On 2017-11-08 18:18:22, user Gregory Connor wrote:

Updated version https://www.biorxiv.org/con... …<br /> discusses prewhitening of the dependent variable (phenotype) for univariate GWAS regression.

On 2021-04-21 21:32:49, user Manendra wrote:

That's an amazing work which will be really helpful... Is it possible to increase the number of approved drugs (not just 2016). Atleast 10 years of FDA approved drugs... But this is a very good start

On 2018-12-06 21:57:02, user Devang Mehta wrote:

This preprint references Supplementary Tables that are not provided. I request the authors to upload a complete document.

On 2019-02-26 13:10:37, user Laurentius Huber wrote:

Please find a formatted version of this response letter with figures here: https://goo.gl/3czXWG

Response Letter:<br /> We thank the referees for reviewing our manuscript entitled “Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex”. The critical reading of this manuscript is highly appreciated, and we believe that the comments have helped to improve the manuscript and clarify the interpretation of the presented results. The manuscript has been modified according to the reviewers’ suggestions.<br /> All points raised by the reviewers have been addressed in detail below.

Reviewer #1:<br /> R1.1 <br /> This is a very interesting study investigating the spatial organization of hand movement representations in M1. Certainly the hand representation in M1 is likely complex and therefore requires advanced methods to probe. Both imaging and neurophysiological evidence clearly suggests that M1 is not so much concerned with the representation of fingers, but rather of complex hand movements. The use of a winner-take-all map for fingers is therefore likely a less effective way of gaining a deeper understanding of the organization of M1.

We thank the reviewer for his/her expert assessment and for appreciating the necessity of advance methodology to investigate the complex representations in M1.

We would like to comment on the reviewer’s statement that “imaging and neurophysiological evidence clearly suggests that M1 is not so much concerned with the representation of fingers, but rather of complex hand movements”. We agree that there is imaging and electrophysiological evidence that parts of M1 can represent complex hand movements. However, we take issue that it would be established that the entire M1 must behave like this. We believe this is only part of the entire picture. <br /> In fact, physiological support of the control of the mentioned “complex hand movement” and muscle and movement synergies comes from investigations of cortico-motoneuronal (CM) cells, (CM cells are the ones with motor neurons innervating shoulder, elbow, and finger muscles). Note, however, that these representations and these cells are confined to the caudal part of M1 (also known as the “new” M1 or Brodmann area BA4p). This is the evolutionary younger part of M1 that is located deep in the central sulcus. In this part of M1, individual body parts are largely overlapping (probably to facilitate complex hand movement) and a finger dominance maps might be misleading (as the reviewer suggested).

However, we would like to note that there are no such CM cells in the rostal M1 (Rathelot and Strick, 2006, 2009). As pointed out in Fig. S9 of or manuscript, the new finding of mirrored finger representations are solely visible in the rostal M1 (a.k.a. “old” M1 or BA4a). In this evolutinary old part of M1, body part movements (e.g. hand, elbow, shoulder) have locally distinct domains with less overlap compared to BA4p.<br /> Thus, we respectfully disagree with the reviewer about the effectiveness of finger dominance maps. These maps are extensively used in imaging and electrophysiology and have efficiently lead to important findings throughout the last century (Woolsey 1979; Hlustik 2001; Idovina 2001; Sanes 1995; Penfield 1937; Schieber 1993; Schellekens 2018; Olman 2012; Siero 2014). We don’t want to discredit this large body of literature of body part maps. And we would also like to use the tool of finger dominance maps, when appropriate.

We would also like to point out that at no point in this analysis, we are estimating “winner-takes-all maps”. We are aware of the shortcoming of winner-takes all maps and thus, the finger-dominance maps that we are depicting in many figures, are not binary. Instead, our finger-dominance maps are shown with a continuous color scale. Every voxel has a relative regime (from 0 to 1) of how much it is dominated from that finger. This analysis retains the fact that multiple fingers can be represented in the same voxel.<br /> For even more quantitative interpretations, (e.g. to avoid that the color of one fingers covers the color of another fingers that is more weakly represented) we included Fig. 3B that shows the mirrored representation in column profiles.

The methods presented in this paper are carefully applied and well documented. In fact the authors have made the tools and data available in an open repository, for which they are to be commended. I really have no quibbles with the processing or VASO approach, both of which have extensive prior publication history.

We thank the reviewer for recognizing the importance of investigating the organization of M1 and we are delighted that the reviewer considers out methods adequate.

R1.2 <br /> The paper is clearly written and illustrated. However the crux of the problem lies in the extent of the novelty of the imaging sequence versus the lack of novelty in the neuroscience findings. Certainly practioners of VASO have made a convincing argument for its superiority over GE-EPI BOLD for the localization of function at the mesoscopic scale and I certainly am convinced of that. Nonetheless researchers around the globe have used GE-EPI to look at various columnar structures in animal and human brain with some degree of success. While the results in this paper are the amongst the clearest, the spatial resolution doesn't really go beyond what Cheng et al. used in their Neuron paper in 2001. So while VASO is certainly a viable and perhaps better alternative to BOLD, this manuscript doesn't really advance the MRI side of the equation much beyond what these authors and others have already shown.

We thank the reviewer for appreciating the clarity of the manuscript and for appreciating the value of VASO in high-resolution fMRI.<br /> Given the reviewer’s doubts about the novelty, we would like to explicitly point out the methodological advancements we achieved and novel neuroscience finding that we found.

Methodological Novelty:<br /> We agree with reviewer, that previous studies could already achieve sub-millimeter in-plane resolutions. Note, however that previous papers (including the Cheng paper) relied on flat portions of cortices and collapsed the third dimension along 3-4mm thick MR-slices. This means that precious MRI methods to investigate “columnar” alignment where not applicable across people and certainly not across the entire precentral M1-gray matter bank with its characteristic Omega-like folding pattern. VASO has never before proven its applicability for sub-millimeter “columnar” imaging. And certainly not for along the curved cortex. This is a novel achievement. <br /> We agree with the reviewer that we could previously already show indications of layer results (with submillimeter in-plane resolution). Please note however, that our previous methodology was limited to a very small FOV of less than 3cm in read direction and less than 2cm in slice direction, resulting in a coverage that could only capture 0.8% of the cortex. In previous studies, this was sufficient to address research questions about individual chunks of the cortex. However, it is not sufficient for topographical mapping of “columnar” organization. One fundamental achievement of this study is that we developed a fundamentally new acquisition approach that allows us to achieve 22% of brain coverage. This was achieved with the novel development of advanced readout strategies. As such, we invested two years of development for the inclusion of advanced GRAPPA reconstruction, asymmetric echoes, and corresponding reconstruction to image space. Compared to our previous methods, the resulting coverage is more than an order of magnitude bigger. This is fundamentally novel and enabled the present study in the first place. <br /> In this study we developed a fundamentally new analysis methodology. The corresponding LAYNII software package used here allows columnar and laminar signal pooling in the voxel space of the native EPI space. There is no other analysis method that can achieve this. While there are previous automatic software packages (e.g. FreeSurfer, CBS-Tools etc.) that allow similar analysis steps, they are not suitable to detect ‘columnar’ structures that are smaller than 1mm (5 digits in 3mm) within the curved cortex. These methods require closed surfaces (not possible with, partial brain coverage), alignment with ‘anatomical’ data (which requires spatial resampling=blurring). Previous methods work in vertex space (not voxel space) and thus are associated with resolution loss during spatial resampling, which makes the neighboring finger representations merge and disappear. The mirrored finger results are only as clearly visible with all the above analysis advancements. And thus, we consider these advancements as a fundamental methodological novelty. <br /> Other methodological analysis novelties developed here are the columnar smoothing without signal leakage across sulci, laminar Point-spread function estimation (Fig. S3, S8), layering in 3D with isotropic voxels (not only 2D as previously), cortical unfolding in voxel space.

Biological novelty<br /> With respect to the referenced study from Cheng et al., we would like to point out that they showed patterns that resembled the expected shape and size as columns but never established such structure and organization. There is no expected ground truth of ocular dominance columns alignment (e.g. where to find which columns). This is different for our study. We can differentiate between any random columnar pattern compared to a meaningful somatotopic organization, with neighbouring fingers being represented in neighbouring columns. This form of meaningful columnar mapping at submillimeter scale is novel compared to Cheng et al.<br /> As opposed to previous columnar fMRI studies, we do not simply try to depict known structures with known shape and size as proof-of-principle for a method as previous studies. Instead here, we are finding previously unknown organization principles of sub-millimeter representations in M1. This is a fundamentally new approach and a paradigm shift for the field of “columnar” and “laminar” fMRI. <br /> We report fundamentally new neuroscientific insights about how the previously described action representations in the microscopic regime are integrated into previously described body-part representations in the macroscopic regime This was not described until now and is a fundamental novelty of this study.<br /> We agree with the reviewer that previous studies (including Ejaz et al.,) found deviations of the homunculus model. It is not clear until now, however, how these deviations (multiple representations and fractionalizations) are coming about. Are these deviation of the linear body-part alignments just randomly aligned? Or do the deviations follow a specific geometric order? If yes, which one? According to which order are the movement actions aligned? In this study we find -for the first time- mirrored representations of individual digits in the primary motor cortex that are differently engages for different actions. This is novel and has not been described previously.

In the revised version of the manuscript, we tried to stress the novelty of the study.

R1.2 <br /> So we are left with the importance of the neuroscientific findings, and here I have some more serious issues. The organization of M1 and S1 along an action-axis is well known and certainly not as mysterious as the authors would represent.

We agree with the reviewer that there are previous accounts of action representations in the motor cortex. We are describing them as part of our introduction and discussion section. We did not intend to describe them as ‘mysterious’ by any means. The point that we are trying to make is that these action representations are partly in conflict with somatotopic organization principles that are found in most of the high-resolution imaging literature (e.g. Schellekens 2018; Olman 2012; Siero 2014).

In the revised version of the manuscript, we emphasize the [Ejaz et al., 2015] even more in a dedicated paragraph about it.

R1.3 <br /> Furthermore, they have dismissed a paper that shows a similar result using MRI by misrepresenting the findings of that paper as I understand them (Ejaz et al., 2015, Nature Neurosci). <br /> Specifically, in reference to that paper, Huber et al. state that 1) the work argues for a simple topographic arrangement of single finger representations in S1, and 2) that the overlap between finger activation patterns is "due to noise". In that work (Ejaz et al., 2015), they used BOLD fMRI to measure the activity patterns evoked by single- and multi-finger movements in M1 and S1. The spatial arrangements of these patterns in both regions were stable within each participant (compared across different scanning sessions), but highly variable across participants. These finger patterns are shown in Fig. 1 of that paper. Close visual inspection of the patterns reveals they do not follow a clear linear arrangement in either S1 or M1, and perhaps some evidence of digit "mirroring" can be observed - definitely there are parts of the cortex activated for the thumb at the dorsal end of the hand region.

They then calculate the dissimilarity between all pairs of finger patterns for M1 and S1, separately. Importantly, the relative dissimilarity between any pair of activity patterns (within a participant) was highly stable across participants. This is notable given the spatial arrangements of these patterns was highly variable across individuals. One stable characteristic was that the thumb pattern was more similar to the little finger than to the ring finger. This finding clearly shows - contrary to what Huber et al. claim it shows - that a simple linear somatotopic arrangement cannot account for the digit representations in M1 or S1.

1.) Our justification for the statements in the previous version of the paper:<br /> We assume the reviewer refers to the citation on page 5 of the original manuscript:

“In the primary somatosensory cortex, we find no clear deviations from the homunculus model as shown previously in humans (Ejaz 2015; Schluppeck 2017; Olman 2012; Kolasinski 2016; Shellekens 2018).”

This statement in our manuscript was based on the following paragraph in [Ejaz et al., 2015] from page 1034:

“There was some consistency: when averaging activity patterns across participants (Fig. 1), a blurry somatotopic arrangement became visible with the thumb activating more ventral and the other fingers more dorsal areas of the motor strip.”

Figure caption: adapted screenshot from Fig. 1 of Ejaz et al. Subject average activation maps show rough features of linear somatotopic arrangement (with secondary deviations). Thumb representations peaks at the bottom (pink arrow) and the remaining fingers are linearly aligned with the little finger representation peaking at the top (red arrow).<br /> We also noticed indications of a secondary thumb representation in Fig. 1 of [Ejaz et al., 2015] next to the index finger. We discussed these double-thumb indications in the Ejaz et al. figures extensively among ourselves and eventually decided not elaborate on them in our manuscript for the following reasons:<br /> In our own pilot studies, we noticed that for some kinds of thumb movement tasks, the thumb-movement can come along with unwanted secondary wrist movement. This was not the case for index/middle/ring/pinky-finger movements. Since the wrist movement representations are expected to be located next to the pinky-finger, we were sceptical that the secondary thumb representation form Ejaz might actually refer to unwanted wrist movement?<br /> In our own BOLD data, we find some cases of signal leakage from S1 to M1 (across the central sulcus), which might introduce artifactual double representation in M1. Since, Ejaz et al., also used BOLD sequences, we speculate that this might have been the case in those data too? <br /> The text of the paper [Ejaz et al., 2015] does not discuss the secondary blob at all. Neither does it mention it in the context of a potential double-representations or mirrored representation. Thus we are hesitant to include it as a reference for this feature. If would be more appropriate for us to give the authors of [Ejaz et al., 2015] full credit for the discovery of mirrored representations, if they would have described it and discussed it consistently across people.

It is further to note that the above statement in our preprint referred to the sensory cortex, not the motor cortex.

Revision to avoid future misunderstandings:<br /> We think this misunderstanding can be resolved by removing the [Ejaz et al. 2015] citation on page 5. Instead we discuss the paper in more depth on page 7.

R1.4 <br /> Furthermore, they (Ejaz et al.) go on to show that the stable structure of overlap of finger representations in M1 and S1 can be accounted for by the statistics of everyday hand movement. They did not interpret the spatial variability of these patterns as "noise due to inter-individual variability in every day hand movements". On the contrary, the statistics of hand use they showed is stable across individuals (also see Ingram et al., 2008, Exp. Brain Res.), as is the organizing principle underlying the spatial organization of activity patterns in M1 and S1.

1.) Justification for our statements in the previous version of the paper:<br /> We assume the comment from the reviewer refers to the following section of our manuscript on page 6:

“Previous studies by Sane et al. (1995) and by Ejaz et al. (2015) already identified deviations from linear organizations for finger representations in the human motor cortex with GE-BOLD at 2.5 mm and 1.4 mm resolutions, respectively. However, without the localization specificity, a consistent spatial layout principle, such as the mirrored finger representation alignment, was not found. Instead, the exact pattern of overlapping and segregated representations was interpreted as noise due to inter-individual variability in every day hand movements (Ejaz 2015).”

We included this interpretation of Ejatz’ results based on the first few sentences of the discussion section in [Ejaz et al., 2015] on page 1039:

“The relative similarities between activity patterns were preserved across individuals, despite the substantial spatial inter-subject variability of the activity patterns themselves. The representational structure remained invariant even when the shared somatotopic arrangement of the digits was removed from the data. This suggests an organizing mechanism that shapes the overlap between patterns without enforcing a regular spatial layout. The representational structure could be predicted by the natural statistics of hand use.“

If we understand the highlighted section correctly, Ejaz et al. found that there are deviations from a simple somatotopic organization. And the patterns of these deviations have a considerable variability across people. It is not clear, however, according to which consistent organization principle this variability comes about.

In our view, we thus (mis-)described the phrase “inter-individual variability without given structure” with the term “noise due to inter-individual variability”.

Revision to avoid future misunderstandings:<br /> We agree that the term “noise due to inter-individual variability” might be misleading to describe “inter-individual variability”. In the revised version of the manuscript, the corresponding section is revised as follows:<br /> A previous study by Ejaz et al. (2015) already identified deviations from linear organizations for finger representations in the human motor cortex with GE-BOLD at 2.5 mm and 1.4 mm resolutions, respectively. These data already showed some indications of multiple finger representations (e.g. Fig. 1 in (Ejaz et al. 2015)). However, these data were not discussed with respect to an alternative geometric somatotopic organization principle such as a mirrored representation.

R1.5 <br /> I definitely agree with the authors that M1 organization is more complex arrangement than simple linear finger organization. Whether the organization really is best described by two discrete finger maps with phase reversal, however, really has to await a more rigorous experimental and statistical evaluation than even what is presented in Huber et al. Whatever the answer may be, however, I do think that the improved specificity of VASO sequence may play an important role in uncovering such representations in the future, but I don't feel that what has been shown goes much beyond what is known from the literature already.

We are glad that the reviewer agrees with our work showing that the M1 representations can be complex. We agree that the literature needs to be augmented with more rigorous studies.<br /> In fact, with the manuscript at hand we intent to do just that: providing a more rigorous experimental evaluation. We aim to move beyond the position of Ejaz et al. Namely, we aim to go beyond the conclusion “that the motor cortex is more complicated than individual finger representations”, . and describe how it is different, how these differences are geometrically organized, and whether they are stable across people.<br /> Accounting also the large bulk of electrophysiological and micro-stimulation evidence about the body-part sub-divisions in M1 we opt to see how these representation are in agreement with the results from Ejaz.<br /> In previous imaging studies (including Ejaz et al.,) it was common to view M1 as one large chunk of cortex that would follow the same architectonic principle. There is a large body of invasive literature, however, that suggests that this is not correct, neither functionally (Rathelot and Strick, 2006, 2009) nor anatomically (Geyer 1996). Thus, we intend to describe the body-part representations with a more rigorous fine-scale evaluation. To get there, we developed the advanced methodology as described here. And we start to describe the simplest movement principle of the literature (finger tapping) in the simplest part of M1, namely the evolutionary “old” M1 that has been described as body part representations. <br /> Thus, we feel that our findings go beyond what it known form the literature already.

Reviewer #3: <br /> General Comments: <br /> This paper uses the vascular space occupancy (VASO) method of measuring cerebral blood volume (fMRI) to explore the somatotopy of the finger representation at a sub-millimeter resolution in M1 and S1 of humans. This is an important problem as prior fMRI papers exploring this issue did not have sufficient resolution to adequately address a fine grained topography for fingers. This paper appears to have adequate resolution (~0.8mm) to make a major contribution to understanding the topography of the hand in M1 as well as S1. As such, this paper is primarily one of anatomical location and fMRI reconstruction. In addition, it addresses the issue of whether a given body part representation is always active when that body part is moved. The answer is that there is functional specialization within each M1finger representation. The figures are complex and it is paramount that their display is straightforward, consistent and simple to understand.

R3.1. The stated goal of this paper is to"non-invasively investigate the functional organization topography across columnar and laminar structures in humans", particularly M1 and S1. To understand the topography of the fingers in M1, the entire extent of the finger representations in M1 must be accurately mapped. Such maps are shown in Figs. 6S and 10S. These maps, for each participant, could form the core of an important paper, but they belong in the main body of the paper. They also need to be shown systematically for each participant. The data showing the columnar organization of M1 and S1 seem like important validating information for the reconstruction of the central sulcus. Some of this could be moved to the Supplementary information. What is currently displayed in Figs. 1-5 is just a small sample from the entire extent of slices through M1. Although the concept of mirror hand representations derived from single slices is appealing, it is only represents a small fraction of the entire map of the central sulcus. Furthermore, the single fMRI slices totally ignore the finger representations present in the depth of the central sulcus.

We would like to clarify our goal of this study. We feel the quoted section was taken out of context. As mentioned in the abstract, it was not our goal to ‘investigate the complete topographical organization of the motor cortex at its entirety’. Instead, the quoted section comes from an introductory sentence that states that our goal actually was to ‘develop imaging and analysis methodology, which -in principle- allows us to investigate topographical features’. In a next step we then use the M1/S1 system as a test bed to investigate the neuroscientific usefulness of that methodology. Given that we find -previously not described- neuroscience findings of the mirrored digit representation, we think that the neuroscientific usefulness it confirmed. In this sense, we see our manuscript to lie along a fine line between a methods paper and neuroscience paper.

We agree with the reviewer that every figure in the Manuscript and the Supplementary information is “tuned” to a specific message that we want to bring across. We further agree that Figs. 1-5 in the main manuscript are just a small sample of the main story and there is much more information to be seen. We don’t see this as a weakness of the manuscript. But as a means to follow the comment R3.14, namely selectively showing figures that have a specific message, which comes across as intuitive as possible.

In order to discuss the mirrored pattern of digit representations, we find it most natural to zoom into the hand area (Fig. 1). Correspondingly, when it comes to showing the inter-participant consistently of this feature (Fig. 2), we find it advantageous to use the same imaging procedure across all people as in Fig. 1. However, when it comes to explaining where these features are located across the dimensions of the central sulcus, we show additional unzoomed images. <br /> We agree with the reviewer that entire maps of the unflattened sensory-motor-system would give a more comprehensive view. However, it would distract the reader from the feature of interest. Those entire maps would mostly contain nothing (e.g. all the non-stimulated body parts, trunk, face, feet, etc.) and the 3-8mm of interest would be tiny (e.g. See Fig. S6). <br /> To address the reviewers comment, we included the full maps of the central sulcus into the manuscript main body (new figure 3), additional to the zoomed images.<br /> Furthermore, we included additional IMAGIRO maps (as requested) of for more participants with zoomed and unzoomed sections to guide the reader which part of the superior part of M1 it refers to (See new Fig. S6E).

The of laminar and columnar fMRI is still emerging. Thus, not all potential sources of analysis artifacts are fully described and understood. To minimize potential misinterpretation it has been suggested to depict the final results as close to the raw data as possible (Polimeni 2017; Kay 2019). Thus we try to show the activation maps in the raw EPI space (Fig. 1,2,4), when possible. This way, it can be easily be directly appreciated that the mirrored finger pattern is not an artifact of a flawed infolding artifact. Furthermore, the activity maps in EPI space best depict the spatial scale of columnar size with respect to the cortical thickness and location at the hand knob. Flattened maps are produced by several additional steps and are presented in an very abstract space where, these reference dimensions are lost. Thus, we are hesitant to remove the activation maps on the folded cortex from the manuscript. However, we included additional unfolded flattened maps in the supplementary material.

Please note that we are also required to following the Journal’s Guidelines to only include material that is central to the narrative. In doing so, we follow the rule of not having more than double of supplementary figures as figures in the main text. Thus, is included the some of additional maps as figure-panels, not as additional stand-alone figures.

We revised the manuscript to account for the reviewer’s comment. Specifically, we rephrased the abstract and introduction section to make our goals clearer. We also tried to make it clearer what the message is for each figure, in the figure captions respectively.

Kay, K., Jamison, K., Vizioli, L., Zhang, R., Margalit, E., & Ugurbil, K. (2019). A critical assessment of data quality and venous effects in sub-millimeter fMRI. NeuroImage, 189, 847–869. http://doi.org/10.1016/j.ne... <br /> Polimeni, J. R., Renvall, V., Zaretskaya, N., & Fischl, B. (2017). NeuroImage Analysis strategies for high-resolution UHF-fMRI data. NeuroImage, (April), 1–25. http://doi.org/10.1016/j.ne...

R3.2. The orientation of brain images and reconstructions should be the same in every figure. For example, Fig. 1A and 1E seem to have the right side of the brain image toward the right whereas Fig. 1B-D has it to the left. In Fig. 6S, the orientation of the CS appears to be opposite to that shown in Fig. 10S. Continually forcing the reader to flip the images creates unnecessary confusion. Since this paper shows the right hemisphere, left/medial should be on page left and right/lateral should be on page right. The terms medial and lateral are preferable to left and right. In Figs. 6S, 10S, the actual location of the medial wall/sagittal fissure should be indicated. Without this marker, the CS just floats in space with no anchor to the actual brain image. A calibration should be included on each image.

We agree that the orientation is confusing. This comes from the fact that the convention of MRI images is to view them as they would look like from the experimenter perspective. E.g. looking at an axial cut from the perspective of the participants feet. The right motor cortex of the person is then depicting on the left. This is contradicting to the 3D-head-models from viewing from above. Thus, the 3D-views and the 2D-views were confusing.<br /> Based on the reviewers comments, we tried to make it more consistent in Fig. 1, S6 and S10. This means however, that the 3D-head-models are mirrored representations compared to their real-live pendants. <br /> We included additional calibration markers and the landmarks of the medial wall in multiple figures. E.g. Fig. S6, S9, S3.

R3.3. The term 'multiple' is used incorrectly throughout the manuscript. Multiple means 'more than 2'.

We respectfully disagree with the reviewer on this point. In our understanding, the term ‘multiple’ refers to ‘more than one’ (source: https://en.oxforddictionari... "https://en.oxforddictionaries.com/definition/us/multi-)"). We chose this term deliberately vague. We find only two mirrored representation consistently across all participants. However, we cannot exclude the possibility that there are more representation hidden below the detection threshold. Since absence of evidence is not the same as evidence of absence, we would like to refrain from calling it “double” representation. This excludes the possibility of a third or fourth representation. <br /> In one participant, with a large tilting angle, and with a very low threshold, we see indications of a third representation. However, since its not reproducible across participants, its discussion is subject to future experiments with more sensitive imaging methodology only.

R3.4. It is unclear how the images in Fig. 1E were developed. What do the colors mean? Why is this representation shown here when it is not used until Figs. 3S, 6S.

Fig. 1 was intended as a figure describing the methods applied in this study. Thus, we included the coordinate system of layers and columns in 3D-grids as they are used for the directional smoothing. We agree with the reviewer that it can be confusing, we thus removed the panel E from the figure in the revised version of the manuscript.

R3.5. Discussion- <br /> The requested revisions in the data presentation will require revision of comparisons to other fMRI papers. <br /> The Discussion would be improved by a more extensive comparison to studies in monkeys where most of the mapping of M1 has occurred. An excellent brief summary of the monkey literature may be found in the section written by Paul Cheney in Omrani et al, 2017. The discussion should address two issues. <br /> First, a comparison of the organization of human M1 to the anatomical and physiological explorations of this region in the monkey. Second, the issue of specialization (separate regions of grasping and retraction) has its basis in monkey data that indicates specialization of M1 neurons for specific tasks.

We agree with the reviewer that the summary from Cheney provides a nice summary about representations in the motor cortex learned from monkey experiments. Based on this summary, we included an additional paragraph into the discussion section that should address the two issues.

Most of the knowledge on the functional representation of movements in the primary motor cortex has been obtained from countless experiments in monkeys over the last century. The current state of consensus in the field is nicely summarized by Paul Cheney in (Omrani 2017; see also referenced therein); Overall, corticomotoneuronal cells in the primary motor encode muscle-related parameters of movement such as muscle activity and muscle force. Although some corticomotoneuronal cells in the primary motor cortex (particularly those involved with finger movements) have their terminations confined to motoneurons of single muscles, a large amount of corticomotoneuronal cells are not rigidly coupled to the activity of its target muscles but show specialization for particular movements or categories of muscle activity. Namely, almost half of the corticomotoneuronal cells facilitate muscles involving at least one distal and one proximal joint and are specialized for specific muscle synergies, E.g. for reach-to-grasp movements. With respect to action representations shown in Fig. 2B, it is important to note that Cheney and Fetz (1985) had previously identified the muscle fields of neighboring corticomotoneuronal cells. They showed that neighboring corticomotoneuronal had muscle fields that were very similar. Hence, the notion of cortical patches that are preferentially activated for grasping and retraction actions (Fig. 2B) has its basis in previous monkey data and could refer to these previously described muscle fields.

Specific Comments:

R3.6. The first sentence of the Significance statement is incomprehensible. In general, the significance of this study is not well explained.

Since the significance statement is removed from the revised version of the manuscript.

R3.7. Introduction- Sanes et al., 1995 did not study monkeys.

We agree with the reviewer. The Sanes reference is moved to a different section now.

R3.8. "However, the organizational principle of smaller body parts such as individual digits could not be resolved due to the lack of localization specificity of conventional GE-BOLD fMRI and the sparse sampling of invasive electrophysiological recordings." This may be true for fMRI but the electrophysiological stimulation in monkeys (Kwan et al.l 1978; Strick and Preston, 1982 [up to 16 penetrations per 1mm2]) and Park et al. 2001) can hardly be described as sparse.

We agree with the reviewer that the term “sparse” might be misleading and does not give those experiments’ justice. The point we were trying to make is, that fMRI is inherently a continuous mapping technique that continuously samples the entire cortical sheath without any holes between electrodes. Which is true even at low resolutions. To address the reviewers comment, we revised the paragraph in the introduction section.

R3.9. Lin et al 2011 is often used as evidence that VASO accurately measures CBV. However, close examination of Fig. 1 in Lin et al reveals that the VASO and Gd-DTPA blood volume measurements often do not occupy the same voxels. That is, many VASO voxels with significant activation have no significant Gd-DTPA activation and many Gd-DTPA voxels with significant activation have no VASO activation. This observation suggests that VASO does not accurately represent CBV when voxel to voxel comparisons are made by the two different methods of measuring CBV. What other evidence, other than theoretical, indicates that VASO accurately measures CBV? (Lin AL, Lu H, Fox PT, Duong TQ. Cerebral blood volume measurements- Gd-DTPA vs. VASO - and their relationship with cerebral blood flow in activated human visual cortex. Open Neuroimag. J. 2011; 5: 90-95.)

We share the reviewer’s concerns whether VASO is a good measure for CBV. For this reason, we validated our SS-SI-VASO variant with gold-standard methods in multiple setups across the last 5 years. Ranging from concomitant VASO imaging with optical imaging spectroscopy in rats, up to validations of layer-dependent VASO signal with MION/Ferraheme imaging in rats and monkeys.

While we agree that Fig. 1 in Lin et al., shows deviations of VASO and Gd-DTPA, we would like to refrain from speculating what might be the reason for this. Reasons could range from acquisition challenges up to analysis inconsistencies. See the following reference:

Huber, L., et al (2015). Micro- and macrovascular contributions to layer-dependent blood blood volume fMRI: A multi-modal, multi-species comparison. ISMRM. doi: http://dx.doi.org/10.7490/f... ).

Note that our validation studies are quantitative in physical units of ml. This is in contrast to significance maps in Lin et al., that might be prone to biases in different noise characteristics post-injection of GD. <br /> Also note that our validations are carried out across columnar structures (B) and laminar structures (C).

See figures from:<br /> Huber, L., Goense, J.B.M., Kennerley, A.J., Guidi, M., Trampel, R., Turner, R., and Möller, H.E. (2015). Micro- and macrovascular contributions to layer-dependent blood blood volume fMRI: A multi-modal, multi-species comparison. In Proceedings of the International Society of Magnetic Resonance in Medicine, p. 2114. Doi: http://dx.doi.org/10.7490/f...<br /> Huber, L., Goense, J.B.M., Kennerley, A.J., Trampel, R., Guidi, M., Ivanov, D., Gauthier, C.J., Turner, R., Möller, H.E., Reimer, E., et al. (2015). Cortical lamina-dependent blood volume changes in human brain at 7T. Neuroimage 107, 23–33.<br /> Huber, L. (2015). Mapping human brain activity by functional magnetic resonance imaging of blood volume. University of Leipzig. https://fim.nimh.nih.gov/fi... <br /> Kennerley, A.J., Huber, L., Mildner, T., Mayhew, J.E., Turner, R., Möller, H.E., and Berwick, J. (2013). Does VASO contrast really allow measurement of CBV at high field (7 T)? An in-vivo quantification using concurrent optical imaging spectroscopy. In Proceedings of the International Society of Magnetic Resonance in Medicine, p. 0757.

In the revised version of the manuscript, we included the following additional paragraph into the discussion section:

Note that the CBV weighting in VASO has been extensively validated by comparisons with gold-standard methods in rats and monkeys across layer and columns (Huber et al., 2015a-c; Kennerley et al., 2013).

R3.10. The voxel size is listed as 0.89mm x 0.99mm on page 2 versus 0.79mmx0.79mmx 0.99mm on page 1. Which is correct?

The correction resolution is 0.79 mm. This typo is corrected in the revised version of the manuscript.

R3.11. Was the smoothing across layers a directional smoothing?

The reviewer is correct. The layer-smoothing was applied in specific directions only. It was only applied in the direction that is parallel to the column. There was no smoothing perpendicular to this direction. <br /> Note that this way of “directional” smoothing refers to cortical directions. The smoothing was independent of the direction in the laboratory frame of reference. As such, the smoothing is applied independent of the orientation of read-direction, slice-direction and phase direction. The LAYNII program LN_DIRECT_SMOOTH was not applied in this study. <br /> An additional sentence about this is included in the revised version of the manuscript.

R3.12. Page 13- "...primary motor cortex is 4 mm (Fischl and Dale 2000), the resolution of 0.79 mm used here allows us to obtain 5-7 independent data points across the 20 layers. The number of 20 layers is chosen based on previous experience in finding a compromise". This description is hard to understand. Suggest something like- The cortical thickness of the primary motor cortex is 4 mm (Fischl and Dale 2000). With our resolution of 0.79 mm, we obtained 5-7 independent data points across the thickness of the cortex. These data points were upsampled to create 20 layers across the thickness of the cortex. Twenty layers was chosen based on previous experience in finding a compromise... These 20 layers were smoothed and extracted (tell me what you did here) in sheets to produce a reconstruction of the face of the anterior bank of the central sulcus (Figs. 3S, 6S, 10S).

Based on the reviewer’s suggestion, we tried provide a more detailed description of the underlying assumptions and the necessity of using so many layers in a recent blog post: https://layerfmri.com/2019/... <br /> In the revised version of the manuscript, we the included the following summarizing statement:

The cortical thickness of the primary motor cortex is 4 mm (Fischl and Dale 2000). With our resolution of 0.79 mm, we obtained 5-7 independent data points across the thickness of the cortex. Across these data points, we created 20 layers across the thickness of the cortex on a 4-fold finer grid than the effective resolution. The number of twenty layers was chosen based on previous experience in finding a compromise data size and smoothness (see Fig. S6 in (Huber 2018)). Columnar profiles in Fig. 3 and Fig. S4 are generated from unsmoothed data. For Figs. S3 and S6, the functional signal was smoothed with 0.5 mm within columns and extracted in sheets to produce a reconstruction of the face of the anterior bank of the central sulcus. No smoothing was applied across columns.

R3.13. Fig. 2B- For participant 5, the copper and turquoise outlines are reversed. Hue of copper and turquoise colors are not consistent in each panel. <br /> In last panel of 2B, first line- there is a hand in this panel. What is its purpose? If the purpose is to be a key for finger color, the thumb should be magenta.

The reviewer is right, the copper and turquoise patch seems reversed in participant 5. Note, however that this is not a presentation error in the preparation of the images. We find that the grasping-extension patches do not follow a the same organization principle along the medial-lateral direction across participants. It is highly dependent on the position of the axial projection chosen. E.g. it can be seen in Fig. S6 (and previous version of Fig. S9) that, dependent on the depth of the central sulcus, the copper and turquoise patches are either on the medial or lateral side. Please also note that participant 5 is not an outlier here; in fact, participant 1 (in the same figure) has the same copper-turquoise alignment as participant 5. Please also note, that the sensory cortex consistently shows a grasping preference, across all participants.

The additional hand pictogram had been included as a figure key to remind the reader, which color refers to which finger. Based in the reviewers comments, it is excluded in the revised version of the manuscript. It is already shown in panel A) anyway.

R3.14. Fig. S3C- Several features of this figure make it hard to decipher and undermine the explanation of the reconstruction method. I am assuming that the little squares in panel B are equivalent to columns. This should be stated explicitly. If the colors correspond to the fingers, then the mirror representation of the hand shown in Figs. 1-3 is nowhere to be found. This is confounding. It may be useful to show the location of the slice in panel D. Panel D is reversed from panel A, creating needless confusion. In panel C, the laminar thickness of the cortex is greater than the depth of the central sulcus. Calibrations would help but why not make the laminar thickness accurate? State explicitly that the IMAGIRO reconstruction consists of 20 layers, each like the one in B. Spelling- Columnar 'distance' <br /> It took me a long time to understand what you were doing. The descriptions of the reconstruction needs to be simple, clear and intuitive or very few will comprehend them. It all makes sense but the reader should not have to go to the blog (which I did) to understand them.

We thank the reviewer for the suggestions to make this figure clearer. We also applaud the reviewers level of commitment to check the description on our blog.<br /> -> The little squares indeed refer to the columnar dimension. Additional comments are included in the caption.<br /> -> The colors do not refer to finger dominance, but to the medial-lateral position. This is included in the caption now.<br /> -> The location colors are now included in panel C, as suggested.<br /> -> Panels C and D are now switched, as suggested.<br /> -> If, the laminar thickness could be accurately depicted, all 20 layers would be 2-3 mm apart in the figure. If we would depict it in the right geometry, the layers could not be separated with the naked eye. Scale bars are included as suggested, which points out how they are distorted.<br /> -> An explicit reference about 20 layers is included.<br /> -> The typo is corrected in “distance”

Updated Fig. 3:

We agree, that an intuitive image is helpful. Here, we tried to find a compromise of simple intuitive figures that are representing the complexity of the analysis without making the supplementary material too long. The reviewer’s comments are appreciated to achieve this.

R3.15. Fig. 4S part B- Should note that this is upsampled to produce 20 layers.

The revised version of the manuscript has an additional statement included:

Note that the size of layer and column structures are smaller than the effective resolution of 0.79 mm. They are estimated in an upscaled space.

R3.16. Fig. 9S- Why is the background of the VASO view of the anterior bank of the CS entirely red? This implies that the entire CS is related to the 5th finger. How is that possible? Why are there yellow and green patches distributed all along the CS? This arrangement is different from any of the other figures. There does not seem to be a double mirror representation in this participant. <br /> In the bottom panels, why is the view limited to just part of M1 instead of the whole of M1? In general, this figure is quite confusing and really difficult to interpret. The organization of the grasping and retraction patches is an important issue. A better explanation (illustration?) of what you are trying convey in this figure is necessary.

We agree with the reviewer that previous Figure S9 could be confusing. We tried to show too many features in one Figure. Our goal of this figure was to show the consistency of the finger representations across the different tasks and also to show the position of the mirrored representation along the depth of the central sulcus. Based on the reviewer’s comments, we decided to remove Fig. S9. From the manuscript. We believe that these to messages already come across from Fig. S5, S6, S9 (new).

To answer the reviewer’s questions (for the sake of his/her curiosity): <br /> -> The top-right figure was included for the sake of orientation. It was not included to suggest the significance of the mirrored pattern. Thus, we did not threshold the finger dominances at all. In areas outside the hand-knob, therefore, the finger-preference measure for all fingers is close to 0. The red color outside the hand knob does not mean that this finger is represented there. It only means that all the other fingers are even noisier. E.g. that the finger preference for the index finger is 0.0014 compared to other fingers with a finger preference of 0.0005. For reference, in the hand knob, the finger preferences are in the regime 0.3-1 (please, see Fig. 3B about the absolute selectivity strengths in an outside the hand knob). The previous figure S9 corresponds to the line graph in Fig. 3B from above. <br /> -> We believe that there is, in fact, a mirrored pattern visible in this figure. Within the Brodman area subsection BA4A, the color pattern is reversed.

R3.17. Fig. 10S- in the right panel, the orientation seems to be incorrect. That is, left is lateral and right is medial which means the left ear arrow should be pointing to the right.

We agree, the arrow description now says “right” ear.

R3.18. I suggest alphabetizing the reference list.

In the updated reference list “S” is after “O”.

R3.19. The correct citation is- Meier JD, Aflalo TN, Kastner S, Graziano MS. Complex organization of human primary motor cortex: a high-resolution fMRI study. J Neurophysiol. 2008 Oct;100(4):1800-12. doi: 10.1152/jn.90531.2008. Epub 2008 Aug 6

The reference is updated.

On 2019-11-21 07:33:52, user Saravanan vijayakumar wrote:

Though the authors created "Consensus_NR" from the previous data set for this study, the authors should clearly state what is "Positive" and what is "Negative" data set. This is because, for instance (on random check) IEDB ID 107354, which is classified as "POSITIVE" in the "Consensus_NR" data set is reported to have 10 B-cell assays in IEDB, out of which 8 are negative 1 is positive and 1 is low-positive. Does this data should be in Negative? Similarly, IEDB ID 93172 which is also classified as "POSITIVE" in this study, but the IEDB reports 4 B-cell assay out of which 2 are negative and 2 are positive-low! Hence, authors should clearly mention what is "POSITIVE" and what is "NEGATIVE" according to this study. Also, it will be great if authors provide the sequences of both positive and negative data set they used in this study as supplementary, rather than only IEDB ids, because IEDB doesn't allow bulk epitope download via IDs.

On 2021-02-25 03:55:18, user Colin Dunstan wrote:

This is an interesting and insightful manuscript advancing the technologies available for in vitro developmental patterning of human stem cells with promise for 3D fabrication of human tissues accomplished via rational design of architecture, mechanical properties and composition of a 3D printed matrix scaffold.

On 2019-12-01 01:30:49, user DeboraMarks wrote:

No surprise that our lab likes this work - ha ha but seriously it says something about power of evolutionary information, deep generative models -and - oh - the ”un-supervision” keeping us honest on training/test that’s so hard with biological data. Now let’s do that for design

On 2020-03-03 15:20:54, user Sebastien LEON wrote:

Very interesting. Do you guys have any idea of how "strong" is the energy depletion caused by 2DG + antimycin? In terms of ATP levels, kinase activation etc.

On 2025-10-07 06:57:42, user Stéphane Mazières wrote:

The fetomaternal incompatibility between Neanderthals and H. sapiens is a very original hypothesis brought from "novel" genetic markers in the era of genomics: red cell polymorphisms. We have previously raised it in 2021 in our study of red cell blood groups (PMID: 34320013, not cited in the preprint)<br /> I have few more methodological concerns about this preprint: <br /> - the study does not precise which Neanderthal genome(s) has been studied and how the authors have identified the p.Gly307Ser.<br /> - all 3 high-quality Neandertal genomes have several other. missense polymorphisms in the PIEOZ1 exons. I don't understand why the authors have focused only on the p.Gly307Ser.<br /> - By the way, Denisova also carries the p.Gly307Ser.

On 2020-06-14 14:26:36, user Neeta Tanna wrote:

There is no supplementary materials

On 2017-10-25 15:57:00, user René L. Warren wrote:

ARCS has now been peer-reviewed:

Abstract:<br /> https://urldefense.proofpoi...

Article (free access):<br /> https://urldefense.proofpoi...

These are persistent URLs.

On 2017-03-16 22:33:41, user jstitlow wrote:

This probe synthesis technique worked well for us. Quality of the smFISH signal with these probes is comparable to commercial systems. Josh Titlow- Davis Lab, Oxford

On 2016-06-08 22:21:54, user John Urban wrote:

Oh - I may have found the commands and such in the supplement.

On 2017-12-15 13:07:47, user Tom Wallis wrote:

This paper presents an investigation of the relationship between voluntary attention and illusory contour perception using a novel stimulus and the classification image technique. The paper is interesting and well-written; I have some minor comments.

• I’m not convinced by line 176. The effect itself is pretty weak / small, and to show that a weak effect disappears is maybe not surprising. Do the BFs at least show evidence supporting no difference?

• I think the red line showing the illusory edge row is confusing - I initially mistook it for the area of pixels you were in fact testing, which of course didn’t make sense. I think it would be better to have something below the edge, spanning the illusory portion.

• Line 197: The authors hypothesise that the illusory star form constrains voluntary interpolation of the illusory triangle edge. They could presumably test this by measuring classification images after rotating the non-target pacmen by 90 degrees (breaking the star but largely preserving local contrast). I think this condition would actually be useful as a baseline for the plots in Figure 2b: how strong could we expect the middle of the contour to be in the absence of the illusory star? For example, the authors could state something like “the presence of the competing illusory form reduces the strength of the illusory contour by 3-fold”. Is there any other data that could speak to this - perhaps Jason Gold’s work?

• what exactly does the SVM fitting add? The pictures are nice to explicitly show the result of two / three potential hypotheses for how to do the task, but then these are not directly tested against the data. Rather it’s left up to the readers’ impression of the classification images and their correspondence to the three models (which is admittedly much more than most classification image studies do). While I do think it’s nice to have those hypothesis images generated from understandable models, I also wonder what value that’s added beyond just sketching those hypotheses by hand. Can the authors think of a way to test those hypotheses against the data more formally?

On 2020-03-21 14:37:39, user Peter Ellis wrote:

Hi,

In relation to our model of mtDNA partitioning between sperm (your ref 36), we fully accept that some kind of autosomal "permission" factor is likely to be necessary to allow paternal transmission of mitochondria.

Our argument was that any such factor is not sufficient to explain the observed quasi-Mendelian inheritance, and that there must in addition be some kind of competitive proliferation dynamics that leads to the production of two different classes of sperm bearing a single mtDNA haplotype each. I'm absolutely thrilled to see that your single cell analysis bears this out and provides evidence that a significant fraction of sperm are indeed homoplasmic for either the maternal or paternal mtDNA haplotype.

Peter

On 2021-03-17 03:18:46, user Sam Kariuki wrote:

CORRECTION on last sentence in abstract:<br /> The high rate of multidrug-resistant H58 S. Typhi, and the close phylogenetic relationships between carriers and controls, provides evidence for the role of carriers as a reservoir for the community spread of typhoid in this setting.

Should read:<br /> The high rate of multidrug-resistant H58 S. Typhi, and the close phylogenetic relationships between cases and carriers, provides evidence for the role of carriers as a reservoir for the community spread of typhoid in this setting.

On 2020-12-17 08:04:10, user Joerg Schneider wrote:

Can you please point me to or post supplemental material referred to in the text? (supplemental Table 1, supplemental information 1, supplement information 2). Thank you.

On 2022-06-13 08:08:35, user Olivier Gandrillon wrote:

This is a quite provocative view of the absence of cell types that could be identified through specific gene expression patterns in single-cell RNAseq data. My first comment is that Waddington was not the first one to propose the existence of distinct cell types, harbouring different functions. My second comment is more of a question: when you are using differentially expressed genes why do you still find no cluster structure? This seems weird to me. By definition of DE genes, they should define clusters. I fully agree that there should be some continuity in between cell types but at the same time tehre should be differences in between cell types.

On 2024-12-02 09:30:09, user Rebecca McDougal wrote:

This has now been published: https://doi.org/10.1186/s42483-024-00255-8

On 2016-06-23 18:22:45, user Yoav Gilad wrote:

I am not sure that the conclusions of this study are warranted. I wonder if the authors can comment on the following possible concern:

If I understand correctly, empirical normalization was performed, namely the same amount of RNA was labeled and hybridized to the array, regardless of time after death. If so, this means that genes that decay at a lower rate than the mean decay (or degradation) rates, will seem as highly expressed. This pattern is not expected to be linear, as it relates to the relationship between the decay rate of a specific gene and the overall decay of the entire sample (either of which is not necessarily linear). In addition to this issue, I could not find an explicit mention of modeling the RINs of the samples, or global analytical normalization of the data (did I miss it?). If this is indeed the case, the effects of the empirical normalization are confounded with differences in RNA quality across sample; this would make it even harder to predict what proportion of genes are expected to be 'seen' as highly expressed after death because of technical considerations alone.

On 2020-05-11 13:27:48, user Liz Miller wrote:

This paper was the subject of the Miller lab journal club and, following a fun discussion of the findings, we have the following comments to make.

In this paper, Clancy and colleagues confirm that the de-ubiquitylase enzyme USP9X regulates the stability of the RING E3 ligase ZNF598. They also establish an additional RING E3 ligase, MKRN2, as a new substrate of USP9X. Further, the authors identify a high specificity inhibitor of USP9X, and proteomics analysis confirms ZNF598 and MKRN2 to be amongst the proteins that are destabilised in response to inhibition of USP9X activity. MKRN2 and ZNF598 both play key roles in stalling and resolution of di-ribosomes, and consequently loss of USP9X activity, either through knock out or inhibition, impairs the cells’ ability to respond to ribosome stalling sequences.

Whilst USP9X inhibition is unlikely to be a useful pharmaceutical target due to its broad and important roles in various cellular processes, the identification of the highly selective inhibitor FT709 provides a useful tool for future studies into USP9X and its role in the regulation of ribosome stalling.

USP9X is implicated in ribosome stalling through its stabilising activity on ZNF598. We would be interested to know if this activity is dynamically regulated, such that it is increased in scenarios when ribosome stalling is more prevalent. For example, would its stabilising effect on ZNF598 increase above basal level if ribosome stalls were acutely induced by low doses of emetine (such as used by Juszkiewicz et al, 2018)?

We discussed the interesting problem of how USP9X might achieve specificity to ZNF598 and MKNR2 when there are other important ubiquitination events taking place at the ribosome (on 40S subunits and nascent polypeptide chain). The authors address that it is difficult to tease out these separate events, and this will certainly be an interesting area for future work.

Overall, we enjoyed reading this concise and clear story, and thank you for sharing your work on BioRXiv. We hope our comments are of some interest to the community.

On 2025-09-01 08:12:19, user Stefan wrote:

We thank the authors for their comment on our paper, however, we feel that some of the key take home messages have been misunderstood, and we wanted to provide some clarification.

“Recent work proposed that APOBEC-signature mutations in MPXV are enriched in cruciform structures formed by inverted repeats”

We never actually explicitly make the claim that the SNPs in question are ‘enriched’ within inverted repeats, as this was not the intent of the manuscript. Neither do we explicitly claim that these mutations are driven by APOBEC, and we agree that experimental evidence is necessary. We only state that ‘X’ number of SNPs fall within or near sequences with the potential to form IRs in the 2018 isolate.

The main point of interest and where the mutation hypothesis arose was when this earlier strain (2018) was compared to newer strains. Here we found that the newer strains shared far fewer of the SNP-associated IR sequences identified in the 2018 strain. Most of the IRs that were ‘lost’ more frequently were those that the SNPs fell within, and not the ones where SNPs were ‘near’ to. Which suggested to us that mutations were arising in these regions more frequently. This also made sense based on observations in other organisms. Moreover, the previous bias of including those near to would not have skewed the overall observations as most of these IRs were retained in the new strains.

The APOBEC hypothesis itself was based on previous observations by Isidro et al which formed the rationale for this study. They propose that the SNPs might be due to APOBEC mutations, and we were only trying to provide an observation that might support their hypothesis. Our mentioning of APOBEC was only to give our study some logical context. We agree that experimental evidence is absolutely critical to confirm these observations and apologise if we have misinterpreted the biological outcomes.

Hopefully this has provided some clarification for our study.

Best,

The Authors

On 2020-08-17 07:54:36, user Andrea Zaliani wrote:

Sirs<br /> I have problem with your definition of synergy. HSA should be positive for synergy and should use the max activity of single components alone, not the minimum. At least so, is here described https://bpspubs.onlinelibra....<br /> Am I wrong?<br /> Best<br /> Andrea Zaliani

On 2018-09-22 12:01:14, user Peter Andolfatto wrote:

The paper is similar to the Multiplexed Shotgun Genotyping paper of Andolfatto et al. 2011 https://www.ncbi.nlm.nih.go... . The latter uses an HMM to assign posterior probabilities of ancestry for each individual and imputes missing data.

On 2021-06-09 19:31:12, user Eden Tefera wrote:

Hello, Dr.Tian. My name is Eden Tefera and I am a current undergraduate student at UCLA in the Biomedical Research Minor. I wanted to thank you for your work in this paper as our journal club really appreciated the opportunity to read and learn from it. As we were reading there were some comments and suggestions that I would like to offer!<br /> Our comments have been detailed through your paper, but I wanted to reiterate the question about the timing of Mask OE. To avoid any developmental side effects, we suggest that Mask Overexpression occurs after the flies have grown up to avoid any complications. Doing this may also elucidate what exactly the flies are dying of that Mask circumvents. <br /> In reference to Figure 2, we were wondering what the downstream effects of upregulating GPCR signaling in DANs are. Additionally, a graph comparing the lifespan extension due to upregulating GPCRs vs Mask overexpression could be helpful in showing the importance of Mask to this system. It’s possible that Mask OE is restructuring the Dopaminergic system, maybe creating a separate pathway through which lifespan extension is facilitated. <br /> In reference to Figure 7, we suggest using a bar graph to visualize the data rather than survivorship curves. Switching to a bar graph could make the increases/decreases in lifespan in each experimental condition more clear. <br /> This was a fascinating paper to read and thank you again for the opportunity to read your science!

On 2024-12-01 16:53:12, user Clement Kent wrote:

Interesting work which advances the field. <br /> Just recording a few typos here. <br /> Line 103 refers to Allan(19) but no such item in the references.<br /> Line 8,16,18 - author Erclik listed with 2 identical affiliations.<br /> Line 188- put in the reference number. <br /> Lines 214 and 257 - tup is the Flybase standard name for this gene. I don't see why you insist on Islet. At any rate, italicize tup in 214.<br /> Line 393: optogenetically instead of optogenetic.<br /> Line 447: "for each of 32 hemilineages"<br /> Line 628 " obtained from"<br /> Lines 833,894,967 - update references

On 2023-02-25 01:22:56, user Kostas Tsirigos wrote:

It's interesting to see that papers publishing topology prediction methods in 2023 only compare themselves to one (1) method (!) and, of all methods, this is TMHMM (published in 2001). You could at least compare to DeepTMHMM, that has replaced it.

Not sure how that signifies any progress in the field of TM topology prediction methods.

Also, where are the training data? Could not see them on Github. These should be made available to the community.

On 2025-10-21 05:01:38, user Beatrice Opoku wrote:

This study by Bento et. al (2024) revealed that both Anopheles mosquitoes and plasmodium parasites are influenced by rhythms that have evolved to synchronize in order to optimize infection and transmission timing within the hole. Researchers asked whether the biological clocks helped the organism anticipate blood meals and modulate, parasite behavior, which would ultimately affect malaria transmission. There are three primary findings in this paper. The first finding was that roughly half of the mosquito salivary gland transcriptome exhibit 24 hour rhythmic expression. The expression peaks occurred twice daily once early in the day and once early in the night, which alliance with the gnome mosquito feeding patterns. These changes persist in both light/dark (LD) and constant darkness (DD) conditions. This was an indication that the genes in the salivary glands were driven by an internal circadian clock rather than by light cues or environmental factors. The second finding is that the Plasmodium sporozoite in the salivary gland also show cyclic transcription, that included cells involved in motility and host cell invasion. The authors ruled out the possibility that the observed rhythmic expression could be due to changes in sporozoite numbers due to replication or migration. After using immunofluorescence to visualize sporozoites at specific time points and testing for DNA replication, they found no evidence that replication explained the cyclic patterns. These results strengthened to idea that both mosquito and parasite are synchronized by daily cycles. Lastly, the found that mosquitoes are more likely feed at night and ingest more blood. Mice infected with sporozoites at night show higher liver parasites loads than mice infected during the day, indicating that sporozoites more infective at night. Together, these results suggest that the mammalian host, Plasmodium parasite and the Anopheles mosquito have evolved coordinated daily rhythms that maximize transmission success.<br /> The paper’s central contribution is the discovery that both the Anopheles mosquito vector and the Plasmodium parasite operate under circadian control and that their biological rhythms are synchronized to optimize malaria transmission. <br /> A critique that I would give to the main contribution is that experiments use Anopheles mosquitoes and Plasmodium berghei (a rodent malaria model). While appropriate for a controlled lab setting, it does not fully represent human malaria species or the natural mosquito environment. Conducting similar experiment in the future on human malaria parasites and field collected mosquitoes would strength relevance to the human population. <br /> If I were to write the significance of the paper on a 1-5 scale, I would give it a 4. It provides an important discovery with clear implication for control strategies. <br /> Overall, the methodology is mostly convincing. The researchers sampling every 4 hours, replication cross LD and DD conditions and combined transcriptomic and proteomic approaches provide a strong support for the presence of rhythmic gene expression. However, I have some questions about the results. The paper shows differences in expression levels between LD and DD conditions. What explains the discrepancies. This study would benefit from more discussion or experiments that addressed the source of LD and DD differences. Also, not all genes within the sporozoite showed clean cyclic patterns. For example, p52, CS, and AMA-1, displayed irregular or weak oscillations. It would be helpful if the authors addressed whether their irregulars reflect true biological variability or something external among the sporozoites. Also, a direct manipulation of clock components would strength the link between salivary gland genes and sporozoites. <br /> The most important limitation is generalizability. This study’s experiments focused on rodent-parasite/mosquito system under controlled conditions. This makes it unclear whether the observed data of rhythmic gene expression operates identically in natural setting with human malaria species and diverse mosquito environments. <br /> I would rate the writing 3/5 for clarity. The method’s section was dense and difficult to follow in some parts. There were technical terms and procedural details that were not fully explained for a non-specialist reader. For example. algorithms were named (e.g., ARSER, JTK_CYCLE), but a brief, plain-language description of what each contributes would help readers unfamiliar with circadian statistics. Also, some of the figures lacked proper labeling so it as hard to interpret what the data without having to refer back to the paper.  However, the abstract, introduction, results and discussion were readable and logically organized. <br /> Overall, the paper reframes malaria as a time-dependent system in which the vector, parasite and host rhythms are important for infection and transmission. Practical implication includes creating strategies that uses this knowledge such as insecticide spraying, bed-net use or prophylactic drug delivery to disrupt transmission timing. Moreover, this study can extend to other vector-borne disease such as Zika and dengue. Ultimately, this research contributes to the growing appreciation that temporal organization is central to biological function.

On 2017-09-16 16:07:35, user R. Ahmad wrote:

I was curious about the same thing. It seems like there should be a difference in total reads since you’d start with half the material. Although since they are blastomeres, you’d have to amplify for sequencing, and illumina uses additional amplification. There is the possibility for bias in that case. That’s probably why allele dropout has been such an issue. Science put this out in their blog, “This possibility of “allele dropout” has been the subject of discussion in the field ever since the Nature paper was published, says developmental biologist Robin Lovell-Badge of the Francis Crick Institute in London. Many scientists are now waiting for a response from Mitalipov, he says.”

On 2020-03-17 17:09:13, user Bryan J. Gonzalez wrote:

Amazing work and exciting findings about relationship of cell cycle progression and beta-cell differentiation. This is approach is very helpful to improve differentiation protocols and prevent teratomas when grafting.

On 2024-12-06 20:40:30, user Laurie Ailles wrote:

A more recent version of this article has now been published in Nature Communications: Nat Commun. 2024 Sep 19;15(1):8232. doi: 10.1038/s41467-024-52507-y

On 2019-06-06 01:36:12, user Joe Flood wrote:

Very irritating the way these authors insist on using ISOGG nomenclature for Y haplogroups, but can't be bothered keeping abreast of changes. 'R1b1a2' used for the Catacomb samples has at various times in the past been Z2103, M269, V88 and is now BY15383. My guess is they probably mean the Z2103 'Yamna' subclade'

On 2019-09-19 11:55:27, user yochannah wrote:

Hey y'all - nice preprint!

This popped up in my alerts as I'm one of the developers of InterMine, the software that PhytoMine is based on. I had a tiny improvement to suggest for the links you've included, e.g. https://phytozome.jgi.doe.g... - this format of link isn't necessarily permanent, so it's possible that in the future that link might not point where you'd expect it to.

The good news is that you _can_ get permanent links - just go to each page and look for the (admittedly rather tiny) "share" button on the top right. That will give a link that looks like this: https://phytozome.jgi.doe.g... rather than the original link, and it should remain up in the longer term. I hope that helps! :)

If you need any more help or advice, please contact support@intermine.org or the phytomine support line :)

On 2020-01-21 20:15:17, user Vincent Prevosto wrote:

A classic reference of interest here is Kuyper 1982's review. A quote:

The fibers to the dorsal horn apparently constitute the primordial component of the corticospinal tract, because they are present in all mammals. These fibers probably subserve a sensory control and in cat and monkey are mainly derived from the granular somatosensory cortex

On 2024-11-28 10:07:05, user Sholto David wrote:

Abstract: "Since 2009, successful hoaxes usually appeared at a year of one or more a year" - perhaps this should say "at a rate of one or more a year"? Current phrasing doesn't quite make sense.

On 2019-06-12 20:12:25, user Kayla R Nygaard wrote:

New version published in Autism Research: https://onlinelibrary.wiley.com/doi/full/10.1002/aur.2154

On 2025-08-13 17:31:55, user Ruslan Dmitriev wrote:

Now published in: <br /> ACS Nano<br /> Cite this: ACS Nano 2024, 18, 19, 12168–12186<br /> https://doi.org/10.1021/acsnano.3c12539 <br /> Published April 30, 2024<br /> https://pubs.acs.org/doi/full/10.1021/acsnano.3c12539

On 2017-10-20 03:19:52, user Grégoire Altan-Bonnet wrote:

A very nifty result linking cell-to-cell variability and sharpness of dose response in population of cells: this will find applications in tons of quantitative biological studies.

On 2020-08-26 16:46:04, user Jordan Berg wrote:

Metaboverse v0.2.0b is live!<br /> https://github.com/Metabove...

• Improved metabolite synonym mapping
• New time-course and multi-condition features added during motif exploration

See https://metaboverse.readthe... for a full description of updates.

On 2024-10-08 00:33:10, user Alexis Rohou wrote:

This comment is based on the version of the article published by Cell.

I have concerns about the validity of the cryoEM result presented in this paper.

The authors claim they obtained “a structure of the NINJ1 segment at 4.3 Ŕ. At this resolution, the following features should be clearly resolved in a cryoEM map of a protein containing alpha helices:<br /> • The pitch of alpha helices (~5.5 Å)<br /> • Large amino acid side residue side chains (e.g. Phe or Tyr, of which there are several in Ninj1)<br /> Neither of these features are demonstrated in the figures prepared by the authors.

Figure panels S2D and E are consistent with a resolution of ~ 8-10 Å, where alpha helices are resolved as tubular features. No regular indentation or ridging corresponding to the helical pitch is apparent in the figures, or upon visual inspection of the deposited map ( https://www.ebi.ac.uk/emdb/EMD-42301) "https://www.ebi.ac.uk/emdb/EMD-42301)") . No features corresponding to Phe or Tyr side chains are visible. At the resolution claimed by the authors, features corresponding to the side chains of residues Phe100, Phe117, Phe127 and Phe135, which are all located in alpha-helical segments (not in loops), would be expected to be resolved in the map, but they are not.

In my view, based on this map inspection the authors should not have made this resolution claim.

To support their resolution claim, the authors present Fourier shell correlation (FSC) curves from the cryoSPARC software in Figure S2C.

While the FSC curve shown by the authors does cross the 0.143 threshold at ~4.3 Å, the FSC curve exhibits pathologies, which should have alerted the authors to the possibility that the 4.3 Å resolution estimate may be unreliable and that the map should be interpreted with caution. Most notably the curve starts dropping off at around 15 Å, indicating that the signal-to-noise ratio in the map is significantly deteriorated at resolution of ~15 Å and beyond.

After completion of 3D refinement, cryoSPARC also outputs a second FSC figure, which includes an additional curve (“Corrected”), which accounts for effects of masking on resolution estimation by the FSC. It is unfortunate that the authors didn’t include this in their manuscript.

The most likely explanation for this pathological FSC resolution estimate and its mismatch with the features resolved in the map is that the 3D refinement failed (due to high noise, or preferred orientation, or other pathologies in the dataset or in the refinement parameters), leading to overfitting to a local minimum in the scoring function.

Indeed, the validation report for the deposited map and PDB (EMDB: 42301; PDB: 8UIP; https://files.rcsb.org/pub/pdb/validation_reports/ui/8uip/8uip_full_validation.pdf ) contains evidence of overfitting during refinement. The orthogonal projections of the raw map (Section 6.1.2 of the validation report) show overfitting artefacts, as do the orthogonal standard deviation projections in false color of the raw map (Section 6.4.2).

Such overfitting causes artifically inflated FSC values, which may help explain why the FSC-based estimate of resolution was wrong in this case.

Given a map of this quality, it’s unclear how the authors built an atomic model of Ninj1. In that respect, the publication’s methods section is not detailed enough. Table S2 indicates that the authors started from a computational structure prediction from AlphaFold2. Given the lack of any features in the map to help place any residue side chains, I assume the location of key residues mentioned in the paper originated from this computational prediction rather than from the cryoEM result itself.

The lack of support for the modeled atomic coordinates from cryoEM is also made evident in the EMDB/PDB validation report by the unusually poor Q and atom inclusion scores for a map/model of the claimed resolution. A Q-score of 0.04 is unusually low and shows very little map-to-model fit.

These metrics, together with a visual inspection of the map and model as deposited, suggests that a significant fraction of the sequence was built outside the map and that the cryoEM result does not support the authors’ atomic model beyond the general shape and relative orientation of parts of the alpha-helical segments. The positioning of individual amino acid residues is not directly specified by the cryoEM result and may have come mostly from the AlphaFold model the authors used as a starting point.

Note: the above observations should be not be taken as having any bearing on other key cryoEM-based observations in the paper, such as the curvatures of Ninj1 assemblies, which are supported by two-dimensional class averages of cryoEM images and not affected by overfitting in the later 3D refinement process.

On 2024-02-12 19:19:36, user Alejandro wrote:

The Supplementary Information is missing

On 2024-03-12 20:49:58, user Bennett Fox wrote:

Hi bioRxiv, this paper is now published: https://doi.org/10.1038/s41...

thanks!

On 2020-07-01 13:06:21, user Jan Nagel wrote:

Very nice paper! Ancestral state reconstruction is quite sensitive to missing data and taxa. Do you think the conclusion that homothalism is the ancestral state in Phyllostictaceae will remain unchanged if more species' thallism is determined and added to the reconstruction?

On 2021-10-01 02:23:20, user John McBride wrote:

Thanks for this work, it's good to see someone evaluating AlphaFold in this way.

Maybe I missed something in the article, but I fail to see why one should expect differences in pLDDT to correlate with either changes in stability, or fluorescence. To my understanding, changes in pLDDT can be either that the algorithm performs worse, or that the protein is predicted to be more disordered / flexible. My personal impression is that there is no robust rationale for expecting a priori that pLDDT should strongly correlate with either stability, or fluorescence. This point is pivotal to the entire paper, so I'd be more convinced if it were addressed.

On 2023-09-14 13:49:29, user Benoit B. wrote:

We advise readers that our preprint has been published and a link will be forthcoming.

On 2020-03-30 12:26:25, user Afolake wrote:

Waoh! I have been working on this gene for five years now and is amazing and as well mixed feeling to see this paper published before we could get my manuscript out. I hope we will be able to collaborate with your research group on FAM111B proteins for future work. Congrats on this beautiful and thorough work.

On 2020-09-09 09:33:15, user Yorgo Modis ???????????????? wrote:

This article has been accepted for publication by Nucleic Acids Research, DOI: 10.1093/nar/gkaa785

On 2025-08-26 14:09:40, user Jiajun Zhang wrote:

This preprint seems to be published on 07 May 2025, but doesn't get the link updated yet.<br /> (Tavakoli, M.R., Lyudchik, J., Januszewski, M. et al. Light-microscopy-based connectomic reconstruction of mammalian brain tissue. Nature 642, 398–410 (2025). https://doi.org/10.1038/s41586-025-08985-1 )

On 2020-05-27 14:40:38, user Vaughn Cooper wrote:

Note: this was submitted on 20-05-11 but the journal delayed posting here.

On 2020-09-15 15:27:02, user Duggy wrote:

Can we get the exact link for the this dataset? <br /> I am unable to find the dataset in the provided link (http://gigadb.org/site/index) "http://gigadb.org/site/index)")

On 2018-10-30 20:44:03, user Michael Hoffman wrote:

This preprint seems to be missing a Methods section referred to within. Methods are not described with the minimum level of detail one might expect for a scientific publication.

On 2020-01-09 18:49:08, user Hannah Takasuka wrote:

Under the "SHIELD Processing" section under methods, I believe that the first reference should be 13 and not 3.

On 2020-10-10 18:34:08, user Kristen Panfilio wrote:

Now published in Communications Biology: https://doi.org/10.1038/s42...<br /> With RNA-seq data available at NCBI under BioProject #PRJNA645519

On 2020-06-23 16:05:55, user ???? Dr. Jennifer Glass ???? wrote:

What a fascinating study! Wonder if you had a chance to look at our recent study on a similar topic: https://www.biorxiv.org/con...

On 2020-06-05 16:46:53, user Mariana Mateos wrote:

The final version of this manuscript is now published and freely available at https://www.frontiersin.org/articles/10.3389/fmicb.2020.01080/full

On 2025-11-06 19:00:54, user Joyce Lee wrote:

https://www.nature.com/articles/s41467-022-31238-y

On 2020-05-12 11:33:05, user Taekjip Ha wrote:

Thank you very much for sharing your interesting manuscript!<br /> We used your preprint as one of the journal club papers in the Single<br /> Molecule & Single Cell Biophysics course for graduate students of Johns<br /> Hopkins University during the Covid-19 lockdown. Students also practiced peer<br /> reviews as the final assignment. I am submitting their formal reviews here <br /> and hope you find them useful.

Taekjip Ha

Reviewer 1.

In this study, Laprade et al. engineered Hela 1.3 based cell lines withCRISPR-Cas9,<br /> such that telomerase RNA (hRT) can be tracked with the MS2-MCP system.Fluorescence<br /> signals from single hrT molecules were used as readout of telomerase activity.Previous studies<br /> employing FISH identified hRT to be predominantly localized in Cajal bodies.Laprade et al.<br /> present results that contradicts this, showing that only 10% of hRT reside inCBs. The conflicting<br /> results can be attributed to the longer residence time of hRT in CBs as opposedto that in the<br /> nucleoplasm. This finding boasts the strength of studies interrogating thedynamics, and not<br /> static properties, of a biological system, made possible with technologicaladvancements in<br /> super resolution microscopy.<br /> The argument presented in this paper is that short interaction times are basedon<br /> TPP1-TERT interaction and RNA-DNA base-pairing is responsible for the longerinteraction<br /> times. These two types of interactions are correlated with the scanning andengaged diffusive<br /> behavior of telomerase bound to telomere. One important control construct tosupport this<br /> argument may be a TERT knockout cell line. The propensity for hRT alone tointeract with the<br /> telomere solely based on base-pairing can be probed, which seems plausible in aPOT1<br /> OB-fold deletion context. The observation that short interactions becomenonexistent as<br /> long-lasting co-localization and slow diffusive states persist would furtherstrengthen the model.<br /> Since the study infers endogenous TERT binding based on hTERT and hTERT-K78E<br /> overexpression assays, that telomere-bound hRT is accompanied by TERT is mostlyassumed,<br /> but confirmation studies of hRT dynamics without endogenous TERT would stillhelp the<br /> interpretation of telomerase diffusive and interactive behavior. On a relatednote, comparing<br /> telomerase diffusion coefficients with that of existing literature, especiallyfrom single particle<br /> tracking studies utilizing Halo-tagged TERT, can strengthen the authors’ claimsof the different<br /> diffusive states. Taking the logarithm of the diffusion coefficient seems to becustomary in this<br /> field, but I am curious whether the raw distribution of diffusion coefficientscan also resolve<br /> different diffusive states.<br /> One reservation I have about the mechanistic explanations in this paper is thatHeLa 1.3<br /> cells are known to have long telomerase. It’s tempting to ask whether it ispossible that the<br /> scanning behavior of telomerase differs based on the length of the telomerase,since the<br /> telomerase retention step has not been delineated in lower eukaryotes. It wouldbe interesting to<br /> test if the proportions or lengths of short and long interactions scale with thelength of the<br /> telomerase.<br /> Provided that cancer lines see inherent variability in TERT expression, it isperhaps<br /> unsurprising that only up to 50% of cells found hRT-bound telomeres. On top ofthat, it’s difficult<br /> to tell how often one would come across false negatives i.e. bound hRT withoutthe<br /> TRF1-mCherry signal. Some measure of the variation of the number of visibletelomeres would<br /> be good, just to have an idea of the error associated with “% telomeres withhTR” data points.<br /> Grace Taumoefolau<br /> Such multidimensional variability has implications for statistical power, sosome notes on the<br /> specific statistical test employed in relation to the number of measurements andeffect size in<br /> the methodology section would be nice. Notably, several analyses comparecategorical data of<br /> multiple categorical treatments, so a pairwise t-test would be insufficient,assuming that’s what<br /> the p-values are based on. It is also stated that the first decay rate term ofvehicle and<br /> GRN163L in Figure S5H is statistically dissimilar but the short interactionsstill seem somewhat<br /> impacted by GRN163L. I would like to see the exact p-values that were deemed<br /> “non-significant”. To take things further, perhaps a maximum likelihood estimateensuring that<br /> the survival probability curves are bi-exponential and not single exponentialwould raise the<br /> confidence that RNA-DNA base pairing leads to long retention times.<br /> One final minor comment I have is that there is a typo in the figure legend ofFigure S5. It<br /> should be Figure S5E (F) and Figure 5F (G).<br /> All together, the article presented compelling evidence for their final model oftelomerase<br /> dynamics and interactions, complementing and expanding known details abouttelomerase<br /> maturation and recruitment process.

Reviewer 2

In this paper, Laplade and colleagues aim to explore how telomerase andtelomeres are<br /> spatiotemporally coordinated within the nucleus to enable telomereelongation. To this end, they<br /> examine the dynamics of telomerase assembly andits recruitment to telomeres at the single-molecule<br /> level. Their experimentalapproach is to use the MS2-GFP RNA tagging system, in conjunction with<br /> cleveruse of photoconvertible fluorophores and FRAP, to visualise and track individualRNA molecules<br /> of the telomerase ribonucleoprotein in living cells. The authorsare able to observe hTR (RNA<br /> component of telomerase) trafficking dynamics inand out of Cajal bodies, where the proposed site of<br /> telomerase holoenzymeassembly. They also measure the relative dynamics of telomerase colocalized<br /> totelomeres and propose a novel “Recruitment-Retention” model of telomerasetargeting. Finally, the<br /> authors apply their system to show that acancer-associated shelterin complex mutation, POT1, may<br /> elongate telomeres bypromoting retention of telomerase.

Given that telomerase dysregulation is a common feature of cancers and animportant potential target<br /> for therapy, the direct measurement of individualmolecular dynamics provides valuable experimental<br /> data that furthers ourunderstanding of telomere homeostasis mechanisms. The authors make<br /> thoroughefforts to validate their system and characterise the dynamics of differentbinding states<br /> before manipulating conditions. Through their live-cellhTR-targeted single molecule approach, the<br /> authors make several novelobservations. Some of these challenge previous assumptions in the field<br /> that hadbeen based on fixed bulk IF-FISH data. Notably, they show telomerase and S-phasetelomeres do<br /> not interact within CBs, making the widely accepted “handovermodel” of CBs bringing together<br /> telomerase and telomere sequences highlyunlikely. They also show that contrary to IF-FISH reports,<br /> only a small subsetof hTRs within the nucleus localise to the CB. Most importantly, the authorsfind<br /> that telomerase oscillates between an initial high-mobility “scanning”state and a low-mobility<br /> “engaged” state at the telomere, in a manner dependenton template RNA-ssDNA base-pairing<br /> interactions. Moreover, the two modes aredifferentially targeted by drugs and mutations, suggesting<br /> novel functions thathave not been characterised before.

Overall, the authors use appropriate controls and are careful to validate theirmethods. However, one<br /> major issue present throughout the paper is a lack ofproper statistical information. While the<br /> figures do show p-values, the authorsnever indicate what statistical tests were used to obtain those<br /> p-values.Without this crucial information, it is difficult to determine how appropriateor accurate<br /> the statistics are for these data.

Another important point is that the telomerase holoenzyme requires both thecatalytic hTERT and the<br /> RNA template hTR. Although some experiments involvemanipulation of hTERT expression levels or<br /> activity, this study largely followsonly the single-molecule dynamics of hTR. Although the authors<br /> mention in theirdiscussion that several key findings (including the “scanning” vs<br /> “engaged”behaviours at the telomeres) diverge from previous single-molecule dynamicsstudies using<br /> Halo-tagged hTERT and offer possible explanations for thesedifferences, the fact remains that we<br /> cannot ascertain if the dynamics describedin the paper actually reflect those of telomerase or just<br /> the isolated hTR. Asuggestion for revision would be an experiment where both MS2-tagged hTR<br /> andHalo-tagged hTERT are expressed together in the same cell, enabling the trackingof both enzyme<br /> components and determining where the dynamics do not coincide.

A minor suggestion is that the authors may wish to edit their introduction forgreater clarity,<br /> coherence and cohesiveness. For example, the authors mentionthe possibility of a “potential… backup<br /> pathway during telomerase assembly” withregards to coilin loss, but this is never again referenced<br /> within the main bodyof the paper. A substantial section of the introduction also focuses on<br /> theinteraction between hTERT and TPP1; while this interaction provides therationale for some of the<br /> deletion experiments in the paper, it is not the mainfocus, and a more organised overview of ideas<br /> in the field concerning telomerasetargeting to telomeres would be more useful for the reader.

On 2016-08-22 05:17:31, user Rajat Rohatgi wrote:

Check out our recent 3.2 Angstrom crystal structures of near-full length Smoothened in its active, cholesterol-bound state in comparison to an inactive state bound to the anti-cancer drug vismodegib:<br /> http://www.nature.com/natur...

On 2017-10-11 20:27:16, user Keith Robison wrote:

Some typographic notes. TBLASTN should not be written 'tBLASTn', particularly in a variable space font. "CLUSATLX" probably should be CLUSTALX. Default parameters is never a reproducible description; these can be changed in future versions.

In introduction, it is incorrect to say "Homology can be divided into orthology and paralogy according to whether they are present in the same species" -- two genes can be different species yet still be either orthologs or paralogs, depending on whether their ancestral relationship involves a gene duplication event.

On 2024-07-02 11:48:52, user Hervé Acloque wrote:

The revised and peer-reviewed version of this paper is now available in the special issue of Genomics dedicated to FAANG:<br /> https://doi.org/10.1016/j.y...

On 2019-04-25 03:42:07, user Dawen Cai wrote:

This manuscript is published on:<br /> https://academic.oup.com/bi...

On 2017-03-27 15:13:48, user Casey Dunn wrote:

Great paper! A very minor comment - "although" should be capitalized on line 105.

On 2024-06-28 09:25:30, user Helle Jorgensen wrote:

Published after peer review<br /> Lambert, J., Oc, S., Worssam, M.D. et al. Network-based prioritization and validation of regulators of vascular smooth muscle cell proliferation in disease. Nat Cardiovasc Res 3, 714–733 (2024). https://doi.org/10.1038/s44...

On 2023-07-04 06:56:22, user Eduardo Oliveira wrote:

Would it be possible to share the sequences of the 228 generated proteins?

On 2021-10-20 09:46:31, user Shenghong He wrote:

This study has now been published in Journal of Neuroscience (https://doi.org/10.1523/JNE... "https://doi.org/10.1523/JNEUROSCI.0770-21.2021)").

On 2016-05-26 03:25:27, user Charles Simmins wrote:

Two observations. As to method of transmission, is Zika conjunctivitis infectious? Many forms of viral conjunctivitis are highly contagious and I would suggest this may be the case with Zika.

My second point has to do with the reported numbers of Zika cases. The numbers for laboratory verified cases are quite low in Brazil. Most diagnoses seem to be made clinically, and that is a problem.

Dasgupta, et al, found that 85 percent of patients with at least one Zika symptom tested negative for the virus. <br /> http://dx.doi.org/10.15585/...

Dirlikov and colleagues looked at a similar set of patients in Puerto Rico, http://dx.doi.org/10.15585/...

The total for both studies was over 10,000 subjects with good reason to believe they had been exposed to Zika. Combining both studies, less than 15 percent of those tested had Zika. It appears that diagnosing Zika through signs and symptoms produces a serious rate of error. I would suggest that the strong possibility exists that the number of Zika viral illnesses in Brazil is much smaller than first believed, due to incorrect diagnosing.

On 2017-08-25 05:52:23, user C. Ghang wrote:

Does hydrogen peroxide damage the RNAs and effect the result of sequencing?

On 2018-03-25 16:50:43, user Sven Bradler wrote:

Great study,but one major reference has been overlooked that already reported the horizontal gene transfer of pectinase genes from bacteria in Phasmatodea: Shelomi et al. 2016 https://www.nature.com/arti...

On 2019-03-19 06:33:52, user Xing Xu wrote:

I will suggest putting APOE somewhere in the title. It will give the article a huge boost, considering how notorious APOE4 is in Alzheimer's community.

On 2020-04-25 13:17:54, user David Sherman wrote:

Nice work and glad to see you had success with various megaenzymes, including PikAIII. We found significant value in our recent paper <br /> DOI:10.1021/acssynbio.0c00038 using Orbitrap proteomics to confirm presence of the key biosynthetic enzymes. Nice analysis of phosphopantetheinylation of the PCP/ACP. Any sense of IVTT system to handle proteins that express poorly or are insoluble from traditional E. coli production methods?

On 2020-03-11 03:02:04, user V Zenkov wrote:

Summary of the paper:

The authors propose a new deep learning framework, NetFLICS-CR, which speeds up hyperspectral lifetime imaging. It is an extension of a previous product, NetFLICS (from https://arxiv.org/abs/1711.... "https://arxiv.org/abs/1711.06187)"). Both strategies use single-pixel imaging, which is typically based on an inverse problem that reconstructs 2D images from measurements featuring a series of patterns. They speed up the process using compression sensing. NetFLICS is 4 orders of magnitude faster than the previous solution and requires less photons, but it has a compression ratio of 50% for 32x32 images. NetFLICS-CR improves by staying fast, and works on 128x128 images. For perspective, NetFLICS-CR reduces the acquisition time from 2.5 hours at 50% compression to 3 minutes at 99% compression while using a single-pixel Hyperspectral Macroscopic Fluorescence Lifetime Imaging (HMFLI) system.

Comments:

• is there a reference for TVRecon? There is a summary in the supplement, perhaps it should be referenced in the text.

• there should be a consistent numbers of iterations. Why are there sometimes only five? Some justification would be appreciated.

• it would be very helpful to have the code on GitHub for reusability.

• what are the limitations of this research?

• Figures 1,2,3,4: The legends would benefit from having summary sentences and conclusions. (The current legends only guide the reader to what is in the figure.)

• Figures 1,3: If the paper is printed in gray scale, the lines in the graphs look largely similar.

• Figure 1b: What are the units? Also, why is mean absolute error (MAE) used instead of mean squared error (MSE)? Mean squared error is traditional, as far as I know.

• Figure 1b: The top right of both graphs shows a "zoomed in" view of the bottom right rectangles. However, it is not immediately clear that that is what is happening because the rectangles have different proportions. One way to help would be to add numbers on the axes to show that the epoch number / MAE are "zoomed in".

• Figure 1b: It could help to add the title "compression ratios" next to the compression ratios at the top of the figure.

• Figure 1b: The legend could say "six different compression ratios" to be even clearer.

• Page 2: The text below Figure 1b defines Compression Ratios as #acquired patterns / #full patterns, but the examples in the text are instead the ratio (#full patterns - #acquired patterns) / #full patterns. I believe the latter is used throughout the paper.

• Figure 2b, especially the top graph: The legend may be confusing because all the lines seem to overlap, with the only visible color in the graph being yellow. This might be fixable with smaller points.

• Only one metric was used (Structural Similarity Index Metric) in the paper, which could lead to bias. The optimization is then based on the same metric used to assess the performance of the model, which could be biased toward that metric. Having multiple metrics / measures of performance could help remove this possible bias.

• Figure 3c: I would find 3 panels easier to read than the 3D graph.

• Figure 3c: P and I/R are labeled backwards.

• Figure 3c: The legend says NetFLICS, but it seems like it should say NetFLICS-CR. (This is confusing because NetFLICS is also a thing!) The legend says "per CR" and that would make more sense in the context of the paper.

• Figure 4a: Next to the red images, why is External CCD turned 90 degrees to the left while other text in the figure is turned 90 degrees to the right? If all the text is turned in the same direction then it will be more consistent.

• Figure 4b: TVRecon and NetFLICS-CR are not next to each other in the graph, which makes it difficult to compare them.

• Figure 4b: Why is this the only figure with error bars?

• There is no actual labeled conclusion section - the conclusion begins with "in conclusion" in the text. Having a set-off conclusion section would make it easier for someone to quickly read the conclusion.

On 2023-11-13 18:38:19, user Curtis Loer wrote:

This paper needs to adopt standard C. elegans nomenclature for strains / mutants. For example, the strain designation for cat-1(ok411) is RB681 (from the CGC, if not outcrossed). 'UA57' IS a proper strain designation, but it would likely be clearer to indicate overexpression of TH in dopaminergic cells. In the abstract it would be appropriate to use the 'vesicular monoamine transporter / VMAT' terminology for the protein encoded by the cat-1 gene, and also simply state this is a cat-1 mutant (familiar to C. elegans biologists). Also, simply making a double mutant by routine genetics methods does not warrant calling a new genetic construct (MBIA) as a 'novel strain.'

On 2025-03-19 18:15:47, user Ana Nascimento wrote:

The peer-reviewed version of this pre-print can be found at: https://www.nature.com/articles/s41598-020-69944-6

On 2022-07-20 18:37:37, user Ariel Mundo wrote:

This paper has been published in Statistics in Medicine

On 2018-02-14 02:26:29, user James Lloyd wrote:

First of all, thank you for this really useful resource, I look forward to being able to get my hands on it as it will help with ongoing work.

One thing I noticed, in Table S2, the plasmid pCK020 (Pro+5U) is described as WUSCHEL but then the rest of the description describes the gene ID (At3g111260) and function (root stem cell niche / quiescent center-specific promoter) of WOX5. I assume what is present in this plasmid is the promoter + 5' UTR of WOX5 and not WUS. Is that correct?

Also, Table S2 has multiple asterisks after the plasmid names but this is not defined within the Tables legend.

Thanks again for creating this terrific resource.

On 2019-02-27 16:08:28, user HYC wrote:

Excellent work, congrats Lorenzo @lorenzpasquali, Mireia @mireia_bioinfo and all the team!!

On 2020-06-01 04:19:59, user Lachlan Coin wrote:

Hi authors, nice work! However this statement: " However, prokaryotic transcriptomes have not been characterized on the genome wide level by native RNA-seq approaches so far as prokaryotic RNAs lack a poly(A) tail, which is required to capture the RNA and feed it into the nanopore. " is not correct. We did something similar in K. Pneumoniae : https://doi.org/10.1093/gig...

On 2022-10-24 20:02:15, user Maiwenn KERSAUDY-KERHOAS wrote:

Supplementary videos are available from https://www.youtube.com/pla...

On 2023-08-09 18:40:04, user Jiahua Tan wrote:

The perspective of tackling the ratio compression caused by the isolation interference in this paper is interesting. It seems that the tool is designed for single plex experiment if I am correct. Are there some ways to run the tool for multiplex experiments simutaneously so that we can make use of all available cores in parallel processing to reduce the run time?

On 2019-03-20 16:25:43, user Rosa Fasching wrote:

That's why the pioneers (#KrebstherapiederAltmeister) said long time ago, no matter what kind of diet it is (Bircher-Benner, Gerson, Mayer, Kuhl etc.) it all depends on the microbes in your gut. They are the key players and "chefs".

On 2017-09-28 23:52:51, user Josie Briggs wrote:

Interesting study. Lectin sorting isperhaps a better way to get cell or segment specific isolates. Certainly beats the hard work of microdissection.

On 2018-04-12 23:08:18, user Pedro Duarte wrote:

(121-2) ..."while both I and P exclude the many Hymenoptera that are parasitic on other parasitoids (“hyperparasitoids”)."<br /> So, hyperparasitoids are in... C?

On 2020-01-27 10:40:55, user New CoV wrote:

the generation time is fixed for the new coronavirus to be the same as for Sars in the analysis and r0 for new coronavirus is somewhat higher - an alternate explanation would be shorter generation time for the new coronavirus?

On 2024-01-14 22:54:39, user Keji Zhao wrote:

Very interesting study --- providing insights into how MutSb and CNG cooperate to drive the expansion of trinucleotide repeats in Huntington's and other relevant diseases.

Do the authors know how well these trinucleotide repeats form nucleosome structure in cells?

On 2020-12-04 18:48:54, user Reza and Nasim Bekheirnia wrote:

Very interesting work. Congratulations! I was wondering if FACS is a required step? It seems that scRNA-seq could be done w/o FACS?

On 2019-08-09 22:58:37, user microbial_minded wrote:

I would like to thank the authors for uploading this interesting manuscript. I am hoping the authors can provide some additional comments and clarification. In figure 1, Ralstonia is indicated present in the basal plate from collected placentae based on fluorescence signal from a Bacterial-specific probe (panels C and H) and a Ralstonia-specific probe (panels D and I). DAPI counterstaining is shown in panels E and J. Arrows denote the location of Ralstonia in panels C, D, H, and I.

However, there are several punctate foci (bottom right of panels – two in C/D and one in H/I) that are not denoted as being Ralstonia in these same panels despite having signal in both channels. These same unlabeled points are also present in composite image panels G and L. There are other points that appear in the panels with the bacterial-specific probe and Ralstonia-specific probe, but those points do not appear to overlap, unlike the above mentioned three points and the arrow-labeled points. Of further note, DAPI staining of total nucleic acid does not appear where the Ralstonia cells are denoted, even in cases where the Ralstonia cells do not overlap with the DAPI-stained host cells. This is especially pronounced in panel L, with the bottom point being ~5um away from the nearest stained nuclei. This is a curious observation given that DAPI has been used to stain Ralstonia in the past (https://doi.org/10.1128/AEM... and http://dx.doi.org/10.1016/j... "http://dx.doi.org/10.1016/j.colsurfb.2012.09.044)") and the authors clearly demonstrate the ability of DAPI to stain Ralstonia in culture in supplemental figures S1 and S2.

I have three questions related to these data:

1a. Do the authors consider those unlabeled points in Fig. 1 to be Ralstonia? <br /> 1b. If not, what differentiates the unlabeled points from the labeled points?

1. Why is there lack of DAPI staining where the Ralstonia cells are in Fig. 1, as DAPI should also stain bacterial DNA?

Thank you in advance for your time and response.

On 2024-11-06 01:23:49, user sun zhen wrote:

The results of this paper are not credible.

Based on the gel result, we can see that only introns are removed by oligo dT purification, lots of precursor and nicked RNA still in the products. Obviously, there will be a decrease in dsRNA compared with RNA kit purification which removed nothing from the crude circRNA products.

When it comes to in vitro expression test, I am surprised to see even the expression of RNA kit circRNA is better than HPLC purified circRNA.We have also tested and compared RP-HPLC purified circRNA (circEGFP and circFLuc from GenScript) before, they got much higher expression than RNA kit purified circRNA or RNase R treatment crude circRNA.

As the purity of circRNAs is very important for their expression (which can reduce their immunogenicity) based on previous reports, the in vitro or in vivo data in this paper are not convincing.

On 2022-02-23 07:50:47, user Stefan Oehlers wrote:

This manuscript has been published in Scientific Reports with a slightly different title:<br /> Morris S, Cholan PM, Britton WJ, Oehlers SH. Glucose inhibits haemostasis and accelerates diet-induced hyperlipidaemia in zebrafish larvae. Sci Rep. 2021 Sep 24;11(1):19049. doi: 10.1038/s41598-021-98566-9. PubMed PMID: 34561530; PubMed Central PMCID: PMC8463691.

On 2018-03-26 20:25:53, user stephen shepherd wrote:

My excellent reviewers at Frontiers pushed for a reliability assessment of our behavioral scoring. Since this is the foundation for behavioral analysis, this is a very reasonable requirement.

Unfortunately, after conducting a partial rescoring of the data, the reliability was substantially lower than I had hoped. Mismatches reflected both uncertainty in timing of events and soft category boundaries around behaviors like 'facing' and 'looking'.

While I believe the gist of the scoring, and the subsequent analyses, are correct, please take these findings with a large grain of salt pending reliable rescoring of subjects' behaviors by multiple observers. This will be a large undertaking, and I am not sure when it will be completed.

On 2019-11-07 16:10:35, user Ashot Margaryan wrote:

The paper is currently in review, so it might take few more months after the resubmission.

On 2024-07-17 15:41:07, user Matilda wrote:

https://www.cell.com/iscience/fulltext/S2589-0042(24)01665-1

On 2019-05-19 03:06:39, user Mingye Wang wrote:

Now http://doi.org/10.1093/infd...

On 2021-03-15 16:17:39, user Kai He wrote:

We also found the Qingling-Daba Corridor in small mammals <br /> https://onlinelibrary.wiley...

On 2024-04-24 14:37:31, user Bonnie Thiel wrote:

This manuscript has been peer reviewed and published: https://journals.plos.org/p...

On 2023-10-05 22:27:50, user VK Ulaganathan PhD????????, M.Tech ( wrote:

Chimeric Cytokine Receptor expressing T cells (CCR-T cells)??

On 2021-07-01 04:07:56, user Javier Rasero wrote:

Hi,

First of all, congratulations for this amazing manuscript.

Could it be possible to also show the predictive accuracies using the coefficient of determination R2? Maybe in a supplementary material? This is usually the recommended metric for quantifying regression accuracy [1]. Pearson correlations for estimating out-of-sample performances can sometimes be biased.

In any case, wonderful and insightful paper!

[1] Poldrack RA, Huckins G, Varoquaux G. Establishment of Best Practices for Evidence for Prediction: A Review. JAMA Psychiatry. 2020;77(5):534-540. doi:10.1001/jamapsychiatry.2019.3671

On 2022-08-25 15:11:03, user Sam Nooij wrote:

I would like to thank the authors for sharing an updated version (v3) of such a fascinating manuscript. I enjoyed reading it and I have a couple of questions/points of feedback that I would like to share.<br /> 1. Do I understand correctly that in lines 133-134 the authors state that higher percentages of read recruitment to donor MAGs suggest bacterial colonisation? This seems to me very indirect evidence and not a justifiable conclusion based on this observation alone.<br /> 2. In figure 1, Canada is shown slightly bigger than the other countries. Is this because the donors and patients from the current study are also from Canada? I could not find this information in the text.<br /> Furthermore, I like that the authors made a distinction between industrialised and less industrialised countries. Would it be possible to change the colours slightly (e.g. make the red magenta) to make it easier for colour blind people to see?

Also, the blue and purple rows are somewhat difficult to distinguish for me. Is this intentional, as the donor and post-FMT recipient microbiota are supposed to be similar?<br /> 3. I find it interesting that the authors found that only 16 and 44% of donor microbial genomes were detected in all donor metagenomes, suggesting that only a minority of bacteria is stably present over longer periods. (Lines 180-182.) Have the authors considered doing similar analyses on these genomes to find if they are HMI and LMI bacteria?<br /> 4. I find the comparison made between short-read taxonomy and donor population detection (lines 184-188) a little difficult to follow. Would it be possible to rephrase this part or add a little explanation of what exactly is compared?<br /> 5. Lines 190-194 are also fascinating! Even with such small numbers, it is striking to see that more bacteria colonise from pills that from colonoscopic transfer. Could the authors speculate or provide additional info on why this may be the case?<br /> 6. From the final conclusion (lines 431-435) I gather that FMT or similar microbiota therapeutics are unlikely to (temporarily?) cure IBD. Do the authors have suggestions as to what might work better, and would they like to share their perspectives on promising new treatment options?<br /> 7. And finally, what do the ellipses in supplementary figures 2 and 3 represent? I suppose they show some sort of area around each cluster centroid. A few extra words of explanation in the figure captions would be nice. This information is also not easily found in the analysis scripts. (And by the way, it is wonderful that the authors share all code and instructions on how to reproduce the analyses!)

On 2024-10-15 18:37:28, user Jacek Majewski wrote:

This article has been published in Cell Reports:

Chromatin dysregulation associated with NSD1 mutation in head and neck squamous cell carcinoma

Cell Rep. 2021 Feb 23;34(8):108769. doi: 10.1016/j.celrep.2021.108769

On 2019-06-27 01:34:16, user Marcello wrote:

The corresponding author of this paper was mistakenly listed as Marcello Rosa. Please send any correspondence to the first author, Dr. Hsin-Hao Yu, at the address: hhyu00{at}gmail.com.

On 2021-05-24 22:36:53, user Howard Salis wrote:

Readers note: The authors make the following assumptions in all their analysis, which critically undermines several of their most important (and controversial) conclusions:

1. The authors are treating their ribosome profiling experiments as a measurement of translation rate. That's incorrect. Ribosome profiling measures the relative density of ribosomes bound to mRNAs. It is not a direct measurement of translation rate nor is it a direct measurement of translational efficiency.

Let me be clear with these terms: Translation rate is the number of proteins produced per mRNA per time. Translational efficiency is the number of proteins produced by a single transcript before it is degraded. Ribosome density is the translation initiation rate divided by the mRNA's translation elongation rate.

1. The authors assume that all mRNAs have the same translation elongation rate in order to convert ribosome densities into translation rates. For example, in their Equation 5, epsilon (translation elongation rate) is just a single constant, used throughout to relate translation rates to protein synthesis rates and protein concentrations. This is incorrect. mRNAs do not all have the same translation elongation rate. There is a whole genre of literature on synonymous codon usage, the effects of synonymous codons on ribosome translocation speeds, and the use of codon optimization to increase heterologous protein expression levels. Epsilon is not a single constant. At the very least, if we only care about protein concentrations, one must consider the CDS-averaged translation elongation rate for each mRNA transcript in the transcriptome.

The authors' conclusion "Translation initiation rates are similar across mRNAs and growth conditions" needs to be revisited without assuming that all translation elongation rates are the same. Likewise, the conclusion that "Total mRNA abundance matches the translational capacity" depends on the veracity of this conclusion. The conclusion that "Constancy of ribosome spacing across mRNA and nutrient conditions" is more accurately viewed as "Constancy of *ribosome density* across mRNA and nutrient conditions". Finally, the conclusion that "Total mRNA synthesis flux is adjusted to match translational capacity" is again dependent on the veracity of the first conclusion.

Concluding that all mRNAs have the same translation rate is bold. But it's not supported by data. If you see someone using ribosome profiling to make this claim, be skeptical!

On 2019-10-03 22:50:47, user Laura Hayward wrote:

The Supplementary Information will be posted later on. Meanwhile, the manuscript is largely self-contained

On 2017-09-05 13:36:00, user Stephen Buratowski wrote:

This is our first time using Biorxiv, so it's a bit of an experiment. I'm very excited about the model we propose here, which I believe explains a lot of previously puzzling TFIID findings. That's why I wanted to get it out to the field early. All I ask in return is that you cite us (this preprint or eventually the published paper) if you use our ideas. I look forward to hearing about any other data that support (or argue against) our model!

On 2021-09-08 08:19:11, user Rony wrote:

Published version: https://doi.org/10.3390/ijm...

On 2021-02-19 13:48:13, user robin khosla wrote:

Good Work. Very Informative app to survey the target reserach groups for initial stage researchers.

On 2021-05-11 06:38:22, user Christophe Leterrier wrote:

The manuscript refers to numerous Supplementary Figures and material, but they are not provided next to the preprint. Would it be possible to upload them as well? Thank you.

On 2022-01-12 14:21:34, user Rafael Najmanovich wrote:

Review of Improving AlphaFold modeling using implicit information from experimental density maps. https://doi.org/10.5281/zen...

On 2017-01-07 00:22:56, user Manuel Corpas wrote:

Please note that the 23andMe chip has a number of versions, and not all the analysed SNPs come from the Illumina HumanOmniExpress-24 format chip.

Hence you may find customers that joined 23andMe before this chip version whose SNPs may be slightly different, hence affecting results.

On 2017-09-04 10:57:08, user Sotirios Tsaftaris wrote:

To all interested readers, I also bring to your attention Phenotiki and the Phenotiki software (http://phenotiki.com/) "http://phenotiki.com/)"), which was published a year ago in the plant journal (http://onlinelibrary.wiley.... "http://onlinelibrary.wiley.com/doi/10.1111/tpj.13472/abstract)"). It is open, free, based in Matlab, but also available as executable, and relies heavily on award-winning machine learning to segment plants and count leaves, making it more robust to environmental variations and laboratory conditions.

On 2017-06-20 13:11:48, user @hugospiers wrote:

Would be great if the model could predict the distortions in judgments of space and time found in our new article in Cognition: http://www.sciencedirect.co...

On 2023-01-31 18:53:04, user Marco Incarbone wrote:

Pre-print review for Nielsen et al, bioRxiv 2023 <br /> (doi: 10.1101/2023.01.10.523395)

The manuscript by Nielsen and colleagues proposes a novel function of Arabidopsis DCL2 as a sensor of double-stranded RNA (dsRNA) that activates innate immunity through NLR receptors. This immune function of DCL2 is independent of its canonical function in RNA interference through small RNA. The strength of the study is in the genetic dissection of the autoimmune phenotype caused by DCL2 that was previously described in literature. The authors use phenotypic analysis combined with RNA sequencing in Arabidopsis to show that DCL2 triggers activation of immunity and an autoimmune phenotype through at least two NLR proteins (L5 and RPP9). This is a novel and important discovery. They show that this activity is cytoplasmic and not nuclear. Interestingly, immunity is also activated in tomato by DCL2 and moss by DCL3, suggesting this mechanism is evolutionarily highly conserved. These conclusions are well supported by data and well presented.<br /> However, the evidence for an antiviral function of this pathway is not sufficient for the strong claims made in this paper. References to virus are made in the title, the abstract, results and conclusions, and are an important part of the paper, so major concerns need to be addressed. These are listed below, with suggested experiments.

• The virus infection results are not conclusive. For example, in Fig 4A dcl4/l5 line 1 shows more virus accumulation than dcl4/l5 line 2 (which in turn accumulates as much virus as the dcl4 single mutant), while in Fig 4B the opposite is true. In fact, in Fig 4B the l5 mutation is not additive to dcl4 mutation in terms of antiviral defense. In Fig 4B the dcl2 mutant shows significantly less accumulation than WT, which is not the case in Fig 4A and in literature. All this points to high stochastic variability between samples, which can be expected when working with pathogens. It is highly appreciated that the authors show conflicting results between experiments, but these results need to be robust given their importance for the conclusions of the paper. One possible explanation for this variability could be the use of labile in vitro transcripts as inoculum. This could be solved by either randomizing the plants when infecting (not one genotype after the other, if this wasn’t done) and/or use systemically infected tissues from inoculated dcl2/dcl4 mutants as inoculum for rub inoculation (although this TCV clone has no CP, which could be a problem). In any case, the infection experiments need to be repeated with more biological replicates per genotype, in the key genotypes Col, dcl4, l5, rpp9, dcl4/l5, dcl4/rpp9.

• A VSR-impaired virus should not be used to test whether DCL2 has an RNAi-independent function. As this virus is very sensitive to RNAi, in this experimental setup this pathway cannot be uncoupled from the proposed dsRNA-DCL2-NLR sensing pathway. In addition, the P38 VSR is also the viral coat protein, loss of which could impact the viral life cycle and accessibility of dsRNA structures in vivo. On the other hand, the use of wild-type TCV, encoding a very efficient VSR, would reliably show whether DCL2 has a small RNA/AGO-independent antiviral function. In this case WT, dcl4, l5 and rpp9 should show similar levels of virus accumulation, while dcl4/l5 and dcl4/rpp9 should show increased accumulation, if the hypothesis is valid. As above, several replicates should be analyzed.

• A central conclusion in this paper is that DCL2 is a sensor of dsRNA that activates innate immunity through NLRs. While the fact that DCL2 is an RNAseIII strongly suggests that it senses dsRNA, there is limited experimental evidence for this (the knock-out of RNA decay factors in literature and the experiments with TCV-deltaP38 - see comments above). Two experimental approaches would significantly strengthen these conclusions. (1) Treatment with a dsRNA analogue such as poly I:C of the various dcl/NLR mutants described in the paper. Activation of immunity can be assessed by quantifying expression of immune-responsive genes as in Supplementary Figure 1 or as described in literature (DOI: 10.1111/pbi.13327, DOI: 10.1042/EBC20210100). Performing this experiment on WT, dcl2, l5, rpp9, dcl2/l5 and dcl2/rpp9 should genetically determine whether DCL2 activates innate immunity through L5 and RPP9 NLR receptors upon detection of dsRNA. (2) Perform infection experiments with phylogenetically distant RNA viruses, and test activation of immunity as in (1). Available and routinely used in Arabidopsis are, among others, Cucumber mosaic virus (CMV), Turnip mosaic virus (TuMV), Tobacco rattle virus (TRV), Turnip yellow mosaic virus (TYMV), Tomato bushy stunt virus (TBSV) and Turnip crinkle virus (TCV – see above). These virus species have very different proteomes, replicate on different organelles, have different capsid shapes, etc. If several of these activate immunity in a DCL2/NLR-dependent fashion, the case for DCL2 being a sensor for dsRNA in vivo would be far more robust. Using these viruses to infect Col, dcl4, l5, rpp9, dcl4/l5, dcl4/rpp9, then assessing viral accumulation, would indicate that in addition to activating immunity, this mechanism also has antiviral activity.

If these points are not addressed, I believe that claims of immune activation by viruses and antiviral activity should not be present as major conclusions in the paper. They could be suggested in the discussion. In addition, conclusions regarding dsRNA as the trigger for immunity should be tempered.<br /> It is my hope that these experiments will be conducted, and if the current conclusions of the authors are substantiated, this is a very significant discovery and advancement in plant virology and immunity.

Next are listed some minor points to address.

• The authors refer to antiviral RNAi function of DCL2 as being dependent on the genetic abrogation of DCL4. The authors state more than once that DCL4 is the main antiviral Dicer, with DCL2 dicing only when DCL4 is somehow overwhelmed by viral dsRNA. This view is not up to date, as more recent studies have shown that in WT plants DCL2 can be an active antiviral RNAi player, depending on virus species and/or tissue (e.g.: doi: 10.1038/nplants.2017.94; doi: 10.1093/nar/gkab802), suggesting that precise function and context of DCL2 in antiviral RNAi remain poorly understood. This should at least be mentioned.
• A whole section in the middle of the manuscript delineates the model proposed by the authors, while describing some of the results. In my opinion this interrupts the flow of the manuscript. The results should be presented as in the rest of the manuscript, while the model should be built in the discussion or at the end of the results. A partial model is also presented in Figure 2, taking away space for data.
• The authors end the final paragraph of the results with “when RNAi has been abrogated by mutation of DCL4”. KO of DCL4 does not abrogate antiviral RNAi, it reduces its efficacy. This should be corrected.

On 2019-02-19 13:39:41, user Marco Tripodi wrote:

Matsuyama et al. have made an early report in bioRxiv on a technical concern regarding the self-inactivating rabies (SiR) virus vector on which we recently published. In the absence of any peer-review we feel obliged to respond directly to Matsuyama et al. in <br /> order to give a context to their report and to obviate any concerns regarding SiR that others may have.

Further, although it is clear that Matsuyama have been investigating this issue for some time, we only learned of it a few days ago. We shall produce experimental data to illustrate our case in due course, but for now, with time pressing, we offer a reply of concept.

It appears that Matsuyama et al. received from a third party batches of the SiR in which mutations targeted the PEST domain, a critical regulator of self-inactivation of the vector. As the authors point out, these mutations (which are absent in the Addgene deposited plasmids) can naturally emerge during the viral amplification process due to the high rate of replication errors of rhabdoviruses. In particular, the characterised mutants carry a nonsense mutation just upstream to the PEST site, effectively reverting the SiR to a wild-type (WT), and hence toxic, ?G Rabies. The rest of their findings, and the observed cytotoxicity, are therefore the obvious consequence of the presence of the reported PEST mutation that transforms the SiR into a WT ?G Rabies.

Indeed, what they refer to as “SiR” in the manuscript is, in fact, a “SiR mutant PEST”.

The results of Matsuyama et al. confirm the requirement for an intact PEST sequence to sustain the self-inactivating behaviour of the SiR. This is perfectly in line with our model and the results of our published work. Once the PEST is rendered ineffective (via a mutation or otherwise) the SiR reverts functionally to a first generation, and hence toxic, <br /> SADB19.

Matsuyama et al. also claim that the substitution of a Cherry reporter with the CRE in a wild type SADB19 ?G Rabies would be, in itself, sufficient to eliminate cytotoxicity, albeit via an unspecified biological mechanism. However, when the SiR PEST mutant (i.e a wild type revertant SADB19 ?G-CRE-Cherry Rabies) is compared to a wild type SADB19 ?G-CRE Rabies, the former would appear to be more toxic than the latter. Given that the two <br /> viruses are functionally identical (since the SiR has lost the PEST domain and since both express the CRE) these findings appear to defy logic.

In fact, it is most likely that both viruses are equally toxic. However, the lack of an early fluorescent reporter in the ?G-CRE Rabies used by Matsuyama et al. probably does not allow them to observe most of the early cytotoxicity elicited during the early stages of the infection, due to the delay in the expression of the transgenic CRE-driven reporter. Instead, for the SiR PEST mutant (i.e a wild type revertant SADB19 ?G) they are able to <br /> track neurons for the entire infective period, given the early expression of the CRE-independent Cherry reporter. Once that is taken into account, cytotoxicity will likely result to be present, and identical, for the two strains, which are, in fact, wild type SADB19 ?G <br /> viruses.

Overall, these findings show that revertant mutants can arise during viral amplification of the SiR virus and that, therefore, care must be taken during SiR production to avoid selecting mutant variants with advantageous replication kinetics (such as the reported PEST mutants). The key issue is to be aware of this and to implement quality-control <br /> procedures that prevent the emergence of such mutants during viral <br /> production.

As noted above, we shall soon be able to address this and the other points made <br /> by Matsuyama et al. in a follow up experimental paper.

On 2019-07-07 00:03:23, user Buert Sohrrem wrote:

The amounts of CDNF released from ischemic/ reperfused hearts are sufficient to generate protection in another heart?<br /> Ischemic preconditioning is able to increase CDNF secretion? Does the activation of the KDEL receptor by the CDNF have any genomic effects?

On 2020-02-03 09:31:09, user Douglas Philip Dyer wrote:

Interesting paper, we had a couple of recent (ish) papers that might be of interest to you in respect to the interaction between CCL5 and GAGs and the biophysical effects of this interaction.<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.ncbi.nlm.nih.go...

On 2021-04-26 12:10:00, user Thomas D Alcock wrote:

Very nice work! Could I please ask if you would expect Si to be taken up through OsNIP2;1 as silica or as silicic acid? I am curious, as you state in the introduction that silicic acid is the naturally occurring bioavailable form of Si, but in later figures, it looks like silica is represented as transiting through NIP2;1. Is silicic acid transformed to silica prior to plant uptake/translocation? Many thanks, Tom

On 2016-05-27 21:46:03, user K. wrote:

Two years of daily 9 hour exposure in rats is equivalent to about 56 human years. <br /> What if the exposure had been more than 9 hours? Would that have changed the outcome? Many people are exposed day and night.<br /> Just looking at the group with low-level exposure: with 1 in 15 male rats developing cancer or pre-cancerous lesions when exposed to LOW levels common from cell phones, and when levels may be similar or higher in conjunction with other wireless devices (utility meters, Ipads, routers, etc.)--what does this mean for public wireless and liability?

On 2018-06-07 21:15:24, user Brendan O'Fallon wrote:

Nice work! You should consider providing a breakdown of the sizes (number of overlapping enrichment targets) for the CNV calls, since larger CNVs are much easier to detect than smaller CNVs. Grouping them all together has the potential to obscure reduced sensitivities for smaller CNVs

Also, it might be important to clarify the statement that "A true positive (TP) call is defined as a Genalice CNV call that overlaps an MLPA CNV call." Right now it sounds like ANY Genalice CNV call that overlaps the true CNV will be labeled as a correct call. Other studies have used a 50% or 80% reciprocal overlap rule to determine the correct calls (this prevents really large called CNVs that just happen to overlap a couple true CNVs from contributing to sensitivity)

On 2018-12-06 06:25:17, user Sam Diaz-Munoz wrote:

**<br /> This review was conducted by the Díaz-Muñoz Lab, as part of a pre-print journal club (inspired by Prof. Prachee Avasthi’s journal club: https://asapbio.org/preprin... "https://asapbio.org/preprint-journal-clubs)"). We provide the review in the best spirit of open science and to contribute to the virology field.<br /> -Sam Diaz-Munoz, Ilechukwu Agu, Sari Mäntynen, Alex Wilcox, Ivy José<br /> **

This manuscript aims to quantify the contribution of influenza virus genetic variation to the observed heterogeneity of virus infection outcome. The experiments first enriched for interferon activated cells (which are rare in normal infections), then infected with well-characterized viral stocks, and finally characterized the full genotypes and transcriptomes of all viral genes present within single cells. This transcriptomic data is used to derive the viral transcriptional burden on cells and to quantify innate immune activation (IFN, ISG) on a per-cell basis. The genotype data provides information on mutations, deletions, and other changes in the viral genome(s) that infected those same cells. The results from these two data sets show that a majority of cells infected by viruses with mutations are IFN+ compared to cells infected by wild type viruses, but this difference was not statistically significant. Further analysis shows statistically significant associations with IFN+ cells and specific mutations, particularly in segments PB1 and NS1. Some of these mutations were engineered into viruses and experiments showed that 5/8 of these mutations increased the percentage of IFN+ cells relative to wildtype, with the greatest effect mutation yielding ~17% IFN+ cells.

This paper presents leading edge methods to investigate the outcomes of viral infections at the single cell level. A major strength of the text is the explanation of the methods, which briefly and very clearly explains the overall methodology with plenty of supplementary information. The presentation of results and data is clear, with increasing levels of detail in the multiple supplements. The data in Figure 4 certainly represent a milestone in the influenza virus field, made all the more impactful by the exquisite presentation.

This manuscript could be improved by contextualizing the statistical significance and magnitude of the observed effects with regard to the role of viral genetic variation in the heterogeneity of innate immune activation. In a few sections, some words could be interpreted by readers as an overstatement of the support of the data for this topic. We present specific suggestions (see Major Comment 1) to improve this aspect of the text. The abstract could clarify the background of interferon in the context of influenza infection for readers not familiar. Finally, a summary figure tracking the overall results (see Major Comment 1) might benefit the reader.

Overall, we find this manuscript highly stimulating and useful for the innovation in the methods. We anticipate some aspects of this paper (esp. Figure 4) will become textbook knowledge in influenza virology. This paper is an exciting contribution to the influenza field, and indeed the broader virology community, which is currently characterizing viral infection dynamics in increasing detail.

Major comments:

1. One of our major points of our discussion reviewing this paper centered around contextualizing the statistical significance and magnitude of the observed effects with regard to the role of viral genetic heterogeneity in innate immune activation. Particularly given that the activation of IFN is so rare to begin with and that the data seem to support modest effects that are not always statistically significant. We have some suggestions to this effect.

First, some of the language in the discussion and abstract should be qualified to benefit the reader’s understanding (e.g. the word “crucial” in Line 286-287). Second, the data in Figure 3G can be used to provide some context showing that viral infection indeed increases the proportion of IFN+ cells, relative to uninfected cells. This result can also be highlighted with a statistical test of these proportions and the discussion could expand on how cell state could influence spontaneous IFN activation. Third, it may be helpful to conduct a post-hoc power test of data in Fig 5B to assess whether there was enough power to detect a significant difference between these proportions (0.184 and 0.306) given the sample size imposed by the single cell methodology. Fourth, it may be worth placing more emphasis on the fact that although the stated NS1 and PB1 mutations were well known to activate immunity, this is (if we are not mistaken) the first characterization at the single cell level. Finally, the discussion should place more emphasis on the fact that although IFN activation is rare, it could affect the course of the entire infection when founding infection size is small.

Additionally, it would be very useful to have a single figure that summarizes the major results addressing the central question of the paper. This figure could summarize the infected/uninfected proportions, immune activation/genotypes, and specific mutations that impact IFN, thus tracking the broad outlines of the full experiment. Readers may get lost in the rich data and helpful figures, which could dilute the main message of the text.

1. We find Figure 4 a very interesting and effective figure. It is an accomplishment in both experimental methods and data visualization. I (Sam Díaz-Muñoz) anticipate routine use of this figure in the classroom and we expect it will become textbook knowledge in virology.

2. There are a couple of terms used throughout the paper that could be substituted or clarified to benefit the reader. Viral infection outcomes is used throughout, but it is not always clear what this term refers to. In the context of the paper, the two measured infection outcomes we found were mRNA levels and immune state. If this is the case, “expression and immune state” is only one more word and provides flexibility to refer to one or the other.

The second term is the reference to mutations, deletions, and other modifications from “wild-type” as defects. While this is certainly commonplace in genetics and is convenient shorthand, it does have a connotation of being uncommon and negative. As Figure 4 so exquisitely shows (among many, many other studies), this heterogeneity is probably the norm for influenza viruses. As Brooke (2014) and many others have advocated, as a field we perhaps should be moving towards investigating and recognizing this heterogeneity as a standard part of influenza virus biology.

1. We find it very interesting that the coinfection rate is estimated to be 63% given a rough, “effective” MOI ~ 0.241 (290/1200). While the text explained this could be due to methodological biases, it may be worth indicating if there were any relationships between coinfection and expression or immune activation.

Minor Comments:

Line 16 - This line could be confusing to some readers if they do not have the background (stated well in the Introduction line 32) that influenza virus is very good at preventing IFN induction.

Line 37 - For the introduction, this statement could be written with more accessible language, paralleling the wording in the discussion (Lines 325-332), which is very understandable.

Line 41 - This paragraph could perhaps more appropriately start by discussing NS1’s well known role in suppressing IFN, setting a baseline for what the expectation should be. This could better contextualize the results for readers not familiar with influenza virus immunology.

Line 87 - To benefit the reader, the text could indicate (as done in Fig1 Supp4) that the Sendai infection was done at high MOI to validate the IFN activation of cells. As written may not be clear if text is still referring to Steuerman data.

Line 106 - May want to spell out IFN negative, instead of IFN-, some readers found it easy to miss the - sign

Line 201 - This is a really nice summary of the criteria used to call mutations and a frank assessment of the limitations of the approach

Line 216 - A very nice figure, with a wealth of information, indeed

Line 226 - A “favorable outcome for the virus” could be interpreted as increased viral replication, which doesn’t necessarily follow from the proportion of viral mRNA expressed in the cell.

Line 235 - “Also” is confusing in this context. Delete if retains intended meaning, otherwise should be rephrased

Line 325 - This paragraph is a really good discussion of the importance of IFN+ cells in light of the fact that they are very rare in influenza infection.

Line 431 - This section is a very detailed description of the methods. Highly useful for the community.

Line 608 - The computational analyses are helpfully described and the full pipeline is open and available in GitHub and explained in several Jupyter Notebooks

On 2025-07-15 08:25:43, user Patricio Fuentes Bravo wrote:

Nice and insightful article!

Have you explored alternative protocols for generating organoids, such as those developed by Pasca or Muotri?

I’m curious whether your criteria or determinants might be generalizable across different methodologies.

Many thanks.<br /> Patricio

On 2020-12-17 16:02:34, user Lamya Ghenim wrote:

The text has been improved as well as some of the figures, and that a link will be forthcoming to the accepted version shortly. The paper has been accepted in Scientific reports (Nature).

On 2022-09-21 03:31:40, user Daniel E. Weeks wrote:

What a fun and interesting paper! It even applies Student's t test to Student's data!

I am interested in your variable selection (Table S2) with the disparate results between the different methods.

One minor suggestion would be to merge Table S3 into Table S2, as it would be nice to be able to see these metrics at the same time as we're seeing which variables were selected.

Regarding variable selection, I really like this discussion of variable selection issues:

Heinze G, Wallisch C, Dunkler D. Variable selection – A review and recommendations for the practicing statistician. Biometrical Journal. 2018;60(3):431–449. DOI: https://doi.org/10.1002/bim...

I like their recommendation to "assess selection stability and model uncertainty", which is what we ended up doing in this recent paper:

Heinsberg LW, Carlson JC, Pomer A, Cade BE, Naseri T, Reupena MS, Weeks DE, McGarvey ST, Redline S, Hawley NL. Correlates of daytime sleepiness and insomnia among adults in Samoa. Sleep Epidemiology. 2022 Dec;2:100042. DOI: https://doi.org/10.1016/j.s...

We had originally wanted to use a lasso where we forced in a few variables that we thought had to be in all models, but couldn't get existing software to work in our hands to enable proper post-selection inference. When no variables are forced in, proper post-selection inference after variable selection via lasso can be done using these approaches:

1. Taylor J, Tibshirani R. Post-Selection Inference for l1-Penalized Likelihood Models. Can J Stat. 2018 Mar;46(1):41–61. PMID: 30127543 PMCID: PMC6097808 DOI: https://doi.org/10.1002/cjs...

2. Lee JD, Sun DL, Sun Y, Taylor JE. Exact post-selection inference, with application to the lasso. The Annals of Statistics. Institute of Mathematical Statistics; 2016 Jun;44(3):907–927. DOI: https://doi.org/10.1214/15-...

On 2020-07-06 01:13:30, user Brent Lengel wrote:

From the study's methodology:

"...***We acknowledge that this study was conducted with untrained individuals and not transgender athletes.*** Thus, while this gave us the important opportunity to study the effect of the cross-hormone treatment alone, and as such the study adds important data to the field, it is still uncertain how the findings would translate to transgender athletes undergoing advanced training regimens during the gender-affirming intervention. It is also important to recognize that we only assessed proxies for athletic performance, such as muscle mass and strength. Future studies are needed to examine a more comprehensive battery of performance outcomes in transgender athletes. Given the marked changes in hemoglobin concentration in the current study, it is possible that gender-affirming treatment also has effects on endurance performance and aerobic capacity. Furthermore, since the TM and to some extent also the TW demonstrated progressive changes in muscle strength, and strikingly some of the TW individuals did not lose any muscle mass at all, follow-ups longer than 12 months are needed to better characterize the long-term consequences and individual responsiveness to gender affirming interventions. Future studies should also include age- and body size-matched cisgender control groups undergoing the same assessment timepoints without the therapies."

On 2020-11-30 09:16:49, user Niko Popitsch wrote:

Nanopanel2 is now released on github: https://github.com/popitsch/nanopanel2

On 2021-08-07 12:30:57, user ????????? ??????? wrote:

There are 0 not predicted proteins, not 29. Please fix it.