This is a review of the preprint "A tunable dual-input system for 'on-demand' dynamic gene expression regulation" by Elisa Pedone and colleagues. The ITQB Preprint Journal Club met to discuss the article on September 21, 2018. Dr. Federico Herrera led the discussion and wrote this review. Dr. Zach Hensel edited the review for publication.

The preprint “Revisiting tradeoffs in Rubisco kinetic parameters” by Flamholz et al. 2018 (https://doi.org/10.1101/470021) investigates the tradeoffs between catalytic efficiency and rate, of the central enzyme in carbon fixation, Ribulose-1,6-bisphosphate Carboxylase/Oxygenase (RuBisCO), using kinetic modeling based on biochemical data. The manuscript builds on previous work from the group (Savir et al. 2010; doi: 10.1073/pnas.0911663107), including an expanded dataset of kinetic parameters of ~250 RuBisCOs from 286 different species extracted from the literature. We thought it was an important topic with potential interest for a wide range of researchers working on photosynthesis and evolution.The main questions the paper seeks to address are:Which trade-offs are inherent in Rubisco kinetics. Has evolution resulted in optimal kinetics within the constraints of those inherent trade-offs.The preprint challenges the theory that increasing RuBisCO activity reduces enzyme specificity as described by Tcherkez et al. (2006), which was based on a model of enzyme activity the discriminates between CO2 and O2 in a transition state. Data supporting this theory has been reported widely in the literature. However, the authors propose that there is little/no correlation between specificity and activity, and most previously-found correlations (KcatC and SC/O etc) are smaller for the new dataset, except for KcatC/KC and KcatO/KO. We really enjoyed reading the manuscript and as it challenged our preconceptions about RuBisCO activity. We found it interesting (and surprising!) that the data contradicts a well-established theory, and it increased our awareness out current models of enzyme activity. We also thought it was interesting that they were able to collect data from so many species across many previous studies and also break down trends/relationships between different clades or physiologies. It was also interesting that given that they were using data from studies that showed the opposite, they were able to come to the conclusion they found.   We particularly liked the authors' suggestions about how to move the field forward and the call for an improved understanding of RuBisCO kinetic mechanism.There were a few areas we thought it would be useful to clarify:Providing a cartoon model of the proposed mechanism would help readers unfamiliar with the nuances of the models being assessed. More information about how the authors selected and filtered data collecting from the literature and how the different datasets were taken into account in the statistical analysis. i.e. how many measurements per species, types of values (mean/median) etc.Figure 4 and 5: how was the conclusion reached that there was no correlation between parameters?Organization, it would make things stronger to layout the arguments clearly between what came before and after, for those not familiar with the academic argument.As the paper argues against what most people are reading it might expect, additional text theorizing why this is the case would be useful to guide readers.Minor commentsFigure 2A y-axis labelsPut a key at the top as the symbols can get buried in the text

The UIUC Plant physiology journal club chose to review the preprint “Triose phosphate utilization and beyond: from photosynthesis to end-product synthesis” (http://dx.doi.org/10.1101/434928), by McClain and Sharkey, about the role of phosphate levels in relation to CO2 assimilation during photosynthesis. The paper explains that when the rate of photophosphorylation which produces ATP, exceeds that of starch or sucrose synthesis which consumes ATP, phosphate levels drop causing photosynthetic electron transport rates to slow. This process is defined as Triose phosphate limitation (TPU). We found the paper provided a very thorough definition of TPU and thought it was a really good introduction to the topic. In particular, the explanation about the way TPU limitation can be identified by no change in photosynthesis and decline in PhiPSII at high CO2 was very useful. The paper raises a number of points we were interested to discuss further including why TPU limitation is only slightly more than photosynthetic rate? Are some plants are more subjected to TPU limitation than others? Why is TPU limitation is slightly more than photosynthetic rate (one might predict that natural selection would select for utilization of all their photosynthate)? Why C4 plants do not experience any TPU limitation? Is it more common to see TPU limitation in Li6800s?The question of the potential impact of a rising atmospheric CO2 concentrations on TPU is a fascinating topic.  We thought it would be beneficial for readers to include reference to Experimental free air CO2 enrichment (FACE) experiments which have sought to address this issue, and some degree of guidance about what types of increases in atmospheric CO2 would be required to potentially see an effect. In addition it would be useful to mention that many of the TPU studies have been performed in pot bound plants, which experience an altered shoot/root ratio creating an artificial sink limitation, and a bit more discussion about how often TPU limitation might occur under field conditions (Arp 1991). Finally it might be good to include a bit more discussion about how the xanthophyll cycle kicks in to protect the plant under stress and that decreased PhiPSII could be an example of non-photochemical quenching.In summary, we thoroughly enjoyed the paper, we learnt a lot and look forward to seeing it published.Minor pointsIn equation 5 it is not entirely clear what the phi symbol is?  The symbol R is used to represent three different variables, could maybe consider changing this to prevent confusion.Many color combinations used in the figures would be unsuitable for those with color blindness, and suggest using color oracle (https://colororacle.org/) to help.Fig 2: Clarification on what ‘a little’ and ‘a lot’ means would be helpfulFig 2: We found it a bit confusing, many members were unclear about what it was demonstrating and suggest considering reformulating the description as we believe it could be useful as a teaching aid.Fig 3: Many members really liked this figure and thought it provided a clear image to explain the various limitations a plant experiences during an A/Ci curve. There was some discussion about where the boundary lines were drawn, this might be addressed by having zones of colour fade into each other rather than represented as sharp boundaries. It was also suggested that some kind of upper bound to J limitation of ETR might be included (possibly fading out to white?)ReferencesArp (1991) https://doi.org/10.1111/j.1365-3040.1991.tb01450.xRodgers et al. (2004) https://doi.org/10.1111/j.1365-3040.2004.01163.x

This is a preprint journal club review of "Age-related decline in behavioral discrimination of amplitude modulation frequencies compared to envelope-following responses" by Jesyn Lay, Edward L. Bartlett. The preprint was posted on bioRxiv on Sep 28, 2017 (DOI: http://dx.doi.org/10.1101/193268).Our group reviewed this paper on October 13, 2017 and used the PREreview quick participant worksheet.

Metabolic Interactions Between Dynamic Bacterial Subpopulations  Adam Z Rosenthal, Yutao Qi, Sahand Hormoz, Jin Park, Sophia Hsin-Jung Li, Michael ElowitzVersion reviewed: V1. October 25, 2017. bioRxiv 208686; doi: https://doi.org/10.1101/208686Note: This review incorporates input from attendees to the ITQB Preprint Journal Club on November 9, 2017.Overview:Rosenthal et al. demonstrate that stochastic changes between metabolic gene expression states underlie the population-averaged progression through acetate production to acetoin detoxification during exponential growth of B. subtilis. They show that the probabilities of cells being in the slow-growing, acetate-producing (sucC+) and acetoin-producing (alsS+) states depend upon competence regulators (comK) as well as environmental acetate concentration. The work combines population averaged experiments with single-cell fluorescence snapshot and timelapse microscopy experiments to conclusively prove the claims in the abstract. The work was very interesting for members of our journal club coming from several different fields. We were particularly interested in the the implications of the final gel-pad timelapse experiment and its discussion in the main text—the possibility that competence, toxic-product secretion, and subsequent detoxification could be coupled to give B. subtilis an advantage in a complex environment will be a great problem to investigate in the future.

As part of our first Live PREreview Journal Club (#LivePREJC), we discussed the bioRxiv preprint "A Measure of Open Data: A Metric and Analysis of Reusable Data Practices in Biomedical Data Resources" by Seth Carbon,  Robin Champieux, Julie McMurry,  Lilly Winfree,  Letisha R Wyatt, and Melissa Haendel. doi: https://doi.org/10.1101/282830.  We were joined by two of the preprint authors: Robin Champieux and Lilly Winfree.  Below is a summary of our discussion. 

Review of:Evolutionary dynamics of bacteria in the gut microbiome within and across hostsNandita R. Garud*, Benjamin H. Good*, Oskar Hallatschek, Katherine S.Pollard*co-first authorsOriginally published on biorxivdoi: https://doi.org/10.1101/210955

In our journal club this week, we went a little more experimental and selected a BioRxiv paper yet to be published - it can be found at https://www.biorxiv.org/content/early/2017/07/13/163402 and currently bears the title "Heterogeneous Responses of Hematopoietic Stem Cells to Inflammatory Stimuli are Altered with Age".  We were inspired by the eLife Labs article on PREReview to give this a go and hopefully the authors and wider biological community benefit from this interaction.As mentioned above, this was our first attempt at discussing a preprint paper from BioRxiv so we had a few opening questions and general discussion about the concept between the 15 or so members of the journal club.  Our groups (Laurenti and Kent) are based at the WT / MRC Cambridge Stem Cell Institute in Cambridge, UK (https://www.stemcells.cam.ac.uk/) and the opinions and comments found below are a summary of the group discussion rather than the opinion of any single researcher partaking in the journal club.First question:   Was reading this any different than a normal paper?Some people had worried that a pre-publication paper would suffer from major gaps in organisation or presentation and this was not the case here.  Overall, there was not a substantial difference from published papers and people agreed that the structure of the paper was good and the content relatively easy to follow.  That said, one cynical comment was raised, querying whether the peer review process prohibited authors from telling their version of the story (e.g., one interpretation), that might actually diverge from the interpretation of the broader field (i.e., peer review makes the article "less readable" but also "more accurate").  Second question: Would you continue to explore pre-print servers for new content?Resounding "yes" from majority of researchers (many of whom had not heard of preprint servers prior to the discussion)---We then entered the discussion phase and came up with the following "review" based on the discussion:In their manuscript, "Heterogeneous Responses of Hematopoietic Stem Cells to Inflammatory Stimuli are Altered with Age", Mann et al., identify and transcriptionally profile at the single cell level a novel subset of aged cells primed to respond to  inflammatory stimuli.  Overall, the manuscript read very well, had a logical progression and was easily considered to be "publication standard" with respect to these aspects (i.e., it was in better shape from an editing perspective than some manuscripts!).  The data were presented nicely and generally supported the statements in the abstract, but several areas of concern were identified through our discussion and a number of questions arose that might be considered by the authors: Concerns:1) Unknown LT-HSC frequency of cells isolated from aged animals:  LSK SLAM is a well-described phenotype for functional HSCs in young mice (~35-50% functional HSCs), but it remains unknown what the precise frequency of functional HSCs in that phenotype in aged mice would be.  Perhaps the best evidence comes from the de Haan group (http://jem.rupress.org/content/208/13/2691.long) where they studied LSK SLAM 34- EPCR+ cells and showed that there was at least 2-fold less functional activity on a per HSC basis and this arguably is a much more stringent sorting criteria.  Therefore, the cells being isolated as "aged LT-HSCs" in this current manuscript are likely less than 10% functional HSCs, making interpretation of all downstream data difficult to ascribe to "LT-HSCs".  Moreover, the frequency of phenotypic LT-HSCs is highly variable (from 100 to 4300 per million cells), making the number of input mice (n=4) concerning, since they might represent a large proportion of one single mouse (e.g., 100-fold more phenotypic HSCs).  Overall, our discussion group agreed that the data presented re: inflammatory signatures were still interesting - it was just that the claims that it was a functional LT-HSC specific (or myeloid-biased LT-HSC specific) signature were less robust.2) Proportional, not absolute, changes in chimerism:  Figure 1 appears to display the proportion of cells that were T, B, gran, or myeloid without showing the absolute levels of chimerism.  It therefore precludes any reviewer from appropriately determining whether a "myeloid bias"really exists (e.g., an absolute increase in myeloid cell production) or whether it is an absence of lymphoid cell production (lymphoid deficient).  Without differences in the absolute myeloid cell number, it changes the interpretation of the data since both cell types would produce the same number of cells and young myeloid progenitors would be present (but not responding) to the stimulus.3)  in vitro 2h stimulus vs. in vivo effect:  The in vivo effect of a single dose treatment seems to be temporary (Figure 1 B-E), whereas the in vitro effect (from a 2 hr stimulation) is sufficient to change the fate of the aged LT-HSCs that get transplanted.  It remains unclear how the progeny of these LT-HSCs continue to self-renew in a way which creates progeny HSC with the same properties for sustaining long term hematopoiesis.  A few items came up in discussion:  1) Is there selective killing of balanced/lymphoid biased HSCs?  2)Are secondary transplantations "myeloid-biased" as well (e.g., how strong is the continuation of the pattern?)Additional questions arising in discussion:1)  Could the authors speculate on the origin of the HSC expansion?  It has been described by several groups that the HSC subtype balance shifts with age but nobody has clearly addressed whether the lymphoid deficient HSCs that accumulate with age are "old" balanced HSCs that have lost lymphoid potential or an accumulation of "lymphoid deficient (or alpha / My-biased)" HSCs.  2)  Would young HSCs from malignant mouse models (or patient samples) also bear the aged/inflammatory signature?minor items:the mega-biased HSCs were first described by the Jacobsen and Nakauchi groups, not the de Haan and Graf groups as mentioned in discussion.

Recent demographic histories and genetic diversity across pinnipeds are shaped by anthropogenic interactions and mediated by ecology and life-history  Martin Adam Stoffel, Emily Humble, Karina Acevedo-Whitehouse, Barbara L. Chilvers, Bobette Dickerson, Fillipo Galimberti, Neil Gemmell, Simon D. Goldsworthy, Hazel J. Nichols, Oliver Krueger, Sandra Negro, Amy Osborne, Anneke J. Paijmans, Teresa Pastor, Bruce C. Robertson, Simona Sanvito, Jennifer Schultz, Aaron B.A. Shafer, Jochen B.W. Wolf, Joseph I. Hoffman,  April 12, 2018, version 1, bioRxiv doi: https://doi.org/10.1101/293894Firstly, we thank the authors for their work and for posting it as a preprint on bioRxiv. This work endeavored to evaluate the occurrence and intensity of population bottlenecks in a large number of pinniped species that have been differentially affected by human exploitation. Population bottlenecks can decrease genetic diversity and adversely affect the ability of a species to adapt to modern habitat loss and climate change. Because historical data is sparse and unreliable, the authors applied population genetics methods to a large, multi-species dataset to detect and evaluate past population bottlenecks and then compared the results to life-history traits and current conservation status for each species. The results indicate that 11 of the species included in the analysis experienced a population bottleneck and that land-breeding pinnipeds are more likely to have experienced a bottleneck than ice-breeding pinnipeds. While there was not an overall relationship between IUCN status and past bottleneck events, bottleneck events were detected for 4 of the 6 endangered species included in the study. The breadth of this study is especially important, as it represents a first effort to apply these methods across 30 species in a single analysis. Our overall impression is that this was a large project using an extensive amount of data from multiple sources, which then had to be standardized in order to be analyzed in a novel way. This paper highlights the benefits of open science and open data, as data from multiple studies was reused and analyzed in a far broader context than any single study on a single population or species.This manuscript is exceptionally well-written and uses clear language, making it both easy and enthralling to read. However, there are a few small mistakes that caused some confusion. Particularly, the caption of Figure 4 switches the descriptions for Panels A and B. Additionally, the figures in the main text are numbered 1:4, 6; it appears that Figure 5 may have been moved to the supplemental materials, but the main figure numbering was not adjusted accordingly. All of the figures in the manuscript are very attractive and make good use of consistent coloring. Figure 1 nicely summarizes many of the main findings of the paper. This figure would be even better if Panel A utilized a 2-color scale like Panels B and C. Additionally since Pbot and Pneut are complementary, we suggest that only Pbot needs to be included in Panel C, which will reduce the size of the figure and make it easier to interpret. Figure 2 is very clear and intuitive and nicely illustrates the intensity of population bottlenecks for different species. Additionally, the pinniped drawings are beautiful and the use of original artwork in the paper is commendable. We feel that Figure 4, which displays the expected correlation between global abundance and allelic richness does not necessarily need to be included in the main text. Conversely, we feel that Supplementary Table 1, which contains the sample size, number of microsatellite loci, and citation for each species' dataset, is important for readers to have available in the main text. Overall, the authors did an outstanding job applying population genetics techniques appropriately. In particular, the authors made very good use of the program STRUCTURE. This program was used to detect population substructure and if detected, the largest genetic cluster was selected for inclusion in ensuing analyses. This important step prevents false detection of bottlenecks, which can be a common mistake. It is also appreciated that the authors chose to examine allelic richness rather than allelic frequency. We were left with a few lingering questions about the methods, however. First, we are curious if the authors acquired raw electropherograms or pre-interpreted genotypes for the published datasets used and if there were any measures taken to control for observer bias in interpreting microsatellite genotypes, such as preparing and running samples from other labs and assessing whether similar conclusions were reached. We were also curious about the justification for grouping IUCN categories into "concern" ('near threatened,' 'vulnerable,' and 'endangered') and "least concern" ('least concern'), especially since the IUCN Red List Categories and Criteria groups 'near threatened' with 'least concern' and explicitly distinguishes 'vulnerable,' 'endangered,' and 'critically endangered' as the "threatened" categories. We wonder how the results presented in Figure 6 would be affected by moving the species designated as 'near threatened' out of the "concern" category.Lastly, we would have appreciated a more extensive discussion. For example, the authors describe ice-breeding species as experiencing less historical exploitation than the land-breeding species, but ice-breeding species are likely more susceptible to negative impacts from climate change in the recent past and into the future. The implications of the findings of the study combined with change in anthropogenic disturbance patterns and continuing disturbance  in these habitats into the future could be addressed in the discussion. We are also very interested in what the authors perceive as weaknesses in the approaches used, if there may be alternative interpretations of the results, and especially what future studies they would suggest based on their results and conclusions. Again, it was a great pleasure reading this impressive work. We hope that our comments are useful to the authors and we look forward to reading the final version when it is published. Thank you!The OIST Ecology and Evolution Preprint Journal Club 

This is a review of the bioRxiv preprint "Sex Differences in Aggression: Differential Roles of 5-HT2, Neuropeptide F and Tachykinin" by Andrew N Bubak, Michael J Watt, Kenneth J Renner, Abigail A Luman, Jamie D Costabile, Erin J Sanders,  Jaime L Grace, and John Swallow. DOI: https://doi.org/10.1101/407478 This review was compiled from discussion points raised during a PREreview live-streamed preprint journal club as part of Open Access Week, 2018. The event details can be found here and the collaborative Etherpad showing all the journal club notes can be found here. In addition to those named as authors above, the participants who wished to be acknowledged for their contributions to this review are as follows: Dariusz K. Murakowski, Tim Koder, and Daniela Saderi.

Here is how it all started. Two researchers and ASAPbio Ambassadors met at a Mozilla Working Open Workshop in April 2017. A PhD student (Daniela) and a postdoctoral fellow (Sam) decided to volunteer some of their time to develop guidelines to help researchers from all around the world start preprint journal clubs. We believed this would have contributed positively to spreading the word and value of preprints in the scientific community, as well as helped early-career researchers master their skills in peer review.During the Mozilla Science Global Sprint, June 2-3 2017, we wrote our application to the first Mozilla Science Mini-Grant. We asked for enough money to support 20 beta testers by covering the cost of snacks and beverage for two preprint journal clubs. And we were awarded!Since then, a lot has happened, including starting PREreview thank to the help of the Authorea team and the support of many others who share our mission.Since July, our application has been posted on our project GitHub, but we wanted to have it on PREreview as well. So below is our full proposal. Thank you!

This is a preprint journal club review of "Evolutionary responses to conditionality in species interactions across environmental gradients" by Anna M O'Brien, Ruairidh J.H. Sawers, Jeffrey Ross-Ibarra, Sharon Y Strauss. The preprint was originally posted on bioRxiv on December 10, 2017 (DOI: https://doi.org/10.1101/031195) https://www.biorxiv.org/content/early/2017/12/10/031195.Our group Biotic Interactions and Global Change (BIGC) reviewed this paper on January, 2017 .

This is a review of the preprint "Cell boundary confinement sets the size and position of the E. coli chromosome" by Fabai Wu, Pinaki Swain, Louis Kuijpers, Xuan Zheng, Kevin Felter, Margot Guurink, Debasish Chaudhuri, Bela Mulder and Cees Dekker. The article was posted on bioRxiv on 15 June 2018 (https://doi.org/10.1101/348052). The ITQB Preprint Journal club reviewed the article on 3 July 2018. This review was written by Dr. Ambre Jousselin, who led the Preprint Journal Club. Dr. Manuel Melo made major contributions to analysis of simulations, and Dr. Zach Hensel helped edit the review for posting on PREreview and commented on microscopy methods.

The following notes were taken by Naomi Penfold, eLIFE Innovation Officer, during our session at MozFest 2017. Here is a link to our short slide deck. Welcome everyone! We are excited to see you and we hope you are having a fantastic time at MozFest 2017. Who are we?* Daniela Saderi, neuroscientist at OHSU. I love organising community events, including Science Hack Day Portland. Originally from Sardinia (it's beautiful!)* Samantha Hindle, "post"-postdoc at UCSF, from scarborough, UK. I'm a neuroscientist too.We are ASAPbio ambassadors! We advocate for preprints in the life sciences, and have built PREreview to enable researchers to review preprints in their journal clubs and online.* Naomi Penfold, eLife - I'm here to help Dani and Sam to encourage the adoption of preprints.

Steven Burgess (0000-0003-2353-7794), Samuel Fernandes, Antony Digrado, Charles Pignon, Elsa de Becker, Naomi Housego Day, Lusya Manukyan, Stephanie Cullum, Isla Causon, Iulia Floristeanu, Young Cho, Freya Way, Judy Savitskya, Robert Collison, Aoife Sweeney, Pietro Hughes, Cindy Chan AbstractThe paper “Arabidopsis species employ distinct strategies to cope with drought stress” by Bouzid et al. (https://doi.org/10.1101/341859) investigates whether responses to water limitation vary between closely related species by assessing the growth and survival of A. thaliana, A. lyrata and A. halleri accessions in a dry down experiment. By including multiple accessions of each species the authors were able to analyse variation in response to drought stress within and between species based on eight phenotypic parameters. The authors went on to perform comparative transcriptomic analysis between A. lyrata and A. halleri over a time course of drought treatment and identified differentially expressed genes. GO ontology analysis suggest the species analysed adopt different strategies to cope with drought stress, with A. lyrata employing avoidance and tolerance mechanisms, whereas A. thaliana showed strong avoidance but no tolerance. We were impressed with the amount of work performed and thought the  study aims to address an interesting question. During the hour long journal club participants were asked to focus on three aspects of the paper as part of a training exercise, including novelty, interest, soundness as well as writing and presentation.ReviewThere are several published papers looking at the effect of drought stress in Arabidopsis species including A. lyrata (Sletvold and Agren 2011; Paccard et al. 2014) and A. thaliana (Ferguson et al. 2018; Kalladan et al. 2017). We suggest toning down the assertion on Line 28 that little is known about the physiological response to drought in closely related species. Although not in a single paper, this issue has been addressed within a species by Sletvold and Agren (2012) and Davila Olivas et al. (2017) and there is a fairly large collection of papers looking at this within a species. To our knowledge, the novelty of this work lies in analysis of drought responses within and between several species of Arabidopsis in one article. We thought this was an interesting approach and that the authors can make more of this point, highlighting the new information that it yields. The article is well written in clear sentences and it was easy to read. We felt the authors had collected a lot of data and believe this could be explored further in the discussion, particularly the differential expression data. There is a lot of microarray and transcriptomic data available for the response of A. thaliana to drought conditions and would like to have seen some form of comparison between these data and that collected for A. lyrata and A. halleri.In addition, it would help to provide more information about why analysis of Arabidopsis accessions was limited to late flowering varieties. Does excluding accessions which can terminate life cycle early bias the experiment? Termination is a major strategy for survival and may impacts upon the conclusion that “response to depletion in SWC did not reveal significant differences between accessions (line 591).” Further discussion of this conclusion in the context of previous studies would be illuminating as it appears to contrast with some findings, for example Bouchabke et al. (2008) which suggested there are differences in response to depletion of SWC between A. thaliana accessions. Readers would also benefit from discussion about how the results from the phenotypic analysis relates to other studies which have implicated trichome production, rosette leaf size and flowering time as drivers for drought tolerance.We commend the fact the methods are detailed which should aid anyone wanting to replicate the study. Several aspects were highlighted as excellent practices, in particular: the fact that all program versions are provided, software parameters are included and accessions and materials are well catalogued. To build on this we recommend depositing the data (particularly transcriptomic analysis) in a public repository such as GEO, SRA or Zendo as required by some journals. This means data can be built upon in future studies and could increase the likelihood of the paper being cited. Inclusion of extended methods in an accompanying Bioprotocol paper or on sites such as Protocols.io and sharing custom scripts in a github repository (or on Zendo) would make the reporting of methods outstanding. Minor commentsThe paper may benefit from clarify a number of questions raised by participants:   How was soil mixture chosen?Line 192 under what conditions were the plants grown in the greenhouse? Line 206: How were the first signs of wilting defined? It might be worth mentioning this earlier in the manuscript (line 206)Line 232 - How was loss of turgidity measured?The colors used in the figures will create difficulty for those individuals with color blindness, using color oracle (https://colororacle.org/) can help address this issue.Figure 2 - no scale bars are includedFigure 2 - how many plants per pot were analyzed? This could have impacted on measurements displayed images suggested this was variable.Figure 5 - we thought it was excellent that unit level data is provided, this could be extended to the other figures too.Figure legends could be improved e.g. by changing “halleri” to “A. halleri”*Formating of legend (line 1326 )extra space and period.The article might benefit from consistent formatting/presentation of figuresA table might be more appropriate for Figures 7 and 8Inclusion of n= numbers in the figures would help readers assess the data.We were confused about how the experiment was designed, why is data for only on biological replicate displayed in figures 7 and 8.References Bouchabke et al. 2008 https://doi.org/10.1371/journal.pone.0001705 Davila Olivas et al. (2017) https://doi.org/10.1111/mec.14100Des Marais, et al. (2012) https://doi.org/10.1105/tpc.112.096180 Huttunen et al. 2010 https://doi.org/10.5735/085.047.0304Kalladan et al. (2017) https://doi.org/10.1073/pnas.1705884114 Paccard et al. (2014) doi: 10.1007/s00442-014-2932-8 Sletvold and Agren (2011) https://doi.org/10.1007/s10682-011-9502-x

Hello! Thank you to all who came to our preprint workshop at UCL 30th October, 2017. We thoroughly enjoyed the discussion and we hope it was useful to you too. Due to the superb note-taking skills of Naomi Penfold, I am able to provide a detailed summary of the event as a refresher for those who came, and as info for those who couldn't make it. If you are interested in hosting your own preprint event and would like some help, please let me know (samantha.j.hindle@gmail.com). Enjoy!

bioRxiv articleThe cellular NMD pathway restricts Zika virus infection and is targeted by the viral capsid protein

 Iulia Floristeanu,  Cindy Chan , Steven Burgess (0000-0003-2353-7794), Charles Pignon, Stephanie Cullum, Isla Causon, Pietro Hughes AbstractIn the preprint "StomataCounter: a deep learning method applied to automatic stomatal identification and counting” (doi: https://doi.org/10.1101/327494) Fetter et al. introduced a reliable and automated stomata counting program that is more efficient and accurate than human counting and existing algorithms, with a low false positive rate. The authors report the algorithm can be used for previous uncharacterised species and has a 94.2% transfer accuracy when used on untrained datasets. In addition they provide a publically available webtool which could be very beneficial for researchers working on stomata.ReviewWe really enjoyed the paper and found it to be of high interest as the method presented could make the work of a lot of people easier. We were particularly impressed with the precision score of StomataCounter which compares well with other existing algorithms, especially in terms of the transfer accuracy. To our knowledge this is a novel approach, as there are no automated stomata counting technologies available, and it outperforms existing methods. The DCCN appears to overcomes challenges of stomata counting and it correctly identified stomata showing minimal false positives on non-plant and non-stomata covered tissue.The article was well written and easy to follow. We liked the choice of a Deep Convolutional Neural Network (DCNN) for machine learning and felt the authors could have highlighted the benefits of this method by providing a more detailed justification of why it is superior to other algorithms - as this is what made the paper so interesting to us. The text might be further improved by being more specific about results in the abstract and a clearer statement on supplementary data and sampling used.We were interested to know how the algorithm performs on grass species, as they have “dumbbell-shaped” guard cells and companion cells. It was unclear to us whether grass stomata have been used among the training images so we wondered if the accuracy would be the same for this particular shape of stomata. Including some text in the discussion about this would be illuminating. In addition for the sake of reproducibility and to aid readers comprehension it would be useful to provide (1) a complete list of samples analysed and (2) ideally the whole training dataset in a public repository such as Zendo or Dryad, as it could greatly benefit future comparative studiesQuestions we had it would help to clarifyThe authors state “many researchers are likely to manually count stomata” -  is this because other methods are not good enough, user interface is too complicated or just people liking more traditional things?In the methods it is written “final fully connected layers by convolutions”. Does this mean there's no fully-connected layers, only convolutional layers? It sounds like both are included in other parts of the paper. We were confused by the statement “we argue that the current architecture does not transfer well to different scales and images should be pre-processed to match the training scale of the network,” is that not the reason a fully-convolutional network is used? to avoid the inability to manage different input sizes.Minor commentsConsider making legends of Figure 3 and Figure 4 bigger to improve readabilityFigure 5 is very informative and summarizes the data wellFigure 6 might be improved by including an inset to focus on the down number.Figure 7 - scale barsThere was a strong linear correlation at higher magnification between DCNN and human counts is this a standard magnification for this type of experiment?It would be good to include:Quantification of the accuracy of the automatic separation between abaxial and adaxial datasets. A failure of separation could potentially have contributed to finding stomata on the "adaxial" cuticle. Discussion of whether the magnifications (200x and 400x) are suitable for this kind of analysis.Information about why the 4 sources of images were chosen and discussion of whether they provide enough of a range.An explanation of why data was which showed <98% was discardedStats on Gingko and poplar datasets.Might consider rephrasing “the mean number of stomata detected in the adaxial, aorta, and breast cancer image sets was 1.5, 1.4, and 2.4, respectively, while the  mean value of the abaxial set was 24.1” to better explain the low frequency: to compare with non-stomata dataset vs dataset that has very low number of stomataCode availability: It would be helpful if the code and custom scripts (such as separation of abaxial and adaxial leaf sides) are made available and linked to a repository.