Where can you find preprints?
Where you can find preprints:There are various preprint repositories (see below) and also website platforms where you can search all/most of the preprint repositories, including Prepubmed, Publons, The Winnower, and Academic Karma (please let us know at email@example.com or leave a comment on this page if we missed any). You can search the Research Preprint Servers List to find a preprint server in your field.Below is a list of the most common preprint repositories that post findings in the biological sciences:AgriXiv: a preprint repository for agriculture and allied sciencesarXiv q-bio: a preprint repository for quantitative biology operated by the Cornell University Library. This repository includes manuscripts in the following categories: Biomolecules, Cell Behavior, Genomics, Molecular Networks, Neurons and Cognition, Subcellular Processes, Populations and Evolution, Tissues and Organs, Quantitative Methods, and Other Quantitative BiologybioRxiv: a preprint repository for the biological sciences operated by Cold Spring Harbor Laboratory. This repository includes manuscripts in the following areas: Animal behavior and Cognition, Biochemistry, Bioengineering, Bioinformatics, Biophysics, Cancer Biology, Cell Biology, Clinical Trials, Developmental Biology. Ecology, Epidemiology, Evolutionary Biology, Genetics, Genomics, Immunology, Microbiology, Molecular Biology, Neuroscience, Paleontology, Pathology, Pharmacology and Toxicology, Physiology, Plant Biology, Scientific Communication and Education, Synthetic Biology, Systems Biology, and ZoologyOSF PREPRINTS: a preprint server that hosts preprints from a broad range of disciplines, including the Life Sciences, and Medicine and Health Sciences PeerJ Preprints: a preprint repository for the biology and computer sciencesPreprints.org: a preprint repository that posts manuscripts covering many areas of the biology and biomedical science (and other sciences, arts and humanities), including Behavioral Sciences, Biology, Life Sciences, and Medicine and PharmacologyWellcome Open Research: a preprint repository for research funded by the Wellcome Trust mainly in areas of the biological sciences, population health, applied research, humanities and social scienceINARxiv: the preprint server for Indonesia powered by OSF Preprints hosting preprints from a broad range of disciplines, including the Life Sciences, and Medicine and Health SciencesEarthArXiv: the preprint server for Earth Sciences powered by OSF Preprints
Preprint Journal Clubs: Your Opinions Revealed
In the summer of 2017, we conducted a survey to assess scientists' opinions on the value and potential barriers related to reading and reviewing preprints at journal clubs. In this short article we present and discuss the results of the survey as well as how these results helped us shape our approach at PREreview.
UIUC Plant Physiology JC (2018/12/3): Revisiting tradeoffs in Rubisco kinetic parameters
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
PREreview Journal Club (UCL Great Ormond Street Institute of Child Health)
[ Child Growth Predicts Brain Functional Connectivity and Future Cognitive Outcomes in Urban Bangladeshi Children Exposed to Early Adversities ]
[Wanze Xie, Sarah K. Jensen, Mark Wade, Swapna Kumar, Alissa Westerlund, Shahria H. Kakon, Rashidul Haque, William A. Petri, Charles A. Nelson, 22 Oct 2018, version #1, biorxiv]
[ 10.1101/447722 ]
Overview and take-home messages:
Boston University NeuroPreprint Journal Club
A spatial code in the dorsal lateral geniculate nucleusVincent Hok, Pierre-Yves Jacob, Pierrick Bordiga, Bruno Truchet, Bruno Poucet, Etienne Save doi: https://doi.org/10.1101/473520Version posted on Biorxiv: 11/19/18In the current work, Hok et al. report the existence of spatial receptive fields in the dLGN of rats that are similar in nature to HPC place fields. The authors demonstrate that dLGN place fields, which make up approximately 30% of extracellularly recorded dLGN neurons, are less stable and more variable (on a pass by pass basis) than their HPC counterparts. Further, the authors show theta oscillations in the dLGN local field potential and the presence of individual dLGN neurons that have theta rhythmic spiking. The authors report that, unlike CA3 neurons, most dLGN neurons do not exhibit theta phase precession suggesting that the dLGN spatial code does not carry sequential information about the animal’s current, past, or future trajectories. Finally, Hok and colleagues show that a sub-population of dLGN neurons can be modulated by visual stimulation but that this population does not overlap with neurons that exhibit place fields. Interestingly, dLGN place fields are insensitive to the presence or absence of visual information (as determined by similarity in responses in recordings from light vs. dark conditions) but remap when the color of the environment is altered (from all white to all black). By and large the results are robust and the reported findings will be of interest to researchers considering interactions between different forms of neural spatial representations and visual information. Further, the observation of spatial receptive fields in the dLGN raises important questions about fundamentals of visual processing. We enjoyed the manuscript and have the following suggestions for the authors.Major suggestions: • There is little discussion about dLGN visual sensitivity in the rat despite there being extensive literature on the subject. There is known retinotopy in dLGN (see for example, Montero et al. 1968). How, if at all, does this relate to the spatial response properties of dLGN neurons? For example, is there any correspondence between neurons that would be sensitive to the lower half of the visual field and remapping in the light vs. dark arena conditions? A similar question could be raised regarding the object sensitive sub-population. The authors should attempt to address the relationship between their observations and visual sensitivity of dLGN in more detail. • The object sensitive sub-population is intriguing, but the details concerning this finding are ambiguous. It is mentioned briefly in the main manuscript but the percentage of neurons exhibiting this tendency is unclear, even when examining the extended data concerning this observation. As a result, the reader is left a bit distracted by this information, especially when considering that object sensitive neurons are not place cells. The authors should consider presenting this component of the manuscript in greater detail. • The visual stimulation experiment is posed as a control for determining that dLGN place cells were indeed recorded in a visually sensitive area (dLGN), but this experiment was not conducted for CA3 neurons. How can we know for certain that the visual stimulation protocol wouldn’t have elicited modulation in CA3 neurons as well, thus rendering this component of the study inconclusive? • Tissue can often become displaced for extended periods of time when moving tetrodes along the D/V axis. How confident are the authors in the correspondence between their tetrode turning records and final tetrode placement? The waveform related analyses are pretty convincing and seems to cluster nicely into two groups, but waveform shape is not a definitive metric for determining the location of a recorded neuron. Is it possible to record from dLGN without moving electrodes through the hippocampus first? It would be helpful if the authors could provide more histology so readers can examine for themselves how displaced CA3 tissue was. • It seems that LFPs were primarily referenced to another tetrode. Instead, LFPs should be referenced against an electrode outside of either the hippocampus or dLGN such as a skull screw above the cerebellum. Because of the current referencing scheme and many other factors, theta oscillations recorded in the dLGN could be explained by volume conduction yet this is not considered. These issues may be of great importance to the theta phase precession analysis. Did the authors attempt to look at phase precession for dLGN place cells against both HPC LFPs as well as dLGN LFPs? If not, this would be an important analysis to consider. • No power spectral density plots are shown for theta oscillations in CA3 and dLGN. It would be interesting to see if the dLGN theta peak in the power spectrum is broader compared to CA3, which might explain the higher theta frequency and would also affect phase-related analyses. Additionally, a consideration of speed related modulation of theta dynamics should likely be included. Minor Concerns: • Table 1 should include a #/% of cells which were “object specific” • Re Overdispersion analysis (line 460): A linearly scaled Poisson distribution will itself be poisson distributed, not normal, with the approximation being more valid at higher firing rates. The Z value described here will therefore be biased to higher values for lower firing rate neurons. Because of this, it is unclear if the results in lines 90-92 are artificially strong or weak. If thalamic neurons have a higher firing rate than CA3 neurons, they may have an even higher difference in dispersion than reported. A consideration of percentiles rather than standard deviations would likely be revealing. • Quantification of the object rotation sub-experiment (lines 144-147) should be included in the main manuscript text. • More details on the properties of theta modulation in dLGN should be presented. What percentage of dLGN neurons had peaks in the theta frequency range in the FFT of their spike train autocorrelations (i.e. how many were significantly theta rhythmic)? What percentage of dLGN neurons were theta phase locked and was there a bias to a particular theta phase? Was theta oscillatory activity for single dLGN neurons modulated by running speed? • Was a velocity filter used when creating spatial firing rate maps? In a related point, did the authors examine potential differences in mobile vs. immobile activation for dLGN vs. HPC? • Perhaps we missed it, but what size was the smoothing kernel for 2D ratemaps (in cms)? • Did the authors examine the spatial distribution of dLGN place fields and, if so, were they uniformly distributed? It would be interesting to know if the fields clustered near objects for example. • The explanation of the “field index” metric is difficult to understand in the methods and should be clarified if possible. Figure 2: The color-coded pass index trajectory plots are not very informative because the dots are all layered on top of each other. Authors could do one or multiple of the following to enhance interpretation: make the dots smaller, make the dots transparent, make the color map sequential rather than qualitative. It would also be helpful to provide an “n” of the number of place cells in each region in Fig 2g. Figure 3: The close up of a single pass in the upper right corner of sub-plots 3b and 3c are confusing. The legend could be revised to clarify what we are looking at. In general, we are a bit unclear as to why the close up of a single pass is necessary. Are we supposed to see rhythmic activity in the spikes? The close up of a single pass is not useful for this because the rat may not be moving at a constant velocity. It may be the case that the spike train above is sufficient. - BU NeuroPreprint JC
Present and past LivePREJCs: PREreview's community live-streamed preprint journal clubs
Below is a list of upcoming live-streamed preprint journal clubs (LivePREJCs) as well as those hosted in the past. To learn more about what to expect and how to solicit our help to organize one, please read here
PREreview-PLOS Open Access Week Preprint Journal Club Information
Join us for the OA Week PLOS/PREreview live-streamed preprint journal clubs! REGISTER HERESCHEDULEDONE! Neuroscience – Monday October 22, 2018 – 9am PDT / 12pm EDT / 4pm UTCPreprint: Sex Differences in Aggression: Differential Roles of 5-HT2, Neuropeptide F and Tachykinin Facilitators: Samantha Hindle (PREreview) and Daniela Saderi (PREreview)Expert: Dr. Tim MoscaCollaborative notes and info to join this call: Neuroscience EtherpadPREreview available here. DONE! Bioinformatics – Tuesday October 23, 2018 – 9am PDT / 12pm EDT / 4pm UTCPreprint: EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma Facilitators: Daniela Saderi (PREreview) and Monica Granados (PREreview)Experts: Dr. Shannon McWeeney and Dr. Ted LaderasCollaborative notes and info to join this call: Bioinformatics EtherpadPREreview available here. DONE! Ecology – Wednesday October 24, 2018 – 9am PDT / 12pm EDT / 4pm UTCPreprint: Host-parasite interaction explains variation in prevalence of avian haemosporidians at the community level Facilitators: Jessica Polka (ASAPbio) and Steven Burgess (University of Illinois)Expert: Dr. Timothée PoisotCollaborative notes and info to join this call: Ecology EtherpadPREreview available here.What are PREreview Live-Streamed Preprint Journal Clubs? #LivePREJC
OIST E&E PREreview JC "Biodiversity trends are stronger in marine than terrestrial assemblages"
Biodiversity trends are stronger in marine than terrestrial assemblagesShane Blowes, Sarah Supp, Laura Antao, Amanda Bates, Helge Bruelheide, Jonathan Chase, Faye Moyes, Anne Magurran, Brian McGill, Isla Myers-Smith, Marten Winter, Anne Bjorkman, Diana Bowler, Jarrett EK Byrnes, Andrew Gonzalez, Jes Hines, Forest Isbell, Holly Jones, Laetitia Navarro, Patrick Thompson, Mark Vellend, Conor Waldock, Maria DornelasbioRxiv, October 30th, 2018doi: https://doi.org/10.1101/457424Overview and take-home messages:Blowes et al. tackle an impressive and large undertaking in this paper by attempting to disentangle global biodiversity trends through a meta-analysis of data from 358 studies. By dividing the available data by biome and taxa, the authors were able to detect different biodiversity trends in marine and terrestrial biomes. Tropical marine biomes, particularly the Caribbean, have a more negative deviation from the mean trend in species richness and more positive deviations from the overall trend in species turnover--species are turning over more quickly in marine biomes. The analyses demonstrate that mean local species richness is not decreasing, but many individual regions deviate significantly from the overall mean. The results have important implications for how we discuss changes in biodiversity in the anthropocene, but it is important to make clear that locally static species richness does not equate to globally static species richness, and species are going extinct at an alarming rate. Overall, this paper presents careful analyses and is clearly written, however, there are a few issues that, if addressed, we feel could improve future versions of the manuscript.
Live-streamed preprint Journal Club on "EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma" – October 23, 2018
This is a review of the bioRxiv preprint "EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma" by Borong Shao, Maria Bjaanæs, Åslaug Helland, Christof Schütte, Tim Conrad, doi:10.1101/410472. This review was compiled from a discussion during the live-streamed Bioinformatics preprint journal club as part of an Open Access Week effort organized by the PREreview team and PLOS. 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: Samantha Hindle, Paul Goetsch, and Bradly Alicea.