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Rxivist combines biology preprints from bioRxiv and medRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 118,816 papers from 512,446 authors.

Most tweeted biology preprints, last 24 hours

*There are gaps in historical Twitter data, most notably in spring 2020. This may result in some preprints appearing with less tweets than they should.

123 results found. For more information, click each entry to expand.

1: Nonsense correlations in neuroscience
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Posted 30 Nov 2020

Nonsense correlations in neuroscience
36 tweets bioRxiv neuroscience

Kenneth D. Harris

Most neurophysiological signals exhibit slow continuous trends over time. Because standard correlation analyses assume that all samples are independent, they can yield apparently significant "nonsense correlations" even for signals that are completely unrelated. Here we compare the performance of several methods for assessing correlations between timeseries, using simulated slowly drifting signals with and without genuine correlations. The best performance was obtained from a "pseudosession method", which relies on one of the signals being randomly generated by the experimenter, or a "session perturbation" method which requires multiple recordings under the same conditions. If neither of these is applicable, we find that a "linear shift method can work well, but only when one of the signals is stationary. Methods based on cross-validation, circular shifting, phase randomization, or detrending gave up to 100% false positive rates in our simulations. We conclude that analysis of neural timeseries is best performed when stationarity and randomization is built into the experimental design.

2: Synonymous mutations and the molecular evolution of SARS-Cov-2 origins
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Posted 21 Apr 2020

Synonymous mutations and the molecular evolution of SARS-Cov-2 origins
33 tweets bioRxiv evolutionary biology

Hongru Wang, Lenore Pipes, Rasmus Nielsen

Human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is most closely related, by average genetic distance, to two coronaviruses isolated from bats, RaTG13 and RmYN02. However, there is a segment of high amino acid similarity between human SARS-CoV-2 and a pangolin isolated strain, GD410721, in the receptor binding domain (RBD) of the spike protein, a pattern that can be caused by either recombination or by convergent amino acid evolution driven by natural selection. We perform a detailed analysis of the synonymous divergence, which is less likely to be affected by selection than amino acid divergence, between human SARS-CoV-2 and related strains. We show that the synonymous divergence between the bat derived viruses and SARS-CoV-2 is larger than between GD410721 and SARS-CoV-2 in the RBD, providing strong additional support for the recombination hypothesis. However, the synonymous divergence between pangolin strain and SARS-CoV-2 is also relatively high, which is not consistent with a recent recombination between them, instead it suggests a recombination into RaTG13. We also find a 14-fold increase in the dN/dS ratio from the lineage leading to SARS-CoV-2 to the strains of the current pandemic, suggesting that the vast majority of non-synonymous mutations currently segregating within the human strains have a negative impact on viral fitness. Finally, we estimate that the time to the most recent common ancestor of SARS-CoV-2 and RaTG13 or RmYN02 based on synonymous divergence, is 51.71 years (95% C.I., 28.11-75.31) and 37.02 years (95% C.I., 18.19-55.85), respectively. ### Competing Interest Statement The authors have declared no competing interest.

3: Mapping-based genome size estimation
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Posted 13 Apr 2019

Mapping-based genome size estimation
20 tweets bioRxiv genomics

Boas Pucker

While the size of chromosomes can be measured under a microscope, the size of genomes cannot be measured precisely. Biochemical methods and k-mer distribution-based approaches allow only estimations. An alternative approach to predict the genome size based on high contiguity assemblies and short read mappings is presented here and optimized on Arabidopsis thaliana and Beta vulgaris. Brachypodium distachyon, Solanum lycopersicum, Vitis vinifera, and Zea mays were also analyzed to demonstrate the broad applicability of this approach. Mapping-based Genome Size Estimation (MGSE) and additional scripts are available on github: https://github.com/bpucker/MGSE.

4: A comparison of blood and brain-derived ageing and inflammation-related DNA methylation signatures and their association with microglial burdens
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Posted 01 Dec 2020

A comparison of blood and brain-derived ageing and inflammation-related DNA methylation signatures and their association with microglial burdens
16 tweets bioRxiv neuroscience

Anna J Stevenson, Daniel L McCartney, Gemma L Shireby, Robert Francis Hillary, Declan King, Makis Tzioras, Nicola Wrobel, Sarah McCafferty, Lee Murphy, Barry W McColl, Paul Redmond, Adele M. Taylor, Sarah E. Harris, Tom C Russ, Eilis J Hannon, Andrew M McIntosh, Jonathan Mill, Colin Smith, Ian J Deary, Simon R Cox, Riccardo E Marioni, Tara L. Spires-Jones

Inflammation and ageing-related DNA methylation patterns in the blood have been linked to a variety of morbidities, including cognitive decline and neurodegenerative disease. However, it is unclear how these blood-based patterns relate to patterns within the brain, and how each associates with central cellular profiles. In this study, we profiled DNA methylation in both the blood and in five post-mortem brain regions (BA17, BA20/21, BA24, BA46 and hippocampus) in 14 individuals from the Lothian Birth Cohort 1936. Microglial burdens were additionally quantified in the same brain regions. DNA methylation signatures of five epigenetic ageing biomarkers ('epigenetic clocks'), and two inflammatory biomarkers (DNA methylation proxies for C-reactive protein and interleukin-6) were compared across tissues and regions. Divergent correlations between the inflammation and ageing signatures in the blood and brain were identified, depending on region assessed. Four out of the five assessed epigenetic age acceleration measures were found to be highest in the hippocampus ({beta} range=0.83-1.14, p[≤]0.02). The inflammation-related DNA methylation signatures showed no clear variation across brain regions. Reactive microglial burdens were found to be highest in the hippocampus ({beta}=1.32, p=5x10-4); however, the only association identified between the blood- and brain-based methylation signatures and microglia was a significant positive association with acceleration of one epigenetic clock (termed DNAm PhenoAge) averaged over all five brain regions ({beta}=0.40, p=0.002). This work highlights a potential vulnerability of the hippocampus to epigenetic ageing and provides preliminary evidence of a relationship between DNA methylation signatures in the brain and differences in microglial burdens.

5: SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome
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Posted 30 Jun 2020

SARS-CoV-2 Simulations Go Exascale to Capture Spike Opening and Reveal Cryptic Pockets Across the Proteome
13 tweets bioRxiv biophysics

Maxwell I. Zimmerman, Justin R. Porter, Michael D Ward, Sukrit Singh, Neha Vithani, Artur Meller, Upasana L. Mallimadugula, Catherine E. Kuhn, Jonathan H. Borowsky, Rafal P. Wiewiora, Matthew F. D. Hurley, Aoife M Harbison, Carl A. Fogarty, Joseph E. Coffland, Elisa Fadda, Vincent A. Voelz, John D Chodera, Gregory R. Bowman

SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression, and replication, which depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate an unprecedented 0.1 seconds of the viral proteome. Our simulations capture dramatic opening of the apo Spike complex, far beyond that seen experimentally, which explains and successfully predicts the existence of "cryptic" epitopes. Different Spike homologues modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also observe dramatic conformational changes across the proteome, which reveal over 50 "cryptic" pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas. ### Competing Interest Statement The authors have declared no competing interest.

6: Neuroinvasion of SARS-CoV-2 in human and mouse brain
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Posted 26 Jun 2020

Neuroinvasion of SARS-CoV-2 in human and mouse brain
10 tweets bioRxiv microbiology

Eric Song, Ce Zhang, Benjamin Israelow, Alice Lu-Culligan, Alba Vieites Prado, Sophie Skriabine, Peiwen Lu, Orr-El Weizman, Feimei Liu, Yile Dai, Klara Szigeti-Buck, Yuki Yasumoto, Guilin Wang, Christopher Castaldi, Jaime Heltke, Evelyn Ng, John Wheeler, Mia Madel Alfajaro, Etienne Levavasseur, Benjamin Fontes, Neal G. Ravindra, David Van Dijk, Shrikant Mane, Murat Gunel, Aaron Ring, Syed A. Jaffar Kazmi, Kai Zhang, Craig B. Wilen, Tamas L Horvath, Isabelle Plu, Stephane Haik, Jean-Leon Thomas, Angeliki Louvi, Shelli F. Farhadian, Anita Huttner, Danielle Seilhean, Nicolas Renier, Kaya Bilguvar, Akiko Iwasaki

Although COVID-19 is considered to be primarily a respiratory disease, SARS-CoV-2 affects multiple organ systems including the central nervous system (CNS). Yet, there is no consensus whether the virus can infect the brain, or what the consequences of CNS infection are. Here, we used three independent approaches to probe the capacity of SARS-CoV-2 to infect the brain. First, using human brain organoids, we observed clear evidence of infection with accompanying metabolic changes in the infected and neighboring neurons. However, no evidence for the type I interferon responses was detected. We demonstrate that neuronal infection can be prevented either by blocking ACE2 with antibodies or by administering cerebrospinal fluid from a COVID-19 patient. Second, using mice overexpressing human ACE2, we demonstrate in vivo that SARS-CoV-2 neuroinvasion, but not respiratory infection, is associated with mortality. Finally, in brain autopsy from patients who died of COVID-19, we detect SARS-CoV-2 in the cortical neurons, and note pathologic features associated with infection with minimal immune cell infiltrates. These results provide evidence for the neuroinvasive capacity of SARS-CoV2, and an unexpected consequence of direct infection of neurons by SARS-CoV-2. ### Competing Interest Statement The authors have declared no competing interest.

7: Connecting structure to function with the recovery of over 1000 high-quality activated sludge metagenome-assembled genomes encoding full-length rRNA genes using long-read sequencing
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Posted 12 May 2020

Connecting structure to function with the recovery of over 1000 high-quality activated sludge metagenome-assembled genomes encoding full-length rRNA genes using long-read sequencing
9 tweets bioRxiv microbiology

Caitlin Singleton, Francesca Petriglieri, Jannie Munk Kristensen, Rasmus H Kirkegaard, Thomas Y Michaelsen, Martin H Andersen, Zivile Kondrotaite, Søren M Karst, Morten S Dueholm, Per H. Nielsen, Mads Albertsen

Microorganisms are critical to water recycling, pollution removal and resource recovery processes in the wastewater industry. While the structure of this complex community is increasingly understood based on 16S rRNA gene studies, this structure cannot currently be linked to functional potential due to the absence of high-quality metagenome-assembled genomes (MAGs) with full-length rRNA genes for nearly all species. Here, we sequence 23 Danish full-scale wastewater treatment plant metagenomes, producing >1 Tbp of long-read and >0.9 Tbp of short-read data. We recovered 1083 high-quality MAGs, including 57 closed circular genomes. The MAGs accounted for ~30% of the community, and meet the stringent MIMAG high-quality draft requirements including full-length rRNA genes. We show how novel high-quality MAGs in combination with >13 years of amplicon data, Raman microspectroscopy and fluorescence in situ hybridisation can be used to uncover abundant undescribed lineages belonging to important functional groups. ### Competing Interest Statement R.H.K., M.A., S.M.K. and P.H.N. own DNASense ApS. The remaining authors declare no competing interests.

8: Making the invisible enemy visible
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Posted 07 Oct 2020

Making the invisible enemy visible
5 tweets bioRxiv molecular biology

Tristan I. Croll, Kay Diederichs, Florens Fischer, Cameron Fyfe, Yunyun Gao, Sam Horrell, Agnel Praveen Joseph, Luise Kandler, Oliver Kippes, Ferdinand Kirsten, Konstantin Müller, Kristoper Nolte, Alex Payne, Matthew G. Reeves, Jane Richardson, Gianluca Santoni, Sabrina Stäb, Dale Tronrud, Christopher Williams, Andrea Thorn

During the COVID-19 pandemic, structural biologists have rushed to solve the structures of the 28 proteins encoded by the SARS-CoV-2 genome in order to understand the viral life cycle and enable structure-based drug design. In addition to the 200 structures from SARS-CoV previously solved, 367 structures covering 16 of the viral proteins have been released in the span of only 6 months. These structural models serve as basis for research worldwide to understand how the virus hijacks human cells, for structure-based drug design and to aid in the development of vaccines. However, errors often occur in even the most careful structure determination - and are even more common among these structures, which were solved under immense pressure. From the beginning of the pandemic, the Coronavirus Structural Taskforce has categorized, evaluated and reviewed all of these experimental protein structures in order to help downstream users and original authors. Our website also offers improved models for many key structures, which have been used by Folding@Home, OpenPandemics, the EU JEDI COVID-19 challenge, and others. Here, we describe our work for the first time, give an overview of common problems, and describe a few of these structures that have since acquired better versions in the worldwide Protein Data Bank, either from new data or as depositor re-versions using our suggested changes. ### Competing Interest Statement The authors have declared no competing interest.

9: SARS-CoV-2 infection of human iPSC-derived cardiac cells predicts novel cytopathic features in hearts of COVID-19 patients
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Posted 25 Aug 2020

SARS-CoV-2 infection of human iPSC-derived cardiac cells predicts novel cytopathic features in hearts of COVID-19 patients
5 tweets bioRxiv cell biology

Juan A Pérez-Bermejo, Serah Kang, Sarah J. Rockwood, Camille R. Simoneau, David A. Joy, Gokul N. Ramadoss, Ana C. Silva, Will R. Flanigan, Huihui Li, Ken Nakamura, Jeffrey D. Whitman, Melanie Ott, Bruce R. Conklin, Todd C McDevitt

Although COVID-19 causes cardiac dysfunction in up to 25% of patients, its pathogenesis remains unclear. Exposure of human iPSC-derived heart cells to SARS-CoV-2 revealed productive infection and robust transcriptomic and morphological signatures of damage, particularly in cardiomyocytes. Transcriptomic disruption of structural proteins corroborated adverse morphologic features, which included a distinct pattern of myofibrillar fragmentation and numerous iPSC-cardiomyocytes lacking nuclear DNA. Human autopsy specimens from COVID-19 patients displayed similar sarcomeric disruption, as well as cardiomyocytes without DNA staining. These striking cytopathic features provide new insights into SARS-CoV-2 induced cardiac damage, offer a platform for discovery of potential therapeutics, and raise serious concerns about the long-term consequences of COVID-19. ### Competing Interest Statement B.R.C. is a founder of Tenaya Therapeutics (https://www.tenayatherapeutics.com/), a company focused on finding treatments for heart failure, including genetic cardiomyopathies. B.R.C. and T.C.M. hold equity in Tenaya.

10: MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance
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Posted 07 Oct 2020

MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance
5 tweets bioRxiv bioinformatics

Samuel M. Nicholls, Radoslaw Poplawski, Matthew J. Bull, Anthony Underwood, Michael Chapman, Khalil Abu-Dahab, Ben Taylor, Ben Jackson, Sara Rey, Roberto Amato, Rich Livett, Sónia Gonçalves, Ewan M. Harrison, Sharon J Peacock, David M. Aanensen, Andrew Rambaut, Thomas R. Connor, Nick J Loman, The COVID-19 Genomics UK (COG-UK) Consortium

Genomic epidemiology has become an increasingly common tool for epidemic response. Recent technological advances have made it possible to sequence genomes rapidly enough to inform outbreak response, and cheaply enough to justify dense sampling of even large epidemics. With increased availability of sequencing it is possible for agile networks of sequencing facilities to collaborate on the sequencing and analysis of epidemic genomic data. In response to the ongoing SARS-CoV-2 pandemic in the United Kingdom, the COVID-19 Genomics UK (COG-UK) consortium was formed with the aim of rapidly sequencing SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of Majora, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network. The system was designed and implemented pragmatically to stand up capacity rapidly in a pandemic caused by a novel virus. This approach has underpinned the success of COG-UK, which has rapidly become the leading contributor of SARS-CoV-2 genomes to international databases and has generated over 60,000 sequences to date. ### Competing Interest Statement The authors have declared no competing interest.

11: Distributed control of motor circuits for backward walking in Drosophila
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Posted 12 Jul 2020

Distributed control of motor circuits for backward walking in Drosophila
4 tweets bioRxiv neuroscience

Kai Feng, Rajyashree Sen, Ryo Minegishi, Michael Dübbert, Till Bockemühl, Ansgar Büschges, Barry J. Dickson

How do descending inputs from the brain control leg motor circuits to change the way an animal walks? Conceptually, descending neurons are thought to function either as command-type neurons, in which a single type of descending neuron exerts a high-level control to elicit a coordinated change in motor output, or through a more distributed population coding mechanism, whereby a group of neurons, each with local effects, act in combination to elicit a global motor response. The Drosophila Moonwalker Descending Neurons (MDNs), which alter leg motor circuit dynamics so that the fly walks backwards, exemplify the command-type mechanism. Here, we identify several dozen MDN target neurons within the leg motor circuits, and show that two of them mediate distinct and highly-specific changes in leg muscle activity during backward walking: LIN156 neurons provide the hindleg power stroke during stance phase; LIN128 neurons lift the legs at the end of stance to initiate swing. Through these two effector neurons, MDN directly controls both the stance and swing phases of the backward stepping cycle. MDN exerts these changes only upon the hindlegs; the fore- and midlegs follow passively through ground contact. These findings suggest that command-type descending neurons can also operate through the distributed control of local motor circuits. ### Competing Interest Statement The authors have declared no competing interest.

12: A comprehensive data-driven model of cat primary visual cortex
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Posted 24 Sep 2018

A comprehensive data-driven model of cat primary visual cortex
4 tweets bioRxiv neuroscience

Jan Antolik, Cyril Monier, Yves Frègnac, Andrew P. Davison

Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue: a comprehensive spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code. This integrative model offers numerous insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception. Significance statement Computational modeling can integrate fragments of understanding generated by experimental neuroscience. However, most previous models considered only a few features of neural computation at a time, leading to either poorly constrained models with many parameters, or lack of expressiveness in over-simplified models. A solution is to commit to detailed models, but constrain them with a broad range of anatomical and functional data. This requires a long-term systematic approach. Here we present a first snapshot of such an integrative program: a large-scale spiking model of V1, that is constrained by an unprecedented range of anatomical and functional features. Together with the associated modeling infrastructure, this study lays the groundwork for a broad integrative modeling program seeking an in-depth understanding of vision.

13: Limited evidence of tumour mutational burden as a biomarker of response to immunotherapy
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Posted 04 Sep 2020

Limited evidence of tumour mutational burden as a biomarker of response to immunotherapy
4 tweets bioRxiv genomics

Carino Gurjao, Dina Tsukrov, Maxim Imakaev, Lovelace J. Luquette, Leonid A Mirny

Cancer immunotherapy by immune checkpoint blockade (ICB) is effective for several cancer types, however, its clinical use is encumbered by a high variability in patient response. Several studies have suggested that Tumor Mutational Burden (TMB) correlates with patient response to ICB treatments, likely due to immunogenic neoantigens generated by novel mutations accumulated during cancer progression. Association of TMB and response to checkpoint inhibitors has become widespread in the oncoimmunology field, within and across cancer types, and has led to the development of commercial TMB-based biomarker platforms. As a result, patient prioritization for ICB based on individual TMB level was recently approved by the FDA. Here we revisit the association of mutational burden with response to checkpoint inhibitors by aggregating pan-cancer data of ICB-treated patients with whole-exome sequencing and clinical annotation. Surprisingly, we find little evidence that TMB is predictive of patient response to immunotherapy. Our analysis suggests that previously reported associations arise from a combination of confounding disease subtypes and incorrect statistical testing. We show that using a TMB threshold for clinical decisions regarding immunotherapy could deprive potentially responding patients of receiving efficacious and life-extending treatment. Finally, we present a simple mathematical model that extends the neoantigen theory, is consistent with the lack of association between TMB and response to ICB and highlights the role of immunodominance. Our analysis calls for caution in the use of TMB as a biomarker and emphasizes the necessity of continuing the search for other genetic and non-genetic determinants of response to immunotherapy. ### Competing Interest Statement The authors have declared no competing interest.

14: A zebrafish model of Granulin deficiency reveals essential roles in myeloid cell differentiation
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Posted 23 Jul 2020

A zebrafish model of Granulin deficiency reveals essential roles in myeloid cell differentiation
4 tweets bioRxiv cell biology

Clyde A. Campbell, Oksana Fursova, Xiaoyi Cheng, Elizabeth Snella, Abbigail McCune, Liangdao Li, Barbara Solchenberger, Bettina Schmid, Debashis Sahoo, Mark Morton, David Traver, Raquel Espín-Palazón

Granulin (GRN) is a pleiotropic protein involved in inflammation, wound healing, neurodegenerative disease, and tumorigenesis. These roles in human health have prompted research efforts to utilize Granulin in the treatment of rheumatoid arthritis, frontotemporal dementia, and to enhance wound healing. How granulin contributes to each of these diverse biological functions, however, remains largely unknown. Here, we have uncovered a new role for granulin during myeloid cell differentiation. Using a zebrafish model of granulin deficiency, we reveal that in the absence of granulin a (grna), myeloid progenitors are unable to terminally differentiate into neutrophils and macrophages during normal and emergency myelopoiesis. In addition, macrophages fail to recruit to the wound, resulting in abnormal healing. Our CUT&RUN experiments identify Pu.1, which together with Irf8 positively regulate grna expression. Importantly, we demonstrate functional conservation between the mammalian granulin and the zebrafish orthologue grna. Our findings uncover a previously unrecognized role for granulin during myeloid cell differentiation, opening a new field of study that has the potential to impact different aspects of the human health.

15: The psychological arrow of time and the human brain dynamics of event ordering
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Posted 25 Aug 2017

The psychological arrow of time and the human brain dynamics of event ordering
3 tweets bioRxiv neuroscience

Baptiste Gauthier, Karin Pestke, Virginie van Wassenhove

When navigating the real-world, the spatiotemporal sequencing of events is intrinsically bound to one's physical trajectory; when recollecting the past or imagining the future, the temporal and spatial dimension of events can be independently manipulated. Yet, the rules enabling the flexible use of spatial and temporal cognitive maps likely differ in one major way as time is directional (oriented from past-to-future) whereas space is not. Using combined magneto- and electroencephalography, we sought to capture such differences by characterizing time-resolved brain activity while participants mentally ordered memories from different mental perspectives in time (past/future) or space (west/east). We report two major neural dissociations underlying the mental ordering of events in time and in space: first, brain responses evoked by the temporal order and the temporal distance of events-to-self dissociated at early and late latencies, respectively whereas spatial order and distance of events-to-self elicited late brain responses simultaneously. Second, brain responses distinguishing self-position in time and the temporal order of events involved sources in the hippocampal formation; spatial perspective, order and distance did not. These results suggest that the neural dynamics evoked by the temporal ordering of a series of events retrieved from long-term memory, i.e. the psychological time arrow, entails dedicated cognitive processes in the hippocampal formation that are fundamentally distinct from the mapping of spatial location.

16: Query to reference single-cell integration with transfer learning
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Posted 16 Jul 2020

Query to reference single-cell integration with transfer learning
3 tweets bioRxiv bioinformatics

Mohammad Lotfollahi, Mohsen Naghipourfar, MD Luecken, Matin Khajavi, Maren Büttner, Ziga Avsec, Alexander V. Misharin, Fabian J Theis

Large single-cell atlases are now routinely generated with the aim of serving as reference to analyse future smaller-scale studies. Yet, learning from reference data is complicated by batch effects between datasets, limited availability of computational resources, and sharing restrictions on raw data. Leveraging advances in machine learning, we propose a deep learning strategy to map query datasets on top of a reference called single-cell architectural surgery (scArches, https: //github.com/theislab/scarches). It uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building, and the contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, and whole organism atlases, we showcase that scArches preserves nuanced biological state information while removing batch effects in the data, despite using four orders of magnitude fewer parameters compared to de novo integration. To demonstrate mapping disease variation, we show that scArches preserves detailed COVID-19 disease variation upon reference mapping, enabling discovery of new cell identities that are unseen during training. We envision our method to facilitate collaborative projects by enabling the iterative construction, updating, sharing, and efficient use of reference atlases. ### Competing Interest Statement F.J.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc.

17: The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area
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Posted 16 Jul 2019

The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area
3 tweets bioRxiv neuroscience

Amanda K. Tilot, Ekaterina A. Khramtsova, Katrina Grasby, Neda Jahanshad, Jodie Painter, Lucía Colodro-Conde, J. Bralten, Derrek P. Hibar, Penelope A. Lind, Siyao Liu, Sarah M. Brotman, Paul M. Thompson, Sarah E. Medland, Fabio Macciardi, Barbara E. Stranger, Lea K Davis, SE Fisher, Jason L. Stein

Structural brain changes along the lineage that led to modern Homo sapiens have contributed to our unique cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens in living humans, to reveal how selective pressures have shaped neocortical surface area. We show that variation within human gained enhancers active in the developing brain is associated with global surface area as well as that of specific regions. Moreover, we find evidence of recent polygenic selection over the past 2,000 years influencing surface area of multiple cortical regions, including those involved in spoken language and visual processing.

18: Predicting and validating protein degradation in proteomes using deep learning
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Posted 29 Nov 2020

Predicting and validating protein degradation in proteomes using deep learning
3 tweets bioRxiv bioinformatics

Matiss Ozols, Alexander Eckersley, Christopher Platt, Callum Stewart Mcguinness, Sarah Hibbert, Jerico Revote, Fuyi Li, Christopher Griffiths, Rachel Watson, Jiangning Song, Mike Bell, Michael Sherratt

Age, disease, and exposure to environmental factors can induce tissue remodelling and alterations in protein structure and abundance. In the case of human skin, ultraviolet radiation (UVR)-induced photo-ageing has a profound effect on dermal extracellular matrix (ECM) proteins. We have previously shown that ECM proteins rich in UV-chromophore amino acids are differentially susceptible to UVR. However, this UVR-mediated mechanism alone does not explain the loss of UV-chromophore-poor assemblies such as collagen. Here, we aim to develop novel bioinformatics tools to predict the relative susceptibility of human skin proteins to not only UVR and photodynamically produced ROS but also to endogenous proteases. We test the validity of these protease cleavage site predictions against experimental datasets (both previously published and our own, derived by exposure of either purified ECM proteins or a complex cell-derived proteome, to matrix metalloproteinase [MMP]-9). Our deep Bidirectional Recurrent Neural Network (BRNN) models for cleavage site prediction in nine MMPs, four cathepsins, elastase-2, and granzyme-B perform better than existing models when validated against both simple and complex protein mixtures. We have combined our new BRNN protease cleavage prediction models with predictions of relative UVR/ROS susceptibility (based on amino acid composition) into the Manchester Proteome Susceptibility Calculator (MPSC) webapp http://www.manchesterproteome.manchester.ac.uk/#/MPSC (or Application of the MPSC to the dermal proteome suggests that fibrillar collagens and elastic fibres will be preferentially degraded by proteases alone and by UVR/ROS and protease in combination, respectively. We also identify novel targets of oxidative damage and protease activity including dermatopontin (DPT), fibulins (EFEMP-1,-2, FBLN-1,-2,-5), defensins (DEFB1, DEFA3, DEFA1B, DEFB4B), proteases and protease inhibitors themselves (CTSA, CTSB, CTSZ, CTSD, TIMPs-1,-2,-3, SPINK6, CST6, PI3, SERPINF1, SERPINA-1,-3,-12). The MPSC webapp has the potential to identify novel protein biomarkers of tissue damage and to aid the characterisation of protease degradomics leading to improved identification of novel therapeutic targets.

19: Generalization in data-driven models of primary visual cortex
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Posted 07 Oct 2020

Generalization in data-driven models of primary visual cortex
3 tweets bioRxiv neuroscience

Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Willeke, Akshay K. Jagadish, Eric Wang, Edgar Y. Walker, Santiago A Cadena, Taliah Muhammad, Erick Cobos, Andreas S. Tolias, Alexander S. Ecker, Fabian H. Sinz

Deep neural networks (DNN) have set new standards at predicting responses of neural populations to visual input. Most such DNNs consist of a convolutional network (core) shared across all neurons which learns a representation of neural computation in visual cortex and a neuron-specific readout that linearly combines the relevant features in this representation. The goal of this paper is to test whether such a representation is indeed generally characteristic for visual cortex, i.e. generalizes between animals of a species, and what factors contribute to obtaining such a generalizing core. To push all non-linear computations into the core where the generalizing cortical features should be learned, we devise a novel readout that reduces the number of parameters per neuron in the readout by up to two orders of magnitude compared to the previous state-of-the-art. It does so by taking advantage of retinotopy and learns a Gaussian distribution over the neuron's receptive field position. With this new readout we train our network on neural responses from mouse primary visual cortex (V1) and obtain a gain in performance of 7% compared to the previous state-of-the-art network. We then investigate whether the convolutional core indeed captures general cortical features by using the core in transfer learning to a different animal. When transferring a core trained on thousands of neurons from various animals and scans we exceed the performance of training directly on that animal by 12%, and outperform a commonly used VGG16 core pre-trained on imagenet by 33%. In addition, transfer learning with our data-driven core is more data-efficient than direct training, achieving the same performance with only 40% of the data. Our model with its novel readout thus sets a new state-of-the-art for neural response prediction in mouse visual cortex from natural images, generalizes between animals, and captures better characteristic cortical features than current task-driven pre-training approaches such as VGG16. ### Competing Interest Statement The authors have declared no competing interest.

20: A comprehensive map of the dendritic cell transcriptional network engaged upon innate sensing of HIV
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Posted 18 Mar 2019

A comprehensive map of the dendritic cell transcriptional network engaged upon innate sensing of HIV
3 tweets bioRxiv immunology

Jarrod S Johnson, Nicholas De Veaux, Alexander W Rives, Xavier Lahaye, Sasha Y Lucas, Brieuc Pérot, Marine Luka, Lynn M Amon, Aaron Watters, Alan Aderem, Nicolas Manel, Dan R. Littman, Richard Bonneau, Mickaël M. Ménager

Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly understood. During infection with HIV-1, human dendritic cells (DCs) can detect the virus through an innate sensing pathway leading to antiviral interferon and DC maturation. Here, we developed an iterative experimental and computational approach to map the innate response circuitry during HIV-1 infection. By integrating genome-wide chromatin accessibility with expression kinetics, we inferred a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observed that an interferon response is required, yet insufficient to drive DC maturation, and identified PRDM1 and RARA as essential regulators of the interferon response and DC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the DC response to infection.

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