bluuebunny/crossref_metadata_2025_split huggingface dataset:
sdmTMB: An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields
Authors: Anderson Sean C. , Ward Eric J. , English Philina A. , Barnett Lewis A. K. , Thorson James T. 
Published: March 2022

AbstractGeostatistical spatial or spatiotemporal data are common across scientific fields. However, appropriate models to analyse these data, such as generalised linear mixed effects models (GLMMs) with Gaussian Markov random fields (GMRFs), are computationally intensive and challenging for many users to implement. Here, we introduce the R packagesdmTMB, which extends the flexible interface familiar to users oflme4, glmmTMB, andmgcvto include spatial and spatiotemporal latent GMRFs using an SPDE-(stochastic partial differential equation) based approach. SPDE matrices are constructed withfmesherand estimation is conducted via maximum marginal likelihood withTMBor via Bayesian inference withtmbstanandrstan. We describe the model and explore case studies that illustratesdmTMB’s flexibility in implementing penalised smoothers, non-stationary processes (time-varying and spatially varying coefficients), hurdle models, cross-validation and anisotropy (directionally dependent spatial correlation). Finally, we compare the functionality, speed, and interfaces of related software, demonstrating thatsdmTMBcan be an order of magnitude faster than R-INLA. We hopesdmTMBwill help open this useful class of models to a wider field of geostatistical analysts.
Indirect decoding of behavior from brain signals via reconstruction of experimental conditions
Authors: Soch Joram , Haynes John-Dylan 
Published: March 2022

AbstractThe data acquired during a functional magnetic resonance imaging (fMRI) experiment usually comprise experimental conditions, brain signals and behavioral responses. This reflects the underlying causal flow where the experimental conditions evoke brain responses that in turn result in behavior. In multivariate analyses, a common approach is to focus on the second step of that chain and to decode behavioral responses from brain signals. However, a different approach would be to first reconstruct the experimental conditions from brain signals and in turn use these to predict behavior. While this indirect approach would go against the causal chain of events, it might work better under certain circumstances, especially when the experimental conditions evoke much stronger measurable brain signals than the overt motor behavior. Here we tested this question directly by assessing the various mappings between conditions, brain signals and behavior in an open dataset. We found that the path of first decoding experimental conditions works surprisingly well, even though in our example data set, it is still outperformed by directly decoding the behavior from brain responses.
AIRRscape: an interactive tool for exploring B-cell receptor repertoires and antibody responses
Authors: Waltari Eric , Nafees Saba , Wong Joan , McCutcheon Krista M. , Pak John E. 
Published: March 2022

AbstractThe sequencing of antibody repertoires of B-cells at increasing coverage and depth has led to the identification of vast numbers of immunoglobulin heavy and light chains. However, the size and complexity of these Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) datasets makes it difficult to perform exploratory analyses. To aid in data exploration, we have developed AIRRscape, an R Shiny-based interactive web browser application that enables B-cell receptor (BCR) and antibody feature discovery through comparisons among multiple repertoires. Using AIRR-seq data as input, AIRRscape starts by aggregating and sorting repertoires into interactive and explorable bins of germline V-gene, germline J-gene, and CDR3 length, providing a high-level view of the entire repertoire. Interesting subsets of repertoires can be quickly identified and selected, and then network topologies of CDR3 motifs can be generated for further exploration. Here we demonstrate AIRRscape using patient BCR repertoires and sequences of published monoclonal antibodies to investigate patterns of humoral immunity to three viral pathogens: SARS-CoV-2, HIV-1, and DENV (dengue virus). AIRRscape reveals convergent antibody sequences among datasets for all three pathogens, although HIV-1 antibody datasets display limited convergence and idiosyncratic responses. We have made AIRRscape available on GitHub to encourage its open development and use by immuno-informaticians, virologists, immunologists, vaccine developers, and other scientists that are interested in exploring and comparing multiple immune receptor repertoires.Author SummaryTechnological advances in next generation sequencing have allowed for broad experimental sampling of immune repertoires, providing insight into how our immune system responds to infection, vaccination, autoimmunity, and cancer. The scale of these “big data”, however, make it difficult to bioinformatically extract the key sequence features that are shared across multiple repertoires. With AIRRscape, we enable large-scale immune repertoire visualization and analysis that requires no knowledge of the command line or advanced programming. By providing the community with an open-source, interactive, and user-friendly interface, we reduce the barriers to exploring immune repertoires at scale. We demonstrate the use of AIRRscape to characterize features of immune responses to viral infection that are shared across multiple repertoire datasets.
SegVeg: Segmenting RGB images into green and senescent vegetation by combining deep and shallow methods
Authors: Serouart Mario , Madec Simon , David Etienne , Velumani Kaaviya , Lozano Raul Lopez , Weiss Marie , Baret Frédéric 
Published: March 2022

AbstractThe pixels segmentation of high resolution RGB images into background, green vegetation and senescent vegetation classes is a first step often required before estimating key traits of interest including the vegetation fraction, the green area index, or to characterize the sanitary state of the crop. We developed the SegVeg model for semantic segmentation of RGB images into the three classes of interest. It is based on a U-net model that separates the vegetation from the background. It was trained over a very large and diverse dataset. The vegetation pixels are then classified using a SVM shallow machine learning technique trained over pixels extracted from grids applied to images. The performances of the SegVeg model are then compared to a three classes U-net model trained using weak supervision over RGB images with predicted pixels by SegVeg as groundtruth masks.Results show that the SegVeg model allows to segment accurately the three classes, with however some confusion mainly between the background and the senescent vegetation, particularly over the dark and bright parts of the images. The U-net model achieves similar performances, with some slight degradation observed for the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent ones. Finally, the models are used to predict the fraction of the three classes over the grids pixels or the whole images. Results show that the green fraction is very well estimated (R2=0.94) by the SegVeg model, while the senescent and background fractions show slightly degraded performances (R2=0.70 and 0.73, respectively).We made SegVeg publicly available as a ready-to-use script, as well as the entire dataset, rendering segmentation accessible to a broad audience by requiring neither manual annotation nor knowledge, or at least, offering a pre-trained model to more specific use.
Genome polarisation for detecting barriers to geneflow
Authors: Baird Stuart J. E. , Petružela Jan , Jaroň Izar , Škrabánek Pavel , Martínková Natália 
Published: March 2022

AbstractSemi-permeable barriers to geneflow in principle allow distantly related organisms to capture and exchange pre-adapted genes potentially speeding adaptation. However, describing barriers to geneflow on a genomic scale is non-trivial.We extend classic diagnostic allele counting measures of geneflow across a barrier to the case of genome-scale data. Diagnostic index expectation maximisation (diem) polarises the labelling of bistate markers with respect to the sides of a barrier. An initial state of ignorance is enforced by starting with randomly generated marker polarisations. This means there is no prior on population or taxon membership of the genomes concerned. Using a deterministic data labelling, small numbers of classic diagnostic markers can be replaced by large numbers of markers, each with a diagnostic index. Individuals’ hybrid indices (genome admixture proportions) are then calculated genome wide conditioned on marker diagnosticity; within diploid, haplodiploid and/or haploid genome compartments; or indeed over any subset of markers, allowing classical cline width/barrier strength comparisons along genomes. Along-genome barrier strength hetero-geneity allows for barrier regions to be identified. Further, blocks of genetic material that have introgressed across a barrier are easily identified with high power.diem indicates panmixis among Myotis myotis bat genomes, with a barrier separating low data quality outliers. In a Mus musculus domesticus/Mus spretus system, diem adds multiple introgressions of olfactory (and vomeronasal) gene clusters in one direction to previous demon-strations of a pesticide resistance gene introgressing in the opposite direction across a strong species barrier.diem is a genomes analysis solution which scales over reduced representation genomics of thousands of markers to treatment of all variant sites in large genomes. While the method lends itself to visualisation, its output of markers with barrier-informative annotation will fuel research in population genetics, phylogenetics and association studies. diem can equip such downstream applications with millions of informative markers.
CellSium – Versatile Cell Simulator for Microcolony Ground Truth Generation
Authors: Sachs Christian Carsten , Ruzaeva Karina , Seiffarth Johannes , Wiechert Wolfgang , Berkels Benjamin , Nöh Katharina 
Published: March 2022

AbstractSummaryTo train deep learning based segmentation models, large ground truth data sets are needed. To address this need in microfluidic live-cell imaging, we present CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks. Synthetic time-lapse videos with and without fluorescence, using programmable cell growth models, and simulation-ready 3D colony geometries for computational fluid dynamics (CFD) are also supported.Availability and ImplementationCellSium is free and open source software under the BSD license, implemented in Python, available at https://github.com/modsim/cellsium (DOI: 10.5281/zenodo.6193033), along with documentation, usage examples and Docker images.Contactk.noeh@fz-juelich.deSupplementary informationSupplementary data are available online.
Scalable Generation of Pseudo-Unipolar Sensory Neurons from Human Pluripotent Stem Cells
Authors: Deng Tao , Tristan Carlos A. , Weber Claire , Chu Pei-Hsuan , Ryu Seungmi , Jovanovic Vukasin M. , Ormanoglu Pinar , Twumasi Prisca , Shim Jaehoon , Jayakar Selwyn , Zhang Han-Xiong Bear , Jo Sooyeon , Voss Ty C. , Simeonov Anton , Bean Bruce P. , Woolf Clifford J. , Singeç Ilyas 
Published: March 2022

ABSTRACTDevelopment of new non-addictive analgesics requires advanced strategies to differentiate human pluripotent stem cells (hPSCs) into relevant cell types amenable for translational research. Here, we developed a highly efficient and reproducible method that differentiates hPSCs into peptidergic and non-peptidergic nociceptors. By modulating specific cell signaling pathways, hPSCs were first converted into SOX10+ neural crest cells, followed by differentiation into sensory neurons with an in vivo-like pseudo-unipolar morphology. Detailed characterization confirmed that the hPSC-derived nociceptors displayed molecular and cellular features comparable to native dorsal root ganglion (DRG) neurons, and expressed high-threshold primary sensory neuron markers, transcription factors, neuropeptides, and over 150 ion channels and receptors, including critical pain-relevant drug targets (e.g., TRPV1, TAC1, CALCA, NAV1.7, NAV1.8). Moreover, after confirming robust functional activities and differential response to noxious stimuli and specific drugs, a robotic cell culture system was employed to produce large quantities of human sensory neurons, which can be used to develop nociceptor-selective analgesics.
Microglial TNFα orchestrates brain phosphorylation during the sleep period and controls homeostatic sleep
Authors: Pinto Maria J , Cottin Léa , Dingli Florent , Laigle Victor , Ribeiro Luís F. , Triller Antoine , Henderson Fiona J , Loew Damarys , Fabre Véronique , Bessis Alain 
Published: March 2022

AbstractThe time we spend asleep is adjusted to previous time spent awake, and therefore believed to be under tight homeostatic control. Here, we establish microglia as a new cellular component of the sleep homeostat circuit. By using quantitative phosphoproteomics we demonstrate that microglia-derived TNFα controls thousands of phosphorylation sites during the sleep period. Substrates of microglial TNFα comprise sleep-promoting kinases and numerous synaptic proteins, including a subset whose phosphorylation status codes sleep need and determines sleep duration. As a result, lack of microglial TNFα attenuates the build-up of sleep need, as measured by slow wave activity, and prevents immediate compensation for loss of sleep. Together, we propose that microglia control sleep homeostasis by releasing TNFα that acts at the neuronal circuitry through dynamic control of phosphorylation.
ViPal: A Framework for Virulence Prediction of Influenza Viruses with Prior Viral Knowledge Using Genomic Sequences
Authors: Yin Rui , Luo Zihan , Zhuang Pei , Kwoh Chee Keong , Lin Zhuoyi 
Published: March 2022

AbstractInfluenza viruses pose significant threats to public health and cause enormous economic loss every year. Previous work has revealed the viral factors that influence the virulence of influenza viruses. However, taking prior viral knowledge represented by heterogeneous categorical and discrete information into account is scarce in the existing work. How to make full use of the preceding domain knowledge into virulence study is challenging but beneficial. This paper proposes a general framework named ViPal for virulence prediction that incorporates discrete prior viral mutation and reassortment information based on all eight influenza segments. The posterior regularization technique is leveraged to transform prior viral knowledge to constraint features and integrated into the machine learning models. Experimental results on influenza genomic datasets validate that our proposed framework can improve virulence prediction performance over baselines. The comparison between ViPal and other existing methods shows the computational efficiency of our framework with superior performance. Moreover, the interpretable analysis through SHAP identifies the scores of constraint features contributing to the prediction. We hope this framework could provide assistance for the accurate detection of influenza virulence and facilitate flu surveillance.
Gata2-regulated Gfi1b expression controls endothelial programming during endothelial-to-hematopoietic transition
Authors: Koyunlar Cansu , Gioacchino Emanuele , Vadgama Disha , de Looper Hans , Zink Joke , Hoogenboezem Remco , Havermans Marije , Bindels Eric , Dzierzak Elaine , Touw Ivo P , de Pater Emma 
Published: March 2022

AbstractThe first hematopoietic stem cells (HSCs) are formed through endothelial-to-hematopoietic transition (EHT) events during embryonic development. The transcription factor GATA2 is a crucial regulator of EHT and HSC function throughout life. Because GATA2 haploinsufficiency patients have inborn mutations, prenatal defects are likely to have an influence on disease development. In mice, Gata2 haploinsufficiency (Gata2+/-) reduces the number and the functionality of embryonic hematopoietic stem and progenitor cells (HSPCs) generated through EHT. However, the embryonic HSPC pool is heterogeneous and the mechanisms underlying this defect in Gata2+/- embryos are unclear. Here, we investigated whether Gata2 haploinsufficiency selectively affects a cellular subset undergoing EHT. We show that Gata2+/- HSPCs initiate but cannot fully activate hematopoietic programming during EHT. In addition, due to reduced activity of the endothelial repressor Gfi1b, Gata2+/- HSPCs cannot repress the endothelial identity to complete maturation. Finally, we show that hematopoietic-specific induction of gfi1b can restore HSC production in gata2b-null (gata2b-/-) zebrafish embryos. This study illustrates pivotal roles of Gata2 on the regulation of transcriptional network governing HSPC identity throughout EHT.HighlightsMaturation of embryonic Gata2+/- HSPCs is disturbed due to aberrant endothelial gene expression and incomplete activation of hematopoietic transcriptional programming.Gata2 activates Gfi1b to repress endothelial identity of embryonic HSPCs during maturation.Hematopoietic-specific induction of gfi1b restores the number of embryonic HSCs in gata2b-/- zebrafish.
Germline/soma distinction in Drosophila embryos requires regulators of zygotic genome activation
Authors: Colonnetta Megan M. , Schedl Paul , Deshpande Girish 
Published: March 2022

AbstractIn Drosophila melanogaster embryos, somatic versus germline identity is the first cell fate decision. Zygotic genome activation (ZGA) orchestrates regionalized gene expression, imparting specific identity on somatic cells. ZGA begins with a minor wave that commences at nuclear cycle (NC) 8 under the guidance of chromatin accessibility factors (Zelda, CLAMP, GAF), followed by the major wave during NC14. By contrast, primordial germ cell (PGC) specification requires maternally deposited and posteriorly anchored germline determinants. This is accomplished by a centrosome coordinated release and sequestration of germ plasm during the precocious cellularization of PGCs in NC10. Here we report a novel requirement for Zelda and CLAMP during the establishment of the germline/soma distinction. When their activity is compromised, PGC determinants are not properly sequestered, and specification is disrupted. Conversely, the spreading of PGC determinants from the posterior pole adversely influences transcription in the neighboring somatic nuclei. These reciprocal aberrations can be correlated with defects in centrosome duplication/separation that are known to induce inappropriate transmission of the germ plasm. Interestingly, consistent with the ability of BMP signaling to influence specification of embryonic PGCs, reduction in the transcript levels of a BMP family ligand, decapentaplegic (dpp), is exacerbated at the posterior pole.
Protein synthesis inhibition and loss of homeostatic functions in astrocytes from an Alzheimer’s disease mouse model: a role for ER-mitochondria interaction
Authors: Tapella Laura , Dematteis Giulia , Moro Marianna , Pistolato Beatrice , Tonelli Elisa , Vanella Virginia Vita , Giustina Daniele , La Forgia Aleida , Restelli Elena , Barberis Elettra , Cali Tito , Brini Marisa , Villani Salvatore , Del Grosso Erika , Grilli Mariagrazia , Manfredi Marcello , Corazzari Marco , Grolla Ambra A , Genazzani Armando A , Lim Dmitry 
Published: March 2022

ABSTRACTDeregulation of protein synthesis and ER stress/unfolded protein response (ER stress/UPR) have been reported in astrocytes. However, the relationships between protein synthesis deregulation and ER stress/UPR, as well as their role in the altered homeostatic support of Alzheimer’s disease (AD) astrocytes remain poorly understood. Previously, we reported that in astrocytic cell lines from 3xTg-AD mice (3Tg-iAstro) protein synthesis was impaired and ER-mitochondria distance was reduced. Here we show that impaired protein synthesis in 3Tg-iAstro is associated with an increase of p-eIF2α and downregulation of GADD34. Although mRNA levels of ER stress/UPR markers were increased two-three-fold, we found neither activation of PERK nor downstream induction of ATF4 protein. Strikingly, the overexpression of a synthetic ER-mitochondrial linker (EML) resulted in a reduced protein synthesis and augmented p-eIF2α without any effect on ER stress/UPR marker genes.In vivo, in hippocampi of 3xTg-AD mice, reduced protein synthesis, increased p-eIF2α and downregulated GADD34 protein were found, while no increase of p-PERK or ATF4 proteins was observed, suggesting that in AD astrocytes, bothin vitroandin vivo, phosphorylation of eIF2α and impairment of protein synthesis are PERK-independent. Next, we investigated the ability of 3xTg-AD astrocytes to support metabolism and function of other cells of the central nervous system. Astrocyte conditioned medium (ACM) from 3Tg-iAstro cells significantly reduced protein synthesis rate in primary hippocampal neurons. When added as a part of pericyte/endothelial cell (EC)/astrocyte 3D co-culture, 3Tg-iAstro, but not WT-iAstro, severely impaired formation and ramification of tubules, the effect, replicated by EML overexpression in WT-iAstro cells. Finally, a chemical chaperone 4-phenylbutyric acid (4-PBA) rescued protein synthesis, p-eIF2α levels in 3Tg-iAstro cells and tubulogenesis in pericyte/EC/3Tg-iAstro co-culture. Collectively, our results suggest that a PERK-independent, p-eIF2α-associated impairment of protein synthesis compromises astrocytic homeostatic functions, and this may be caused by the altered ER-mitochondria interaction.
Proton-transporting heliorhodopsins from marine giant viruses
Authors: Hososhima Shoko , Mizutori Ritsu , Abe-Yoshizumi Rei , Rozenberg Andrey , Shigemura Shunta , Pushkarev Alina , Konno Masae , Katayama Kota , Inoue Keiichi , Tsunoda Satoshi P. , Béjà Oded , Kandori Hideki 
Published: March 2022

AbstractRhodopsins convert light into signals and energy in animals and microbes. Heliorhodopsins (HeRs), a recently discovered new rhodopsin family, are widely present in archaea, bacteria, unicellular eukaryotes, and giant viruses, but their function remains unknown. Here we report that a viral HeR from Emiliania huxleyi virus 202 (V2HeR3) is a light-gated proton channel. V2HeR3 absorbs blue-green lights, and the active intermediate contains the deprotonated retinal Schiff base. Site-directed mutagenesis study revealed that E191 in TM6 constitutes the gate together with the retinal Schiff base. E205 and E215 form a proton accepting group of the Schiff base, whose mutations converted the protein into an outward proton pump. Three environmental viral HeRs from the same group, as well as a more distantly related HeR exhibited similar proton-transport activity, indicating that HeR functions might be diverse similarly to type-1 microbial rhodopsins. Some strains of E. huxleyi contain one HeR that is related to the viral HeRs, while its viruses EhV-201 and EhV-202 contain two and three HeRs, respectively. Except for V2HeR3 from EhV-202, none of these proteins exhibit ion-transport activity. Thus, when expressed in the E. huxleyi cell membranes, only V2HeR3 has the potential to depolarize the host cells by light, possibly to overcome the host defense mechanisms or to prevent superinfection. The neuronal activity generated by V2HeR3 suggests that it can potentially be used as an optogenetic tools, like type-1 microbial rhodopsins.