bluuebunny/crossref_metadata_2025_split huggingface dataset:
Persistence of anti-SARS-CoV-2 antibodies in non-hospitalized COVID-19 convalescent health care workers
Authors: Bruni Margherita , Cecatiello Valentina , Diaz-Basabe Angelica , Lattanzi Georgia , Mileti Erika , Monzani Silvia , Pirovano Laura , Rizzelli Francesca , Visintin Clara , Bonizzi Giuseppina , Giani Marco , Lavitrano Marialuisa , Faravelli Silvia , Forneris Federico , Caprioli Flavio , Pelicci Pier Giuseppe , Natoli Gioacchino , Pasqualato Sebastiano , Mapelli Marina , Facciotti Federica 
Published: August 2020

AbstractBackgroundCoronavirus disease-19 (COVID-19) is a respiratory illness caused by the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), a novel beta-coronavirus. Although antibody response to SARS-CoV-2 can be detected early during the infection, several outstanding questions remain to be addressed regarding magnitude and persistence of antibody titer against different viral proteins and their correlation with the strength of the immune response, as measured by serum levels of pro-inflammatory mediators.MethodsAn ELISA assay has been developed by expressing and purifying the recombinant SARS-CoV-2 Spike Receptor Binding Domain (RBD), Soluble Ectodomain (Spike), and full length nucleocapsid protein (N protein). Sera from healthcare workers affected by non-severe COVID-19 were longitudinally collected over four weeks, and compared to sera from patients hospitalized in Intensive Care Units (ICU) and SARS-CoV-2-negative subjects for the presence of IgM, IgG and IgA antibodies as well as soluble pro-inflammatory mediators in the sera.ResultsSpecificity and sensitivity of the ELISA assays were high for anti-RBD IgG and IgA (92-97%) and slightly lower for IgM and the Spike and N proteins (70-85%). The ELISA allowed quantification of IgM, IgG and IgA antibody responses against all the viral antigens tested and showed a correlation between magnitude of the antibody response and disease severity. Non-hospitalized subjects showed lower antibody titers and blood pro-inflammatory cytokine profiles as compared to patients in Intensive Care Units (ICU), irrespective of the antibodies tested. Noteworthy, in non-severe COVID-19 infections, antibody titers against RBD and Spike, but not against the N protein, as well as pro-inflammatory cytokines decreased within a month after viral clearance.ConclusionsRapid decline in antibody titers and in pro-inflammatory cytokines may be a common feature of non-severe SARS-CoV-2 infection, suggesting that antibody-mediated protection against re-infection with SARS-CoV-2 is of short duration. These results suggest caution in use serological testing to estimate the prevalence of SARS-CoV-2 infection in the general population.
NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity
Authors: Barot Meet , Gligorijević Vladimir , Cho Kyunghyun , Bonneau Richard 
Published: August 2020

AbstractTransferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to proteome and biological network functional annotation use sequence similarity to transfer knowledge between species. These similarity-based approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular or organismal context for meaningful function prediction. In order to supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, the majority of these methods are tied to a network for a single species, and many species lack biological networks. In this work, we integrate sequence and network information across multiple species by applying an IsoRank-derived network alignment algorithm to create a meta-network profile of the proteins of multiple species. We then use this integrated multispecies meta-network as input features to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and more diverse examples from multiple organisms, and consequently leads to significant improvements in function prediction performance. Further, we evaluate our approach in a setting in which an organism’s PPI network is left out, using other organisms’ network information and sequence homology in order to make predictions for the left-out organism, to simulate cases in which a newly sequenced species has no network information available.
Representation of contralateral visual space in the human hippocampus
Authors: Silson Edward H , Zeidman Peter , Knapen Tomas , Baker Chris I 
Published: August 2020

AbstractThe initial encoding of visual information primarily from the contralateral visual field is a fundamental organizing principle of the primate visual system. Recently, the presence of such retinotopic sensitivity has been shown to extend well beyond early visual cortex to regions not historically considered retinotopically sensitive. In particular, human scene-selective regions in parahippocampal and medial parietal cortex exhibit prominent biases for the contralateral visual field. Here we used fMRI to test the hypothesis that the human hippocampus, which is thought to be anatomically connected with these scene-selective regions, would also exhibit a biased representation of contralateral visual space. First, population receptive field mapping with scene stimuli revealed strong biases for the contralateral visual field in bilateral hippocampus. Second, the distribution of retinotopic sensitivity suggested a more prominent representation in anterior medial portions of the hippocampus. Finally, the contralateral bias was confirmed in independent data taken from the Human Connectome Project initiative. The presence of contralateral biases in the hippocampus – a structure considered by many as the apex of the visual hierarchy - highlights the truly pervasive influence of retinotopy. Moreover, this finding has important implications for understanding how this information relates to the allocentric global spatial representations known to be encoded therein.Significance StatementRetinotopic encoding of visual information is an organizing principle of visual cortex. Recent work demonstrates this sensitivity in structures far beyond early visual cortex, including those anatomically connected to the hippocampus. Here, using population receptive field modelling in two independent sets of data we demonstrate a consistent bias for the contralateral visual field in bilateral hippocampus. Such a bias highlights the truly pervasive influence of retinotopy, with important implications for understanding how the presence of retinotopy relates to more allocentric spatial representations.
Topographic connectivity reveals task-dependent retinotopic processing throughout the human brain
Authors: Knapen Tomas 
Published: August 2020

AbstractThe human visual system is organized as a hierarchy of maps that share the topography of the retina. These retinotopic maps have been identified throughout the brain, but how much of the brain is visually organized remains unknown. Here we demonstrate widespread stable visual organization beyond the traditional visual system. We analyzed detailed topographic connectivity with primary visual cortex during moviewatching, rest, and retinotopic mapping experiments to reveal that visual-spatial representations are warped by experimental condition and cognitive state. Specifically, traditionally visual regions alternate with default mode network and hippocampus in preferentially representing the center of the visual field. This visual role of hippocampus would allow it to implement sensory predictions by interfacing between abstract memories and concrete perceptions. These results indicate that pervasive sensory coding facilitates the communication between far-flung brain regions.
Decoding visual colour from scalp electroencephalography measurements
Authors: Hajonides Jasper E. , Nobre Anna C. , van Ede Freek , Stokes Mark G. 
Published: August 2020

AbstractRecent advances have made it possible to decode various aspects of visually presented stimuli from patterns of scalp EEG measurements. As of recently, such multivariate methods have been commonly used to decode visual-spatial features such as location, orientation, or spatial frequency. In the current study, we show that it is also possible to track visual colour processing by using Linear Discriminant Analysis on patterns of EEG activity. Building on other recent demonstrations, we show that colour decoding: (1) reflects sensory qualities (as opposed to, for example, verbal labelling) with a prominent contribution from posterior electrodes contralateral to the stimulus, (2) conforms to a parametric coding space, (3) is possible in multi-item displays, and (4) is comparable in magnitude to the decoding of visual stimulus orientation. Through subsampling our data, we also provide an estimate of the approximate number of trials and participants required for robust decoding. Finally, we show that while colour decoding can be sensitive to subtle differences in luminance, our colour decoding results are primarily driven by measured colour differences between stimuli. Colour decoding opens a relevant new dimension in which to track visual processing using scalp EEG measurements, while bypassing potential confounds associated with decoding approaches that focus on spatial features.
Co-evolved maternal effects selectively eliminate offspring depending on resource availability
Authors: Hsu Bin-Yan , Müller Martina S. , Gahr Christoph L. , Dijkstra Cor , Groothuis Ton G. G. G. 
Published: August 2020

AbstractMany plants and animals adaptively downsize the number of already-produced propagules if resources become insufficient to raise all of them. In birds, mothers often induce hatching asynchrony by incubating first eggs before last eggs are laid, creating an age/size hierarchy within broods which selectively eliminates the smallest chicks in poor food conditions. However, mothers also deposit more testosterone into late-laid eggs, which boosts competitive abilities of younger chicks, counteracts the competitive hierarchy, and ostensibly creates a paradox. Since testosterone also carries costs, we hypothesized that benefits of maternally deposited testosterone outweigh its costs in good food conditions, but that testosterone has a net detrimental effect in poor food conditions. We found experimental evidence that elevated maternal testosterone in the egg caused higher chick mortality in poor food conditions but better chick growth in good food conditions. These context-dependent effects resolve the paradox, suggesting co-evolution of two maternal effects, and explain inconsistent results of egg hormone manipulations in the literature.
Assessment of differentially methylated loci in individuals with end-stage kidney disease attributed to diabetic kidney disease
Authors: Smyth LJ , Kilner J , Nair V , Liu H , Brennan E , Kerr K , Sandholm N , Cole J , Dahlström E , Syreeni A , Salem RM , Nelson RG , Looker HC , Wooster C , Anderson K , McKay GJ , Kee F , Young I ,   NICOLA Collaborative Team,   Warren 3 and Genetics of Kidneys in Diabetes (GoKinD) Study Group, Andrews D , Forsblom C , Hirschhorn JN , Godson C , Groop PH , Maxwell AP , Susztak K , Kretzler M , Florez JC , McKnight AJ ,   on behalf of the GENIE consortium
Published: August 2020

AbstractA subset of individuals with type 1 diabetes mellitus (T1DM) are predisposed to developing diabetic kidney disease (DKD), which is the most common cause globally of end-stage kidney disease (ESKD). Emerging evidence suggests epigenetic changes in DNA methylation may have a causal role in both T1DM and DKD. The aim of this investigation was to assess differences in blood-derived DNA methylation patterns between individuals with T1DM-ESKD and individuals with long-duration T1DM but no evidence of kidney disease upon repeated testing. Blood-derived DNA from individuals (107 cases, 253 controls and 14 experimental controls) were bisulphite treated before DNA methylation patterns from both groups were generated and analysed using Illumina’s Infinium MethylationEPIC BeadChip arrays (n=862,927 sites). Differentially methylated CpG sites (dmCpGs) were identified (false discovery rate adjusted p≤×10−8and fold change ±2) by comparing methylation levels between ESKD cases and T1DM controls at single site resolution. Gene annotation and functionality was investigated to enrich and rank methylated regions associated with ESKD in T1DM.Top-ranked genes within which several dmCpGs were located and supported byin silicofunctional data, and replication where possible, include;AFF3, ARID5B, CUX1, ELMO1,FKBP5,HDAC4, ITGAL, LY9,PIM1, RUNX3, SEPTIN9, andUPF3A. Top-ranked enrichment pathways included pathways in cancer, TGF-β signalling and Th17 cell differentiation.Epigenetic alterations provide a dynamic link between an individual’s genetic background and their environmental exposures. This robust evaluation of DNA methylation in carefully phenotyped individuals, has identified biomarkers associated with ESKD, revealing several genes and implicated key pathways associated with ESKD in individuals with T1DM.
Selective attention in hypothesis-driven data analysis
Authors: Yanai Itai , Lercher Martin 
Published: August 2020

AbstractWhen analyzing the results of an experiment, the mental focus on a specific hypothesis might prevent the exploration of other aspects of the data, effectively blinding one to new ideas. To test this notion, we performed an experiment in which we asked undergraduate students to analyze a fictitious dataset. In addition to being asked what they could conclude from the dataset, half of the students were asked to also test specific hypotheses. In line with our notion, students in the hypothesis-free group were almost 5 times more likely to observe an image of a gorilla when simply plotting the data, a proxy for an initial step towards data analysis. If these findings are representative also of scientific research as a whole, they warrant concern about the current emphasis on hypothesis-driven research, especially in the context of information-rich datasets such as those now routinely created in the biological sciences. Our work provides evidence for a link between the psychological effect of selective attention and hypothesis-driven data analysis, and suggests a hidden cost to having a hypothesis when analyzing a dataset.
Repeated evolution of inactive pseudonucleases in a fungal branch of the Dis3/RNase II family of nucleases
Authors: Ballou Elizabeth R. , Cook Atlanta G. , Wallace Edward W.J. 
Published: August 2020

AbstractThe RNase II family of 3′-5′ exoribonucleases are present in all domains of life, and eukaryotic family members Dis3 and Dis3L2 play essential roles in RNA degradation. Ascomycete yeasts contain both Dis3 and inactive RNase II-like “pseudonucleases”. These function as RNA-binding proteins that affect cell growth, cytokinesis, and fungal pathogenicity. Here, we show how these pseudonuclease homologs, includingSaccharomyces cerevisiaeSsd1, are descended from active Dis3L2 enzymes. During fungal evolution, active site mutations in Dis3L2 homologs have arisen at least four times, in some cases following gene duplication. The N-terminal cold-shock domains and regulatory features are conserved across diverse dikarya and mucoromycota, suggesting that the non-nuclease function require this region. In the basidiomycete pathogenic yeastCryptococcus neoformans, the single Ssd1/Dis3L2 homolog is required for cytokinesis from polyploid “titan” growth stages and yet retains an active site sequence signature. We propose that that a nuclease-independent function for Dis3L2 arose in an ancestral hyphae-forming fungus. This second function has been conserved across hundreds of millions of years, while the RNase activity was lost repeatedly in independent lineages.
Cell-type-specific alternative polyadenylation (APA) genes reveal the function of dynamic APA in complex tissues
Authors: Bai Yulong , Qin Yidi , Fan Zhenjiang , Morrison Robert M. , Nam KyongNyon , Zarour Hassane Mohamed , Koldamova Radosveta , Padiath Quasar Saleem , Kim Soyeon , Park Hyun Jung 
Published: August 2020

ABSTRACTAlternative polyadenylation (APA) causes shortening or lengthening of the 3’-untranslated region (3’-UTR) of genes across multiple cell types. Bioinformatic tools have been developed to identify genes that are affected by APA (APA genes) in single-cell RNA-Seq (scRNA-Seq) data. However, they suffer from low power, and they cannot identify APA genes specific to each cell type (cell-type-specific APA) when multiple cell types are analyzed. To address these limitations, we developed scMAPA that systematically integrates two novel steps. First, scMAPA quantifies 3’-UTR long and short isoforms without requiring assumptions on the read density shape of input data. Second, scMAPA estimates the significance of the APA genes for each cell type while controlling confounders. In the analyses on our novel simulation data and human peripheral blood mono cellular data, scMAPA showed enhanced power in identifying APA genes. Further, in mouse brain data, scMAPA identifies cell-type-specific APA genes, improving interpretability for the cell-type-specific function of APA. We further showed that this improved interpretability helps to understand a novel role of APA on the interaction between neurons and blood vessels, which is critical to maintaining the operational condition of brains. With high sensitivity and interpretability, scMAPA shed novel insights into the function of dynamic APA in complex tissues.Key PointsWe developed a bioinformatic tool, scMAPA, that identifies dynamic APA across multiple cell types and a novel simulation pipeline to assess performance of such tools in APA calling.In simulation data of various scenarios from our novel simulation pipeline, scMAPA achieves sensitivity with a minimal loss of specificity.In human peripheral blood monocellular data, scMAPA identifies APA genes accurately and robustly, finding unique associations of APA with hematological processes.scMAPA identifies APA genes specific to each cell type in mouse brain data while controlling confounders that sheds novel insights into the complex molecular processes.
Network variants are similar between task and rest states
Authors: Kraus Brian T. , Perez Diana , Ladwig Zach , Seitzman Benjamin A. , Dworetsky Ally , Petersen Steven E. , Gratton Caterina 
Published: August 2020

AbstractRecent work has demonstrated that individual-specific variations in functional networks (that we call “network variants”) can be identified in individuals using functional magnetic resonance imaging (fMRI). These network variants exhibit reliability over time with resting-state fMRI data. These properties have suggested that network variants may be trait-like markers of individual differences in brain organization. Another test of this conclusion would be to examine if network variants are stable between task and rest states. Here, we use precision data from the Midnight Scan Club (MSC) to demonstrate that (1) task data can be used to identify network variants reliably, (2) these network variants show substantial spatial overlap with those observed in rest, although state-specific effects are present, (3) network variants assign to similar canonical functional networks in different states, and (4) single tasks or a combination of multiple tasks produce similar network variants to rest. Together, these findings further reinforce the trait-like nature of network variants and demonstrate the utility of using task data to define network variants.
Inference of ploidy by leveraging read depth from amplicon sequencing
Authors: Delomas Thomas A. , Willis Stuart C. , Schreier Andrea , Narum Shawn 
Published: August 2020

AbstractVariation in ploidy occurs naturally in select plant and animal species. Ploidy variation can also occur spontaneously or be induced during artificial propagation of fish and shellfish. Studying species and systems that have variable ploidy requires techniques to infer ploidy of individuals. Massively parallel sequencing of biallelic SNPs has been used to infer ploidy, but existing techniques have several drawbacks. These include being limited to only comparing a fixed number of ploidies (diploidy, triploidy, and tetraploidy) and requiring that heterozygous genotypes in an individual be identified prior to ploidy inference. We describe a method of inferring ploidy from sequencing of biallelic SNPs based on beta-binomial mixture models. This method is generalized to apply to any ploidy and does not require prior identification of heterozygous genotypes. We demonstrate efficacy of this method for comparing ancestral octoploidy, decaploidy, and dodecaploidy (tetraploidy, pentaploidy, and hexaploidy for the sequenced SNPs) in white sturgeon and diploidy and triploidy in Chinook salmon with amplicon sequencing (GT-seq) data. Results indicated that ploidy could be reliably estimated for individuals based on distinct distribution of log-likelihood ratios (LLR) for known ploidy samples of both species that were tested. Confidence in ploidy estimates increased with sequencing depth. We encourage users to explore the sequencing depths and LLR critical values that provide reliable estimates of ploidy for a given organism and set of SNPs. We expect that the R package provided will empower studies of genetic variation and inheritance in organisms that vary in ploidy naturally or as a result of artificial propagation practices.
Spatially localized cluster solutions in inhibitory neural networks
Authors: Ryu Hwayeon , Miller Jennifer , Teymuroglu Zeynep , Wang Xueying , Booth Victoria , Campbell Sue Ann 
Published: August 2020

AbstractNeurons in the inhibitory network of the striatum display cell assembly firing patterns which recent results suggest may consist of spatially compact neural clusters. Previous computational modeling of striatal neural networks has indicated that non-monotonic, distance-dependent coupling may promote spatially localized cluster firing. Here, we identify conditions for the existence and stability of cluster firing solutions in which clusters consist of spatially adjacent neurons in inhibitory neural networks. We consider simple non-monotonic, distance-dependent connectivity schemes in weakly coupled 1-D networks where cells make strong connections with theirkthnearest neighbors on each side. Using the phase model reduction of the network system, we prove the existence of cluster solutions where neurons that are spatially close together are also synchronized in the same cluster, and find stability conditions for these solutions. Our analysis predicts the long-term behavior for networks of neurons, and we confirm our results by numerical simulations of biophysical neuron network models. Additionally, we add weaker coupling between closer neighbors as a perturbation to our network connectivity. We analyze the existence and stability of cluster solutions of the perturbed network and validate our results with numerical simulations. Our results demonstrate that an inhibitory network with non-monotonic, distance-dependent connectivity can exhibit cluster solutions where adjacent cells fire together.