bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semisupervised definition of hippocampal ripples
Authors: Watanabe Yusuke , Okada Mami , Ikegaya Yuji 
Published: October 2020

AbstractHippocampal ripples are transient neuronal features observed in high-frequency oscillatory bands of local field potentials, and they occur primarily during periods of behavioral immobility and slow-wave sleep. Ripples have been defined based on mathematically engineered features, such as magnitudes, durations, and cycles per event. However, the “ripples” could vary from laboratory to laboratory because their definition is subject to human bias, including the arbitrary choice of parameters and thresholds. In addition, local field potentials are often influenced by myoelectric noise arising from animal movement, making it difficult to distinguish ripples from high-frequency noises. To overcome these problems, we extracted ripple candidates under few constraints and labeled them as binary or stochastic “true” or “false” ripples using Gaussian mixed model clustering and a deep convolutional neural network in a weakly supervised fashion. Our automatic method separated ripples and myoelectric noise and was able to detect ripples even when the animals were moving. Moreover, we confirmed that a convolutional neural network was able to detect ripples defined by our method. Leave-one-animal-out cross-validation estimated the area under the precision-recall curve for ripple detection to be 0.72. Finally, our model establishes an appropriate threshold for the ripple magnitude in the case of the conventional detection of ripples.
Evaluation of Zoetis GeneMax Advantage genomic predictions in commercialBos taurusAngus cattle
Authors: Arisman Brian. C. , Rowan Troy N. , Thomas Jordan M. , Durbin Harly J. , Lamberson William R. , Patterson David J. , Decker Jared E. 
Published: October 2020

ABSTRACTThe GeneMax (GMX) Advantage test, developed by Zoetis, uses approximately 50,000 single nucleotide polymorphisms (SNP) to predict the genomic potential of a commercial Angus heifer. Genetic predictions are provided for Calving Ease Maternal, Weaning Weight, Heifer Pregnancy, Milk, Mature Weight, Dry Matter Intake, Carcass Weight, Marbling, Yield, and three economic selection indices. Test results can inform selection and culling decisions made by commercial beef cattle producers. To measure the accuracy of the trait predictions, data from commercial Angus females and their progeny at the University of Missouri Thompson Research Center were utilized to analyze weaning weight, milk, marbling, fat, ribeye area, and carcass weight. Progeny phenotypic data were matched to the respective dam, then the cow’s genomic predictions were compared to the calf’s age-adjusted phenotypes using correlation and linear model effect sizes. All tested GeneMax scores of the dam were significantly correlated with and predicted calf performance. Our predicted effect sizes, except for fat thickness, were similar to those reported by Zoetis. In conclusion, the GeneMax Advantage test accurately ranks animals based on their genetic merit and is an effective selection tool in commercial cowherds.
The papain-like protease of coronaviruses cleaves ULK1 to disrupt host autophagy
Authors: Mohamud Yasir , Xue Yuan Chao , Liu Huitao , Ng Chen Seng , Bahreyni Amirhossein , Jan Eric , Luo Honglin 
Published: October 2020

ABSTRACTThe ongoing pandemic of COVID-19 alongside the outbreaks of SARS in 2003 and MERS in 2012 underscore the significance to understandbetacoronavirusesas a global health challenge. SARS-CoV-2, the etiological agent for COVID-19, has infected more than 29 million individuals worldwide with nearly ~1 million fatalities. Understanding how SARS-CoV-2 initiates viral pathogenesis is of the utmost importance for development of antiviral drugs. Autophagy modulators have emerged as potential therapeutic candidates against SARS-CoV-2 but recent clinical setbacks underline the urgent need for better understanding the mechanism of viral subversion of autophagy. Using murine hepatitis virus-A59 (MHV-A59) as a modelbetacoronavirus, time-course infections revealed a significant loss in the protein level of ULK1, a canonical autophagy regulating serine-threonine kinase, and the concomitant appearance of a possible cleavage fragment. To investigate whether virus-encoded proteases target this protein, we conductedin vitroand cellular cleavage assays and identified ULK1 as a novel bona fide substrate of SARS-CoV-2 papain-like protease (PLpro). Mutagenesis studies discovered that ULK1 is cleaved at a conserved PLprorecognition sequence (LGGG) after G499, separating its N-terminal kinase domain from the C-terminal substrate recognition region. Consistent with this, over-expression of SARS-CoV-2 PLprois sufficient to impair starvation-induced canonical autophagy and disrupt formation of ULK1-ATG13 complex. Finally, we demonstrated a dual role for ULK1 in MHV-A59 replication, serving a pro-viral functions during early replication that is inactivated at late stages of infection. In conclusion, our study identified a new mechanism by which PLproofbetacoronavirusesinduces viral pathogenesis by targeting cellular autophagic pathway (Word count=250)IMPORTANCEThe recent COVID-19 global pandemic alongside the 2003 SARS and 2012 MERS outbreaks underscore an urgent need to better understandbetacoronavirusesas pathogens that pose global challenge to human health. Studying the underlying biology of howbetacoronavirusessubvert innate cellular defense pathways such as autophagy will help to guide future efforts to develop anti-viral therapy. (Word count= 55)
Silver nanoparticles induce atriclosan-likeantibacterial action mechanism in multi-drug resistantKlebsiella pneumoniae
Authors: Pareek Vikram , Devineau Stéphanie , Sivasankaran Sathesh K. , Bhargava Arpit , Panwar Jitendra , Srikumar Shabarinath , Fanning Séamus 
Published: October 2020

AbstractInfections associated with antimicrobial-resistant bacteria now represent a significant threat to human health using conventional therapy, necessitating the development of alternate and more effective antibacterial compounds. Silver nanoparticles (Ag NPs) have been proposed as potential antimicrobial agents to combat infections. A complete understanding of their antimicrobial activity is required before these molecules can be used in therapy. Lysozyme coated Ag NPs were synthesized and characterized by TEM-EDS, XRD, UV-vis, FTIR spectroscopy, zeta potential and oxidative potential assay. Biochemical assays and deep level transcriptional analysis using RNA sequencing were used to decipher how Ag NPs exert their antibacterial action against multi-drug resistantKlebsiella pneumoniaeMGH78578. RNAseq data revealed that Ag NPs induced atriclosan-likebactericidal mechanism responsible for the inhibition of the type II fatty acid biosynthesis. Additionally, released Ag+generated oxidative stress both extra- and intracellularly inK. pneumoniae. The data showed thattriclosan-likeactivity and oxidative stress cumulatively underpinned the antibacterial activity of Ag NPs. This result was confirmed by the analysis of the bactericidal effect of Ag NPs against the isogenicK. pneumoniaeMGH78578ΔsoxSmutant, which exhibits a compromised oxidative stress response compared to the wild type. Silver nanoparticles induce atriclosan-likeantibacterial action mechanism in multi-drug resistantK. pneumoniae. This study extends our understanding of anti-Klebsiellamechanisms associated with exposure to Ag NPs. This allowed us to model how bacteria might develop resistance against silver nanoparticles, should the latter be used in therapy.
nearBynding: A flexible pipeline characterizing protein binding to local RNA structure
Authors: Busa Veronica F. , Favorov Alexander V. , Fertig Elana J. , Leung Anthony K. L. 
Published: October 2020

AbstractThe etiology of diseases driven by dysregulated mRNA metabolism can be elucidated by characterizing the responsible RNA-binding proteins (RBPs). Although characterizations of RBPs have been mainly focused on their binding sequences, not much has been investigated about their preferences for RNA structures. We present nearBynding, an R/Bioconductor pipeline that incorporates RBP binding sites and RNA structure information to discern structural binding preferences for an RBP. nearBynding visualizes RNA structure at and proximal to sites of RBP binding transcriptome-wide, analyzes CLIP-seq data without peak-calling, and provides a flexible scaffold to study RBP binding preferences relative to diverse RNA structure data types.
Fitting quantum machine learning potentials to experimental free energy data: Predicting tautomer ratios in solution
Authors: Wieder Marcus , Fass Josh , Chodera John D. 
Published: October 2020

AbstractThe computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios—the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery—is surprisingly diffcult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.
A tunable population timer in multicellular consortia
Authors: Toscano-Ochoa Carlos , Garcia-Ojalvo Jordi 
Published: October 2020

ABSTRACTProcessing time-dependent information requires cells to quantify the duration of past regulatory events and program the time span of future signals. At the single-cell level, timer mechanisms can be implemented with genetic circuits: sets of genes connected to achieve specific tasks. However, such systems are difficult to implement in single cells due to saturation in molecular components and stochasticity in the limited intracellular space. Multicellular implementations, on the other hand, outsource some of the components of information-processing circuits to the extracellular space, and thereby might escape those constraints. Here we develop a theoretical framework, based on a trilinear coordinate representation, to study the collective behavior of a cellular population composed of three cell types under stationary conditions. This framework reveals that distributing different processes (in our case the production, detection and degradation of a time-encoding signal) across distinct cell types enables the robust implementation of a multicellular timer. Our analysis also shows the circuit to be easily tunable by varying the cellular composition of the consortium.
Male sex hormones increase excitatory neuron production in developing human neocortex
Authors: Kelava Iva , Chiaradia Ilaria , Pellegrini Laura , Kalinka Alex T. , Lancaster Madeline A. 
Published: October 2020

AbstractThe presence of male-female brain differences has long been a controversial topic. Yet simply negating the existence of biological differences has detrimental consequences for all sexes and genders, particularly for the development of accurate diagnostic tools, effective drugs and understanding of disease. The most well-established morphological difference is size, with males having on average a larger brain than females; yet a mechanistic understanding of how this difference arises remains to be elucidated. Here, we use brain organoids to test the roles of sex chromosomes and sex steroids during development. While we show no observable differences between XX and XY brain organoids, sex steroids, namely androgens, increase proliferation of cortical neural progenitors. Transcriptomic analysis reveals effects on chromatin remodelling and HDAC activity, both of which are also implicated in the male-biased conditions autism spectrum disorder and schizophrenia. Finally, we show that higher numbers of progenitors result specifically in increased upper-layer excitatory neurons. These findings uncover a hitherto unknown role for male sex hormones in regulating excitatory neuron number within the human neocortex and represent a first step towards understanding the origin of human sex-related brain differences.
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network
Authors: Confavreux Basile , Agnes Everton J. , Zenke Friedemann , Lillicrap Timothy , Vogels Tim P. 
Published: October 2020

AbstractThe search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by – and fitted to – experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce. Briefly, we parameterize synaptic plasticity rules by a Volterra expansion and then use supervised learning methods (gradient descent or evolutionary strategies) to minimize a problem-dependent loss function that quantifies how effectively a candidate plasticity rule transforms an initially random network into one with the desired function. We first validate our approach by re-discovering previously described plasticity rules, starting at the single-neuron level and “Oja’s rule”, a simple Hebbian plasticity rule that captures the direction of most variability of inputs to a neuron (i.e., the first principal component). We expand the problem to the network level and ask the framework to find Oja’s rule together with an anti-Hebbian rule such that an initially random two-layer firing-rate network will recover several principal components of the input space after learning. Next, we move to networks of integrate-and-fire neurons with plastic inhibitory afferents. We train for rules that achieve a target firing rate by countering tuned excitation. Our algorithm discovers a specific subset of the manifold of rules that can solve this task. Our work is a proof of principle of an automated and unbiased approach to unveil synaptic plasticity rules that obey biological constraints and can solve complex functions.
Parasitic success and venom composition evolve upon specialization of parasitoid wasps to different host species
Authors: Cavigliasso Fanny , Mathé-Hubert Hugo , Gatti Jean-Luc , Colinet Dominique , Poirié Marylène 
Published: October 2020

AbstractFemale endoparasitoid wasps usually inject venom into hosts to suppress their immune response and ensure offspring development. However, the parasitoid’s ability to evolve towards increased success on a given host simultaneously with the evolution of the composition of its venom has never been demonstrated. Here, we designed an experimental evolution to address this question. We crossed two parasitoid lines ofLeptopilina boulardidiffering both in parasitic success on differentDrosophilahosts and venom composition. F2descendants were reared on three differentDrosophilaspecies for nine generations. We tested for evolution of parasitic success over the generations and for the capacity of parasitoids selected on a given host to succeed on another host. We also tested whether the venom composition - based on a statistical analysis of the variation in intensity of the venom protein bands on SDS-PAGE 1D - evolved in response to different host species. Results showed a specialization of the parasitoids on their selection host and a rapid and differential evolution of the venom composition according to the host. Overall, data suggest a high potential for parasitoids to adapt to a new host, which may have important consequences in the field as well in the context of biological control.
Comparative genomic analysis ofMycobacterium tuberculosisreveals evolution and genomic instability within Uganda I sub-lineage
Authors: Kanyerezi Stephen , Nabisubi Patricia 
Published: October 2020

AbstractIntroductionTuberculosis (TB) is the leading cause of morbidity and mortality globally, responsible for an estimated annual 10.0 million new cases and 1.3 million deaths among infectious diseases with Africa contributing a quarter of these cases in 2019. Classification ofMycobacterium tuberculosis(MTB) strains is important in understanding their geographical predominance and pathogenicity. Different studies have gone ahead to classify MTB using different methods. Some of these include; RFLP, spoligotyping, MIRU-VNTR and SNP set based phylogeny. The SNP set based classification has been found to be in concordance with the region of difference (RD) analysis of MTB complex classification system. In Uganda, the most common cause of pulmonary tuberculosis (PTB) is Uganda genotype of MTB and accounts for up to 70 % of isolates.MethodsSequenced MTB genome samples were retrieved from NCBI and others from local sequencing projects. The genomes were subjected to snippy (a rapid haploid variant calling and core genome alignment) to call variants and annotate them. Outputs from snippy were used to classify the isolates into Uganda genotypes and Non Ugandan genotypes based on 62 SNP set. The Ugandan genotype isolates were later subjected to 413 SNP set and then to a pan genome wide association analysis.Results6 Uganda genotype isolates were found not to classify as either Uganda I or II genotypes based on the 62 SNP set. Using the 413 SNP set, the 6 Uganda genotype isolates were found to have only one SNP out of the 7 SNPs that classify the Uganda I genotypes. They were also found to have both missense and frameshift mutations within thectpHgene whereas the rest of Uganda I that had a mutation within this gene, was a missense.ConclusionAmong the Uganda genotypes genomes, Uganda I genomes are unstable. We used publicly available datasets to perform analysis like mapping, variant calling, mixed infection, pan-genome analysis to investigate and compare evolution of the Ugandan genotype.
Combining protein-based transcriptome assembly, and efficient MinION long read sequencing for targeted transcript sequencing in orphan species. Validation on herbicide targets and low copy number genes in Gymnosperms, Juncaceae and Pteridophyta
Authors: Evans Dyfed Lloyd 
Published: October 2020

AbstractOrphan species that are evolutionarily distant from their closest sequenced/assembled neighbour provide a significant challenge in terms of gene or transcript assembly for functional analysis. This is because 30% sequence divergence from the closest available reference sequence means that, even with a complete genome or transcriptome sequence, mapping-based or reference-based approaches to gene assembly and gene identification break down.A new approach is required for reference-guided gene and transcript assembly in such orphan species, or species that are evolutionarily very divergent from their closest relatives. When annotating genes, the protein sequence is often preferred as it diverges less than the DNA/RNA sequence and it is often simpler to find meaningful homology at the protein level. This greater conservation of protein sequence across evolutionary time also makes proteins a prime candidate for use as the basis for sequence assembly. A protein-based pipeline was developed for transcript assembly between distantly related species. This was tested on three evolutionarily divergent species with little sequence information available for them and for which the closest genome representatives were at least 40 million years divergent as well as one species (Azolla filiculoides) for which a genome assembly is available. All the species have the potential to be weeds and herbicide targets were chosen as functional genes, whilst low copy number genes were chosen for evolutionary studies. Transcriptomic sequences were assembled using a bait and assemble strategy and final assemblies were verified by direct sequencing.
Inheritance of DNA methylation differences in the mangrove Rhizophora mangle
Authors: Mounger Jeannie , Boquete M. Teresa , Schmid Marc W. , Granado Renan , Robertson Marta H. , Voors Sandy A. , Langanke Kristen L. , Alvarez Mariano , Wagemaker Cornelis A.M. , Schrey Aaron W. , Fox Gordon , Lewis David B. , Lira Catarina Fonseca , Richards Christina L. 
Published: October 2020

AbstractThe capacity to respond to environmental challenges ultimately relies on phenotypic variation which manifests from complex interactions of genetic and non-genetic mechanisms through development. While we know something about genetic variation and structure of many species of conservation importance, we know very little about the non-genetic contributions to variation.Rhizophora mangleis a foundation species that occurs in coastal estuarine habitats throughout the neotropics where it provides critical ecosystem functions, and is potentially threatened by climate change. Several studies have documented landscape level patterns of genetic variation in this species, but we know virtually nothing about the inheritance of non-genetic variation. To assess one type of non-genetic variation, we examined the patterns of DNA sequence and DNA methylation in maternal plants and offspring from natural populations ofR. manglefrom the Gulf Coast of Florida. We used a reduced representation bisulfite sequencing approach (epi-genotyping by sequencing or epiGBS) to address the following questions: a) What are the levels of genetic and epigenetic diversity in natural populations ofR. mangle? b) How are genetic and epigenetic variation structured within and among populations? c) How faithfully is epigenetic variation inherited? We found low genetic diversity but high epigenetic diversity from natural populations of maternal plants in the field and that a large portion (up to ~25%) of epigenetic differences among offspring grown in common garden was explained by maternal family. Therefore, epigenetic variation could be an important source of response to challenging environments in the genetically depauperate populations of this foundation species.