bluuebunny/crossref_metadata_2025 huggingface dataset:
Longitudinal Network Re-organization Across Learning and Development
Authors: McCormick Ethan M. , Peters Sabine , Crone Eveline A. , Telzer Eva H.
Published: October 2020

While it is well understood that the brain experiences changes across short-term experience/learning and long-term development, it is unclear how these two mechanisms interact to produce developmental outcomes. Here we test an interactive model of learning and development where certain learning-related changes are constrained by developmental changes in the brain against an alternative development-as-practice model where outcomes are determined primarily by the accumulation of experience regardless of age. Participants (8-29 years) participated in a three-wave, accelerated longitudinal study during which they completed a feedback learning task during an fMRI scan. Adopting a novel longitudinal modeling approach, we probed the unique and moderated effects of learning, experience, and development simultaneously on behavioral performance and network modularity during the task. We found nonlinear patterns of development for both behavior and brain, and that greater experience supported increased learning and network modularity relative to naïve subjects. We also found changing brain-behavior relationships across adolescent development, where heightened network modularity predicted improved learning, but only following the transition from adolescence to young adulthood. These results present compelling support for an interactive view of experience and development, where changes in the brain impact behavior in context-specific fashion based on developmental goals.
Semisupervised definition of hippocampal ripples
Authors: Watanabe Yusuke , Okada Mami , Ikegaya Yuji
Published: October 2020

Hippocampal 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

The 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.
A Convolutional Network Architecture Driven by Mouse Neuroanatomical Data
Authors: Shi Jianghong , Buice Michael A. , Shea-Brown Eric , Mihalas Stefan , Tripp Bryan
Published: October 2020

Convolutional neural networks trained on object recognition derive some inspiration from the neuroscience of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the hierarchical organization of primates, the visual system of the mouse has flatter hierarchy. Since mice are capable of visually guided behavior, this raises questions about the role of architecture in neural computation. In this work, we introduce a framework for building a biologically constrained convolutional neural network model of lateral areas of the mouse visual cortex. The structural parameters of the network are derived from experimental measurements, specifically estimates of numbers of neurons in each area and cortical layer, the interareal connec-tome, and the statistics of connections between cortical layers. This network is constructed to support detailed task-optimized models of mouse visual cortex, with neural populations that can be compared to specific corresponding populations in the mouse brain. The code is freely available to support such research.
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

The 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)

IMPORTANCE

The 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

Infections 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

The 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

The 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

Processing 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

The 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

The 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

Abstract

Female 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

Introduction

Tuberculosis (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.

Methods

Sequenced 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.

Results

6 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.

Conclusion

Among 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

Orphan 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

The 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.
Quantitative trait locus mapping for common scab resistance in a tetraploid potato full-sib population
Authors: da Silva Pereira Guilherme , Mollinari Marcelo , Qu Xinshun , Thill Christian , Zeng Zhao-Bang , Haynes Kathleen , Yencho G. Craig
Published: October 2020

Despite the negative impact of common scab (Streptomycesspp.) to the potato industry, little is known about the genetic architecture of resistance to this bacterial disease in the crop. We evaluated a mapping population (~150 full-sibs) derived from a cross between two tetraploid potatoes (‘Atlantic’ × B1829-5) in three environments (MN11, PA11, ME12) under natural common scab pressure. Three measures to common scab reaction were assessed, namely percentage of scabby tubers, and disease area and lesion indices, which were highly correlated (>0.76). Due to large environmental effect, heritability values were zero for all three traits in MN11, but moderate to high in PA11 and ME12 (0.44~0.79). We identified a single quantitative trait locus (QTL) for lesion index in PA11, ME12 and joint analyses on linkage group 3, explaining 22~30% of the total variation. The identification of QTL haplotypes and candidate genes contributing to disease resistance can support genomics-assisted breeding approaches.
Microbial diversity estimation and hill number calculation using the hierarchical Pitman-Yor process
Authors: Mcgregor Kevin , Labbe Aurélie , Greenwood Celia M.T. , Parsons Todd , Quince Christopher
Published: October 2020

Background

The human microbiome comprises the microorganisms that inhabit the various locales of the human body and plays a vital role in human health. The composition of a microbial population is often quantified through measures of species diversity, which summarize the number of species along with their relative abundances into a single value. In a microbiome sample there will certainly be species missing from the target population which will affect the diversity estimates.

Methods

We employ a model based on the hierarchical Pitman-Yor (HPY) process to model the species abundance distributions over multiple populations. The model parameters are estimated using a Gibbs sampler. We also derive estimates of species diversity, conditional and unconditional on the observed data, as a function of the HPY parameters Finally, we derive a general formula for the Hill numbers in the HPY context.

Results

We show that the Gibbs sampler for the HPY model performs well in simulations. We also show that the conditional estimates of diversity from the HPY model improve over naïve estimates when species are missing. Similarly the conditional HPY estimates tend to perform better than the naïve estimates especially when the number of individuals sampled from a population is small.
Origin and evolution of the Haustoriidae (Amphipoda): A eulogy for the Haustoriidira
Authors: Hancock Zachary B. , Ogawa Hiroshi , Light Jessica E. , Wicksten Mary K.
Published: October 2020

Haustoriid amphipods, despite their ubiquity in coastal sand or mud, have received little recent attention and their systematics and phylogenetics are largely unresolved. Some efforts have been made at classifying the family within the broader Amphipoda, but there is persistent incongruence in its placement among different authors and techniques. Furthermore, there exists no phylogenetic hypothesis of intrafamilial relationships despite the potential for rich biogeographic information to be gained given the specific habitat requirements of haustoriids and their limited dispersal abilities. In this work, we evaluate the competing hypotheses on the phylogenetic position of the Haustoriidae within Amphipoda by examining new and previously published sequences of nearly 100 species across 38 families. We find strong support for the Haustoriidae as basal gammarids, and that other families placed within the parvorder “Haustoriidira” are spread across Amphipoda. The radiation began during the Eocene and may have been driven in North America by the rapid filling of a coastal niche opened by the Chesapeake Bay impact crater. Unlike previous work, we find that the Pacific-endemic genusEohaustoriusis the most basal haustoriid, and that it separated from the rest of the family ~31 Mya. Finally, based on ancestral reconstructions, we provide taxonomic recommendations for relationships within Haustoriidae, including the elevation of a new genus,Cryptohaustorius. We conclude by recommending that the “Haustoriidira” be abandoned.
A Fast Data-Driven Method for Genotype Imputation, Phasing, and Local Ancestry Inference: MendelImpute.jl
Authors: Chu Benjamin B. , Sobel Eric M. , Wasiolek Rory , Sinsheimer Janet S. , Zhou Hua , Lange Kenneth
Published: October 2020

1

Current methods for genotype imputation and phasing exploit the sheer volume of data in haplotype reference panels and rely on hidden Markov models. Existing programs all have essentially the same imputation accuracy, are computationally intensive, and generally require pre-phasing the typed markers. We propose a novel data-mining method for genotype imputation and phasing that substitutes highly efficient linear algebra routines for hidden Markov model calculations. This strategy, embodied in our Julia programMendelImpute.jl, avoids explicit assumptions about recombination and population structure while delivering similar prediction accuracy, better memory usage, and an order of magnitude or better run-times compared to the fastest competing method.MendelImputeoperates on both dosage data and unphased genotype data and simultaneously imputes missing genotypes and phase at both the typed and untyped SNPs. Finally,MendelImputenaturally extends to global and local ancestry estimation and lends itself to new strategies for data compression and hence faster data transport and sharing.
Assessing tiger corridor functionality with landscape genetics and modelling across Terai-Arc Landscape, India
Authors: Biswas Suvankar , Bhatt Supriya , Sarkar Debanjan , Talukdar Gautam , Pandav Bivash , Mondol Samrat
Published: October 2020

India led the global tiger conservation initiatives since last decade and has doubled its wild tiger population to 2967 (2603-3346). The survival of these growing populations residing inside the continuously shrinking habitats is a major concern, which can only be tackled through focused landscape-scale conservation planning across five major extant Indian tiger landscapes. The Terai-Arc landscape (TAL) is one of the ‘global priority’ tiger conservation landscapes holding 22% of the country’s wild tigers. We used intensive field-sampling, genetic analyses and GIS modelling to investigate tiger population structure, source-sink dynamics and functionality of the existing corridors across TAL. Genetic analyses with 219 tigers revealed three low, but sigficantly differentiated tiger subpopulations. Overall, we identified Seven source and 10 sink areas in TAL through genetic migrant and gene flow analyses. GIS modelling identified total 19 (10 high, three medium and six low conductance) corridors in this landscape, with 10 being critical to maintain landscape connectivity. We suggest urgent management attention towards 2707 sq. km. non-protected habitat, mitigation measures associated with ongoing linear infrastructure developments and transboundary coordination with Nepal to ensure habitat and genetic connectivity and long-term sustainability of tigers in this globally important landscape.
Two-species community design of Lactic Acid Bacteria for optimal production of Lactate
Authors: Ibrahim Maziya , Raman Karthik
Published: October 2020

Microbial communities that metabolise pentose and hexose sugars are useful in producing high-value chemicals, as this can result in the effective conversion of raw materials to the product, a reduction in the production cost, and increased yield. Here, we present a computational approach called CAMP (Co-culture/Community Analyses for Metabolite Production) that simulates and identifies appropriate communities to produce a metabolite of interest. To demonstrate this approach, we focus on optimal production of lactate from various Lactic Acid Bacteria. We used genome-scale metabolic models (GSMMs) belonging toLactobacillus, Leuconostoc, andPediococcusspecies from the Virtual Metabolic Human (VMH;https://vmh.life/) resource and well-curated GSMMs ofL. plantarumWCSF1 andL. reuteriJCM 1112. We studied 1176 two-species communities using a constraint-based modelling method for steady-state flux-balance analysis of communities. Flux variability analysis was used to detect the maximum lactate flux in a community. Using glucose or xylose as substrates separately or in combination resulted in either parasitism, amensalism, or mutualism being the dominant interaction behaviour in the communities. Interaction behaviour between members of the community was deduced based on variations in the predicted growth rates of monocultures and co-cultures. Acetaldehyde, ethanol, NH4+, among other metabolites, were found to be cross-fed between community members.L. plantarumWCSF1 was a member of communities with high lactate yields.In silicocommunity optimisation strategies to predict reaction knock-outs for improving lactate flux were implemented. Reaction knock-outs of acetate kinase, phosphate acetyltransferase, and fumarate reductase in the communities were found to enhance lactate production.

Importance

Understanding compatibility and interactions based on growth between the members of a microbial community is imperative to exploit these communities for biotechnological applications. Towards this goal, here, we introduce a computational analysis framework that evaluates all possible two-species communities generated from a given set of microbial species on single or multiple substrates to achieve optimal production of a target metabolite. As a case study, we analysed communities of Lactic Acid Bacteria to produce lactate. Lactate is a platform chemical produced experimentally from lignocellulosic biomass, which constitutes pentoses and hexoses, such as xylose and glucose. Metabolic engineering strategies, such as reaction knock-outs that can improve product flux while retaining the community’s viability are identified usingin silicooptimisation methods. Our approach can guide in the selection of most promising communities for experimental testing and validation to produce valuable bio-based chemicals.
An Algorithmic framework for genome-wide identification of Sugarcane (Saccharum officinarumL.)-encoded microRNA targets against SCBV
Authors: Ashraf Muhammad Aleem , Feng Xiaoyan , Hu Xiaowen , Ashraf Fakiha , Shen Linbo , Zhang Shuzhen
Published: October 2020

Sugarcane Bacilliform Virus(SCBV) is considered an economically the most damaging pathogen for sugarcane production worldwide. Three ORFs are characterized in a single molecule of circular, ds-DNA genome of the SCBV, encoding for hypothetical protein (ORF1), DNA binding protein (ORF2) and Polyprotein (ORF3). The study was aimed to predict and comprehensively evaluate sugarcane miRNAs for the silencing of SCBV genome usingin-silicoalgorithms. Computational methods were used for prediction of candidate miRNAs from sugarcane (S. officinarumL.) to silence the expression of SCBV genes through translational inhibition by mRNA cleavage. Mature sugarcane miRNAs were retrieved and were assessed to hybridization with the SCBV genome. A total of fourteen potential candidate miRNAs from sugarcane were computed by all the algorithms used for the silencing of SCBV. A consensus of three algorithms predicts hybridization sites of sof-miR159e at common locus 5534. The miRNA-mRNA interaction was estimated by computing free-energy of miRNA-mRNA duplex using RNAcofold algorithm. Regulatory network of predicted candidate miRNAs of sugarcane with SCBV ORFs, generated using Circos, identify novel targets. Consequently, detecting and discarding inefficient amiRNAs prior to cloning would help suppressed mutants faster. The efficacy of predicted candidate miRNAs was evaluated to test the survival rate of thein vitroamiRNA-mediated effective badnaviral silencing and resistance in sugarcane cultivars.
Spatio-temporal modeling of the effect of selected environmental and land-use drivers on acid grassland vegetation
Authors: Damgaard Christian
Published: October 2020

Spatial and temporal pin-point plant cover monitoring data are fitted in a structural equation model in order to understand and quantify the effect of selected environmental and land-use drivers on the observed variation and changes in the vegetation of acid grasslands. The important sources of measurement- and sampling uncertainties have been included using a hierarchical model structure. Furthermore, the measurement- and sampling uncertainties are separated from the process uncertainty, which is important when generating ecological predictions. Generally, increasing atmospheric nitrogen deposition leads to more grass-dominated acid grassland habitats at the expense of the cover of forbs. Sandy soils are relatively more acidic, and the effect of soil type on the vegetation includes both direct effects of soil type and indirect effects mediated by the effect of soil type on soil pH. Both soil type and soil pH affected the vegetation of acid grasslands. Even though only a small part of the temporal variation in cover was explained by the model, it will still be useful to quantify the uncertainties when using the model for generating local ecological predictions and adaptive management plans.
MRS-measured Glutamate versus GABA reflects excitatory versus inhibitory neural activities in awake mice
Authors: Takado Yuhei , Takuwa Hiroyuki , Sampei Kazuaki , Urushihata Takuya , Takahashi Manami , Shimojo Masafumi , Uchida Shoko , Nitta Nobuhiro , Shibata Sayaka , Nagashima Keisuke , Ochi Yoshihiro , Ono Maiko , Maeda Jun , Tomita Yutaka , Sahara Naruhiko , Near Jamie , Aoki Ichio , Shibata Kazuhisa , Higuchi Makoto
Published: October 2020

To assess if magnetic resonance spectroscopy (MRS)-measured Glutamate (Glu) and GABA reflect excitatory and inhibitory neural activities, respectively, we conducted MRS measurements along with two-photon mesoscopic imaging of calcium signals in excitatory and inhibitory neurons of living, unanesthetized mice. For monitoring stimulus-driven activations of a brain region, MRS signals and mesoscopic neural activities were measured during two consecutive sessions of 15-min prolonged sensory stimulations. In the first session, putative excitatory neuronal activities were increased, while inhibitory neuronal activities remained at the baseline level. In the second half, while excitatory neuronal activities remained elevated, inhibitory neuronal activities were significantly enhanced. We also assessed regional neurochemical and functional statuses related to spontaneous neural firing by measuring MRS signals and neuronal activities in a mouse model of Dravet syndrome under a resting condition. Mesoscopic assessments showed that activities of inhibitory neurons in the cortex were diminished relative to wild-type mice in contrast to spared activities of excitatory neurons. Consistent with these observations, the Dravet model exhibited lower concentrations of GABA than wild-type controls. Collectively, the current investigations demonstrate that the MRS-measured Glu and GABA can reflect spontaneous and stimulated activities of neurons producing and releasing these neurotransmitters in an awake condition.
Neural networks to learn protein sequence-function relationships from deep mutational scanning data
Authors: Gelman Sam , Fahlberg Sarah A. , Heinzelman Pete , Romero Philip A. , Gitter Anthony
Published: October 2020

The mapping from protein sequence to function is highly complex, making it challenging to predict how sequence changes will affect a protein’s behavior and properties. We present a supervised deep learning framework to learn the sequence-function mapping from deep mutational scanning data and make predictions for new, uncharacterized sequence variants. We test multiple neural network architectures, including a graph convolutional network that incorporates protein structure, to explore how a network’s internal representation affects its ability to learn the sequence-function mapping. Our supervised learning approach displays superior performance over physics-based and unsupervised prediction methods. We find networks that capture nonlinear interactions and share parameters across sequence positions are important for learning the relationship between sequence and function. Further analysis of the trained models reveals the networks’ ability to learn biologically meaningful information about protein structure and mechanism. Finally, we demonstrate the models’ ability to navigate sequence space and design new proteins beyond the training set. We applied the GB1 models to design a sequence that binds to IgG with substantially higher affinity than wild-type GB1. Our software is available fromhttps://github.com/gitter-lab/nn4dms.

Significance

Understanding the relationship between protein sequence and function is necessary to design new and useful proteins with applications in bioenergy, medicine, and agriculture. The mapping from sequence to function is tremendously complex because it involves thousands of molecular interactions that are coupled over multiple lengths and timescales. In this work, we show neural networks can learn the sequence-function mapping from large protein datasets. Neural networks are appealing for this task because they can learn complicated relationships from data, make few assumptions about the nature of the sequencefunction relationship, and can learn general rules that apply across the length of the protein sequence. We demonstrate the learned models can be applied to design new proteins with properties that exceed natural sequences.
Transsynaptic modulation of presynaptic short-term plasticity induction in hippocampal mossy fiber synapses
Authors: Vandael David , Okamoto Yuji , Jonas Peter
Published: October 2020

The hippocampal mossy fiber synapse is a key synapse of the trisynaptic circuit of the hippocampus. Post-tetanic potentiation (PTP) is the most powerful form of plasticity at this synaptic connection. It is widely believed that mossy fiber PTP is an entirely presynaptic phenomenon, implying that PTP induction is input-specific, and requires neither activity of multiple inputs nor stimulation of postsynaptic neurons for induction. Thus, mossy fiber PTP appears to lack cooperativity and associativity that characterize other forms of plasticity. To directly test these predictions, we made paired recordings between single mossy fiber terminals and postsynaptic CA3 pyramidal neurons in rat brain slices. By stimulating parallel but non-overlapping mossy fiber bouton (MFB) inputs converging onto single CA3 neurons, we confirmed that PTP was inputspecific and non-cooperative. Unexpectedly, mossy fiber PTP showed anti-associative induction properties. Mossy fiber excitatory postsynaptic currents (EPSCs) showed only minimal PTP after combined pre- and postsynaptic high-frequency stimulation (HFS) with intact postsynaptic Ca2+signaling (0.1 mM EGTA), but marked PTP in the absence of postsynaptic spiking and after suppression of postsynaptic Ca2+signaling (10 mM EGTA). PTP was rescued by blocking Ca2+entry via voltage-gated R-type and to a smaller extent L-type Ca2+channels. PTP was also recovered by extracellular application of group II metabotropic glutamate receptor (mGluR) antagonists and vacuolar-type (v-type) H+-ATPase inhibitors, suggesting the involvement of retrograde vesicular glutamate signaling. Transsynaptic regulation of PTP induction may increase the computational power of mossy fiber synapses, and implement a break on hippocampal mossy fiber detonation.
Application of precise neutron focusing mirrors for neutron reflectometry: latest results and future prospects
Authors: Yamada Norifumi L. , Hosobata Takuya , Nemoto Fumiya , Hori Koichiro , Hino Masahiro , Izumi Jun , Suzuki Kota , Hirayama Masaaki , Kanno Ryoji , Yamagata Yutaka
Published: October 2020

Neutron reflectometry (NR) is a powerful tool for providing insight into the evolution of interfacial structures, for example via operando measurements for electrode–electrolyte interfaces, with a spatial resolution of nanometres. The time resolution of NR, which ranges from seconds to minutes depending on the reflection intensity, unfortunately remains low, particularly for small samples made of state-of-the-art materials even with the latest neutron reflectometers. To overcome this problem, a large-area focusing supermirror manufactured with ultra-precision machining has been employed to enhance the neutron flux at the sample, and a gain of approximately 100% in the neutron flux was achieved. Using this mirror, a reflectivity measurement was performed on a thin cathode film on an SrTiO3 substrate in contact with an electrolyte with a small area of 15 × 15 mm. The reflectivity data obtained with the focusing mirror were consistent with those without the mirror, but the acquisition time was shortened to half that of the original, which is an important milestone for rapid measurements with a limited reciprocal space. Furthermore, a method for further upgrades that will reveal the structural evolution with a wide reciprocal space is proposed, by applying this mirror for multi-incident-angle neutron reflectometry.
Laboratory-modified powder diffraction specimen holder for filter paper
Authors: Rowles Matthew R.
Published: October 2020

A simple modification to an existing powder diffraction specimen holder is given to allow for the presentation of filter papers for analysis. A mild steel annulus holds the filter paper in place using magnets added to the rear of the specimen holder.