bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractDespite 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

BackgroundThe 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.MethodsWe 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.ResultsWe 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

ABSTRACTHaustoriid 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

1AbstractCurrent 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

AbstractIndia 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

AbstractMicrobial 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.ImportanceUnderstanding 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

AbstractSugarcane 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

AbstractSpatial 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

AbstractTo 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

ABSTRACTThe 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.SignificanceUnderstanding 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

SUMMARYThe 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.
Revisiting risk management in a time of crisis
Authors: Sheehan Norman T. 
Published: October 2020

Purpose
The global pandemic provides boards and executives an opportunity to evaluate if their organizations’ risk management systems were up to the challenge. Organizational risk management systems act like the body’s immune system, protecting the organization from threats. Most organizations struggled to deal with the crisis caused by global pandemic, with some failing to survive. This paper reviews the risk management steps and then examines at why some organizational risk management systems may have failed to protect their organizations from harm.

Design/methodology/approach
This topic was inspired by discussions with executives and board members of organizations dealing with the pandemic. The paper was written based on their comments and was supplemented and contrasted with a review of risk management literature.

Findings
The paper reviews what risk responses are effective in coping with the global pandemic and then discusses how a risk tool, the risk-value curve, can help boards and executive determine the optimal level of risk for their organizations going forward.

Practical implications
Boards and executives can use the risk responses and risk tools reviewed to evaluate the quality of their risk management systems in light of the global pandemic.

Originality/value
The paper is one of the first to critically examine why some organizational risk management systems failed and proposes risk management tools to help organizations deal with the next global crisis.