bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds
Authors: Eisaguirre Joseph M. , Booms Travis L. , Barger Christopher P. , Lewis Stephen B. , Breed Greg A. 
Published: October 2019

AbstractHuman modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality. Here, we used satellite telemetry combined with step-selection functions designed to explicitly incorporate the energy landscape (el-SSFs) to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagleAquila chrysaetos, during migration. Our sample consisted of 32 adult eagles tracked for 45 spring and 39 fall migrations from 2014-2017. Fitted el-SSFs indicated eagles had a strong general preference for south-facing slopes, where thermal uplift develops predictably, and that these areas are likely important aspects of migratory pathways. el-SSFs also revealed that roads and railroads affected movement during both spring and fall migrations, but eagles selected areas near roads to a greater degree in spring compared to fall and at higher latitudes compared to lower latitudes. During spring, time spent near linear features often occurred during slower-paced or stopover movements, perhaps in part to access carrion produced by vehicle collisions. Regardless of the behavioral mechanism of selection, use of these features could expose eagles and other soaring species to elevated risk via collision with vehicles and/or transmission lines. Linear features have been previously documented to affect the ecology of terrestrial species (e.g., large mammals) by modifying individuals’ movement patterns; our work shows these effects on movement extend to avian taxa.
Inhibition of ATR Reverses a Mitochondrial Respiratory Insufficiency
Authors: Borror Megan B. , Girotti Milena , Kar Adwitiya , Cain Meghan , Gao Xiaoli , MacKay Vivian L. , Herron Brent , Bhaskaran Shylesh , Becerra Sandra , Novy Nate , Ventura Natascia , Johnson Thomas E. , Kennedy Brian K. , Rea Shane L. 
Published: October 2019

ABSTRACTDiseases that affect the mitochondrial electron transport chain (ETC) often manifest as threshold effect disorders, meaning patients only become symptomatic once a certain level of ETC dysfunction is reached. Multiple processes work to control proximity to the critical ETC threshold and as a consequence there can be significant variability in disease presentation among patients. Identification of such control processes remains an ongoing goal. Checkpoint signaling comprises a collection of alert mechanisms activated in cells in response to nuclear DNA damage. Well-defined hierarchies of proteins are involved in both sensing and signaling DNA damage, with ATM (ataxia telangiectasia mutated) and ATR (ATM and Rad3-related) acting as pivotal signaling kinases. In the nematode C. elegans, severe reduction of mitochondrial ETC activity shortens life, as in humans, but mild reduction extends life as a consequence of survival strategies that are invoked under these circumstances. Here we show that removal of ATL-1, the worm ortholog of ATR, unexpectedly lessens the severity of ETC dysfunction, but removal of ATM does not. Multiple genetic and biochemical tests show no evidence for increased mutation or DNA breakage in animals exposed to ETC disruption. Instead, we find that reduced ETC function alters nucleotide ratios within both the ribo- and deoxyribo-nucleotide pools, and causes stalling of RNA polymerase, which is also known to activate ATR. Unexpectedly, atl-1 mutants confronted with mitochondrial ETC disruption maintain normal levels of oxygen consumption and have an increased abundance of translating ribosomes. This suggests checkpoint signaling by ATL-1 normally dampens cytoplasmic translation. Taken together, our data suggests a model whereby ETC insufficiency in C. elegans results in nucleotide imbalances leading to stalling of RNA polymerase, activation of ATL-1, dampening of global translation and magnification of ETC dysfunction. Loss of ATL-1 effectively reverses the severity of ETC disruption so that animals become phenotypically closer to wild type.
Demonstration of altruistic behaviour in rats
Authors: Das Ushnik , Kumari Anshu , Sharma Shruthi , Rao Laxmi T. 
Published: October 2019

AbstractA pilot study demonstrating complex cognitive abilities of altruism in rats by observing if rats would voluntarily put themselves in distress solely in order to help another conspecific irrespective of kinship and without any expectation of reward in terms of food or mate in return.
Tunings for leapfrog integration of Hamiltonian Monte Carlo for estimating genetic parameters
Authors: Arakawa Aisaku , Hayashi Takeshi , Taniguchi Masaaki , Mikawa Satoshi , Nishio Motohide 
Published: October 2019

AbstractA Hamiltonian Monte Carlo algorithm is a Markov Chain Monte Carlo method that is considered more effective than the conventional Gibbs sampling method. Hamiltonian Monte Carlo is based on Hamiltonian dynamics, and it follows Hamilton’s equations, which are expressed as two differential equations. In the sampling process of Hamiltonian Monte Carlo, a numerical integration method called leapfrog integration is used to approximately solve Hamilton’s equations, and the integration is required to set the number of discrete time steps and the integration stepsize. These two parameters require some amount of tuning and calibration for effective sampling. In this study, we applied the Hamiltonian Monte Carlo method to animal breeding data and identified the optimal tunings of leapfrog integration for normal and inverse chi-square distributions. Then, using real pig data, we revealed the properties of the Hamiltonian Monte Carlo method with the optimal tuning by applying models including variance explained by pedigree information or genomic information. Compared with the Gibbs sampling method, the Hamiltonian Monte Carlo method had superior performance in both models. We have provided the source codes of this method written in the R language.
A meta-analysis of global avian survival across species and latitude
Authors: Scholer Micah N. , Strimas-Mackey Matt , Jankowski Jill E. 
Published: October 2019

AbstractTropical birds are purported to be longer lived than temperate species of similar size, but it has not been shown whether avian survival rates covary with a latitudinal gradient worldwide. Here, we perform a global-scale meta-analysis to investigate the extent of the latitudinal survival gradient. We modeled survival as a function of latitude for the separate northern and southern hemispheres, and considered phylogenetic relationships and extrinsic (climate) and intrinsic (life history) predictors hypothesized to moderate these effects. Using a database of 1,004 estimates from 246 studies of avian survival, we demonstrate that in general a latitudinal survival gradient exists in the northern hemisphere, is dampened or absent for southern hemisphere species, and that survival rates of passerine birds largely account for these trends. We found no indication that the extrinsic climate factors were better predictors of survival than latitude alone, but including species’ intrinsic traits improved model predictions. Notably, species with smaller clutch size and larger body mass showed higher survival. Our results illustrate that while some tropical birds may be longer lived than their temperate counterparts, the shape of the latitude-survival gradient differs by geographic region and is strongly influenced by species’ intrinsic traits.
Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning
Authors: Ditria Ellen M. , Lopez-Marcano Sebastian , Sievers Michael K. , Jinks Eric L. , Brown Christopher J. , Connolly Rod M. 
Published: October 2019

AbstractAquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomised 80:20 ratios of training:validation data-sets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%), and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer’s performance in determining MaxN was 7.1% better than human marine experts, and 13.4% better than citizen scientists in single image test data-sets, and 1.5% and 7.8% higher in video data-sets, respectively. We show that deep learning is a more accurate tool than humans at determining abundance, and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance. Deep learning techniques have much to offer the field of aquatic ecology.
Environmental correlates of the European common toad hybrid zone
Authors: Arntzen Jan W. , Canestrelli Daniele , Martínez-Solano Iñigo 
Published: October 2019

AbstractThe interplay between intrinsic (development, physiology, behavior) and extrinsic (landscape features, climate) factors determines the outcome of admixture processes in hybrid zones, in a continuum from complete genetic merger to full reproductive isolation. Here we assess the role of environmental correlates in shaping admixture patterns in the long hybrid zone formed by the toadsBufo bufoandB. spinosusin western Europe. We used species-specific diagnostic SNP markers to genotype 6584 individuals from 514 localities to describe the contact zone and tested for association with topographic, bioclimatic and land use variables. Variables related to temperature and precipitation contributed to accurately predict the distribution of pure populations of each species, but the models did not perform well in areas where genetically admixed populations occur. A sliding window approach proved useful to identify different sets of variables that are important in different sections of this long and heterogeneous hybrid zone, and offers good potential to predict the fate of moving contact zones in global change scenarios.
OCSANA+: Optimal Control and Simulation of Signaling Networks from Network Analysis
Authors: Marazzi Lauren , Gainer-Dewar Andrew , Vera-Licona Paola 
Published: October 2019

AbstractSummaryOCSANA+ is a Cytoscape app for identifying nodes to drive the system towards a desired long-term behavior, prioritizing combinations of interventions in large scale complex networks, and estimating the effects of node perturbations in signaling networks, all based on the analysis of the network’s structure. OCSANA+ includes an update to OCSANA (optimal combinations of interventions from network analysis) software tool with cutting-edge and rigorously tested algorithms, together with recently-developed structure-based control algorithms for non-linear systems and an algorithm for estimating signal flow. All these algorithms are based on the network’s topology. OCSANA+ is implemented as a Cytoscape app to enable a user interface for running analyses and visualizing results.Availability and ImplementationOCSANA+ app and its tutorial can be downloaded from the Cytoscape App Store or https://veraliconaresearchgroup.github.io/OCSANA-Plus/. The source code and computations are available in https://github.com/VeraLiconaResearchGroup/OCSANA-Plus_SourceCode.
Selective 40S footprinting reveals that scanning ribosomes remain cap-tethered in human cells
Authors: Bohlen Jonathan , Fenzl Kai , Kramer Günter , Bukau Bernd , Teleman Aurelio A. 
Published: October 2019

SUMMARYTranslation regulation occurs largely during initiation. Currently, translation initiation can be studied in vitro, but these systems lack features present in vivo and on endogenous mRNAs. Here we develop selective 40S footprinting for visualizing initiating 40S ribosomes on endogenous mRNAs in vivo. It pinpoints where on an mRNA initiation factors join the ribosome to act, and where they leave. We discover that in human cells most scanning ribosomes remain attached to the 5’ cap. Consequently, only one ribosome scans a 5’UTR at a time, and 5’UTR length affects translation efficiency. We discover that eIF3B, eIF4G1 and eIF4E remain on translating 80S ribosomes with a decay half-length of ∼12 codons. Hence ribosomes retain these initiation factors while translating short upstream Open Reading Frames (uORFs), providing an explanation for how ribosomes can re-initiate translation after uORFs in humans. This method will be of use for studying translation initiation mechanisms in vivo.HIGHLIGHTSSelective 40S FPing visualizes regulation of translation initiation on mRNAs in vivoScanning ribosomes are cap-tethered in human cellsOnly one ribosome scans a 5’UTR at a time in human cellsRibosomes retain eIFs during early translation, allowing reinitiation after uORFs
A selective inference approach for FDR control using multi-omics covariates yields insights into disease risk
Authors: Yurko Ronald , G’Sell Max , Roeder Kathryn , Devlin Bernie 
Published: October 2019

AbstractTo correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive p-value thresholding (Lei & Fithian 2018, AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association p-values play the role of the primary data for AdaPT; SNPs are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically-correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene-gene coexpression, captured by subnetwork (module) membership. In all 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefontal cortex (Werling et al. 2019). We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.
Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models
Authors: Robinson O.J. , Ruiz-Gutierrez V. , Reynolds M.D. , Golet G.H. , Strimas-Mackey M. , Fink D. 
Published: October 2019

AbstractInformation on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, are a fundamental source of ecological knowledge. However, collecting biological information at relevant scales if often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen-science data has been signaled as an increasingly important source of biological information needed to fill in data gaps needed to make more comprehensive and robust inferences on species distributions. However, there are perceived trade-offs of combining highly structured, scientific survey data with largely unstructured, citizen-science data. As a result, the focus of most methodological advances to combine these sources of information has been on treating these sources as independent. The degree to which each source of information is allowed to directly inform a common underlying process (e.g. species distribution) depends on the perceived quality of the data. In this paper, we explore these trade-offs by applying a simplified approach of filtering citizen-science data to resemble structured survey data, and analyze both sources of data under a common framework. To accomplish this, we explored ways of integrating high-resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality. The integration of survey data with the filtered citizen-science data in eBird resulted in improved inference and predictive ability, and increased the extent and accuracy of inferences on shorebirds for the Central Valley. The structured surveys were found to improve the overall accuracy of ecological inference based only on citizen-science data, by increasing the representation of data collected from high quality habitats for shorebirds (e.g. rice fields). The practical approach we have shown for data integration can be also be used to improve the efficiency of designing biological surveys in the context of larger, citizen-science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple processing and filtering method we present can be used to integrate other types of data (e.g. camera traps) with more localized efforts (e.g. research projects), ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.
Principles of RNA processing from analysis of enhanced CLIP maps for 150 RNA binding proteins
Authors: Van Nostrand Eric L , Pratt Gabriel A , Yee Brian A , Wheeler Emily , Blue Steven M , Mueller Jasmine , Park Samuel S , Garcia Keri E , Gelboin-Burkhart Chelsea , Nguyen Thai B , Rabano Ines , Stanton Rebecca , Sundararaman Balaji , Wang Ruth , Fu Xiang-Dong , Graveley Brenton R , Yeo Gene W 
Published: October 2019

AbstractA critical step in uncovering rules of RNA processing is to study the in vivo regulatory networks of RNA binding proteins (RBPs). Crosslinking and immunoprecipitation (CLIP) methods enabled mapping RBP targets transcriptome-wide, but methodological differences present challenges to large-scale integrated analysis across datasets. The development of enhanced CLIP (eCLIP) enabled the large-scale mapping of targets for 150 RBPs in K562 and HepG2, creating a unique resource of RBP interactomes profiled with a standardized methodology in the same cell types. Here we describe our analysis of 223 enhanced (eCLIP) datasets characterizing 150 RBPs in K562 and HepG2 cell lines, revealing a range of binding modalities, including highly resolved positioning around splicing signals and mRNA untranslated regions that associate with distinct RBP functions. Quantification of enrichment for repetitive and abundant multi-copy elements reveals 70% of RBPs have enrichment for non-mRNA element classes, enables identification of novel ribosomal RNA processing factors and sites and suggests that association with retrotransposable elements reflects multiple RBP mechanisms of action. Analysis of spliceosomal RBPs indicates that eCLIP resolves AQR association after intronic lariat formation (enabling identification of branch points with single-nucleotide resolution) and provides genome-wide validation for a branch point-based scanning model for 3’ splice site recognition. Further, we show that eCLIP peak co-occurrences across RBPs enables the discovery of novel co-interacting RBPs. Finally, we present a protocol for visualization of RBP:RNA complexes in the eCLIP workflow using biotin and standard chemiluminescent visualization reagents, enabling simplified confirmation of ribonucleoprotein enrichment without radioactivity. This work illustrates the value of integrated analysis across eCLIP profiling of RBPs with widely distinct functions to reveal novel RNA biology. Further, our quantification of both mRNA and other element association will enable further research to identify novel roles of RBPs in regulating RNA processing.
Genomic analyses of population structure reveal metabolism as a primary driver of local adaptation in Daphnia pulex
Authors: Maruki Takahiro , Ye Zhiqiang , Lynch Michael 
Published: October 2019

ABSTRACTElucidating population structure is important for understanding evolutionary features of an organism. In the freshwater microcrustacean Daphnia pulex, an emerging model system in evolutionary genomics, previous studies using a small number of molecular markers indicated that genetic differentiation among populations is high. However, the dispersal ability of D. pulex is potentially high, and evolutionary forces shaping genetic differentiation among populations are not understood well. In this study, we carried out genomic analyses using high-throughput sequencing to investigate the population structure of D. pulex. We analyzed 10 temporary-pond populations widely distributed across the midwestern United States, with each sample consisting of 71 to 93 sexually reproducing individuals. The populations are generally in Hardy-Weinberg equilibrium and have relatively large effective sizes. The genetic differentiation among the populations is moderate and positively correlated with geographic distance. To find outlier regions showing significantly high or low genetic differentiation, we carried out a sliding-window analysis of the differentiation estimates using the bootstrap. Genes with significantly high genetic differentiation show striking enrichment of gene ontology terms involved in food digestion, suggesting that differences in food quality and/or quantity among populations play a primary role in driving local adaptation of D. pulex.
Identification of scavenger receptor B1 as the airway microfold cell receptor forMycobacterium tuberculosis
Authors: Khan Haaris S. , Nair Vidhya R. , Ruhl Cody R. , Alvarez-Arguedas Samuel , Galvan Resendiz Jorge L. , Franco Luis H. , Huang Linzhang , Shaul Philip W. , Mitchell Ron B. , Shiloh Michael U. 
Published: October 2019

AbstractMycobacterium tuberculosis(Mtb) can enter the body through multiple routes, including via specialized transcytotic cells called microfold cells (M cell). However, the mechanistic basis for M cell entry remains undefined. Here, we show that M cell transcytosis depends on the Mtb Type VII secretion machine and its major virulence factor EsxA. We identify scavenger receptor B1 (SR-B1) as an EsxA receptor on airway M cells. SR-B1 is required for Mtb binding to and translocation across M cells in mouse and human tissue. Together, our data demonstrate a previously undescribed role for Mtb EsxA in mucosal invasion and identify SR-B1 as the airway M cell receptor for Mtb.