bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cellular labelling favours unfolded proteins
Authors: Minde David-Paul , Ramakrishna Manasa , Lilley Kathryn S. 
Published: July 2018

AbstractFolded enzymes are essential for life, but there is limited in vivo information about how locally unfolded protein regions contribute to biological functions. Intrinsically Disordered Regions (IDRs) are enriched in disease-linked and multiply post-translationally modified proteins. The extent of foldability of predicted IDRs is difficult to measure due to significant technical challenges to survey in vivo protein conformations on a proteome-wide scale. We reasoned that IDRs should be more accessible to targeted in vivo biotinylation than more ordered protein regions, if they retain their flexibility in vivo. Indeed, we observed a positive correlation of predicted IDRs and biotinylation density across four independent large-scale proximity proteomics studies that together report >20 000 biotinylation sites. We show that biotin ‘painting’ is a promising approach to fill gaps in knowledge between static in vitro protein structures, in silico disorder predictions and in vivo condition-dependent subcellular plasticity using the 80S ribosome as an example.
Bat trypanosomatids (first report of T. wauwau) in Triângulo Mineiro, Brazil
Authors: Pegorari Pablo de Oliveira , Gómez-Hernández César , Barbosa Cecilia G. , Rezende-Oliveira Karine , Pedrosa André Luiz , Ramirez Juan David , Ramirez Luis E. 
Published: July 2018

AbstractIn this study, trypanosomatids commonly found in bats, including Trypanosoma cruzi marinkellei, T. dionisii, and Leishmania braziliensis, were identified. Additionally, T. wauwau was identified in one specimen of Anoura caudifer, and represents the first report of this parasite from the Central West region of Brazil. T. wauwau was previously identified by other researchers in the North of the country, in only three species of bats in the genus Pteronotus: P. parnellii (Pará and Rondônia states), and P. personatus and P. gymnonotus (Rondônia). The identification of T. wauwau indicates how different trypanosomatids are able to adapt to new host species of bats. This is owing to bats’ high mobility, wide geographic distribution, social behavior, and ability to coexist in large colonies. These characteristics may facilitate the transmission of infectious agents in nature, which are responsible for outbreaks of some zoonoses. Therefore, health authorities should focus on both vertebrates and vectors associated with the environments where these bats are found.Author summaryThe prevalence of Trypanosoma in bats is high, with T. cruzi, T. cruzi marinkellei, and T. dionisii as the most prevalent infective species. This study reports for the first time the presence of T. wauwau in the southeast region of Brazil in the bat Anoura caudifer. Although this species of Trypanosoma has been found in bats of the genus Pteronotus, it was not detected in any other genus, including in the bats that share the same shelter with Pteronotus. The species T. wauwau was found infecting bats only in Brazil. Its occurrence was restricted to the northern region of the country, in the states of Pará, infecting the species P. parnellii and in Rondônia infecting P. personatus, P. gymnonotus as well as P. parnellii. Although its morphology is similar to that of T. cruzi, little is known about the development of T. wauwau, both in its vertebrate host and the existence of a plausible invertebrate vector. Its characteristics include its inability to develop in mammalian cells and its non-infectiousness in mice and triatomine insects. Further research, through molecular studies, may provide important and valuable data for understanding the origin, evolution, and global distribution of, and the association between the different species of Trypanosoma and their hosts.
Cyborg Groups Enhance Face Recognition in Crowded Environments
Authors: Valeriani Davide , Poli Riccardo 
Published: July 2018

AbstractRecognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create “cyborgs” that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.
Seroprevalence of fourteen human polyomaviruses determined in blood donors
Authors: Kamminga Sergio , van der Meijden Els , Feltkamp Mariet C.W. , Zaaijer Hans L. 
Published: July 2018

AbstractThe polyomavirus family currently includes thirteen human polyomavirus (HPyV) species. In immunocompromised and elderly persons HPyVs are known to cause disease, such as progressive multifocal leukoencephalopathy (JCPyV), haemorrhagic cystitis and nephropathy (BKPyV), Merkel cell carcinoma (MCPyV), and trichodysplasia spinulosa (TSPyV). Some recently discovered polyomaviruses are of still unknown prevalence and pathogenic potential. Because HPyVs infections persist and might be transferred by blood components to immunocompromised patients, we studied the seroprevalence of fourteen polyomaviruses in adult Dutch blood donors. For most polyomaviruses the observed seroprevalence was high (60–100%), sometimes slightly increasing or decreasing with age. Seroreactivity increased with age for JCPyV, HPyV6 and HPyV7 and decreased for BKPyV and TSPyV. The most recently identified polyomaviruses HPyV12, NJPyV and LIPyV showed low overall seroprevalence (∼5%) and low seroreactivity, questioning their human tropism. Altogether, HPyV infections are common in Dutch blood donors, with an average of nine polyomaviruses per subject.
Effects of chronic spinal cord injury on relationships among ion channel and receptor mRNAs in mouse lumbar spinal cord
Authors: Garcia Virginia B. , Abbinanti Matthew D. , Harris-Warrick Ronald M. , Schulz David J. 
Published: July 2018

ABSTRACTSpinal cord injury (SCI) causes widespread changes in gene expression of the spinal cord, even in the undamaged spinal cord below the level of the lesion. Less is known about changes in the correlated expression of genes after SCI. We investigated gene co-expression networks among voltage-gated ion channel and neurotransmitter receptor mRNA levels using quantitative RT-PCR in longitudinal slices of the mouse lumbar spinal cord in control and chronic SCI animals. These longitudinal slices were made from the ventral surface of the cord, thus forming slices relatively enriched in motor neurons or interneurons. We performed absolute quantitation of mRNA copy number for 50 ion channel or receptor transcripts from each sample, and used multiple correlation analyses to detect patterns in correlated mRNA levels across all pairs of genes. The majority of channels and receptors changed in expression as a result of chronic SCI, but did so differently across slice levels. Furthermore, motor neuron enriched slices experienced an overall loss of correlated channel and receptor expression, while interneuron slices showed a dramatic increase in the number of positively correlated transcripts. These correlation profiles suggest that spinal cord injury induces distinct changes across cell types in the organization of gene co-expression networks for ion channels and transmitter receptors.
Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing
Authors: Ros-Freixedes Roger , Mara Battagin , Johnsson Martin , Gorjanc Gregor , Mileham Alan J , Rounsley Steve D , Hickey John M 
Published: July 2018

AbstractBackgroundInherent sources of error and bias that affect the quality of the sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing there is a need to understand the impact of these errors and bias on resulting genotype calls.ResultsWe used a dataset of 26 pigs sequenced both at 2x with multiplexing and at 30x without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, a default and desired step for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points.ConclusionsWe propose a simple pipeline to correct this bias and we recommend that users of low-coverage sequencing be wary of unexpected biases produced by tools designed for high-coverage sequencing.
Prevalence and determinants of smoking status among university students: Artvin Çoruh University sample
Authors: Karadoğan Dilek , Önal Özgür , Kanbay Yalçın 
Published: July 2018

AbstractBackground and aimWhile smoking prevalence has decreased in many developed countries, in others it is still an increasing risk factor for public health, especially among young adults. In this study we aimed to find the factors affecting university students’ smoking status.MethodsThis cross-sectional study was conducted between March and June 2017 with a simple random sampling method. A self-administered questionnaire was used to collect information on sociodemographic characteristics, cigarette smoking status, and the related risk factors. Univariate analysis and multivariate logistic regression analysis with the Backward:LR test were performed.ResultsOut of 2723 students, 2505 students’ data were available and suitable for analysis. Mean participant age was 20.9±2.5, with female dominance (58.9%). Students studying in two-year degree departments comprised 45.6% of the study population, and the remaining 54.4% of the students were in 4-year degree departments. In terms of parental smoking habits, 36.1% reported a smoker father, while that rate was 10.3% for mothers and 15.0% for siblings. Current smokers represented 27.9% of the group: 46% among males and 15.3% among females. Mean age for beginning smoking was 16.34±2.72, 15.65±2.67 for males, 16.34±2.72 for females (p<0.05). Mean Fagerströmtest score was 4.43±1.82, and female students had lower test scores than males (p<0.05). After controlling for potential confounders in multivariate analysis, it was seen that five factors were positively associated with current smoking (p<0.05): male gender (OR:3.43; 95%, CI:2.75-4.28), studying in two-year programs (OR: 1.74; 95% CI: 1.39-2.18), having at least one close family member who is a smoker (OR:1.63; 95% CI:1.31-2.04), having close friends who are all smokers (OR:1.81; 95% CI: 1.40-2.33), and alcohol consumption (OR:4.39; 95% CI: 3.51-5.49).ConclusionOur study showed a higher smoking rate among our study population, both compared to similar nationalstudies and the country’s overall smoking rate. Underlying factors should be evaluated via qualitative studies and preventive strategies should be implemented accordingly.
Exploring the Genetic Basis of Human Population Differences in DNA Methylation and their Causal Impact on Immune Gene Regulation
Authors: Husquin Lucas T. , Rotival Maxime , Fagny Maud , Quach Hélène , Zidane Nora , McEwen Lisa M. , MacIsaac Julia L. , Kobor Michael S , Aschard Hugues , Patin Etienne , Quintana-Murci Lluis 
Published: July 2018

AbstractDNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, its genetic determinants, and their respective causal impact on immune gene regulation remain elusive. We report extensive population differences in DNA methylation between individuals of African and European descent — detected in primary monocytes that were used as a model of a major innate immunity cell type. Most of these differences (~70%) were driven by DNA sequence variants nearby CpG sites (meQTLs), which account for ~60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation. Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.
Clinical Impact and Cost-Effectiveness of a 176-Condition Expanded Carrier Screen
Authors: Beauchamp Kyle A. , Johansen Taber Katherine A. , Muzzey Dale 
Published: July 2018

AbstractPurposeCarrier screening identifies couples at high risk for conceiving offspring affected with serious heritable conditions. Minimal screening guidelines mandate testing for cystic fibrosis and spinal muscular atrophy, but expanded carrier screening (ECS) assesses reproductive risk for hundreds of conditions simultaneously. Although medical societies consider ECS an acceptable practice, the health economics of ECS remain incompletely characterized.MethodsThe clinical impact and cost-effectiveness of a 176-condition ECS panel were investigated using a decision-tree model comparing minimal screening and ECS in a preconception setting. Carrier rates from >50,000 patients informed disease-incidence estimates, while cost and life-years-lost data were aggregated from the literature and a cost-of-care database. Model robustness was evaluated using one-way and probabilistic sensitivity analyses.ResultsFor every 100,000 pregnancies, 300 are predicted to be affected by ECS-panel conditions, which, on average, individually incur $1,300,000 in lifetime costs and increase mortality by 26 undiscounted life-years on average. Relative to minimal screening, ECS reduces the affected-birth rate and is cost-effective (i.e., <$50,000 incremental cost per life-year), findings robust to reasonable model-parameter perturbation.ConclusionECS is predicted to reduce the population burden of Mendelian disease in a cost-effective manner compared to many other common medical interventions.
Regulatory mechanism predates the evolution of ant-like swarm intelligence in simulated robots
Authors: Fujisawa Ryusuke , Ichinose Genki , Dobata Shigeto 
Published: July 2018

AbstractThe evolution of complexity is one of the prime features of life on Earth. Although well accepted as the product of adaptation, the dynamics underlying the evolutionary build-up of complex adaptive systems remains poorly resolved. Using simulated robot swarms that exhibit ant-like group foraging with trail pheromones, we show that their swarm intelligence paradoxically involves regulatory behavior that arises in advance. We focused on a “traffic rule” on their foraging trail as a regulatory trait. We allowed the simulated robot swarms to evolve pheromone responsiveness and behaviors simultaneously. In most cases, the traffic rule, initially arising as selectively neutral component behaviors, assisted the group foraging system to bypass a fitness valley caused by overcrowding on the trail. Our study reveals a hitherto underappreciated role of regulatory mechanisms in the origin of swarm intelligence, as well as highlights the importance of embodiment in the study of their evolution.
ParGenes: a tool for massively parallel model selection and phylogenetic tree inference on thousands of genes
Authors: Morel Benoit , Kozlov Alexey M. , Stamatakis Alexandros 
Published: July 2018

ABSTRACTMotivationCoalescent- and reconciliation-based methods are now widely used to infer species phylogenies from genomic data. They typically use per-gene phylogenies as input, which requires conducting multiple individual tree inferences on a large set of multiple sequence alignments (MSAs). At present, no easy-to-use parallel tool for this task exists. Ad hoc scripts for this purpose do not only induce additional implementation overhead, but can also lead to poor resource utilization and long times-to-solution. We present ParGenes, a tool for simultaneously determining the best-fit model and inferring maximum likelihood (ML) phylogenies on thousands of independent MSAs using supercomputers.ResultsParGenes executes common phylogenetic pipeline steps such as model-testing, ML inference(s), bootstrapping, and computation of branch support values via a single parallel program invocation. We evaluated ParGenes by inferring > 20, 000 phylogenetic gene trees with bootstrap support values from Ensembl Compara and VectorBase alignments in 28 hours on a cluster with 1024 nodes.AvailabilityGNU GPL at https://github.com/BenoitMorel/ParGenes.ContactBenoit.Morel@h-its.orgSupplementary informationSupplementary material is available at Bioinformatics online.
Prediction of subgenome additive and interaction effects in allohexaploid wheat
Authors: Santantonio Nicholas , Jannink Jean-Luc , Sorrells Mark E. 
Published: July 2018

1AbstractWhole genome duplications have played an important role in the evolution of angiosperms. These events often occur through hybridization between closely related species, resulting in an allopolyploid with multiple subgenomes. With the availability of affordable genotyping and a reference genome to locate markers, breeders of allopolyploids now have the opportunity to manipulate subgenomes independently. This also presents a unique opportunity to investigate epistatic interactions between homeologous orthologs across subgenomes. We present a statistical framework for partitioning genetic variance to the subgenomes of an allopolyploid, predicting breeding values for each subgenome, and determining the importance of inter-genomic epistasis. We demonstrate using an allohexaploid wheat breeding population evaluated in Ithaca, NY and an important wheat dataset previously shown to demonstrate non-additive genetic variance. Subgenome covariance matrices were constructed and used to calculate subgenome interaction covariance matrices across subgenomes for variance component estimation and genomic prediction. We propose a method to extract population structure from all subgenomes at once before covariances are calculated to reduce collinearity between subgenome estimates. Variance parameter estimation was shown to be reliable for additive subgenome effects, but was less reliable for subgenome interaction components. Predictive ability was equivalent to current genomic prediction methods. Including only inter-genomic interactions resulted in the same increase in accuracy as modeling all pairwise marker interactions. Thus, we provide a new tool for breeders of allopolyploid crops to characterize the genetic architecture of existing populations, determine breeding goals, and develop new strategies for selection of additive effects and fixation of inter-genomic epistasis.
Fast and flexible linear mixed models for genome-wide genetics
Authors: Runcie Daniel E , Crawford Lorin 
Published: July 2018

AbstractLinear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.Author summaryThe goal of quantitative genetics is to characterize the relationship between genetic variation and variation in quantitative traits such as height, productivity, or disease susceptibility. A statistical method known as the linear mixed effect model has been critical to the development of quantitative genetics. First applied to animal breeding, this model now forms the basis of a wide-range of modern genomic analyses including genome-wide associations, polygenic modeling, and genomic prediction. The same model is also widely used in ecology, evolutionary genetics, social sciences, and many other fields. Mixed models are frequently multi-faceted, which is necessary for accurately modeling data that is generated from complex experimental designs. However, most genomic applications use only the simplest form of linear mixed methods because the computational demands for model fitting can be too great. We develop a flexible approach for fitting linear mixed models to genome scale data that greatly reduces their computational burden and provides flexibility for users to choose the best statistical paradigm for their data analysis. We demonstrate improved accuracy for genetic association tests, increased power to discover causal genetic variants, and the ability to provide accurate summaries of model uncertainty using both simulated and real data examples.