bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mango: Distributed Visualization for Genomic Analysis
Authors: Morrow Alyssa Kramer , He George Zhixuan , Nothaft Frank Austin , Tu Eric Tongching , Paschall Justin , Yosef Nir , Joseph Anthony D. 
Published: July 2018

SummaryThe decreasing cost of DNA sequencing over the past decade has led to an explosion of available sequencing datasets, leaving us with terabytes to petabytes of data to explore and analyze. It is critical for analysts in research and clinical settings to be able to develop new data-driven hypotheses from these datasets through bias identification, analysis of data quality, and testing different algorithms and parameter settings. However, current interactive tools for sequence analysis are designed to run on single machines that do not scale to the size of modern genomic datasets, and rely on precomputed static views, rather than allowing direct interaction with the primary dataset. Mango is a genomic sequence visualization and analysis platform that removes these constraints regarding scalability and staticity by leveraging the power of multi-node compute clusters in the cloud to allow interactive analysis over terabytes of sequencing data. Mango provides both a genome browser graphical user interface and programmable notebook form factor to allow users of varying analytical experience to explore large sequencing datasets on both private clusters and in the cloud. These tools provide a flexible environment for interactive exploration of genomic datasets, while surpassing the computational limits of single-node genomic visualization tools.
Evaluating Named-Entity Recognition approaches in plant molecular biology
Authors: Do Huy , Than Khoat , Larmande Pierre 
Published: July 2018

AbstractText mining research is becoming an important topic in biology with the aim to extract biological entities from scientific papers in order to extend the biological knowledge. However, few thorough studies on text mining and applications are developed for plant molecular biology data, especially rice, thus resulting a lack of datasets available to train models able to detect entities such as genes, proteins and phenotypic traits. Since there is rare benchmarks for rice, we have to face various difficulties in exploiting advanced machine learning methods for accurate analysis of rice bibliography. In this article, we developed a new training datasets (Oryzabase) as the benchmark. Then, we evaluated the performance of several current approaches to find a methodology with the best results and assigned it as the state of the art method for our own technique in the future. We applied Name Entities Recognition (NER) tagger, which is built from a Long Short Term Memory (LSTM) model, and combined with Conditional Random Fields (CRFs) to extract information of rice genes and proteins. We analyzed the performance of LSTM-CRF when applying to the Oryzabase dataset and improved the results up to 86% in F1. We found that on average, the result from LSTM-CRF is more exploitable with the new benchmark.
Astrocytes amplify neuronal dendritic volume transmission
Authors: Chen Chun , Jiang ZhiYing , Fu Xin , Tasker Jeffrey G. 
Published: July 2018

AbstractIn addition to their support role in neurotransmitter and ion buffering, astrocytes directly regulate neurotransmission at synapses via local bidirectional signaling with neurons. Here, we reveal a new form of neuronal-astrocytic signaling that transmits retrograde dendritic signals to upstream neurons to activate recurrent synaptic circuits. Norepinephrine activates α1-adrenoreceptors in hypothalamic corticotropin releasing factor (CRF) neurons to stimulate dendritic release, which triggers an astrocytic calcium response and release of ATP; ATP stimulates action potentials in upstream glutamate and GABA neurons to activate recurrent excitatory and inhibitory synaptic circuits to the CRF neurons. Thus, norepinephrine activates a novel retrograde signaling mechanism in CRF neurons that engages astrocytes in order to extend dendritic volume transmission to reach distal presynaptic glutamate and GABA neurons, thereby amplifying volume transmission mediated by dendritic release.
Feedback-mediated signal conversion promotes viral fitness
Authors: Vardi Noam , Chaturvedi Sonali , Weinberger Leor S. 
Published: July 2018

ABSTRACTA fundamental signal-processing problem is how biological systems maintain phenotypic states (i.e., canalization) long after degradation of initial catalyst signals. For example, to efficiently replicate, herpesviruses (e.g., human cytomegalovirus, HCMV) rapidly counteract cell-mediated silencing using trans-activators packaged in the tegument of the infecting virion particle. But, the activity of these tegument trans-activators is inherently transient—they undergo immediate proteolysis but delayed synthesis—and how transient activation sustains lytic viral gene expression despite cell-mediated silencing is unclear. By constructing a two-color, conditional-feedback HCMV mutant, we find that positive feedback in HCMV’s Immediate Early 1 (IE1) protein is of sufficient strength to sustain HCMV lytic expression. Single-cell time-lapse imaging and mathematical modeling show that IE1 positive feedback converts transient transactivation signals from tegument pp71 proteins into sustained lytic expression, which is obligate for efficient viral replication, whereas attenuating feedback decreases fitness by promoting a reversible silenced state. Together, these results identify a regulatory mechanism enabling herpesviruses to sustain expression despite transient activation signals—akin to early electronic transistors—and expose a potential target for therapeutic intervention.
Profiling the genome-wide landscape of tandem repeat expansions
Authors: Mousavi Nima , Shleizer-Burko Sharona , Yanicky Richard , Gymrek Melissa 
Published: July 2018

AbstractTandem Repeat (TR) expansions have been implicated in dozens of genetic diseases, including Huntington’s Disease, Fragile X Syndrome, and hereditary ataxias. Furthermore, TRs have recently been implicated in a range of complex traits, including gene expression and cancer risk. While the human genome harbors hundreds of thousands of TRs, analysis of TR expansions has been mainly limited to known pathogenic loci. A major challenge is that expanded repeats are beyond the read length of most next-generation sequencing (NGS) datasets and are not profiled by existing genome-wide tools. We present GangSTR, a novel algorithm for genome-wide genotyping of both short and expanded TRs. GangSTR extracts information from paired-end reads into a unified model to estimate maximum likelihood TR lengths. We validate GangSTR on real and simulated data and show that GangSTR outperforms alternative methods in both accuracy and speed. We apply GangSTR to a deeply sequenced trio to profile the landscape of TR expansions in a healthy family and validate novel expansions using orthogonal technologies. Our analysis reveals that healthy individuals harbor dozens of long TR alleles not captured by current genome-wide methods. GangSTR will likely enable discovery of novel disease-associated variants not currently accessible from NGS.
Temporal expectations modulate face image repetition suppression as indexed by event-related potentials
Authors: Feuerriegel Daniel , Churches Owen , Coussens Scott , Keage Hannah A.D. 
Published: July 2018

AbstractRepeated exposure to a stimulus leads to reduced responses of stimulus-selective sensory neurons, an effect known as repetition suppression or stimulus-specific adaptation. Several influential models have been proposed to explain repetition suppression within hierarchically-organised sensory systems, with each specifying different mechanisms underlying repetition effects. We manipulated temporal expectations within a face repetition experiment to test a critical prediction of the predictive coding model of repetition suppression: that repetition effects will be larger following stimuli that appear at expected times compared to stimuli that appear at unexpected times. We recorded event-related potentials from 18 participants and mapped the spatiotemporal progression of repetition effects using mass univariate analyses. We then assessed whether the magnitudes of observed face image repetition effects were influenced by temporal expectations. In each trial participants saw an adapter face, followed by a 500ms or 1000ms interstimulus interval (ISI), and then a test face, which was the same or a different face identity to the adapter. Participants’ expectations for whether the test face would appear after a 500ms ISI were cued by the sex of the adapter face. Our analyses revealed multiple repetition effects with distinct scalp topographies, extending until at least 800ms from stimulus onset. An early (158-203ms) repetition effect was larger for stimuli following surprising, rather than expected, 500ms ISI durations, contrary to the model predictions of the predictive coding model of repetition suppression. Later (230-609ms) repetition effects tended to be larger following expected stimulus onset times, in line with predictive coding models. Our results indicate that the relationship between repetition suppression and temporal expectation differs across the time course of the stimulus-evoked response, suggesting multiple distinct mechanisms driving repetition suppression that operate at different latencies within the visual hierarchy.Highlights- Multiple face image repetition effects identified from 162-800ms post stimulus onset- Temporal expectations influenced the magnitudes of repetition effects- Temporal expectation effects differed for early and late stimulus-evoked responses
Pyramids and cascades: a synthesis of food chain functioning and stability
Authors: Barbier Matthieu , Loreau Michel 
Published: July 2018

AbstractFood chain theory is one of the cornerstones of ecology, providing many of its basic predictions, such as biomass pyramids, trophic cascades and predator-prey oscillations. Yet, ninety years into this theory, the conditions under which these patterns may occur and persist in nature remain subject to debate. Rather than address each pattern in isolation, we propose that they must be understood together, calling for synthesis in a fragmented landscape of theoretical and empirical results. As a first step, we propose a minimal theory that combines the long-standing energetic and dynamical approaches of food chains. We chart theoretical predictions on a concise map, where two main regimes emerge: across various functioning and stability metrics, one regime is characterized by pyramidal patterns, the other by cascade patterns. The axes of this map combine key physiological and ecological variables, such as metabolic rates and self-regulation. A quantitative comparison with data sheds light on conflicting theoretical predictions and empirical puzzles, from size spectra to causes of trophic cascade strength. We conclude that drawing systematic connections between various existing approaches to food chains, and between their predictions on functioning and stability, is a crucial step in confronting this theory to real ecosystems.
Embodied precision: Intranasal oxytocin modulates multisensory integration
Authors: Crucianelli Laura , Paloyelis Yannis , Ricciardi Lucia , Jenkinson Paul M , Fotopoulou Aikaterini 
Published: July 2018

AbstractMultisensory integration processes are fundamental to our sense of self as embodied beings. Bodily illusions, such as the rubber hand illusion (RHI) and the size-weight illusion (SWI), allow us to investigate how the brain resolves conflicting multisensory evidence during perceptual inference in relation to different facets of body representation. In the RHI, synchronous tactile stimulation of a participant’s hidden hand and a visible rubber hand creates illusory bodily ownership; in the SWI, the perceived size of the body can modulate the estimated weight of external objects. According to Bayesian models, such illusions arise as an attempt to explain the causes of multisensory perception and may reflect the attenuation of somatosensory precision, which is required to resolve perceptual hypotheses about conflicting multisensory input. Recent hypotheses propose that the precision or salience of sensorimotor representations is determined by modulators of synaptic gain, like dopamine, acetylcholine and oxytocin. However, these neuromodulatory hypotheses have not been tested in the context of embodied multisensory integration. The present, double-blind, placebo-controlled, crossed-over study (N = 41 healthy volunteers) aimed to investigate the effect of intranasal oxytocin (IN-OT) on multisensory integration processes, tested by means of the RHI and the SWI. Results showed that IN-OT enhanced the subjective feeling of ownership in the RHI, only when synchronous tactile stimulation was involved. Furthermore, IN-OT increased the embodied version of the SWI (quantified as weight estimation error). These findings suggest that oxytocin might modulate processes of visuo-tactile multisensory integration by increasing the precision of top-down signals against bottom-up sensory input.
Duplications and functional specialization force distinct evolution of isoflavonoid biosynthetic genes in legumes
Authors: Jiao Yin Shan , Zhao Yu , Chen Wen Feng 
Published: July 2018

AbstractIsoflavonoids are specialized plant metabolites, almost exclusive to legumes, and synthesized by the phenylpropanoid pathway. Leguminous plants produce 5-deoxyflavonoids and 5-deoxyisoflavonoids that act in symbiosis with nitrogen-fixing bacteria and involved in plant pathogen and stress response. However, little is known about evolutional origin of legume-specific isoflavonoid biosynthesis pathway. Here, we explored the genome-wide analysis of key genes: chalcone synthase (CHS), chalcone reductase (CHR), isoflavone synthase (IFS) and isoflavone reductase (IFR), encoding enzymes involved in the biosynthesis of (iso) flavonoids in legumes and nonlegumes. Among them,CHS,CHRandIFRcomprise multigene families, underling the significant role of gene duplication in the evolutionary. Most duplications ofCHSwere highly the conventional leguminous type, whereas some were grouped with nonleguminousCHSgenes. We also found thatCHRhomologs in soybean andSesbania rostratapreviously reported were ambiguous and should be re-identified. Phylogenetic analysis and protein sequences alignment indicated that IFSs in legumes are highly conserved. Intriguingly, unlike other IFRs in legumes, IFR-like homologs inSophora flavescensandLupinusangustifolius shared high sequence similarity and protein structures with homologs in nonlegumes. Overall, these results offer reasonable gene annotations and comparative analysis and also provided a glimpse into evolutional route of legume-specific isoflavonoid biosynthesis.HighlightIsoflavonoids are specialized plant metabolites, almost exclusive to legumes. We firstly provide evidence that evolutional origin of legume-specific isoflavonoid biosynthesis may be driven by gene duplications and functional specialization.
Neural oligarchy: how synaptic plasticity breeds neurons with extreme influence
Authors: Kleberg Florence I. , Triesch Jochen 
Published: July 2018

AbstractSynapses between cortical neurons are subject to constant modifications through synaptic plasticity mechanisms, which are believed to underlie learning and memory formation. The strengths of excitatory and inhibitory synapses in the cortex follow a right-skewed long-tailed distribution. Similarly, the firing rates of excitatory and inhibitory neurons also follow a right-skewed long-tailed distribution. How these distributions come about and how they maintain their shape over time is currently not well understood. Here we propose a spiking neural network model that explains the origin of these distributions as a consequence of the interaction of spike-timing dependent plasticity (STDP) of excitatory and inhibitory synapses and a multiplicative form of synaptic normalisation. Specifically, we show that the combination of additive STDP and multiplicative normalisation leads to lognormal-like distributions of excitatory and inhibitory synaptic efficacies as observed experimentally. The shape of these distributions remains stable even if spontaneous fluctuations of synaptic efficacies are added. In the same network, lognormal-like distributions of the firing rates of excitatory and inhibitory neurons result from small variability in the spiking thresholds of individual neurons. Interestingly, we find that variation in firing rates is strongly coupled to variation in synaptic efficacies: neurons with the highest firing rates develop very strong connections onto other neurons. Finally, we define an impact measure for individual neurons and demonstrate the existence of a small group of neurons with an exceptionally strong impact on the network, that arise as a result of synaptic plasticity. In summary, synaptic plasticity and small variability in neuronal parameters underlie a neural oligarchy in recurrent neural networks.Author summaryOur brain’s neural networks are composed of billions of neurons that exchange signals via trillions of synapses. Are these neurons created equal, or do they contribute in similar ways to the network dynamics? Or do some neurons wield much more power than others? Recent experiments have shown that some neurons are much more active than the average neuron and that some synaptic connections are much stronger than the average synaptic connection. However, it is still unclear how these properties come about in the brain. Here we present a neural network model that explains these findings as a result of the interaction of synaptic plasticity mechanisms that modify synapses’ efficacies. The model reproduces recent findings on the statistics of neuronal firing rates and synaptic efficacies and predicts a small class of neurons with exceptionally high impact on the network dynamics. Such neurons may play a key role in brain disorders such as epilepsy.
Powerful Gene Set Analysis in GWAS with the Generalized Berk-Jones Statistic
Authors: Sun Ryan , Hui Shirley , Bader Gary D. , Lin Xihong , Kraft Peter 
Published: July 2018

AbstractA common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.
The effect of silencing immunity related genes on longevity in the naturally occurring Anopheles arabiensis mosquito population of Southwest Ethiopia
Authors: Debalke Serkadis , Habtewold Tibebu , Duchateau Luc , Christophides George K. 
Published: July 2018

AbstractBackgroundIn the fight against malaria, vector control remains the most important tool, butit is now severely constrained by the spread of insecticide or behavioral resistance by mosquito populations. Therefore, new vector control tools are warranted. Such novel tools include anti-mosquito vaccines or mosquito genetic modifications targeting the mosquito midgut homeostasis and reducing the mosquito lifespan beyond a stage they can transmit malaria.MethodsWe assessed the effect of RNA interference silencing of the midgut homeostasis regulators FN3D1, FN3D2, FN3D3, GPRGR9 and PGRPLC3 in populations of Anopheles arabiensis reared at nearly natural setting. We monitored the survival of gene-silenced mosquitoes and assessed the load of their midgut microbiota using flow cytometry. The effect of gene silencing was modeled by the Cox proportional hazards frailty model, and bacterial counts were first log transformed and then compared by a mixed model.ResultSignificantly higher mortality rates were observed for the FN3D1 (Hazard ratio =1.64, P=0.004), FN3D3 (HR=1.79, P<0.001) and GPRGr9 silenced mosquitoes (HR=2.00, P<0.001) as compared to a control group injected with dsRNA against a non-related bacterial gene LacZ. The bacterial load ratios for all target gene silenced mosquitoes compared to control mosquitoes were above 1, with the highest value for FN3D1 equal to 2.66 (95%CI: [0.94;7.57]) but no statistically significant difference could be demonstrated. Interestingly, there was a strong correlation (r=0.61) between the mortality hazard ratio and the bacterial count ratio of the gene-silenced mosquitoes. Increased mortality rates were reversed when the gene-silenced mosquitoes were treated with antibiotic mixtures suggesting that gut microbiota play a key role in the observed reduction of mosquito survival.ConclusionWe demonstrate that interfering with the expression of theFN3D1, FN3D3 or GPRGr9 genes can cause a significant reduction of the longevity of An. arabiensis mosquitoes due to the disruption of the mosquito gut homeostasis.
Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias
Authors: Wang Yong , Tian Pengfei , Boomsma Wouter , Lindorff-Larsen Kresten 
Published: July 2018

AbstractEnergy landscape theory suggests that native interactions are a major determinant of the folding mechanism of a protein. Thus, structure-based (Gō) models have, aided by coarse-graining techniques, shown great success in capturing the mechanisms of protein folding and conformational changes. In certain cases, however, non-native interactions and atomic details are also essential to describe the protein dynamics, prompting the development of a variety of structure-based models which include non-native interactions, and differentiate between different types of attractive potentials. Here, we describe an all-protein-atom hybrid model, termed ProfasiGo, that integrates an implicit solvent all-atom physics-based model (called Profasi) and a structure-based Gō potential, and its implementation in two software packages (PHAISTOS and ProFASi) that are developed for Monte Carlo sampling of protein molecules. We apply the ProfasiGo model to study the folding free energy landscapes of four topologically similar proteins, one of which can be folded by the simplified potential Profasi, and two that have been folded by explicit solvent, all-atom molecular dynamics simulations with the CHARMM22∗ force field. Our results reveal that the hybrid ProfasiGo model is able to capture many of the details present in the physics-based potentials, while retaining the advantages of Gō models for sampling and guiding to the native state. We expect that the model will be widely applicable to study the folding of more complex proteins, or to study conformational dynamics and integration with experimental data.