bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reactivation of neural patterns during memory reinstatement supports encoding specificity
Authors: Staudigl Tobias , Hanslmayr Simon 
Published: January 2018

AbstractEncoding specificity or transfer appropriate processing state that memory benefits when items are encoded and retrieved in the same modality compared to when encoding and retrieval is conducted in different modalities. In neural terms, these effects can be expressed by a resonance process between a memory cue and a stored engram; the more the two overlap the better memory performance. We here used temporal pattern analysis in MEG to tap into this resonance process. We predicted that reactivation of sensory patterns established during encoding has opposing effects depending on whether there is a match or mismatch between the memory cue and the encoding modality. To test this prediction items were presented either visually or aurally during encoding and in a recognition test to create match (e.g. “dog” presented aurally during encoding and recognition) and mismatch conditions (e.g. “dog” presented aurally during encoding and shown visually during recognition). Memory performance was better for items in the match compared to the mismatch condition. MEG results showed that memory benefitted from neural pattern reinstatement only in the match condition, but suffered from reinstatement in the mismatch condition. These behavioural and neural effects were asymmetric in that they were only obtained for aurally encoded words but not for visually encoded words. A simple computational model was generated in order to simulate these opposing effects of neural pattern reactivation on memory performance. We argue that these results suggest that reactivation of neural patterns established during encoding underlies encoding specificity or transfer appropriate processing.
Motion regions are modulated by scene content
Authors: Korkmaz Hacialihafiz Didem , Bartels Andreas 
Published: January 2018

AbstractCreating a stable perception of the world during pursuit eye movements is one of the everyday roles of visual system. Some motion regions have been shown to differentiate between motion in the external world from that generated by eye movements. However, in most circumstances, perceptual stability is consistently related to content: the surrounding scene is typically stable. However, no prior study has examined to which extent motion responsive regions are modulated by scene content, and whether there is an interaction between content and motion response. In the present study we used a factorial design that has previously been shown to reveal regional involvement in integrating efference copies of eye-movements with retinal motion to mediate perceptual stability and encode real-world motion. We then added scene content as a third factor, which allowed us to examine to which extent real-motion, retinal motion, and static responses were modulated by meaningful scenes versus their Fourier scrambled counterpart. We found that motion responses in human motion responsive regions V3A, V6, V5+/MT+ and cingulate sulcus visual area (CSv) were all modulated by scene content. Depending on the region, these motion-content interactions differentially depended on whether motion was self-induced or not. V3A was the only motion responsive region that also showed responses to still scenes. Our results suggest that contrary to the two-pathway hypothesis, scene responses are not isolated to ventral regions, but also can be found in dorsal areas.
A robot-based gait training therapy for pediatric population with Cerebral Palsy: goal setting, proposal and preliminary clinical implementation
Authors: Bayón C. , Martín-Lorenzo T. , Moral-Saiz B. , Ramírez O. , Pérez-Somarriba A. , Lerma-Lara S. , Martínez I. , Rocon E. 
Published: January 2018

ABSTRACTBACKGROUNDThe use of robotic trainers has increased with the aim of improving gait function in patients with limitations. Nevertheless, there is an absence of studies that deeply describe detailed guidelines of how to correctly implement robot-based treatments for gait rehabilitation. This contribution proposes an accurate robot-based training program for gait rehabilitation of pediatric population with Cerebral Palsy (CP).METHODSThe program is focused on the achievement of some specifications defined by the International Classification of Functioning, Disability and Health framework, Children and Youth version (ICF-CY). It is framed on 16 non-consecutive sessions where motor control, strength and power exercises of lower limbs are performed in parallel with a postural control strategy. A clinical evaluation with four pediatric patients with CP using the CPWalker robotic platform is presented.RESULTSThe preliminary evaluation with patients with CP shows improvements in several aspects as strength (74.03±40.20%), mean velocity (21.46±33.79%), step length (17.95±20.45%) or gait performance (e.g. 18.88±14.31% in Gross Motor Function Measure-88 items, E and D dimensions).CONCLUSIONSThe improvements achieved in the short term show the importance of working strength and power functions meanwhile over-ground training with postural control. This research could serve as preliminary support for future clinical implementations in any robotic device.TRIAL REGISTRATIONThe study was carried out with the number R-0032/12 from Local Ethical Committee of the Hospital Infantil Niño Jesús. Public trial registration: ISRCTN18254257. Registered 23 March 2017, retrospectively registered.
Evolution at two time frames: polymorphisms from an ancient singular divergence event fuel contemporary parallel evolution
Authors: Van Belleghem Steven M. , Vangestel Carl , De Wolf Katrien , De Corte Zoë , Möst Markus , Rastas Pasi , De Meester Luc , Hendrickx Frederik 
Published: January 2018

AbstractWhen species occur in repeated ecologically distinct habitats across their range, adaptation may proceed surprisingly fast and result in parallel evolution. There is increasing evidence that such cases of rapid parallel evolution are fueled by standing genetic variation, but the origin of this genetic variation remains poorly understood. In Pogonus chalceus beetles, short- and long-winged ecotypes have diverged in response to contrasting hydrological regimes and can be repeatedly found along the Atlantic European coast. By analyzing genomic variation across the beetles’ distribution, we reveal that genomically widespread short-wing selected alleles evolved during a singular divergence event, estimated at ~0.19 Mya. The ancient and differentially selected alleles are currently polymorphic in all populations across the range, allowing for the fast evolution of one ecotype from a small number of random individuals, as low as 5 to 15, of the populations of the other ecotype. Our results suggest that cases of fast parallel ecological divergence might be the result of evolution at two different time frames: divergence in the past, followed by repeated selection on the divergently evolved alleles after admixture. We suggest that this mechanism may be common and potentially further driven by periods of geographic isolation imposed by large-scale environmental changes such as glacial cycles.
Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
Authors: Steele Andrew J. , Aylin Cakiroglu S. , Shah Anoop D. , Denaxas Spiros C. , Hemingway Harry , Luscombe Nicholas M. 
Published: January 2018

AbstractPrognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values in datasets are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning.Using a cohort of over 80,000 patients from the CALIBER programme, we performed a systematic comparison of several machine-learning approaches in EHR. We used Cox models and random survival forests with and without imputation on 27 expert-selected variables to predict all-cause mortality. We also used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input.We observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values, and with no need to scale or transform continuous data. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values.We also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate.This demonstrates that machine-learning approaches applied to raw EHR data can be used to build reliable models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.
Asymptotically optimal minimizers schemes
Authors: Marçais Guillaume , DeBlasio Dan , Kingsford Carl 
Published: January 2018

AbstractMotivationThe minimizers technique is a method to sample k-mers that is used in many bioinformatics software to reduce computation, memory usage and run time. The number of applications using minimizers keeps on growing steadily. Despite its many uses, the theoretical understanding of minimizers is still very limited. In many applications, selecting as few k-mers as possible (i.e. having a low density) is beneficial. The density is highly dependent on the choice of the order on the k-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density, and thereby making existing and future bioinformatics tools even more efficient.ResultsFrom the analysis of the asymptotic behavior of minimizers, forward and local schemes, we show that the previously believed lower bound on minimizers schemes does not hold, and that schemes with density lower than thought possible actually exist. The proof is constructive and leads to an efficient algorithm to compare k-mers. These orders are the first known orders that are asymptotically optimal. Additionally, we give improved bounds on the density achievable by the 3 type of schemes.Contactgmarcais@cs.cmu.educkingsf@cs.cmu.edu
Opposing effects of population density and stress on Escherichia coli mutation rate
Authors: Krašovec Rok , Richards Huw , Gifford Danna R. , Belavkin Roman V. , Channon Alastair , Aston Elizabeth , McBain Andrew J. , Knight Christopher G. 
Published: January 2018

AbstractEvolution depends on mutations. For an individual genotype, the rate at which mutations arise is known to increase with various stressors (stress-induced mutagenesis – SIM) and decrease at high population density (density-associated mutation-rate plasticity – DAMP). We hypothesised that these two forms of mutation rate plasticity would have opposing effects across a nutrient gradient. Here we test this hypothesis, culturing Escherichia coli bacteria in increasingly rich media. We distinguish an increase in mutation rate with added nutrients through SIM (dependent on error-prone polymerases Pol IV and Pol V) and an opposing effect of DAMP (dependent on MutT, which removes oxidised G nucleotides). The combination of DAMP and SIM result in a mutation rate minimum at intermediate nutrient levels (which can support 7×108 cells ml−1). These findings demonstrate a strikingly close and nuanced relationship of ecological factors – stress and population density – with mutation, the fuel of all evolution.
Durability of environment-recruitment relationships in aquatic ecosystems: insights from long-term monitoring in a highly modified estuary and implications for management
Authors: Tamburello Natascia , Connors Brendan M. , Fullerton David , Phillis Corey C. 
Published: January 2018

AbstractThe environment can strongly influence the survival of aquatic organisms and their resulting dynamics. Our understanding of these relationships, typically based on correlations, underpins many contemporary resource management decisions and conservation actions. However, such relationships can break down over time as ecosystems evolve. Even when durable, they may not be very useful for management if they exhibit high variability, context dependency, or non-stationarity. Here, we systematically review the literature to identify trends across environment-recruitment relationships for aquatic taxa from California’s San Francisco Bay and Sacramento-San Joaquin Delta Estuary. This is one of the most heavily modified aquatic ecosystems in North America, and home to numerous species of concern whose relationships with the environment inform regulatory actions and constraints. We retested 23 of these relationships spanning 9 species using data that have accumulated in the years since they were first published (9-40 additional years) to determine whether they persisted. Most relationships were robust (i.e., same or stronger in magnitude) to the addition of new data, but the ability to predict how a species will respond to environmental change did not generally improve with more data. Instead, prediction error generally increased over time and in some cases very quickly, suggesting a rapid regime shift. Our results suggest that more data alone will not necessarily improve the ability of these relationships to inform decision making. We conclude by synthesizing emerging insights from the literature on best practices for the analysis, use, and refinement of environment-recruitment relationships to inform decision making in dynamic ecosystems.
A phenomenological model of the time course of maximal voluntary isometric contraction force for optimization of complex loading schemes
Authors: Herold Johannes L. , Kirches Christian , Schlöder Johannes P. 
Published: January 2018

AbstractWe construct a simple and predictive ordinary differential equation model to describe the time course of maximal voluntary isometric contraction (MVIC) force during voluntary isometric contractions and at rest. These time courses are of particular interest whenever force capacities are a limiting factor, e.g. during heavy manual work or resistance training (RT) sessions. Our model is able to describe MVIC force under complex loading schemes and is validated with a comprehensive set of published data from the elbow flexors. We use the calibrated model to analyze fatigue and recovery patterns observed in the literature. Due to the model’s structure, it can be efficiently employed to optimize complex loading schemes. We demonstrate this by computing a work-rest schedule that minimizes fatigue and an optimal isometric RT session as examples.
Integration of single-cell RNA-seq data into metabolic models to characterize tumour cell populations
Authors: Damiani Chiara , Maspero Davide , Di Filippo Marzia , Colombo Riccardo , Pescini Dario , Graudenzi Alex , Westerhoff Hans Victor , Alberghina Lilia , Vanoni Marco , Mauri Giancarlo 
Published: January 2018

AbstractMotivationMetabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. Computational models hold the promise to bridge this gap, by estimating fluxes across metabolic pathways. Yet they currently portray the average behavior of intermixed subpopulations, masking their inherent heterogeneity known to hinder cancer diagnosis and treatment. If complemented with the information on single-cell transcriptome, now enabled by RNA sequencing (scRNA-seq), metabolic models of cancer populations are expected to empower the characterization of the mechanisms behind metabolic heterogeneity. To this aim, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate sc-transcriptomes into single-cell fluxomes.ResultsWe show that the integration of scRNA-seq profiles of cells derived from lung ade-nocarcinoma and breast cancer patients, into a multi-scale stoichiometric model of cancer population: 1) significantly reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites.AvailabilityThe scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.Contactchiara.damiani@unimib.it
Antimalarial pantothenamide metabolites target acetyl-CoA synthesis inPlasmodium falciparum
Authors: Schalkwijk Joost , Allman Erik L. , Jansen Patrick A.M. , de Vries Laura E. , Jackowski Suzanne , Botman Peter N. M. , Beuckens-Schortinghuis Christien A. , Koolen Karin M.J. , Bolscher J. M. , Vos Martijn W. , Miller Karen , Reeves Stacy A. , Pett Helmi , Trevitt Graham , Wittlin Sergio , Scheurer Christian , Sax Sibylle , Fischli Christoph , Josling Gabrielle , Kooij Taco W.A. , Bonnert Roger , Campo Brice , Blaauw Richard H. , Rutjes Floris P.J.T. , Sauerwein Robert W. , Llinás Manuel , Hermkens Pedro H.H. , Dechering Koen J. 
Published: January 2018

AbstractMalaria eradication is critically dependent on novel drugs that target resistantPlasmodiumparasites and block transmission of the disease. Here we report the discovery of potent pantothenamide bioisosteres that are active against blood-stageP. falciparumand also block onward mosquito transmission. These compounds are resistant to degradation by serum pantetheinases, show favorable pharmacokinetic properties and clear parasites in a humanized rodent infection model. Metabolomics revealed that CoA biosynthetic enzymes convert pantothenamides into drug-conjugates that interfere with parasite acetyl-CoA anabolism.In vitrogenerated resistant parasites showed mutations in acetyl-CoA synthetase and acyl-CoA synthetase 11, confirming the key roles of these enzymes in the sensitivity to pantothenamides. These new pantothenamides provide a promising class of antimalarial drugs with a unique mode of action.One sentence summaryPantothenamides form antimetabolites that interfere with acetyl-CoA metabolism in the human malaria parasitePlasmodium falciparum
SPADIS: An Algorithm for Selecting Predictive and Diverse SNPs in GWAS
Authors: Yilmaz Serhan , Tastan Oznur , Cicek A. Ercument 
Published: January 2018

AbstractPhenotypic heritability of complex traits and diseases is seldom explained by individual genetic variants. Algorithms that select SNPs which arecloseand connected on a biological network have been successful in finding biologically-interpretable and predictive loci. However, we argue that the connectedness constraint favors selecting redundant features that affect similar biological processes and therefore does not necessarily yield better predictive performance. In this paper, we propose a novel method called SPADIS that selects SNPs thatcoverdiverse regions in the underlying SNP-SNP network. SPADIS favors the selection of remotely located SNPs in order to account for the complementary additive effects of SNPs that are associated with the phenotype. This is achieved by maximizing a submodular set function with a greedy algorithm that ensures a constant factor (1−1/e) approximation. We compare SPADIS to the state-of-the-art method SConES, on a dataset of Arabidopsis Thaliana genotype and continuous flowering time phenotypes. SPADIS has better regression performance in 12 out of 17 phenotypes on average, it identifies more candidate genes and runs faster. We also investigate the use of Hi-C data to construct SNP-SNP network in the context of SNP selection problem for the first time, which yields slight but consistent improvements in regression performance. SPADIS is available athttp://ciceklab.cs.bilkent.edu.tr/spadis
Spatio-Temporal Gene Discovery For Autism Spectrum Disorder
Authors: Norman Utku , Cicek A. Ercument 
Published: January 2018

AbstractWhole exome sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited in power. Here, we present a spatio-temporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm solves a variant of prize-collecting Steiner forest-based problem on coexpression networks to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on WES data of 3,871 samples and identify risk clusters using BrainSpan coexpression networks of earlyand mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the prediction power: Predicted clusters are hit more and show higher enrichment in ASD-related functions compared to the state-of-the-art. Code is available at http://ciceklab.cs.bilkent.edu.tr/ST-Steiner/.
A proposal for a standardized bacterial taxonomy based on genome phylogeny
Authors: Parks Donovan H. , Chuvochina Maria , Waite David W. , Rinke Christian , Skarshewski Adam , Chaumeil Pierre-Alain , Hugenholtz Philip 
Published: January 2018

AbstractTaxonomy is a fundamental organizing principle of biology, which ideally should be based on evolutionary relationships. Microbial taxonomy has been greatly restricted by the inability to obtain most microorganisms in pure culture and, to a lesser degree, the historical use of phenotypic properties as the basis for classification. However, we are now at the point of obtaining genome sequences broadly representative of microbial diversity by using culture-independent techniques, which provide the opportunity to develop a comprehensive genome-based taxonomy. Here we propose a standardized bacterial taxonomy based on a concatenated protein phylogeny that conservatively removes polyphyletic groups and normalizes ranks based on relative evolutionary divergence. From 94,759 bacterial genomes, 99 phyla are described including six major normalized monophyletic units from the subdivision of the Proteobacteria, and amalgamation of the Candidate Phyla Radiation into the single phylum Patescibacteria. In total, 73% of taxa had one or more changes to their existing taxonomy.