bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stimulus presentation rates affect performance but not the acquired knowledge – Evidence from procedural learning
Authors: Kiss Mariann , Nemeth Dezso , Janacsek Karolina 
Published: May 2019

AbstractPresentation rates – the tempo in which we encounter subsequent items – can alter both our behavioral and neural responses in cognitive domains such as learning, memory, decision-making, perception and language. However, it is still unclear to what extent presentation rates affect the momentary performance versus the underlying cognitive function or mental representation. Here we systemically tested the effect of presentation rate on performance versus competence in procedural learning – a fundamental cognitive function that underlies the acquisition of skills and habits. Procedural learning was assessed by a probabilistic sequence learning task, in which learning typically occurs implicitly. Stimulus presentation rates were systematically manipulated by the Response-to-Stimulus-Intervals (RSIs). Participants were assigned to a fast (120ms) RSI or a slow (850ms) RSI group during the Learning phase, and were tested with both RSIs 24 hours later (Testing phase). We also tested whether they gained explicit knowledge about the sequence or their knowledge remained implicit. We found that the slower RSI led to weaker performance in the Learning phase. Importantly, however, the results of the Testing phase showed that this effect was primarily due to the expression of the acquired knowledge and not the learning itself. Thus, the slower presentation rate affected performance rather than competence. The acquired knowledge remained implicit in both groups, regardless of the presentation rate during learning. These findings call for tasks that can dissociate competence from performance both in experimental and clinical settings. Additionally, our findings can help design more efficient training protocols in education and clinical rehabilitation.
A Universal Nonparametric Event Detection Framework for Neuropixels Data
Authors: Chen Hao , Chen Shizhe , Deng Xinyi 
Published: May 2019

SummaryNeuropixels probes present exciting new opportunities for neuroscience, but such large-scale high-density recordings also introduce unprecedented challenges in data analysis. Neuropixels data usually consist of hundreds or thousands of long stretches of sequential spiking activities that evolve non-stationarily over time and are often governed by complex, unknown dynamics. Extracting meaningful information from the Neuropixels recordings is a non-trial task. Here we introduce a general-purpose, graph-based statistical framework that, without imposing any parametric assumptions, detects points in time at which population spiking activity exhibits simultaneous changes as well as changes that only occur in a subset of the neural population, referred to as “change-points”. The sequence of change-point events can be interpreted as a footprint of neural population activities, which allows us to relate behavior to simultaneously recorded high-dimensional neural activities across multiple brain regions. We demonstrate the effectiveness of our method with an analysis of Neuropixels recordings during spontaneous behavior of an awake mouse in darkness. We observe that change-point dynamics in some brain regions display biologically interesting patterns that hint at functional pathways, as well as temporally-precise coordination with behavioral dynamics. We hypothesize that neural activities underlying spontaneous behavior, though distributed brainwide, show evidences for network modularity. Moreover, we envision the proposed framework to be a useful off-the-shelf analysis tool to the neuroscience community as new electrophysiological recording techniques continue to drive an explosive proliferation in the number and size of data sets.
In-vitro and in-vivo Characterization of a Multi-Stage Enzyme-Responsive Nanoparticle-in-Microgel Pulmonary Drug Delivery System
Authors: Mejías Joscelyn C. , Roy Krishnendu 
Published: May 2019

AbstractAlthough the lung is an obvious target for site-specific delivery of many therapeutics for respiratory airway diseases such as asthma, COPD, and cystic fibrosis, novel strategies are needed to avoid key physiologic barriers for efficient delivery and controlled release of therapeutics to the lungs. Specifically, deposition into the deep lung requires particles with a 1-5 µm aerodynamic diameter; however, particles with a geometric diameter less than 6 µm are rapidly cleared by alveolar macrophages. Additionally, epithelial, endothelial, and fibroblast cells prefer smaller (< 300 nm) nanoparticles for efficient endocytosis. Here we address these contradictory design requirements by using a nanoparticle-inside-microgel system (Nano-in-Microgel). Using an improved maleimide-thiol based Michael Addition during (water-in-oil) Emulsion (MADE) method, we fabricated both trypsin-responsive and neutrophil elastase-responsive polymeric Nano-in-Microgel to show the versatility of the system in easily exchanging enzyme-responsive crosslinkers for disease-specific proteases. By varying the initial macromer concentration, from 20-50 % w/v, the size distribution means ranged from 4-8 µm, enzymatic degradation of the microgels is within 30 minutes, and in vitro macrophage phagocytosis is lower for the higher % w/v. We further demonstrated that in vivo lung delivery of the multi-stage carriers through the pulmonary route yields particle retention up to several hours and followed by clearance within in naïve mice. Our results provide a further understanding of how enzymatically-degradable multi-stage polymeric carriers can be used for pulmonary drug delivery.Graphical Abstract
Population-based neuroimaging reveals traces of childbirth in the maternal brain
Authors: de Lange Ann-Marie G. , kaufmann Tobias , van der Meer Dennis , Maglanoc Luigi , Alnæs Dag , Moberget Torgeir , Douaud Gwenaëlle , Andreassen Ole A. , Westlye Lars T. 
Published: May 2019

AbstractPregnancy and childbirth involve maternal brain adaptations that promote attachment to and protection of the newborn. Using brain imaging and machine learning, we provide evidence for a positive relationship between number of childbirths and a ‘younger-looking’ brain in 12,021 women, which could not be explained by common genetic variation. The findings demonstrate that parity can be linked to brain health later in life.
Metric Learning on Expression Data for Gene Function Prediction
Authors: Makrodimitris Stavros , Reinders Marcel J.T. , Ham Roeland C.H.J. van 
Published: May 2019

AbstractMotivationCo-expression of two genes across different conditions is indicative of their involvement in the same biological process. However, using RNA-Seq datasets with many experimental conditions from diverse sources introduces batch effects and other artefacts that might obscure the real co-expression signal. Moreover, only a subset of experimental conditions is expected to be relevant for finding genes related to a particular Gene Ontology (GO) term. Therefore, we hypothesize that when the purpose is to find similar functioning genes that the co-expression of genes should not be determined on all samples but only on those samples informative for the GO term of interest.ResultsTo address both types of effects, we developed MLC (Metric Learning for Co-expression), a fast algorithm that assigns a GO-term-specific weight to each expression sample. The goal is to obtain a weighted co-expression measure that is more suitable than the unweighted Pearson correlation for applying Guilt-By-Association-based function predictions. More specifically, if two genes are annotated with a given GO term, MLC tries to maximize their weighted co-expression, and, in addition, if one of them is not annotated with that term, the weighted co-expression is minimized. Our experiments on publicly available Arabidopsis thaliana RNA-Seq data demonstrate that MLC outperforms standard Pearson correlation in term-centric performance.AvailabilityMLC is available as a Python package at www.github.com/stamakro/MLCContacts.makrodimitris@tudelft.nlSupplementary informationSupplementary data are available online.
Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex
Authors: Toro-Serey Claudio , Tobyne Sean M. , McGuire Joseph T. 
Published: May 2019

AbstractRegions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into “DN” and “non-DN” subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.HighlightsThe topography of Default Network cortical regions varies across individuals.A community detection algorithm, spectral partitioning, was applied to rs-fMRI data.The algorithm identified individualized Default Network regions in mPFC and PCC.Default Network topography varied across individuals in mPFC, moreso than in PCC.Overlap of task effects with DN regions should be assessed at the individual level.
Automated cryo-EM sample preparation by pin-printing and jet vitrification
Authors: Ravelli Raimond B.G. , Nijpels Frank J.T. , Henderikx Rene J.M. , Weissenberger Giulia , Thewessem Sanne , Gijsbers Abril , Beulen Bart W.A.M.M. , López-Iglesias Carmen , Peters Peter J. 
Published: May 2019

AbstractThe increasing demand for cryo-electron microscopy (cryo-EM) reveals drawbacks in current sample preparation protocols, such as sample waste and lack of reproducibility. Here, we present several technical developments that provide controlled and efficient sample preparation for cryo-EM studies. Pin printing substantially reduces sample waste by depositing only a sub-nanoliter volume of sample on the carrier surface. Sample evaporation is mitigated by dewpoint control feedback loops. The deposited sample is vitrified by jets of cryogen followed by submersion into a cryogen bath. Because the cryogen jets cool the sample from the center, premounted autogrids can be used and loaded directly into automated cryo-EMs. We integrated these steps into a single device, named VitroJet. The device’s performance was validated by resolving 4 standard proteins (apoferritin, GroEL, worm hemoglobin, beta-galactosidase) to ~3 Å resolution using a 200-kV electron microscope. The VitroJet offers a promising solution for improved automated sample preparation in cryo-EM studies.
The Human Dynamic Clamp reveals the fronto-parietal network linking real-time social coordination and cognition
Authors: Dumas G. , Moreau Q. , Tognoli E. , Kelso J.A.S. 
Published: May 2019

AbstractHow does the brain allow us to interact with others, and above all how does it handle situations when the goals of the interactors overlap (i.e. cooperation) or differ (i.e. competition)? Social neuroscience has already provided some answers to these questions but has tended to treat high-level, cognitive interpretations of social behavior separately from the sensorimotor mechanisms upon which they rely. The goal here is to identify the underlying neural processes and mechanisms linking sensorimotor coordination and intention attribution. We combine the Human Dynamic Clamp (HDC), a novel paradigm for studying realistic social behavior between self and other in well-controlled laboratory conditions, with high resolution electroencephalography (EEG). The collection of humanness and intention attribution reports, kinematics and neural data affords an opportunity to relate brain activity to the behavior of the HDC as well as to what the human is doing. Behavioral results demonstrate that sensorimotor coordination influences judgements of cooperativeness and humanness. Analysis of brain dynamics reveals two distinct networks related to integration of visuo-motor information from self and other. The two networks overlap over the right parietal region, an area known to be important for interpersonal motor interactions. Furthermore, connectivity analysis highlights how the judgement of humanness and cooperation of others modulate the connection between the right parietal hub and prefrontal cortex. These results reveal how distributed neural dynamics integrates information from ‘low-level’ sensorimotor mechanisms and ‘high-level’ social cognition to support the realistic social behaviors that play out in real time during interactive scenarios.Significance StatementDaily social interactions require us to coordinate with others and to reflect on their potential motives. This study investigates the brain and behavioral dynamics of these two key aspects of social cognition. Combining high-density electroencephalography and the Human Dynamic Clamp (a Virtual Partner endowed with human-based coordination dynamics), we show first, that several features of sensorimotor coordination influence attribution of intention and judgement of humanness; second, that the right parietal lobe is a key integration hub between information related to self- and other-behavior; and third, that the posterior online social hub is functionally coupled to anterior offline brain structures to support mentalizing about others. Our results stress the complementary nature of low-level and high-level mechanisms that underlie social cognition.
Resolving kinesin stepping: one head at a time
Authors: Stepp Willi L. , Ökten Zeynep 
Published: May 2019

SummaryKinesins are well-known to power diverse long-range transport processes in virtually all eukaryotic cells. The ATP-dependent processive stepping as well as the regulation of kinesin’ activity have thus been focus of extensive studies over the past decades. It is widely accepted that kinesin motors can self-regulate their activity by suppressing the catalytic activity of the ‘heads’. The distal random coil at the C-terminus, termed ‘tail domain’, is proposed to mediate this autoinhibition, however, a direct regulatory influence of the tail on the processive stepping of kinesin proved difficult to capture. Here, we simultaneously tracked the two distinct head domains in the kinesin-2 motor using dual-color super resolution microscopy (dcFIONA) and reveal for the first time their individual properties during processive stepping. We show that the autoinhibitory wild type conformation selectively impacts one head in the heterodimer but not the other. Our results provide key insights into the regulated kinesin stepping that had escaped experimental scrutiny.
Evolution in action: Habitat-transition leads to genome-streamlining in Methylophilaceae (Betaproteobacteriales)
Authors: Salcher Michaela M. , Schaefle Daniel , Kaspar Melissa , Neuenschwander Stefan M. , Ghai Rohit 
Published: May 2019

AbstractThe most abundant aquatic microbes are small in cell and genome size. Genome-streamlining theory predicts gene loss caused by evolutionary selection driven by environmental factors, favouring superior competitors for limiting resources. However, evolutionary histories of such abundant, genome-streamlined microbes remain largely unknown. Here we reconstruct the series of steps in the evolution of some of the most abundant genome-streamlined microbes in freshwaters (‘Ca. Methylopumilus’) and oceans (marine lineage OM43). A broad genomic spectrum is visible in the family Methylophilaceae (Betaproteobacteriales), from sediment microbes with medium-sized genomes (2-3 Mbp genome size), an occasionally blooming pelagic intermediate (1.7 Mbp), and the most reduced pelagic forms (1.3 Mbp). We show that a habitat transition from freshwater sediment to the relatively oligotrophic pelagial was accompanied by progressive gene loss and adaptive gains. Gene loss has mainly affected functions not necessarily required or advantageous in the pelagial or are encoded by redundant pathways. Likewise, we identified genes providing adaptations to oligotrophic conditions that have been transmitted horizontally from pelagic freshwater microbes. Remarkably, the secondary transition from the pelagial of lakes to the oceans required only slight modifications, i.e., adaptations to higher salinity, gained via horizontal gene transfer from indigenous microbes. Our study provides first genomic evidence of genome-reduction taking place during habitat transitions. In this regard, the family Methylophilaceae is an exceptional model for tracing the evolutionary history of genome-streamlining as such a collection of evolutionarily related microbes from different habitats is practically unknown for other similarly abundant microbes (e.g., ‘Ca. Pelagibacterales’, ‘Ca. Nanopelagicales’).
Fast quantification of uncertainty in non-linear diffusion MRI models for artifact detection and more power in group studies
Authors: Harms R.L. , Fritz F.J. , Schoenmakers S. , Roebroeck A. 
Published: May 2019

AbstractDiffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. The uncertainty in these dMRI measures is often ignored, while previous work in functional MRI has shown that incorporating uncertainty estimates can lead to group statistics with a higher statistical power. We propose the Fisher Information Matrix (FIM) as a generally applicable method for quantifying the parameter uncertainties in non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo sampling, the FIM produces similar uncertainty estimates at lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, like data complexity and signal-to-noise ratio. In individual subjects, the parameter standard deviations can help in detecting white matter artifacts as patches of relatively large standard deviations. In group statistics, we recommend using the parameter standard deviations by means of variance weighted averaging. Doing so can reduce the overall variance in group statistics and reduce the effect of data artifacts without discarding data from the analysis. Both these effects can lead to a higher statistical power in group studies.
High-throughput smFRET analysis of freely diffusing nucleic acid molecules and associated proteins
Authors: Segal Maya , Ingargiola Antonino , Lerner Eitan , Chung Sang Yoon , White Jonathan A. , Streets Aaron , Weiss S. , Michalet X. 
Published: May 2019

AbstractSingle-molecule Förster resonance energy transfer (smFRET) is a powerful technique for nanometer-scale studies of single molecules. Solution-based smFRET, in particular, can be used to study equilibrium intra- and intermolecular conformations, binding/unbinding events and conformational changes under biologically relevant conditions without ensemble averaging. However, single-spot smFRET measurements in solution are slow. Here, we detail a high-throughput smFRET approach that extends the traditional single-spot confocal geometry to a multispot one. The excitation spots are optically conjugated to two custom silicon single photon avalanche diode (SPAD) arrays. Two-color excitation is implemented using a periodic acceptor excitation (PAX), allowing distinguishing between singly- and doubly-labeled molecules. We demonstrate the ability of this setup to rapidly and accurately determine FRET efficiencies and population stoichiometries by pooling the data collected independently from the multiple spots. We also show how the high throughput of this approach can be used to increase the temporal resolution of single-molecule FRET population characterization from minutes to seconds. Combined with microfluidics, this high-throughput approach will enable simple real-time kinetic studies as well as powerful molecular screening applications.
Motor axon excitability measures in the rat tail are the same awake or anaesthetized using sodium pentobarbital
Authors: Jones Kelvin E , Bennett David J 
Published: May 2019

AbstractBackgroundNerve excitability tests in sciatic motor axons are sensitive to anaesthetic choice. Results using ketamine/xylazine (KX) are different from those using sodium pentobarbital (SP). It is not clear which results are most similar to the awake condition, though results using SP appear more similar to human results.MethodsNerve excitability in tail motor axons was tested in 8 adult female rats with a chronic sacral spinal cord injury. These animals have no behavioural response to electrical stimulation of the tail and were tested awake and then anaesthetized using SP.ResultsThe nerve excitability test results in the awake condition were indistinguishable from the results when the same rats were anaesthetized with sodium pentobarbital. Summary plots of the test results overlap within the boundaries of the standard error and paired t-tests on the 42 discrete measures generated by nerve excitability testing yielded no significant differences (after Bonferroni correction for multiple comparisons).ConclusionsNerve excitability test results in rat motor axons are the same whether the animals are awake or anesthetized using sodium pentobarbital.
Spectral clustering in regression-based biological networks
Authors: Gaynor Sheila M. , Lin Xihong , Quackenbush John 
Published: May 2019

AbstractBiological networks often have complex structure consisting of meaningful clusters of nodes that are integral to understanding biological function. Community detection algorithms to identify the clustering, or community structure, of a network have been well established. These algorithms assume that data used in network construction is observed without error. However, oftentimes intermediary analyses such as regression are performed before constructing biological networks and the associated error is not propagated in community detection. In expression quantitative trait loci (eQTL) networks, one must first map eQTLs via linear regression in order to specify the matrix representation of the network. We study the effects of using estimates from regression models when applying the spectral clustering approach to community detection. We demonstrate the impacts on the affinity matrix and consider adjusted estimates of the affinity matrix for use in spectral clustering. We further provide a recommendation for selection of the tuning parameter in spectral clustering. We evaluate the proposed adjusted method for performing spectral clustering to detect gene clusters in eQTL data from the GTEx project and to assess the stability of communities in biological data.