bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication
Authors: De Filippi Eleonora , Wolter Mara , Melo Bruno , Tierra-Criollo Carlos J. , Bortolini Tiago , Deco Gustavo , Moll Jorge 
Published: July 2020

AbstractDuring the last decades, neurofeedback training for emotional self-regulation has received significant attention from both the scientific and clinical communities. However, most studies have focused on broader emotional states such as “negative vs. positive”, primarily due to our poor understanding of the functional anatomy of more complex emotions at the electrophysiological level. Our proof-of-concept study aims at investigating the feasibility of classifying two complex emotions that have been implicated in mental health, namely tenderness and anguish, using features extracted from the electroencephalogram (EEG) signal in healthy participants. Electrophysiological data were recorded from fourteen participants during a block-designed experiment consisting of emotional self-induction trials combined with a multimodal virtual scenario. For the within-subject classification, the linear Support Vector Machine was trained with two sets of samples: random cross-validation of the sliding windows of all trials; and 2) strategic cross-validation, assigning all the windows of one trial to the same fold. Spectral features, together with the frontal-alpha asymmetry, were extracted using Complex Morlet Wavelet analysis. Classification results with these features showed an accuracy of 79.3% on average when doing random cross-validation, and 73.3% when applying strategic cross-validation. We extracted a second set of features from the amplitude time-series correlation analysis, which significantly enhanced random cross-validation accuracy while showing similar performance to spectral features when doing strategic cross-validation. These results suggest that complex emotions show distinct electrophysiological correlates, which paves the way for future EEG-based, real-time neurofeedback training of complex emotional states.Significance statementThere is still little understanding about the correlates of high-order emotions (i.e., anguish and tenderness) in the physiological signals recorded with the EEG. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, concerning the therapeutic application, EEG is a more suitable tool with regards to costs and practicability. Therefore, our proof-of-concept study aims at establishing a method for classifying complex emotions that can be later used for EEG-based neurofeedback on emotion regulation. We recorded EEG signals during a multimodal, near-immersive emotion-elicitation experiment. Results demonstrate that intraindividual classification of discrete emotions with features extracted from the EEG is feasible and may be implemented in real-time to enable neurofeedback.
NEURAL ENCODING OF FELT AND IMAGINED TOUCH WITHIN HUMAN POSTERIOR PARIETAL CORTEX
Authors: Chivukula Srinivas , Zhang Carey , Aflalo Tyson , Jafari Matiar , Pejsa Kelsie , Pouratian Nader , Andersen Richard A. 
Published: July 2020

ABSTRACTIn the human posterior parietal cortex (PPC), single units encode high-dimensional information withpartially mixedrepresentations that enable small populations of neurons to encode many variables relevant to movement planning, execution, cognition, and perception. Here we test whether a PPC neuronal population previously demonstrated to encode visual and motor information is similarly selective in the somatosensory domain. We recorded from 1423 neurons within the PPC of a human clinical trial participant during objective touch presentation and during tactile imagery. Neurons encoded experienced touch with bilateral receptive fields, organized by body part, and covered all tested regions. Tactile imagery evoked body part specific responses that shared a neural substrate with experienced touch. Our results are the first neuron level evidence of touch encoding in human PPC and its cognitive engagement during tactile imagery which may reflect semantic processing, sensory anticipation, and imagined touch.
Neuropeptidome regulation after baculovirus infection. A focus on proctolin and its relevance in locomotion and digestion
Authors: Llopis-Giménez Angel , Parenti Stefano , Han Yue , Ros Vera I.D. , Herrero Salvador 
Published: July 2020

AbstractBaculoviruses constitute a large group of invertebrate DNA viruses, predominantly infecting larvae of the insect order Lepidoptera. During a baculovirus infection, the virus spreads throughout the insect body producing a systemic infection in multiple larval tissues. Some behavioral and physiological changes in lepidopteran larvae have been described following a baculovirus infection and those changes could be connected with alterations in the host’s central nervous system (CNS). As a main component of the CNS, neuropeptides are small protein-like molecules functioning as neurohormones, neurotransmitters or neuromodulators. These peptides are involved in regulating animal physiology and behaviour and could also be targeted by baculoviruses in order to achieve host behavioural manipulations leading to increased viral fitness. In this study, we have investigated the effect of aSpodoptera exigua multiple nucleopolyhedrovirus(SeMNPV) infection on the neuropeptidome gene expression ofSpodoptera exigualarval heads and brains. Expression of the gene encoding the neuropeptide proctolin was severely downregulated following infection and was chosen for further analysis. A recombinantAutographa californica multiple nucleopolyhedrovirus(AcMNPV) overexpressing theS. exigua proctolingene was generated and used in bioassays usingS. exigualarvae to study its influence on the viral infection. AcMNPV-proctolin infected larvae showed less locomotion activity and suffered a loss of weight compared to larvae infected with wild type AcMNPV or mock-infected larvae. These results provide additional information on the role ofproctolinduring a baculovirus infection, and offers a novel hypothesis for the molecular bases for the behavioral changes associated with a baculovirus infection.
Evaluation of Polygenic Prediction Methodology within a Reference-Standardized Framework
Authors: Pain Oliver , Glanville Kylie P. , Hagenaars Saskia P. , Selzam Saskia , Fürtjes Anna E. , Gaspar Héléna A. , Coleman Jonathan R. I. , Rimfeld Kaili , Breen Gerome , Plomin Robert , Folkersen Lasse , Lewis Cathryn M. 
Published: July 2020

AbstractBackgroundThe predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores.MethodsEight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDPred1, LDPred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value threshold and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models.ResultsLDPred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs and DBSLMM, with a relative improvement of >10% over other pseudovalidation and infinitesimal methods (lassosum, SBLUP, SBayesR, LDPred1, LDPred2). PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score.ConclusionWithin a reference-standardized framework, the best polygenic prediction was achieved using LDPred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods.
Predictive visuo-motor communication through neural oscillations
Authors: Benedetto Alessandro , Binda Paola , Costagli Mauro , Tosetti Michela , Morrone Maria Concetta 
Published: July 2020

SummaryThe mechanisms coordinating action and perception over time are poorly understood. The sensory cortex needs to prepare for upcoming changes contingent on action, and this requires temporally precise communication that takes into account the variable delays between sensory and motor processing. Several theorists1,2have proposed synchronization of the endogenous oscillatory activity observed in most regions of the brain3as the basis for an efficient and flexible communication protocol between distal brain areas2,4, a concept known as “communication through coherence”. Synchronization of endogenous oscillations5,6occurs after a salient sensory stimulus, such as a flash or a sound7–11, and after a voluntary action12–18, and this impacts directly on perception, causing performance to oscillate rhythmically over time. Here we introduce a novel fMRI paradigm to probe the neural sources of oscillations, based on the concept of perturbative signals, which overcomes the low temporal resolution of BOLD signals. The assumption is that a synchronized endogenous rhythm will modulate cortical excitability rhythmically, which should be reflected in the BOLD responses to brief stimuli presented at different phases of the oscillation cycle. We record rhythmic oscillations of V1 BOLD synchronized by a simple voluntary action, in phase with behaviourally measured oscillations in visual sensitivity in the theta range. The functional connectivity between V1 and M1 also oscillates at the same rhythm. By demonstrating oscillatory temporal coupling between primary motor and sensory cortices, our results strongly implicate communication through coherence to achieve precise coordination and to encode sensory-motor timing.
Unimodal effects of pigment richness on niche and fitness differences explain species richness and ecosystem function in light-limited phytoplankton communities
Authors: Spaak Jurg Werner , De Laender Frederik 
Published: July 2020

AbstractTrait diversity is traditionally seen as promoting species richness and ecosystem function. Species with dissimilar traits would partition available resources, increasing niche differences, facilitating coexistence and increasing ecosystem function. Here we first show, using theory and simulations for light-limited phytoplankton, that combing photosynthetic pigments is indeed a necessary condition for coexistence and stimulates ecosystem function. However, pigment richness does mostly not permit the coexistence of more than two species, and increases productivity at most 60% compared to single-pigment communities. Surprisingly, combining all nine pigments known to date leads to a 2.5% probability that four species would coexist, illustrating that the coexistence of a high number of species along a continuous niche axis is constrained by limiting similarity. We explain these constraints by unimodal effects of pigment richness on niche and fitness differences, which jointly limit the positive effect of pigment on species richness. Empirical data and additional simulations suggest that pigment richness effects can be stronger during transient dynamics but inevitably weaken with time, i.e. pigment richness effects on species richness and function are likely short-lived. Our results highlight the need to apply coexistence theory to understand the long-term effects of trait diversity on biodiversity and ecosystem function.Statement of authorshipJ.W.S. and F.dL. developed the ideas and wrote the manuscript. J.W.S developed the mathematics and the python code to conduct the study. J.W.S conducted the literature review.
MAGIC: A label-free fluorescence method for 3D high-resolution reconstruction of myelinated fibers in large volumes
Authors: Costantini Irene , Baria Enrico , Sorelli Michele , Matuschke Felix , Giardini Francesco , Menzel Miriam , Mazzamuto Giacomo , Silvestri Ludovico , Cicchi Riccardo , Amunts Katrin , Axer Markus , Pavone Francesco Saverio 
Published: July 2020

AbstractAnalyzing the structure of neuronal fibers with single axon resolution, in large volumes, remains an unresolved challenge in connectomics. Here, we propose MAGIC (Myelin Autofluorescence imaging by Glycerol Induced Contrast enhancement), a simple tissue preparation method to perform label-free fluorescence imaging of myelinated fibers. We demonstrate its broad applicability by performing mesoscopic reconstruction at sub-micron resolution of mouse, rat, monkey, and human brain samples and by quantifying the different fiber organization in Control and Reeler mouse’s hippocampal sections.
Confirmation bias is adaptive when coupled with efficient metacognition
Authors: Rollwage Max , Fleming Stephen M. 
Published: July 2020

AbstractBiases in the consideration of evidence can reduce the chances of consensus between people with different viewpoints. While such altered information processing typically leads to detrimental performance in laboratory tasks, the ubiquitous nature of confirmation bias makes it unlikely that selective information processing is universally harmful. Here we suggest that confirmation bias is adaptive to the extent that agents have good metacognition, allowing them to downweight contradictory information when correct but still able to seek new information when they realise they are wrong. Using simulation-based modelling, we explore how the adaptiveness of holding a confirmation bias depends on such metacognitive insight. We find that the behavioural consequences of selective information processing are systematically affected by agents’ introspective abilities. Strikingly, we find that selective information processing can even improve decision-making when compared to unbiased evidence accumulation, as long as it is accompanied by good metacognition. These results further suggest that interventions which boost people’s metacognition might be efficient in alleviating the negative effects of selective information processing on issues such as political polarisation.
Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes
Authors: Kilic Zeliha , Sgouralis Ioannis , Pressé Steve 
Published: July 2020

AbstractThe hidden Markov model (HMM) is a framework for time series analysis widely applied to single molecule experiments. It has traditionally been used to interpret signals generated by systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, originally developed for applications outside the Natural Sciences, such as speech recognition, transitions between states, such as molecular conformational states, are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, while measurements are often performed at discrete time levels in the Natural Sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, as the transition rates are unknown in principle, it is unclear,a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, as these switch between discrete states incontinuous time. We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process (HMJP). We explicitly show in what limit the HMJP reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.
Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites
Authors: Schmidt Marco , Maié Tiago , Dahl Edgar , Costa Ivan G. , Wagner Wolfgang 
Published: July 2020

AbstractBackgroundThe complex composition of different cell types within a tissue can be estimated by deconvolution of omics datasets. For example, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. In this study, we investigated if deconvolution is also feasible with individual cell-type-specific CG dinucleotides (CpG sites), which can be addressed by targeted analysis, such as pyrosequencing.ResultsWe compiled and curated a dataset of 579 samples from Illumina 450k BeadChip technology that comprised 14 different purified and characterized human cell types. A training and validation strategy was applied to identify and test cell-type-specific CpGs. Initially, the amount of fibroblasts was estimated using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. This FibroScore correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts and induced pluripotent stem cells. Using previously published BeadChip datasets with cell mixtures the accuracy of this eight CpG signature was comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types.ConclusionThis proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.
Unsupervised analysis of multi-experiment transcriptomic patterns with SegRNA identifies unannotated transcripts
Authors: Mendez Mickaël ,   FANTOM Consortium Main Contributors, Scott Michelle S. , Hoffman Michael M. 
Published: July 2020

AbstractBackgroundExploratory analysis of complex transcriptomic data presents multiple challenges. Many methods often rely on preexisting gene annotations, impeding identification and characterization of new transcripts. Even for a single cell type, comprehending the diversity of RNA species transcribed at each genomic region requires combining multiple datasets, each enriched for specific types of RNA. Currently, examining combinatorial patterns in these data requires time-consuming visual inspection using a genome browser.MethodWe developed a new segmentation and genome annotation (SAGA) method, SegRNA, that integrates data from multiple transcriptome profiling assays. SegRNA identifies recurring combinations of signals across multiple datasets measuring the abundance of transcribed RNAs. Using complementary techniques, SegRNA builds on the Segway SAGA framework by learning parameters from both the forward and reverse DNA strands. SegRNA’s unsupervised approach allows exploring patterns in these data without relying on pre-existing transcript models.ResultsWe used SegRNA to generate the first unsupervised transcriptome annotation of the K562 chronic myeloid leukemia cell line, integrating multiple types of RNA data. Combining RNA-seq, CAGE, and PRO-seq experiments together captured a diverse population of RNAs throughout the genome. As expected, SegRNA annotated patterns associated with gene components such as promoters, exons, and introns. Additionally, we identified a pattern enriched for novel small RNAs transcribed within intergenic, intronic, and exonic regions. We applied SegRNA to FANTOM6 CAGE data characterizing 285 lncRNA knockdowns. Overall, SegRNA efficiently summarizes diverse multi-experiment data.
Substrate-induced clustering activates Trim-Away of pathogens and proteins
Authors: Zeng Jingwei , Santos Ana Filipa , Mukadam Aamir , Osswald Mariana , Luptak Jakub , Jacques David , Dickson Claire , Renner Nadine , Johnson Chris , Vaysburd Marina , McEwan William A. , Morais-de-Sá Eurico , Clift Dean , James Leo C. 
Published: July 2020

SUMMARYTrim-Away is a powerful new technology that exploits off-the-shelf antibodies and the E3 RING ligase and cytosolic antibody receptor TRIM21 to carry out rapid protein depletion. How TRIM21 is catalytically-activated upon substrate engagement during either its normal immune function or when re-purposed for targeted protein degradation is unknown. Here we show that a mechanism of substrate-induced clustering triggers intermolecular dimerization of the RING domain to switch on the ubiquitination activity of TRIM21 and induce an antiviral response or drive Trim-Away. We harness this mechanism to expand the Trim-Away toolbox with highly-active TRIM21-nanobody chimeras that can also be controlled optogenetically. This work provides a mechanism for cellular activation of TRIM RING ligases and has important implications for targeted protein degradation technologies.
Association of Metabolic and DNA-repair gene polymorphisms with Longevity: a role forGSTT1, GSTPandXPCgenes
Authors: Scarfò Manuel , Sciandra Chiara , Santovito Alfredo 
Published: July 2020

AbstractAging and longevity are complex processes controlled at different levels, including genetic level. We evaluated the association of seven drug and DNA-repair gene polymorphisms with longevity in an Italian cohort. A sample of 756 subjects aged 18-98 was genotyped forCYP1A1 exon 7 A>G, GSTT1 null, GSTM1 null, GSTP A>G, XRCC1exon 6 C>T, XRCC1 exon 9 A> G and XPC exon 15 A>C gene polymorphisms. The association between the analyzed gene polymorphisms and longevity was evaluated by dividing the sample into three age groups: 10-50, 51-85, and 86-98.We observed a significant decrease in the frequency of theGSTT1null,GSTPG andXPCC alleles in the oldest group with respect to the youngest one and with respect to 51-85 age group. We obtained the same results also subdividing the sample into 1-85 and 86-98 age groups. The general linear model analyses confirmed a significant decreasing trend of the above mentioned alleles with age. We hypothesized that these minor alleles, being important in the sensitivity against the development of different types of cancer, may reflect a reduced life-expectancy in carrier subjects and may explain their significantly lower frequency observed among subjects belonging to oldest age group.