bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neural Correlates of Statistical Learning in Developmental Dyslexia: An Electroencephalography Study
Authors: Daikoku Tatsuya , Jentschke Sebastian , Tsogli Vera , Bergström Kirstin , Lachmann Thomas , Ahissar Merav , Koelsch Stefan 
Published: July 2022

AbstractThe human brain extracts statistical regularities from the surrounding environment in a process referred to as statistical learning. Recent behavioural evidence suggests that developmental dyslexia affects statistical learning. However, surprisingly few neurophysiological studies have assessed how developmental dyslexia affects the neural processing underlying statistical learning. In this study, we used electroencephalography to explore the neural correlates of an important aspect of statistical learning – sensitivity to transitional probabilities – in individuals with developmental dyslexia. Adults diagnosed with developmental dyslexia (n = 17) and controls (n = 19) were exposed to a continuous stream of sound triplets in which a few triplet endings were location deviants (i.e., were presented from an unexpected speaker direction) or statistical deviants (i.e., had a low transitional probability given the triplet’s first two sounds). Location deviants elicited a large location mismatch negativity (MMN), which was larger in controls than dyslexics. Statistical deviants elicited a small, yet significant statistical MMN (sMMN) in controls, whereas the dyslexic individuals did not exhibit a statistical MMN. These results suggest that the neural mechanisms underlying statistical learning are impaired in developmental dyslexia.Significance statementWe assessed the neural correlates of statistical learning in individuals with developmental dyslexia. Statistical deviants, namely word endings with a low transitional probability (as compared to high probability transitions) elicited a small, yet significant statistical MMN in controls, whereas the dyslexic individuals did not exhibit a statistical MMN. Location deviants elicited a MMN, which was larger in controls than dyslexics. These results suggest that the neural mechanisms underlying statistical learning are impaired in developmental dyslexia.
Introducing Mouffet, a unified framework to make model creation easier and more reproducible
Authors: Christin Sylvain , Lecomte Nicolas 
Published: July 2022

AbstractBiological and ecological models are being increasingly used to explain the natural world. Model creation is an iterative process requiring two steps: training and evaluating the models. However, this process can become complex when multiple models are trained and evaluated at the same time. Besides, development steps can be lost, reducing the reproducibility of model creation.We introduce Mouffet, an open-source Python framework that aims to make model creation easier, more robust, and more reproducible. It provides a set of configuration files and high-level Python interfaces that help managing data, training, and evaluating models. To improve reproducibility, every step of the model creation process, including the options used, are saved.Mouffet introduces the notion of scenarios that allow users to define multiple training or evaluation tasks in a single configuration file. This not only facilitates model creation but enables users to define experimental plans to study the effect of selected parameters on training or evaluation.While initially developed for deep learning models, Mouffet is independent of the implementation of the models. Therefore, it could be successfully used to compare different modelling approaches. Besides, its ease of use makes it a choice tool for ecologists, even when not familiar with complex model creation.
Coherent pathway enrichment estimation by modeling inter-pathway dependencies using regularized regression
Authors: Jablonski Kim Philipp , Beerenwinkel Niko 
Published: July 2022

AbstractGene set enrichment methods are a common tool to improve the interpretability of gene lists as obtained, for example, from differential gene expression analyses. They are based on computing whether dysregulated genes are located in certain biological pathways more often than expected by chance. Gene set enrichment tools rely on pre-existing pathway databases such as KEGG, Reactome, or the Gene Ontology. These databases are increasing in size and in the number of redundancies between pathways, which complicates the statistical enrichment computation. Here, we address this problem and develop a novel gene set enrichment method, calledpareg, which is based on a regularized generalized linear model and directly incorporates dependencies between gene sets related to certain biological functions, for example, due to shared genes, in the enrichment computation. We show thatparegis more robust to noise than competing methods. Additionally, we demonstrate the ability of our method to recover known pathways as well as to suggest novel treatment targets in an exploratory analysis using breast cancer samples from TCGA.paregis freely available as an R package on Bioconductor (https://bioconductor.org/packages/release/bioc/html/pareg.html) as well as onhttps://github.com/cbg-ethz/pareg. The GitHub repository also contains the Snakemake workflows needed to reproduce all results presented here.
Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity
Authors: Karagiannis Tanya T. , Dowrey Todd W. , Villacorta-Martin Carlos , Montano Monty , Reed Eric , Andersen Stacy L. , Perls Thomas T. , Monti Stefano , Murphy George J. , Sebastiani Paola 
Published: July 2022

AbstractAge-related changes in immune cell composition and functionality are associated with multimorbidity and mortality. However, many centenarians delay the onset of aging-related disease suggesting the presence of elite immunity that remains highly functional at extreme old age. To identify immune-specific patterns of aging and extreme human longevity, we analyzed novel single cell profiles from the peripheral blood mononuclear cells (PBMCs) of 7 centenarians (mean age 106) and publicly available single cell RNA-sequencing (scRNA-seq) datasets that included an additional 7 centenarians as well as 52 people at younger ages (20-89 years). The analysis confirmed known shifts in the ratio of lymphocytes to myeloid cells, and noncytotoxic to cytotoxic cell distributions with aging, but also identified significant shifts from CD4+T cell to B cell populations in centenarians suggesting a history of exposure to natural and environmental immunogens. Our transcriptional analysis identified cell type signaturesspecificto exceptional longevity that included genes with age-related changes (e.g., increased expression ofSTK17A, a gene known to be involved in DNA damage response)as well as genes expressed uniquely in centenarians’ PBMCs (e.g.,S100A4, part of the S100 protein family studied in age-related disease and connected to longevity and metabolic regulation). Collectively, these data suggest that centenarians harbor unique, highly functional immune systems that have successfully adapted to a history of insults allowing for the achievement of exceptional longevity.
MC profiling: a novel approach to analyze DNA methylation heterogeneity from bulk bisulfite sequencing data
Authors: De Riso Giulia , Sarnataro Antonella , Scala Giovanni , Cuomo Mariella , Della Monica Rosa , Amente Stefano , Chiariotti Lorenzo , Miele Gennaro , Cocozza Sergio 
Published: July 2022

ABSTRACTDNA methylation is an epigenetic mark implicated in crucial biological processes. Most of the knowledge about DNA methylation is based on bulk experiments, in which DNA methylation of genomic regions is reported as average methylation. However, average methylation does not inform on how methylated cytosines are distributed in each single DNA molecule.Here, we propose Methylation Class (MC) profiling as a genome-wide approach to the study of DNA methylation heterogeneity from bulk bisulfite sequencing experiments. The proposed approach is built on the concept of MCs, groups of DNA molecules sharing the same number of methylated cytosines. The relative abundances of MCs from sequencing reads incorporates the information on the average methylation, and directly informs on the methylation level of each molecule.By applying our approach to publicly available bisulfite-sequencing datasets, we individuated cell-to-cell differences as the prevalent contributor to methylation heterogeneity. Moreover, we individuated signatures of loci undergoing imprinting and X-inactivation, and highlighted differences between the two processes. When applying MC profiling to compare different conditions, we identified methylation changes occurring in regions with almost constant average methylation.Altogether, our results indicate that MC profiling can provide useful insights on the epigenetic status and its evolution at multiple genomic regions.
Evaluation of epigenetic age acceleration scores and their associations with CVD related phenotypes in a population cohort
Authors: Chervova Olga , Chernysheva Elizabeth , Panteleeva Kseniia , Widayati Tyas Arum , Hrbkova Natalie , Schneider Jadesada , Maximov Vladimir , Ryabikov Andrew , Tillmann Taavi , Pikhart Hynek , Bobak Martin , Voloshin Vitaly , Malyutina Sofia , Beck Stephan 
Published: July 2022

AbstractWe evaluated associations between nine epigenetic age acceleration (EAA) scores and 18 cardio-metabolic phenotypes using an Eastern European ageing population cohort richly annotated for a diverse set of phenotypes (subsample, n = 306; aged 45-69 years). This was implemented by splitting the data into groups with positive and negative EAAs. We observed strong association between all epigenetic age acceleration scores and sex, suggesting that any analysis of EAAs should be adjusted by sex. We found that some sex-adjusted EAA scores were significantly associated with several phenotypes such as blood levels of gamma-glutamyl transferase and low-density lipoprotein, smoking status, annual alcohol consumption, multiple carotid plaques, and incident coronary heart disease status (not necessarily the same phenotypes for different EAAs). We demonstrated that even after adjusting EAAs for sex, EAA-phenotype associations remain sex-specific, which should be taken into account in any downstream analysis involving EAAs. The obtained results suggest that in some EAA-phenotype associations, negative EAA scores (i.e. epigenetic age below chronological age) indicated more harmful phenotype values, which is counter-intuitive. Among all considered epigenetic clocks, GrimAge was significantly associated with more phenotypes than any other EA scores in this Russian sample.
A deep learning and graph-based approach to characterise the immunological landscape and spatial architecture of colon cancer tissue
Authors: Parreno-Centeno Mario , Malagoli Tagliazucchi Guidantonio , Withnell Eloise , Pan Shi , Secrier Maria 
Published: July 2022

ABSTRACTTumour immunity is key for the prognosis and treatment of colon adenocarcinoma, but its characterisation remains cumbersome and expensive, requiring sequencing or other complex assays. Detecting tumour-infiltrating lymphocytes in haematoxylin and eosin (H&E) slides of cancer tissue would provide a cost-effective alternative to support clinicians in treatment decisions, but inter- and intra-observer variability can arise even amongst experienced pathologists. Furthermore, the compounded effect of other cells in the tumour microenvironment is challenging to quantify but could yield useful additional biomarkers. We combined RNA sequencing, digital pathology and deep learning through the InceptionV3 architecture to develop a fully automated computer vision model that detects prognostic tumour immunity levels in H&E slides of colon adenocarcinoma with an area under the curve (AUC) of 82%. Amongst tumour infiltrating T cell subsets, we demonstrate that CD8+ effector memory T cell patterns are most recognisable algorithmically with an average AUC of 83%. We subsequently applied nuclear segmentation and classification via HoVer-Net to derive complex cell-cell interaction graphs, which we queried efficiently through a bespoke Neo4J graph database. This uncovered stromal barriers and lymphocyte triplets that could act as structural hallmarks of low immunity tumours with poor prognosis. Our integrated deep learning and graph-based workflow provides evidence for the feasibility of automated detection of complex immune cytotoxicity patterns within H&E-stained colon cancer slides, which could inform new cellular biomarkers and support treatment management of this disease in the future.
Electrical stimulation of temporal and limbic circuitry produces distinct responses in human ventral temporal cortex
Authors: Huang Harvey , Gregg Nicholas M. , Valencia Gabriela Ojeda , Brinkmann Benjamin H. , Lundstrom Brian N. , Worrell Gregory A. , Miller Kai J. , Hermes Dora 
Published: July 2022

ABSTRACTThe human ventral temporal cortex (VTC) is highly connected to integrate visual perceptual inputs with feedback from cognitive and emotional networks. In this study, we used electrical brain stimulation to understand how different inputs from multiple brain regions drive unique electrophysiological responses in the VTC.We recorded intracranial EEG data in 6 patients implanted with intracranial electrodes for epilepsy surgery evaluation. Pairs of electrodes were stimulated with single pulse electrical stimulation, and corticocortical evoked potential (CCEP) responses were measured at electrodes in the collateral sulcus and lateral occipitotemporal sulcus of the VTC. Using a novel unsupervised machine learning method, we uncovered 2 to 4 distinct response shapes, termed basis profile curves (BPCs), at each recording electrode in the 11 to 500 ms post-stimulation interval.CCEPs of unique shape and high amplitude were elicited following stimulation of several regions and classified into a set of four consensus BPCs across subjects. One of the consensus BPCs was primarily elicited by stimulation of the hippocampus; another by stimulation of the amygdala; a third by stimulation of lateral cortical sites, such as the middle temporal gyrus; and the final one by stimulation of multiple distributed sites. Stimulation also produced sustained high frequency power decreases and low frequency power increases that spanned multiple BPC categories.Characterizing distinct shapes in stimulation responses provides a novel description of connectivity to the VTC and reveals significant differences in input from cortical and limbic structures.SIGNIFICANCE STATEMENTDisentangling the numerous input influences on highly connected areas in the brain is a critical step toward understanding how different brain networks work together to produce function. Single pulse electrical stimulation is an effective tool to accomplish this goal because the shapes and amplitudes of signals recorded from electrodes are informative of the synaptic physiology of the stimulation-driven inputs. We focused on targets in the ventral temporal cortex because it is an area strongly implicated in visual object perception. By using a data-driven clustering algorithm, we identified anatomical regions with distinct input connectivity profiles to the ventral temporal cortex. Examining high frequency power changes revealed possible modulation of excitability at the recording site induced by electrical stimulation of connected regions.
In vivoAntidiarrheal activities of the hydro alcoholic extracts ofSchinus molleL. (Anarcardiaceae) leaf in mice
Authors: Mengistu Getnet , Hailay Kidan , Misganaw Desye , Andualem Abel , Belayneh Yaschilal Muche 
Published: July 2022

AbstractBackgrounddiarrhea is the second leading cause of death among children under five globally next to pneumonia and it kills around 760, 000 children every year in the world. The available synthetic drugs to treat diarrhea have limited safety and efficacy. Thus, people are still dependent on the herbal remedies for managing diarrhea.ObjectiveTo evaluate in vivo antidiarrheal activities of the hydro-alcoholic leaf extracts ofSchinus molleL. (Anacardiaceae) in mice.MethodsThe 80% methanol leaf extract was prepared by successive maceration technique. The antidiarrheal activity of the extract was evaluated using castor oil induced diarrhea, enteropooling and small intestine transit models. The test groups received various doses (100, 200 and 400 mg/kg) of the extract, whereas positive controls received Loperamide (3 mg/kg) and negative controls received vehicle (distilled water, 10 ml/kg). Data were analysed using one-way ANOVA followed by Tukey’s posthoc test.ResultIn the castor oil induced diarrheal model, the 80% methanol extract delayed onset of defecation and reduced the number and weight of feces at all tested doses significantly as compared to the negative control. In the enteropooling test, the 80ME significantly (P<0.001) reduced the weight and volume of intestinal fluid at all tested doses when compared to negative control. Results from the charcoal meal test revealed that the extracts produced a significant anti-motility effect at all tested doses as compared to negative control.ConclusionThis study confirmed the antidiarrheal activity of the hydro-alcoholic extract and the highest test dose produced the maximum antidiarrheal activity in all models
FAVA: High-quality functional association networks inferred from scRNA-seq and proteomics data
Authors: Koutrouli Mikaela , Líndez Pau Piera , Nastou Katerina , Bouwmeester Robbin , Rasmussen Simon , Martens Lennart , Jensen Lars Juhl 
Published: July 2022

AbstractProtein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, co-expression analysis becomes complex. To address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4,210 interactions between 1,039 understudied proteins. Our findings showcase FAVA’s capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source.
Metabolic differences and differentially expressed genes between C57BL/6J and C57BL/6N mice substrains
Authors: Nemoto Shino , Kubota Tetsuya 
Published: July 2022

AbstractC57BL/6J (B6J) and C57BL/6N (B6N) mice are the most frequently used substrains in C57BL/6 (B6) inbred mice, serving as physiological models for in vivo studies and as background strains to build transgenic mice. However, little attention has been paid to the phenotypic differences between B6J and B6N mice, and they have often been attributed to mutations in the nicotinamide nucleotide transhydrogenase (Nnt) gene, which was found only in B6J. Nevertheless, phenotypic differences between the two that cannot be explained by Nnt mutations alone, especially in metabolic traits, indicate the presence of genetic variants associated with metabolism other than Nnt. We aimed to identify these genetic differences between B6J and B6N mice. Our results provide insights into differentially expressed genes (DEGs) in adipose tissues, skeletal muscle, liver, hypothalamus, and hippocampus, and phenotypic differences in metabolic traits between B6J and B6N. B6J mice had significantly lower body weight than B6N mice, regardless of a normal or high-fat diet. Blood insulin levels in B6J mice were significantly lower than those in B6N mice and glucose levels during dietary obesity were higher in B6J mice. Metabolic assessments revealed greater physical activity, less food intake, and higher energy expenditure in B6J mice than in B6N mice. Among the DEGs that were highly expressed in B6J mice compared to B6N mice, three genes—insulin degrading enzyme, adenylosuccinate synthase 2, and ectonucleotide triphosphate diphosphohydrolase 4—and the DEGs that had lower expression in B6J mice compared to B6N mice—Nnt, WD repeat and FYVE domain containing 1, and dynein light chain Tctex-type 1—were overlapped in all seven tissues. Our study provides insights into DEGs between B6J and B6N, which will be useful for substrain selection for mouse experiments, avoiding erroneous experimental results, and reviewing the results of studies that have used B6J, B6N, or mixed substrains.
How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment
Authors: Duchet Benoit , Sermon James J. , Weerasinghe Gihan , Denison Timothy , Bogacz Rafal 
Published: July 2022

AbstractWhile brain stimulation therapies such as deep brain stimulation for Parkinson’s disease can be effective, they have yet to reach their full potential across neurological disorders. Entraining neuronal rhythms using rhythmic brain stimulation has been suggested as a new therapeutic mechanism to restore neurotypical behavior in conditions such as chronic pain, depression, and Alzheimer’s disease. However, theoretical and experimental evidence indicate that brain stimulation can also entrain neuronal rhythms at sub- and super-harmonics, far from the stimulation frequency. Crucially, these counterintuitive effects can be harmful to patients, for example by triggering debilitating involuntary movements in Parkinson’s disease. We therefore propose a principled approach to selectively promote rhythms close to the stimulation frequency, while avoiding potential harmful effects by preventing entrainment at sub- and super-harmonics. Our open-loop approach to selective entrainment, dithered stimulation, consists in adding white noise to the stimulation period. We theoretically establish the ability of dithered stimulation to selectively entrain a given brain rhythm, and verify its efficacy in simulations of uncoupled neural oscillators, and networks of coupled neural oscillators. Furthermore, we show that dithered stimulation can be implemented in neurostimulators with limited capabilities by toggling within a finite set of stimulation frequencies. Likely implementable across a variety of existing brain stimulation devices, dithering-based selective entrainment has potential to enable new brain stimulation therapies, as well as new neuroscientific research exploiting its ability to modulate higher-order entrainment.
A minimum effective dose for (transcranial) alternating current stimulation
Authors: Alekseichuk Ivan , Wischnewski Miles , Opitz Alexander 
Published: July 2022

AbstractTranscranial alternating current stimulation (tACS) is a popular method for non-invasive neuromodulation in human research and therapy. However, what constitutes an effective dose in tACS applications is still a matter of investigation and debate. Here, we examined available literature data regarding the effects of alternating current (AC)-induced electric fields on cellular-level neural activity. The literature search identified 16 relevant experimental reports that utilized brain slices, anesthetized rodents, and awake/behaving animal models. We implemented a probabilistic meta-analysis to estimate the minimum effective dose (MED) required for inducing detectable significant neural changes with AC stimulation. The results showed that AC stimulation at 0.3 mV/mm in awake/behaving mammals leads to an 80% probability of inducing minimum neural effects. A similar level of effectiveness in brain slices and anesthetized mammals required 0.7 mV/mm. In conclusion, (transcranial) alternating current stimulation is significantly more effective in awake than in anesthetized brains. The proposed dose targets can serve as a practical guideline for tACS in humans.