bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accumulation of meningeal lymphocytes, but not myeloid cells, correlates with subpial cortical demyelination and white matter lesion activity in progressive MS patients
Authors: Ahmed Shanzeh M. , Fransen Nina , Touil Hanane , Michailidou Iliana , Huitinga Inge , Gommerman Jennifer L. , Bar-Or Amit , Ramaglia Valeria 
Published: December 2021

AbstractSubpial cortical demyelination is an important component of multiple sclerosis (MS) pathology contributing to disease progression, yet mechanism(s) underlying its development remain unclear. Compartmentalized inflammation involving the meninges may drive this type of injury. Given recent findings identifying substantial white matter (WM) lesion activity in patients with progressive MS, elucidating whether and how WM lesional activity relates to meningeal inflammation and subpial cortical injury is of interest. Using post-mortem formalin-fixed paraffin-embedded tissue blocks (range, 5-72 blocks; median, 30 blocks) for each of 27 progressive MS patients, we assessed the relationship between meningeal inflammation, the extent of subpial cortical demyelination, and the state of subcortical WM lesional activity. Meningeal accumulations of T cells and B cells, but not myeloid cells, were spatially adjacent to subpial cortical lesions and greater immune-cell accumulation was associated with higher subpial lesion numbers. Patients with a higher extent of meningeal inflammation harboured a greater proportion of active and mixed (active-inactive) WM lesions, and an overall lower proportion of inactive and remyelinated WM lesions. Our findings support the involvement of meningeal lymphocytes in subpial cortical injury, and also point to a potential link between inflammatory subpial cortical demyelination and pathological mechanisms occurring in the subcortical white matter.
A tractometry investigation of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis
Authors: Jandric Danka , Parker Geoff JM , Haroon Hamied , Tomassini Valentina , Muhlert Nils , Lipp Ilona 
Published: December 2021

ABSTRACTUnderstanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts share a similar network structure it would be expected that tracts within a network are similarly affected by MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in diffusion metrics across white matter (WM) tracts. We investigated whether separate networks, based on normal variation or pathology, appear, and how this relates to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract components to predict test performance across cognitive domains. We found that a single component which captured pathology across all tracts explained the most variance and that there was little evidence for separate, smaller network patterns of pathology. WM tract components were weak, but significant, predictors of cognitive function in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.
Spatiotemporal patterns and progression of the Delta variant of COVID-19 and their health intervention linkages in Southeast Asia
Authors: Wei Luo , Zhaoyin Liu , Yuxuan Zhou , Yumin Zhao , Elita Li Yunyue , Masrur Arif , Yu Manzhu 
Published: December 2021

AbstractThe global pandemic of COVID-19 presented an unprecedented challenge to all countries in the world, among which Southeast Asia (SEA) countries managed to maintain and mitigate the first wave of COVID-19 in 2020. However, these countries were caught in the crisis after the Delta variant was introduced to SEA, though many countries had immediately implemented non-pharmaceutical intervention (NPI) measures along with vaccination in order to contain the disease spread. To investigate the potential linkages between epidemic dynamics and public health interventions, we adopted a prospective space-time scan method to conduct spatiotemporal analysis at the district level in the seven selected countries in SEA from June 2021 to October 2021. Results reveal the spatial and temporal propagation and progression of COVID-19 risks relative to public health measures implemented by different countries. Our research benefits continuous improvements of public health strategies in preventing and containing this pandemic.
Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy
Authors: Wu Zhenke , Li Zehang R. , Chen Irena , Li Mengbing 
Published: December 2021

SummaryDetermining causes of deaths (COD) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or “domains”) necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this paper, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a pre-specified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. Posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation data set. The paper concludes with a discussion on limitations and future directions.
Statistical Forecasting : Third Wave of COVID-19-With an Application to India
Authors: Rajeshbhai Sabara Parshad , Dhar Subhra Sankar , Shalabh  
Published: December 2021

AbstractThe pandemic due to the SARS-CoV-2 virus impacted the entire world in different waves. An important question that arise after witnessing the first and second waves of COVID-19 is - Will the third wave also arrive and if yes, then when. Various types of methodologies are being used to explore the arrival of third wave. A statistical methodology based on the fitting of mixture of Gaussian distributions is explored in this paper and the aim is to forecast the third wave using the data on the first two waves of pandemic. Utilizing the data of different countries that are already facing the third wave, modelling of their daily cases data and predicting the impact and timeline for the third wave in India is attempted in this paper. The Gaussian mixture model based on algorithm for clustering is used to estimate the parameters.
Oxytocin Signaling Regulates the Homeostatic Response to Cold Stress in Poikilothermic Vertebrates
Authors: Segev-Hadar Adi , Krispin Shani , Olthof Anouk M. , Hyatt Katery C. , Haller Liran , Barki Assaf , Nitzan Tali , Levkowitz Gil , Kanadia Rahul N. , Cnaani Avner , Biran Jakob 
Published: December 2021

AbstractWhen exposed to low temperature, homeothermic vertebrates maintain internal body temperature by activating thermogenesis and by altered metabolism, synchronized by neuroendocrine responses. Although such physiological responses also occur in poikilothermic vertebrates, the prevailing notion is that their reactions are passive. Here, we explored molecular hypothalamic and physiological responses to cold stress in the tropical poikilotherm Nile tilapia (Oreochromis niloticus). We show that cold exposed tilapia exhibit complex homeostatic responses, including increased hypothalamic oxytocin, plasma glucose and cortisol concomitant with reduced plasma lactate and metabolic rate. Pharmacological or genetic blockage of oxytocin signaling further affected metabolic rate in two cold-exposed poikilothermic models. This indicates that oxytocin, a known thermoregulator in homeotherms, actively regulates temperature-related homeostasis in poikilotherms. Overall, our findings show that the brain of poikilotherms actively responds to cold temperature by regulating metabolic physiology. Moreover, we identify oxytocin signaling as an adaptive and evolutionarily conserved metabolic regulator of temperature-related homeostasis.
Membrane voltage fluctuations in human breast cancer cells
Authors: Quicke Peter , Sun Yilin , Arias-Garcia Mar , Acker Corey D. , Djamgoz Mustafa B. A. , Bakal Chris , Foust Amanda J. 
Published: December 2021

ABSTRACTCancer cells feature a resting membrane potential (Vm) that is depolarized compared to normal cells, and express active ionic conductances, which factor directly in their pathophysiological behavior. Despite similarities to ‘excitable’ tissues, relatively little is known about cancer cell Vm dynamics. With high-throughput, cellular-resolution Vm imaging, we characterized Vm fluctuations of hundreds of human triple-negative breast cancer MDA-MB-231 cells and compared to non-cancerous breast epithelial MCF-10A cells. By quantifying their Dynamic Electrical Signatures (DESs) through an unsupervised machine-learning protocol, we identified four classes ranging from “noisy” to “blinking/waving”. The Vm of MDA-MB-231 cells exhibited spontaneous, transient hyperpolarizations that were inhibited by the voltage-gated sodium channel blocker tetrodotoxin. The Vm of MCF-10A cells was comparatively static, but fluctuations increased following treatment with transforming growth factor-β1, a canonical inducer of the epithelial-to-mesenchymal transition. These data suggest that the ability to generate Vm fluctuations is acquired during transformation and may participate in oncogenesis.
Gene regulation network inference using k-nearest neighbor-based mutual information estimation-Revisiting an old DREAM
Authors: Shachaf Lior I. , Roberts Elijah , Cahan Patrick , Xiao Jie 
Published: December 2021

AbstractBackgroundA cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past twenty years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization.ResultsIn this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution using k-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov-Stoögbauer-Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods.ConclusionsUsing three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction - which combines CMIA, and the KSG-MI estimator - achieves an improvement of 20-35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or choose gene candidates for experimental validations.
A Cartography of Differential Gene Methylation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Different Network Roles in the Protein-Protein Interactions Network Play Different, Biologically Relevant, Roles
Authors: Wilberforce Aurelia , Riva Giulio Valentino Dalla 
Published: December 2021

AbstractMyalgic Encephalomyelitis, or Chronic Fatigue Syndrome (ME/CFS), is characterised by severe fatigue and associated with immune dysfunction. Previous studies of DNA methylation have found evidence of changes in immune cells for ME/CFS. However these studies have been limited by their small sample size. Here, we aggregate three comparable datasets to achieve a larger sample size and detect small changes to DNA methylation. We find 10,824 differentially methylated genes, with a small average change. Next, from the currently known interactions of relevant proteins, we build a Protein-Protein interaction network and,localising the network cartography analysis, we identify 184 hub genes. We find that different hub types play different, and meaningful, biological roles. Finally, we perform Gene ontology enrichment analysis, and we find that these hubs are involved in immune system processes, including response to TGF-β and LPS, as well as mitochondrial functioning, supporting previous theories about ME/CFS. We also show that dopaminergic signalling may potentially contribute to immune pathology in ME/CFS, suggesting a possible interplay with Long Covid. Our results demonstrate the potentiality of network analysis in shedding light on the epigenetic contribution to the immune dysregulation of ME/CFS.
Geminivirus C4 proteins inhibit GA signaling via prevention of NbGAI degradation, to promote viral infection and symptom development in N. benthamiana
Authors: Li Pengbai , Guo Liuming , Lang Xinyuan , Li Mingjun , Wu Gentu , Wu Rui , Wang Lvxin , Zhao Meisheng , Qing Ling 
Published: December 2021

AbstractPhytohormone gibberellin (GA) is an important plant signaling molecule that regulates plant growth and defense against abiotic and biotic stresses. To date, the molecular mechanism of the plant responses to viral infection mediated by GA is still undetermined. DELLA is a repressor of GA signaling and is recognized by the F-box protein, a component of the SCFSLY1/GID2 complex. The recognized DELLA is degraded by the ubiquitin-26S proteasome, leading to the activation of the GA signaling. Here, we report that ageratum leaf curl Sichuan virus (ALCScV)-infected N. benthamiana plants showed dwarfing symptom and abnormal flower development. The infection of ALCScV alters the expressions of GA pathway-related genes and decreases the content of endogenous GA significantly in N. benthamiana. Further, ALCScV-encoded C4 protein interacts with the DELLA protein NbGAI, and interferes with the interaction between NbGAI and NbGID2 to prevent the degradation of NbGAI, leading to the inhibition of the GA signaling pathway. Silencing of NbGAI or exogenous GA3 treatment significantly reduces viral accumulation and disease symptoms in N. benthamiana plants. The same results were proved by the experiments with C4 protein encoded by tobacco curly shoot virus (TbCSV). Therefore, we propose a novel mechanism of geminivirus C4 proteins controling virus infection and disease symptom development through interfering GA signaling pathway.Author SummaryGibberellins (GAs) are plant hormones that are essential for many developmental processes in plants. It has indicated that plant virus infection can induce abnormal flower development and influence GA pathway resulting the plant dwarfing symptom, but the underlying mechanisms is still not well described. Here, we demonstrate that geminivirus - encoded C4 protein regulates the GA signaling pathway to promote viral accumulation and disease symptom development. Through directly interacting with NbGAI, the C4 protein interferes with the interaction between NbGAI and NbGID2, which inhibits the degradation of NbGAI. As a result, the GA signaling pathway is blocked, and the infected plants display symptoms of typical dwarfing and delayed flowering. Our results reveal a novel mechanism by which geminivirus C4 proteins influence viral pathogenicity via interfering the GA signaling pathway, and provide new insights into the interaction between virus and host.
hapCon: Estimating contamination of ancient genomes by copying from reference haplotypes
Authors: Huang Yilei , Ringbauer Harald 
Published: December 2021

AbstractMotivationHuman ancient DNA (aDNA) studies have surged in recent years, revolutionizing the study of the human past. Typically, aDNA is preserved poorly, making such data prone to contamination from other human DNA. Therefore, it is important to rule out substantial contamination before proceeding to downstream analysis. As most aDNA samples can only be sequenced to low coverages (<1x average depth), computational methods that can robustly estimate contamination in the low coverage regime are needed. However, the ultra low-coverage regime (0.1x and below) remains a challenging task for existing approaches.ResultsWe present a new method to estimate contamination in aDNA for male individuals. It utilizes a Li&Stephen’s haplotype copying model for haploid X chromosomes, with mismatches modelled as genotyping error or contamination. We assessed an implementation of this new approach, hapCon, on simulated and down-sampled empirical aDNA data. Our results demonstrate that hapCon outperforms a commonly used tool for estimating male X contamination (ANGSD), with substantially lower variance and narrower confidence intervals, especially in the low coverage regime. We found that hapCon provides useful contamination estimates for coverages as low as 0.1x for SNP capture data (1240k) and 0.02x for whole genome sequencing data (WGS), substantially extending the coverage limit of previous male X chromosome based contamination estimation methods.Availability and ImplementationA implementation of our software (hapCON) using Python and C has been deposited at https://github.com/hyl317/hapROH. We make hapCon available as part of a python package (hapROH), which is available at the Python Package Index (https://pypi.org/project/hapROH) and can be installed via pip. The documentation provides example use cases as blueprints for custom applications (https://haproh.readthedocs.io).
“Prefrontal cortical contributions to working memory loading, maintenance and recall are parsed by hippocampal-prefrontal oscillatory assembly dynamics”
Authors: Domanski Aleksander P.F. , Kucewicz Michal T. , Russo Eleonora , Tricklebank Mark D. , Robinson Emma S.J. , Durstewitz Daniel , Jones Matt W. 
Published: December 2021

AbstractWorking memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both prefrontal cortex and hippocampus, where neurons encode task cues, rules and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant nonmatch to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes, but were not 4-5Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding; pharmacological disruption of CA1-mPFC assembly rhythmicity impaired task performance. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.
Different excitation-inhibition correlations between spontaneous and tone-evoked activity in primary auditory cortex neurons
Authors: Chew Katherine C. M. , Kumar Vineet , Tan Andrew Y. Y. 
Published: December 2021

AbstractTone-evoked synaptic excitation and inhibition are highly correlated in many neurons with V-shaped tuning curves in the primary auditory cortex of pentobarbital-anesthetized rats. In contrast, there is less correlation between spontaneous excitation and inhibition in visual cortex neurons under the same anesthetic conditions. However, it was not known whether the primary auditory cortex resembles visual cortex in having spontaneous excitation and inhibition that is less correlated than tone-evoked excitation and inhibition. Here we report whole-cell voltage-clamp measurements of spontaneous excitation and inhibition in primary auditory cortex neurons of pentobarbital-anesthetized rats. The larger excursions of both spontaneous excitatory and inhibitory currents appeared to consist of distinct events, with the inhibitory event rate typically lower than the excitatory event rate. We use the ratio of the excitatory event rate to the inhibitory event rate, and the assumption that the excitatory and inhibitory synaptic currents can each be reasonably described as a filtered Poisson process, to estimate the maximum spontaneous excitatory-inhibitory correlation for each neuron. In a subset of neurons, we also measured tone-evoked excitation and inhibition. In neurons with V-shaped tuning curves, although tone-evoked excitation and inhibition were highly correlated, the spontaneous inhibitory event rate was typically sufficiently lower than the spontaneous excitatory event rate to indicate a lower excitatory-inhibitory correlation for spontaneous activity than for tone-evoked responses.