bluuebunny/crossref_metadata_2025_split huggingface dataset:
Source-sink connectivity: A novel interictal EEG marker for seizure localization
Authors: Gunnarsdottir Kristin M. , Li Adam , Smith Rachel J. , Kang Joon-Yi , Korzeniewska Anna , Crone Nathan E. , Rouse Adam G. , Cheng Jennifer J. , Kinsman Michael J. , Landazuri Patrick , Uysal Utku , Ulloa Carol M. , Cameron Nathaniel , Cajigas Iahn , Jagid Jonathan , Kanner Andres , Elarjani Turki , Bicchi Manuel Melo , Inati Sara , Zaghloul Kareem A. , Boerwinkle Varina L. , Wyckoff Sarah , Barot Niravkumar , Gonzalez-Martinez Jorge , Sarma Sridevi V. 
Published: October 2021

AbstractOver 15 million epilepsy patients worldwide have drug-resistant epilepsy (DRE). Successful surgery is a standard of care treatment for DRE but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Surgical success rates vary between 20-80% because no clinically validated biological markers of the EZ exist. Localizing the EZ is a costly and time-consuming process beginning with non-invasive neuroimaging and often followed by days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity (e.g., low-voltage high frequency activity) on individual channels occurring immediately before seizures or spikes that occur on interictal iEEG (i.e., between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients.Intracranial EEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aim to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the EZ. We hypothesize that when a patient is not clinically seizing, it is because the EZ is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighboring nodes (“sources”) and the inhibited nodes themselves (“sinks”). Specifically, patient-specific dynamical network models (DNMs) were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics (SSMs). We validated the SSMs in a retrospective analysis of 65 patients by using the SSMs of the annotated EZ to predict surgical outcomes. The SSMs predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians’ predictions (surgical success rate of this dataset). In failed outcomes, we identified regions of the brain with high SSMs that were untreated. When compared to high frequency oscillations, the most commonly proposed interictal iEEG feature for EZ localization, SSMs outperformed in predictive power (by a factor of 1.2) suggesting SSMs may be an interictal iEEG fingerprint of the EZ.
A longer stance is more stable for a standing horse
Authors: Gellman Karen , Ruina Andy 
Published: October 2021

AbstractWhat is the effect of posture on the stability of a standing horse? We address this with a 2D quasistatic model. The model horse has 3 rigid parts: a trunk, a massless fore-limb and a massless rear limb, and has hinges at the shoulder, hip, and hooves. The postural parameterℓgis the distance between the hooves. For a givenℓg, statics finds an equilibrium configuration which, with no muscle stabilization, is unstable. To measure the neuro-muscular effort to maintain stability, we add springs at the shoulder and hip; the larger the springs needed to stabilize the model, the more the neuro-muscular effort needed for stabilization. We find that a canted-in posture (smallℓg), observed in some pathological domestic horses, requires about twice the spring stiffness (representing twice the neuromuscular effort) as is needed for postures with vertical or slightly splayed-out (largeℓg) legs.
A new fMRI localizer for preoperative language mapping using a sentence completion task: Validity, choice of baseline condition, and test-retest reliability
Authors: Elin Kirill , Malyutina Svetlana , Bronov Oleg , Stupina Ekaterina , Marinets Aleksei , Dragoy Olga 
Published: October 2021

AbstractTo avoid post-neurosurgical language deficits, intraoperative mapping of the language function in the brain can be complemented with preoperative mapping with fMRI. The validity of an fMRI ‘language localizer’ paradigm crucially depends on the choice of an optimal language task and baseline condition. This study presents a new fMRI ‘language localizer’ in Russian using overt sentence completion, a task that comprehensively engages the language function by involving both production and comprehension at the word and sentence level. The paradigm was validated in 18 neurologically healthy volunteers who participated in two scanning sessions, for estimating test-retest reliability. For the first time, two baseline conditions for the sentence completion task were compared.At the group level, the paradigm significantly activated both anterior and posterior language-related regions. Individual-level analysis showed that activation was elicited most consistently in the inferior frontal regions, followed by posterior temporal regions and the angular gyrus. Test-retest reliability of activation location, as measured by Dice coefficients, was moderate and thus comparable to previous studies. Test-retest reliability was higher in the frontal than temporo-parietal region and with the most liberal statistical thresholding compared to two more conservative thresholding methods. Lateralization indices were expectedly left-hemispheric, with greater lateralization in the frontal than temporo-parietal region, and showed moderate test-retest reliability. Finally, the pseudoword baseline elicited more extensive and more reliable activation, although the syllable baseline appears more feasible for future clinical use.Overall, the study demonstrated the validity and reliability of the sentence completion task for mapping the language function in the brain. The paradigm needs further validation in a clinical sample of neurosurgical patients. Additionally, the study contributes to general evidence on test-retest reliability of fMRI
Intravenous corticosteroid treatment in adult patients with sepsis defined by the Sepsis-3 criteria: a systematic review and meta-analysis
Authors: Wu Yu-Pu , Lin Cheng-Kuan , Hamaya Rikuta , Huang Fei-Yang , Chien Yung-Shin , Hsu Yu-Tien , Chen Szu-Ta , Papatheodorou Stefania 
Published: October 2021

AbstractObjectivesTo summarize the effects of intravenous corticosteroid treatment for sepsis defined by the Sepsis-3 criteria in adult patients.DesignSystematic review and meta-analysis.MethodsWe searched RCTs from PubMed, Embase,ClinicalTrials.gov, Cochrane Central Register of Controlled Trials, Web of Science, and International Clinical Trials Registry Platform from inception to July 12th, 2019 and updated on June 28th, 2020. Conference proceedings from relevant societies and the reference lists of previous reviews were manually screened. Abstract or full-text articles were screened by two independent investigators. We included RCTs where (1) the participants had infections and the baseline Sequential Organ Failure Assessment (SOFA) score ≥ 2 (the Sepsis-3 definitions) (2) the intervention involved any intravenous corticosteroids; (3) the control group received placebo or standard of care (4) the outcomes of interest included mortality or clinical recovery. We chose the 28-day mortality as the pre-specified primary outcome and risk ratio (RR) as the effect measure. We followed PRISMA guidelines and chose random-effects models for the pooled analyses.ResultsThis study included 24 RCTs and 19 of them (7,115 participants) reported the 28-day mortality. Pooled analyses showed that intravenous corticosteroid treatment compared to placebo or standard of care was not associated with a lower risk of 28-day mortality (RR, 0.88; 95%CI, 0.73 to 1.05), but with a higher risk of hyperglycemia (RR, 1.16; 95%CI, 1.06 to 1.27). Sensitivity analysis of high-quality studies revealed a similar result for the 28-day mortality (RR, 0.95; 95%CI, 0.86 to 1.05).ConclusionsOur findings suggested that intravenous corticosteroids compared to placebo or standard of care may not reduce the 28-day mortality in adult patients with sepsis defined by the Sepsis-3 criteria. Further studies are warranted to clarify the roles of disease severity and treatment timing in the effects of corticosteroid treatment in this population.PROSPERO registration numberCRD42019143083Strengths and limitations of this studyThis is the first systematic review and meta-analysis that summarized the effects of intravenous corticosteroid treatments in patients with sepsis defined by the Sepsis-3 criteria.We provide the quality of evidence to support the development of treatment guidelines specific to the Sepsis-3 cohort.We only include randomized controlled trials in this systematic review and meta-analysis, which exclude less controlled evidences from clinical settings closer to our daily practice.The Sepsis-3 definitions will be retrospectively applied to the included studies, so clinical trials without enough reported baseline data available may be excluded.
Long-term manifestations and modifiers of prevalence estimates of the post-COVID-19 syndrome: A systematic review and meta-analysis
Authors: De-la-Rosa-Martinez Daniel , Delaye-Martínez Marco Antonio , Bello-Chavolla Omar Yaxmehen , Sicilia-Andrade Alejandro , Juárez-Cruz Isaac David , Fermín-Martínez Carlos A. , Márquez-Salinas Alejandro , Cañedo-Guerra Enrique , Fernández-Chirino Luisa , Martínez-Gutiérrez Mónica , Sandoval-Colin Daniel Eduardo , Vilar-Compte Diana 
Published: October 2021

AbstractBackgroundPost-acute COVID-19 syndrome (PACS) is a multi-system disease comprising persistent symptomatology after the acute phase of infection. Long-term PACS effects significantly impact patient outcomes, but their incidence remains uncharacterized due to high heterogeneity between studies. Therefore, we aimed to summarize published data on PACS, characterizing the clinical presentation, prevalence, and modifiers of prevalence estimates.MethodIn this systematic review and meta-analysis, we research MEDLINE for original studies published from January 1st, 2020, to January 31st, 2021, that reported proportions of PACS manifestations. Studies were eligible for inclusion if they included patients aged ≥18 years with confirmed COVID-19 by RT-PCR or antigen testing and a minimum follow-up of 21 days. The prevalence of individual manifestations across studies was pooled using random-effects meta-analysis. For evaluating determinants of heterogeneity, meta-regression analysis was performed. This study was registered in PROSPERO (CRD42019125025).ResultsAfter screening 1,235 studies, we included 29 reports for analysis. Twenty-seven meta-analyses were performed, and 61 long-term manifestations were described. The pooled prevalence of PACS was 56% (95%CI 45-66%), with the most common manifestations being diminished health status, fatigue, asthenia, dyspnea, myalgias, hyposmia and dysgeusia. Most of the included studies presented high heterogeneity. After conducting the meta-regression analysis, we identified that age, gender, number of comorbidities, and reported symptoms significantly modify the prevalence estimation of PACS long-term manifestations.ConclusionPACS is inconsistently reported between studies, and population characteristics influence the prevalence estimates due to high heterogeneity. A systematized approach for the study of PACS is needed to characterize its impact adequately.Fundingnone
Doors and corners of variance partitioning in statistical ecology
Authors: Schulz Torsti , Saastamoinen Marjo , Vanhatalo Jarno 
Published: October 2021

AbstractVariance partitioning is a common tool for statistical analysis and interpretation in both observational and experimental studies in ecology. Its popularity has led to a proliferation of methods with sometimes confusing or contradicting interpretations. Here, we present variance partitioning as a general tool in a model based Bayesian framework for summarizing and interpreting regression-like models. To demonstrate our approach we present a case study comprising of a simple occupancy model for a metapopulation of the Glanville fritillary butterfly. We pay special attention to the thorny issue of correlated covariates and random effects, and highlight uncertainty in variance partitioning. We recommend several alternative measures of variance, which jointly can be used to better interpret variance partitions. Additionally, we extend the general approach to encompass partitioning of variance within and between groups of observations, an approach very similar to analysis of variance. While noting that many troublesome issues relating to variance partitioning, such as uncertainty quantification, have been neglected in the literature, we likewise feel that the rather general applicability of the methods as an extension of statistical model-based analyses has not been fully utilized by the ecological research community either.
Evaluation role of miR-124 in neurodegenerative diseases: literature review and in silico analysis
Authors: Amini Javad , Bibak Bahram , Afshar Amir R , Sahebkar Amirhossein 
Published: October 2021

AbstractNeurodegenerative diseases (ND) are characterized by loss of function and structure of neurons. NDs like Alzheimer’s disease (AD) and Parkinson’s disease (PD) have high burden on the society and patients. Currently microRNAs (miRNAs) approach is growing. miRNAs express in different tissues, especially in the central neuron systems (CNS). miRNAs have a dynamic role in the CNS among this miRNAs, miR-124 significantly express in the CNS. Studies on miR-124 have shown that miR-124 improves ND. In this study, we evaluated the role of miR-124 in the ND by literature review and in silico analysis. We used Pubmed database to find miR-124 function in the Alzheimer’s disease, Parkinson’s disease, Multiple sclerosis, Huntington’s disease and amyotrophic lateral sclerosis. To better understand the role of miR-124 in the neurons, RNA-seq data form miR-124-deleted neuronal cells extracted from GEO database and analyzed in Galaxy platform. According literature review miR-124 attenuates inflammation and apoptosis in the ND by target NF-kb signaling pathway and regulation of BAX/BCL-2. miR-124 targets BACE1 and decreases level of Aβ. RNA-seq data showed miR-124 downregulation, an increase in chemokine gene like CCL1 and cytokine-cytokine receptor-interaction, as well as MAPK-signaling pathway. Our study shows that miR-124 can be promising therapeutic approaches to ND.
Extrasynaptic NMDA receptors bidirectionally modulate intrinsic excitability of inhibitory neurons
Authors: Yao Lulu , Rong Yi , Ma Xiaoyan , Li Haifu , Deng Di , Chen YongJun , Yang Sungchil , Peng Tao , Ye Tao , Liang Feixue , Xu Nenggui , Zhou Qiang 
Published: October 2021

AbstractThe NMDA subtype glutamate receptors (NMDARs) play important roles in both physiological and pathological processes in the brain. Comparing to their critical roles in synaptic modifications and excitotoxicity in the excitatory neurons, much less is understood about the functional contributions of NMDARs to the inhibitory/GABAergic neurons. By using selective NMDAR inhibitors and potentiators, we here show that NMDARs bi-directionally modulate the intrinsic excitability (defined as spontaneous/evoked spiking activity and EPSP-spike coupling) in the inhibitory/GABAergic neurons. This modulation depends on GluN2C/2D-but not GluN2A/2B-containing NMDARs. We further show that NMDAR modulator EU1794-4 mostly enhances extrasynaptic NMDAR activity, and by using it we demonstrate a significant contribution of extrasynaptic NMDARs to the modulation of intrinsic excitability in the inhibitory neurons. Altogether, this bidirectional modulation of intrinsic excitability reveals a previously less appreciated importance of NMDARs in the second-to-second functioning of inhibitory/GABAergic neurons.
Improving MHC Class I antigen processing predictions using representation learning and cleavage site-specific kernels
Authors: Lawrence Patrick J. , Ning Xia 
Published: October 2021

AbstractIn this work, we propose a new deep learning model, MHCrank, to predict the probability that a peptide will be processed for presentation within the MHC Class I pathway. We find that the performance of our model is significantly higher than two previously published baseline methods: MHCflurry and netMHCpan. Gains in performance result from the utilization of cleavage site-specific kernels and learned representations for amino acids. By visualizing the site-specific amino acid enrichment among top-ranked peptides, we find MHCrank’s top-ranked peptides are enriched at biologically relevant positions with amino acids that are consistent with previous work. Furthermore, the cosine similarity matrix derived from MHCrank’s learned embeddings for amino acids correlate highly with physiochemical properties that have been experimentally shown to be important in determining a peptide’s favorability to be processed. Altogether, the results reported in this work indicate that the proposed MHCrank demonstrates strong performance compared to existing methods and could have vast applicability to aid drug and vaccine development.
Identification of age-associated proteins and functional alterations in human primary retinal pigment epithelium cells
Authors: Jin Xiuxiu , Liu Jingyang , Wang Weiping , Li Jiangfeng , Liu Guangming , Qiu Ruiqi , Yang Mingzhu , Liu Meng , Yang Lin , Du Xiaofeng , Lei Bo 
Published: October 2021

AbstractBackgroundRetinal pigmented epithelium (RPE) has essential functions to nourish and support the neural retina, and is of vital importance in the pathogenesis of age-related retinal degeneration. However, the exact molecular changes of RPE in aging remain poorly defined.MethodsWe isolated human primary RPE (hRPE) cells from 18 eye donors distributed over a wide age range (10 - 67 years). A quantitative proteomic analysis was performed to analyze their intracellular and secreted protein changes, and potential age-associtated mechanisms were validated by ARPE-19 and hRPE cells.ResultsAge-stage related subtypes and age-associtated proteins and functional alterations were revealed. Proteomic data and verifications showed that RNF123 and RNF149 related ubiquitin-mediated proteolysis might be an important clearance mechanism in elimination of oxidative damaged proteins in aged hRPE. In older hRPE cells, apoptotic signaling related pathways were up-regulated and endoplasmic reticulum organization was down-regulated both in intracellular and secreted proteome.ConclusionsThis work paints a detailed molecular picture of human RPE in aging process and provides new insights for molecular characteristics of RPE in aging and related clinical retinal conditions.
Elevated islet prohormone ratios as indicators of insulin dependency in islet transplant recipients
Authors: Chen Yi-Chun , Klimek-Abercrombie Agnieszka M. , Potter Kathryn J. , Pallo Lindsay P. , Soukhatcheva Galina , Lei Dai , Bellin Melena D. , Verchere C. Bruce 
Published: October 2021

AbstractAutologous pancreatic islet transplantation is an established therapy for patients with chronic pancreatitis. However, the long-term transplant outcomes are modest. Identifying indicators of graft function will aid the preservation of transplanted islets and glycemic control. To this end, we analyzed beta cell prohormone peptide levels in a retrospective cohort of total pancreatectomy autologous islet transplant patients (n=28). Proinsulin-to-C-peptide (PI/C) and proIAPP-to-total IAPP (proIAPP/IAPP) ratios measured at 3 months post-transplant were significantly higher in patients who remained insulin dependent at 1 year follow-up. In a mouse model of human islet transplantation, recipient mice that later became hyperglycemic displayed significantly higher PI/C ratios than mice that remained normoglycemic. Histological analysis of islet grafts showed reduced insulin- and proinsulin-positive area, but elevated glucagon-positive area in grafts that experienced greater secretory demand. Increased prohormone convertase 1/3 was detected in glucagon-positive cells, and glucagon-like peptide 1 (GLP-1) area was elevated in grafts from mice that displayed hyperglycemia or elevated plasma PI/C ratios, demonstrating intra-islet incretin production in metabolically challenged human islet grafts. These data indicate that in failing grafts, alpha cell prohormone processing is likely altered, and incomplete beta cell prohormone processing may be an early indicator of insulin dependency.
SIMBA: SIngle-cell eMBedding Along with features
Authors: Chen Huidong , Ryu Jayoung , Vinyard Michael E. , Lerer Adam , Pinello Luca 
Published: October 2021

AbstractRecent advances in single-cell omics technologies enable the individual and joint profiling of cellular measurements. Currently, most single-cell analysis pipelines are cluster-centric and cannot explicitly model the interactions between different feature types. In addition, single-cell methods are generally designed for a particular task as distinct single-cell problems are formulated differently. To address these current shortcomings, we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin accessible regions, and transcription factor binding sequences into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal, and omics data integration. SIMBA has been extensively applied to scRNA-seq, scATAC-seq, and dual-omics data. We show that SIMBA provides a single framework that allows diverse single-cell analysis problems to be formulated in a unified way and thus simplifies the development of new analyses and integration of other single-cell modalities. SIMBA is implemented as an efficient, comprehensive, and extensible Python library (https://simba-bio.readthedocs.io) for the analysis of single-cell omics data using graph embedding.