bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impaired functional connectivity of the hippocampus in murine models of NMDA-receptor antibody associated pathology
Authors: Kuchling Joseph , Jurek Betty , Kents Mariya , Kreye Jakob , Geis Christian , Wickel Jonathan , Mueller Susanne , Koch Stefan Paul , Boehm-Sturm Philipp , Prüss Harald , Finke Carsten 
Published: January 2022

AbstractIntroductionWhile decreased hippocampal connectivity and disruption of functional networks are established MRI features in human anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, the underlying pathophysiology for brain network alterations remains poorly understood. Application of patient-derived monoclonal antibodies against the NR1 subunit of the NMDAR allows for the investigation of potential functional connectivity alterations in experimental murine NMDAR antibody disease models.ObjectiveTo explore functional connectivity changes in NR1 antibody mouse models using resting-state functional MRI (rs-fMRI).MethodsAdult C57BL/6J mice (n=10) were intrathecally injected with a recombinant human NR1 antibody over 14 days and then studied using rs-fMRI at 7 Tesla. In addition, a newly established mouse model with in utero exposure to a human recombinant NR1 antibody characterized by a neurodevelopmental disorder (NR1-offspring) was investigated with rs-fMRI at the age of 8 weeks (n=15) and 10 months (n=14). Mice exposed to isotype-matched control antibodies served as controls. Independent component analysis (ICA) and dual regression analysis were performed to compare functional connectivity between NMDAR antibody mouse models and control mice.ResultsAdult NR1-antibody injected mice showed significantly impaired functional connectivity within the dentate gyrus of the left hippocampus in comparison to controls, resembling impaired hippocampal functional connectivity patterns observed in human patients with NMDAR encephalitis. Similarly, analyses showed significantly reduced functional connectivity in the dentate gyrus in NR1-offspring compared after 8 weeks, and impaired connectivity in the dentate gyrus and CA3 hippocampal subregion in NR1-offspring at the age of 10 months.ConclusionFunctional connectivity changes within the hippocampus resulting from both direct application and in utero exposure to NMDAR antibodies can be modeled in experimental murine systems. With this translational approach, we successfully reproduced functional MRI alterations previously observed in human NMDAR encephalitis patients. Future experimental studies will identify the detailed mechanisms that cause functional network alterations and may eventually allow for non-invasive monitoring of disease activity and therapeutic effects in autoimmune encephalitis.
Improving the time and space complexity of the WFA algorithm and generalizing its scoring
Authors: Eizenga Jordan M. , Paten Benedict 
Published: January 2022

AbstractMotivationModern genomic sequencing data is trending toward longer sequences with higher accuracy. Many analyses using these data will center on alignments, but classical exact alignment algorithms are infeasible for long sequences. The recently proposed WFA algorithm demonstrated how to perform exact alignment for long, similar sequences in O(sN) time and O(s2) memory, where s is a score that is low for similar sequences (Marco-Sola et al., 2021). However, this algorithm still has infeasible memory requirements for longer sequences. Also, it uses an alternate scoring system that is unfamiliar to many bioinformaticians.ResultsWe describe variants of WFA that improve its asymptotic memory use from O(s2) to O(s3/2) and its asymptotic run time from O(sN) to O(s2 + N). We expect the reduction in memory use to be particularly impactful, as it makes it practical to perform highly multithreaded megabase-scale exact alignments in common compute environments. In addition, we show how to fold WFA’s alternate scoring into the broader literature on alignment scores.AvailabilityAll code is publicly available for use and modification at https://github.com/jeizenga/wfalm.Contactjeizenga@ucsc.eduSupplementary informationSupplementary data are available online.
BlenderPhotonics – an integrated open-source software environment for 3-D meshing and photon simulations in complex tissues
Authors: Zhang Yuxuang , Fang Qianqian 
Published: January 2022

AbstractSignificanceRapid advances in biophotonics techniques require quantitative, model-based computational approaches to obtain functional and structural information from increasingly complex and multi-scaled anatomies. The lack of efficient tools to accurately model tissue structures and subsequently perform quantitative multi-physics modeling greatly impedes the clinical translation of these modalities.AimWhile the mesh-based Monte Carlo (MMC) method expands our capabilities in simulating complex tissues by using tetrahedral meshes, the generation of such domains often requires specialized meshing tools such as Iso2Mesh. Creating a simplified and intuitive interface for tissue anatomical modeling and optical simulations is essential towards making these advanced modeling techniques broadly accessible to the user community.ApproachWe responded to the above challenge by combining the powerful, open-source 3-D modeling software, Blender, with state-of-the-art 3-D mesh generation and MC simulation tools, utilizing the interactive graphical user interface (GUI) in Blender as the front-end to allow users to create complex tissue mesh models, and subsequently launch MMC light simulations.ResultsHere we present a tutorial to our newly developed Python-based Blender add-on – BlenderPhotonics – to interface with Iso2Mesh and MMC, allowing users to create, configure and refine complex simulation domains and run hardware-accelerated 3-D light simulations with only a few clicks. We provide a comprehensive introduction to this new tool and walk readers through 5 examples, ranging from simple shapes to sophisticated realistic tissue models.ConclusionBlenderPhotonics is user-friendly and open-source, leveraging the vastly rich ecosystem of Blender. It wraps advanced modeling capabilities within an easy-to-use and interactive interface. The latest software can be downloaded at http://mcx.space/bp.
Emergence of two distinct SARS-CoV-2 Gamma variants and the rapid spread of P.1-like-II SARS-CoV-2 during the second wave of COVID-19 in Santa Catarina, Southern Brazil
Authors: Padilha Dayane A. , Benetti-Filho Vilmar , Moreira Renato S. , Soratto Tatiany A. T. , Maia Guilherme A. , Christoff Ana P. , Barazzetti Fernando H. , Schörner Marcos A. , Ferrari Fernanda L. , Martins Carolina L. , Kawagoe Eric K. , Wachter Julia K. , Sacchet Paula , Baptistella Antuani R. , Schlindwein Aline D. , Coelho Bruna K. , Fernandes Sandra B. , Rovaris Darcita B. , Anjos Marlei P. D. , Melo Fernanda R. , Bittencourt Bianca , Cunha Sthefani , Meneghetti Karine L. , Wendt Nestor , Madaloz Tamela Z. , Rodrigues Marcus V. D. , Souza Doris S. M. , Moraes Milene H. , Baptista Rodrigo P. , Toledo-Silva Guilherme , Maciel Guilherme R. , Grisard Edmundo C. , Stoco Patrícia H. , Oliveira Luiz F. V. , Bazzo Maria L. , Fongaro Gislaine , Wagner Glauber 
Published: January 2022

ABSTRACTThe Western mesoregion, the State of Santa Catarina (SC), Southern Brazil, was heavily affected as a whole by the COVID-19 pandemic in early 2021. This study aimed to evaluate the dynamics of the SARS-CoV-2 virus spreading patterns in the SC state through March 2020 to April 2021 using genomic surveillance. During this period, 23 distinct variants, including Beta and Gamma, among which, the Gamma and related lineages were predominant in the second pandemic wave within SC. A regionalization of P.1-like-II in the Western-SC region was observed, concomitant to the increase in cases, mortality, and case fatality rate (CFR) index. This is the first evidence of the regionalization of the SARS-CoV-2 in SC transmission and highlights the importance of tracking variants, dispersion, and impact of SARS-CoV-2 on the public health systems.
Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: Insights from Rapid COVID-19 Diagnosis by Adversarial Learning
Authors: Yang Jenny , Soltan Andrew A. S. , Yang Yang , Clifton David A. 
Published: January 2022

AbstractMachine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how machine learning may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection or magnified during model development. For example, if one class is over-presented or errors/inconsistencies in practice are reflected in the training data, then a model can be biased by these. To evaluate our adversarial training framework, we used the statistical definition of equalized odds. We evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments, and aimed to mitigate regional (hospital) and ethnic biases present. We trained our framework on a large, real-world COVID-19 dataset and demonstrated that adversarial training demonstrably improves outcome fairness (with respect to equalized odds), while still achieving clinically-effective screening performances (NPV>0.98). We compared our method to the benchmark set by related previous work, and performed prospective and external validation on four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.
Effects of medical school on mental health and sleep habits
Authors: McKinley Blake , Daines Bryan , Allen Mitchell , Pulsipher Kayd , Zapata Isain , Wilde Benjamin 
Published: January 2022

ABSTRACTBACKGROUND and OBJECTIVESThis study aims to define changes in anxiety and depression among medical students while evaluating the association of sleep habits and other risk factors, including exercise habits and a diagnosis of chronic disease. The effect of the COVID-19 pandemic was also evaluated.DESIGNA cohort of first- and second-year medical students was evaluated longitudinally using survey methods to quantify changes from pre-medical school and summer break to each semester in medical school throughout years one and two.METHODSData was analyzed using Generalized Linear Mixed Models (GLMMs) on the numeric responses of General Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Pittsburg Sleep Quality Index. Additional assessments evaluated exercise habits, chronic disease, and impact of COVID-19 Pandemic.RESULTSDepression, anxiety, and sleep habits displayed a cyclical change that was associated with the academic cycle. The COVID-19 pandemic was never significant. Medical students who had a chronic disease diagnosis had increased severity. Exercise did not play a role.CONCLUSIONThe main driver for depression, anxiety, and poor sleep quality was the academic cycle, while the COVID-19 pandemic did not have an impact on mental health.
ExPRSweb - An Online Repository with Polygenic Risk Scores for Common Health-related Exposures
Authors: Ma Ying , Patil Snehal , Zhou Xiang , Mukherjee Bhramar , Fritsche Lars G. 
Published: January 2022

AbstractComplex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Since measurements are often unfeasible, Exposure Polygenic Risk Scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks, the Michigan Genomics Initiative and the UK Biobank. We established ExPRS for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially, the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.
Two Pools of ATP Detected in the Brains of Pediatric Patients with Myelin Oligodendrocyte Glycoprotein Antibody Disorders (MOGAD) by 3D31P MR Spectroscopic Imaging (MRSI) at 7T
Authors: Ren Jimin , Yu Fang , Greenberg Benjamin M. 
Published: January 2022

AbstractOver the past four decades, ATP, the obligatory energy molecule for keeping all cells alive and functioning, was thought to contribute only one set of31P MR signals in the human brain. Here we report for the first time the simultaneous detection of two pools of ATP in the human brain by high-resolution 3D31P MRSI at ultrahigh field 7T. These two ATP pools differ in cytosolic Mg2+concentration (1:0.5 ratio), with a resonance separation of 0.5 ppm at β-ATP, a well-established imaging marker of intracellular Mg2+concentration. Mg2+is a cofactor of ATPase and its deficiency is associated with immune dysfunction, free radical damage, perturbations in Ca2+homeostasis, and development of atherosclerosis, dyslipidemia and a number of neurological disorders, such as cerebral vasospasm, stroke, migraine, Alzheimer’s disease, and Parkinson’s disease. Our study documents reduced Mg levels in the brain of patients with myelin oligodendrocyte glycoprotein antibody disorders (MOGAD), which is an idiopathic, inflammatory, demyelinating condition of the central nervous system (CNS) more common in pediatric patients. Low-Mg2+ATP signals were detected mostly in the white matter regions in MOGAD, suggesting an association between Mg2+deficiency and compromised functions of oligodendrocytes in maintenance and generation of the axonal myelin sheath. This preliminary study demonstrates the utility of the 7T 3D31P MSRI for probing altered energy metabolism at reduced availability of Mg2+rather than ATP itself. The potential correlation between [Mg2+] and disease progression over time should be assessed in larger cohorts.Author ApprovalYes
Prognostic Value of Serum/Plasma Neurofilament Light Chain for COVID-19 Associated Mortality
Authors: Masvekar Ruturaj R. , Kosa Peter , Jin Kimberly , Dobbs Kerry , Stack Michael A. , Castagnoli Riccardo , Quaresima Virginia , Su Helen C. , Imberti Luisa , Notarangelo Luigi D. , Bielekova Bibiana 
Published: January 2022

ABSTRACTGiven the continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), early predictors of coronavirus disease 19 (COVID-19) mortality might improve patients’ outcomes. Increased levels of circulating neurofilament light chain (NfL), a biomarker of neuro-axonal injury, have been observed in patients with severe COVID-19. We investigated whether NfL provides non-redundant clinical value to previously identified predictors of COVID-19 mortality.We measured serum or plasma NfL concentrations in a blinded fashion in 3 cohorts totaling 338 COVID-19 patients. In cohort 1, we found significantly elevated NfL levels only in critically ill COVID-19 patients compared to healthy controls. Longitudinal cohort 2 data showed that NfL is elevated late in the course of the disease, following two other prognostic markers of COVID-19: decrease in absolute lymphocyte count (ALC) and increase in lactate dehydrogenase (LDH). Significant correlations between LDH and ALC abnormalities and subsequent rise of NfL implicate multi-organ failure as a likely cause of neuronal injury at the later stages of COVID-19. Addition of NfL to age and gender in cohort 1 significantly improved the accuracy of mortality prediction and these improvements were validated in cohorts 2 and 3.In conclusion, although substantial increase in serum/plasma NfL reproducibly enhances COVID-19 mortality prediction, NfL has clinically meaningful prognostic value only close to death, which may be too late to alter medical management. When combined with other prognostic biomarkers, rising longitudinal NfL measurements triggered by LDH and ALC abnormalities would identify patients at risk of COVID-19 associated mortality who might still benefit from escalated care.
Association of Retinal Age Gap with Arterial Stiffness and Incident Cardiovascular Disease
Authors: Zhu Zhuoting , Chen Yifan , Wang Wei , Wang Yueye , Hu Wenyi , Shang Xianwen , Liao Huan , Shi Danli , Huang Yu , Ha Jason , Tan Zachary , Kiburg Katerina , Zhang Xueli , Tang Shulin , Yu Honghua , Yang Xiaohong , He Mingguang 
Published: January 2022

AbstractBackgroundRetinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for ageing and mortality.ObjectiveTo investigate associations of retinal age gap with arterial stiffness index (ASI) and incident cardiovascular disease (CVD).MethodsA deep learning (DL) model was trained based on 19,200 fundus images of 11,052 participants without any past medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35,917 participants. Regression models were used to assess the association between retinal age gap and ASI. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD.ResultsWe found each one-year increase in retinal age gap was associated with increased ASI (β=0.002, 95% confidence interval [CI]: 0.001-0.003, P<0.001). After a median follow-up of 5.83 years (interquartile range [IQR]: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each one-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio [HR]=1.03, 95% CI: 1.01-1.06, P=0.012). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (HR=1.05; 95% CI, 1.00-1.10; P-overall <0.0001; P-nonlinear=0.0681).ConclusionWe found that retinal age gap was significantly associated with ASI and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.
Characterizing the conformational free-energy landscape of RNA stem-loops using single-molecule field-effect transistors
Authors: Jang Sukjin S. , Dubnik Sarah , Hon Jason , Hellenkamp Björn , Lynall David G. , Shepard Kenneth L. , Nuckolls Colin , Gonzalez Ruben L. 
Published: January 2022

ABSTRACTWe have developed and used high-time-resolution, single-molecule field-effect transistors (smFETs) to characterize the conformational free-energy landscape of RNA stem-loops. Stem-loops are some of the most common RNA structural motifs and serve as building blocks for the formation of more complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been recently interpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or on the application of mechanical (un)folding forces, they provide a unique and complementary approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 μs time resolution. Our results show that under our experimental conditions, stem-loops fold and unfold over a 1-200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1-200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which unfolded conformations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several misfolded conformations or, alternatively, the natively folded conformation. Our findings demonstrate how the combination of single-molecule sensitivity and high time resolution makes smFETs unique and powerful tools for characterizing the conformational free-energy landscape of RNA and highlight the extremely rugged landscape on which even the simplest RNA structural elements fold.
SH3-domain mutations selectively disrupt Csk homodimerization or PTPN22 binding
Authors: Brian Ben F. , Sjaastad Frances V. , Freedman Tanya S. 
Published: January 2022

AbstractThe kinase Csk is the primary negative regulator of the Src-family kinases (SFKs, e.g., Lck, Fyn, Lyn, Hck, Fgr, Blk, Yes), phosphorylating a tyrosine on the SFK C-terminal tail that mediates autoinhibition. Csk also binds phosphatases, including PTPN12 (PTP-PEST) and immune-cell PTPN22 (LYP/Pep), which dephosphorylate the SFK activation loop to promote autoinhibition. Csk-binding proteins (e.g., CBP/PAG1) oligomerize within membrane microdomains, and high local concentration promotes Csk function. Purified Csk homodimerizes in solution through an interface that overlaps the phosphatase binding footprint. Here we demonstrate that Csk can homodimerize in Jurkat T cells, in competition with PTPN22 binding. We designed SH3-domain mutations in Csk that selectively impair homodimerization (H21I) or PTPN22 binding (K43D) and verified their kinase activity in solution. Disruption of either interaction, however, decreased the negative-regulatory function of Csk in cells. Csk W47A, a substitution previously reported to block PTPN22 binding, also impairs homodimerization. Csk H21I and K43D will be useful tools for dissecting the protein-specific drivers of autoimmunity mediated by the human polymorphism PTPN22 R620W, which impairs interaction with Csk and with the E3 ubiquitin ligase TRAF3. Future investigations of Csk homodimer activity and phosphatase interactions may reveal new facets of SFK regulation in hematopoietic and non-hematopoietic cells.
coveR: An R package for processing Digital Cover Photography images to retrieve forest canopy attributes
Authors: Chianucci Francesco , Ferrara Carlotta , Puletti Nicola 
Published: January 2022

AbstractDigital Cover Photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for fisheye photography, whereas open-access tools for DCP are lacking.We developed an R package (coveR) to support the whole processing of DCP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline, while also allowing simple implementation for batch-processing bunches of images.A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning.By providing a simple, transparent, and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the implementability of DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also by non-experts.