bluuebunny/crossref_metadata_2025_split huggingface dataset:
Circulating Metabolites May Illustrate Relationship of Alcohol Consumption with Cardiovascular Disease
Authors: Li Yi , Wang Mengyao , Liu Xue , Rong Jian , Miller Patricia Emogene , Joehanes Roby , Huan Tianxiao , Guo Xiuqing , Rotter Jerome , Smith Jennifer , Yu Bing , Nayor Matthew , Levy Daniel , Liu Chunyu , Ma Jiantao 
Published: May 2023

ABSTRACTBackgroundMetabolite signatures of long-term alcohol consumption are lacking. To better understand the molecular basis linking alcohol drinking and cardiovascular disease (CVD), we investigated circulating metabolites associated with long-term alcohol consumption and examined whether these metabolites were associated with incident CVD.MethodsCumulative average alcohol consumption (g/day) was derived from the total consumption of beer, wine and liquor on average of 19 years in 2,428 Framingham Heart Study Offspring participants (mean age 56 years, 52% women). We used linear mixed models to investigate the associations of alcohol consumption with 211 log-transformed plasma metabolites, adjusting for age, sex, batch, smoking, diet, physical activity, BMI, and familial relationship. Cox models were used to test the association of alcohol-related metabolite scores with fatal and nonfatal incident CVD (myocardial infarction, coronary heart disease, stroke, and heart failure).ResultsWe identified 60 metabolites associated with cumulative average alcohol consumption (p<0.05/211≈0.00024). For example, one g/day increase of alcohol consumption was associated with higher levels of cholesteryl esters (e.g., CE 16:1, beta=0.023±0.002, p=6.3e-45) and phosphatidylcholine (e.g., PC 32:1, beta=0.021±0.002, p=3.1e-38). Survival analysis identified that 10 alcohol-associated metabolites were also associated with a differential CVD risk after adjusting for age, sex, and batch. Further, we built two alcohol consumption weighted metabolite scores using these 10 metabolites and showed that, with adjustment age, sex, batch, and common CVD risk factors, the two scores had comparable but opposite associations with incident CVD, hazard ratio 1.11(95% CI=[1.02, 1.21],p=0.02) vs 0.88 (95% CI=[0.78, 0.98], p=0.02).SummaryWe identified 60 long-term alcohol consumption-associated metabolites. The association analysis with incident CVD suggests a complex metabolic basis between alcohol consumption and CVD.
Distinct and synergistic immunological responses to SARS-CoV-2 andMycobacterium tuberculosisduring co-infection identified by single-cell-RNA-seq
Authors: Sheerin Dylan , Phan Thanh Kha , Eriksson Emily M. ,   COVID PROFILE Consortium, Coussens Anna K. 
Published: May 2023

AbstractCOVID-19 and tuberculosis (TB) exhibit similar symptomatic presentation, clinical parameters and co-diagnosis increases COVID-19 mortality yet there is limited understanding of the mechanisms underlying their immunopathogenic interactions. Here we show by single-cell RNA-sequencing of 18,990 cells from whole blood uninfected or infected withMycobacterium tuberculosis(Mtb), SARS-CoV-2, or both pathogens, their shared, distinct, and synergistic immunological interactions. The greatest transcriptional divergence occurred within monocytes and two neutrophil subsets at early timepoints of infection. Co-infection had the greatest synergistic effect 24 hours post-infection including enrichment of IFN-γ and TNF production, whilst 96 hours post-infectionMtb, SARS-CoV-2 and co-infection shared considerable pathway overlap. SARS-CoV-2 infection alone resulted in widespread cell death 96 hours post-infection, whilstMtband co-infection had enhanced cell survival at 96 hrs, sharing negative regulation of extrinsic apoptosis. Our findings elucidate potential pathways for targeted host-directed therapies, which is particularly crucial for settings where these pathogens are now endemic.
Prediction of Overall Patient Characteristics that Incorporate Multiple Outcomes in Acute Stroke: Latent Class Analysis
Authors: Uchida Junya , Yamada Moeka , Nagayama Hirofumi , Tomori Kounosuke , Ikeda Kohei , Yamauchi Keita 
Published: May 2023

AbstractBackgroundPrevious prediction models have predicted a single outcome (e.g. gait) from several patient characteristics at one point (e.g. on admission). However, in clinical practice, it is important to predict an overall patient characteristic by incorporating multiple outcomes. This study aimed to develop a prediction model of overall patient characteristics in acute stroke patients using latent class analysis.MethodsThis retrospective observational study analyzed stroke patients admitted to acute care hospitals (37 hospitals, N=10,270) between January 2005 and March 2016 from the Japan Association of Rehabilitation Database. Overall, 6,881 patients were classified into latent classes based on their outcomes. The prediction model was developed based on patient characteristics and functional ability at admission. We selected the following outcome variables at discharge for classification using latent class analysis: Functional Independence Measure (functional abilities and cognitive functions), subscales of the National Institutes of Health Stroke Scale (upper extremity function), length of hospital stay, and discharge destination. The predictor variables were age, Functional Independence Measure (functional abilities and comprehension), subscales of the National Institutes of Health Stroke Scale (upper extremity function), stroke type, and amount of rehabilitation (physical, occupational, and speech therapies) per day during hospitalization.ResultsPatients (N=6,881) were classified into nine classes based on latent class analysis regarding patient characteristics at discharge (class size: 4–29%). Class 1 was the mildest (shorter stay and highest possibility of home discharge), and Class 2 was the most severe (longer stay and the highest possibility of transfers including deaths). Different gradations characterized Classes 3–9; these patient characteristics were clinically acceptable. Predictor variables at admission that predicted class membership were significant (odds ratio: 0.0– 107.9,P<.001).ConclusionsBased on these findings, the model developed in this study could predict an overall patient characteristic combining multiple outcomes, helping determine the appropriate rehabilitation intensity. In actual clinical practice, internal and external validation is required.
Regional Variation in Antenatal Late Preterm Steroid Use following the ALPS Trial
Authors: Freret Taylor S. , Cohen Jessica L. , Gyamfi-Bannerman Cynthia , Kaimal Anjali J. , Lorch Scott A , Wright Jason D. , Melamed Alexander , Clapp Mark A. 
Published: May 2023

AbstractObjectiveTo assess regional variation in the use of late preterm steroids use after the publication of the Antenatal Late Preterm Steroids (ALPS) Trial and to understand factors associated with a region’s pace of adoption.MethodsThis was a repeated cross-sectional study using US natality data across hospital referral regions (HRRs) within the US from February 2015 to October 2017. Inclusion criteria included live-born, non-anomalous, singleton, late preterm (34-36 completed weeks of gestation) neonates born to individuals without pregestational diabetes. HRRs were categorized as either a “slower” or “faster” adopter of antenatal late preterm steroids based on the observed versus expected pace of antenatal steroid adoption in a 1-year period after the trial’s dissemination compared to a 1-year pre-dissemination period. Patient and regional factors hypothesizeda priorito be associated with the uptake of late preterm steroids were compared between faster and slower adopters.ResultsThere were 666,097 late preterm births in 282 HRRs during the study period. Of the included HRRs, 136 (48.2%) were considered faster adopters and 146 (51.8%) slower adopters. Faster adopters increased their steroid use by 12.1-percentage points (5.9% to 18.0%) compared to a 5.5-point increase (3.7% to 9.2%) among slower adopters (p<0.001). Most examined patient and regional factors were not associated with a region’s pace of adoption. The only factor associated with being a faster adopter was the regional prevalence of prior preterm birth (adjusted odds ratio 2.04, 95% confidence interval 1.48-2.82).ConclusionThere was widespread geographic variation in the adoption of antenatal steroid administration for late preterm births that largely remained unexplained by population factors. Future research should focus on patient, provider, and system-level factors that may be influencing the adoption of late preterm steroids. Namely, this work can provide insights to barriers to timely or equitable access to new evidence-based practices and can guide future dissemination strategies with the goal of more uniform adoption.
Adolescent gulls have the opportunity for social development at breeding colonies
Authors: Taylor Liam U. 
Published: May 2023

ABSTRACTMost seabirds delay reproduction for multiple years. The long-standing ecological hypothesis is that seabirds delay reproduction until they pass a foraging efficiency threshold. This foraging development hypothesis is puzzling for seabirds with progressive delayed plumage maturation, such as American Herring Gulls (Larus argentatus smithsonianus). Young American Herring Gulls pass through a distinct series of predefinitive plumages in their early years, suggesting a process of adolescence rather than a binary switch between energetic immaturity and maturity. Drawing on facts from both colonial seabirds and tropical lekking birds, I propose an additional life history hypothesis: young gulls undergo a phase of social development—rather than foraging development alone—during which time predefinitive plumages function as signals that reduce the costs of social engagement at breeding colonies. I tested one facet of the hypothesis: predefinitive gulls have an opportunity for social development at colonies before breeding. A unique prediction is that predefinitive gulls are common at breeding colonies, socially engaged, and not breeding. I conducted counts, quantitative tests of territoriality and conflict, and qualitative behavioral observations of American Herring Gulls at two northwest Atlantic breeding colonies in the summer of 2022. Results supported all three prediction criteria. Birds in an advanced predefinitive plumage stage were common at colonies (2.0-5.8% of census) even while birds in earlier plumage stages were nearly absent (generally <1% of census). These predefinitive birds were socially engaged while loafing on—and losing fights in—foreign territories. Yet only one out of hundreds of predefinitive birds held a territory or nest. This phenomenon suggests the social conditions of breeding colonies can set the stage for social development that, in turn, sets the stage for life history and plumage evolution.
Glaucoma-associated Optineurin mutations increase transmitophagy in a vertebrate optic nerve
Authors: Jeong Yaeram , Davis Chung-ha O. , Muscarella Aaron M. , Deshpande Viraj , Kim Keun-Young , Ellisman Mark H. , Marsh-Armstrong Nicholas 
Published: May 2023

AbstractWe previously described a process referred to as transmitophagy where mitochondria shed by retinal ganglion cell (RGC) axons are transferred to and degraded by surrounding astrocytes in the optic nerve head of mice. Since the mitophagy receptor Optineurin (OPTN) is one of few large- effect glaucoma genes and axonal damage occurs at the optic nerve head in glaucoma, here we explored whether OPTN mutations perturb transmitophagy. Live-imaging ofXenopus laevisoptic nerves revealed that diverse human mutant but not wildtype OPTN increase stationary mitochondria and mitophagy machinery and their colocalization within, and in the case of the glaucoma-associated OPTN mutations also outside of, RGC axons. These extra-axonal mitochondria are degraded by astrocytes. Our studies support the view that in RGC axons under baseline conditions there are low levels of mitophagy, but that glaucoma-associated perturbations in OPTN result in increased axonal mitophagy involving the shedding and astrocytic degradation of the mitochondria.Graphical Abstract
Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity
Authors: Ahmadipour Parima , Sani Omid G. , Pesaran Bijan , Shanechi Maryam M. 
Published: May 2023

AbstractLearning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales. Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior. We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower computational cost while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity. Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest.
Flexible Analysis of Spatial Transcriptomics Data (FAST): A Deconvolution Approach
Authors: Zhang Meng , Liu Yiwen , Parker Joel , An Lingling , Sun Xiaoxiao 
Published: May 2023

AbstractMotivationSpatial transcriptomics is a state-of-art technique that allows researchers to study gene expression patterns in tissues over the spatial domain. As a result of technical limitations, the majority of spatial transcriptomics techniques provide bulk data for each sequencing spot. Consequently, in order to obtain high-resolution spatial transcriptomics data, performing deconvolution becomes essential. Deconvolution enables the determination of the proportions of different cell types along with the corresponding gene expression levels for each cell type within each spot. Most existing deconvolution methods rely on reference data (e.g., single-cell data), which may not be available in real applications. Current reference-free methods encounter limitations due to their dependence on distribution assumptions, reliance on marker genes, or the absence of leveraging histology and spatial information. Consequently, there is a critical demand for the development of highly adaptable, robust, and user-friendly reference-free deconvolution methods capable of unifying or leveraging case-specific information in the analysis of spatial transcriptomics data.ResultsWe propose a novel reference-free method based on regularized non-negative matrix factorization (NMF), named Flexible Analysis of Spatial Transcriptomics (FAST), that can effectively incorporate gene expression data, spatial coordinates, and histology information into a unified deconvolution framework. Compared to existing methods, FAST imposes fewer distribution assumptions, utilizes the spatial structure information of tissues, and encourages interpretable factorization results. These features enable greater flexibility and accuracy, making FAST an effective tool for deciphering the complex cell-type composition of tissues and advancing our understanding of various biological processes and diseases. Extensive simulation studies have shown that FAST outperforms other existing reference-free methods. In real data applications, FAST is able to uncover the underlying tissue structures and identify the corresponding marker genes.
Protein-protein interaction prediction via structure-based deep learning
Authors: Liu Yucong , Li Zhenhai 
Published: May 2023

AbstractProtein-protein interactions (PPIs) play an essential role in life activities. Many machine learning algorithms based on protein sequence information have been developed to predict PPIs. However, these models have difficulty dealing with various sequence lengths and suffer from low generalization and prediction accuracy. In this study, we proposed a novel end-to-end deep learning framework, RSPPI, combining Residual Neural Network (ResNet) and Spatial Pyramid Pooling (SPP), to predict PPIs based on the protein sequence physicochemistry properties and spatial structural information. In the RSPPI model, ResNet was employed to extract the structural and physicochemical information from the protein 3D structure and primary sequence; the SPP layer was used to transform feature maps to a single vector and avoid the fixed-length requirement. The RSPPI model possessed excellent cross-species performance and outperformed several state-of-the-art methods based either on protein sequence or gene ontology in most evaluation metrics. The RSPPI model provides a novel strategic direction to develop an AI PPI prediction algorithm.
ElecFeX: A user-friendly toolkit for efficient feature extraction from single-cell electrophysiological recordings
Authors: Ma Xinyue , Miraucourt Loïs S. , Qiu Haoyi , Xu Mengyi , Sharif-Naeini Reza , Khadra Anmar 
Published: May 2023

MotivationProfiling neurons by their electrophysiological phenotype is essential for understanding their roles in information coding within and beyond the nervous systems. Technological development has unleashed our power to record neurons more than ever before, yet the booming size of the dataset poses new challenges for data analysis. Current software tools require users to have either significant programming knowledge or to devote great time and effort, which impedes their prevalence and adoption among experimentalists. To address this problem, here we present ElecFeX, a MATLAB-based graphical user interface designed for a more accessible and efficient analysis of single-cell electrophysiological recordings. ElecFeX has a simple and succinct graphical layout to enable effortless handling of large datasets. This tool includes a set of customizable methods for most common electrophysiological features, and these methods can process multiple files all at once in a reliable and reproducible manner. The output is assembled in a properly formatted file which is exportable for further analysis such as statistical comparison and clustering. By providing such a streamlined and user-friendly open-sourced interface, we hope ElecFeX can benefit broader users for their studies associated with neural activity.SummaryCharacterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Recent developments in electrode technologies have enabled the collection of hundreds of neural recordings; that necessitated the development of new toolkits capable of performing feature extraction efficiently. To address this urgent need for a powerful and accessible tool, we present ElecFeX, an open-source MATLAB-based toolbox that (1) has a succinct and intuitive graphical user interface, (2) provides generalized methods for wide-ranging electrophysiological features, (3) processes large-size dataset effortlessly, and (4) yields formatted output for further analysis such as neuronal characterization and classification. We implemented the toolbox on a diverse set of neural recordings and demonstrated its functionality, efficiency, and versatility in capturing features that can well-distinguish neuronal subgroups across brain regions and species. ElecFeX is thus presented as a powerful tool to significantly promote future studies on neuronal electrical activity.
Lyssa excreta: Defining parameters for fecal samples as a rabies virus surveillance method
Authors: Walker Faith M. , Upton Jordyn R. , Erickson Daryn , Barrand Zachary A. , Brock Breezy , Valentine Michael , Federman Emma L. , Froehlich Emma M. , Van Pelt Lolita , Hastings Lias , Bergman David L. , Engelthaler David M. , Hepp Crystal M. 
Published: May 2023

ABSTRACTIt is not possible to systematically screen the environment for rabies virus (RABV) using current approaches. We sought to determine under what conditions RABV is detectable from feces and other accessible samples from infected wildlife to broaden the number of biological samples that could be used to test for RABV. We employed a recently-developed quantitative RT-PCR assay called the “LN34 panlyssavirus real-time RT-PCR assay”, which is highly sensitive and specific for all variants of RABV. We harvested and tested brain tissue, fecal, and/or mouth swab samples from 25 confirmed RABV positive bats of six species. To determine if rabies RNA lasts in feces sufficiently long post-defecation to use it as a surveillance tool, we tested fecal samples from 10 bats at the time of sample collection and after 24 hours of exposure to ambient conditions, with an additional test on six bats out to 72 hours. To assess whether we could pool fecal pellets and still detect a positive, we generated dilutions of known positives at 1:1, 1:10, 1:50, and 1:200. For six individuals for which matched brain, mouth swab, and fecal samples were tested, results were positive for 100%, 67%, and 67%, respectively. For the first time test to 24 hours, 63% of feces that were positive at time 0 were still positive after 24 hours, and 50% of samples at 72 hours were positive across all three replicates. Pooling tests revealed that fecal positives were detected at 1:10 dilution, but not at 1:50 or 1:200. Our preliminary results suggest that fecal samples hold promise for a rapid and non-invasive environmental screening system.
Slow ramping emerges from spontaneous fluctuations in spiking neural networks
Authors: Gavenas Jake , Rutishauser Ueli , Schurger Aaron , Maoz Uri 
Published: May 2023

AbstractHighlights1. We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements.2. Slow synapses stabilize spontaneous fluctuations in spiking neural network.3. We validate model predictions in human frontal cortical single-neuron recordings.4. The model recreates the readiness potential in an EEG proxy signal.5. Neurons that ramp together had correlated activity before ramping onset.The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.