bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Transformer Approach for Cognitive Impairment Classification and Prediction
Authors: Liu Houjun , Weakley Alyssa M. , Zhang Jiawei , Liu Xin 
Published: May 2024

Introduction:
Early classification and prediction of Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI) with noninvasive approaches is a long-standing challenge. This challenge is further exacerbated by the sparsity of data needed for modeling. Deep learning methods offer a novel method to help address these challenging multiclass classification and prediction problems.

Methods:
We analyzed 3 target feature-sets from the National Alzheimer’s Coordinating Center (NACC) dataset: (1) neuropsychological (cognitive) data; (2) patient health history data; and (3) the combination of both sets. We used a masked Transformer-encoder without further feature selection to classify the samples on cognitive status (no cognitive impairment, aMCI, AD)—dynamically ignoring unavailable features. We then fine-tuned the model to predict the participants’ future diagnosis in 1 to 3 years. We analyzed the sensitivity of the model to input features via Feature Permutation Importance.

Results:
We demonstrated (1) the masked Transformer-encoder was able to perform prediction with sparse input data; (2) high multiclass current cognitive status classification accuracy (87% control, 79% aMCI, 89% AD); (3) acceptable results for 1- to 3-year multiclass future cognitive status prediction (83% control, 77% aMCI, 91% AD).

Conclusion:
The flexibility of our methods in handling inconsistent data provides a new venue for the analysis of cognitive status data.
Efficient and accurate mixed model association tool for single-cell eQTL analysis
Authors: Zhou Wei , Cuomo Anna S.E. , Xue Angli , Kanai Masahiro , Chau Grant , Krishna Chirag , Xavier Ramnik J. , MacArthur Daniel G. , Powell Joseph E. , Daly Mark J. , Neale Benjamin M. 
Published: May 2024

ABSTRACTUnderstanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the “pseudobulk” approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.
Identifying drug targets for schizophrenia through gene prioritization
Authors: Kraft Julia , Braun Alice , Awasthi Swapnil , Panagiotaropoulou Georgia , Schipper Marijn , Bell Nathaniel , Posthuma Danielle , Pardiñas Antonio F. ,   Schizophrenia Working Group of the Psychiatric Genomics Consortium, Ripke Stephan , Heilbron Karl 
Published: May 2024

AbstractBackgroundSchizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome.MethodsTo more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). To validate our findings, we compared them with previous prioritization efforts, known neurodevelopmental genes, and results from the PsyOPS tool.ResultsWe prioritized 62 schizophrenia genes, 41 of which were also highlighted by our validation methods. In addition toDRD2, the principal target of antipsychotics, we prioritized 9 genes that are targeted by approved or investigational drugs. These included drugs targeting glutamatergic receptors (GRIN2AandGRM3), calcium channels (CACNA1CandCACNB2), and GABABreceptor (GABBR2). These also included genes in loci that are shared with an addiction GWAS (e.g. PDE4BandVRK2).ConclusionsWe curated a high-quality list of 62 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.
"Exploring Cardiac Vector Propagation in Acute Myocardial Infarction: A Spatial Velocity Perspective"
Authors: Ghosal Tania , Hembram Anjan , Ahmed Imran , Goswami Damodar Prasad , Bandyopadhyay Anupam , Sengupta Arnab 
Published: May 2024

AbstractAimsTo objectively characterize the spatial-velocity dynamics of the QRS-loop in the vectorcardiogram (VCG) of patients with acute myocardial infarction (AMI).MethodsVCG was constructed as space curve from three orthogonal ECG leads of 25 healthy individuals and 50 AMI patients. Spatial-velocity (SV) of the dynamic QRS loop was recorded, along with component attributes such as changing spatial distance (SD) and magnitude.ResultsDecreased SV and SD and altered spatial propagation patterns of ventricular vectors in AMI was recorded, with changes in specific axes based on infarct location. Reduced left-ventricular ejection-fraction indicates systolic dysfunction in AMI.ConclusionAltered spatial propagation patterns of vectors and ventricular dysfunction may be explained by complex interplay between mechanical and electrical factors in AMI.DeclarationThe present manuscript has been submitted in the journalFuture Cardiology, and currently under peer reviewNone of the manuscript’s contents have yet been published.All authors have read and approved the manuscript and approved its submission to MedRxiv, as preprint server. They also agreed the order of authorship.
Cell autonomous microglia defects in a stem cell model of frontotemporal dementia
Authors: Iyer Abhirami K. , Vermunt Lisa , Mirfakhar Farzaneh S. , Minaya Miguel , Acquarone Mariana , Koppisetti Rama Krishna , Renganathan Arun , You Shih-Feng , Danhash Emma P. , Verbeck Anthony , Galasso Grant , Lee Scott M. , Marsh Jacob , Nana Alissa L. , Spina Salvatore , Seeley William W. , Grinberg Lea T. , Temple Sally , Teunissen Charlotte E. , Sato Chihiro , Karch Celeste M. 
Published: May 2024

AbstractNeuronal dysfunction has been extensively studied as a central feature of neurodegenerative tauopathies. However, across neurodegenerative diseases, there is strong evidence for active involvement of immune cells like microglia in driving disease pathophysiology. Here, we demonstrate that tau mRNA and protein are expressed in microglia in human brains and in human induced pluripotent stem cell (iPSC)-derived microglia like cells (iMGLs). Using iMGLs harboring theMAPTIVS10+16 mutation and isogenic controls, we demonstrate that a tau mutation is sufficient to alter microglial transcriptional states. We discovered thatMAPTIVS10+16 microglia exhibit cytoskeletal abnormalities, stalled phagocytosis, disrupted TREM2/TYROBP networks, and altered metabolism. Additionally, we found that secretory factors fromMAPTIVS10+16 iMGLs impact neuronal health, reducing synaptic density in neurons. Key features observedin vitrowere recapitulated in human brain tissue and cerebrospinal fluid fromMAPTmutations carriers. Together, our findings thatMAPTIVS10+16 drives cell-intrinsic dysfunction in microglia that impacts neuronal health has major implications for development of therapeutic strategies.
Necessary for seizure forecasting outcome metrics: seizure frequency and benchmark model
Authors: Chang Chi-Yuan , Zhang Boyu , Moss Robert , Picard Rosalind , Westover M. Brandon , Goldenholz Daniel 
Published: May 2024

AbstractWork is ongoing to advance seizure forecasting, but the performance metrics used to evaluate model effectiveness can sometimes lead to misleading outcomes. For example, some metrics improve when tested on patients with a particular range of seizure frequencies (SF). This study illustrates the connection between SF and metrics. Additionally, we compared benchmarks for testing performance: a moving average (MA) or the commonly used permutation benchmark. Three data sets were used for the evaluations: (1) Self-reported seizure diaries of 3,994 Seizure Tracker patients; (2) Automatically detected (and sometimes manually reported or edited) generalized tonic-clonic seizures from 2,350 Empatica Embrace 2 and Mate App seizure diary users, and (3) Simulated datasets with varying SFs. Metrics of calibration and discrimination were computed for each dataset, comparing MA and permutation performance across SF values. Most metrics were found to depend on SF. The MA model outperformed or matched the permutation model in all cases. The findings highlight SF’s role in seizure forecasting accuracy and the MA model’s suitability as a benchmark. This underscores the need for considering patient SF in forecasting studies and suggests the MA model may provide a better standard for evaluating future seizure forecasting models.
Single-cell transcriptomics reveals immunosuppressive microenvironment and highlights stumor-promoting macrophage cells in Glioblastoma
Authors: Cheng Han , Yan Yan , Zhang Biao , Ma Zhuolin , Fu Siwen , Ji Zhi , Zou Ziji , Wang Qin 
Published: May 2024

AbstractGlioblastoma (GBM) is the most prevalent and aggressive primary brain malignancy in adults. Nevertheless, the cellular heterogeneity and complexity within the GBM microenvironment (TME) are still not fully understood, posing a significant obstacle in the advancement of more efficient immunotherapies for GBM. In this study, we conducted an integrated analysis of 48 tumor fragments from 24 GBM patients at the single-cell level, uncovering substantial molecular diversity within immune infiltrates. We characterized molecular signatures for five distinct tumor-associated macrophage (TAM) subtypes. Notably, the TAM_MRC1 subtype displayed a pronounced M2 polarization signature. Additionally, we identified a subtype of natural killer (NK) cells, designated CD56dim_DNAJB1. This subtype is characterized by an exhausted phenotype, evidenced by an elevated stress signature and enrichment in the PD-L1/PD-1 checkpoint pathway. Our findings also highlight significant cell-cell interactions among malignant glioma cells, TAM, and NK cells within the TME. Overall, this research sheds light on the functional heterogeneity of glioma and immune cells in the TME, providing potential targets for therapeutic intervention in this immunologically cold cancer.
Clonal expansion dictates the efficacy of mitochondrial lineage tracing in single cells
Authors: Wang Xin , Wang Kun , Zhang Weixing , Tang Zhongjie , Zhang Hao , Cheng Yuying , Zhou Da , Zhang Chao , Zhong Wen-Zhao , Ma Qing , Xu Jin , Hu Zheng 
Published: May 2024

AbstractAlthough mitochondrial DNA (mtDNA) variants hold promise as endogenous barcodes for tracking human cell lineages, their efficacy as reliable lineage markers is hindered by the complex dynamics of mtDNA in somatic tissues. Here, we utilized computational modeling and single-cell genomics to thoroughly interrogate the origin and clonal dynamics of mtDNA lineage markers across various biological settings. Our findings revealed the majority of purported “clone informative variants (CIVs)” were pre-existing heteroplamies in the first cell instead ofde novosomatic mutations during divisions. Moreover, CIVs demonstrated limited discriminatory power among different lineages during normal development; however, certain CIVs with consistently high allele frequencies proved capable of faithfully labeling cell lineages in scenarios of stringent clonal expansion. Inspired by our simulations, we introduced a lineage informative (LI) score, facilitating the identification of reliable mitochondrial lineage markers across different modalities of single-cell genomic data.
Nimodipine-Associated Standard Dose Reductions and Neurologic Outcomes After Aneurysmal Subarachnoid Hemorrhage: The Era of Pharmacogenomics
Authors: Vázquez-Medina Adriana , Turnbull Marion T. , James Courtney L. , Cowart Jennifer B. , Lesser Elizabeth , Carter Rickey E. , Ross Owen A. , Miller David A. , Meschia James F. , De Jesús Espinosa Aixa , Weinshilboum Richard , Freeman W. David 
Published: May 2024

AbstractNimodipine, an L-type cerebroselective calcium channel antagonist, is the only drug approved by the US Food and Drug Administration for the neuroprotection of patients with aneurysmal subarachnoid hemorrhage (aSAH). Four randomized, placebo-controlled trials of nimodipine demonstrated clinical improvement over placebo; however, these occurred before precision medicine with pharmacogenomics was readily available. The standard enteral dose of nimodipine recommended after aSAH is 60 mg every 4 hours. However, up to 78% of patients with aSAH develop systemic arterial hypotension after taking the drug at the recommended dose, which could theoretically limit its neuroprotective role and worsen cerebral perfusion pressure and cerebral blood flow, particularly when concomitant vasospasm is present. We investigated the association between nimodipine dose changes and clinical outcomes in a consecutive series of 150 patients (mean age, 56 years; 70.7% women) with acute aSAH. We describe the pharmacogenomic relationship of nimodipine dose reduction with clinical outcomes. These results have major implications for future individualized dosing of nimodipine in the era of precision medicine.
Using models to identify the causes of pre-symptomatic transmission from human infection data
Authors: Zhang Kayla , Pak Damie , Greischar Megan A. 
Published: May 2024

AbstractWhen disease transmission can precede symptom onset, containing outbreaks requires distinct strategies, like active surveillance. Yet it is rarely clear in advance when such interventions are needed, especially for emerging pathogens. Predicting pre-symptomatic transmission would be easier with knowledge of the within-host dynamics that enable pre-symptomatic transmission. To investigate those dynamics, we survey controlled human infection (CHI) trials with viral agents, which contain data on incubation times, infection duration, and transmission potential following inoculation with a known dose. We find that all studies report information on the duration of viral shedding, but few report the timing of symptoms. Only one study provided data on the timing of shedding and symptoms for individual participants, following norovirus inoculation. We apply a statistical model to individual time series to show significantly greater potential for pre-symptomatic transmission with faster viral replication, but no evidence for a tradeoff between transmission rate and duration during the pre-symptomatic phase. We then compare within-host models of pathogen replication, immune clearance, and symptom onset to identify plausible assumptions about the causes of pre-symptomatic transmission. We recover the pattern that peak shedding can precede symptom onset if we assume that symptoms are triggered by immune responses rather than pathogen abundance. Only by relaxing the standard assumption of exponential growth can we recover the pattern that faster viral replication enables pre-symptomatic transmission. Thus, data on symptom onset in CHI trials, paired with models, can illuminate the within-host dynamics underpinning pre-symptomatic transmission, guiding efforts to improve control strategies.Significance statementThe COVID-19 pandemic was exacerbated by the potential for transmission before symptoms. Yet the causes of pre-symptomatic transmission remain unclear, hindering efforts to predict disease spread and tailor control efforts for novel pathogens. For known pathogens, the potential for pre-symptomatic transmission varies across individuals, but patterns may emerge from controlled human infection (CHI) trials. We surveyed CHI trials, finding that only one reported data on individual participants. We fit a simple model to those data, finding that faster viral replication correlates with pre-symptomatic transmission. We used more detailed models to identify plausible assumptions about the causes of symptom onset, e.g., that immune responses trigger symptoms. Thus, applying models to CHI trial data gives insight into the drivers of pre-symptomatic transmission.
Regulation of Neuronal Chloride Homeostasis by Pro- and Mature Brain-Derived Neurotrophic Factor (BDNF) via KCC2 Cation-Chloride Cotransporters in Rat Cortical Neurons
Authors: Hamze Mira , Brier Cathy , Buhler Emmanuelle , Zhang Jinwei , Medina Igor , Porcher Christophe 
Published: May 2024

ABSTRACTThe strength of inhibitory neurotransmission depends on intracellular neuronal chloride concentration, primarily regulated by the activity of cation-chloride cotransporters NKCC1 and KCC2. Brain-derived neurotrophic factor (BDNF) influences the functioning of these co-transporters. BDNF is synthesized from precursor proteins (proBDNF), which undergo proteolytic cleavage to yield mature BDNF (mBDNF). While previous studies have indicated the involvement of BDNF signaling in the activity of KCC2, its specific mechanisms are unclear. We investigated the interplay between both forms of BDNF and chloride homeostasis in rat hippocampal neurons andin-uteroelectroporated cortices of rat pups, spanning the behavioral, cellular, and molecular levels. We found that both pro– and mBDNF play a comparable role in immature neurons by inhibiting the capacity of neurons to extrude chloride. Additionally, proBDNF increases the endocytosis of KCC2 while maintaining a depolarizing shift of EGABAin maturing neurons. Behaviorally, proBDNF-electroporated rat pups in the somatosensory cortex exhibit sensory deficits delayed huddling and cliff avoidance. These findings emphasize the role of BDNF signaling in regulating chloride transport through the modulation of KCC2. In summary, this study provides valuable insights into the intricate interplay between BDNF, chloride homeostasis, and inhibitory synaptic transmission, shedding light on the underlying cellular mechanisms involved.
Multimessenger search for electrophilic feebly interacting particles from supernovae
Authors: Luque Pedro De la Torre , Balaji Shyam , Carenza Pierluca 
Published: May 2024

We study MeV-scale electrophilic feebly interacting particles (FIPs), that may be abundantly produced in supernova explosions, escape the star and decay into electrons and positrons. This exotic injection of leptons in the Milky Way leaves an imprint in both photon and cosmic ray fluxes. Specifically, positrons lose energy and annihilate almost at rest with background electrons, producing photons with 511 keV energy. In addition, electrons and positrons radiate photons through bremsstrahlung emission and upscatter the low-energy galactic photon fields via the inverse Compton process generating a broad emission from x-ray to γ ray energies. Finally, electrons and positrons are directly observable in cosmic ray experiments. In order to describe the FIP-induced lepton injection in full generality, we use a model-independent parametrization which can be applied to a host of FIPs such as axionlike particles, dark photons and sterile neutrinos. Theoretical predictions are compared to experimental data to robustly constrain FIP-electron interactions with an innovative multimessenger analysis.

Published by the American Physical Society
2024
An incoming threat: the influence of automation potential on job insecurity
Authors: Cao Jieqiong , Song Zhaoli 
Published: May 2024

PurposeIn today’s digital age, news and social media are abuzz with ChatGPT and a myriad of advanced AI tools. Experts from disciplines like computer science and socioeconomics have discussed the profound transformations AI can bring. While certain industries have embraced AI, its penetration across all sectors remains uneven. Yet, even with this limited adoption, the psychological ramifications it presents for workplace employees are profound. Our study integrated social information processing and transactional stress theories to analyze the effect of automation brought by AI on job insecurity. Our study also considers whether moderating factors like supervisor–subordinate relationships and social media engagement can alleviate the adverse consequences of automation.Design/methodology/approachWe empirically test our research hypotheses with longitudinal data from the US General Social Survey (GSS).FindingsUsing US General Social Survey data, our findings indicate that employees in industries with high automation potential are more susceptible to job insecurity. Interestingly, social media engagement appears to dampen this relationship, while the quality of the supervisor–subordinate relationship shows negligible impact.Originality/valueThis study provides valuable insights into the effects of automation potential and the role of social media engagement in coping with it, making a meaningful contribution to the existing literature in this area.
Assessing the effect of open innovation on firm performance: a study of Indian IT organisations
Authors: Grandhi Srimannarayana , Chhetri Prem , Molla Alemayehu 
Published: May 2024

PurposeThere is a growing interest among academics, government agencies and private organisations to examine the scale, characteristics, and impact of Open Innovation (OI). Studies have examined these issues mainly in the context of a developed world. Because firms in developing economies face unique challenges of OI such as building networks, inter-firm interactions, collaboration for resource utilisation and knowledge sharing, these warrant an examination of the theoretical relationships between the antecedents of OI and their impact on performance as well as mediators of these relationships. Therefore, this study develops a comprehensive OI framework to measure open innovation and analyse its effect on the innovation performance of Indian IT organisations.Design/methodology/approachTheoretically, the study draws upon the Resource-Based View, Relational View, and Absorptive Capacity theories. Empirically, a survey questionnaire was distributed to Indian IT organisations through the online survey tool “Qualtrics”. The research framework was tested using the data collected from 346 Indian IT organisations.FindingsThe results highlight the positive effect of OI activities on innovation performance and the mediating role of absorptive capacity. IT organisations with a higher inbound knowledge and absorptive capacity demonstrated better innovation performance.Research limitations/implicationsThis study is limited to understanding the mediating effect of absorptive capacity for inbound innovation. Future studies into the mediating role of desorption capacity could reveal its impact on innovation performance.Practical implicationsFrom a management perspective, this knowledge will enable managers and policymakers to emphasise OI to achieve better innovation performance. This knowledge will provide both government decision-makers and IT managers with definite OI implications for innovation performance.Originality/valueThe main contribution of this study lies in exploring the interconnectedness among IT organisations and collaborative processes on OI and innovation performance. This empirical study pinpoints the causes and sources of OI that would lead to innovation performance and the mediating role of absorptive capacity in achieving innovation performance. It extends the empirical base of OI scholarship based on firms in an emerging economy.