bluuebunny/crossref_metadata_2025_split huggingface dataset:
Corticospinal excitability at rest outside of a task does not differ from task intertrial intervals in healthy adults
Authors: Bakken Kate , Horton Chris , Fisher Mitchell , Wadsley Corey G. , Greenhouse Ian 
Published: February 2024

AbstractHuman corticospinal excitability modulates during movement, when muscles are active, but also at rest, when muscles are not active. These changes in resting motor system excitability can be transient or longer lasting. Evidence from transcranial magnetic stimulation (TMS) studies suggests even relatively short periods of motor learning on the order of minutes can have lasting effects on resting corticospinal excitability. Whether individuals are able to return corticospinal excitability to out-of-task resting levels during the intertrial intervals of behavioral tasks that do not include an intended motor learning component is an important question. Here, in twenty-six healthy young adults, we used single-pulse TMS and electromyography (EMG) to measure motor evoked potentials (MEPs) during two different resting contexts: 1) intertrial intervals of a choice-reaction time task, and 2) outside the task. In both contexts, five TMS intensities were used to evaluate possible differences in recruitment of corticospinal output. We hypothesized resting state excitability would be greater during intertrial intervals than out-of-task rest, reflected in larger MEP amplitudes. Contrary to our hypothesis, we observed no significant difference in MEP amplitudes between out-of-task rest and in-task intertrial intervals, and instead found evidence of equivalence, indicating that humans are able to return to a stable motor resting state within seconds after a response. These data support the interpretation that rest is a uniform motor state in the healthy nervous system. In the future, our data may be a useful reference for motor disorder populations with an impaired ability to return to rest.
Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function
Authors: Spaeth Alex , Haussler David , Teodorescu Mircea 
Published: February 2024

AbstractDue to the complexity of neuronal networks and the nonlinear dynamics of individual neurons, it is challenging to develop a systems-level model which is accurate enough to be useful yet tractable enough to apply. Mean-field models which extrapolate from single-neuron descriptions to large-scale models can be derived from the neuron’s transfer function, which gives its firing rate as a function of its synaptic input. However, analytically derived transfer functions are applicable only to the neurons and noise models from which they were originally derived. In recent work, approximate transfer functions have been empirically derived by fitting a sigmoidal curve, which imposes a maximum firing rate and applies only in the diffusion limit, restricting applications. In this paper, we propose an approximate transfer function called Refractory SoftPlus, which is simple yet applicable to a broad variety of neuron types. Refractory SoftPlus activation functions allow the derivation of simple empirically approximated mean-field models using simulation results, which enables prediction of the response of a network of randomly connected neurons to a time-varying external stimulus with a high degree of accuracy. These models also support an accurate approximate bifurcation analysis as a function of the level of recurrent input. Finally, the model works without assuming large presynaptic rates or small postsynaptic potential size, allowing mean-field models to be developed even for populations with large interaction terms.Author SummaryAs one of the most complex systems known to science, modeling brain behavior and function is both fascinating and extremely difficult. Empirical data is increasingly available fromex vivohuman brain organoids and surgical samples, as well asin vivoanimal models, so the problem of modeling the behavior of large-scale neuronal systems is more relevant than ever. The statistical physics concept of a mean-field model offers a tractable approach by modeling the behavior of a single representative neuron and extending this to the population. However, most mean-field models work only in the limit of weak interactions between neurons, where synaptic input behaves more like a diffusion process than the sum of discrete synaptic events. This paper introduces a data-driven mean-field model, estimated by curve-fitting a simple transfer function, which works with larger interaction strengths. The resulting model can predict population firing rates and bifurcations of equilibria, as well as providing a simple dynamical model that can be the basis for further analysis.
Use of large language models as a scalable approach to understanding public health discourse
Authors: Espinosa Laura , Salathé Marcel 
Published: February 2024

AbstractOnline public health discourse is becoming more and more important in shaping public health dynamics. Large Language Models (LLMs) offer a scalable solution for analysing the vast amounts of unstructured text found on online platforms. Here, we explore the effectiveness of Large Language Models (LLMs), including GPT models and open-source alternatives, for extracting public stances towards vaccination from social media posts. Using an expert-annotated dataset of social media posts related to vaccination, we applied various LLMs and a rule-based sentiment analysis tool to classify the stance towards vaccination. We assessed the accuracy of these methods through comparisons with expert annotations and annotations obtained through crowdsourcing. Our results demonstrate that few-shot prompting of best-in-class LLMs are the best performing methods, and that all alternatives have significant risks of substantial misclassification. The study highlights the potential of LLMs as a scalable tool for public health professionals to quickly gauge public opinion on health policies and interventions, offering an efficient alternative to traditional data analysis methods. With the continuous advancement in LLM development, the integration of these models into public health surveillance systems could substantially improve our ability to monitor and respond to changing public health attitudes.Authors summaryWe examined how Large Language Models (LLMs), including GPT models and open-source versions, can analyse online discussions about vaccination from social media. Using a dataset with expert-checked posts, we tested various LLMs and a sentiment analysis tool to identify public stance towards vaccination. Our findings suggest that using LLMs, and prompting them with labelled examples, is the most effective approach. The results show that LLMs are a valuable resource for public health experts to quickly understand the dynamics of public attitudes towards health policies and interventions, providing a faster and efficient option compared to traditional methods. As LLMs continue to improve, incorporating these models into digital public health monitoring could greatly improve how we observe and react to dynamics in public health discussions.
symptomcheckR: an R package for analyzing and visualizing symptom checker performance
Authors: Kopka Marvin , Feufel Markus A. 
Published: February 2024

AbstractBackgroundA major stream of research on symptom checkers aims at evaluating the technology’spredictive accuracy, but apart from general trends, the results are marked by high variability. Several authors suggest that this variability might in part be due to different assessment methods and a lack of standardization. To improve the reliability of symptom checker evaluation studies, several approaches have been suggested, including standardizing input procedures, the generation of test vignettes, and the assignment of gold standard solutions for these vignettes. Recently, we suggested a third approach––test-theoretic metrics for standardized performance reporting–– to allow systematic and comprehensive comparisons of symptom checker performance. However, calculating these metrics is time-consuming and error prone, which could hamper the use and effectiveness of these metrics.ResultsWe developed the R package symptomcheckR as an open-source software to assist researchers in calculating standard metrics to evaluate symptom checker performance individually and comparatively and produce publicationready figures. These metrics include accuracy (by triage level), safety of advice (i.e., rate of correct or overtriage), comprehensiveness (i.e., how many cases could be entered or were assessed), inclination to overtriage (i.e., how risk-averse a symptom checker is) and a capability comparison score (i.e., a score correcting for case difficulty and comprehensiveness that enables a fair and reliable comparison of different symptom checkers). Each metric can be obtained using a single command and visualized with another command. For the analysis of individual or the comparison of multiple symptom checkers, single commands can be used to produce a comprehensive performance profile that complements the standard focus on accuracy with additional metrics that reveal strengths and weaknesses of symptom checkers.ConclusionsOur package supports ongoing efforts to improve the quality of vignette-based symptom checker evaluation studies by means of standardized methods. Specifically, with our package, adhering to reporting standards and metrics becomes easier, simple, and time efficient. Ultimately, this may help users gain a more systematic understanding of the strengths and limitations of symptom checkers for different use cases (e.g., all-purpose symptom checkers for general medicine versus symptom checkers that aim at improving triage in emergency departments), which can improve patient safety and resource allocation.
A single-cell 3D spatiotemporal multi-omics atlas fromDrosophilaembryogenesis to metamorphosis
Authors: Wang Mingyue , Hu Qinan , Tu Zhencheng , Kong Lingshi , Yao Jiajun , Xiang Rong , Chen Zhan , Zhao Yan , Zhou Yanfei , Yu Tengxiang , Wang Yuetian , Jia Zihan , Ouyang Kang , Wang Xianzhe , Bai Yinqi , Lian Mingwei , Yang Zhenyu , Yang Tao , Chen Jing , Huang Yunting , Yin Ni , Mo Wenyuan , Liang Wenfu , Liu Chang , Lin Xiumei , Liu Chuanyu , Gu Ying , Chen Wei , Liu Longqi , Xu Xun , Hu Yuhui 
Published: February 2024

SUMMARYThe development of a multicellular organism is a highly intricate process tightly regulated by numerous genes and pathways in both spatial and temporal manners. Here, we present Flysta3D, a comprehensive multi-omics atlas of the model organismDrosophila, spanning its developmental lifespan from embryo to pupa. Our datasets encompass 3D single-cell spatial transcriptomic, single-cell transcriptomic, and single-cell chromatin accessibility information. By integrating these multi-dimensional data, we constructed cell state trajectories that uncover the detailed profiles of tissue development. With a focus on the central nervous system (CNS) and midgut, we dissected the spatiotemporal dynamics of gene regulatory networks, cell type diversity, and morphological changes from a multi-omics perspective. This extensive atlas provides an unprecedentedly rich resource and serves as a systematic platform for studyingDrosophiladevelopment with integrated single-cell data at an ultra-high spatiotemporal resolution.
QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport
Authors: Wang Zexuan , Zhan Qipeng , Yang Shu , Mu Shizhuo , Chen Jiong , Garai Sumita , Orzechowski Patryk , Wagenaar Joost , Shen Li 
Published: February 2024

AbstractSingle-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The data’s innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.
Unveiling Multi-level and Multi-modal Semantic Representations in the Human Brain using Large Language Models
Authors: Nakagi Yuko , Matsuyama Takuya , Koide-Majima Naoko , Yamaguchi Hiroto Q. , Kubo Rieko , Nishimoto Shinji , Takagi Yu 
Published: February 2024

AbstractIn recent studies, researchers have used large language models (LLMs) to explore semantic representations in the brain; however, they have typically assessed different levels of semantic content, such as speech, objects, and stories, separately. In this study, we recorded brain activity using functional magnetic resonance imaging (fMRI) while participants viewed 8.3 hours of dramas and movies. We annotated these stimuli at multiple semantic levels, which enabled us to extract latent representations of LLMs for this content. Our findings demonstrate that LLMs predict human brain activity more accurately than traditional language models, particularly for complex background stories. Furthermore, we identify distinct brain regions associated with different semantic representations, including multi-modal vision-semantic representations, which highlights the importance of modeling multi-level and multi-modal semantic representations simultaneously. We will make our fMRI dataset publicly available to facilitate further research on aligning LLMs with human brain function. Please check out our webpage athttps://sites.google.com/view/llm-and-brain/.
Brain and grammar: revealing electrophysiological basic structures with competing statistical models
Authors: Cometa Andrea , Battaglini Chiara , Artoni Fiorenzo , Greco Matteo , Frank Robert , Repetto Claudia , Bottoni Franco , Cappa Stefano F , Micera Silvestro , Ricciardi Emiliano , Moro Andrea 
Published: February 2024

AbstractAcoustic, lexical and syntactic information is simultaneously processed in the brain. Therefore, distinguishing the electrophysiological activity pertaining to these components requires complex and indirect strategies. Capitalizing on previous works which factor out acoustic information, we could concentrate on the lexical and syntactic contribution to language processing by testing competing statistical models. We exploited EEG recordings and compared different surprisal models selectively involving lexical information, part of speech or syntactic structures in various combinations. EEG responses were recorded in 32 participants during listening to affirmative active declarative sentences and compared the activation corresponding to basic syntactic structures, such as noun phrases vs verb phrases. Lexical and syntactic processing activates different frequency bands, different time windows and different networks. Moreover, surprisal models based on part of speech inventory only do not explain well the electrophysiological data, while those including syntactic information do. Finally, we confirm previous measures obtained with intracortical recordings independently supporting the original hypothesis addressed here in a robust way.
Proactive selective attention across competition contexts
Authors: Aguado-López Blanca , Palenciano Ana F. , Peñalver José M.G. , Díaz-Gutiérrez Paloma , López-García David , Avancini Chiara , Ciria Luis F. , Ruz María 
Published: February 2024

AbstractSelective attention is a cognitive function that helps filter out unwanted information. Theories such as the biased competition model (Desimone & Duncan, 1995) explain how attentional templates bias processing towards targets in contexts where multiple stimuli compete for resources. However, it is unclear how the anticipation of different levels of competition influences the nature of attentional templates, in a proactive fashion. In this study, we used EEG to investigate how the anticipated demands of attentional selection (either high or low stimuli competition contexts) modulate target-specific preparatory brain activity and its relationship with task performance. To do so, participants performed a sex judgement task in a cue-target paradigm where, depending on the block, target and distractor stimuli appeared simultaneously (high competition) or sequentially (low competition). Multivariate Pattern Analysis (MVPA) showed that, in both competition contexts, there was a preactivation of the target category to select with a ramping-up profile at the end of the preparatory interval. However, cross-classification showed no generalization across competition conditions, suggesting different preparatory formats. Notably, time-frequency analyses showed differences between anticipated competition demands, reflecting higher theta band power for high than low competition, which mediated the impact of subsequent stimuli competition on behavioral performance. Overall, our results show that, whereas preactivation of the internal templates associated with the category to select are engaged in advance in both competition contexts, their underlying neural patterns differ. In addition, these codes could not be associated with theta power, suggesting different preparatory processes. The implications of these findings are crucial to increase our understanding of the nature of top-down processes across different contexts.
Protein-protein interactions with G3BPs drive stress granule condensation and gene expression changes under cellular stress
Authors: Liboy-Lugo José M. , Espinoza Carla A. , Sheu-Gruttadauria Jessica , Park Jesslyn E. , Xu Albert , Jowhar Ziad , Gao Angela L. , Carmona-Negrón José A. , Wittmann Torsten , Jura Natalia , Floor Stephen N. 
Published: February 2024

ABSTRACTStress granules (SGs) are macromolecular assemblies that form under cellular stress. Formation of these condensates is driven by the condensation of RNA and RNA-binding proteins such as G3BPs. G3BPs condense into SGs following stress-induced translational arrest. Three G3BP paralogs (G3BP1, G3BP2A, and G3BP2B) have been identified in vertebrates. However, the contribution of different G3BP paralogs to stress granule formation and stress-induced gene expression changes is incompletely understood. Here, we identified key residues for G3BP condensation such as V11. This conserved amino acid is required for formation of the G3BP-Caprin-1 complex, hence promoting SG assembly. Total RNA sequencing and ribosome profiling revealed that disruption of G3BP condensation corresponds to changes in mRNA levels and ribosome engagement during the integrated stress response (ISR). Moreover, we found that G3BP2B preferentially condenses and promotes changes in mRNA expression under endoplasmic reticulum (ER) stress. Together, this work suggests that stress granule assembly promotes changes in gene expression under cellular stress, which is differentially regulated by G3BP paralogs.
mTORC1 activity oscillates throughout the cell cycle promoting mitotic entry and differentially influencing autophagy induction
Authors: Joshi Jay N. , Lerner Ariel D. , Scallo Frank , Grumet Alexandra N. , Matteson Paul , Millonig James H. , Valvezan Alexander J. 
Published: February 2024

SummaryMechanistic Target of Rapamycin Complex 1 (mTORC1) is a master metabolic regulator that stimulates anabolic cell growth while suppressing catabolic processes such as autophagy. mTORC1 is active in most, if not all, proliferating eukaryotic cells. However, it remains unclear whether and how mTORC1 activity changes from one cell cycle phase to another. Here we tracked mTORC1 activity through the complete cell cycle and uncover oscillations in its activity. We find that mTORC1 activity peaks in S and G2, and is lowest in mitosis and G1. We further demonstrate that multiple mechanisms are involved in controlling this oscillation. The interphase oscillation is mediated through the TSC complex, an upstream negative regulator of mTORC1, but is independent of major known regulatory inputs to the TSC complex, including Akt, Mek/Erk, and CDK4/6 signaling. By contrast, suppression of mTORC1 activity in mitosis does not require the TSC complex, and instead involves CDK1-dependent control of the subcellular localization of mTORC1 itself. Functionally, we find that in addition to its well-established role in promoting progression through G1, mTORC1 also promotes progression through S and G2, and is important for satisfying the Wee1- and Chk1-dependent G2/M checkpoint to allow entry into mitosis. We also find that low mTORC1 activity in G1 sensitizes cells to autophagy induction in response to partial mTORC1 inhibition or reduced nutrient levels. Together these findings demonstrate that mTORC1 is differentially regulated throughout the cell cycle, with important phase-specific functional consequences in proliferating cells.
Performance investigation of conduction-cooled cryogenic permanent magnet undulator at high beam currents
Authors: Huang Jui-Che , Kitamura Hideo , Yang Chih-Shen , Yang Ching-Kang , Chen Chih-Wei , Chuang Yu-Chun 
Published: February 2024

A conduction-cooled cryogenic permanent magnet undulator, CU15, with a period length of 15 mm and 133 periods has been successfully developed. This undulator has been operated at the Taiwan Photon Source (TPS) storage ring at a beam current of 500 mA for more than 3 years. In CU15, two cryocoolers are used to cool the PrFeB magnets to 80 K. The effective magnetic field measures 1.33 Tesla for a magnet gap of 4.2 mm, maintaining an rms phase error below 2.6°. The bunch length of the electron beam in TPS is as short as 16 psec, leading to a serious issue with high beam-induced heating in CU15. This report addresses the above issues and provides detailed adaptations of CU15 to operate at a beam current of 500 mA.

Published by the American Physical Society
2024