bluuebunny/crossref_metadata_2025_split huggingface dataset:
Convergent Transcriptomic Evidence Reveals the Dysfunctional Quantitative Mechanism of Synaptic Plasticity Control in ASD
Authors: Kong Chao , Bing Zhi-Tong , Yang Lei , Huang Zi-Gang , Wang Wen-Xu 
Published: December 2023

AbstractA prominent endophenotype in Autism Spectrum Disorder (ASD) is synaptic plasticity dysfunction, yet the molecular mechanism remains elusive. As a prototype, we investigated the postsynaptic signal transduction network in glutamatergic neurons and integrated transcriptomics to unveil the malfunction of translation control.We devised an innovative and highly dependable pipeline to transform our acquired signal transduction network into a mRNA Signaling-Regulatory Network (mSiReN) and analyze it at the RNA level. We employed Cell-Specific Network Inference via Integer Value Programming and Causal Reasoning (CS-NIVaCaR) to identify core modules and Cell-Specific Probabilistic Contextualization for mRNA Regulatory Networks (CS-ProComReN) to quantitatively reveal activated sub-pathways involving MAPK1, MKNK1, RPS6KA5, and MTOR across different cell types in ASD.The results indicate that specific pivotal molecules, such as EIF4EBP1 and EIF4E, lacking Differential Expression (DE) characteristics and responsible for protein translation with long-term potentiation (LTP) or long-term depression (LTD), are dysregulated. We further uncovered distinct activation patterns causally linked to the EIF4EBP1-EIF4E module in excitatory and inhibitory neurons.Importantly, our work has introduced a methodology for leveraging extensive transcriptomics data to parse the signal transduction network, transforming it into mSiReN, and mapping it back to the protein level. These algorithms can serve as potent tools in systems biology to analyze other omics and regulatory networks. Furthermore, the biomarkers within the activated sub-pathways, revealed by identifying convergent dysregulation, illuminate potential diagnostic and prognostic factors in ASD.
Assess Alzheimer’s Disease via Plasma Extracellular Vesicle-derived mRNA
Authors: Phu Pham Le Hoang , Chang Ching-Fang , Tuchez Katherine , Chen Yuchao 
Published: December 2023

AbstractAlzheimer’s disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern, particularly due to the increasing aging population. Recently, it has been revealed that extracellular vesicles (EVs) originating from neurons play a critical role in AD pathogenesis and progression. These neuronal EVs can cross the blood-brain barrier and enter peripheral circulation, offering a less invasive means for assessing blood-based AD biomarkers. In this study, we analyzed plasma EV-derived messenger RNA (mRNA) from 82 subjects, including individuals with AD, mild cognitive impairment (MCI), and healthy controls, using next-generation sequencing (NGS) to profile their gene expression for functional enrichment and pathway analysis. Based on the differentially expressed genes identified in both MCI and AD groups, we established a diagnostic model by implementing a machine learning classifier. The refined model demonstrated an average diagnostic accuracy over 98% and showed a strong correlation with different AD stages, suggesting the potential of plasma EV-derived mRNA as a promising non-invasive biomarker for early detection and ongoing monitoring of AD.
An accurate and rapidly calibrating speech neuroprosthesis
Authors: Card Nicholas S. , Wairagkar Maitreyee , Iacobacci Carrina , Hou Xianda , Singer-Clark Tyler , Willett Francis R. , Kunz Erin M. , Fan Chaofei , Vahdati Nia Maryam , Deo Darrel R. , Srinivasan Aparna , Choi Eun Young , Glasser Matthew F. , Hochberg Leigh R. , Henderson Jaimie M. , Shahlaie Kiarash , Brandman David M. , Stavisky Sergey D. 
Published: December 2023

AbstractBrain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output. A man in his 40’s with ALS with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled into the BrainGate2 clinical trial. He underwent surgical implantation of four microelectrode arrays into his left precentral gyrus, which recorded neural activity from 256 intracortical electrodes. We report a speech neuroprosthesis that decoded his neural activity as he attempted to speak in both prompted and unstructured conversational settings. Decoded words were displayed on a screen, then vocalized using text-to-speech software designed to sound like his pre-ALS voice. On the first day of system use, following 30 minutes of attempted speech training data, the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. On the second day, the size of the possible output vocabulary increased to 125,000 words, and, after 1.4 additional hours of training data, the neuroprosthesis achieved 90.2% accuracy. With further training data, the neuroprosthesis sustained 97.5% accuracy beyond eight months after surgical implantation. The participant has used the neuroprosthesis to communicate in self-paced conversations for over 248 hours. In an individual with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore naturalistic communication after a brief training period.
Evolutionary trends of alternative splicing
Authors: de la Fuente Rebeca , Díaz-Villanueva Wladimiro , Arnau Vicente , Moya Andrés 
Published: December 2023

AbstractAlternative splicing is the post-transcriptional mechanism by which many different isoforms are generated from a single gene, contributing to increasing spatio-temporal transcriptome complexity. We propose a novel genome-level measure of alternative splicing, which associates it with a single value for each species. Thus, a comparative analysis of species spanning the whole tree of life has revealed certain evolutionary trends in alternative splicing, prevalence in specific lineages, and relation to genome compositional structures.
GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach
Authors: Zhu Cheng , Zhang Chengyun , Shang Tianfeng , Zhang Chenhao , Zhai Silong , Su Zhihao , Duan Hongliang 
Published: December 2023

AbstractThe identification of protein-peptide binding sites significantly advances our understanding of their interaction. Recent advancements in deep learning have profoundly transformed the prediction of protein-peptide binding sites. In this work, we describe the Geometric Attention-based networks for Peptide binding Sites identification (GAPS). The GAPS constructs atom representations using geometric feature engineering and employs various attention mechanisms to update pertinent biological features. In addition, the transfer learning strategy is implemented for leveraging the pre-trained protein-protein binding sites information to enhance training of the protein-peptide binding sites recognition, taking into account the similarity of proteins and peptides. Consequently, GAPS demonstrates state-of-the-art (SOTA) performance in this task. Our model also exhibits exceptional performance across several expanded experiments including predicting the apo protein-peptide, the protein-cyclic peptide, and the predicted protein-peptide binding sites. Overall, the GAPS is a powerful, versatile, stable method suitable for diverse binding site predictions.
Vocalization Patterns in Laying Hens - An Analysis of Stress-Induced Audio Responses
Authors: Neethirajan Suresh 
Published: December 2023

AbstractThis study leverages Convolutional Neural Networks (CNN) and Mel Frequency Cepstral Coefficients (MFCC) to analyze the vocalization patterns of laying hens, focusing on their responses to both visual (umbrella opening) and auditory (dog barking) stressors at different ages. The aim is to understand how these diverse stressors, along with the hens’ age and the timing of stress application, affect their vocal behavior. Utilizing a comprehensive dataset of chicken vocal recordings, both from stress-exposed and control groups, the research enables a detailed comparative analysis of vocal responses to varied environmental stimuli. A significant outcome of this study is the distinct vocal patterns exhibited by younger chickens compared to older ones, suggesting developmental variations in stress response. This finding contributes to a deeper understanding of poultry welfare, demon-strating the potential of non-invasive vocalization analysis for early stress detection and aligning with ethical live-stock management practices. The CNN model’s ability to distinguish between pre- and post-stress vocalizations highlights the substantial impact of stressor application on chicken vocal behavior. This study not only sheds light on the nuanced interactions between stress stimuli and animal behavior but also marks a significant advancement in smart farming. It paves the way for real-time welfare assessments and more informed decision-making in poultry management. Looking forward, the study suggests avenues for longitudinal research on chronic stress and the application of these methodologies across different species and farming contexts. Ultimately, this research represents a pivotal step in integrating technology with animal welfare, offering a promising approach to transforming welfare assessments in animal husbandry.
Parallel genetic screens identify nuclear envelope homeostasis as a key determinant of telomere entanglement resolution in fission yeast
Authors: Nageshan Rishi Kumar , Krogan Nevan , Cooper Julia Promisel 
Published: December 2023

AbstractIn fission yeast lacking the telomere binding protein, Taz1, replication stalls at telomeres, triggering deleterious downstream events. Strand invasion from onetaz1Δtelomeric stalled fork to another on a separate (non-sister) chromosome leads to telomere entanglements, which are resolved in mitosis at 32°C; however, entanglement resolution fails at ≤20°C, leading to cold-specific cell lethality. Previously, we found that loss of the mitotic function of Rif1, a conserved DNA replication and repair factor, suppresses cold sensitivity by promoting resolution of entanglements without affecting entanglement formation. To understand the underlying pathways of mitotic entanglement resolution, we performed a series of genomewide synthetic genetic array screens to generate a comprehensive list of genetic interactors oftaz1Δ andrif1Δ. We modified a previously described screening method to ensure that the queried cells were kept in log phase growth. In addition to recapitulating previously identified genetic interactions, we find that loss of genes encoding components of nuclear pore complexes (NPCs) promotes telomere disentanglement and suppressestaz1Δcold sensitivity; we attribute this to more rapid anaphase midregion nuclear envelope (NE) breakdown in the absence of these NPC components. Moreover, loss of genes involved in lipid metabolism reverses the ability ofrif1+ deletion to suppresstaz1Δcold sensitivity, again pinpointing NE modulation. Arif1+ separation-of-function mutant that specifically loses Rif1’s mitotic functions yields similar genetic interactions. Genes promoting membrane fluidity were enriched in a paralleltaz1+synthetic lethal screen at permissive temperature, cementing the idea that the cold specificity oftaz1Δlethality stems from altered NE homeostasis.
MnM: a machine learning approach to detect replication states and genomic subpopulations for single-cell DNA replication timing disentanglement
Authors: Josephides Joseph M. , Chen Chun-Long 
Published: December 2023

ABSTRACTWe introduce MnM, an efficient tool for characterising single-cell DNA replication states and revealing genomic subpopulations in heterogeneous samples, notably cancers. MnM uses single-cell copy-number data to accurately perform missing-value imputation, classify cell replication states and detect genomic heterogeneity, which allows to separate somatic copy-number alterations from copy-number variations due to DNA replication. By applying our machine learning methods, our research unveils critical insights into chromosomal aberrations and showcases ubiquitous aneuploidy in tumorigenesis. MnM democratises single-cell subpopulation detection which, in hand, enables the extraction of single-cell DNA replication timing (scRT) profiles from genomically-heterogenous subpopulations detected by DNA content and issued from single samples. By analysing over 119,000 human single cells from cultured cell lines, patient tumours as well as patient-derived xenograft samples, the copy-number and replication timing profiles issued in this study lead to the first multi-sample subpopulation-disentangled scRT atlas and act as data contribution for further cancer research. Our results highlight the necessity of studyingin vivosamples to comprehensively grasp the complexities of DNA replication, given that cell lines, while convenient, lack dynamic environmental factors. This tool offers to advance our understanding of cancer initiation and progression, facilitating further research in the interface of genomic instability and replication stress.GRAPHICAL ABSTRACT
The role of digital public services in promoting international tourism: empirical evidence from European regions
Authors: Ha Le Thanh 
Published: December 2023

PurposeThe present study investigates a nexus between digital public services (DPS) and international tourism empirically.Design/methodology/approachThis article analyzes the nexus of DPS and international tourism by using the international sample of 23 European countries in the span of nearly 10 years from 2011 to 2019. Various econometric techniques, including the panel-corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model, are employed to confirm the author’s findings. Furthermore, the autoregressive distributed lag (ARDL) method is applied to measure the short- and long-run effects of DPS on international tourism developments.FindingsTourism is positively influenced by digitalization, implying that the enhancement of digital public service usage results in the development of the tourism industry. However, when looking at the effect of DPS in the short term, a negative impact can be found on tourism, as the density reported in the previous analysis stated a negative response to the tourism density. This effect spans the course of several facets, such as international tourism arrivals, international tourism receipt, international tourism, receipts (% of total exports) and global tourism expenditure (% of total imports). Although the result is unfavorable in the short term, digitalization promises great prospects for tourism in the long term. Notably, an improvement in economic growth, financial development as well a reduction in the pervasiveness of corruption and an improvement of environmental quality are transmission channels through which DPS have favorable influences on tourism activities.Practical implicationsThe author’s findings are vital for managers and policymakers to establish a comprehensive grasp of digitalization's role in deciding tourist adoption. This is because digitalization has been proven to play a role in determining tourism adoption.Originality/valueThe present study is the first to examine the relationship between DPS and international tourism empirically. The author is also the first to distinguish the effects of digitalization in the short and long run.
Different forms of supplier involvement, knowledge orchestration capability, technological uncertainty and new product development performance: a test of three-way interaction
Authors: Zhang Cailin , Li Suicheng 
Published: December 2023

PurposeThis paper examines the role of purchasing in facilitating early supplier involvement in new product development (NPD) in contexts of technological uncertainty (TU). Taking a purchasing perspective, it develops a moderate model to explain the effects of supplier involvement on NPD performance and whether and how knowledge orchestration capability (KOC) and TU affect these relationships. Additionally, KOC drivers are defined.Design/methodology/approachA total of 317 usable questionnaires from Chinese high-technology firms were collected. Moderated multiple regression (MMR) was used to test all hypotheses. Resource orchestration theory (ROT) was the adopted theoretical lens.FindingsTwo forms of supplier involvement (as knowledge source and co-creator) were found to distinctly affect NPD performance and have potential substitutive relationships. Purchasing KOC positively moderates the relationships between forms of supplier involvement on NPD performance. TU strengthens the moderating role of purchasing KOC. Furthermore, purchasing status and supply complexity are important antecedents for purchasing KOC.Practical implicationsThese findings serve as a blueprint for involving purchasing in technologically uncertain NPD projects and improve supplier NPD integration. Additionally, management should recognize the purchasing function's role and empower it to identify ideas, knowledge and solutions within supply networks.Originality/valueThis research contributes to the ROT by examining the role of purchasing KOC on supplier involvement in NPD performance, especially under TU. Moreover, it demonstrates significant and positive relations between purchasing department status and external supply complexity on its KOC.
A big data analytics method for the influence of WeChat public platform articles on blood donation behavior
Authors: Guo Xinyu , Chen Xu , Liang Xiaoke 
Published: December 2023

PurposeThe purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.Design/methodology/approachThis paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.FindingsThe results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.Originality/valueFirst, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.
An empirical analysis of the dynamic impact of DeFi on GCC foreign exchange forward markets: portfolio implication
Authors: Tien Ho Thuy , Dang Nguyen Mau Ba , Hung Ngo Thai 
Published: December 2023

Purpose
This paper aims to investigate the conditional equicorrelation and cross-quantile dependence between the DeFi, European and GCC currency markets (Oman, Qatar, Bahrain, Kuwait, Saudi Arabia and the United Arab Emirates).

Design/methodology/approach
This study applies the GARCH-DECO model and cross-quantilogram framework.

Findings
The findings reveal evidence of weak and negative average equicorrelations between the examined markets through time, excluding the COVID-19 outbreak and Russia–Ukraine conflict, which is consistent with the literature examining relationships in different markets. From the cross-quantilogram model, the authors note that the dependence between DeFi, EURO and GCC foreign exchange rate markets is greatest in the short run and diminishes over the medium- and long-term horizons, indicating rapid information processing between the markets under consideration, as most innovations are transmitted in the short term.

Practical implications
For the pairs of DeFi and currency markets, the static and dynamic optimal weights and hedging ratios are also estimated, providing new empirical data for portfolio managers and investors.

Originality/value
To the best of the authors’ knowledge, this is one of the most important research looking into the conditional correlation and predictability between the DeFi, EURO and GCC foreign exchange markets. More importantly, this study provides the first empirical proof of the safe-haven, hedging and diversification qualities of DeFi, EURO and GCC currencies, and this work also covers the COVID-19 pandemic and the Russia–Ukraine war with the use of a single dynamic measure produced by the GARCH-DECO model. In addition, the directional predictability between variables under consideration using the cross-quantilogram model is examined, which can be capable of capturing the asymmetry in the quantile dependent structure. The findings are helpful for both policymakers and investors in improving their trading selections and strategies for risk management in different market conditions.
Analyzing the role of Multi-Agent Technology in minimizing breakdown probabilities in Manufacturing Industries
Authors: Singh Vikram , Sharma Nirbhay , Sharma Somesh Kumar 
Published: December 2023

Purpose Every company or manufacturing system is vulnerable to breakdowns. This research aims to analyze the role of Multi-Agent Technology (MAT) in minimizing breakdown probabilities in Manufacturing Industries.Design/methodology/approach This study formulated a framework of six factors and twenty-eight variables (explored in the literature). A hybrid approach of Multi-Criteria Decision-Making Technique (MCDM) was employed in the framework to prioritize, rank and establish interrelationships between factors and variables grouped under them.Findings The research findings reveal that the “Manufacturing Process” is the most essential factor, while “Integration Manufacturing with Maintenance” is highly impactful on the other factors to eliminate the flaws that may cause system breakdown. The findings of this study also provide a ranking order for variables to increase the performance of factors that will assist manufacturers in reducing maintenance efforts and enhancing process efficiency.Practical implications The ranking order developed in this study may assist manufacturers in reducing maintenance efforts and enhancing process efficiency. From the manufacturer’s perspective, this research presented MAT as a key aspect in dealing with the complexity of manufacturing operations in manufacturing organizations. This research may assist industrial management with insights into how they can lower the probability of breakdown, which will decrease expenditures, boost productivity and enhance overall efficiency.Originality/value This study is an original contribution to advancing MAT’s theory and empirical applications in manufacturing organizations to decrease breakdown probability.