bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Multiple Session Dataset of NIRS Recordings From Stroke Patients Controlling Brain–Computer Interface
Authors: Isaev Mikhail R. , Mokienko Olesya A. , Lyukmanov Roman Kh. , Ikonnikova Ekaterina S. , Cherkasova Anastasiya N. , Suponeva Nataliya A. , Piradov Michael A. , Bobrov Pavel D. 
Published: March 2024

AbstractThis paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real– time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near– infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.
Epigenetic Reprogramming Mediates Monocyte and Heterologous T Cell-derived Cytokine Responses after BCG Vaccination
Authors: Qi Cancan , Liu Zhaoli , Kilic Gizem , Sarlea Andrei S. , Debisarun Priya A. , Liu Xuan , Mekonnen Yonatan Ayalew , Li Wenchao , Grasshoff Martin , Alaswad Ahmed , Petkoglou Apostolos , Koeken Valerie A.C.M. , Moorlag Simone J.C.F.M. , J. de Bree L. Charlotte , Mourits Vera P. , Joosten Leo A.B. , Li Yang , Netea Mihai G. , Xu Cheng-Jian 
Published: March 2024

AbstractEpigenetic reprogramming plays an important role in shaping immune memory traits within both innate (trained immunity) and adaptive immune cells following Bacillus Calmette-Guérin (BCG) vaccination. However, the precise impact of dynamic DNA methylation alterations on immunological responses after BCG vaccination remains inadequately elucidated. To address this knowledge gap, we conducted a comprehensive study by integrating longitudinal analysis and systems biology approaches. We established a cohort of 284 healthy Dutch individuals, capturing data on genetics, cytokine responses toex vivostimulation and genome-wide DNA methylation at baseline, as well as at 14 days and 90 days after BCG vaccination. Our findings revealed distinct patterns of DNA methylation alternations in the short- and long-term following BCG vaccination. Moreover, we established that baseline DNA methylation profiles exert influence on the change in interferon-γ (IFN-γ) production upon heterologous (Staphylococcus aureus)stimulation before and after BCG vaccination. Specifically, we identified the regulation of kisspeptin as a novel pathway implicated in the modulation of IFN-γ production, and this finding has been substantiated through experiment validation. We also observed associations between BCG-induced DNA methylation changes and increased IFN-γ and Interleukin-1 β (IL-1β) production uponS. aureusstimulation. Interestingly, by integrating with genetic, epigenetic, and cytokine response data from the same individuals, mediation analysis demonstrated that most of the identified DNA methylation changes played a mediating role between genetic variants and cytokine responses, for example, the changes of cg21375332 nearSLC12A3gene mediated the regulation of genetic variants on IFN-γ changes after BCG vaccination. Sex-specific effects consistently manifested in DNA methylation changes after BCG vaccination and in the association between baseline methylation and cytokine responses. Together, our findings provide deeper insights into immune response mechanisms, crucial for developing effective epigenetic-based medical interventions for personalized medicine.
Applying Deep Learning in Heart Failure: Hospital Readmission is Not Like Other Health Quality Metrics
Authors: Shepherd Hailey M. , Heaton Jeffrey T. , Marghitu Theodore , Bai Yun Z. , Subramanian Melanie P. , Roberts Sophia H. , McGilvray Martha M.O. , Pawale Amit A. , Ewald Gregory A. , Cupps Brian P. , Pasque Michael K. , Foraker Randi E. 
Published: March 2024

AbstractBackgroundEarly identification of heart failure patients at increased risk for near-term adverse outcomes would assist clinicians in efficient resource allocation and improved care. Deep learning can improve identification of these patients.MethodsThis retrospective study examined adult heart failure patients admitted to a tertiary care institution between January 2009 and December 2018. A deep learning model was constructed with a dense input layer, three long short-term memory (LSTM) layers, and a dense hidden layer to cohesively extract features from time-series and non-time-series EHR data. Primary outcomes were all-cause hospital readmission or death within 30 days after hospital discharge.ResultsAmong a final subset of 49,675 heart failure patients, we identified 171,563 hospital admissions described by 330 million EHR data points. There were 22,111 (13%) admissions followed by adverse 30-day outcomes, including 19,122 readmissions (87%) and mortality in 3,330 patients (15%). Our final deep learning model achieved an area under the receiver-operator characteristic curve (AUC) of 0.613 and precision-recall (PR) AUC of 0.38.ConclusionsThis EHR-based deep learning model developed from a decade of heart failure care achieved marginal clinical accuracy in predicting very early hospital readmission or death despite previous accurate prediction of 1-year mortality in this large study cohort. These findings suggest that factors unavailable in standard EHR data play pivotal roles in influencing early hospital readmission.Clinical PerspectiveWhat is new?We developed an EHR-based deep learning model trained by 330 million data points from one of the largest cohorts of heart failure patients to date. Despite this model’s highly accurate prediction of long-term outcomes, such as mortality and disease progression, our findings suggest that EHR data alone offers limited predictive power for predicting the short-term outcomes of 30-day hospital readmission or death.What are the clinical implications?Our study supports the notion that hospital readmission, in contrast to other health outcomes, is uniquely driven by additional factors beyond traditional EHR variables. Once identified, incorporation of these determinants into future deep learning models could allow for accurate heart failure risk-stratification at hospital discharge to facilitate more efficient allocation of limited resources to the most vulnerable patients.
Hypercholesterolemia-induced impairment in sorafenib functionality is overcome by avasimibe co-treatment
Authors: Athavale Dipti , Yaduvanshi Himanshi , Bhati Firoz Khan , Mayengbam Shyamananda Singh , More Tushar , Rapole Srikanth , Bhat Manoj Kumar 
Published: March 2024

AbstractAvasimibe; a cholesterol-lowering drug with a proven safety in clinical trials, has recently been repositioned as an anticancer agent in various preclinical investigations. A study from our group reported that hypercholesterolemia promotes hepatocellular carcinoma (HCC) cell survival and hampers the anticancer effect of sorafenib, a kinase inhibitor. In the present study, we demonstrate that in HCC under hypercholesterolemic conditions the anticancer property of sorafenib is potentiated by avasimibe (AVA) co-treatment. Further, to elucidate the role of hypercholesterolemia on sorafenib efficacy,in vitroandin vivomodels of HCC were used.In vitro, co-treatment of both drugs synergistically inhibited HCC cell viability and induced cell death under normal and hypercholesterolemic conditions. At the molecular level, downregulation of ERK signalling and induction of endoplasmic reticulum stress are likely to contribute to the combinatorial cytotoxic effect of sorafenib and avasimibein vitro. In mice, fed on a high-cholesterol diet (HCD), the efficacy of sorafenib was restored by co-administration of AVA. Collectively, these findings suggest that impairment in the efficacy of sorafenib because of hypercholesterolemic phenotype could be restored by AVA co-treatment, which may have implications towards treatment strategy.HighlightsCholesterol impedes sorafenib efficacy in Hepatocellular carcinoma cells.Avasimibe restores the functionality of sorafenib under hypercholesterolemic environment.Combine treatment of sorafenib and avasimibe synergistically enhances cytotoxicity in hepatocellular carcinoma.Sorafenib and avasimibe treatment in the presence of LDLc.is associated with diminished ERK activation and increased ER stress.
Bidirectional modulation of reward-guided decision making by dopamine
Authors: Dias Maile Ana Antonia , Gründler Theo OJ , Froböse Monja I , Kurtenbach Hannah , Kaiser Luca F , Jocham Gerhard 
Published: March 2024

AbstractThe neuromodulator dopamine is known to play a key role in reward-guided decision making, where choice options are often characterized by multiple attributes. Different decision strategies can be used to merge these choice attributes with personal preferences (e.g. risk preferences) and integrate them into a single subjective value. While the influence of dopamine on risk preferences has been investigated, it is unknown whether dopamine is also involved in arbitrating between decision strategies. We investigated this using a reward-guided decision-making task which was performed by 31 healthy participants under the influence of the dopamine D2/D3-receptor antagonist amisulpride, the dopamine precursor L-DOPA, or placebo in a double-blind within-subject design. Notably, we observed that the dopaminergic interventions shifted the (overall) weighting of option attributes without changing how option attributes are integrated into a subjective value (decision strategy). These effects were bidirectional: Amisulpride reduced the degree to which choices were influenced by both reward magnitude and reward probability, whereas the opposite was observed under L-DOPA, where we found an increased effect of reward magnitude and reward probability on choice. These effects occurred in the absence of changes in statistically optimal behavior. Together, our data provide evidence for a role of dopamine in controlling the influence of value parameters on choice irrespective of decision strategies.
Global variation in prior exposure shapes antibody neutralization profiles of SARS-CoV-2 variants up to BA.2.86
Authors: Turner Sam , Amirthalingam Gayatri , Bailey Dalan , Barouch Dan H. , Bewley Kevin R. , Brown Kevin , Cao Yunlong , Chan Yung-Wai , Charlton Sue ,   DOVE consortium, Coombes Naomi S. , Hallis Bassam , Ho David D. , Jian Fanchong , Lasrado Ninaad , Lassaunière Ria , Liu Lihong , Montefiori David C. , Moss Paul , Newman Joseph , Parry Helen , Polacek Charlotta , Rasmussen Morten , Shao Fei , Shen Xiaoying , Thakur Nazia , Thomson Emma C. , Wang Jing , Wang Peng , Wang Qian , Willett Brian J. , Yisimayi Ayijiang , Smith Derek J. 
Published: March 2024

AbstractThe highly mutated SARS-CoV-2 variant, BA.2.86, and its descendants are now the most frequently sequenced variants of SARS-CoV-2. We analyze antibody neutralization data from eight laboratories from the UK, USA, Denmark, and China, including two datasets assessing the effect of XBB.1.5 vaccines, to determine the effect of infection and vaccination history on neutralization of variants up to and including BA.2.86, and produce antibody landscapes to describe these neutralization profiles. We find evidence for lower levels of immune imprinting on pre-Omicron variants in sera collected from Denmark and China, which may be explained by lower levels of circulation of the ancestral variant in these countries, and the use of an inactivated virus vaccine in China.
Molecular dynamics unveils multiple-site binding of inhibitors with reduced activity on the surface of dihydrofolate reductase
Authors: Araki Mitsugu , Ekimoto Toru , Takemura Kazuhiro , Matsumoto Shigeyuki , Tamura Yunoshin , Kokubo Hironori , Bekker Gert-Jan , Yamane Tsutomu , Isaka Yuta , Sagae Yukari , Kamiya Narutoshi , Ikeguchi Mitsunori , Okuno Yasushi 
Published: March 2024

ABSTRACTThe sensitivity to protein inhibitors is altered by modifications or protein mutations, as represented by drug resistance. The mode of stable drug binding to the protein pocket has been experimentally clarified. However, the nature of the binding of inhibitors with reduced sensitivity remains unclear at the atomic level. In this study, we analyzed the thermodynamics and kinetics of inhibitor binding to the surface of wild-type and mutant dihydrofolate reductase (DHFR) using molecular dynamics simulations combined with Markov state modeling. A strong inhibitor of methotrexate (MTX) showed a preference for the active site of wild-type DHFR with minimal binding to unrelated (secondary) sites. Deletion of a side-chain fragment in MTX largely destabilized the active site-bound state, with clear evidence of binding to secondary sites. Similarly, the F31V mutation in DHFR diminished the specificity of MTX binding to the active site. These results reveal the presence of multiple-bound states whose stabilities are comparable to or higher than those of the unbound state, suggesting that a reduction in the binding affinity for the active site significantly elevates the fractions of these states. This study sheds light on the specific drug recognition by proteins and the selectivity of drug binding sites on protein surfaces. (199 words)
Transcript-specific enrichment enables profiling rare cell states via scRNA-seq
Authors: Abay Tsion , Stickels Robert R. , Takizawa Meril T. , Nalbant Benan N. , Hsieh Yu-Hsin , Hwang Sidney , Snopkowski Catherine , Yu Kenny Kwok Hei , Abou-Mrad Zaki , Tabar Viviane , Ludwig Leif S. , Chaligné Ronan , Satpathy Ansuman T. , Lareau Caleb A. 
Published: March 2024

AbstractSingle-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing (scRNA-seq) identifies many rare populations of interest that express specific marker transcript combinations, traditional flow sorting limits our ability to enrich these populations for further profiling, including requiring cell surface markers with high-fidelity antibodies. Additionally, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. To address these limitations, we describe Programmable Enrichment via RNA Flow-FISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations from complex cellular mixtures defined by the presence or absence of specific RNA transcripts. Across immune populations (n= 141,227 cells) and fresh-frozen and formalin-fixed paraffin-embedded brain tissue (n= 29,522 nuclei), we demonstrate the sorting logic that can be used to enrich for cell populations via RNA-based cytometry followed by high-throughput scRNA-seq. Our approach provides a rational, programmable method for studying rare populations identified by one or more marker transcripts.
Addiction recovery stories: Ceri Pimblett in conversation with Lisa Ogilvie
Authors: Pimblett Ceri , Ogilvie Lisa 
Published: March 2024

Purpose
The purpose of this paper is to examine recovery through lived experience. It is part of a series that explores candid accounts of addiction and recovery to identify important components in the recovery process.

Design/methodology/approach
The G-CHIME model comprises six elements important to addiction recovery (growth, connectedness, hope, identity, meaning in life and empowerment). It provides a standard against which to consider addiction recovery, having been used in this series, as well as in the design of interventions that improve well-being and strengthen recovery. In this paper, a first-hand account is presented, followed by a semi-structured e-interview with the author of the account. Narrative analysis is used to explore the account and interview through the G-CHIME model.

Findings
This paper shows that addiction recovery is a remarkable process that can be effectively explained using the G-CHIME model. The significance of each component in the model is apparent from the account and e-interview presented.

Originality/value
Each account of recovery in this series is unique and, as yet, untold.
Global fast terminal sliding mode control for automatic carrier landing with environmental disturbances
Authors: Yao Zhuoer , Kan Zi , Li Daochun , Shao Haoyuan , Xiang Jinwu 
Published: March 2024

Purpose
The purpose of this paper is to solve the challenging problem of automatic carrier landing with the presence of environmental disturbances. Therefore, a global fast terminal sliding mode control (GFTSMC) method is proposed for automatic carrier landing system (ACLS) to achieve safe carrier landing control.

Design/methodology/approach
First, the framework of ACLS is established, which includes flight glide path model, guidance model, approach power compensation system and flight controller model. Subsequently, the carrier deck motion model and carrier air-wake model are presented to simulate the environmental disturbances. Then, the detailed design steps of GFTSMC are provided. The stability analysis of the controller is proved by Lyapunov theorems and LaSalle’s invariance principle. Furthermore, the arrival time analysis is carried out, which proves the controller has fixed time convergence ability.

Findings
The numerical simulations are conducted. The simulation results reveal that the proposed method can guarantee a finite convergence time and safe carrier landing under various conditions. And the superiority of the proposed method is further demonstrated by comparative simulations and Monte Carlo tests.

Originality/value
The GFTSMC method proposed in this paper can achieve precise and safe carrier landing with environmental disturbances, which has important referential significance to the improvement of ACLS controller designs.
Explicating the mapping between big data and knowledge management: a systematic literature review and future directions
Authors: Goswami Anil Kumar , Sinha Anamika , Goswami Meghna , Kumar Prashant 
Published: March 2024

PurposeThis study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.Design/methodology/approachThe study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.FindingsThe study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.Research limitations/implicationsThis study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.Practical implicationsThis study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.Originality/valueTo the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.
Unpacking the sociomaterial parameters of connectivity management practices in the Saudi academic context
Authors: Aljabr Njod , Petrakaki Dimitra , Chamakiotis Petros 
Published: March 2024

PurposeExisting research on how professionals manage after-hours connectivity to work has been dominated by studies on the strategies/practices individuals develop. In these studies, mobile technology is perceived as a tool or an enabler that supports otherwise human-centric connectivity decisions. This view sees technology as separate or external to the organisation, missing out on its nuanced role in shaping connectivity decisions. Our study aims to bring technology back into the sociomaterially imbricated context of connectivity and to unpack its parameters.Design/methodology/approachDrawing on data collected from documents and semi-structured interviews, we adopt the framework of “sociomaterial imbrications” (Leonardi, 2011) to understand the social and material parameters that influence connectivity management practices at two different academic institutions in Saudi Arabia.FindingsThe study identifies a set of social and material parameters (organisational, individual, technological and situational) that imbricate to shape, collectively and not individually, professionals’ connectivity management practices. Connectivity decisions to change practice (such as decisions of where, when or why to connect) or technology (how to connect) are not as distinct as they appear but originate from, and are founded on, imbricated sociomaterial parameters. Our study further suggests that connectivity decisions are shaped by individuals’ perceptions of sociomaterial imbrications, but decisions are not solely idiosyncratic. The context within which connectivity decisions are taken influences the type of decisions made.Originality/valueConnectivity management emerged from sociomaterial imbrications within a context constitutive of four interacting parameters: organisational, technological, situational and individual. Decisions around the “how” and the “what” of connectivity – i.e. the practice of connectivity and its underpinning technology – originate from how people perceive sociomaterial imbrications as enabling or constraining within a context. Individual perceptions account for changes in practice and in technology, but the context they find themselves in is also important. For instance, we show that professionals may perceive a certain technology as affording, but eventually they may use another technology for communications due to social norms.
The effect of green intellectual capital and innovative work behavior on green process innovation performance in the hospitality industry
Authors: Shahbaz Muhammad Hamid , Naseem Muhammad Akram , Battisti Enrico , Alfiero Simona 
Published: March 2024

PurposeThis study examines the direct and indirect effects of green intellectual capital (GIC) and innovative work behavior (IWB) on green process innovation performance (GPIP), with green knowledge sharing (GKS) as a mediator, in Pakistan’s hospitality industry. The aim is to provide a paradigm for assisting companies in transforming strategic green processes of green hotel innovation and its practices.Design/methodology/approachA total of 203 questionnaires were administered to front-desk officers of 15 hotels in Pakistan. Smart PLS-SEM 4 was used for analysis, and demographic statistics were analyzed using SPSS 21.0.FindingsGIC (green human capital, green organizational capital and green relational capital) and IWB significantly and positively influence GPIP. GKS strengthens the relationships of GIC and IWB with GPIP. Finally, all hypotheses were significant and the constructs showed a positive association.Originality/valueResearch studies have revealed the impact of GIC on the hotel industry’s competitive advantage. However, the mechanisms underlying those impacts remain relatively underexplored. This study makes valuable contributions by providing crucial evidence from Pakistan’s hospitality industry.