bluuebunny/crossref_metadata_2025_split huggingface dataset:
Body Mass Index and Non-invasive Cardiovascular Parameters
Authors: Wu Shenghui , Meucci Marco , Liu Zhong 
Published: May 2024

AbstractBackgroundEpidemiological studies on body mass index (BMI) and non-invasive cardiovascular parameters are limited and inconsistent. To provide more informative data for further prevention and control, we examined associations between BMI, as well as overweight/obesity, and non-invasive cardiovascular parameters and their dose-response relationships in North Carolina Appalachian adults.MethodsA total of 71 participants were included in this analysis. Non-invasive cardiovascular parameters included carotid-femoral pulse wave velocity for measuring central arterial stiffness, augmentation index at 75 bpm for gauging peripheral arterial stiffness, ejection time for indicating left ventricular performance, and Buckberg index for measuring coronary microvascular circulation. Logistic regression models were used for analysis.ResultsEvery unit (kg/m2) increase in BMI was associated with a 25% statistically significant increased multivariable-adjusted odds of higher central arterial stiffness (odds ratio: 1.25; 95% confidence interval: 1.04-1.51), a 31% increased adjusted odds of higher peripheral arterial stiffness, a 23% statistically significant increased adjusted odds of worse left ventricular performance, and a 25% statistically significant increased adjusted odds of worse coronary microvascular circulation. Overweight/obesity was associated with a 532% statistically significant increased odds of higher arterial stiffness (6.32; 1.42-28.09), and a 704% statistically significant increased odds of worse left ventricular performance after adjusting for age, sex, physical activity, and body fat percentage.ConclusionIncreased BMI, especially overweight/obesity, was significantly associated with the increased risk of worse cardiovascular health, measured by non-invasive cardiovascular parameters. Efforts need to be focused on improving interventions to lower BMI/reduce overweight and obesity in North Carolina, especially in Appalachian populations.
Hidden state inference requires abstract contextual representations in ventral hippocampus
Authors: Mishchanchuk Karyna , Gregoriou Gabrielle , Qü Albert , Kastler Alizée , Huys Quentin , Wilbrecht Linda , MacAskill Andrew F. 
Published: May 2024

ABSTRACTThe ability to form and utilize subjective, latent contextual representations to influence decision making is a crucial determinant of everyday life. The hippocampus is widely hypothesized to bind together otherwise abstract combinations of stimuli to represent such latent contexts, and to allow their use to support the process of hidden state inference. Yet, direct evidence for this remains limited. Here we show that the CA1 area of the ventral hippocampus is necessary for mice to perform hidden state inference during a 2-armed bandit task. vCA1 neurons robustly differentiate between the two abstract contexts required for this strategy in a manner similar to the differentiation of spatial locations, despite the contexts being formed only from past probabilistic outcomes. These findings offer insight into how latent contextual information is used to optimize decision-making processes, and emphasize a key role of the hippocampus in hidden state inference.
Advancing Privacy-Aware Machine Learning on Sensitive Data via Edge-Based Continualµ-Training for Personalized Large Models
Authors: Huang Zhaojing , Yu Leping , Herbozo Contreras Luis Fernando , Eshraghian Kamran , Truong Nhan Duy , Nikpour Armin , Kavehei Omid 
Published: May 2024

AbstractThis paper introduces an innovative method for fine-tuning a larger multi-label model for abnormality detection, utilizing a smaller trainer and advanced knowledge distillation techniques. It delves into the effects of fine-tuning on various abnormalities, noting varied improvements based on the Original Model’s performance in specific tasks. The experimental setup, optimized for on-device inference and fine-tuning with limited computational resources, demonstrates moderate yet promising enhancements in model performance post-fine-tuning. Key insights from the study include the importance of aligning theµ-Trainer’s behavior with the Original Model and the influence of hyper-parameters like the batch size on fine-tuning outcomes. The research acknowledges limitations such as the limited exploration of loss functions in multi-label models and constraints in architectural design, suggesting potential avenues for future investigation. While the proposed Naive Continual Fine-tuning Process is in its early stages, it highlights the potential for long-term model personalization. Moreover, using weight transfer exclusively for fine-tuning amplifies user privacy protection through on-device fine-tuning, devoid of transferring data or gradients to the server. Despite modest performance improvements after fine-tuning, these layers represent a small fraction (0.7%) of the total weights in the Original Model and 1.6% in theµ-Trainer. This study establishes a foundational framework for advancing personalized model adaptation, on-device inference, and fine-tuning while emphasizing the importance of safeguarding data privacy in model development.
Predicting 30-Day In-Hospital Mortality in Surgical Patients: A Logistic Regression Model Using Comprehensive Perioperative Data
Authors: Hofmann Jonathan , Bouras Andrew , Patel Dhruv , Chetla Nitin , Balaji Nia , Boulis Michael 
Published: May 2024

AbstractBackgroundAccurate prediction of postoperative outcomes, particularly 30-day in-hospital mortality, is crucial for improving surgical planning, patient counseling, and resource allocation. This study aimed to develop and validate a logistic regression model to predict 30-day in-hospital mortality using comprehensive perioperative data from the INSPIRE dataset.MethodsWe conducted a retrospective analysis of the INSPIRE dataset, comprising approximately 130,000 surgical cases from Seoul National University Hospital between 2011 and 2020. The primary objective was to develop a logistic regression model using preoperative and intraoperative variables. Key predictors included demographic information, clinical variables, laboratory values, and the emergency status of the operation. Missing data were addressed through multiple imputation, and feature selection was performed using univariate analysis and clinical judgment. The model was validated using cross-validation and assessed for performance using ROC AUC and precision-recall AUC metrics.ResultsThe logistic regression model demonstrated high predictive accuracy, with an ROC AUC of 0.978 and a precision-recall AUC of 0.958. Significant predictors of 30-day in-hospital mortality included emergency status of the operation (OR: 1.56), preoperative prothrombin time (PT/INR) (OR: 1.53), potassium levels (OR: 1.49), body mass index (BMI) (OR: 1.37), serum sodium (OR: 1.11), creatinine levels (OR: 1.04), and albumin levels (OR: 0.85).ConclusionThis study successfully developed and validated a logistic regression model to predict 30-day in-hospital mortality using comprehensive perioperative data. The model’s high predictive accuracy and reliance on routinely collected clinical and laboratory data enhance its feasibility for integration into existing clinical workflows, providing real-time risk assessments to healthcare providers. Future research should focus on external validation in diverse clinical settings and prospective studies to assess the practical impact of this predictive model.
Leveraging regulatory monitoring data for quantitative microbial risk assessment ofLegionella pneumophilain cooling towers
Authors: Sylvestre Émile , Charron Dominique , Lefebvre Xavier , Bedard Emilie , Prévost Michèle 
Published: May 2024

AbstractCooling towers are critical engineered water systems for air conditioning and refrigeration but can create favorable conditions forLegionella pneumophilagrowth and aerosolization. Human exposure toL. pneumophila-contaminated aerosols can cause Legionnaire’s disease. Routine monitoring ofL. pneumophilain cooling towers offers possibilities to develop quantitative microbial risk assessment (QMRA) to guide system design, operation, control, and maintenance. Here, we used the regulatory monitoring database from Quebec, Canada, to develop statistical models for predictingL. pneumophilaconcentration variability in cooling towers and integrate these models into a screening-level QMRA to predict human health risks. Analysis of 105,463 monthlyL. pneumophilatest results revealed that the exceedance rate of the 104colony forming unit (CFU) per liter threshold was constant at 10% from 2016 to 2020, emphasizing the need to better validate the efficacy of corrective measures following the threshold exceedances. Among 2,852 cooling towers, 51.2% reported no detections, 38.5% had up to nine positives, and 10.2% over ten. The gamma or the lognormal distributions adequately described site-specific variations inL. pneumophilaconcentrations, but parametric uncertainty was very high for the lognormal distribution. We showed that rigorous model comparison is essential to predict peak concentrations accurately. Using QMRA, we found that, to meet a health-based target of 10-6DALY/pers.-year for clinical severity infections, an averageL. pneumophilaconcentration below 1.4 × 104CFU L-1should be maintained in cooling towers. We identified 137 cooling towers at risk of exceeding this limit, primarily due to the observation or prediction of rare peak concentrations above 105CFU L-1. Effective mitigation of those peaks is critical to controlling public health risks associated withL. pneumophila.
Deep-Learning Structure Elucidation from Single-Mutant Deep Mutational Scanning
Authors: Drake Zachary C. , Day Elijah , Toth Paul , Lindert Steffen 
Published: May 2024

AbstractDeep learning has revolutionized the field of protein structure prediction. AlphaFold2, a deep neural network, vastly outperformed previous algorithms to provide near atomic-level accuracy when predicting protein structures. Despite its success, there still are limitations which prevent accurate predictions for numerous protein systems. Here we show that sparse residue burial restraints from deep mutational scanning (DMS) can refine AlphaFold2 to significantly enhance results. Burial information extracted from DMS is used to explicitly guide residue placement during structure generation. DMS-Fold was validated on both simulated and experimental single-mutant DMS, with DMS-Fold outperforming AlphaFold2 for 89% of protein targets and with 253 proteins having an improvement greater than 0.1 in TM-score. DMS-Fold is free and publicly available:https://github.com/LindertLab/DMS-Fold.
Cryo-EM structure of the bicarbonate receptor GPR30
Authors: Kaneda Shota , Jo-Watanabe Airi , Akasaka Hiroaki , Oshima Hidetaka S. , Yokomizo Takehiko , Shihoya Wataru , Nureki Osamu 
Published: May 2024

AbstractG-protein-coupled receptor 30 (GPR30) is a bicarbonate receptor that plays a vital role in cellular responses to extracellular pH and ion homeostasis. Despite its significance, the mechanisms by which GPR30 interacts with bicarbonate ions remain elusive. There is no consensus on a drug that targets GPR30, and the difficulty in the pharmacological analysis has limited biological and drug discovery researches on GPR30. Here, we present the cryo-electron microscopy structure of human GPR30 in the presence of bicarbonate ions at 3.2 Å resolution. Our structure reveals unique extracellular pockets and critical residues for bicarbonate binding and activation. Functional assays demonstrate that mutations in these residues impair bicarbonate-induced GPR30 activation, underscoring their importance in receptor function. This study also provides insights into the G-protein coupling, highlighting the structural divergence between GPR30 and other GPCRs. Our findings not only advance the understanding of the role of GPR30 in pH homeostasis but also pave the way for the development of high-affinity drugs targeting GPR30 for therapeutic interventions in diseases associated with acid-base imbalance.
Hospital Characteristics Associated with Observed Transcatheter Aortic Valve Replacement Prices
Authors: Ochakovski Roni , Zhang Yuqi , Cerullo Marcelo 
Published: May 2024

AbstractTranscatheter aortic valve replacement (TAVR) has emerged as a revolutionary treatment for aortic stenosis. However, TAVR prices vary considerably, and factors associated with this variation remain unclear. We aim to describe the variation in TAVR prices in relation to hospital financial performance among institutions ranked by the U.S. News and World Report (USNWR). Using a modified two-part model, we examined financial and operational characteristics (TAVR performance scores, median all-payer within-hospital TAVR price, net hospital profit margin, hospital markups [i.e., charge-to-cost ratio], bed days available, and CMS wage index) of 640 TAVR-performing hospitals ranked by the USNWR. After determining observed to expected (O:E) ratios for TAVR prices for each hospital, we then examined hospital characteristics across O:E quintiles. Overall, price disclosure was 48.6% (n=311). Between the lowest and highest O:E quintiles, median hospital markup (4.75 vs 5.33; p=0.41) and median net hospital margin (1.76 vs 3.15; p=0.12) were comparable. The highest O:E ratio quintile had lower median TAVR prices compared to the lowest O:E ratio quintile ($72,129.12 vs $49,022.03; p<0.001). Most significantly, TAVR price IQR’s within hospitals had a linear decline from the lowest to the highest O:E ratio quintiles ($119,043 vs $27,240; p<0.001). USNWR ranking scores had no significant variation across the quintiles (p=0.95). We concluded that hospitals that charge more than expected for TAVRs do not have higher profit margins nor markups and are not higher ranked by USNWR as those that charge less than expected. Additionally, with higher observed over expected TAVR prices, the variation in TAVR rates within hospitals decreased linearly. Finally, O:E TAVR price ratios appear to have no association with publicly reported hospital quality.
Impact of COVID-19 pandemic on rates of congenital heart disease procedures among children: Prospective cohort analyses of 26,270 procedures in 17,860 children using CVD-COVID-UK consortium record linkage data
Authors: Karthikeyan Suseeladevi Arun , Denholm Rachel , Babu-Narayan Sonya , Sinha Shubhra , Stoica Serban , Dong Tim , Angelini Gianni , Sudlow Cathie , Walker Venexia , Brown Katherine , Caputo Massimo , Lawlor Deborah A 
Published: May 2024

AbstractBackgroundThe COVID-19 pandemic necessitated major re-allocation of health care services. Our aim was to assess the impact on paediatric congenital heart disease procedures during different pandemic periods compared to the pre-pandemic period, to inform appropriate responses to future major health services disruptions.Methods and ResultsWe analysed 26,270 procedures from 17,860 children between 01-Jan-2018 and 31-Mar-2022 in England, linking them to primary/secondary care data. The study period included pre-pandemic and pandemic phases, with latter including three restriction periods and corresponding relaxation periods. We compared procedure characteristics and outcomes between each pandemic periods and the pre-pandemic period. There was a reduction in all procedures across all pandemic period with the largest reductions during the first, most severe restriction period (23-Mar-2020 to 23-Jun-2020), and the relaxation period following second restrictions (03-Dec-2020 to 04-Jan-2021) coinciding with winter pressures. During the first restrictions, median procedures per week dropped by 51 compared with the pre-pandemic period(80 vs 131 per week, p = 4.98×10-08). Elective procedures drove these reductions, falling from 96 to 44 per week, (p = 1.89×10-06), while urgent (28 vs 27 per week, p = 0.649) and life-saving/emergency procedures (7 vs 6 per week, p = 0.198) remained unchanged. Cardiac surgery rates increased, and catheter-based procedure rates reduced during the pandemic. Procedures for children under 1-year were prioritized, especially during the first four pandemic periods. No evidence was found for differences in post procedure complications (age adjusted odds ratio 1.1 (95%CI: 0.9, 1.4) or post procedure mortality (age and case mix adjusted odds ratio 0.9 (0.6, 1.3)).ConclusionsPrioritization of urgent, emergency and life-saving procedures during the pandemic, particularly in infants, did not impact paediatric CHD post procedure complications or mortality. This information is valuable for future major health services disruptions, though longer-term follow-up of the effects of delaying elective surgery is needed.
Exploring nonstandard
Hbb¯
interactions at future electron-proton colliders
Authors: Haghighat Gholamhossein , Jafari Reza , Khanpour Hamzeh , Mohammadi Najafabadi Mojtaba 
Published: May 2024

In this paper, we use the charged-current Higgs boson production process at future electron-proton colliders, e−p→Hjνe, with the subsequent decay of the Higgs boson into a bb¯ pair, to probe the Standard Model effective field theory with dimension-six operators involving the Higgs boson and the bottom quark. The study is performed for two proposed future high-energy electron-proton colliders, the Large Hadron Electron Collider (LHeC) and the Future Circular Collider (FCC-he) at the center-of-mass energies of 1.3 TeV and 3.46 TeV, respectively. Constraints on the CP-even and CP-odd Hbb¯ couplings are derived by analyzing the simulated signal and background samples. A realistic detector simulation is performed and a multivariate technique using the gradient boosted decision trees algorithm is employed to discriminate the signal from background. Expected limits are obtained at 95% confidence level for the LHeC and FCC-he assuming the integrated luminosities of 1, 2 and 10  ab−1. We find that using 1  ab−1 of data, the CP-even and CP-odd Hbb¯ couplings can be constrained with accuracies of the order of 10−3 and 10−2, respectively, and a significant region of the unprobed parameter space becomes accessible.

Published by the American Physical Society
2024
Framework of managerial wisdom: an analysis using the TOPSIS and MICMAC process
Authors: Valiyan Hasan , Abdoli Mohammadreza , Ashrafi Mehdi , Barati Hadi 
Published: May 2024

PurposeBehavioral characteristics and the existence of personal knowledge and skills that are essential in forming a capable manager with specialized insight, is one of the most important evaluation processes in appointing a manager. Wisdom as a trait of a set of personal characteristics and cognitive knowledge of a capable manager will gain competitive advantages for the company and external stakeholders. The purpose of this study is to present the framework of managerial financial wisdom through qualitative and quantitative research methodologies.Design/methodology/approachThe study uses grounded theory methodology to identify factors for managerial financial wisdom and uses a MICMAC process to evaluate the power of driving and dependence among factors. MaxQDA software was used for data coding and analysis. The MICMAC process by determining the location of each of the sub-themes in the four-dimensional matrix, seeks to determine the most effective criterion in the context of the study. The method of data collection is mixed and in terms of philosophical nature, this study is inductive/comparative.FindingsThe results of the research in the qualitative part indicate the presentation of a framework based on two categories, four components, and thirty themes. The results of the quantitative part of the study also showed that the most important factor of managerial financial wisdom is market-oriented values. Finally, it was found that the social trust caused by managerial financial wisdom is considered a factor in gaining more shares in the capital market due to the positive stimulus in this dimension.Originality/valueThe study is relevant for both practitioners and academia and has significant implications. For practitioners, the structural linkages identified will help enhance managerial wisdom effectiveness and drive the critical metrics for financial decisions and shareholders' rights protection. The study may help financial market practitioners better understand future policies. Academia and researchers can take reference from methodologies used in this study for exploring factors of interest and developing matrix linkages among them.
Competency development opportunities and organizational citizenship behaviors: the mediating role of subjective career success
Authors: Teow Hui Hui , Au Wee Chan , Ahmed Pervaiz 
Published: May 2024

PurposeBuilding upon Social Exchange Theory, this study explores the relationship between perceived opportunities for competency development (POCD), subjective career success (SCS), and organizational citizenship behaviors (OCBs). Given the importance of SCS in providing mutual benefits for both the employee and the organization, this study examines how SCS plays a mediating role in the relationship between POCD and OCBs.Design/methodology/approachData was collected from 353 full-time working professionals in Malaysian organizations through a two-wave hardcopy questionnaire survey. The data was then analyzed using structural equation modeling and the bootstrapping method.FindingsThe study found no significant direct relationship between POCD and OCBs. However, the bootstrapping analysis uncovered that SCS mediated this relationship, indicating a significant full mediation effect.Research limitations/implicationsTo increase the external validity of findings, future studies should include samples from various industries in both the public and private sectors.Originality/valueIn contrast to the findings from previous studies, this study adds value to the career literature by demonstrating that without a sense of SCS, even with competency development opportunities, employees are less likely to engage in OCBs. It highlights the importance of SCS in fostering mutual benefits for both the employee and the organization.
Big data, machine learning and uncertainty in foresight studies
Authors: Muraro Vinicius , Salles-Filho Sergio 
Published: May 2024

Purpose
Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.

Design/methodology/approach
The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.

Findings
It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.

Originality/value
This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.