bluuebunny/crossref_metadata_2025_split huggingface dataset:
Determinants of financial distress in the building construction sub-sector companies listed on the Indonesia Stock Exchange
Authors: Setowening Sekar , Djuminah Djuminah 
Published: May 2024

Purpose — This study aims to analyze the influence of profitability, leverage, and intellectual capital on financial distress in companies within the building construction sub-sector.Method — The research method involves quantitative and regression analysis. The sample consists of companies within the building construction sub-sector that consistently published financial reports during the period 2016-2021. The research population comprises 96 building construction companies listed on the Indonesia Stock Exchange, with 16 companies meeting the sample requirements. Data collection is performed using purposive sampling, and the analysis is conducted using EViews 10. Various tests, including classic assumption tests, feasibility analysis models, panel regression analysis, and coefficient of determination tests, are employed in the analysis.Result — The study results indicate a significant positive effect of profitability on the level of financial distress, suggesting that higher levels of profitability correspond to lower financial distress. Conversely, leverage demonstrates a significant negative effect on financial distress, implying that higher levels of leverage are associated with increased financial distress for the company. However, the study did not identify a significant relationship between intellectual capital and the level of financial distress, suggesting that the level of intellectual capital does not significantly influence the level of financial distress.Practical implications — Management in the building construction sub-sector is encouraged to prioritize strategies and tactics aimed at enhancing company profitability. Focusing on efforts to improve operational efficiency, optimize asset utilization, and enhance the effectiveness of marketing strategies can contribute to an increase in the company's profitability.
Excess Fatal Overdoses in the United States During the COVID-19 Pandemic by Geography and Substance Type: March 2020–August 2021
Authors: Chandra Jay , Charpignon Marie-Laure , Bhaskar Anushka , Therriault Andrew , Chen Yea-Hung , Mooney Alyssa , Dahleh Munther A. , Kiang Mathew V. , Dominici Francesca 
Published: May 2024

Objectives. To assess heterogeneity in pandemic-period excess fatal overdoses in the United States, by location (state, county) and substance type. Methods. We used seasonal autoregressive integrated moving average (SARIMA) models to estimate counterfactual death counts in the scenario that no pandemic had occurred. Such estimates were subtracted from actual death counts to assess the magnitude of pandemic-period excess mortality between March 2020 and August 2021. Results. Nationwide, we estimated 25 668 (95% prediction interval [PI] = 2811, 48 524) excess overdose deaths. Specifically, 17 of 47 states and 197 of 592 counties analyzed had statistically significant excess overdose-related mortality. West Virginia, Louisiana, Tennessee, Kentucky, and New Mexico had the highest rates (20–37 per 100 000). Nationally, there were 5.7 (95% PI = 1.0, 10.4), 3.1 (95% PI = 2.1, 4.2), and 1.4 (95% PI = 0.5, 2.4) excess deaths per 100 000 involving synthetic opioids, psychostimulants, and alcohol, respectively. Conclusions. The steep increase in overdose-related mortality affected primarily the southern and western United States. We identified synthetic opioids and psychostimulants as the main contributors. Public Health Implications. Characterizing overdose-related excess mortality across locations and substance types is critical for optimal allocation of public health resources. ( Am J Public Health. 2024;114(6):599–609. https://doi.org/10.2105/AJPH.2024.307618 )
STEM PjBL Sparks Global Ecoliteracy Advancement
Authors: Indrasari Ayu , Wulandari Fitria Eka 
Published: May 2024

This study examines the impact of a STEM-based Project-Based Learning (PjBL) model on Class VIII students' ecoliteracy abilities at SMP Negeri 1 Tanggulangin using a quantitative pre-experimental approach. The research design employs a One Group Pretest-Posttest Design with non-probability purposive sampling. Results from administering valid ecoliteracy tests and analyzing data with the N-Gain Test and One Way ANOVA reveal a significant increase in ecoliteracy abilities after implementing the STEM-based PjBL model, indicating its effectiveness in enhancing students' environmental literacy. These findings have implications for incorporating innovative teaching methods to improve environmental education in schools.

Highlight:

Evaluates STEM-based PjBL for ecoliteracy in students.
Uses purposive sampling for targeted research.
Shows significant increase in ecoliteracy skills post-implementation.

Keyword: STEM-based PjBL, Ecoliteracy, Quantitative Approach, Pre-experimental Methods, Environmental Education
Programable Albumin-Hitchhiking Nanobodies Enhance the Delivery of STING Agonists to Potentiate Cancer Immunotherapy
Authors: Wilson John , Kimmel Blaise , Arora Karan , Chada Neil , Bharti Vijaya , Kwiatkowski Alexander , Finklestein Jonah , Hanna Ann , Arner Emily , Sheehy Taylor , Pastora Lucinda , Yang Jinming , Pagendarm Hayden , Stone Payton , Taylor Brandie , Hubert Lauren , Gibson-Corley Kathern , May Jody , McLean John , Rathmell Jeffrey , Richmond Ann , Rathmell Wendy , Balko Justin , Fingleton Barbara , Hargrove-Wiley Ebony 
Published: May 2024

Abstract

Stimulator of interferon genes (STING) is a promising target for potentiating antitumor immunity, but multiple pharmacological barriers limit the clinical utility, efficacy, and/or safety of STING agonists. Here we describe a modular platform for systemic administration of STING agonists based on nanobodies engineered for in situ hitchhiking of agonist cargo on serum albumin. Using site-selective bioconjugation chemistries to produce molecularly defined products, we found that covalent conjugation of a STING agonist to anti-albumin nanobodies improved pharmacokinetics and increased cargo accumulation in tumor tissue, stimulating innate immune programs that increased the infiltration of activated natural killer cells and T cells, which potently inhibited tumor growth in multiple mouse tumor models. We also demonstrated the programmability of the platform through the recombinant integration of a second nanobody domain that targeted programmed cell death ligand-1 (PD-L1), which further increased cargo delivery to tumor sites while also blocking immunosuppressive PD-1/PD-L1 interactions. This bivalent nanobody carrier for covalently conjugated STING agonists stimulated robust antigen-specific T cell responses and long-lasting immunological memory, conferred enhanced therapeutic efficacy, and was effective as a neoadjuvant treatment for improving responses to adoptive T cell transfer therapy. Albumin-hitchhiking nanobodies thus offer an enabling, multimodal, and programmable platform for systemic delivery of STING agonists with potential to augment responses to multiple immunotherapeutic modalities.
Targeting Fatty Acid Synthase Reduces Aortic Atherosclerosis and Inflammation
Authors: zayed Mohamed , Meade Rodrigo , Engel Connor , Belaygorod Larisa , Arif Batool , Hsu Fong-Fu , Adak Sangeeta , Catlett Ryan , Semenkovich Clay 
Published: May 2024

Abstract

Fatty acid synthase (FAS) is predominantly expressed in the liver and adipose tissue. It plays vital roles in de novo synthesis of saturated fatty acid and regulates insulin sensitivity. We previously demonstrated that serum circulating FAS (cFAS) is a clinical biomarker for advanced atherosclerosis, and that it is conjugated to low-density lipoproteins (LDL). However, it remains unknown whether cFAS can directly impact atheroprogression. To investigate this, we evaluated whether cFAS impacts macrophage foam cell formation – a important cellular process leading to atheroprogression. Macrophages exposed to human serum containing high levels of cFAS showed increased foam cell formation as compared to cells exposed to serum containing low levels of cFAS. This difference was not observed using serum containing either high or low LDL. Pharmacological inhibition of cFAS using Platensimycin (PTM) decreased foam cell formation in vitro. In Apoe−/− mice with normal FAS expression, administration of PTM over 16 weeks along with a high fat diet decreased cFAS activity and aortic atherosclerosis without affecting circulating total cholesterol. This effect was also seen in Apoe−/− mice with liver-specific deletion of hepatic FAS. Reductions in aortic root plaque were associated with decreased macrophage infiltration. These findings demonstrate that cFAS can impact arterial atheroprogression.
Label free metabolic imaging to enhance the efficacy of chimeric antigen receptor T cell therapy
Authors: Skala Melissa , Pham Dan , Cappabianca Daniel , Forsberg Matthew , Weaver Cole , Mueller Katherine , Tommasi Anna , Vidugiriene Jolanta , Lauer Anthony , Sylvester Kayla , Bugel Madison , Capitini Christian , Saha Krishanu 
Published: May 2024

Abstract

Chimeric antigen receptor (CAR) T cell therapy for solid tumors is challenging not only because of the immunosuppressive tumor microenvironment, but also because of a complex manufacturing process that is difficult to monitor. Manufacturing directly impacts CAR T cell yield, phenotype, and metabolism, which correlate with in vivo potency and persistence. In particular, though metabolic fitness is a critical quality attribute, how T cell metabolic requirements vary throughout manufacturing remains unexplored. Here, we address this limitation with optical metabolic imaging (OMI), a non-invasive, label-free method to evaluate single-cell metabolism based on autofluorescent metabolic coenzymes NAD(P)H and FAD. Using OMI, we identified the dominating impacts of media composition over the selection of antibody stimulation and/or cytokines on anti-GD2 CAR T cell metabolism, activation strength and kinetics, and phenotype. We demonstrated that OMI parameters can indicate cell cycle stage and optimal gene transfer conditions for both viral transduction and electroporation-based CRISPR/Cas9. Notably, in a virus-free CRISPR-edited anti-GD2 CAR T cell model, OMI measurements allowed accurate prediction of an oxidative metabolic phenotype that yielded higher in vivo potency against neuroblastoma. Our data supports OMI’s potential as a robust, sensitive analytical tool to identify optimal manufacturing conditions and monitor cell metabolism throughout manufacturing for increased CAR T cell yield and metabolic fitness.
Empowered, Accountable, and Committed? Applying self-determination theory to examine work-place procrastination.
Authors: Kausar Fazia , Ijaz Muhammad Umer , Rasheed Maria , Suhail Aamir , Islam Usman 
Published: May 2024

Abstract

Background – This research examines the role of psychological characteristics (psychological empowerment and psychological ownership) in reducing employees’ workplace procrastination. Further, using organizational commitment theory, this research examines the mediating mechanism of organizational commitment in the relationship between psychological characteristics and employees’ workplace procrastination.
Methods– Two studies were conducted to test our hypotheses. In Study 1, we collected online data from 146 working individuals via the crowdsourcing platform Prolific. For Study 2, we gathered data from 384 employees in the banking sector in Pakistan using a paper-and-pencil survey questionnaire. Both studies employed a time-lagged data collection approach to minimize common-method bias.
Results – The research findings supported our hypotheses that organizational commitment mediated the positive relationship between psychological characteristics and employees’ workplace procrastination.
Conclusion – Previous studies have focused on the factors responsible for employees’ workplace procrastination. This study advances our knowledge by identifying the antecedents of employees’ workplace procrastination and examining the underlying mediating mechanism.
Quantifying the impact of the food we eat on species extinctions
Authors: Ball Thomas , Dales Michael , Eyres Alison , Green Jonathan , Madhavapeddy Anil , Williams David , Balmford Andrew 
Published: May 2024

Abstract

Agriculturally-driven habitat degradation and destruction is the biggest threat to global biodiversity, yet the impacts on extinctions of different types of food and where they are produced and the mitigation potential of different interventions remain poorly quantified. Here we link the LIFE biodiversity metric – a high-resolution global layer describing the marginal impact of land-use on extinctions of ~30K vertebrate species – with food consumption and production data and provenance modelling. Using an opportunity-cost framing we discover that the impact of what we eat on species extinctions varies widely both across and within foods, in many cases by more than an order of magnitude. Despite marked differences in per-capita impacts across countries, there are consistent patterns that could be leveraged for mitigating harm to biodiversity. We anticipate the approach and results outlined here could inform decision-making across many levels, from national policies to individual dietary choices.
SegPhase: Development of Arrival Time Picking Models for Japan’s Seismic Network Using the Hierarchical Vision Transformer
Authors: Katoh Shinya , Iio Yoshihisa , Nagao Hiromichi , Katao Hiroshi , Sawada Masayo , Tomisaka Kazuhide 
Published: May 2024

Abstract

A seismic arrival time picking model, SegPhase, is introduced to automatically process a large amount of seismic data recorded by large dense seismic networks with different sampling frequencies and numbers of observed components. Three models were created to address different sampling frequencies and the number of observed components in each network. The model structure uses a hierarchical Vision Transformer structure, which has not previously been used in seismic arrival time picking models and shows superior performance compared to conventional models using convolutional layers. The performance of SegPhase models was verified in terms of the relationship between arrival time residuals, output probability values, epicentral distance, signal-to-noise ratio, and magnitude, and compared to the PhaseNet models. The SegPhase models had better picking performance and number of seismic detections. Moreover, when the SegPhase models are applied to continuous waveforms, the relationship between the number of detections, O-C values and hypocenter determination error, and the threshold of output probability values used in the analysis was then investigated. It was found that when the threshold was lowered, more arrival times were used for earthquake detection not only with lower output probability values but also with higher output probability. Therefore, lowering the threshold allows the Phase association to make better use of the arrival times that the model assumes to be highly accurate. Although lowering the threshold value increases the error, its effect does not significantly impact the overall result.
Exploring Internet Use and Parental Mediation in Pakistan: A Thematic Analysis
Authors: Ali Sana , Riaz Saqib , Pasha Saadia Anwar , Youseef Enaam 
Published: May 2024

Abstract

The Internet has become one of the most influential components of a child’s everyday life. While it offers certain benefits, it also contains some darker sides, needing strong consideration by the parents and guardians. This research examines parents' perceptions and role in ensuring safe internet use among Pakistani children. Theoretically supported by parental mediation theory, this research gathered qualitative data from 12 parents with children under 18. Data analyzed using thematic qualitative analysis revealed that parents showed a subtle understanding of children's internet usage patterns among participants. While acknowledging the educational benefits of the internet, parents also share concerns about its possible risks, including exposure to inappropriate content and online predators. Regular communication, confidence-building practices, and active monitoring approaches are essential for protecting children's online experiences. These results highlight the critical role of parental mediation in navigating the complexities of children's internet use and encouraging a secure online environment. Considering the findings, it is concluded that parents can effectively mitigate possible risks associated with children's internet use by promoting open communication and implementing assertive monitoring strategies. These insights highlight the need for ongoing research and collaborative efforts among parents, educators, and policymakers to develop extensive frameworks prioritizing children's digital well-being in an increasingly connected world.
Advancing Sepsis Diagnosis and Immunotherapy Machine Learning-Driven Identification of Stable Molecular Biomarkers and Therapeutic Targets
Authors: Liu Fangpeng , Xiong Weichuan , Xiao Rui , Zhan Yian 
Published: May 2024

Abstract

This research presents a novel integrated approach combining genomic analysis and machine learning to identify biomarkers and drug sensitivities specific to sepsis, aiming to facilitate personalized treatment strategies. We comprehensively examined gene expression profiles from sepsis patients and healthy controls by utilizing the Gene Expression Omnibus (GEO) database, specifically datasets GSE154918 and GSE134347. Through the application of the ESTIMATE algorithm, weighted gene co-expression network analysis (WGCNA), and a range of machine learning techniques, we identified crucial Sepsis-Related Genes (SRGs), Immune-Related Differentially Expressed Genes (IRDEGs), and Important Immune-related genes (IIRGs). Our analysis revealed significant differences in immune and stromal scores between sepsis patients and controls, highlighting the altered immune landscape in sepsis. The study also uncovered specific genes associated with drug sensitivity, providing insights into potential therapeutic targets. The predictive model developed demonstrated high accuracy in sepsis diagnosis and prognosis, validated by independent datasets. These findings contribute to understanding sepsis at a molecular level and open new avenues for developing personalized therapeutic interventions, underscoring the potential of integrating genomic analysis and machine learning in sepsis research.
Evaluating Machine Learning Approaches for Detecting Fake News on Social Media
Authors: Negi Jyoti , Sood Sumesh , kumari Kritika 
Published: May 2024

Abstract

The study aims to evaluate the efficiency of machine learning and ensemble methods in identifying fake news using the liar dataset. A novel model is proposed that utilizes a voting ensemble method to achieve optimal accuracy in fake news detection. The proposed methodology involves data collection from Kaggle, preprocessing to clean and prepare the dataset, and feature selection using term frequency and inverse document frequency. Subsequently, machine learning algorithms are trained and tested, and model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Various classifiers, including Naive Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, and Random Forest and the vote method were utilized. During the implementation phase, Support Vector Machine achieved the highest accuracy among individual machine learning algorithms i.e. 62%. However, upon applying the vote algorithm on various machine learning models, the best combination, consisting of Naive Bayes, Support Vector Machine, K-Nearest Neighbors, and Random Forest, achieved the highest accuracy of 63.07%. This indicates that combining multiple classifiers into a single classifier using the vote algorithm produced the best results compared to individual machine learning algorithms. The research introduces novel contributions to fake news detection by proposing a unique model utilizing a voting ensemble method. The effectiveness of ensemble methods compared to individual machine learning algorithms is explored, and combining classifiers trained on different feature sets is investigated. Through experimentation with the Liar dataset, the optimal combination of classifiers for fake news detection is identified.
Optimum Strata Boundaries for Breast Cancer Patients Using Dynamic Programming Approach
Authors: Danish Faizan 
Published: May 2024

Abstract

The study examines the determination of stratification points for two study variables within the framework of simple random sampling, particularly focusing on calculating the population mean using a closely associated variable. Utilizing a superpopulation model, the investigation seeks to derive minimal equations by minimizing aggregated variance with the assistance of the variables under scrutiny. In order to improve the accuracy of estimating population parameters within a stratified sampling framework, it is suggested in the article to establish optimal strata boundaries (OSB) instead of categorizing variables, the study focuses on continuous variables for analysis. The creation of homogeneous units within each stratum, facilitated by optimally constructed OSB, leads to more effective sample sizes (SS) and consequently enhances precision in estimation. However, it's noted that the OSB and SS may not remain optimal in cases where a fixed total sample size is mandated, particularly in survey designs constrained by a fixed budget. To address this challenge, the article outlines a methodology for calculating the OSB and SS under the condition that the survey's per-unit stratum measurement costs or its probability density function is understood. An empirical demonstration of a design-based stratification approach is showcased using breast cancer data, where the mean perimeter is estimated based on mean radius and mean texture. Furthermore, numerical examples are provided for hypothetical study variables following Cauchy and standard power distributions. The novel approach has been integrated into the revised stratifyR package and LINGO software. The research outcomes indicate that the newly introduced method demonstrates superior efficiency or comparable performance when aiming to refine the precision of population parameter estimations.
Fas Ligand enhances vessel maturation and inhibits vascular leakage associated with age-related macular degeneration
Authors: Koirala Adarsha , Marshak-Rothstein Ann , Ksander Bruce R. , Gregory-Ksander Meredith 
Published: May 2024

Abstract

Neovascular age-related macular degeneration (AMD), results from choroidal neovascularization (CNV), retinal edema and loss of photoreceptors. Previous studies suggested that Fas Ligand (FasL) on retinal pigment epithelial cells inhibited CNV by inducing apoptosis of infiltrating Fas + vascular endothelial cells. However, induction of apoptosis depends on membrane-bound (mFasL) while the FasL cleavage product (sFasL) is neuroprotective. To better understand how FasL regulates the development of CNV, we used a mouse model of laser CNV to evaluate the development of CNV in mice with a FasL cleavage site mutation (ΔCS) and can only express the membrane-bound form of FasL. There was no significant difference in CNV size and area of vascular leakage in homozygous FasLΔCS/ΔCS mice when compared to wild type mice. Unexpectedly, heterozygous FasLΔCS/WT mice developed significantly less vascular leakage and showed accelerated neovessel maturation. However, CNV was not prevented in heterozygous FasLΔCS/WT mice if the Fas receptor was deleted in myeloid cells (FasLΔCS/+ Fasflox/flox CreLysM). Thus, FasL-mediated CNV inhibition depends on the extent of FasL cleavage, and on FasL engagement of Fas + myeloid cells. Moreover, accelerated neovessel maturation prevents vascular leakage in AMD.