bluuebunny/crossref_metadata_2025_split huggingface dataset:
Innovative deep learning techniques for monitoring aggressive behavior in social media posts
Authors: Han Huimin , Asif Muhammad , Awwad Emad Mahrous , Sarhan Nadia , Ghadi Yazeed Yasid , Xu Bo 
Published: January 2024

AbstractThe study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors. With the rising prevalence of online harassment, developing effective automated tools for aggression detection in digital communications has become imperative. This research assesses the efficacy of Random Forest, Light Gradient Boosting Machine (LightGBM), Logistic Regression, Support Vector Machine (SVM), and Naive Bayes classifiers in identifying cyber troll posts within a publicly available dataset. Each ML classifier was trained and tested on a dataset curated for the detection of cyber trolls. The performance of the classifiers was gauged using confusion matrices, which provide detailed counts of true positives, true negatives, false positives, and false negatives. These metrics were then utilized to calculate the accuracy, precision, recall, and F1 scores to better understand each model’s predictive capabilities. The Random Forest classifier outperformed other models, exhibiting the highest accuracy and balanced precision-recall trade-off, as indicated by the highest true positive and true negative rates, alongside the lowest false positive and false negative rates. LightGBM, while effective, showed a tendency towards higher false predictions. Logistic Regression, SVM, and Naive Bayes displayed identical confusion matrix results, an anomaly suggesting potential data handling or model application issues that warrant further investigation. The findings underscore the effectiveness of ensemble methods, with Random Forest leading in the cyber troll detection task. The study highlights the importance of selecting appropriate ML algorithms for text classification tasks in social media contexts and emphasizes the need for further scrutiny into the anomaly observed among the Logistic Regression, SVM, and Naive Bayes results. Future work will focus on exploring the reasons behind this occurrence and the potential of deep learning techniques in enhancing detection performance.
Causal relationships between type 1 diabetes mellitus and Alzheimer’s disease and Parkinson’s disease: a bidirectional two-sample Mendelian randomization study
Authors: Geng Chaofan , Meng Ke , Zhao Bo , Liu Xiaoduo , Tang Yi 
Published: January 2024

Abstract
Background
Previous compelling evidence suggests an association between Type 2 diabetes (T2D) and neurodegenerative diseases. However, it remains uncertain whether Type 1 diabetes mellitus (T1DM) exerts a causal influence on the risk of Alzheimer's disease (AD) and Parkinson's disease (PD). Consequently, this study employed a bidirectional two-sample Mendelian Randomization (MR) approach to investigate the causal relationship between T1DM and the genetic susceptibility to AD and PD.

Methods
We utilized large-scale cohorts derived from publicly available genome-wide association study datasets involving European populations to perform MR analyses. The primary analytical method employed was the inverse-variance weighted (IVW) approach. Furthermore, sensitivity analyses, including assessments of heterogeneity and horizontal pleiotropy, were carried out using Cochran's Q, MR-Egger intercept, and MR-PRESSO tests to enhance the robustness of our conclusions.

Results
Using the IVW-based method, the MR analysis indicated no significant association between genetically determined T1DM and AD (OR = 0.984, 95% CI: 0.958–1.011, p = 0.247). Conversely, T1DM appeared to be associated with a reduced risk of genetic susceptibility to PD (IVW: OR = 0.958, 95% CI: 0.928–0.989, p = 0.001). In the reverse direction, no evidence of reverse causality was observed between AD (OR = 1.010, 95% CI: 0.911–1.116, p = 0.881) or PD (OR = 1.164, 95% CI: 0.686–2.025, p = 0.5202) and T1DM. Additionally, our analysis found no indications of the results being influenced by horizontal pleiotropy.

Conclusion
This MR study reveals that T1DM is associated with a reduced genetic susceptibility to PD, whereas no significant genetic susceptibility is observed between T1DM and AD. These findings suggest that T1DM may have a distinct role in the development of neurodegenerative diseases compared to T2D. Further investigations are warranted to elucidate the underlying mechanisms and provide a more comprehensive understanding of this relationship.
Targeting TIGIT for cancer immunotherapy: recent advances and future directions
Authors: Zhang Peng , Liu Xinyuan , Gu Zhuoyu , Jiang Zhongxing , Zhao Song , Song Yongping , Yu Jifeng 
Published: January 2024

AbstractAs a newly identified checkpoint, T cell immunoreceptor with immunoglobulin and tyrosine-based inhibitory motif (ITIM) domain (TIGIT) is highly expressed on CD4+ T cells, CD8+ T cells, natural killer (NK) cells, regulatory T cells (Tregs), and tumor-infiltrating lymphocytes (TILs). TIGIT has been associated with NK cell exhaustion in vivo and in individuals with various cancers. It not only modulates NK cell survival but also mediates T cell exhaustion. As the primary ligand of TIGIT in humans, CD155 may be the main target for immunotherapy due to its interaction with TIGIT. It has been found that the anti-programmed cell death protein 1 (PD-1) treatment response in cancer immunotherapy is correlated with CD155 but not TIGIT. Anti-TIGIT alone and in combination with anti-PD-1 agents have been tested for cancer immunotherapy. Although two clinical studies on advanced lung cancer had positive results, the TIGIT-targeted antibody, tiragolumab, recently failed in two new trials. In this review, we highlight the current developments on TIGIT for cancer immunotherapy and discuss the characteristics and functions of TIGIT.
Extreme weather events on energy systems: a comprehensive review on impacts, mitigation, and adaptation measures
Authors: Gonçalves Ana C. R. , Costoya Xurxo , Nieto Raquel , Liberato Margarida L. R. 
Published: January 2024

AbstractEnergy systems (ES) are seriously affected by climate variability since energy demand and supply are dependent on atmospheric conditions at several time scales and by the impact of severe extreme weather events (EWEs). EWEs affect ES and can cause partial or total blackouts due to energy supply disruptions. These events significantly impact essential infrastructures and are considered one of the main causes of wide-area electrical disturbances worldwide. A comprehensive review is carried out based on 210 published studies using searches from Scopus and Google Scholar databases, to assess the impacts of EWEs—such as extreme storms, wind, and lightning events, heat, or cold waves, and freezing—on ES and their associated infrastructures—production, transmission, and distribution—worldwide, with a particular focus on wind energy systems (WES). Strategies and measures are critically reviewed and synthesized to minimize and mitigate the impact of EWEs, protect, and adapt the systems to maintain regular operations even when these events occur. Finally, physical modifications to systems and the incorporation of new technological solutions such as energy storage systems (ESS), distributed energy systems (DES), and microgrids, can enhance the network resilience and mitigate the EWEs effects.
The rheological and filtration properties of black seed (Nigella Sativa L.) ester as a base fluid in drilling fluid
Authors: Olaniyan Damilola Deborah , Sarah Akintola A. 
Published: January 2024

AbstractThe unceasing utilization of diesel oil, the regular base fluid, as the base fluid of drilling mud has brought about severe ecological worries and regulation because of its poisonousness, non-biodegradability, and similarity issues. Notwithstanding, drilling more complex wells is turning out to be progressively significant in satisfying the world’s need for oil and gas, bringing about increased commercial synthetic-based fluid importation. In light of the ecological worries associated with the usage of diesel-based drilling fluids as well as reducing commercial synthetic-based fluid imports, black seed oil (BSO) was utilized to make an ester for a new ecologically safe drilling fluid. This research explored the potential use of black seed ester in formulating oil-based muds with standard additives. It compares its qualities to that of diesel following a thorough mud check on the mud samples to evaluate the rheological properties and the impact of aging at test temperatures of 86°F, 120°F, and 150°F. Fourier transform infrared (FTIR) spectrometry technique affirmed that BSO ester is a natural compound. About the flow properties, the kinematic thickness at 40 °C of the biodiesel is 4.31 mm2/s and is higher than that of petroleum diesel, which is 3.52 mm2/s. Densities for the ester and diesel oil-based mud tests were 7.9 ppg, 7.8 ppg, 7.3 ppg, and 6.8 ppg, respectively, at 70/30, 75/25, and 80/20, oil–water-proportion (OWR), as utilized in the mud formulation. The prepared mud was aged for 24 h under static conditions to guarantee total hydration. Black seed oil (BSO) ester mud exhibited lower viscosity at all temperatures, aging conditions, and shear rates analyzed, making it more suitable for oil-well drilling fluids when compared with Diesel. BSO being a naturally occurring seed oil could be important for drilling contractors and service companies due to its good ecological acceptability and its applicability.
eRegMat – a digital registry for improved quality of antenatal care: a cluster-randomized trial in a rural area in Bangladesh
Authors: Venkateswaran Mahima , Pervin Jesmin , Dolphyne Akuba , Friberg Ingrid K. , Fjeldheim Ingvild , Frøen J. Frederik , Khatun Fatema , O’Donnell Brian , Rahman Monjur , Rahman A. M. Quaiyum , Nu U Tin , Rose Christopher James , Sarker Bidhan Krishna , Rahman Anisur 
Published: January 2024

Abstract
Introduction
Longitudinal client tracking systems with digital health interventions are recommended for implementation in resource-limited settings but lack evidence of benefits, harms, and implementation. In the eRegMat cluster-randomized controlled trial, we aimed to assess the effectiveness of an eRegistry versus an unshared digital client record.

Methods
Fifty-nine primary health care facilities in Matlab, Bangladesh were randomized with a 1:1 allocation ratio to receive an eRegistry (intervention, 30 health facilities) with decision support, feedback dashboards and targeted client communication, or an unshared digital client record without digital health interventions (control, 29 health facilities). We assessed timely antenatal care attendance, quality of care, and health outcomes. Outcome data were captured in the eRegistry, or unshared digital client record used by health workers, and through a postpartum household survey. We estimated adjusted relative risks (ARRs) following the intention-to-treat principle and adjusted for cluster randomization.

Results
From October 2018 to June 2020, 3023 pregnant women were enrolled in the intervention and 2746 in the control groups through community and facility registrations. Intervention and control groups did not differ for the primary outcomes: timely attendance at eligible antenatal care visits (42.5% vs. 40.3%, ARR 0.96, 95% CI 0.89–1.05, p-value 0.4) and hypertension screening and management (95.1% vs. 94.7%, ARR 1.00, 95% CI 0.96–1.03, p-value 0.8). The secondary outcome of perinatal mortality and severe perinatal morbidities was lower in the intervention (14.6%) compared to the control group (15%) (ARR 0.74, 95% CI 0.58–0.96, p-value 0.02), with the change mostly attributed to morbidity outcomes.

Conclusion
Due to technical and implementation challenges we were unable to estimate the effect of the intervention with sufficient precision. Challenges included delays in rollout of the digital health interventions and outcome data collection, existence of parallel documentation systems on paper and digital and the COVID-19 pandemic. Given these methodological constraints, we are unable to draw definitive interpretations of trial results.

Trial registration
ISRCTN Registry ISRCTN69491836; https://www.isrctn.com/ISRCTN69491836. Date of registration 06.12.2018.
Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance
Authors: Yuan Min , Ding Haolun , Guo Bangwei , Yang Miaomiao , Yang Yaning , Xu Xu Steven 
Published: January 2024

PURPOSE To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. MATERIALS AND METHODS Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. RESULTS Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival ( P = .003). Apparent association was also observed for disease-specific survival ( P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. CONCLUSION Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.
Portfolio diversification with varying investor abilities
Authors: James N. , Menzies M. 
Published: January 2024

Abstract
We introduce new mathematical methods to study the optimal portfolio size of investment portfolios over time, considering investors with varying skill levels. First, we explore the benefit of portfolio diversification on an annual basis for poor, average and strong investors defined by the 10th, 50th and 90th percentiles of risk-adjusted returns, respectively. Second, we conduct a thorough regression experiment examining quantiles of risk-adjusted returns as a function of portfolio size across investor ability, testing for trends and curvature within these functions. Finally, we study the optimal portfolio size for poor, average and strong investors in a continuously temporal manner using more than 20 years of data. We show that strong investors should hold concentrated portfolios, poor investors should hold diversified portfolios; average investors have a less obvious distribution with the optimal number varying materially over time.
Behavior of a chain of coupled self-propelled particles with two-dimensional potential and noise
Authors: Wang Bing , Cheng Zhiguo 
Published: January 2024

Abstract
The behavior of an elastic chain of coupled self-propelled particles in a two-dimensional potential with Gaussian colored noise is investigated. The channel and colored noise regulates the directed transport of the system. The chain of particles changes the moving direction with increasing modulation constant of potential. The average velocity of the chain shows periodic fluctuation with increasing phase shift of the potential. Strong coupling between particles inhibits the directed transport speed of the system. Large translation noise intensity is good for the directed transport in the

- or

, but large self-correlation time inhibits this phenomenon. The effect of the self-propulsion speed is influenced by the modulation parameter of the potential. The elastic chain moves in the

when the angle noise intensity is zero. Noise intensity and self-correlation time of the angle noise have a complicated effect on the system.
Urinary Androgens Provide Additional Evidence Related to Metabolism and Are Correlated With Serum Androgens in Girls
Authors: Scott Sasinya N , Siguencia Marvin , Stanczyk Frank Z , Hartmann Michaela F , Wudy Stefan A , White Melissa , Chung Wendy K , Santella Regina M , Terry Mary Beth , Houghton Lauren C 
Published: January 2024

Abstract

Context
Androgen levels are generally measured in serum samples, but urine may be a more feasible option, especially in children, as it is a noninvasive alternative.

Objective
To assess the correlations of 10 urinary androgen metabolites with 4 serum androgens [dehydroepiandrosterone-sulfate (DHEA-S), androstenedione, and total and free testosterone] and assess if their correlations differ by participant characteristics.

Methods
Our study consisted of 44 girls, ages 6-13, who participated in the New York site of the LEGACY Girls Study and had both serum and urine samples collected at the same visit. We performed Pearson's correlation coefficient tests between 4 serum and 10 individual urinary metabolite measures and their sum. We examined the influence of participant characteristics on the magnitude and direction of the correlations.

Results
The summed urinary metabolite measures had the highest correlation with free testosterone in serum (global sum, r = 0.83) and correlated least with DHEA-S in serum (global sum, r = 0.64). The correlation between individual urinary metabolites and serum androgens ranged from 0.08 to 0.84.Two 11-oxygenated urinary metabolites (5α-androstane-3α-ol-11,17-dione5β-androstane-3α,11β-diol-17-one) were weakly correlated with all serum androgens. Participant age, weight, height, waist:hip ratio, and pubic hair growth stage changed the correlations between urinary and serum androgens measures between 10% and 213%.

Conclusion
The sum of urinary androgen metabolites was a good marker of circulating androstenedione, testosterone, and free testosterone. Individual urinary metabolites provide additional information about the metabolic processes of disease development compared to the antecedent serum androgens.
Partnership Status, Health, and Mortality: Selection or Protection?
Authors: Kulu Hill , Mikolai Júlia , Franke Sebastian 
Published: January 2024

Abstract
Married individuals have better health and lower mortality than nonmarried people. Studies show that when cohabitants are distinguished from other nonmarried groups, health differences between partnered and nonpartnered individuals become even more pronounced. Some researchers have argued that partnered individuals have better health and lower mortality because a partnership offers protective effects (protection); others have posited that partnered people have better health and lower mortality because healthy persons are more likely to form a union and less likely to dissolve it (selection). This study contributes to this debate by investigating health and mortality by partnership status in England and Wales and analyzing the causes of mortality differences. We use combined data from the British Household Panel Survey and the UK Household Longitudinal Study and apply a simultaneous-equations hazard model to control for observed and unobserved selection into partnerships. We develop a novel approach to identify frailty based on self-rated health. Our analysis shows that partnered individuals have significantly lower mortality than nonpartnered people. We observe some selection into and out of unions on unobserved health characteristics, but the mortality differences by partnership status persist. The study offers strong support for the marital protection hypothesis and extends it to nonmarital partnerships.
Sequential Search with Acquisition Uncertainty
Authors: Brown David B. , Uru Cagin 
Published: January 2024

We study a variation of the classical Pandora’s problem in which a decision maker (DM) sequentially explores alternatives from a given set and learns their values while trying to acquire the best alternative. The variations in the model we study are (i) alternatives randomly become unavailable during exploration and (ii) the DM’s ability to acquire a remaining alternative is uncertain and depends on a chosen offer price. Such acquisition uncertainties arise in many applications, including housing search, hiring problems, and e-commerce, but greatly complicate the search problem in that optimal policies retain all previously explored alternative values as part of the problem state, as opposed to only the highest explored value as in Pandora’s rule. Our central insight is that despite the complexity that these acquisition uncertainties create, simple greedy policies based on static sequencing and a single threshold value enjoy strong performance guarantees. We develop such a class of policies and show how to compute them using a greedy algorithm whose worst-case run-time scales linearly (up to logarithmic factors) in the number of alternative types. We show that our policies (a) are asymptotically optimal in high multiplicity regimes with many alternatives and (b) obtain at least [Formula: see text] of the optimal value under a broad set of conditions. Extensive numerical examples support this theory: We illustrate our policies on a variation of Pandora’s problem with disappearing alternatives and housing search on models calibrated on data from the online brokerage Redfin. In these examples, our policies significantly outperform policies based on Pandora’s rule. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00203 .