bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rumination and Self‐Compassion Moderate Mindfulness‐Based Cognitive Therapy for Patients With Recurrent and Persistent Major Depressive Disorder: A Controlled Trial
Authors: Lubbers Jelle , Geurts Dirk E. M. , Spinhoven Philip , Cladder-Micus Mira B. , Ennen Demi , Speckens Anne E. M. , Spijker Jan 
Published: November 2024

Background: Mindfulness‐based cognitive therapy (MBCT) is effective in reducing depressive symptoms in patients with major depressive disorder (MDD). Understanding for whom and how MBCT works may allow for improvements in treatment allocation and effectiveness. In this study, our aim was to investigate depressive rumination, content‐independent perseverative thinking, mindfulness skills, and self‐compassion as potential moderators and mediators of MBCT.Methods: In this non‐randomized controlled trial, patients with persistent (n = 53) or recurrent MDD with (n = 31) or without (n = 51) a current depressive episode were assigned to an intervention (MBCT plus treatment as usual [TAU], n = 94) or control group (TAU only, n = 40) based on the time between the date of inclusion and the start of MBCT. Assessments were carried out before, halfway, and after 8 weeks of MBCT + TAU or TAU. Latent growth models were employed to examine moderation, while cross‐lagged structural equation models were used to assess the mediating effects of several possible mediators of MBCT‐induced change in depressive symptoms and overall functional impairment.Results: MBCT + TAU was more effective in reducing depressive symptoms (and overall functional impairment than TAU with a medium [d = −0.54] and small [d = 0.44] effect size, respectively). Higher baseline levels of rumination and perseverative thinking and lower levels of self‐compassion moderated the effect of MBCT on depressive symptoms and overall functional impairment. Task‐based negative intrusive thoughts moderated the effects of MBCT on overall functional impairment. No mediators were established, particularly due to a lack of effect of MBCT on all assessed mediators at mid‐treatment. For interpretative purposes, a sample split (based on Johnson–Newman values) showed moderate‐to‐large effects in depressive symptom reduction for those with high rumination, high perseverative thinking, and low self‐compassion, while negative‐to‐small nonsignificant effects were found for the opposite traits.Conclusion: In the future, MBCT allocation based on levels of rumination and self‐compassion might lead to a more efficient reduction in depressive symptoms. Directions for mediation analysis within the context of MBCT for depression are discussed.Preregistration: This study was initially preregistered in the Dutch National Trial Register (NL7842). However, due to the NTR no longer being available since June 2022, the trial was reregistered at ClinicalTrials.gov (NCT05802966, dd 09‐Apr‐2023). The statistical analysis plan was adjusted after the start of the trial but before the finalization of data collection (NCT05802966; ClinicalTrials.gov).
Happy to Help: Welfare Effects of a Nationwide Volunteering Programme
Authors: Krekel Christian , Shreedhar Ganga , Lee Helen , Marshall Claire , Boler Alison , Smith Allison , Dolan Paul 
Published: November 2024

Abstract
We study the wellbeing returns from volunteering in England's National Health Service (NHS) Volunteer Responders, set up in response to Covid-19. Using linked survey and administrative data, we exploit the oversubscription of volunteers and the random allocation of tasks via an app to establish causality. Volunteers show stronger wellbeing and feelings of belongingness and connectedness to their local area. Welfare analyses suggest that the benefits of the programme substantially exceeded its costs. We are the first to study the welfare effects of a nationwide volunteering programme. Our findings show that pro-social behaviour improves personal wellbeing as well as social welfare.
Multidimensional Inequality Measurement via Optimal Transport
Authors: Fan Yanqin , Henry Marc , Pass Brendan , Rivero Jorge A. 
Published: November 2024

Abstract
The Lorenz curve and Gini index are standard tools for the evaluation of inequality in one dimension. However, inequality is inherently multi-dimensional. Extending the Lorenz curve and Gini index to a multidimensional context has proved controversial. This paper proposes a new multivariate extension based on multivariate rearrangements of optimal transport theory, which shares many of the desirable properties of their univariate counterparts. In particular, the corresponding multivariate inequality ordering is equivalent to preference by any social planner with inequality averse multivariate rank dependent social evaluation functional.
Should Mothers Work? How Perceptions of the Social Norm Affect Individual Attitudes Toward Work in the U.S.
Authors: Cortés Patricia , Koşar Gizem , Pan Jessica , Zafar Basit 
Published: November 2024

Abstract
We study how peer beliefs shape individual attitudes toward maternal labor supply using hypothetical scenarios that elicit recommendations on the labor supply choices of a mother with a young child and an information treatment embedded within geographically representative surveys of the US population. Across scenarios, we find that individuals are systematically misinformed about the extent of gender conservativeness of the people around them. Exposure to information on peer beliefs leads to a shift in recommendations, driven largely by information-based belief updating. The information treatment also increases (intended and actual) donations to a non-profit organization advocating for women in the workplace.
Constitutional Implementation of Affirmative Action Policies in India
Authors: Sönmez Tayfun , Yenmez M. Bumin 
Published: November 2024

Abstract
India is home to a comprehensive affirmative action program that reserves a fraction of positions at governmental institutions for various disadvantaged groups. While there is a Supreme Court-endorsed mechanism to implement these reservation policies when all positions are identical, courts have refrained from endorsing explicit mechanisms when positions are heterogeneous. This lacuna has resulted in widespread adoption of unconstitutional mechanisms, countless lawsuits, and inconsistent court rulings. By formulating mandates from the landmark Supreme Court judgment Saurav Yadav vs The State of U.P. (2020) as technical axioms, we show that the 2SMH-DA mechanism is uniquely suited to overcome these challenges.
Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling
Authors: Zhang Yaqi , Sylvester Karl G. , Wong Ronald J. , Blumenfeld Yair J. , Hwa Kuo Yuan , Chou C. James , Thyparambil Sheeno , Liao Weili , Han Zhi , Schilling James , Jin Bo , Marić Ivana , Aghaeepour Nima , Angst Martin S. , Gaudilliere Brice , Winn Virginia D. , Shaw Gary M. , Tian Lu , Luo Ruben Y. , Darmstadt Gary L. , Cohen Harvey J. , Stevenson David K. , McElhinney Doff B. , Ling Xuefeng B. 
Published: November 2024

Abstract
Background
Preterm birth (PTB) is a serious health problem. PTB complications is the main cause of death in infants under five years of age worldwide. The ability to accurately predict risk for PTB during early pregnancy would allow early monitoring and interventions to provide personalized care, and hence improve outcomes for the mother and infant.

Objective
This study aims to predict the risks of early preterm (< 35 weeks of gestation) or very early preterm (≤ 26 weeks of gestation) deliveries by using high-resolution maternal urinary metabolomic profiling in early pregnancy.

Design
A retrospective cohort study was conducted by two independent preterm and term cohorts using high-density weekly urine sampling. Maternal urine was collected serially at gestational weeks 8 to 24. Global metabolomics approaches were used to profile urine samples with high-resolution mass spectrometry. The significant features associated with preterm outcomes were selected by Gini Importance. Metabolite biomarker identification was performed by liquid chromatography tandem mass spectrometry (LCMS-MS). XGBoost models were developed to predict early or very early preterm delivery risk.

Setting and participants
The urine samples included 329 samples from 30 subjects at Stanford University, CA for model development, and 156 samples from 24 subjects at the University of Alabama, Birmingham, AL for validation.

Results
12 metabolites associated with PTB were selected and identified for modelling among 7,913 metabolic features in serial-collected urine samples of pregnant women. The model to predict early PTB was developed using a set of 12 metabolites that resulted in the area under the receiver operating characteristic (AUROCs) of 0.995 (95% CI: [0.992, 0.995]) and 0.964 (95% CI: [0.937, 0.964]), and sensitivities of 100% and 97.4% during development and validation testing, respectively. Using the same metabolites, the very early PTB prediction model achieved AUROCs of 0.950 (95% CI: [0.878, 0.950]) and 0.830 (95% CI: [0.687, 0.826]), and sensitivities of 95.0% and 60.0% during development and validation, respectively.

Conclusion
Models for predicting risk of early or very early preterm deliveries were developed and tested using metabolic profiling during the 1st and 2nd trimesters of pregnancy. With patient validation studies, risk prediction models may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights of preterm birth.
Loneliness, emotional support and the mental health of young adults and their parents in New York, US during the COVID-19 pandemic: a cohort study
Authors: Akinkuowo Abosede , Cheslack-Postava Keely , Skokauskas Norbert , Hoven Christina W. 
Published: November 2024

Abstract
Background
The coronavirus disease (COVID-19) pandemic led to social isolation and widespread lockdown, resulting in loneliness and lack of emotional support, which have been associated with adverse mental health outcomes. This study aims to explore the relationship of loneliness and emotional support with depression and substance use among young adults and their parents during the COVID-19 pandemic.

Methods
A cohort of 1227 participants was recruited from three ongoing cohort studies in the metropolitan area of New York City, USA. Data was collected through telephone interviews using preset questionnaires during wave 1 (March-August 2020), wave 2 (September 2020-February 2021) and wave 3 (March-August 2021) of the COVID-19 pandemic. Logistic regression models were used to test the association between loneliness and emotional support, and the mental health outcomes; depression and substance use, with adjustments made for age, gender, race, employment status, living conditions, and marital status. Additionally, the effect modification of respondent type and living alone or with others was examined.

Results
At wave 1, loneliness showed a significant positive association with depressive symptoms (OR: 2.56, 95%CI: 2.19-3.00, P = < 0.001) and an increase in substance use, such as smoking tobacco using cigarettes, pipes, or cigar (OR: 1.59, 95%CI: 1.24–2.04, P = < 0.001), alcohol consumption (OR: 1.23, 95%CI: 1.07–1.42, P = 0.003), and marijuana/other substances (OR: 1.57, 95%CI: 1.26–1.96, P = < 0.001). Conversely, emotional support showed a significant negative association with depressive symptoms (OR: 0.71, 95%CI: 0.62–0.81, P = < 0.001) but a non-significant association with increase in tobacco smoking using vapes and e-cigarettes, alcohol consumption and marijuana and other substances. However, a significant negative association was observed between emotional support, and increased tobacco smoking using cigarettes, pipes, or cigar (OR: 0.73, 95%CI: 0.58–0.93, P = 0.011). The associations of loneliness and emotional support with mental health outcomes were stronger at wave 2 compared to wave 1. The modifier effects of respondent type and living condition were non-significant.

Conclusions
This study indicates that increased loneliness is associated with a higher likelihood of depression and substance use, while higher emotional support is linked to a reduced likelihood of depressive symptoms during the COVID-19 pandemic.
Trends in the use and nonmedical use of sedatives-hypnotics in the population aged 12 to 64 years in Taiwan: a comparative analysis of the national surveys in 2014 and 2018
Authors: Tsay Wei-Yi , Chen Lian-Yu , Wu Shang-Chi , Hsiao Po-Chang , Ting Te-Tien , Yen Cheng-Fang , Chang Shu-Sen , Li Chung-Yi , Yang Hao-Jan , Yen Chia-Feng , Chen Chuan-Yu , Huang Jiun-Hau , Tu Yu-Kang , Chen Wei J. 
Published: November 2024

Abstract
Background
Many countries, including Taiwan, have tightened regulations on prescribing sedatives-hypnotics over the concern of their associated adverse health effects. However, it remains seldom investigated whether different age-sex strata have differential trends in national surveys over time for either the use or nonmedical use (NMU) of sedatives-hypnotics. Comparing Taiwan’s two national surveys in 2014 and 2018, we aimed to examine (1) the population’s trends for the prevalence of past-year use and NMU of sedatives-hypnotics overall and in age-sex strata; (2) trends for sociodemographic subgroups for those age-sex strata with significant changes in past-year use and NMU of sedatives-hypnotics over time; and (3) trends for sources of and motives for NMU of sedatives-hypnotics.

Methods
The national survey enrolled 17,837 participants in 2014 (response rate = 62.2%) and 18,626 participants in 2018 (response rate = 64.6%) of citizens aged 12–64 years. Each participant anonymously completed a computer-assisted self-interview. The questionnaire consisted of sociodemographic variables and the use of various psychoactive substances and sedative-hypnotics, among others. NMU of sedative-hypnotics was defined as using the drug without a prescription, or more frequently, or in larger doses than prescribed. To compare the prevalence between the two waves, we conducted multivariable logistic regression analysis and the difference-in-differences in prevalence was examined with an interaction term between survey year and sex.

Results
We found decreasing trends in young adult (18–39 years old) males for both past-year use (3.07–2.29%) and NMU (0.84–0.18%), but increasing trends in adolescents (0.42–0.80%) and young adult females (2.91–3.81%) for past-year use and in adolescents (0.16–0.39%) and middle-aged adult (40–64 years old) females (0.73–1.14%) for past-year NMU of sedatives-hypnotics. Among the young and middle-aged adult females, the increasing trends for past-year use and NMU, respectively, were found to occur mainly in certain sociodemographic subgroups, with alcohol users being the overlapping subgroup.

Conclusions
The differential trends over time of past-year use or NMU of sedatives-hypnotics in different age-sex strata in the population have policy implications to curtail the increasing trend over time.
A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data
Authors: Puntu Jordi Mahardika , Chang Ping-Yu , Amania Haiyina Hasbia , Lin Ding-Jiun , Suryantara M. Syahdan Akbar , Tsai Jui-Pin , Yu Hwa-Lung , Chang Liang-Cheng , Zeng Jun-Ru , Kassie Lingerew Nebere 
Published: November 2024

AbstractThis study presents a comprehensive approach for constructing a 3D Apparent Geological Model (AGM) by integrating multi-resistivity data using statistical methods, supervised machine learning (SML), and Python-based modeling techniques. Demonstrated through a case study in the Choushui River Alluvial Fan (CRAF) in Taiwan, the methodology enhances data coverage significantly, from 62 to 386 points, by incorporating resistivity data sets from Vertical Electrical Sounding (VES), Transient Electromagnetic (TEM), and borehole information. A key contribution of this work is the rigorous harmonization of these data sets, ensuring consistent resistivity values across different methods before constructing the 3D resistivity model, addressing a gap in previous studies that typically handled these data sets separately, either building models individually or comparing results side-by-side without fully integrating the data. Furthermore, python-based modeling and radial basis function interpolation were employed to construct the 3D resistivity model for greater flexibility and effectiveness than conventional software. Subsequently, this model was transformed into a 3D AGM using the SML technique. Four algorithms, namely, random forest (RF), decision tree (DT), support vector machine (SVM), and extreme gradient boosting (XGBoost) were implemented. Following evaluation via confusion matrix analysis, evaluation metrics, and examination of receiver operating characteristics curve, it emerged that the RF algorithm exhibits superior performance when applied to our multi-resistivity data set. The results from the 3D AGM unveil distinct resistivity anomalies correlated with sediment types. The clay layer exhibited low resistivity (≤ 59.98 Ωm), while the sand layer displayed medium resistivity (59.98 < ρ < 136.14 Ωm), and the gravel layer is characterized by high resistivity ( ≥ 136.14 Ωm). Notably, in the proximal fan, gravel layers predominate, whereas the middle fan primarily consists of sandy clay layers. Conversely, the distal fan, located in the western coastal area, predominantly comprises clayey sand. To conclude, the findings of this study provide valuable insights for researchers to construct the 3D AGM from the resistivity data, applicable not only to the CRAF but also to other target areas.
Stress-induced premature senescence in high five cell cultures: a principal factor in cell-density effects
Authors: Min Rui , Zhang Dahe , He Mingzhe , Chen Jingyuan , Yi Xiaoping , Zhuang Yingping 
Published: November 2024

AbstractThe Baculovirus Expression Vector System (BEVS) is highly valued in vaccine development, protein engineering, and drug metabolism research due to its biosafety, operational convenience, rapid scalability, and capacity for self-assembling virus-like particles. However, increasing cell density at the time of inoculation severely compromises the production capacity of BEVS, resulting in the “cell density effect”. This study aimed to explore the mechanisms of the cell density effect through time-series analysis of transcriptomes and proteomes, with the goal of overcoming or alleviating the decline in productivity caused by increased cell density. The dynamic analysis of the omics of High Five cells under different CCI (cell density at infection) conditions showed that the impact of the “cell density effect” increased over time, particularly affecting genetic information processing, error repair, protein expression regulation, and material energy metabolism. Omics analysis of the growth stage of High Five cells showed that after 36 h of culture (cell density of about 1 × 106 cells/mL), the expression of ribosome-related proteins decreased, resulting in a rapid decrease in protein synthesis capacity, which was a key indicator of cell aging. Senescence verification experiments showed that cells began to show obvious early aging characteristics after 36 h, resulting in a decrease in the host cell’s ability to resist stress. Overexpression and siRNA inhibition studies showed that the ndufa12 gene was a potential regulatory target for restricting the “cell density effect”. Our results suggested that stress-induced premature senescence in High Five cell cultures, resulting in reduced energy metabolism and protein synthesis capabilities, was a critical factor contributing to cell density effects, and ultimately affecting virus production. In conclusion, this study provided new insights into managing virus production limitations due to cell density effects and offered innovative strategies to mitigate the adverse effects of cellular aging in biomanufacturing technologies.
Graphical abstract
Co-reactivity pattern of glucose metabolism and blood perfusion revealing DNA mismatch repair deficiency based on PET/DCE-MRI in endometrial cancer
Authors: Li Xiaoran , Cui Bixiao , Wang Shijun , Gao Min , Xing Qiuyun , Liu Huawei , Lu Jie 
Published: November 2024

Abstract
Background
Identifying DNA mismatch repair deficiency (MMRd) is important for prognosis risk stratification in patients with early-stage endometrial cancer (EC), but there is a notable absence of cost-effective and non-invasive preoperative assessment techniques. The study explored the co-reactivity pattern of glucose metabolism and blood perfusion in EC based on hybrid [18F]fluorodeoxyglucose ([18F]FDG) PET/dynamic contrast enhanced (DCE)-MRI to provide an imaging biomarker for identifying MMRd.

Methods
Patients with a history of postmenopausal bleeding and initially diagnosed with EC on ultrasound were recruited to perform a PET/DCE-MRI scan. Glucose metabolism parameters were calculated on PET, and blood perfusion parameters were calculated semi-automatically by the DCE-Tofts pharmacokinetic model. The MMRd of early-stage EC was evaluated by immunohistochemistry. The synchronous variation of PET and DCE-MRI parameters was compared between the MMRd and mismatch repair proficiency (MMRp). The association between PET/DCE-MRI and MMRd was analyzed by logistic regression to establish the digital biomarker for predicting MMRd. Receiver operating characteristic curve, decision curve analysis, and the net reclassification index (NRI) were used to evaluate the value of the digital biomarker in identifying MMRd.

Results
Eighty-six early-stage EC cases (58.92 ± 10.13 years old, 34 MMRd) were enrolled. The max/mean standardized uptake value (SUVmax/SUVmean), metabolic tumor volume, total lesion glycolysis, transfer constant (Ktrans), and efflux rate (Kep) were higher in MMRd than those in MMRp (P < 0.001, < 0.001, 0.002, 0.004, < 0.001, and 0.005, respectively). The correlations between glucose metabolism and blood perfusion were different between the MMRd and MMRp subgroups. SUVmax was correlated with Kep (r = 0.36) in the MMRd. SUVmean (odds ratio [OR] = 1.32, P = 0.006) and Ktrans (OR = 1.90, P = 0.021) were independent risk factors for MMRd. And the digital biomarker that combined SUVmean and Ktrans outperformed in identifying MMRd in early-stage EC more than DCE-MRI (AUC: 0.83 vs. 0.78, NRI = 13%).

Conclusion
A potential digital biomarker based on [18F]FDG PET/DCE-MRI can identify MMRd for prognosis risk stratification in early-stage EC.