bluuebunny/crossref_metadata_2025_split huggingface dataset:
Introduction
Authors: Karim Sabrina , Hill, Jr. Daniel W 
Published: August 2024

Abstract
This introductory chapter introduces the main arguments in the book. It starts out by describing the rising trend of backlash against advances in women’s status globally and explains that this trend could have consequences for political violence as well. In doing so, it highlights the importance of studying the connection between women’s status and political violence. However, in order to study this connection, the chapter argues, problems in the existing literature must be overcome: concept stretching and measurement invalidity. The solution to these problems includes differentiating through careful conceptualization, particularly differentiating women’s status from gender equality, as well as separating out different aspects of women’s status. The solution also includes careful measurement of these differentiated concepts and delineation of specific theories about how women’s inclusion, women’s rights, harm to women, and beliefs about women’s gender roles each affect political violence. The chapter concludes with a section on contributions and the book outline.
Solving the Concept Stretching Problem
Authors: Karim Sabrina , Hill, Jr. Daniel W 
Published: August 2024

Abstract
Chapter 1 reviews the problem of concept stretching in depth and demonstrates how existing scholarship and policymaking suffer from conflating gender equality with women’s status. The chapter begins by providing specific examples of this conflation in the literature and the policymaking world, as well as identifying the problems that result from this conflation. It then goes on to explain these problems and, using the language of concept stretching, the chapter engages in a step-by-step process of developing the concepts of gender equality and women’s status to show how they are different concepts all together. The goal is to show how this concept stretching can limit, even harm, the women, peace, and security policy agenda.
Women’s Health Study (WENDY)—a protocol of a population-based study assessing gynecological and metabolic health in women in their mid-30s
Authors: Piltonen Terhi T , Ohtamaa Maria , Arffman Riikka K , Vuokila Lotta , Hurskainen Elisa , Männikkö Minna , Huilaja Laura , Sinikumpu Suvi-Päivikki , Rautio Tero , Kilpivaara Katariina , Jokelainen Jari , Kiviniemi Eetu , Pinola Pekka , Törnävä Minna , Komsi Elina , Kangasniemi Marika H , Rajecki Maria , Luiro Kaisu , Kinnunen Jenni , Savukoski Susanna M 
Published: August 2024

Abstract
The Women’s Health Study (WENDY) was conducted to improve insights into women’s health and health burden. It provides a unique, comprehensive data source that can be broadly utilized to understand gynecological symptoms, diseases, and their relation to metabolic and overall health more deeply in a population-based setting. The study was conducted in Finland from May 2020 to October 2022. It included 1918 women (33-37 years old) who were born in northern Finland between July 1985 and December 1987. Data collection comprised one 3- to 4-hour study visit that included clinical measurements, biological samples, ultrasound examinations and an extensive questionnaire on gynecological and reproductive history, physical and mental health, quality of life, lifestyles, current life situations, health awareness, and opinions. The study also included a menstrual cycle follow-up and cognitive testing up to 3 months via a mobile application. Given that all participants’ data can be linked to all Finnish national registers, and the Northern Finland Birth Cohort participants’ data can be linked to the birth cohort data set collected from gestational week 24 onward, WENDY study forms one of the largest data sets worldwide to investigate gynecological and metabolic health burden in women.
Invited commentary: improving spatial exposure data for everyone—life-course social context and ascertaining residential history
Authors: Sims Kendra D , Glymour M Maria , Ncube Collette N , Willis Mary D 
Published: August 2024

Abstract
Measuring age-specific, contextual exposures is crucial for life-course epidemiology research. Longitudinal residential data offer a “golden ticket” to cumulative exposure metrics and can enhance our understanding of health disparities. Residential history can be linked to myriad spatiotemporal databases to characterize environmental, socioeconomic, and policy contexts that a person has experienced throughout life. However, obtaining accurate residential history is challenging in the United States due to the limitations of administrative registries and self-reports. In a recent article, Xu et al (Am J Epidemiol. 2024;193(2):348-359) detailed an approach to linking residential history sourced from LexisNexis Accurint to a Wisconsin-based research cohort, offering insights into challenges with collection of residential history data. Researchers must analyze the magnitude of selection and misclassification biases inherent to ascertaining residential history from cohort data. A life-course framework can provide insights into why the frequency and distance of moves is patterned by age, birth cohort, racial/ethnic identity, socioeconomic status, and urbanicity. Historical and contemporary migration patterns of marginalized people seeking economic and political opportunities must guide interpretations of residential history data. In this commentary, we outline methodological priorities for use of residential history in health disparities research, including contextualizing residential history data with determinants of residential moves, triangulating spatial exposure assessment methods, and transparently quantifying measurement error.
Breast density and risk of breast cancer: masking and detection bias
Authors: Lange Jane M , Gard Charlotte C , O’Meara Ellen S , Miglioretti Diana L , Etzioni Ruth 
Published: August 2024

Abstract
Breast density is associated with risk of breast cancer (BC) diagnosis, affecting risk prediction tools and patient notification policies. Density affects mammography sensitivity and may influence screening intensity. Therefore, the observed association between density and BC diagnosis may not reflect the relationship between density and disease risk. We investigated the association between breast density and BC risk using data sourced from 33 542 women in the Breast Cancer Surveillance Consortium, 2000-2018. We estimated mammogram sensitivity and rates of screening mammography among dense (Breast Imaging Reporting and Data System [BI-RADS] breast density categories C and D) and nondense (BI-RADS categories A and B) breasts. We used Kaplan-Meier estimates to summarize the relative risks of BC diagnosis (RRdx) by density and fit a natural history model to estimate the relative risks of BC onset (RRonset) given density-specific sensitivities. The RRdx for dense vs nondense breasts was 1.80 (95% CI, 1.46-2.57). Based on estimated screening sensitivities of 0.88 and 0.78 for nondense and dense breasts, respectively, RRonset was 1.73 (95% CI, 1.43-2.25). Sensitivity analyses suggested higher breast density is robustly associated with increased risk of BC onset, similar in magnitude to the increased risk of BC diagnosis. These findings support laws requiring notifications to women with dense breasts of their increased BC risk.
Comparative risk of opioid overdose in patients who initiated antibiotics for urinary tract infection while on long-term opioid therapy
Authors: Khan Nazleen F , Bykov Katsiaryna , Glynn Robert J , Vine Seanna M , Gagne Joshua J 
Published: August 2024

Abstract
Sulfamethoxazole/trimethoprim, a commonly used antibiotic, has been associated with opioid overdose in patients with long-term opioid use, based on a prior drug-drug interaction screening study. To evaluate whether this finding represents a false-positive signal due to potential confounding, we assessed the safety of sulfamethoxazole/trimethoprim relative to 2 comparable antibiotic treatments in patients receiving long-term opioid therapy. We used data from 4 large administrative claims databases spanning 2000-2019. The study population was restricted to patients aged 18 years or older with urinary tract infection and at least 90 days of continuous prescription opioid use who initiated sulfamethoxazole/trimethoprim, nitrofurantoin, or fluoroquinolone therapy. We used propensity score matching weights to adjust for confounding and Cox proportional hazards models to estimate weighted hazard ratios (HRs) and 95% CIs in a 30-day intention-to-treat analysis. Cumulative 30-day opioid overdose risk ranged between 0.04% and 0.12% across databases and did not differ between antibiotics. Relative to sulfamethoxazole/trimethoprim, the weighted HR of opioid overdose was 1.09 (95% CI, 0.79-1.50) for nitrofurantoin and 0.94 (95% CI, 0.68-1.31) for fluoroquinolones. Potential safety signals identified in high-throughput screening studies, especially for medication combinations with limited biologic plausibility of drug-drug interaction and high frequency of use, should be interpreted with caution.
This article is part of a Special Collection on Pharmacoepidemiology.
DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras
Authors: Li Fenglei , Hu Qiaoyu , Zhou Yongqi , Yang Hao , Bai Fang 
Published: August 2024

AbstractPROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure–activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence–generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer
Authors: Lenhof Kerstin , Eckhart Lea , Rolli Lisa-Marie , Lenhof Hans-Peter 
Published: August 2024

Abstract
With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.
A dual-scale fused hypergraph convolution-based hyperedge prediction model for predicting missing reactions in genome-scale metabolic networks
Authors: Huang Weihong , Yang Feng , Zhang Qiang , Liu Juan 
Published: August 2024

Abstract
Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular metabolic and physiological states. However, there are still missing reactions in GEMs due to incomplete knowledge. Recent gaps filling methods suggest directly predicting missing responses without relying on phenotypic data. However, they do not differentiate between substrates and products when constructing the prediction models, which affects the predictive performance of the models. In this paper, we propose a hyperedge prediction model that distinguishes substrates and products based on dual-scale fused hypergraph convolution, DSHCNet, for inferring the missing reactions to effectively fill gaps in the GEM. First, we model each hyperedge as a heterogeneous complete graph and then decompose it into three subgraphs at both homogeneous and heterogeneous scales. Then we design two graph convolution-based models to, respectively, extract features of the vertices in two scales, which are then fused via the attention mechanism. Finally, the features of all vertices are further pooled to generate the representative feature of the hyperedge. The strategy of graph decomposition in DSHCNet enables the vertices to engage in message passing independently at both scales, thereby enhancing the capability of information propagation and making the obtained product and substrate features more distinguishable. The experimental results show that the average recovery rate of missing reactions obtained by DSHCNet is at least 11.7% higher than that of the state-of-the-art methods, and that the gap-filled GEMs based on our DSHCNet model achieve the best prediction performance, demonstrating the superiority of our method.
GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection
Authors: Xu Yang , Zhang Yuxiang , Cui Yanru , Zhou Kai , Yu Guangning , Yang Wenyan , Wang Xin , Li Furong , Guan Xiusheng , Zhang Xuecai , Yang Zefeng , Xu Shizhong , Xu Chenwu 
Published: August 2024

Abstract
Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).
Exploring the link between dystrophic microglia and the spread of Alzheimer's neuropathology
Authors: Shahidehpour Ryan K , Nelson Peter T , Katsumata Yuriko , Bachstetter Adam D 
Published: August 2024

Abstract
Genetics and other data modalities indicate that microglia play a critical role in Alzheimer's disease progression, but details of the disease-driving influence of microglia are poorly understood. Microglial cells can be parsed into subtypes based on their histological appearance. One subtype of microglia, termed dystrophic microglia, is characterized structurally by fragmented processes and cytoplasmic decay, and their presence has been associated with ageing and neurodegeneration. Recent studies suggest that the interaction between tau proteins and amyloid-β might induce dystrophic changes in microglia, potentially linking amyloid-β and tau pathologies to their effects on these microglia.
We developed a study of human brains to test the hypothesis that dystrophic microglia are involved in Alzheimer's disease progression. We speculated that if their presence is unique to Alzheimer's disease neuropathological change, they would be substantially more common in Alzheimer's disease neuropathological change than in neurodegenerative diseases characterized by other proteinopathies, e.g. α-synuclein or transactive response (TAR) DNA-binding protein 43 kDa (TDP-43) pathology. Our analyses used histologically stained sections from five human brain regions of 64 individuals across six disease states, from healthy controls to advanced Alzheimer's disease stages, including comparative conditions such as Lewy body disease and limbic-predominant age-related TDP-43 encephalopathy neuropathological change. Using stereological sampling and digital pathology, we assessed populations of ramified, hypertrophic and dystrophic microglia. We found a significant increase in dystrophic microglia in areas affected early by Alzheimer's disease neuropathological change, suggesting a disease-specific role in neuropathology. Mediation analysis and structural equation modelling suggest that dystrophic microglia might impact the regional spread of Alzheimer's disease neuropathological change. In the mediation model, tau was found to be the initiating factor leading to the development of dystrophic microglia, which was then associated with the spread of amyloid-β and tau.
These results suggest that a loss of the protective role of microglia could contribute to the spread of Alzheimer's disease neuropathological change and indicate that further research into preserving microglial function might be warranted.
Laboratory Testing for Celiac Disease: Clinical and Methodological Considerations
Authors: Husby Steffen , Choung Rok Seon , Crawley Cæcilie , Lillevang Søren T , Murray Joseph A 
Published: August 2024

Abstract

Background
Celiac disease (CeD) has an estimated prevalence of 1%–3%. The classical clinical presentation is malabsorption, but now patients may present with more subtle symptoms such as constipation, osteoporosis, or iron deficiency anemia. Children may also present with poor growth.
CeD has a strong genetic component, and high-risk groups include first-degree relatives with CeD, patients with co-existing autoimmune diseases, and patients with chromosomal aberrations.

Content
Diagnostic tests for CeD include duodenal histology, serology, and genetic testing. Duodenal histology has traditionally been the gold standard of diagnosis. However, serological tests, especially IgA tissue transglutaminase antibodies (TTG-IgA), are widely used and diagnostic algorithms are based primarily on TTG-IgA as a starting point. Human leukocyte antigen typing may also be incorporated to determine genetic risk for CeD. Guidelines for children endorse biopsy avoidance provided high levels of TTG-IgA, with diagnostic accuracy being comparable to duodenal biopsy. Confirmation may be achieved by identifying IgA endomysial antibodies in a separate blood sample. Subjects with low positive TTG-IgA levels and subjects with IgA deficiency need a biopsy to establish a diagnosis of CeD. The clinical follow-up of CeD usually includes a repeat TTG-IgA examination. In adults, healing may be delayed or incomplete, and a rare consequence of refractory celiac disease is transformation to enteric T-cell lymphoma.

Summary
Laboratory testing, in particular TTG-IgA, plays a central role in the diagnosis and has an accuracy comparable to histology. Diagnostic algorithms utilizing laboratory testing are critical for the development of novel strategies to improve diagnosis.
A high-quality genome assembly reveals adaptations underlying glossy, wax-coated leaves in the heat-tolerant wild raspberry Rubus leucanthus
Authors: Wu Wei , Wang Longyuan , Huang Weicheng , Zhang Xianzhi , Li Yongquan , Guo Wei 
Published: August 2024

Abstract
With glossy, wax-coated leaves, Rubus leucanthus is one of the few heat-tolerant wild raspberry trees. To ascertain the underlying mechanism of heat tolerance, we generated a high-quality genome assembly with a genome size of 230.9 Mb and 24,918 protein-coding genes. Significantly expanded gene families were enriched in the flavonoid biosynthesis pathway and the circadian rhythm-plant pathway, enabling survival in subtropical areas by accumulating protective flavonoids and modifying photoperiodic responses. In contrast, plant–pathogen interaction and MAPK signaling involved in response to pathogens were significantly contracted. The well-known heat response elements (HSP70, HSP90, and HSFs) were reduced in R. leucanthus compared to two other heat-intolerant species, R. chingii and R. occidentalis, with transcriptome profiles further demonstrating their dispensable roles in heat stress response. At the same time, three significantly positively selected genes in the pathway of cuticular wax biosynthesis were identified, and may contribute to the glossy, wax-coated leaves of R. leucanthus. The thick, leathery, waxy leaves protect R. leucanthus against pathogens and herbivores, supported by the reduced R gene repertoire in R. leucanthus (355) compared to R. chingii (376) and R. occidentalis (449). Our study provides some insights into adaptive divergence between R. leucanthus and other raspberry species on heat tolerance.
Digest: Evaluating sexual conflict in the genome
Authors: Chakraborty Avishikta , Brandt Débora Y C 
Published: August 2024

Abstract
Audet et al. (2024) investigate the genomic basis of sexual conflict in response to sexually discordant size selection in Drosophila melanogaster. They report interesting morphological changes in sexual dimorphism and multivariate allometry. Although they do not find any genetic variants that individually show a strong effect on these traits, as expected for a polygenic trait such as body size, they do find a region on chromosome 3L showing signs of sexually discordant selection (i.e., conflict between males and females). This study highlights potential genomic regions involved in sexual conflict, offering insights into sex-specific adaptations.