bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social disparity is associated with an increased risk of acute and chronic pancreatitis
Authors: Becker Ulrik , Timmermann Amalie , Ekholm Ola , Drewes Asbjørn Mohr , Novovic Srdan , Nøjgaard Camilla , Olesen Søren Schou , Tolstrup Janne Schurmann 
Published: July 2024

Abstract

Aim
To study social disparity in acute pancreatitis (AP) and chronic pancreatitis (CP).We also aimed at exploring whether an interaction exists between alcohol intake and socioeconomic factors.

Methods
Prospective cohort study based on data from 271 696 men and women participating in the Danish National Health Surveys 2010, and 2013. Information on alcohol and smoking parameters, body mass index (BMI), diet, and education, were self-reported and information on family income was obtained from administrative registers. Outcome variables (acute and chronic pancreatitis) were obtained from national health registers.

Results
The incidence rate ratio (IRR) of developing AP and CP increased with decreasing family income. Compared to participants in the highest income quintile, participants in the lowest income quintile had 43 (95% CI: 14–80%), 99 (95% CI: 26–214%), and 56% (95% CI: 26–94%) higher incidence rates of AP, CP, and all pancreatitis, respectively. The associations persisted after adjustment for alcohol intake, smoking, BMI, and diet.
Likewise, participants with only primary school education had an IRR for an AP of 1.30 (95% CI: 1.06–1.59) compared to those with higher education after adjustment for baseline year, age, and sex. We found no interactions between alcohol intake and income or between alcohol intake and education in relation to neither AP, CP, nor all pancreatitis.

Conclusion
This large prospective population study showed a significant social disparity in incidence rates of pancreatitis by family income, with higher rates among those with the lowest income and education independent of risk factors such as alcohol intake, smoking, BMI, and diet.
Epigenomics coverage data extraction and aggregation in R with tidyCoverage
Authors: Serizay Jacques , Koszul Romain 
Published: July 2024

Abstract

Summary
The tidyCoverage R package provides a framework for intuitive investigation of collections of genomic tracks over genomic features, relying on the principle of tidy data manipulation. It defines two data structures, CoverageExperiment and AggregatedCoverage classes, directly extending the SummarizedExperiment fundamental class, and introduces a principled approach to exploring genome-wide data. This infrastructure facilitates the extraction and manipulation of genomic coverage track data across individual or multiple sets of thousands of genomic loci. This allows the end user to rapidly visualize track coverage at individual genomic loci or aggregated coverage profiles over sets of genomic loci. tidyCoverage seamlessly combines with the existing Bioconductor ecosystem to accelerate the integration of genome-wide track data in epigenomic analysis workflows. tidyCoverage emerges as a valuable tool, contributing to the advancement of epigenomics research by promoting consistency, reproducibility, and accessibility in data analysis.

Availability and implementation
tidyCoverage is an R package freely available from Bioconductor ≥ 3.19 (https://www.bioconductor.org/packages/tidyCoverage) for R ≥ 4.4. The software is distributed under the MIT License and is accompanied by example files and data.
Imparting chiroptical property to achiral azobenzene derivative via incorporation into chiral-controlled helical nanofibers
Authors: Ito Tomoki , Nakagawa Makoto , Kawai Takeshi 
Published: July 2024

Abstract
Chiral transcription from chiral to achiral organic molecules is a fundamental research area in supramolecular chemistry, organic chemistry, and nanoscience. In this study, we demonstrated that chiral transcription of an achiral azobenzene derivative (Azo) can be achieved by embedding it within helically controlled nanofibers. Helical nanofibers were produced via supramolecular assembly of water-insoluble D-12-hydroxystearic acid (D-HSA) as a chiral source and a water-soluble long-chain amidoamine derivative (C18AA) in water. Azo-incorporated helical nanofibers exhibited a circular dichroism (CD) peak at 350 nm, which was assigned to the π−π* transition band of trans-azobenzene chromophore, suggesting a chiral arrangement of Azo molecules in the nanofibers. Because the nanofibers are dispersed in water and Azo is not soluble in water, the selection of an organic solvent to dissolve Azo is important for the incorporation of Azo molecules. When water-immiscible toluene was used as the solvent, Azo incorporation did not occur, because contact between the nanofibers and Azo was inhibited; however, when water-miscible methanol was used, incorporation was achieved. The incorporation of Azo gradually occurred in the pre-assembled C18AA + D-HSA nanofibers, but co-assembly did not occur during the formation of the C18AA + D-HSA nanofibers. We also showed that Azo-containing nanofibers can undergo a reversible thermal phase transition between gel-to-sol states, switching the CD signal of Azo on and off. Furthermore, trans–cis-photoisomerization of Azo embedded in the nanofibers eliminated the CD peak of the trans-isomer, and no new CD peak corresponding to the cis-isomer appeared.
How should medical society face patient feedback in online review platforms?
Authors: Kaneda Yudai , Ozaki Akihiko , Tachibana Kazunoshin , Wada Masahiro , Gonda Kenji , Tanimoto Tetsuya , Shimmura Hiroaki , Jakovljevic Mihajlo 
Published: July 2024

Abstract
In the medical field, the importance of online reviews is escalating. However, the complexity of responding to these reviews is profound, as such anonymous critiques may encompass not only emotionally distressing content but also potentially malicious criticisms directed at healthcare professionals. While recognizing the vital role of patient feedback, there exists a necessity for a collective approach to managing online commentary. This effort seeks to strike a balance between patient satisfaction and the safeguarding of healthcare practitioners and administrative staff. We believe the global medical community must establish guidelines to effectively handle such scenarios, thereby contributing to the sustainability of patient-centered services.
Markets and morality: how markets shape our (dis)regard for others
Authors: Chen Daniel L , Reinhart Eric 
Published: July 2024

Abstract
Scholars since Hume and Smith have debated possible causal connections between market experiences and moral beliefs. Here, we study the impact of market interactions on utilitarian versus deontological values, charitable donations, and whether individuals have differential in-group/out-group moral views. We randomly assign workers residing across several nations of varying income levels to different market conditions and found that, in low-income nations, tournament-based compensation increased deontological commitments, especially toward out-group members, and donations by productive workers, but decreased donations by less productive workers. In higher-income nations, the effect on deontological commitments reversed, while effects on out-group attitudes and donations became insignificant. These findings suggest that if utilitarian attitudes lead to more market-oriented policies, then multiple steady states arise wherein some countries sustain high levels of utilitarian attitudes and economic growth alongside progressively weakening deontological commitments and interpersonal regard for others, putting economic rationality and liberal moral development at odds.
Walking fingerprinting
Authors: Koffman Lily , Crainiceanu Ciprian , Leroux Andrew 
Published: July 2024

Abstract
We consider the problem of predicting an individual’s identity from accelerometry data collected during walking. In a previous paper, we transformed the accelerometry time series into an image by constructing the joint distribution of the acceleration and lagged acceleration for a vector of lags. Predictors derived by partitioning this image into grid cells were used in logistic regression to predict individuals. Here, we (a) implement machine learning methods for prediction using the grid cell-derived predictors; (b) derive inferential methods to screen for the most predictive grid cells while adjusting for correlation and multiple comparisons; and (c) develop a novel multivariate functional regression model that avoids partitioning the predictor space. Prediction methods are compared on two open source acceleometry data sets collected from: (a) 32 individuals walking on a 1.06 km path; and (b) six repetitions of walking on a 20 m path on two occasions at least 1 week apart for 153 study participants. In the 32-individual study, all methods achieve at least 95% rank-1 accuracy, while in the 153-individual study, accuracy varies from 41% to 98%, depending on the method and prediction task. Methods provide insights into why some individuals are easier to predict than others.
A Mixed Methods Approach to Understand Mother-Father Relationship and Perceived Stress Among Black Pregnant Women
Authors: Wheeler Jenna M. , LaManna Jacqueline , Esparza Lindsey , Khalil Dalia , Davis Jean W. , Xie Rui , Misra Dawn P. , Giurgescu Carmen , Kavanaugh Karen 
Published: July 2024

Background:
Black women in the United States report moderate to high levels of perceived stress during pregnancy. Though lower levels of involvement and support from father of the baby (FOB) and higher levels of conflict have been associated with higher levels of maternal perceived stress, it is not clear how Black pregnant women experience the mother-father relationship and its influence on perceived stress.

Purpose:
To examine and describe the mother-father relationship and its role in experiences of perceived stress from the perspective of Black pregnant women.

Methods:
Using a convergent, mixed methods approach with ideal-type analysis, we conducted a secondary analysis of data among 60 Black pregnant women enrolled in the Biosocial Impact on Black Births study. Women completed online self-report questionnaires and participated in a semi-structured interview by telephone.

Results:
Participants who reported more conflict with FOB also reported higher levels of perceived stress (ρ(47) = .431, P= .002). Themes (importance, communication, support, conflict, satisfaction, and stress) emerged from the data. Five distinct mother-father relationship typologies were identified following an ideal-type analysis of the combined dataset: Cared For; Managing Expectations; Just Friends, For the Kids; It’s Complicated; and Can’t be Bothered.

Conclusions:
These findings are an innovative exemplar of ideal-type analysis and provide a deeper understanding of the nuance and dynamics within the mother-father relationship and how it influences perceived stress among Black pregnant women.

Implications:
Clinicians must recognize the significance of the mother-father relationship and, when appropriate, encourage paternal involvement or intervene if there is conflict during pregnancy.
Integration of Entrustable Professional Activities in a Competency-Based Clinical Assessment Tool in a Nurse Practitioner Program
Authors: Anthamatten Angelina , Pitts Courtney 
Published: July 2024

Background:
Entrustable professional activities (EPAs) are used in competency-based clinical assessment.

Problem:
Faculty in a Family Nurse Practitioner program aimed to improve their clinical assessment tool to more effectively measure students’ clinical performance, outline expectations, track progress, and document evidence related to clinical competence.

Approach:
EPAs that aligned with the American Association of Colleges of Nursing Essentials and National Organization of Nurse Practitioner (NP) Faculties Core NP Competencies were integrated into a clinical assessment tool. Student performance expectations at different points in the program were outlined. Students documented each time they performed an EPA during clinical experiences.

Outcome:
The EPA-based clinical assessment tool captured valuable information about student performance during clinical experiences and contributed to evidence related to competencies.

Conclusions:
EPAs can be used to assess clinical performance and document evidence of competence and readiness for practice. Tracking the frequency of EPA performance was essential to ensure students had sufficient practice.
Faculty Development Needs and Approaches to Support Course Examination Development in Nursing Programs
Authors: Hensel Desirée , Moorman Margaret , Stuffle Megan E. , Holtel Elizabeth A. 
Published: July 2024

Background:
Many nursing faculty lack formal training in item writing and test construction.

Purpose:
This integrative review synthesizes the evidence to gain a comprehensive understanding of the developmental needs and interventions employed to help nursing faculty gain skills to develop course examinations.

Methods:
Original research and non-research reports published since 2012 were included. Independent quality appraisals were completed based on the Johns Hopkins Nursing Evidence-based Practice Model. Data were analyzed using the constant comparison method.

Results:
Nineteen reports met inclusion criteria. Themes related to test development skills were standards support practice, educational preparation, evolving technologies, and writing for clarity. Themes related to faculty development were continuous improvement, professional development plans, peer and expert collaboration, and up-to-date resources.

Conclusions:
Nursing programs need to establish methods to ensure faculty use best practices and are competent in test development. Nursing needs future research to understand optimal faculty development approaches.
Multimodal AI Combining Clinical and Imaging Inputs Improves Prostate Cancer Detection
Authors: Roest Christian , Yakar Derya , Rener Sitar Dorjan Ivan , Bosma Joeran S. , Rouw Dennis B. , Fransen Stefan Johannes , Huisman Henkjan , Kwee Thomas C. 
Published: July 2024

Objectives
Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI.

Materials and Methods
We retrospectively analyzed 932 biparametric prostate MRI examinations performed for suspected csPCa (ISUP ≥2) at 2 institutions. Each MRI scan was automatically analyzed by a previously developed DL model to detect and segment csPCa lesions. Three sets of features were extracted: DL lesion suspicion levels, clinical parameters (prostate-specific antigen, prostate volume, age), and MRI-based lesion volumes for all DL-detected lesions. Six multimodal artificial intelligence (AI) classifiers were trained for each combination of feature sets, employing both early (feature-level) and late (decision-level) information fusion methods. The diagnostic performance of each model was tested internally on 20% of center 1 data and externally on center 2 data (n = 529). Receiver operating characteristic comparisons determined the optimal feature combination and information fusion method and assessed the benefit of multimodal versus unimodal analysis. The optimal model performance was compared with a radiologist using PI-RADS.

Results
Internally, the multimodal AI integrating DL suspicion levels with clinical features via early fusion achieved the highest performance. Externally, it surpassed baselines using clinical parameters (0.77 vs 0.67 area under the curve [AUC], P < 0.001) and DL suspicion levels alone (AUC: 0.77 vs 0.70, P = 0.006). Early fusion outperformed late fusion in external data (0.77 vs 0.73 AUC, P = 0.005). No significant performance gaps were observed between multimodal AI and radiologist assessments (internal: 0.87 vs 0.88 AUC; external: 0.77 vs 0.75 AUC, both P > 0.05).

Conclusions
Multimodal AI (combining DL suspicion levels and clinical parameters) outperforms clinical and MRI-only AI for csPCa detection. Early information fusion enhanced AI robustness in our multicenter setting. Incorporating lesion volumes did not enhance diagnostic efficacy.
Mapping the peripheral immune landscape of Parkinson’s disease patients with single-cell sequencing
Authors: Moquin-Beaudry Gael , Andriamboavonjy Lovatiana , Audet Sebastien , Hamilton Laura K , Duquette Antoine , Chouinard Sylvain , Panisset Michel , Tetreault Martine 
Published: July 2024

AbstractParkinson’s Disease (PD) is most recognized for its impact on the central nervous system. However, recent breakthroughs underscore the crucial role of interactions between central and peripheral systems in PD’s pathogenesis. The spotlight is now shifting as we explore beyond the central nervous system, discovering that peripheral changes such as inflammatory dysfunctions may predict the rate of disease progression and severity. Despite more than 200 years of research on PD, reliable diagnostic or progression biomarkers and effective disease-modifying treatments are still lacking. Additionally, the cellular mechanisms that drive changes in immunity are largely unknown. Thus, understanding peripheral immune signatures could lead to earlier diagnosis and more effective treatments for PD. Here, we sought to define the transcriptomic alterations of the complete peripheral immune cell compartment by single-cell RNA- and T-cell receptor-sequencing with hopes of uncovering PD signatures and potential peripheral blood biomarkers.Following transcriptional profiling of 78 876 cells from 10 healthy controls and 14 PD donors, we observed five major classes of immune cell types; myeloid (monocytes, dendritic cells) and lymphoid (T, B, natural killer) cells from which we identified 38 cellular subtypes following bioinformatic re-clustering. Comparing immune cell subtypes and phenotypes between PD patients and healthy controls revealed notable features of PD: 1) a significant shift of classical CD14+monocytes towards an activated CD14+/CD83+state, 2) changes in lymphocyte subtype abundance, including a significant decrease in CD4+naive and mucosal-associated invariant T-cells subtypes, along with an increase in CD56+natural killer cells, 3) the identification by T-cell receptor sequencing of several PD specific T-cell clones shared between multiple patients, suggesting the implication of common epitopes in PD pathogenesis, 4) a notable increase in the expression of activation signature genes, including the AP-1 stress-response transcription factor complex, across all PD cell types. This signal was not present in atypical parkinsonism patients with multiple systems atrophy or progressive supranuclear palsy.Overall, we present a comprehensive atlas of peripheral blood mononuclear cells from control and PD patients which should serve as a tool to improve our understanding of the role the immune cell landscape plays in PD pathogenesis.
A common pathway controls cell size in the sepal and leaf epidermis leading to a non-random pattern of giant cells
Authors: Clark Frances K. , Weissbart Gauthier , Wang Xihang , Harline Kate , Li Chun-Biu , Formosa-Jordan Pau , Roeder Adrienne H. K. 
Published: July 2024

AbstractArabidopsis leaf epidermal cells have a wide range of sizes and ploidies, but how large cells are spatially patterned alongside smaller cells remains unclear. Here, we demonstrate that the same genetic pathway that creates giant cells in sepals is also responsible for their formation in the leaf epidermis. In both sepals and leaves, giant cells are scattered among smaller cells; therefore, we asked whether the spatial arrangement of giant cells is random. By comparing sepal and leaf epidermises with computationally generated randomized tissues we show that giant cells are clustered more than is expected by chance. Our cell-autonomous and stochastic computational model recapitulates the observed giant cell clustering, indicating that clustering emerges as a result of the cell division pattern. Overall, cell size patterning is developmentally regulated by common mechanisms in leaves and sepals rather than a simple byproduct of cell growth.TeaserThe spatial pattern of giant cells becomes non-random as the surrounding cells divide.
Context-dependence of deterministic and nondeterministic contributions to closed-loop steering control
Authors: Egger Seth W. , Keemink Sander W. , Goldman Mark S. , Britten Kenneth H. 
Published: July 2024

AbstractIn natural circumstances, sensory systems operate in a closed loop with motor output, whereby actions shape subsequent sensory experiences. A prime example of this is the sensorimotor processing required to align one’s direction of travel, or heading, with one’s goal, a behavior we refer to as steering. In steering, motor outputs work to eliminate errors between the direction of heading and the goal, modifying subsequent errors in the process. The closed-loop nature of the behavior makes it challenging to determine how deterministic and nondeterministic processes contribute to behavior. We overcome this by applying a nonparametric, linear kernel-based analysis to behavioral data of monkeys steering through a virtual environment in two experimental contexts. In a given context, the results were consistent with previous work that described the transformation as a second-order linear system. Classically, the parameters of such second-order models are associated with physical properties of the limb such as viscosity and stiffness that are commonly assumed to be approximately constant. By contrast, we found that the fit kernels differed strongly across tasks in these and other parameters, suggesting context-dependent changes in neural and biomechanical processes. We additionally fit residuals to a simple noise model and found that the form of the noise was highly conserved across both contexts and animals. Strikingly, the fitted noise also closely matched that found previously in a human steering task. Altogether, this work presents a kernel-based analysis that characterizes the context-dependence of deterministic and non-deterministic components of a closed-loop sensorimotor task.New and noteworthyWe use nonparametric systems identification techniques to assess the context-dependence of deterministic and nondeterministic contributions to a closed-loop behavior. Classical approaches assume a fixed transformation between sensory input and motor output. Here, we reveal strong changes to the measured sensorimotor transformations with behavioral context. In contrast, noise within the transformation exhibited a consistent form across contexts, subjects, and species. Together, this work demonstrates how context affects the systematic and stochastic components of a closed-loop behavior.