bluuebunny/crossref_metadata_2025_split huggingface dataset:
Professionalizing all-volunteer nonprofit organizations: an intervention study based on the competing values framework and self-determination theory
Authors: De Clerck Tom , Haerens Leen , Van Dyck Delfien , Devos Geert , Willem Annick 
Published: November 2023

PurposeProfessionalization is an important issue in many all-volunteer nonprofit organizations (e.g. recreational sports clubs). Therefore, this study relied on the competing values framework and self-determination theory to investigate whether a newly developed intervention can effectively strengthen the management processes and leadership styles in all-volunteer sports clubs.Design/methodology/approachFor this purpose, a rigorous non-equivalent pre-test post-test control group design was used. The intervention involved two sessions organized in sports clubs in which internal stakeholders (e.g. board members, coaches, volunteers) were invited to discuss change initiatives aimed at enhancing the organizational processes.FindingsAn effect on both the management processes and leadership styles was found. As for the management processes, the intervention had an impact on the internal processes, with especially the development of an internal communication plan and the annual assessment of the organization's operations being promoted by the intervention. Regarding the leadership styles, the intervention had an effect on the controlling and chaotic leadership style, with leaders becoming less chaotic and controlling in situations in which (respectively) the business plan was established and the tasks were distributed within the organization.Originality/valueThis intervention study adopted an innovative approach to organizational intervention research by focusing on the enhancement of both the management processes and the leadership styles. Its principles are also relevant and valuable to organizations operating in other organizational contexts.
Research on heavy-haul adaptive technology and strengthening measures for existing railway steel bridge
Authors: Ju Xiaochen 
Published: November 2023

PurposeThis research addresses the diverse characteristics of existing railway steel bridges in China, including variations in construction age, design standards, structural types, manufacturing processes, materials and service conditions. It also focuses on prominent defects and challenges related to heavy transportation conditions, particularly low live haul reserves and severe fatigue problems.Design/methodology/approachThe study encompasses three key aspects: (1) Adaptability assessment: It begins with assessing the suitability of existing railway steel bridges for heavy-haul operations through comprehensive analyses, experiments and engineering applications. (2) Strengthening: To combat frequent crack defects in the vertical stiffener end structure of girder webs, fatigue performance tests and reinforcement scheme experiments were conducted. These experiments included the development of a hot-spot stress S-N curve for this structure, validating the effectiveness of methods like crack stop holes, ultrasonic hammering and flange angle steel. (3) Service life extension: Research on the cruciform welded joint structure (non-fusion transfer type) focused on fatigue performance over the long life cycle. This led to the establishment of a fatigue S-N curve, enhancing Chinese design codes.FindingsThe research achieved several significant outcomes: (1) Successful implementation of strengthening and retrofitting measures on a 64-m single-span double-track railway steel truss girder on an existing heavy-duty line. (2) Post-reinforcement, a substantial 26% to 32% reduction in live haul stress on bridge members was achieved. (3) The strengthening and retrofitting efforts met design expectations, enabling the bridge to accommodate vehicles with a 30-ton axle haul on the railway line.Originality/valueThis research systematically tackles challenges and defects associated with Chinese existing railway steel bridges, providing valuable insights into adaptability assessment, strengthening techniques and service life extension methods. Furthermore, the development of fatigue S-N curves and the successful implementation of bridge enhancements have practical implications for improving the resilience and operational capacity of railway steel bridges in China.
Supply chain 4.0 ambidexterity and lean supply chain management: interrelationships and effect on the focal firm’s operational performance
Authors: Roldán Bravo María Isabel , Maqueira-Marin Juan Manuel , Moyano-Fuentes José 
Published: November 2023

Purpose
The purpose of this paper is twofold: firstly, to provide a measurement instrument for supply chain 4.0 ambidexterity by applying the theoretical perspective of ambidexterity to advance Industry 4.0; secondly, to empirically analyse how supply chain 4.0 ambidexterity and lean supply chain management contribute to enhancing the focal firm’s operational performance.

Design/methodology/approach
Empirical results are obtained through analysis of survey data from a sample of 209 Spanish focal firms in industrial sectors in an intermediate position in the supply chain. Structural equation modelling was performed to test the three proposed hypotheses.

Findings
Drawing on resource orchestration theory and the relational resource-based view, this study empirically demonstrates the full mediating role of lean supply chain management in the relationship between supply chain 4.0 ambidexterity and the focal firm’s operational performance.

Originality/value
Although recent research has highlighted the pertinence of applying inter-organisational ambidexterity to foster Industry 4.0 (Hofmann et al., 2019), to the best of the authors’ knowledge, this is one of the first studies to apply this theoretical framework to explain the transition to supply chain 4.0. In addition, to date, to the best of the authors’ knowledge, no study exists that has developed a measurement scale and used this concept in an empirical analysis to advance theory development.
Biological characteristics and pathogenicity of a Staphylococcus aureus strain with an incomplete hemolytic phenotype isolated from bovine milk
Authors: Xu Xiuhua , Zhou Tingting , Fang Xueyao , Hu Longhua , Zhu Jin , Zheng Feng 
Published: November 2023

AbstractStaphylococcus aureus is a common pathogen capable of infecting both humans and animals and causing various severe diseases. Here, we aimed to determine the biological features and pathogenicity of S. aureus strain Sa9, of the incomplete hemolysis phenotype, isolated from bovine milk. Sa9 was classified as ST97 by multilocus sequence typing, and it showed increased β‐hemolysin expression and lower Hla and Hld expression levels compared with that in the S. aureus USA300 strain LAC. RT‐PCR and ELISA results showed that the expression levels of inflammatory cytokines were higher in Sa9‐induced mouse primary peritoneal macrophages compared with those induced by the LAC strain. However, the Sa9 strain also mediated anti‐inflammatory effects by upregulating IL‐10 and IFN‐β in macrophages, which were not apparently induced by S. aureus culture supernatants. Phagocytosis and whole‐blood survival assays were also performed to assess the in vitro survival of bacteria, and the virulence was evaluated in mice. Although the Sa9 strain showed lower ability of intracellular survival in macrophages than LAC, similar multiplication in human whole blood and pathogenicity toward mice were observed. Taken together, we report that the distinctive immune response induced by the S. aureus strain with an incomplete hemolysis phenotype occurs in cattle, and its potential pathogenicity and risk of transmission to humans require attention.
Prediction of spontaneous preterm birth using supervised machine learning on metabolomic data: A case–cohort study
Authors: Al Ghadban Yasmina , Du Yuheng , Charnock‐Jones D. Stephen , Garmire Lana X. , Smith Gordon C. S. , Sovio Ulla 
Published: November 2023

AbstractObjectivesTo identify and internally validate metabolites predictive of spontaneous preterm birth (sPTB) using multiple machine learning methods and sequential maternal serum samples, and to predict spontaneous early term birth (sETB) using these metabolites.DesignCase–cohort design within a prospective cohort study.SettingCambridge, UK.Population or sampleA total of 399 Pregnancy Outcome Prediction study participants, including 98 cases of sPTB.MethodsAn untargeted metabolomic analysis of maternal serum samples at 12, 20, 28 and 36 weeks of gestation was performed. We applied six supervised machine learning methods and a weighted Cox model to measurements at 28 weeks of gestation and sPTB, followed by feature selection. We used logistic regression with elastic net penalty, followed by best subset selection, to reduce the number of predictive metabolites further. We applied coefficients from the chosen models to measurements from different gestational ages to predict sPTB and sETB.Main outcome measuressPTB and sETB.ResultsWe identified 47 metabolites, mostly lipids, as important predictors of sPTB by two or more methods and 22 were identified by three or more methods. The best 4‐predictor model had an optimism‐corrected area under the receiver operating characteristics curve (AUC) of 0.703 at 28 weeks of gestation. The model also predicted sPTB in 12‐week samples (0.606, 95% CI 0.544–0.667) and 20‐week samples (0.657, 95% CI 0.597–0.717) and it predicted sETB in 36‐week samples (0.727, 95% CI 0.606–0.849). A lysolipid, 1‐palmitoleoyl‐GPE (16:1)*, was the strongest predictor of sPTB at 12 weeks of gestation (0.609, 95% CI 0.548–0.670), 20 weeks (0.630, 95% CI 0.569–0.690) and 28 weeks (0.660, 95% CI 0.599–0.722), and of sETB at 36 weeks (0.739, 95% CI 0.618–0.860).ConclusionsWe identified and internally validated maternal serum metabolites predictive of sPTB. A lysolipid, 1‐palmitoleoyl‐GPE (16:1)*, is a novel predictor of sPTB and sETB. Further validation in external populations is required.
Single‐cell transcriptomes provide insights into expansion of glial cells in Bombyx mori
Authors: Liu Xiaojing , Zhang Zhongjie , Hu Bo , Chen Kai , Yu Ye , Xiang Hui , Tan Anjiang 
Published: November 2023

AbstractThe diversity of cell types in the brain and how these change during different developmental stages, remains largely unknown. The life cycle of insects is short and goes through 4 distinct stages including embryonic, larval, pupal, and adult stages. During postembryonic life, the larval brain transforms into a mature adult version after metamorphosis. The silkworm, Bombyx mori, is a lepidopteran model insect. Here, we characterized the brain cell repertoire of larval and adult B. mori by obtaining 50 708 single‐cell transcriptomes. Seventeen and 12 cell clusters from larval and adult brains were assigned based on marker genes, respectively. Identified cell types include Kenyon cells, optic lobe cells, monoaminergic neurons, surface glia, and astrocyte glia. We further assessed the cell type compositions of larval and adult brains. We found that the transition from larva to adult resulted in great expansion of glial cells. The glial cell accounted for 49.8% of adult midbrain cells. Compared to flies and ants, the mushroom body kenyon cell is insufficient in B. mori, which accounts for 5.4% and 3.6% in larval and adult brains, respectively. Analysis of neuropeptide expression showed that the abundance and specificity of expression varied among individual neuropeptides. Intriguingly, we found that ion transport peptide was specifically expressed in glial cells of larval and adult brains. The cell atlas dataset provides an important resource to explore cell diversity, neural circuits and genetic profiles.
Investigation of the reasons for patients who choose peritoneal dialysis as kidney replacement therapies to change their decisions
Authors: Altunok Murat , Çankaya Erdem , Sevinç Can , Uyanık Abdullah 
Published: November 2023

AbstractIntroductionPeritoneal dialysis (PD) is one of the kidney replacement therapies (KRT). Patients' choice of KRT is influenced by personal causes, familial factors, factors related to healthcare professionals, and social factors.MethodsThis study included 341 patients. PD patients who changed their KRT selection were asked for the reasons to change with a questionnaire.ResultsOf the patients who initially chose PD, only 48.5% received KRT by PD. Five (20%) of the patients gave up PD compulsorily because they heard that the risk of infection with PD was higher, eight (40%) thought they could not do it, four (20%) because they needed to do assisted PD but had no relatives to do it, and three (15%) because they had abdominal surgery.ConclusionWe believe that the fact that KRT training is carried out by a PD trained team and that patients are provided with assistance for PD will be effective in addressing the concerns of patients with PD undecided.
Out of stock: A brief clinical reference for rough equivalency of glucagon‐like peptide‐1 (GLP‐1) ± glucose‐dependent insulinotropic polypeptide (GIP) receptor agonists for A1c and weight reduction in people with type 2 diabetes
Authors: Perreault Leigh , Bergman Bryan C. 
Published: November 2023

Highlights
Despite the common practice of switching patients from one medicine to another—to improve efficacy, safety, or tolerability—guidance on how to do so is uncommon. During this time of global shortage of glucagon‐like peptide‐1 receptor agonist (GLP‐1 RA) ± glucose‐dependent insulinotropic polypeptide (GIP) RA therapies, this research letter offers a quick clinical reference of rough equivalency between GLP‐1 ± GIP RA for A1c and body weight reduction in people with type 2 diabetes.
Prevalence and treatment rate of gout by depressive symptom severity: A cross‐sectional analysis of NHANES 2007–2018
Authors: Lu Siyi , Qian Tangliang , Cao Fengjiao , Kang Tianlun , Liu Xiaoping , Wang Chaoxin , Xia Yun , Hou Xiujuan 
Published: November 2023

AbstractBackgroundThe co‐disease of depression and gout is becoming more common in the modern era. However, the relationship between the severity of depressive symptoms and gout prevalence and treatment rate was still unclear.ObjectiveThis study aimed to determine the relationship between the prevalence, treatment rate of gout, and the severity of depression in the United States.MethodThe cross‐sectional analysis of the 2007–2018 National Health and Nutrition Examination Survey (NHANES) for participants with depression was performed. According to their Patient Health Questionnaire‐9 (PHQ‐9) scores, participants were categorized as none, mild, moderate, moderately severe, and severe. To learn the correlation between the severity of depressive symptoms and the prevalence and treatment rate of gout using multivariate logistic regression to control for confounder interference.ResultsA total of 25 022 patients were included in this study. As the severity of the depressive symptoms worsened (Mild, Moderate and Moderately severe), the risk of gout increased in non‐adjusted model and model 1,2,3 (p‐value for trend =.01 in non‐adjusted model, <.0001 in model 1, <.01 in models 2 and 3; prevalence group in Model 1, aOR1.71, 95% CI (1.40, 2.08) in the mild group, aOR1.68, 95% CI (1.19, 2.39) in the moderate group, aOR1.31,95% CI (0.82, 2.11) in the moderately severe group, aOR1.21, 95% CI (0.62, 2.38) in the severe group). However, the lower gout prevalence trend has no statistical significance after adjusting all factors in Model 4(p‐value for trend =.98). Compared with patients without depression, only a few patients received treatment, especially patients with severe depression (none, 80.1%; severe, 0.2%). The more severe the depression, the lower the treatment rate (p‐value for trend: non‐adjusted model, p < .001; model 1, p = .05; model 2, p = .02; model 3, p = .03).ConclusionCompared with patients without depression, the patients with depression had a higher risk of gout. With the aggravation of depression, the prevalence of gout and the rate of treatment both were decreased. Patients with gout and depression need to receive multidisciplinary care after diagnosis. However, currently, treatment cannot meet the needs of the current patients.
Posttraumatic stress disorder symptoms in systemic autoimmune rheumatic disease patients during the early COVID‐19 pandemic
Authors: Oakes Emily G. , Ellrodt Jack , Yee Jeong , Guan Hongshu , Kubzansky Laura D. , Koenen Karestan C. , Costenbader Karen H. 
Published: November 2023

AbstractAimGiven reports of increased prevalence of PTSD symptoms at COVID‐19 pandemic onset, we aimed to assess the prevalence of posttraumatic stress disorder (PTSD) symptoms at pandemic onset in individuals with and without systemic autoimmune rheumatic disease (SARD).MethodsIn May 2020, we invited 6678 patients to complete the Brief Trauma Questionnaire and the Posttraumatic Stress Disorder Checklist (PCL‐5), validated PTSD symptom screenings. We compared responses from patients with and without SARD using multivariable logistic regression.ResultsWe received 1473 responses (22% response rate) from 5/2020 to 9/2021 (63 with prior PTSD diagnoses, 138 with SARD history). The SARD population was more female (p .0001) and had a higher baseline prevalence of stress disorders (56% vs. 43%, p .004). SARD subjects reported more experiences with life‐threatening illness, 60%, versus 53% among those without SARD (p .13), and more antidepressant or anxiolytic medication use pre‐pandemic (78% vs. 59%, p .0001). Adjusting for pre‐pandemic PTSD diagnosis, younger age and history of stress disorder were the most significant predictors of PCL‐5 positivity. There were no significant differences in PCL‐5 score or positivity among those with or without SARD.ConclusionIn this population, patients with SARD had a higher pre‐COVID‐19 prevalence of stress‐related conditions, but it was not the case that they had an increased risk of PTSD symptoms in the early pandemic. Younger individuals, those with baseline depression, anxiety, or adjustment disorders, and those taking antidepressant or anxiolytic medications were more likely to have PTSD symptoms in the first waves of the COVID‐19 pandemic.
The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations
Authors: Tan Lin , Luningham Justin M. , Huh David , Zhou Zhengyang , Tanner‐Smith Emily E. , Baldwin Scott A. , Mun Eun‐Young 
Published: November 2023

AbstractUnderstanding the efficacy and relative effectiveness of a brief alcohol intervention (BAI) relies on obtaining a credible intervention effect estimate. Outcomes in BAI trials are often count variables, such as the number of drinks consumed, which may be overdispersed (i.e., greater variability than expected based on a given model) and zero‐inflated (i.e., greater probability of zeros than expected based on a given model). Ignoring such distribution characteristics can lead to biased estimates and invalid statistical conclusions. In this critical review, we identified and reviewed 64 articles that reported count outcomes from a systematic review of BAI trials for adolescents and young adults from 2013 to 2018. Given many statistical models to choose from when analyzing count outcomes, we reviewed the models used and reporting practices in the BAI trial literature. A majority (61.3%) of analyses with count outcomes used linear models despite violations of normality assumptions; 75.6% of outcome variables demonstrated clear overdispersion. We provide an overview of available count models (Poisson, negative binomial, zero‐inflated or hurdle, and marginalized zero‐inflated Poisson regression) and formulate practical guidelines for reporting outcomes of BAIs. We provide a visual step‐by‐step decision guide for selecting appropriate statistical models and reporting results for count outcomes. We list accessible resources to help researchers select an appropriate model with which to analyze their data. Recent advances in count distribution‐based models hold promise for evaluating count outcomes to gauge the efficacy and effectiveness of BAIs and identify critical covariates in alcohol epidemiologic research. We recommend that researchers report the distributional properties of count outcomes, such as the proportion of zero counts, and select an appropriate statistical analysis for count outcomes using the provided decision tree. By following these recommendations, future research may yield more accurate, transparent, and reproducible results.
Predicting fetal alcohol spectrum disorders in preschool‐aged children from early life factors
Authors: Bandoli Gretchen , Coles Claire , Kable Julie , Jones Kenneth Lyons , Wertelecki Wladimir , Yevtushok Lyubov , Zymak‐Zakutnya Natalya , Granovska Iryna , Plotka Larysa , Chambers Christina ,   The CIFASD
Published: November 2023

AbstractBackgroundEarly life factors, including parental sociodemographic characteristics, pregnancy exposures, and physical and neurodevelopmental features measured in infancy are associated with fetal alcohol spectrum disorders (FASD). The objective of this study was to evaluate the performance of a classifier model for diagnosing FASD in preschool‐aged children from pregnancy and infancy‐related characteristics.MethodsWe analyzed a prospective pregnancy cohort in Western Ukraine enrolled between 2008 and 2014. Maternal and paternal sociodemographic factors, maternal prenatal alcohol use and smoking behaviors, reproductive characteristics, birth outcomes, infant alcohol‐related dysmorphic and physical features, and infant neurodevelopmental outcomes were used to predict FASD. Data were split into separate training (80%: n = 245) and test (20%: n = 58; 11 FASD, 47 no FASD) datasets. Training data were balanced using data augmentation through a synthetic minority oversampling technique. Four classifier models (random forest, extreme gradient boosting [XGBoost], logistic regression [full model] and backward stepwise logistic regression) were evaluated for accuracy, sensitivity, and specificity in the hold‐out sample.ResultsOf 306 children evaluated for FASD, 61 had a diagnosis. Random forest models had the highest sensitivity (0.54), with accuracy of 0.86 (95% CI: 0.74, 0.94) in hold‐out data. Boosted gradient models performed similarly, however, sensitivity was less than 50%. The full logistic regression model performed poorly (sensitivity = 0.18 and accuracy = 0.65), while stepwise logistic regression performed similarly to the boosted gradient model but with lower specificity. In a hold‐out sample, the best performing algorithm correctly classified six of 11 children with FASD, and 44 of 47 children without FASD.ConclusionsAs early identification and treatment optimize outcomes of children with FASD, classifier models from early life characteristics show promise in predicting FASD. Models may be improved through the inclusion of physiologic markers of prenatal alcohol exposure and should be tested in different samples.