bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heat-Induced Secretion of Heat Shock Proteins 70 and 90 Does not Affect the Expression of the Glucocorticoid Receptor in Primary Airway Cells in COPD
Authors: Zhou Liang , Fang Lei , Roth Michael , Papakonstantinou Eleni , Tamm Michael , Stolz Daiana 
Published: April 2024

Abstract
Purpose
The response to glucocorticoids is hampered in many COPD patients by a yet unknown mechanism. Earlier we reported that short-term heat exposure of primary human bronchial epithelial cells (BEC) and airway smooth muscle cells (ASMC) of asthma patients increased the expression and secretion of extracellular heat shock proteins (eHSPs) resulting in increased expression of glucocorticoid receptor (GR) in BEC and inhibition of ASMC remodeling. The aim of the present study was to assess if the same mechanism is also present in primary airway wall cells of COPD patients.

Methods
Primary BEC and ASMC were established from endobronchial biopsies obtained from COPD patients (n = 73), who participated in the HISTORIC study, an investigator-initiated and driven clinical trial. Secretion and protein expression of HSPs was assessed by ELISA and Western blotting. Expression of total GR, its isoforms GRα and GRβ and toll-like receptor 4 (TLR4) was determined by Western-blotting.

Results
Short heat exposure (65 °C, 10 s) of BEC resulted in a significant increase of the secretion of eHSP70 and eHSP90, while the intracellular protein was not altered. Heat treatment or exposure to eHSP70 or eHSP90 had no effect on the expression of GR and GR-isoforms. However, eHSP70 and eHSP90 significantly reduced the expression of TLR4.

Conclusions
The results of this study indicate that primary airway cells from COPD patients respond differently to heat exposure and extracellular HSP70 or HSP90 than cells from asthma patients regarding the expression of GR and this may explain the reduced response to glucocorticoids in patients with COPD.
Trial Registration: ISRCTN11017699
Effects of exercise on life satisfaction of people diagnosed with cancer: a systematic review and meta-analysis
Authors: Fernández-Sánchez Javier , Trujillo-Colmena Daniel , Rodríguez-Castaño Adrián , Lavín-Pérez Ana Myriam , Del Coso Juan , Casado Arturo , Collado-Mateo Daniel 
Published: April 2024

Abstract
Purpose
A cancer diagnosis is commonly associated with a decline in patient’s life satisfaction and more pessimistic expectations about the future. The identification of strategies to improve life satisfaction in patients with cancer is of great interest to health practitioners since it may be associated with a better prognosis of cancer and higher survival rates. Previous meta-analyses and reviews concluded that exercise could significantly improve health-related quality of life in this population, but the effects of exercise on life satisfaction are still not well-known. This review aims to analyse the effects of exercise programs on life satisfaction in people with cancer and individuals who have overcome cancer.

Methods
The present systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A thorough search of databases including Web of Science and PubMed/MEDLINE was carried out. Six studies (535 participants) in which the effect of an exercise program was compared to a non-exercise program control condition in patients with cancer were considered eligible. A subsequent meta-analysis was performed using the random effects model to calculate the standardized mean differences (SMD) and 95% confidence intervals (CI).

Results
Exercise intervention improved satisfaction with life compared with a control condition (SMD = 1.28; p = 0.02 with a 95% CI of 0.22 to 2.34).

Conclusion
Exercise could be considered an effective tool to improve life satisfaction in patients with cancer. Hence, professionals might consider the possibility of integrating physical exercise into strategies aimed at enhancing the low life satisfaction often experienced by patients.

PROSPERO
CRD42023438146
Evolution inspired binary flower pollination for the uncapacitated facility location problem
Authors: Ozsoydan Fehmi Burcin , Kasırga Ali Erel 
Published: April 2024

AbstractThe present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to minimize the total cost, which is the sum of facility opening costs and transportation costs. Since UFLP is a binary optimization problem, FPA, which is introduced to solve real-valued optimization problems, is redesigned to be able to conduct search in binary domains. This constitutes one of the contributions of the present study. In this context, some evolutionary operators such as crossover and mutation are adopted by the proposed FPA. Next, the mutation operator is further enhanced by making use of an adaptive procedure that introduces greater level of diversity at earlier iterations and encourages intensification toward the end of search. Thus, while premature convergence and local optima problems at earlier iterations are avoided, a more intensified search around the found promising regions is performed. Secondarily, as demonstrated in this study, by making use of the reported evolutionary procedures, FPA is able to run in binary spaces without employing any additional auxiliary procedures such as transfer functions. All available benchmarking instances are solved by the proposed approach. As demonstrated by the comprehensive experimental study that includes statistically verified results, the developed approach is found as a promising algorithm that can be extended to numerous binary optimization problems.
Multi-view GCN for loan default risk prediction
Authors: Li Zihao , Chen Yakun , Wang Xianzhi , Yao Lina , Xu Guandong 
Published: April 2024

AbstractAs a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each application as an independent individual, neglecting the explicit connections among different application records. Besides, these attempts suffer from the problem of missing data and imbalanced distribution (i.e., the default records are small samples against all the applications). We believe similar records could provide some auxiliary signals, which are of critical importance to alleviate the data missing issue and facilitate data argumentation. To this end, we propose multi-view loan application graphs, dubbed MLAGs. By evaluating the similarity between the records, a loan application graph can be constructed. Furthermore, we arrange different similarity thresholds to organize various graph structures for multi-graph constructions; thus, a variety of representations can be generated via information propagation and aggregation for small sample argumentation. Consequently, the imbalanced data distribution and missing values issues can be alleviated effectively. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models. Ablation studies also illustrated the validity of each module design.
Emplacement of shocked basement clasts during crater excavation in the Ries impact structure
Authors: Dellefant Fabian , Seybold Lina , Trepmann Claudia A. , Gilder Stuart A. , Sleptsova Iuliia V. , Hölzl Stefan , Kaliwoda Melanie 
Published: April 2024

AbstractIn the Aumühle quarry of the Ries impact structure, moderately shocked clasts from the Variscan basement occur sandwiched between overlying suevite and components derived from the Mesozoic sedimentary cover of the underlying Bunte Breccia without distinct shock effects. We analyzed the clasts by optical microscopy, scanning electron microscopy (SEM/EDS/EBSD), and Raman spectroscopy to unravel their emplacement relation to the overlying suevite and the sediment-rock clasts of the Bunte Breccia. Clasts sizes range up to few decimeters and are embedded in a fine-grained lithic matrix; no impact-melt fragments are observed. Amphibolite clasts contain maskelynite with few lamellar remnants of feldspar, indicating shock pressures of 28–34 GPa. Amphiboles have cleavage fractures and ($$\overline{1}$$

1
¯

01) mechanical twins suggesting differential stresses > 400 MPa. Felsic gneiss components have optically isotropic SiO2 indicative of shock pressures ≈35 GPa. Metagranite cataclasite clasts contain shocked calcite aggregates and quartz with a high density of fine rhombohedral planar deformation features indicating shock pressures ≈20 GPa. The moderately shocked basement clasts originate from deeper levels of the transient cavity and lower radial distance to the center of the structure compared to the sediment-rock clasts. Both were ballistically ejected during crater excavation. In accordance with palaeo- and rock magnetic data, they were mixed during turbulent deposition at the top of the Bunte Breccia before the emplacement of suevite. The high amount of basement clasts below suevite and on top of the underlying Bunte Breccia is consistent with the commonly reported inverse stratigraphy in the Ries impact structure.
Graphical Abstract
Rock slope stability analysis under Hoek–Brown failure criterion with different flow rules
Authors: Melentijević Svetlana , Berisavljević Zoran , Berisavljević Dusan , Marañón Claudio Olalla 
Published: April 2024

AbstractThe stability analysis of homogeneous rock slope following the Hoek–Brown failure criterion under the hypothesis of different flow rules is performed based on limit equilibrium and finite element methods. The applied failure criterion is the generalized Hoek–Brown that can be introduced as a shear/normal function in analysis applying different flow rules. The results are compared with those obtained by the application of equivalent shear strength parameters of the Mohr–Coulomb criterion, considering that this is still the most widely used criterion in rock slope stability analysis and is still the base for the shear strength reduction method applied in finite element modelling. Different proposals for estimating the equivalent strength parameters based on confining stress level are evaluated. The limitation of stress-dependent linear Mohr–Coulomb parameters is emphasized by analysing the vertical cut problem, for which, depending on the chosen stress level, different critical heights are obtained for the same material. Sensitivity analysis of geotechnical parameters used as input for failure criterion is performed to determine their influence on slope stability. Probabilistic analysis is conducted to determine the probability of failure when different flow rules are applied. If slope stability analysis is performed with an assumption of associative flow rule, the probability of failure is within the acceptable limits for the considered case study, while employing non-associative flow rule, the probability of failure is rather high. The chart is presented that could be readily used to estimate the combination of σci, GSI, and mi values that produce failure for the analysed case study.
Molecular epidemiological and clinical infection characteristics analysis of Ralstonia
Authors: Sheng Zhaojun , Li Jiaxin , Han Guojing , Fan Ru , Zhu Pingjun , Fang Xiangqun 
Published: April 2024

Abstract
Purpose
This study was to clarify the molecular epidemiology and clinical infection characteristics of Ralstonia pickettii and establish sequence typing system.

Methods
48 nonrepetitive Ralstonia pickettii strains were collected from January 2008 to December 2013 at the Chinese People’s Liberation Army General Hospital (PLAGH) and were identified through a specific PCR experiment, 16 S rDNA experiment and VITEK 2 system to compare the identification accuracy. The sequence types of the strains were analyzed by multilocus sequence typing (MLST) method. The antibiotic sensitivity of these strains was determined with disc diffusion tests and broth microdilution method. The clinical data of Ralstonia pickettii infected patients were collected.

Results
All of the 48 strains were identified as Ralstonia pickettii by VITEK 2 system. 30 and 34 strains were identified as Ralstonia pickettii by PCR and 16 S rDNA experiment respectively. ST9 was the most sequence types (STs) in these 18 STs of 42 strains. 42 strains were divided into 2 groups (A and B) and 18 genotypes. Ralstonia pickettii was sensitive to some cephalosporins, β-lactam/β-lactamase inhibitor, levofloxacin and trimethoprim/sulfamethoxazole. Cough, sputum, shortness of breath and pulmonary rales were the common clinical symptoms of most Ralstonia pickettii infected patients.

Conclusion
We established a sequence typing system with a relatively fine resolution and the PCR assay is a faster and more sensitive method for clinical identification of Ralstonia pickettii. ST9 is the most common sequence types of Ralstonia pickettii. The most common clinical characteristics of Ralstonia pickettii infected patients were cough, sputum, shortness of breath and pulmonary rales.
Extramural ICT factors impact adolescents’ academic performance and well-being differently: Types of self-regulated learners also matter
Authors: Chen Jiangping , Lin Chin-Hsi , Chen Gaowei 
Published: April 2024

AbstractMost studies have explored how information and communication technology (ICT) factors impact adolescents’ schooling, but often ignore the potential influences on their well-being; no research has further scrutinized the moderating role of self-regulated learning (SRL) as a multi-dimensional combination, that is, different types of SRL learners. This cross-cultural study simultaneously scrutinized how distinct outside-of-school ICT factors influenced adolescents’ digital reading and multi-dimensional well-being. It also took a person-centered approach to identify different types of SRL learners and examined whether the influences varied across the learner types. Data were based on 10,527 students in 308 schools from one East Asian region and one Western country participating in Programme for International Student Assessment (PISA) 2018. Multivariate multiple regression analysis revealed that overall, outside-of-school ICT factors impacted adolescents’ digital reading and well-being differently, which also varied across cultures. Latent profile analysis detected culturally-mixed four profiles of SRL learners: High Profile (high in all indicators), Metacognitive Profile (in-between, optimal metacognition), Cognitive Profile (in-between, optimal cognition), and Low Profile (all low). It was the relatively weaker SRL learners in the East but the stronger SRL learners in the West that were particularly susceptible to the influences, either in a beneficial or detrimental way. Moreover, the cognitive, eudemonic, emotional and social dimensions of adolescents’ well-being were all affected by ICT factors, without recurring trends in patterns. The findings provide implications to strictly monitor and guide adolescents’ Internet use to enhance their academic and subjective well-being.
An Evolutionary Approach to Motivation and Learning: Differentiating Biologically Primary and Secondary Knowledge
Authors: Xu Kate M. , Coertjens Sarah , Lespiau Florence , Ouwehand Kim , Korpershoek Hanke , Paas Fred , Geary David C. 
Published: April 2024

AbstractThe ubiquity of formal education in modern nations is often accompanied by an assumption that students’ motivation for learning is innate and self-sustaining. The latter is true for most children in domains (e.g., language) that are universal and have a deep evolutionary history, but this does not extend to learning in evolutionarily novel domains (e.g., mathematics). Learning in evolutionarily novel domains requires more cognitive effort and thus is less motivating. The current study tested the associated hypothesis that learning will feel easier and more motivating for evolutionarily relevant (e.g., “mother,” “food”) than evolutionarily novel (e.g., “computer,” “gravity”) word pairs and that a growth mindset emphasizing the importance of effort in learning might moderate this effect. Specifically, 144 adults were presented with 32 word pairs (half evolutionarily relevant and half evolutionarily novel) and were randomly assigned to a growth mindset or a control condition. Evolutionarily relevant words were better remembered than evolutionarily novel words (d = 0.65), and the learning was reported as more enjoyable (d = 0.49), more interesting (d = 0.38), as well as less difficult (d = − 0.96) and effortful (d = − 0.78). Although the growth mindset intervention fostered a mindset belief, compared to the control condition, it did not lead to improved recall performance or changes in motivational beliefs. These results are consistent with the prediction of higher motivation and better learning of evolutionarily relevant words and concepts than for evolutionarily novel words and concepts. Implications for future research and educational practice are discussed.
Moths are less attracted to light traps than they used to be
Authors: Battles Ian , Burkness Eric , Crossley Michael S. , Edwards Collin B. , Holmstrom Kristian , Hutchison William , Ingerson-Mahar Joseph , Owens David , Owens Avalon C.S. 
Published: April 2024

AbstractAs evidence of global insect declines continues to mount, insect conservationists are becoming increasingly interested in modeling the demographic history of at-risk species from long-term survey data. However, certain entomological survey methods may be susceptible to temporal biases that will complicate these efforts. Entomological light traps, in particular, may catch fewer insects today than they once did due solely to increases in anthropogenic light pollution. Here we investigate this possibility by comparing the demographic histories of corn earworm moths (Helicoverpa zea) estimated from pairs of blacklight and pheromone traps monitored at the same farms. We find a stark decline in blacklight trap efficacy over 25 years of monitoring in Delaware, USA, mirrored over 10 years of monitoring in New Jersey, USA. While the precise causes of this decline remain a subject for discussion, the practical consequences are clear: insect conservationists cannot fully rely on long-term trends from entomological light traps.
Long-Term Outcomes after Behavior-Analytic Intervention for Pediatric Feeding Disorders
Authors: Andersen Ashley S. , Crowley-Zalaket Jaime G. , Engler Christopher W. , Zeleny Jason R. , Peterson Kathryn M. , Miles Aida G. , Piazza Cathleen C. 
Published: April 2024

AbstractMany children with autism spectrum disorder experience feeding difficulties that can lead to increased health risks (e.g., severe nutrition deficiencies, obesity-related illnesses) if left untreated. Intensive feeding programs that use behavior-analytic interventions have reported positive outcomes for treating challenging behavior associated with pediatric feeding disorders at discharge and up to 1 year post discharge. Whether these children achieve and maintain long-term goals is unknown. The purpose of the current study was to evaluate outcomes of behavior-analytic intervention for pediatric feeding disorders among children with and without autism spectrum disorder in an intensive feeding program post discharge. Caregivers of former patients reported on the positive and negative impact of the program and their child’s growth and diet variety at follow-up. 85% of caregivers reported the program had a positive impact. Increases in height-for-age z scores were significant from admission to discharge from the intensive program, and progress in growth and diet variety remained the same or improved for most children at follow-up. Results suggest that behavior-analytic intervention in an intensive feeding program produced positive short- and long-term outcomes for children and their families.
Profiling national institutional archetypes for climate change technology implementation: application in small islands and least developed countries
Authors: Shah Kalim U. 
Published: April 2024

AbstractIn developing countries, when the implementation success of new climate adaptation and mitigation technologies fall short of expectations, the typical “suspects” cited are lack of funding or country expertise and allusions to “lack of institutional capacity.” The premise of this study is that the national institutional environment is the fundamental prerequisite for successful technology implementation, and despite much effort, a diagnostic approach to assessing this prerequisite is missing. Here, I propose an approach to do this, based on an understanding of the dynamics that interconnect country-level legal, regulatory and market mechanisms, societal norms, and inter/intra governmental structures. I estimate levels of country structural and systems supports, operating environment, implementer acceptance and country tractability. A preliminary test of the approach was completed through a survey of experts involved in the United Nations Technology Needs Assessment programs in Least Developed and Small Island Developing Countries. It was found that countries fall into four fundamental archetypes. A country’s archetype suggests characteristics of the institutional environment that help to explain the potential for technology implementation success. A further implication is that some countries that typically would not be considered very similar may possess similar country institutional environments. One consequence of this is that archetype-based groups could work together and learn from each other more effectively.
Leveraging automatic personalised nutrition: food image recognition benchmark and dataset based on nutrition taxonomy
Authors: Romero-Tapiador Sergio , Tolosana Ruben , Morales Aythami , Fierrez Julian , Vera-Rodriguez Ruben , Espinosa-Salinas Isabel , Freixer Gala , Carrillo de Santa Pau Enrique , Ramírez de Molina Ana , Ortega-Garcia Javier 
Published: April 2024

Abstract
Maintaining a healthy lifestyle has become increasingly challenging in today’s sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., “Meat”), 73 subcategories (e.g., “White Meat”), and 893 specific food products (e.g., “Chicken”). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases. All these resources are available in GitHub (https://github.com/BiDAlab/AI4Food-NutritionDB).