bluuebunny/crossref_metadata_2025 huggingface dataset:
Good learning environment of medical schools is an independent predictor for medical students’ study engagement
Authors: Huang Runzhi , Li Yuanan , Gong Meiqiong , Zhang Wei , Xian Shuyuan , Tang Jieling , Lu Bingnan , Yang Yiting , Jin Minghao , Qian Weijin , Liu Zhenglin , Ma Haonan , Wu Xinru , Yin Huabin , Liu Xin , Zhang Chongyou , Du Erbin , Lin Qing , Huang Zongqiang , Lin Min , Wang Xiaonan , Wang Yue , Chen Wenfang , Liu Yifan , Zhang Jie , Ji Shizhao
Published: July 2024

Background

Study engagement is regarded important to medical students’ physical and mental wellbeing. However, the relationship between learning environment of medical schools and the study engagement of medical students was still unclear. This study was aimed to ascertain the positive effect of learning environment in study engagement.

Methods

We collected 10,901 valid questionnaires from 12 medical universities in China, and UWES-S was utilized to assess the study engagement levels. Then Pearson Chi-Square test and Welch’s ANOVA test were conducted to find the relationship between study engagement and learning environment, and subgroup analysis was used to eradicate possible influence of confounding factors. After that, a multivariate analysis was performed to prove learning environment was an independent factor, and we constructed a nomogram as a predictive model.

Results

With Pearson Chi-Square test (p < 0.001) and Welch’s ANOVA test (p < 0.001), it proved that a good learning environment contributed to a higher mean of UWES scores. Subgroup analysis also showed statistical significance (p < 0.001). In the multivariate analysis, we could find that, taking “Good” as reference, “Excellent” (OR = 0.329, 95%CI = 0.295–0.366, p < 0.001) learning environment was conducive to one’s study engagement, while “Common” (OR = 2.206, 95%CI = 1.989–2.446, p < 0.001), “Bad” (OR = 2.349, 95%CI = 1.597–3.454, p < 0.001), and “Terrible” (OR = 1.696, 95%CI = 1.015–2.834, p = 0.044) learning environment only resulted into relatively bad study engagement. Depending on the result, a nomogram was drawn, which had predictive discrimination and accuracy (AUC = 0.680).

Conclusion

We concluded that learning environment of school was an independent factor of medical student’s study engagement. A higher level of learning environment of medical school came with a higher level of medical students’ study engagement. The nomogram could serve as a predictive reference for the educators and researchers.
Serum HDL-C subfractions as predictors of cardiovascular calcification in hemodialysis patients: novel insights and clinical implications
Authors: Li Dong-Yun , Sun Wei , Zhou Xiao-Tao , Wen Yu , Zou Yang
Published: July 2024

Objective

This study aims to explore the relationship between cardiovascular calcification (CVC) and serum levels of high-density lipoprotein cholesterol (HDL-C) and its subfractions in hemodialysis (HD) patients.

Methods

HD patients and healthy participants were recruited based on specific inclusion and exclusion criteria. Various blood indicators were measured, and demographic information was recorded. HDL-C particle levels were quantified using lipophilic fluorescent dye staining and capillary electrophoresis (microfluidic platform). Coronary artery calcium scores and valve calcification were used to classify HD patients into calcification and non-calcification groups.

Results

Compared to healthy participants, HD patients showed a significant increase in HDL-C, high-density lipoprotein 2 cholesterol (HDL2-C), and high-density lipoprotein 3 cholesterol (HDL3-C) levels (p < 0.001). Further division of HD patients into calcification and non-calcification groups revealed higher serum HDL3-C concentrations (p = 0.002) and a higher HDL3-C/HDL-C ratio (p = 0.04) in the calcification group. Additionally, elevated HDL3-C levels were found to be an independent risk factor for CVC in HD patients (p = 0.040). The ROC curve analysis showed an AUC value of 0.706 for HDL3-C (p = 0.002).

Conclusion

Our study indicates that elevated serum HDL3-C levels in HD patients are an independent risk factor for CVC and can serve as a potential predictor for CVC events. However, more studies need to verify its potential as a predictive indicator..
Causal role of immune cells in uveitis: a Mendelian randomization study
Authors: Pu Jianping , Zhao Zhuanghong , Duan Yanping , Lu Jun , Yao Yuchen , Wen Yuxin , Li Yanxun , Zhang Yu , Ye Fengyu
Published: July 2024

Background

Uveitis refers to a group inflammation affecting the uvea, retina, retinal blood vessels as well as vitreous body, which is one of the common causes of blindness. There is growing evidence linking different types of immune cells to uveitis, although it remains uncertain if these associations imply causal relationships. Recent advancements in high-density genetic markers like SNPs or CNVs for genotyping, along with the progress in genome-wide association studies (GWAS) technologies, have improved our understanding of the immunological mechanisms involved in ocular diseases. Therefore, our objective was to investigate the potential causal link between immune cells and uveitis using a Mendelian randomization study.

Methods

The exposure and outcome GWAS data for this study were sourced from an open-access database (https://gwas.mrcieu.ac.uk/). Two-sample MR analysis was utilized to evaluate the causal relationship between 731 immune cell features and uveitis. Various MR methods were employed to reduce bias and obtain dependable estimates of the causal link between the immune cell variables and the outcomes. Instrumental variable selection criteria were carefully chosen to enhance the accuracy and efficacy of the causal relationship between different immune cell types and the risk of uveitis.

Results

Using two-sample MR, IVW modeling showed that GAD had significant effect on immunophenotypes. CD3 levels on CD45RA− CD4+ T cells (OR = 1.087, 95%CI = 1.029 ~ 1.147, p = 0.003) and CD3 levels on CM CD4+ T cells (OR = 1.086, 95%CI = 1.033 ~ 1.141, p = 0.001) were found to be elevated in cases of uveitis. HLA DR levels in CD14− CD16+ monocyte cells (OR = 0.735, 95% CI = 0.635 ~ 0.850, p < 0.001) and HLA DR levels in NK cells (OR = 0.910, 95% CI = 0.851 ~ 0.972, p = 0.005) were observed to be reduced in individuals with uveitis. Furthermore, Two cells were identified to be significantly associated with uveitis risk: HLA DR on in NK cells (OR = 0.938, 95%CI = 0.899 ~ 0.979, p = 0.003), HLA DR on CD14− CD16+ monocytes (OR = 0.924, 95%CI = 0.878 ~ 0.972, p = 0.002).

Conclusion

This study highlights the intricate relationship between immune cells and generalized anxiety disorder using genetic methods, offering valuable insights for future clinical investigations.
BolA-like protein (IbaG) promotes biofilm formation and pathogenicity of Vibrio parahaemolyticus
Authors: Wang Wenchao , Li Yangyang , Lu Shuqi , Liu Pengxuan , Han Xiangan , Sun Weidong , Wang Quan , Fang Weihuan , Jiang Wei
Published: July 2024

Vibrio parahaemolyticus is a gram-negative halophilic bacterium widespread in temperate and tropical coastal waters; it is considered to be the most frequent cause of Vibrio-associated gastroenteritis in many countries. BolA-like proteins, which reportedly affect various growth and metabolic processes including flagellar synthesis in bacteria, are widely conserved from prokaryotes to eukaryotes. However, the effects exerted by BolA-like proteins on V. parahaemolyticus remain unclear, and thus require further investigation. In this study, our purpose was to investigate the role played by BolA-like protein (IbaG) in the pathogenicity of V. parahaemolyticus. We used homologous recombination to obtain the deletion strain ΔibaG and investigated the biological role of BolA family protein IbaG in V. parahaemolyticus. Our results showed that IbaG is a bacterial transcription factor that negatively modulates swimming capacity. Furthermore, overexpressing IbaG enhanced the capabilities of V. parahaemolyticus for swarming and biofilm formation. In addition, inactivation of ibaG in V. parahaemolyticus SH112 impaired its capacity for colonizing the heart, liver, spleen, and kidneys, and reduced visceral tissue damage, thereby leading to diminished virulence, compared with the wild-type strain. Finally, RNA-sequencing revealed 53 upregulated and 71 downregulated genes in the deletion strain ΔibaG. KEGG enrichment analysis showed that the two-component system, quorum sensing, bacterial secretion system, and numerous amino acid metabolism pathways had been altered due to the inactivation of ibaG. The results of this study indicated that IbaG exerts a considerable effect on gene regulation, motility, biofilm formation, and pathogenicity of V. parahaemolyticus. To the best of our knowledge, this is the first systematic study on the role played by IbaG in V. parahaemolyticus infections. Thus, our findings may lead to a better understanding of the metabolic processes involved in bacterial infections and provide a basis for the prevention and control of such infections.
The Montreal cognitive assessment: normative data from a large, population-based sample of Chinese healthy adults and validation for detecting vascular cognitive impairment
Authors: Wei Qiang , Du Baogen , Liu Yuanyuan , Cao Shanshan , Yin Shanshan , Zhang Ying , Ye Rong , Bai Tongjian , Wu Xingqi , Tian Yanghua , Hu Panpan , Wang Kai
Published: July 2024

Background

The Montreal Cognitive Assessment (MoCA) is a valuable tool for detecting cognitive impairment, widely used in many countries. However, there is still a lack of large sample normative data and whose cut-off values for detecting cognitive impairment is considerable controversy.

Methods

The assessment conducted in this study utilizes the MoCA scale, specifically employing the Mandarin-8.1 version. This study recruited a total of 3,097 healthy adults aged over 20 years. We performed multiple linear regression analysis, incorporating age, gender, and education level as predictor variables, to examine their associations with the MoCA total score and subdomain scores. Subsequently, we established normative values stratified by age and education level. Finally, we included 242 patients with vascular cognitive impairment (VCI) and 137 controls with normal cognition, and determined the optimal cut-off value of VCI through ROC curves.

Results

The participants in this study exhibit a balanced gender distribution, with an average age of 54.46 years (SD = 14.38) and an average education period of 9.49 years (SD = 4.61). The study population demonstrates an average MoCA score of 23.25 points (SD = 4.82). The multiple linear regression analysis indicates that MoCA total score is influenced by age and education level, collectively accounting for 46.8% of the total variance. Higher age and lower education level are correlated with lower MoCA total scores. A score of 22 is the optimal cut-off value for diagnosing vascular cognitive impairment (VCI).

Conclusion

This study offered normative MoCA values specific to the Chinese adults. Furthermore, this study indicated that a score of 26 may not represent the most optimal cut-off value for VCI. And for detecting VCI, a score of 22 may be a better cut-off value.
Intranodal palisaded myofibroblastoma in the submandibular gland region: a case report
Authors: Zhang Han , Wang Min , Li Liang , Shao Shuo , Zheng Ning
Published: July 2024

Intranodal palisaded myofibroblastoma (IPM) is a rare benign tumor of the lymph nodes, particularly in inguinal lymph nodes. IPM originating from the submandibular gland lymph nodes is rarely encountered in clinical practice. Herein, we report the case of a 31-year-old male patient with IPM of the submandibular gland region and describe in detail magnetic resonance imaging findings and pathology. Magnetic resonance imaging detected a heterogeneous lesion with a hypointense rim on T2-weighted imaging with specificity in the left submandibular gland region. This case report will contribute to the accumulation of experience in the diagnosis of this disease.
Theory of Turing pattern formation and its experimental realization in the CIMA reaction system in the presence of materials lowering the diffusivity of activators
Authors: Aizawa Amiko , Asakura Kouichi
Published: July 2024

In 1952, Alan Turing accomplished a pioneering theoretical study to show that the coupling of nonlinear chemical reactions and diffusion leads to the instability of spatially homogeneous states. The activator and inhibitor are synthesized as intermediates of the reaction system in the Turing model. Turing found that spatially periodic stationary concentration patterns are spontaneously generated when the diffusion coefficient of the activator is lower than that of the inhibitor. The first experimental realization of the Turing pattern was achieved in 1990 in a chlorite–iodide–malonic acid (CIMA) reaction system. Iodide and chlorite anions act as the activator and inhibitor of this reaction system, respectively. Although there is no significant difference in the diffusion coefficient of iodide and chlorite anions, the Turing pattern was generated because starch was added to the gel reactor to enhance the color tone. This formed a complex with iodide to inhibit its diffusion to satisfy the condition for the Turing instability. Several examples were found after this finding. We focused on the high affinity of quaternary alkyl ammonium cations to iodide. The CIMA reaction was performed in an open gel reactor by adding a quaternary alkyl ammonium cationic surfactant. In addition, the polymer gel consists of the quaternary alkyl ammonium group as the side chain was utilized for the open gel reactor. The micelles of the surfactants and the polymer gels trapped iodide in their vicinity as a counter anion to lower the effective diffusivity to satisfy the condition for the Turing instability.
Root and rhizosphere traits for enhanced water and nutrients uptake efficiency in dynamic environments
Authors: Holz Maire , Zarebanadkouki Mohsen , Benard Pascal , Hoffmann Mathias , Dubbert Maren
Published: July 2024

Modern agriculture’s goal of improving crop resource acquisition efficiency relies on the intricate relationship between the root system and the soil. Root and rhizosphere traits play a critical role in the efficient use of nutrients and water, especially under dynamic environments. This review emphasizes a holistic perspective, challenging the conventional separation of nutrient and water uptake processes and the necessity for an integrated approach. Anticipating climate change-induced increase in the likelihood of extreme weather events that result in fluctuations in soil moisture and nutrient availability, the study explores the adaptive potential of root and rhizosphere traits to mitigate stress. We emphasize the significance of root and rhizosphere characteristics that enable crops to rapidly respond to varying resource availabilities (i.e. the presence of water and mobile nutrients in the root zone) and their accessibility (i.e. the possibility to transport resources to the root surface). These traits encompass for example root hairs, mucilage and extracellular polymeric substance (EPS) exudation, rhizosheath formation and the expression of nutrient and water transporters. Moreover, we recognize the challenge of balancing carbon investments, especially under stress, where optimized traits must consider carbon-efficient strategies. To advance our understanding, the review calls for well-designed field experiments, recognizing the limitations of controlled environments. Non-destructive methods such as mini rhizotron assessments and in-situ stable isotope techniques, in combination with destructive approaches such as root exudation analysis, are proposed for assessing root and rhizosphere traits. The integration of modeling, experimentation, and plant breeding is essential for developing resilient crop genotypes capable of adapting to evolving resource limitation.
Prediction of protein content in paddy rice (Oryza sativa L.) combining near-infrared spectroscopy and deep-learning algorithm
Authors: Yang Ha-Eun , Kim Nam-Wook , Lee Hong-Gu , Kim Min-Jee , Sang Wan-Gyu , Yang Changju , Mo Changyeun
Published: July 2024

Rice is a staple crop in Asia, with more than 400 million tons consumed annually worldwide. The protein content of rice is a major determinant of its unique structural, physical, and nutritional properties. Chemical analysis, a traditional method for measuring rice’s protein content, demands considerable manpower, time, and costs, including preprocessing such as removing the rice husk. Therefore, of the technology is needed to rapidly and nondestructively measure the protein content of paddy rice during harvest and storage stages. In this study, the nondestructive technique for predicting the protein content of rice with husks (paddy rice) was developed using near-infrared spectroscopy and deep learning techniques. The protein content prediction model based on partial least square regression, support vector regression, and deep neural network (DNN) were developed using the near-infrared spectrum in the range of 950 to 2200 nm. 1800 spectra of the paddy rice and 1200 spectra from the brown rice were obtained, and these were used for model development and performance evaluation of the developed model. Various spectral preprocessing techniques was applied. The DNN model showed the best results among three types of rice protein content prediction models. The optimal DNN model for paddy rice was the model with first-order derivative preprocessing and the accuracy was a coefficient of determination for prediction, Rp2 = 0.972 and root mean squared error for prediction, RMSEP = 0.048%. The optimal DNN model for brown rice was the model applied first-order derivative preprocessing with Rp2 = 0.987 and RMSEP = 0.033%. These results demonstrate the commercial feasibility of using near-infrared spectroscopy for the non-destructive prediction of protein content in both husked rice seeds and paddy rice.
Modeling QTL-by-environment interactions for multi-parent populations
Authors: Li Wenhao , Boer Martin P. , Joosen Ronny V. L. , Zheng Chaozhi , Percival-Alwyn Lawrence , Cockram James , Van Eeuwijk Fred A.
Published: July 2024

Multi-parent populations (MPPs) are attractive for genetic and breeding studies because they combine genetic diversity with an easy-to-control population structure. Most methods for mapping QTLs in MPPs focus on the detection of QTLs in single environments. Little attention has been given to mapping QTLs in multienvironment trials (METs) and to detecting and modeling QTL-by-environment interactions (QEIs). We present mixed model approaches for the detection and modeling of consistent versus environment-dependent QTLs, i.e., QTL-by-environment interaction (QEI). QTL effects are assumed to be normally distributed with variances expressing consistency or dependence on environments and families. The entries of the corresponding design matrices are functions of identity-by-descent (IBD) probabilities between parents and offspring and follow from the parental origin of offspring DNA. A polygenic effect is added to the models to account for background genetic variation. We illustrate the wide applicability of our method by analyzing several public MPP datasets with observations from METs. The examples include diallel, nested association mapping (NAM), and multi-parent advanced inter-cross (MAGIC) populations. The results of our approach compare favorably with those of previous studies that used tailored methods.
Predictors of fear of COVID-19 in a low-income country: health literacy is important
Authors: Ranjbaran Soheila , Chollou Khalil Maleki , Abedi-Nerbin Salar , Babazadeh Towhid
Published: July 2024

Introduction

Excessive fear of a threatening condition or situation can result in individuals engaging in unhealthy behaviors, especially during the COVID-19 pandemic. Identifying the influential factors of fear can be effective in designing and implementing health-center interventions to control and reduce the COVID-19 pandemic.

Methods

This study was a cross-sectional design implemented in Iran in 2022. Participants were adults 18–65 years of age recruited randomly from primary Health Care Services Centers (HCSCs) by medical records (n = 380, 64.7% female, mean (SD) age 32.14 ± 10.98 years) in urban and rural areas of the county. The data collection included a demographic form, Fear of COVID-19 questionnaire, Perceived Barriers of COVID-19 preventive behaviors, Self-efficacy, and Health Literacy for Iranian Adults (HELIA) questionnaire.

Results

It was determined that the fear of COVID-19 showed statistically significant differences according to gender (p-value = 0.013), education level (p-value = 0.001), and job status (p-value = 0.001). According to the hierarchical linear regression, educational level (p-value = 0.001; β = 0.665), job (β = 0.126; p-value = 0.018), self-efficacy (p-value = 0.015; β = 0.103), and Health Literacy (HL) (p-value = 0.001; β = 0.446) were significant predictors of fear of COVID-19. Also, among variables, educational level (p-value = 0.001; β = 0.665) and HL (p-value = 0.001; β = 0.446) were the most important predictors for fear of COVID-19.

Discussion

This research shows that HL has a potential and determinative role in controlling fear of COVID-19. The findings can help healthy policymakers and healthcare providers design HL-based programs in similar epidemics and pandemic situations.
Psychological resilience, athletic experience, and competitive level of judokas. A transversal study
Authors: Garrido-Muñoz María , Blanco-García Cecilia , Diez-Vega Ignacio , García-Merino Sonia , Acebes-Sánchez Jorge , Rodríguez-Romo Gabriel
Published: July 2024

Introduction

While there is agreement on the positive link between psychological resilience and athletic performance, conclusive findings regarding the association between psychological resilience and other variables of interest (for example, age, gender, type of sport, or competitive level) remain elusive.

Objective

The study aimed to assess psychological resilience levels among judokas and explore potential associations with demographic factors, judo experience and competitive level.

Methods

A total of 702 judokas (469 men and 233 women) participated in the study, of whom 194 (27.6%) were classified as TOP by their competitive level. Psychological resilience was evaluated using the Spanish version of the 10 item Connor-Davidson Resilience Scale (CD-RISC 10), with a score range from 0 to 40. Independent T-test and Pearson’s coefficient were used for bivariate analysis. A two-way non-parametric ANCOVA was carried out to analyse the impact of gender and competitive level on psychological resilience.

Results

The judokas showed total mean scores in the CD-RISC 10 of 33.08 points (SD = 4.79), considered high. Levels of psychological resilience were significantly higher among men (33.36 ± 4.76) than women (32.53 ± 4.80) and were positively correlated with age and number of years practicing and competing in Judo (p = 0.019). Judokas with a higher competitive level (TOP judokas) showed significantly higher levels of resilience than the others (non-TOP judokas) (p < 0.001). These differences in resilience according to competitive level persisted, among both men and women, when adjusting the model of analysis (two-way ANCOVA) for all variables considered in the study, although with a small effect size.

Conclusion

The results suggest that the practice of Judo, especially over long periods of time, is associated with high scores in psychological resilience. Furthermore, psychological resilience appears to be a differentiating variable among judokas at a high-competitive level, and its evaluation and development using different strategies based on age and gender should be considered by trainers and psychologists.
Patient satisfaction and digital health in primary health care: a scoping review protocol
Authors: Xavier Pedro Bezerra , Silva Ísis de Siqueira , Dantas Thaissa Hamana de Macedo , Lopes Rayssa Horácio , de Araújo Aguinaldo José , de Figueirêdo Renan Cabral , Uchôa Severina Alice da Costa
Published: July 2024

Introduction

Using digital health in primary health care (PHC) contributes to reducing costs and travel time, achieving global development goals, improving access, quality and longitudinality of care, and managing health crises. Its evaluation must go beyond the technical-operational aspects to include patient satisfaction, a key element in assessing the quality of care.

Objective

To identify and map patient satisfaction (expectations, desires, cultural values) about the adoption of digital health strategies and assess their impact on the quality of care in PHC.

Methods

The review will follow the recommendations proposed by the Joanna’s Briggs Institute (JBI) manual, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and the methodology proposed by Arksey and O’Malley and Levac et al. and will be conducted in nine stages. The search will be conducted in health studies databases (MEDLINE via PubMed, EMBASE, CINAHL, Web of Science, and BVS), gray literature, and preprint repositories (Google Scholar and MedRxiv). Two reviewers will select the studies, and the third will analyze possible conflicts. The inclusion criteria comprise studies that have been made available in their entirety, whether they are primary studies or short communications, as well as the following materials extracted from the gray literature: preprints, manuals, government documents, books, guidelines, theses and dissertations. Exclusion criteria include literature reviews, abstracts, books, conference archives, letters to the editor, duplicates and opinion articles. Data will be analyzed by content analysis and inferential statistics. This protocol is registered on the Open Science Framework (OSF) under DOI 10.17605/OSF.IO/PUJDB.

Results

The study aims to understand aspects related to the expectations, desires, and cultural values of patients from different countries, as well as the strengths and critical nodes of the use of digital health on the quality of care in PHC.
Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric emergency department use: a quasi-experimental study interrupted time-series analysis in North Italian hospitals, 2017 to 2022
Authors: Puntoni Matteo , Maglietta Giuseppe , Caminiti Caterina , Miniaci Angela , Lanari Marcello , Caramelli Fabio , Marchetti Federico , De Fanti Alessandro , Iughetti Lorenzo , Biasucci Giacomo , Suppiej Agnese , Miceli Andrea , Ghizzi Chiara , Vergine Gianluca , Aricò Melodie , Stella Marcello , Esposito Susanna ,   the Emilia-Romagna Paediatric COVID-19 network
Published: July 2024

Background

The use of Non-Pharmaceutical Interventions (NPIs) during the COVID-19 pandemic is debated. Understanding the consequences these measures may have on vulnerable populations including children and adolescents is important.

Methods

This is a multicenter, quasi-experimental before-after study involving 12 hospitals of the North Italian Emilia-Romagna Region, with NPI implementation as the intervention event. The 3 years preceding NPI implementation (in March 2020) constituted the pre-pandemic phase. The subsequent 2 years were further subdivided into a school closure phase (SC) and a subsequent mitigation measures phase (MM) with milder restrictions. Interrupted Time Series (ITS) regression analysis was used to calculate PED Standardized Incidence Rate Ratios (SIRR) on the diagnostic categories exhibiting the greatest frequency and/or variation.

Results

In the 60 months of the study there were 765,215 PED visits. Compared to the pre-pandemic rate, overall PED presentations dropped by 58 and 39% during SC and MM, respectively. “Symptoms, signs and Ill-defined conditions,” “Injury and poisoning” and “Diseases of the Respiratory System” accounted for 74% of the reduction. A different pattern was instead seen for “Mental Disorders,” which exhibited the smallest decrease during SC, and is the only category which rose already at the end of SC. ITS analysis confirmed the strong decrease during SC (level change, IRR 0.17, 95%CI 0.12–0.27) and a significant increase in MM (slope change, IRR 1.23, 95%CI 1.13–1.33), with the sharpest decline (−94%) and rise (+36%) observed in the “Diseases of the Respiratory System” category. Mental Disorders showed a significant increasing trend of 1% monthly over the whole study period exceeding pre-pandemic levels at the end of MM. Females and adolescents showed higher increasing rates both in SC and MM.

Conclusion

NPIs appear to have influenced PED attendance in different ways according to diagnostic categories, mirroring different mechanisms of action. These effects are beneficial in some cases and harmful in others, and establishing a clear balance between pros and cons is a difficult task for public health decision makers. The role of NPIs on PED use appropriateness deserves investigation. The rise in pediatric mental disorders independent of the pandemic makes interventions addressing these issues urgent.
A Swarm Intelligence Solution for the Multi-Vehicle Profitable Pickup and Delivery Problem
Authors: Alhujaylan Abeer I. , Hosny Manar I.
Published: July 2024

Delivery apps are experiencing significant growth, requiring efficient algorithms to coordinate transportation and generate profits. One problem that considers the goals of delivery apps is the multi-vehicle profitable pickup and delivery problem (MVPPDP). In this paper, we propose eight new metaheuristics to improve the initial solutions for the MVPPDP based on the well-known swarm intelligence algorithm, Artificial Bee Colony (ABC): K-means-GRASP-ABC(C)S1, K-means-GRASP-ABC(C)S2, Modified K-means-GRASP-ABC(C)S1, Modified K-means-GRASP-ABC(C)S2, ACO-GRASP-ABC(C)S1, ACO-GRASP-ABC(C)S2, ABC(S1), and ABC(S2). All methods achieved superior performance in most instances in terms of processing time. For example, for 250 customers, the average times of the algorithms was 75.9, 72.86, 79.17, 73.85, 76.60, 66.29, 177.07, and 196.09, which were faster than those of the state-of-the-art methods that took 300 s. Moreover, all proposed algorithms performed well on small-size instances in terms of profit by achieving thirteen new best solutions and five equal solutions to the best-known solutions. However, the algorithms slightly lag behind in medium- and large-sized instances due to the greedy randomised strategy and GRASP that have been used in the scout bee phase. Moreover, our algorithms prioritise minimal solutions and iterations for rapid processing time in daily m-commerce apps, while reducing iteration counts and population sizes reduces the likelihood of obtaining good solution quality.
Above- and Below-Ground Interactions and Interspecific Relationships in Wheat/Maize Systems
Authors: Wang Yifan , Chai Qiang , Zhao Cai , Yin Wen , Hu Falong , Yu Aizhong , Fan Zhilong
Published: July 2024

Above- and below-ground interactions play a crucial role in achieving higher yields in intercropping systems. Nonetheless, it remains unclear how these interactions impact intercropping crop growth and regulate interspecific relationships. This study aimed to quantify the impact of above- and below-ground interactions on crop yield by determining the dynamics of dry matter accumulation, photosynthetically active radiation (PAR) transmittance, and leaf area index (LAI) in intercropped wheat and maize. Three below-ground intensities were set for an intercropping system: no root separation (CI: complete interaction below ground), 48 μm nylon mesh separation (PI: partial interaction below ground), and 0.12 mm plastic sheet separation (NI: no interaction below ground). Two densities were set for maize: low (45,000 plants hm−2) and high (52,500 plants hm−2). At the same time, corresponding monoculture treatments were established. The grain yields in the CI and PI treatments were, on average, 23.7% and 13.7% higher than those in the NI treatment at high and low maize densities, respectively. Additionally, the grain yield for high density was 12.3% higher than that of low density in the CI treatment. The dry matter accumulation of intercropped wheat under the CI and PI treatments was, on average, 9.1%, 14.5%, and 9.0% higher than that in the NI treatment at the flowering, filling, and maturity stages, respectively. The dry matter accumulation of intercropped maize at the blister, milk, and physiological maturity stages increased by 41.4%, 32.1%, and 27.8%, respectively, under the CI treatment compared to the NI treatment. The PAR transmittance and LAI of maize at the V6 stage were significantly increased by increasing the intensity of below-ground interactions. This study showed that complete below-ground interaction contributed to a significant increase in the competitiveness of intercropped wheat with respect to maize (Awm) under the high-density maize treatment, especially at the filling stage of wheat. Moreover, the CI treatment enhanced the recovery effects of maize (Rm) after wheat harvesting. Increasing the intensity of below-ground interactions can significantly enhance the Awm and Rm in intercropping systems, favoring the accumulation of crop dry matter mass and light energy utilization to increase system yields.
Assessing the Susceptibility and Tolerance of Oil Palm Cultivars to Leaf-Bending: A Comparative Physiological and Nutritional Balance Study
Authors: Tupaz-Vera Andrés , Ayala-Díaz Iván , Caicedo-Zambrano Arley Fernando , Rincón-Numpaque Alvaro , Romero Hernán Mauricio
Published: July 2024

Selecting the best oil palm cultivars adapted to specific agro-climatic conditions helps ensure fruit and oil production success. Many biotic and abiotic problems can limit the productivity of the crop, some of which have not yet been described, as well as their symptoms and degree of impact. In oil palm, a disturbance known as leaf-bending occurs, in which the palms present a bending or breakage of the petiole in the leaves, representing a significant challenge for oil palm cultivation. Although leaf-bending in oil palm plantations is known, its effect on the palm’s performance and its possible causes are unknown. Thus, the objectives of the present work were to describe and propose a quantitative way to evaluate leaf-bending in different cultivars (Elaeis guineensis Jacq. and the interspecific O×G hybrid) and to evaluate the disturbance effect on the physiology and nutritional balance of the oil palm. The results showed that most leaves from leaf rings three and four of the palm phyllotaxis have the highest affectation, with 74% leaf-bending. The cultivars with a Deli × La Mé and Coarí × La Mé genetic background were the most tolerant; however, the Deli × AVROS and Deli × Yangambi genetic crosses were the most susceptible. The difference in the affectation between the groups was 60% to 70%. Leaf-bending affected physiological parameters such as photosynthesis, transpiration, and stomatal conductance. Thus, below the second ring of leaves, the affected plants’ physiological parameters decreased between 40% and 50%. The nutritional diagnosis showed that the most sensitive cultivars to leaf-bending were Deli × AVROS, which presented the highest average index of nutritional balance (IBNm), indicating that this genetic cross had the highest nutritional imbalance. On the contrary, the most tolerant cultivars were Deli × La Mé, which presented the lowest IBNm, associated with a better nutritional condition.
Synergistic Antioxidant Effects of Cysteine Derivative and Sm-Cluster for Food Applications
Authors: Chen Lingxia , Wang Lijun , Ma Lifu , Wang Chao , Qin Xinshu , Wang Minlong , Zhang Xiaohe , Yang Ruoyan , Fang Bing , An Jie
Published: July 2024

The incorporation of antioxidants in food products is essential to prevent or delay deterioration, thereby addressing food spoilage. Thiol compounds, recognized for their natural antioxidant properties, are widely used in various foods; however, their antioxidant capacity is often limited. This study investigates the potential enhancement of thiol antioxidant capacity through the addition of a soluble, low-toxic inorganic Sm-cluster. Our findings demonstrate that the Sm-cluster significantly bolsters the antioxidant efficacy of thiol compounds. We explored, for the first time, the in vitro antioxidant activities of an Sm-oxo/hydroxy cluster combined with a cysteine derivative for potential food applications. The composition exhibited a robust inhibition of aromatic aldehyde flavor compound oxidation and displayed strong, dose-dependent DPPH (2,2-diphenyl-1-picrylhydrazine) radical scavenging activity. Notably, the antioxidant activity of the Sm-cluster/cysteine derivative was further enhanced under strong visible light conditions, which typically increased the likelihood of oxidation. These results suggest that the combination of inorganic cluster and thiol compounds presents a promising natural alternative to traditional antioxidants in the food industry.
Optical Study on Temperature-Dependent Absorption Edge of γ-InSe-Layered Semiconductor
Authors: Wu Wen-Te , Tiong Kwong-Kau , Tan Shih-Wei , Hu Sheng-Yao , Lee Yueh-Chien , Chen Ruei-San , Wu Chia-Ti
Published: July 2024

We have studied the variations in the temperature-dependent absorption edge of a bulk InSe-layered semiconductor using photoconductivity (PC) measurements. From both the X-ray diffraction (XRD) and Raman experimental results, the structural phase of the as-prepared InSe sample was confirmed to be γ-polytype. Upon heating from 15 K to 300 K, the absorption edge of PC spectra was found to shift significantly toward lower energy, and the absorption edge as a function of temperature was further analyzed by the Varshni’s relationship and Bose–Einstein empirical equation. The Urbach energy as a function of temperature was obtained by fitting the absorption tail below the absorption coefficient of the PC spectrum, and the effective phonon energy can be derived from the temperature-dependent steepness parameter associated with Urbach energy. Our study indicates that the broadening of the absorption edge in the as-synthesized bulk γ-InSe is caused by a combination of electron/exciton–phonon interactions and thermal/structural disorder.
A Geospatial Framework of Food Demand Mapping
Authors: Gruzauskas Valentas , Burinskiene Aurelija , Airapetian Artur , Urbonaitė Neringa
Published: July 2024

Spatial mapping of food demand is essential for understanding and addressing disparities in food accessibility, which significantly impact public health and nutrition. This research presents an innovative geospatial framework designed to map food demand, integrating individual dietary behaviors with advanced spatial analysis techniques. This study analyzes the spatial distribution of eating habits across Lithuania using a geospatial approach. The methodology involves dividing Lithuania into 60,000 points and interpolating survey data with Shepard’s operator, which relies on a weighted average of values at data points. This flexible approach allows for adjusting the number of points based on spatial resolution and sample size, enhancing the reliability and applicability of the generated maps. The procedure includes generating a structured grid system, incorporating measurements into the grid, and applying Shepard’s operator for interpolation, resulting in precise representations of food demand. This framework provides a comprehensive understanding of dietary behaviors, informing targeted policy interventions to improve food accessibility and nutrition. Traditional food spatial mapping approaches are often limited to specific polygons and lack the flexibility to achieve high granular detail. By applying advanced interpolation techniques and ensuring respondent location data without breaching privacy concerns, this study creates high-resolution maps that accurately represent regional differences in eating habits. The methodology’s flexibility allows for adjustments in spatial resolution and sample size, enhancing the maps’ validity and applicability. This novel approach facilitates the creation of detailed food demand maps at any granular level, providing valuable insights for policymakers and stakeholders. These insights enable the development of targeted strategies to improve food accessibility and nutrition. Additionally, the obtained information can be used for computer simulations to further analyze and predict food demand scenarios. By leveraging spatial data integration, this study contributes to a deeper understanding of the complex dynamics of food demand, identifying critical areas such as food deserts and swamps, and paving the way for more effective public health interventions and policies aimed at achieving equitable food distribution and better nutritional outcomes.
Valorization of Grass Clipping Waste: A Sustainable Approach to Cellulose Extraction and Paper Manufacturing
Authors: Taurbekov Azamat , Kaidar Bayan , Baltabay Akniyet , Imash Aigerim , Ko Weon-Bae , Ko Jeong-Won , Atamanov Meiram , Mansurov Zulkhair , Smagulova Gaukhar
Published: July 2024

This study investigates the physical, mechanical, and structural characteristics of handmade paper samples derived from cellulose extracted from grass clippings using two distinct methods as follows: (1) alkali treatment and (2) alkali treatment followed by bleaching, coupled with the incorporation of barium sulfate as a mineral filler. Our investigation revealed that the handmade paper samples’ densities, moisture contents, and thicknesses varied within the ranges of 0.436 to 0.549 g/cm3, 5.60 to 2.51%, and 0.41 to 0.50 mm, respectively. The tensile strength and folding endurance of the papers produced through alkali treatment with barium sulfate were notably superior to those produced from bleached pulp and barium sulfate. Our analysis indicates that several critical factors, including paper density, thickness, the crystallinity index, and the microfibrillar structure of cellulose, intricately influence the mechanical and strength properties of the samples. Using Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD) techniques, we identified characteristic cellulose bonds and examined cellulose’s crystalline and amorphous phases. Additionally, the crystallinity index of the samples was determined using both the Segal and peak deconvolution methods. Scanning electron microscopy (SEM) micrographs revealed interconnected networks of cellulose fibers with varying thicknesses and lengths, along with incorporated mineral filler within the cellulose fiber structure. Variations in mineral particle retention were attributed to the presence or absence of cellulose microfibrils. These findings contribute to our understanding of the observed strength characteristics of the paper samples and underscore the potential applications of cellulose derived from grass clippings, especially when combined with barium sulfate as a mineral filler in paper production.
Systematic Analysis of Spacer and Gate Length Scaling on Memory Characteristics in 3D NAND Flash Memory
Authors: Bae Hee Young , Hong Seul Ki , Park Jong Kyung
Published: July 2024

This study investigates the impact of oxide/nitride (ON) pitch scaling on the memory performance of 3D NAND flash memory. We aim to enhance 3D NAND flash memory by systematically reducing the spacer length (Ls) and gate length (Lg) to achieve improved memory characteristics. Using TCAD simulations, we evaluate the effects of Ls and Lg scaling on the program speed, erase speed, and Z-interference. Furthermore, we examine the influence of concave and convex channel structures in the context of Ls and Lg scaling. By analyzing the distributions of electron and hole-trapped charges, we provide insights into optimizing the trade-offs between the memory window and retention characteristics. This research offers valuable guidelines for improving the reliability and performance of 3D NAND flash memory through a systematic analysis of spacer and gate length scaling.
Rate of Microelement Quantitative Changes during the Composting of Sewage Sludge with Various Bulking Agents
Authors: Jakubus Monika , Graczyk Małgorzata
Published: July 2024

The composting of sewage sludge (SS) with structure-forming additives is a popular and inexpensive method for the management of biodegradable waste. During this process, a number of transformations of organic matter and nutrients occur. This study presents the rates and directions of quantitative changes in Cu, Zn, and Ni during the composting of various mixtures. The following substances were used for preparing compost mixtures: SS, sawdust, straw, and bark. These substances were mixed together in appropriate proportions as follows: C1: 45% SS + 50% sawdust + 5% straw; C2: 45% SS + 50% bark+ 5% straw; and C3: 45% SS + 35% bark + 15% sawdust + 5% straw. Single (DTPA solution) and sequential extraction methods (BCR protocol) were used for microelement mobility assessment. Compost valorization was performed in each individual composting phase. The chain relative increment analysis was used for such assessment. Additionally, the potential metal pollution in the composts was evaluated by applying the following indices: the pollutant accumulation index (PAI), heavy metal enrichment index (HMEI), and heavy metal pollution load index (HMPLI). During composting, generally, the amounts of Ni, Cu, and Zn increased in the various extracted combinations, which was independent of their susceptibility to decomposition. Despite of this, the tested composts should be considered as a source of slowly available microelements for plants. The most intense quantitative changes in metals occurred in the thermophilic phase, and the weakest changes occurred in the cooling phase. At the same time, the calculated indices indicated a lack of contamination of the composts with the analyzed metals, emphasizing their environmental safety and lack of negative impact. The bulking agents used for composting with SS did not significantly influence the intensity of the quantitative changes in the tested metals.
Near-Infrared Spectroscopic Determination of Pentacyclic Triterpenoid Concentrations in Additives for Animal Food
Authors: Sugráñez-Pérez Carmen , Sugráñez-Serrano Rafael , López-González Marta , Martínez-Vaquero Sara , Moral-Martos Daniel , Cortés-Jiménez Sofía , Peragón-Sánchez Juan
Published: July 2024

The nutritional composition of food for animal production can be enhanced using olive tree and plant by-products due to their high content of bioactive compounds such as pentacyclic triterpenes. Here, we present a novel application of near-infrared spectroscopy (NIRS) for the prediction of the total or individual [maslinic acid (MA), oleanolic acid (OA), and uvaol (UO)] pentacyclic triterpene concentrations in a feed additive obtained from a plant mixture. The oxygen radical absorbance capacity of these types of samples demonstrated the existence of a high antioxidant capacity. The conventional determination methods of pentacyclic triterpene concentration are costly, labor-intensive, and not practical for analyzing several lines within a limited timeframe at the factory level. The optimal regression model developed in our work demonstrated high correlation values for the calibration and validation sets, along with a high residual prediction deviation value. We used 63 samples for the development of the model. The NIRS method can be applied directly to dried powder and makes extraction and high-performance liquid chromatography (HPLC) analysis unnecessary. Our results also demonstrate that NIRS can accurately quantify pentacyclic triterpenes even at low concentrations in food additives. It can be used at the factory level to directly determine the pentacyclic triterpene concentrations in the additive powder at the same time that the powder is produced.