bluuebunny/crossref_metadata_2025 huggingface dataset:
Eliciting psychiatric nurses’ preferences for workplace violence prevention: a protocol for discrete choice experiment
Authors: Xie Peng , Li Hui-qin , Tao Li , Yang Hao
Published: July 2024

Introduction

Workplace violence against healthcare workers has become a serious global public health problem. The incidence of workplace violence towards Psychiatric nurses is higher than in all other medical institutions, up to 84.2% per year. It not only negatively affects many aspects of healthcare workers’ lives, but also destroys the harmony of the nurse–patient relationship and reduces the quality of nursing care. The number of psychiatric nurses in China was approximately 96,000, far lower than most other countries and unable to meet the growing demand for mental health. However, the increase in workplace violence has future exacerbates the current shortage of nurses. Therefore, it is necessary to develop effective strategies to prevent psychiatric nurses from suffering from workplace violence, thereby to reduce nurse turnover and improve the quality of nursing care. A comprehensive understanding of psychiatric nurses’ preferences and priorities for preventing workplace violence is an important prerequisite before formulating strategies and taking measures. Unfortunately, to date, no research has investigated the psychiatric nurses’ preferences. Therefore, a discrete choice experiment (DCE) is conducting to explore the psychiatric nurses’ preferences for workplace violence prevention. This article reports on methodological details of the DCE.

Methods and analysis

Six attributes were developed through a literature review, one-on-one interviews and focus group discussions. D-efficient design in NGENE was used to generate choice sets. SPSS 24.0 will be used for descriptive analysis of social Demography, and Stata 16.0 will be used for analysis of DCE data. A multinomial logit model will be used to preliminarily explore trade-offs between workplace violence prevention characteristics included in the choice tasks. Then, in a mixed logit model, we plan to choose some arbitrarily defined base violence prevention program and will use the nlcom command to evaluate the probability of an alternative violence prevention program.

Ethics and dissemination

The study was approved by the relevant ethics committees. Our findings will emphasize priority intervention areas based on the preferences of psychiatric nurses and provide references for hospitals to develop and improve workplace violence prevention strategies. The results will be shared through seminars, policy briefs, peer-reviewed journal articles and online blogs.
Social-emotional problems in 3-to 5-year-old children: a cross-sectional study of highly educated migrants in a Chinese urban area
Authors: Xu Qingning , Liu Shaoying , Zhu Zhijun , Xu Jingwen , Shen Yujuan , Liu Hongyan , Zhou Yanqing , Xu Luxin
Published: July 2024

Background

Previous research has primarily examined the mental well-being of children from labor migrant families, yet there is a lack of understanding regarding the mental well-being of children from highly educated migrant backgrounds. This study investigated the social-emotional problems of 3-5-year-olds from highly educated migrant families residing in an urban area of China, as well as explored potential differences in demographic variables.

Methods

A cross-sectional study was conducted in Qiantang District, Hangzhou, China, with 1,494 (53.3% boys) children selected via a convenient sampling method. The Ages & Stages Questionnaires: social-Emotional, Second Edition (ASQ:SE-2) was used to measure social-emotional problems.

Results

The results showed that 23.6% of the children were at risk for social-emotional problems. More boys (26.7%) than girls (20.1%) had scores above the cut-off. Additionally, more children in the low socioeconomic status (29.9%) had scores above the cut-off than those in the high socioeconomic status (18.9%). There were three common issues among all age groups: “being more active than others,” “excessive attachment to parents,” and “being overly friendly with strangers.

Conclusion

The social-emotional development of children from highly educated migrant families is a significant aspect that deserves recognition, contributing valuable insights to the existing literature on this topic.
Temporal dynamics of socioeconomic inequalities in depressive and anxiety symptoms during the COVID-19 pandemic: a scoping review
Authors: Herrmann Kiara , Beese Florian , Wollgast Lina , Mauz Elvira , Kersjes Christina , Hoebel Jens , Wachtler Benjamin
Published: July 2024

Background

The existence of socioeconomic inequalities in the prevalence of symptoms of depression and anxiety is widely acknowledged, and individuals from lower socioeconomic backgrounds tend to exhibit higher rates of symptoms. However, the direction in which the COVID-19 pandemic has influenced these disparities remains uncertain. We therefore aimed to systematically outline the available evidence on the temporal dynamics of socioeconomic inequalities in symptoms related to depression and anxiety during the COVID-19 pandemic across high-income countries.

Methods

A scoping review was conducted by searching the databases Embase, Scopus and PsycINFO. According to pre-defined eligibility criteria, two reviewers independently screened titles and abstracts as well as full texts of the compiled records. Data from the included studies were extracted using a standardised data-extraction form and analysed numerically and narratively. The scoping review followed the PRISMA-ScR guidelines.

Results

A total of 49 studies comprising 149 analyses of socioeconomic indicators in relation to symptoms of depression and anxiety were included. Despite heterogeneous study designs and results, there was a tendency of increasing (40.9%; n = 61) or persistent (38.2%; n = 57) inequality trends to the detriment of those in socially more disadvantaged positions. Increasing inequalities were most pronounced when income was used as a socioeconomic indicator. Groups with lower socioeconomic status appeared most vulnerable in the initial phase of the COVID-19 pandemic. Throughout the pandemic, dynamics were diverse, with persistent trends most frequently reported.

Conclusion

Overall, to the detriment of those with lower socioeconomic status, mental-health inequalities persisted or increased in most analyses. Continually monitoring socioeconomic inequalities over time is crucial, since this makes it possible to adapt prevention and intervention strategies to specific pandemic phases. Interventions targeting job security, income security and educational attainment could reduce mental-health inequalities. The results can contribute to preparedness plans for future pandemics and crises.
The relationship of intimate partner violence on depression: the mediating role of perceived social support and the moderating role of the Big Five personality
Authors: Luo Zhenni , Guan Yan , Li Yun , Xu Weihong , Li Lu , Liu Siyuan , Zhou Haozheng , Yin Xuanhao , Wu Yibo , Chen Jiangyun
Published: July 2024

Introduction

This study aimed to explore the influence of Intimate Partner Violence (IPV) on depression, the mediating role of social support, and the moderating role of the Big Five personality traits in the relationship between social support and depression.

Methods

Participants were recruited from Mainland China, using a stratified random sampling and quota sampling method. From June to August 2022, a diverse group of 21,916 participants (ranging from 12 to 100 years old) completed the Intimate Partner Violence Scale, Patient Health Questionnaire, Perceived Social Support Scale, and Big Five Inventory-Short Version.

Results

IPV was significantly positively correlated with depression and significantly negatively correlated with perceived social support. Perceived social support plays a mediating role in the link between IPV and depression.

Discussion

Healthcare workers should assess social support and provide adequate care or recommendations for increasing social support when patients with IPV report depressive symptoms. Patients can be coached by professionals to improve their resiliency by developing or nurturing more optimistic personality traits.
How does working time impact perceived mental disorders? New insights into the U-shaped relationship
Authors: Niu Xiaoru , Li Chao , Xia Yuxin
Published: July 2024

Based on a large-scale nationally representative survey in China, this paper uses the exogenous impact of automation on working hours as the instrumental variable to examine working time’s impact on perceived mental disorders, on the basis of dealing with endogeneity. Different from existing literature, it is found that the impact of working time on perceived mental disorders is U-shaped, rather than linear. Mental disorders firstly decrease with working hours. After working more than 48.688 h per week, further increases in working time carry notable mental health costs, leading to a positive relationship between working hours and depression. The turning point of this U-shaped relationship is almost in line with the International Labor Organization’s 48 working hours/week standard, justifying it from a mental health perspective. In addition, we further exclude the possibility of more complex nonlinear relationships between working time and perceived mental disorders. Furthermore, heterogeneities are found in the effects of working hours on mental disorders across different subgroups. Males are more depressed when working overtime. Older workers have a lower tolerance for overwork stress. The turning point is smaller for the highly educated group and they are more sensitive to working longer. Those with higher socioeconomic status are less depressed after exceeding the optimal hours of work. The increase in depression among rural workers faced with overwork is not prominent. Perceived mental disorders are lower among immigrants and those with higher health status. In addition, labor protection and social security help to weaken mental disorders caused by overtime work. In conclusion, this paper demonstrates that working time has a U-shaped impact on perceived mental disorders and highlights the vulnerability of certain groups, providing a reference for setting optimal working hours from a mental health perspective.
The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods
Authors: Saifullah Saifullah , Mercier Dominique , Lucieri Adriano , Dengel Andreas , Ahmed Sheraz
Published: July 2024

Since the advent of deep learning (DL), the field has witnessed a continuous stream of innovations. However, the translation of these advancements into practical applications has not kept pace, particularly in safety-critical domains where artificial intelligence (AI) must meet stringent regulatory and ethical standards. This is underscored by the ongoing research in eXplainable AI (XAI) and privacy-preserving machine learning (PPML), which seek to address some limitations associated with these opaque and data-intensive models. Despite brisk research activity in both fields, little attention has been paid to their interaction. This work is the first to thoroughly investigate the effects of privacy-preserving techniques on explanations generated by common XAI methods for DL models. A detailed experimental analysis is conducted to quantify the impact of private training on the explanations provided by DL models, applied to six image datasets and five time series datasets across various domains. The analysis comprises three privacy techniques, nine XAI methods, and seven model architectures. The findings suggest non-negligible changes in explanations through the implementation of privacy measures. Apart from reporting individual effects of PPML on XAI, the paper gives clear recommendations for the choice of techniques in real applications. By unveiling the interdependencies of these pivotal technologies, this research marks an initial step toward resolving the challenges that hinder the deployment of AI in safety-critical settings.
Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases
Authors: Abbasi Ahtisham Fazeel , Asim Muhammad Nabeel , Ahmed Sheraz , Vollmer Sebastian , Dengel Andreas
Published: July 2024

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.
Sustainable tourism development and Indigenous protected and conserved areas in sub-arctic Canada
Authors: Vandermale Emalee A. , Mason Courtney W.
Published: July 2024

Rural and northern Indigenous communities across Canada are pursuing new Indigenous-led conservation partnerships with Crown governments as critical alternatives to Western conservation and extractive industries regimes. Colonial conservation policies and industrial development continue to displace Indigenous Peoples from their ancestral territories, with great consequences to land-based economies, food security, and knowledges. Indigenous-led conservation is an umbrella term used to describe a variety of initiatives that includes Indigenous Protected and Conserved Areas. Indigenous communities lead the creation, management, and stewardship of these protected areas, which are guided by localized knowledge and priorities. This creates unique opportunities to build new and bolster existing tourism businesses with sustainable socio-economic, cultural, and environmental outcomes. Our research examines Indigenous-led conservation and tourism in the Dene/Métis community of Fort Providence, Northwest Territories, located adjacent to Canada's first official Indigenous Protected and Conserved Area, Edéhzhíe. Guided by Indigenous methodologies and collaborative approaches, this paper presents the analysis of 23 semi-structured interviews with Elders, knowledgeable land users, and community members. While tourism development in the community is currently limited, our results indicate that participants are hopeful about the contributions of Edéhzhíe and tourism to sustainable economies, cultural resurgence, and environmental stewardship in the surrounding communities. Participants demonstrate that Indigenous-led conservation and tourism have the potential to challenge existing colonial, capitalist land use regimes and foster Indigenous governance, reconciliatory processes, and environmental resiliency. Our findings can be used by other Indigenous communities to inform conservation and sustainable development goals related to regional tourism economies.
Effects of intramuscular alfaxalone and dexmedetomidine alone and combined on ocular, electroretinographic, and cardiorespiratory parameters in normal cats
Authors: Guo Yizhe , Mao Sichao , Zhou Zhenlei
Published: July 2024

Background

This study aimed to determine the effects of intramuscular (IM) administration of alfaxalone with or without dexmedetomidine on short electroretinography (ERG), ocular parameters and cardiorespiratory in healthy cats.

Methods

Eight healthy female spayed cats were treated with three sedation protocols: IM administration of 5 μg/kg dexmedetomidine (DEX), 5 mg/kg alfaxalone (ALF), and 5 μg/kg dexmedetomidine plus 5 mg/kg alfaxalone (DEX + ALF). The washout period after each treatment was 2 weeks. Physiological parameters, time metrics, intraocular pressure (IOP), Schirmer tear test 1 (STT-1) and a short ERG protocol were recorded. For age data, weight data, time metrics and ERG data, one-way ANOVA with Bonferroni posterior comparisons were performed. For physiological parameters, IOP and STT-1 data, two-way repeated measures ANOVA with Bonferroni posterior comparisons were performed. Statistical significance was set at a p-value <0.05.

Results

IOPs were increased in all three groups compared to baseline and showed no significant differences among three groups at any time point. STT-1 values were decreased significantly during the process. Significant differences were noticed between a-wave amplitude in the dark-adapted response between DEX and ALF, and a-wave amplitude in light-adapted response between ALF and DEX + ALF.

Conclusion

This study demonstrates the feasibility of three sedation protocols for short ERG recording in cats. All these treatments resulted in increased IOP values and reduced STT-1 values. But baseline data of ERG was not obtained as a blank control in cats.
Pilot Study Outcomes and Recommendations from Developing an Australian Residential Treatment for Eating Disorders
Authors: Day Sinead , Houlihan Catherine , Mitchison Deborah , Conti Janet , Gill Katherine , Mannan Haider , McMahon Kirsten , Ramjan Lucie , Rankin Rebekah , Tannous Wadad Kathy , Utpala Ranjani , Hay Phillipa
Published: July 2024

Individuals with eating disorders often face difficulty accessing sufficiently intensive, recovery-focused treatment. Residential treatment may fill a gap in the spectrum of care, offering 24-h support in a more home-like environment than a hospital and using a holistic approach including individual and group psychological therapy, meal support, and lived experience staff. As residential treatment has not previously been examined in Australia, the current study aimed to document the development, treatment components, and structure of this first Australian residential service for eating disorders and provide a pilot of its treatment outcomes. Preliminary outcomes are included from a sample of 19 individuals from the first six months of admissions, including eating disorder symptoms, eating disorder-related impairment, anxiety, and depression. Significant pre- to post-treatment improvement was found in total eating disorder psychopathology, dietary restraint, eating concerns, body mass index, eating disorder-related impairment, and depression, but not from pre-treatment to a six-month follow-up. Pilot outcomes were positive at end-of-treatment but require further clinical evaluation to examine follow-up effects. Clinical insights are discussed from the establishment of this new treatment service, including recommendations for clinicians involved in the current roll-out of residential programs across Australia.
Environmental Factors Related to Climate Change Alter the Chemical Composition and Biological Activity of Lavandula viridis L’Hér Essential Oil
Authors: Mansinhos Inês , Gonçalves Sandra , Rodríguez-Solana Raquel , Moreno-Rojas José Manuel , Romano Anabela
Published: July 2024

Climate change is affecting all regions of the world, and the Mediterranean region is one of the most affected. Plants accumulate secondary metabolites as an adaptive response to stress circumstances. The present study investigated the effect of different abiotic factor conditions (drought, moderate heat, severe heat, salinity, and UV-B radiation) on the essential oil (EO) yield, composition (volatile profile), and biological activity (enzyme inhibition and antioxidant activity) of Lavandula viridis L’Hér. In general, the environmental conditions increased the extraction yield of EO. Eighty-two compounds were identified in the EO and environmental factors induced some quantitative changes in EO composition. Severe heat and salinity conditions increased the concentration of the two most abundant compounds, 1,8-cineole and camphor. Severe heat also increased the potential of EO to inhibit the enzymes butyrylcholinesterase and tyrosinase. Drought, salinity, and UV-B radiation promoted the ability of EO to inhibit acetylcholinesterase. In addition, heat and drought enhanced the antioxidant activity of EO. These results are relevant for exploring the potential of this EO for industrial applications, although future studies combining the factors studied are important to understand the influence of synergistic effects on the composition and bioactivity of the plant products obtained.
Nitrogen Reduction and Organic Fertiliser Application Benefits Growth, Yield, and Economic Return of Cotton
Authors: He Huangcheng , Lou Xuemei , Liu Jianguo
Published: July 2024

The application of excessive nitrogen fertiliser has been found to have a detrimental impact on the growth and development of cotton in Xinjiang, China. This has resulted in a reduction in cotton yield and economic benefit. The aim of this study was to investigate the potential for reducing the input of inorganic N fertiliser while maintaining the quality and yield formation of cotton. The objective of this study was to examine the growth, photosynthesis, and yield of cotton crops subjected to varying fertiliser treatments. The experiment was conducted in 2021–2022 with eight treatments in the experiment: no fertiliser (CK); conventional application of inorganic nitrogen fertiliser (T0); T1–T3, with 8%, 16%, and 24% reduction in inorganic nitrogen fertiliser application, respectively; and T4–T6, with organic fertilisers replacing the reduced inorganic nitrogen fertiliser application of T1–T3, respectively. In comparison to T0, T5 demonstrated the most notable agronomical performance and yield components across both years. This is attributable to the spatial distribution of cotton bolls, which was more conducive to the net photosynthetic rate and yield formation. This, in turn, led to an augmented photosynthetic capacity, enhanced biomass accumulation, and an elevated harvesting index. The results of the economic benefit analysis demonstrated that in comparison to the control treatment (T0), the net profit of all treatments except T3 increased. In conclusion, the economic benefit reached its maximum in the range of a 9.90–14.10% reduction in nitrogen and a 16.60–17.60% substitution of organic fertiliser.
Insights on the Use of Pesticides in Two Main Food-Supplier Coastal Valleys of Lima City, Peru
Authors: Chávez-Dulanto Perla N. , Vögler Oliver , Helfgott-Lerner Salomón , Carvalho Fernando P.
Published: July 2024

The food security of Lima—Peru’s capital city, which shelters over 30% of the total country’s population—depends on the food production of its nearest agricultural areas, the Chancay-Huaral and Chillón valleys, wherein agrochemicals are widely used. This study primarily aimed to determine the characteristics of pesticide use in these two valleys, located 83 and 30 km north of Lima City, respectively. A second aim was to assess whether proximity to Lima provides access to technical assistance regarding agricultural activities. A questionnaire-based survey assessing socioeconomic aspects, occupational exposure, and agrochemical-related knowledge was conducted on a sample of 102 participants (farmers and fieldworkers). The results revealed that the average age for starting to handle pesticides was 15 years, while life-long occupational-exposure averaged 30 years. Most pesticides used were organophosphates and carbamates. Personal protective equipment was not used and, therefore, dermal exposure and inhalation were major routes of intoxication. Despite their proximity to Lima, both valleys lack an official agronomic advisory agency, and this void has been occupied by agrochemical manufacturing companies and trading houses focused on increasing their sales. Based on the results, it is urgent to implement an official technical advisory service and a capacity-building program on pesticide use in Peru, as well as the implementation of measures for improved control, trade, and storage of pesticides. Simultaneously, a permanent epidemiological surveillance at the country level is needed to improve public health and to contribute to achieving the Sustainable Development Goals of the United Nations’ 2030 Agenda in Peru.
Bio-Inspired Hyperparameter Tuning of Federated Learning for Student Activity Recognition in Online Exam Environment
Authors: Shankarappa Ramu , Prasad Nandini , Guddeti Ram Mohana Reddy , Mohan Biju R.
Published: July 2024

Nowadays, online examination (exam in short) platforms are becoming more popular, demanding strong security measures for digital learning environments. This includes addressing key challenges such as head pose detection and estimation, which are integral for applications like automatic face recognition, advanced surveillance systems, intuitive human–computer interfaces, and enhancing driving safety measures. The proposed work holds significant potential in enhancing the security and reliability of online exam platforms. It achieves this by accurately classifying students’ attentiveness based on distinct head poses, a novel approach that leverages advanced techniques like federated learning and deep learning models. The proposed work aims to classify students’ attentiveness with the help of different head poses. In this work, we considered five head poses: front face, down face, right face, up face, and left face. A federated learning (FL) framework with a pre-trained deep learning model (ResNet50) was used to accomplish the classification task. To classify students’ activity (behavior) in an online exam environment using the FL framework’s local client device, we considered the ResNet50 model. However, identifying the best hyperparameters in the local client ResNet50 model is challenging. Hence, in this study, we proposed two hybrid bio-inspired optimized methods, namely, Particle Swarm Optimization with Genetic Algorithm (PSOGA) and Particle Swarm Optimization with Elitist Genetic Algorithm (PSOEGA), to fine-tune the hyperparameters of the ResNet50 model. The bio-inspired optimized methods employed in the ResNet50 model will train and classify the students’ behavior in an online exam environment. The FL framework trains the client model locally and sends the updated weights to the server model. The proposed hybrid bio-inspired algorithms outperform the GA and PSO when independently used. The proposed PSOGA not only outperforms the proposed PSOEGA but also outperforms the benchmark algorithms considered for performance evaluation by giving an accuracy of 95.97%.
Application of Antioxidant Compounds in Bone Defect Repair
Authors: Wang Jiajia , Zhang Yubing , Tang Qingming , Zhang Yinan , Yin Ying , Chen Lili
Published: July 2024

Bone defects caused by trauma, tumor resection, and infections are significant clinical challenges. Excessive reactive oxygen species (ROS) usually accumulate in the defect area, which may impair the function of cells involved in bone formation, posing a serious challenge for bone repair. Due to the potent ROS scavenging ability, as well as potential anti-inflammatory and immunomodulatory activities, antioxidants play an indispensable role in the maintenance and protection of bone health and have gained increasing attention in recent years. This narrative review aims to give an overview of the main research directions on the application of antioxidant compounds in bone defect repair over the past decade. In addition, the positive effects of various antioxidants and their biomaterial delivery systems in bone repair are summarized to provide new insights for exploring antioxidant-based strategies for bone defect repair.
Growth, Antioxidant Capacity, and Liver Health in Largemouth Bass (Micropterus salmoides) Fed Multi-Strain Yeast-Based Paraprobiotic: A Lab-to-Pilot Scale Evaluation
Authors: Wang Jie , Xie Xiaoze , Liu Yangyang , Liu Jiacheng , Liang Xiaofang , Wang Hao , Li Gang , Xue Min
Published: July 2024

A multi-strain yeast-based paraprobiotic (MsYbP) comprising inactive cells and polysaccharides (β-glucan, mannan oligosaccharides, and oligosaccharides) derived from Saccharomyces cerevisiae and Cyberlindnera jadinii could ensure optimal growth and health in farmed fish. This study assessed the impact of an MsYbP on the growth, immune responses, antioxidant capacities, and liver health of largemouth bass (Micropterus salmoides) through lab-scale (65 days) and pilot-scale (15 weeks) experiments. Two groups of fish were monitored: one fed a control diet without the MsYbP and another fed 0.08% and 0.1% MsYbP in the lab-scale and pilot-scale studies, respectively (referred to as YANG). In the lab-scale study, four replicates were conducted, with 20 fish per replicate (average initial body weight = 31.0 ± 0.8 g), while the pilot-scale study involved three replicates with approximately 1500 fish per replicate (average initial body weight = 80.0 ± 2.2 g). The results indicate that the MsYbP-fed fish exhibited a significant increase in growth in both studies (p < 0.05). Additionally, the dietary MsYbP led to a noteworthy reduction in the liver function parameters (p < 0.05), such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase (AKP), and hepatic nuclear density, indicating improved liver health. Furthermore, the dietary MsYbP elevated the antioxidative capacity of the fish by reducing their malondialdehyde levels and increasing their levels and gene expressions related to antioxidative markers, such as total antioxidant ca-pacity (T-AOC), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px), catalase (CAT), nuclear factor erythroid 2-related factor 2 (nrf2) and kelch-1ike ech-associated protein (keap1) in both studies (p < 0.05). In terms of hepatic immune responses, the lab-scale study showed an increase in inflammation-related gene expressions, such as interleukin-1β (il-1β) and transforming growth factor β1 (tgf-β1), while the pilot-scale study significantly suppressed the expressions of genes related to inflammatory responses, such as tumor necrosis factor α (tnfα) and interleukin-10 (il-10) (p < 0.05). In summary, our findings underscore the role of dietary multi-strain yeast-based paraprobiotics in enhancing the growth and liver health of largemouth bass, potentially through increased antioxidative capacity and the modulation of immune responses, emphasizing the significance of employing yeast-based paraprobiotics in commercial conditions.
CCL2 and Lactate from Chemotherapeutics-Treated Fibroblasts Drive Malignant Traits by Metabolic Rewiring in Low-Migrating Breast Cancer Cell Lines
Authors: Vera Maria Jesus , Ponce Iván , Almarza Cristopher , Ramirez Gonzalo , Guajardo Francisco , Dubois-Camacho Karen , Tobar Nicolás , Urra Félix A. , Martinez Jorge
Published: July 2024

While cytostatic chemotherapy targeting DNA is known to induce genotoxicity, leading to cell cycle arrest and cytokine secretion, the impact of these drugs on fibroblast–epithelial cancer cell communication and metabolism remains understudied. Our research focused on human breast fibroblast RMF-621 exposed to nonlethal concentrations of cisplatin and doxorubicin, revealing reduced proliferation, diminished basal and maximal mitochondrial respirations, heightened mitochondrial ROS and lactate production, and elevated MCT4 protein levels. Interestingly, RMF-621 cells enhanced glucose uptake, promoting lactate export. Breast cancer cells MCF-7 exposed to conditioned media (CM) from drug-treated stromal RMF-621 cells increased MCT1 protein levels, lactate-driven mitochondrial respiration, and a significantly high mitochondrial spare capacity for lactate. These changes occurred alongside altered mitochondrial respiration, mitochondrial membrane potential, and superoxide levels. Furthermore, CM with doxorubicin and cisplatin increased migratory capacity in MCF-7 cells, which was inhibited by MCT1 (BAY-8002), glutamate dehydrogenase (EGCG), mitochondrial pyruvate carrier (UK5099), and complex I (rotenone) inhibitors. A similar behavior was observed in T47-D and ZR-75-1 breast cancer cells. This suggests that CM induces metabolic rewiring involving elevated lactate uptake to sustain mitochondrial bioenergetics during migration. Treatment with the mitochondrial-targeting antioxidant mitoTEMPO in RMF-621 and the addition of an anti-CCL2 antibody in the CM prevented the promigratory MCF-7 phenotype. Similar effects were observed in THP1 monocyte cells, where CM increased monocyte recruitment. We propose that nonlethal concentrations of DNA-damaging drugs induce changes in the cellular environment favoring a promalignant state dependent on mitochondrial bioenergetics.
Evaluation of Decay Kinetics of Black Elderberry Antioxidants from Fruits and Flowers
Authors: Golonka Iwona , Dryś Andrzej , Podgórska Katarzyna , Polewska Joanna , Musiał Witold
Published: July 2024

The health-promoting properties of black elderberry are related to its high content of polyphenols (natural antioxidants), which eliminate free radicals and prevent the formation of oxidative stress responsible for many diseases. The aim of this work was to determine, the anti-radical effect of Sambucus nigra infusions based on the reaction with 2,2-diphenyl-1-picrylhydrazyl (DPPH) and galvinoxyl (Glv) radicals and to determine the function describing the disappearance curves of these radicals. The antioxidant properties of infusions obtained from the flowers and fruits of this plant were tested using the modified Brand-Williams method using DPPH and Glv radicals. Higher antioxidant activity towards both the DPPH and Glv radicals was found in flowers compared to fruits. In addition, it was found that the process of quenching radicals in the reaction with Sambucus nigra infusions proceeds in accordance with the assumptions of second-order reaction kinetics. The infusion obtained from flowers quenched radicals faster than fruit infusions. The applied second-order kinetics equation may enable estimation of antioxidants levels in natural sources of radicals.
Special Issue “International Conference Wood Science and Engineering in the Third Millennium—ICWSE 2023”
Authors: Gurau Lidia , Campean Mihaela , Salca Emilia-Adela
Published: July 2024

Every two years, the Faculty of Furniture Design and Wood Engineering of the Transilvania University of Brasov organises an international conference on wood science and engineering—ICWSE, which was held for the 13th in November 2023 and hosted over 100 participants from 30 countries worldwide [...]
Development of Dehydrogenation System for Liquid Organic Hydrogen Carrier with Enhanced Reaction Rate
Authors: Lee Juhan , Usman Muhammad , Park Sanghyoun , Lee Sangyong , Song Myung Ho
Published: July 2024

Owing to the massive expansion and intermittent nature of renewable power, green hydrogen production, storage, and transportation technologies with improved economic returns need to be developed. Moreover, the slowness of the dehydrogenation reaction is a primary barrier to the commercialization of liquid organic hydrogen carrier (LOHC) technology. The present study focused on increasing the speed of dehydrogenation, resulting in the proposal of a triple-loop dehydrogenation system comprising reaction, heating, and chilling loops. The reactor has a rotating cage containing a packed bed of catalyst pellets, which is designed to enhance both heat and mass transfer by helping to detach precipitated hydrogen bubbles from the catalyst surface. In addition, the centrifugal force aids in isolating the gas phase from the LOHC liquid. A dehydrogenation experiment was conducted using the reaction and chilling loops, which revealed that the average hydrogen production rate during the first hour was 52.6 LPM (liter per minute) from 26.3 L of perhydro-dibenzyl-toluene with 1.5 kg of 0.5 wt% Pt/Al2O3 catalyst. This was approximately 48% more than the value predicted with the reaction kinetics measured with a small-scale plug flow dehydrogenation reactor with less than 1.0 g of 5.0 wt% Pt/Al2O3 catalyst. The concept, construction methods, and results of the preliminary gas infiltration, flow visualization, and reactor pumping experiments are also described in this paper.
Seismic Stability Study of Bedding Slope Based on a Pseudo-Dynamic Method and Its Numerical Validation
Authors: Lu Yulin , Jing Yinuo , He Jinze , Zhang Xingxing , Chen Xiaoran
Published: July 2024

Earthquakes are one of the main causes of bedding slope instability, and scientifically and quantitively evaluating seismic stability is of great significance for preventing landslide disasters. This study aims to assess the bedding slope stability under seismic loading and the influences of various parameters on stability using a pseudo-dynamic method. Based on the limit equilibrium theory, a general solution for the dynamic safety factor of bedding slope is proposed. The effects of parameters such as slope height, slope angle, cohesion, internal friction angle, vibration time, shear wave velocity, seismic acceleration coefficient, and amplification factor on stability are discussed in detail. To evaluate the validity of the pseudo-dynamic solution, the safety factors are compared with those given by early cases, and the results show that the safety factors calculated by the present formulation coincide better with those of previous methods. Moreover, a two-dimensional numerical solution of bedding slope based on Mohr–Coulomb’s elastic–plastic failure criterion is also performed by using the finite element procedure, and the minimum safety factor is essentially consistent with the result of the pseudo-dynamic method. It is proved that the pseudo-dynamic method is effective for bedding slope stability analyses during earthquakes, and it can overcome the limitations of the pseudo-static method.
The Influence of Medial and Lateral Forefoot Height Discrepancy on Lower Limb Biomechanical Characteristics during the Stance Phase of Running
Authors: Cai Jiachao , Sun Dong , Xu Yining , Chen Hairong , Zhang Qiaolin , Baker Julien S. , Gu Yaodong
Published: July 2024

Previous research has predominantly focused on the biomechanical effects of anterior–posterior foot motion during running, with comparatively less attention given to medial–lateral foot motion and its impact on lower limb biomechanical characteristics. We recruited 18 healthy runners who wore five different types of running shoes: regular shoes (NS), those with a 6 mm and 9 mm medial–lateral height difference in the forefoot (M6, M9), and those with a 6 mm and 9 mm lateral–medial height difference (L6, L9). Biomechanical parameters of lower limb joints during the stance phase of running, including range of motion, peak angular velocity, peak moment, power, and work, were analyzed. We used paired-sample t-tests and one-dimensional statistical parametric mapping (SPM1D) to compare joint biomechanics between shoes with varying height differences and NS. Under the L6 condition, notable differences occurred in the hip and knee flexion–extension moments during landing and push-off, accompanied by a significant increase in ankle dorsiflexion work and a significant decrease in inversion–eversion work. In contrast, the M9 condition resulted in decreased hip flexion–extension peak moment, power, and work in the sagittal plane. These findings indicate that varying forefoot medial–lateral height differences in running shoes significantly impact lower limb joint dynamics during the stance phase, particularly the L6 condition, potentially reducing knee injury risk and aiding gait improvement for overpronators. The findings offer valuable insights for sports injury prevention and athletic footwear design. However, further research is needed to understand the underlying mechanisms and practical implications for sports injury prevention and performance enhancement.
Efficient Sleep–Wake Cycle Staging via Phase–Amplitude Coupling Pattern Classification
Authors: Cota Vinícius Rosa , Del Corso Simone , Federici Gianluca , Arnulfo Gabriele , Chiappalone Michela
Published: July 2024

The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we assessed the performance of several architectures, varying among different input choices and numbers of neurons in the hidden layer. The top performing architecture was then tested using distinct extrapolation strategies that would simulate applications in a real lab setting. Although all the different choices of input data displayed high AUC values (>0.85) for all the stages, the ones using larger input datasets performed significantly better. The top performing architecture displayed high AUC values (>0.95) for all the extrapolation strategies, even in the worst-case scenario in which the training with a single day and single animal was used to classify the rest of the data. Overall, the results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages.
Efficient and Intelligent Feature Selection via Maximum Conditional Mutual Information for Microarray Data
Authors: Zhang Jiangnan , Li Shaojing , Yang Huaichuan , Jiang Jingtao , Shi Hongtao
Published: July 2024

The challenge of analyzing microarray datasets is significantly compounded by the curse of dimensionality and the complexity of feature interactions. Addressing this, we propose a novel feature selection algorithm based on maximum conditional mutual information (MCMI) to identify a minimal feature subset that is maximally relevant and non-redundant. This algorithm leverages a greedy search strategy, prioritizing both feature quality and classification performance. Experimental results on high-dimensional microarray datasets demonstrate our algorithm’s superior ability to reduce dimensionality, eliminate redundancy, and enhance classification accuracy. Compared to existing filter feature selection methods, our approach exhibits higher adaptability and intelligence.
Mechanical Properties of Adjacent Pile Bases in Collapsible Loess under Metro Depot
Authors: Liu Xiaohua , Li Mingze , Liao Hongjian , Huang Bingyan , Liu Shaohua
Published: July 2024

Metro transit construction has begun to develop rapidly in northwest China because of the acceleration of urbanization. Accordingly, metro depots are also regarded as an essential auxiliary facility for stopping, operation, and maintenance of trains. Meanwhile, many commercial buildings are constructed over metro depots to improve the utilization rate of land due to the increasingly scarce urban land resources, known as transit-oriented development (TOD). These buildings have a large covered area and transfer concentrated loads to the bases. Therefore, pile bases under metro depots have the bearing characteristics of undertaking large concentrated loads, while lesser loads are placed on the soil between the adjacent pile bases. Additionally, the main ground in northwest China is collapsible loess, so the collapsibility should also be considered. Based on the above background, this research performed static loading tests with and without immersion in a reduced scale of adjacent pile bases under a metro depot in Xi’an. The remolding process of natural loess could destroy its structure and the anisotropy of natural loess could also affect the test results. Therefore, four kinds of artificial collapsible loess with different mass ratios of barite powder, kaolin, river sand, cement, industrial salt, and calcium oxide were made by the free-drop method. This method could make the artificial loess simulate the structure of natural loess reasonably. Then, the artificial loess with the most similar properties to intact loess was selected by comparison. Finally, static loading tests with this artificial loess were implemented. The results showed that the ultimate bearing capacity was 4.5 kN. At the same time, the axial force decreased along depth, since the pile shaft friction was positive, and the load sharing ratio of pile tip force increased to 0.58 when the load exceeded 4.5 kN in the situation without immersion; the settlement of pile bases increased significantly after immersion, while the negative shaft friction occurred at the depth of −8 cm~−35 cm, and the load sharing ratio of pile tip force reached 0.92.
Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
Authors: Geng Yun , Zhang Fengshan , Liu Hongbin
Published: July 2024

To identify the complex time patterns in the process data and monitor the effect of wastewater treatment by predicting effluent chemical oxygen demand more accurately, a soft-sensor modeling method based on the multi-scale temporal convolutional network (MSTCN) was proposed in this paper. Data at different time scales are reconstructed according to the main frequencies determined by the Fourier transform approach, and the correlations between variables during that period are calculated and stored in the corresponding adjacency matrix. The specific temporal convolutional network (TCN) is built to learn the temporal dependencies within each sequence at the current scale, while the graph convolutional layer (GCN) captures the relationships among variables. Finally, predictions with less error can be obtained by integrating output features from GCN and TCN layers. The proposed model is validated on an annual dataset collected from a wastewater treatment plant employing biological processes for organic matter removal. The experimental results indicate that the proposed MSTCN reduces RMSE by 35.71% and 22.56% compared with the convolutional neural network and TCN, respectively. Moreover, MSCTN shortens the training period by 6.3 s and improves RMSE by 30.41% when compared to the long short-term memory network, which is outperformed in extracting temporal dynamic characteristics.