bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding the association of disability with multimorbidity, and healthcare utilization in India’s older adult population: insights from cross-sectional evidence of SAGE-2
Authors: Agrawal Ritik , Sinha Abhinav , Murmu Jogesh , Kanungo Srikanta , Pati Sanghamitra
Published: October 2024

Background

Like other low-and middle-income countries, India is undergoing a demographic and epidemiologic shift that has led to a significant rise in the burden of non-communicable diseases (NCDs). Evidence suggests that chronic illnesses and disability are linked but limited studies have explored the association between disability and multimorbidity (simultaneous presence of two or more chronic conditions). Since the magnitude of multimorbidity is becoming a norm, it is prudent to understand the association between these two. We aimed to estimate the association between disability and multimorbidity and assess their healthcare utilization among older adults in India using a nationally representative data.

Methods

We employed data from the second round of World Health Organization’s Study on Global AGEing and adult health (SAGE) conducted in 2015. SAGE is a nation-wide survey conducted among a representative sample of older adults aged ≥50 years, a total of 7,118 participants aged ≥50 years were included in the analysis. The main outcome of interest was disability for which we used WHODAS 2.0 scoring scale which ranges from 0 to 100. Descriptive statistics such as frequency and proportion were used to report the characteristics of study population, and prevalence. We performed the univariable followed by multiple ordinal logistic regression to assess the association between disability and multimorbidity, reported as adjusted odds ratio (AOR) with 95% confidence interval (CI) and p-value. Healthcare utilization was presented as frequency and proportion.

Results

The overall prevalence of disability was found to be 89.0% (95% CI: 88.3–89.8) while that of multimorbidity was 39.7% (95% CI: 35.6–37.8). Most of the participants had moderate followed by mild disability. Hypertension (32.7%) was found to be the most prevalent chronic condition followed by cataract (21.3%). The chances of having disability among multimorbid individuals was AOR: 1.40 (95% CI: 1.13–1.75). Participants having disability and multimorbidity mostly visited private sector followed by public healthcare facilities.

Conclusion

We observed that nine out of every 10 individuals had some kind of disability in India. We observed multimorbidity to be associated with disability that signifies the need for including disabilities as a part of NCD program as these factors could be bi-directional. Longitudinal studies for disability will be helpful to better understand and address the growing needs of these individuals.
The SINFONIA project repository for AI-based algorithms and health data
Authors: Fernández-Fabeiro Jorge , Carballido Álvaro , Fernández-Fernández Ángel M. , Moldes Manoel R. , Villar David , Mouriño Jose C.
Published: October 2024

The SINFONIA project’s main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental effects of radiation exposure on patients, workers, caretakers, and comforters, the public, and the environment during the management of patients suspected or diagnosed with lymphoma, brain tumors, and breast cancers. The project plan defines a series of key objectives to be achieved on the way to the main objective. One of these objectives is to develop and operate a repository to collect, pool, and share data from imaging and non-imaging examinations and radiation therapy sessions, histological results, and demographic information related to individual patients with lymphoma, brain tumors, and breast cancers. This paper presents the final version of that repository, a cloud-based platform for imaging and non-imaging data. It results from the implementation and integration of several software tools and programming frameworks under an evolutive architecture according to the project partners’ needs and the constraints of the General Data Protection Regulation. It provides, among other services, data uploading and downloading, data sharing, file decompression, data searching, DICOM previsualization, and an infrastructure for submitting and running Artificial Intelligence models.
Patient-centric knowledge graphs: a survey of current methods, challenges, and applications
Authors: Al Khatib Hassan S. , Neupane Subash , Kumar Manchukonda Harish , Golilarz Noorbakhsh Amiri , Mittal Sudip , Amirlatifi Amin , Rahimi Shahram
Published: October 2024

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient’s health information holistically and multi-dimensionally. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient’s health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field.
Augmented reality in engineering education: enhancing learning and application
Authors: Suhail Nasija , Bahroun Zied , Ahmed Vian
Published: October 2024

Introduction

Augmented reality (AR) is a transformative technology that enhances teaching and learning by blending virtual and real environments. While the potential of AR to improve visualization, interaction, and student engagement in engineering education is recognized, its application across various engineering disciplines remains underexplored. This study systematically investigates the use of AR in engineering fields, highlighting its educational impact and identifying gaps for future research.

Methods

A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, analyzing 67 peer-reviewed papers on AR in engineering education. Content analysis was employed to assess AR’s effectiveness in enhancing visualization, interaction, and motivation. A bibliometric analysis identified key AR tools, research trends, geographic distribution, and interdisciplinary collaboration.

Results

AR is extensively applied in civil and mechanical engineering, where tools such as Unity 3D significantly improve visualization and interaction. AR enhances student engagement and comprehension, particularly in complex areas like construction design. However, its adoption in other engineering disciplines remains limited. The analysis also shows the positive impact of AR on motivation and learning outcomes despite challenges such as technical limitations and insufficient training for educators.

Conclusion

Although AR is gaining traction in engineering education, broader adoption is hindered by technical challenges and the need for better curriculum integration. Future research should address these barriers and explore AR’s potential in underutilized engineering disciplines to maximize its educational benefits.
Robotic colorectal surgery in Latin America: a systematic review on surgical outcomes
Authors: Alves Martins Bruno Augusto , Avellaneda Nicolas , Piozzi Guglielmo Niccolò
Published: October 2024

Background and objectives

Robotic approach in colorectal surgery is rapidly gaining interest, particularly in the context of rectal cancer resection. Despite economic barriers, substantial proliferation of robotic colorectal procedures has been observed throughout Latin America. However, there is a lack of data regarding intraoperative and early postoperative outcomes, as well as oncological and long-term results. This systematic review aims to provide an overview of the surgical outcomes of robotic-assisted colorectal approaches across Latin America.

Material and methods

A systematic literature search of electronic databases, including PubMed, LILACS, Scopus, Cochrane Library and Scielo, was performed and reported in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The main target of the literature search was studies that reported outcomes of colorectal robotic surgery in Latin America.

Results

A total of 9,694 published articles were identified from the initial search. Nine thousand six hundred thirty-six publications were excluded after title and abstract review and removal of duplicates. Fifty-eight articles were thoroughly reviewed, and 11 studies met the inclusion criteria. The critical appraisal of study quality (biases risk assessment) was performed according to the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. In general, the overall study quality was poor. Of the 11 studies included in the analysis, ten addressed intraoperative and early postoperative outcomes, seven addressed oncological/pathological outcomes, and just one addressed long-term outcomes. Ten studies evaluated intraoperative and early postoperative outcomes, encompassing a total of 425 patients, the majority of whom were diagnosed with colorectal cancer. Morbidity rates exhibited a range between 0% and 45.9%, while mortality ranged from 0% to 2.5%.

Conclusion

Few studies have been published addressing intraoperative, postoperative, pathological, and oncological outcomes of robotic colorectal surgery in this region. Undoubtedly, there are unique challenges not encountered by developed countries, including economic obstacles in establishing structured training programmes and high-quality centres for the development of robotic surgery. Further studies are needed to assess the real extent of robotic surgery in the region and its results.

Systematic Review Registration

https://www.crd.york.ac.uk/, PROSPERO (CRD42023494112).
Molecular characterization of the tet (M)-carrying transposon Tn7124 and plasmids in Escherichia coli isolates recovered from swine
Authors: Liu Yingying , Qiao Zhu , Ma Yan , Wang Mingcheng , Hu Gongzheng , Li Enzhong
Published: October 2024

Here, we report the genetic features and evolutionary mechanisms of two tet (M)-bearing plasmids (pTA2 and pTA7) recovered from swine Escherichia coli isolates. The genetic profiles of pTA2 and pTA7 and corresponding transconjugants were accessed by S1 nuclease pulsed-field gel electrophoresis and Southern hybridization, followed by whole genome sequencing and bioinformatics analysis. The biological influences of pTA2 and pTA7 were determined by stability and direct competition assays. Both pTA7 and pTA2 had the IncR backbone sequences but differed in the multidrug resistance region (MDR). The MDR of pTA2 consisted of sul3, tet (M), qnrS1, bleO, oqxAB, floR, aadA1, cmlA1, aadA2, and tet (A)-tetR (A) in addition to 22 insertion sequences. Notably, pTA2 carried the novel complex Tn7124 (IS26-ctp-lp-tet (M)-hp-IS406tnp-IntI4-IS26) harboring tet (M). The fragment carrying tet (M) (IS26-ctp-lp-tet (M)-IS406 tnp-ctp-aadA1-cmlA1-aadA2-dfrA12-IntI1), named Tn6942-like, and the two resistance modules ISVsa3-VirD2-floR-lysR and tet (A)-tetR (A) were located in the MDR of pTA7. Both pTA2 and pTA7 were highly stable in E. coli DH5α cells with no fitness cost to the host or disadvantage in growth competition. These results indicate that transposons carrying tet (M) continuously integrate via mediation with an insertion sequence, which accelerates the transmission of tet (M) in E. coli isolates through integration of other drug-resistant genes, thereby posing a potential serious threat to the efficacy of clinical treatment.
The General Elections in Bosnia and Herzegovina in 2022 – the Legal and Political Aspects
Authors: Krysieniel Krzysztof
Published: October 2024

The article analyzes the results of the general elections in Bosnia and Herzegovina, which took place in October 2022, as well as their consequences for the political situation in this country. Key aspects of electoral law were also discussed, with particular emphasis on provisions limiting human and civil rights on the basis of ethnicity, which is the result of the solutions adopted in the Dayton Peace Agreement in 1995.
Mukbang Live Streaming Commerce and Green Agri-Food Products Consumption: Exploring the New Dynamics of Consumer Purchasing Decisions
Authors: Chen Xinqiang , Chen Jiangjie , Cai Zhiwen
Published: October 2024

China’s live streaming boom has led to mukbang live streaming as a unique food marketing tool. Hosts interact with viewers by tasting and showcasing diverse cuisines in real time. This form of mukbang live streaming has recently been utilized to promote and sell green agri-food products. However, in-depth research into how mukbang live streaming affects the purchase intention for green agri-food products and the underlying mechanisms remains scant. We developed a theoretical model based on stimuli–organism–response theory to explore the impact mechanism. Data was collected via a survey of 455 users from agriculture-related live streaming platforms and analyzed using structural equation modeling with partial least squares. The study found that professional recommendation, audiovisual experience, and social interaction enhance consumers’ perceived utilitarian value; green advocacy, audiovisual experience, and social interaction positively affect consumers’ perceived social value. Both perceived utilitarian value and perceived social value positively affect the intention to purchase green agri-food products. Additionally, we used importance-performance map analysis to compare the model’s effects with latent variable averages, revealing each factor’s importance and performance. The findings offer new insights and recommendations for agri-food marketing strategies, particularly in enhancing consumer behaviors towards green agri-food products, aiding suppliers and mukbang live streaming platforms in more effectively promoting these products.
An Intermittent Fertilization Control System for Fruit Tree Crown Detection
Authors: Yin Hao , Jing Pengyu , Ma Chen , Cao Liewang , Li Chengsong , Wang Lihong
Published: October 2024

In light of the current dearth of strip intermittent fertilization devices in standardized orchards, this study presents the design of an intermittent fertilization control system based on fruit tree crown detection, developed with the objective of meeting the agronomic requirements of strip furrow fertilization in standardized orchards. The initial stage of the process entails the design of the essential components of the fertilization apparatus, followed by the construction of the intermittent fertilization control system. The ultrasonic sensor was employed as the fruit tree crown detection module, and a mathematical model of fertilization speed was constructed to achieve uniform intermittent fertilization. Furthermore, in order to enhance the responsiveness and reliability of the fertilization servo motor, MATLAB Simulink was employed to assess the dynamic performance of the system under disparate control strategies. Ultimately, to validate the simulation outcomes, a field trial was conducted to assess the precision and uniformity of intermittent fertilization. The results demonstrate that the dynamic performance of the system under the fuzzy PID control strategy is optimal, and the coefficient of variation of the fertilizer uniformity of the intermittent fertilization device is less than 7%. The mean effective fertilization rate exceeded 85%, with the primary indices satisfying the agronomic criteria.
The Effect of Flame Sterilization on the Microorganisms in Continuously Cultivated Soil and the Yield and Quality of Tobacco Leaves
Authors: Han Xueying , Wang Liang , Li Ruyan , Han Qingli
Published: October 2024

Flame disinfection is a new technology that uses high temperatures to kill pathogens and control soil-borne diseases. In order to determine the feasibility of applying flame disinfection technology to flue-cured tobacco, a field experiment was conducted in Pianpo Village (test site I) and Lühuai Village (test site II), Luquan County, Yunnan Province. The effects of flame disinfection on soil-borne disease control, flue-cured tobacco growth agronomic traits, the tobacco yield and quality, and the soil microbial community in the flue-cured tobacco field were investigated. The results were as follows. (1) After flame disinfection, the control rates of the four main soil-borne diseases—black root rot, root rot, wilt, and root knot nematodes—were all over 70%. (2) Samples were taken from the experimental site of Pianpo Village at 0 and 114 days after disinfection (tobacco boom period) to study the effects of soil microbial communities by high-throughput sequencing. Compared with the control group, after 0 days of flame disinfection, the abundance of bacterial actinobacteria, Nocardia, Streptomyces, and fungal ascomycetes decreased, while the abundance of bacterial Proteobacteria, Bacteroidetes, Arthrobacter, and mycospora increased. After 114 days of disinfection, the abundance of bacterial actinobacteria, Proteobacteria, chloromyces, and fungal ascomycetes decreased. The abundance of Mortierella was recovered, the abundance of Gibberella and Fusarium increased, and the abundance of Trichospora and Basidiomycetes decreased in both periods. (3) After flame disinfection treatment, the tobacco yield in the two experimental areas was increased by 50.80% and 54.70%, respectively, and the proportion of high-quality tobacco was also increased. In conclusion, flame disinfection before tobacco planting can improve the soil conditions, effectively control soil-borne tobacco diseases, and improve the quality and yield of tobacco leaves.
Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (Brassica rapa subsp. Pekinensis) Plants
Authors: Du Xiandan , Zhou Zhongfa , Huang Denghong
Published: October 2024

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization.
Effect of Green Cowpea Manure on the Quality Properties of Sandy Soils Under the Specific Conditions of the Dry Climate in Romania
Authors: Drăghici Reta , Nanu Ștefan , Diaconu Aurelia , Bîrsoghe Cristina , Netcu Florentina , Paraschiv Alina Nicoleta , Dima Milica
Published: October 2024

This study was carried out on the rotation of cowpea-rye + cowpea in successive crops for green manure-sorghum grains, with the objective of increasing the fertility of sandy soils by incorporating green cowpea manure into the soil in combination with chemical fertilization with NPK. In this sense, after harvesting the rye plant, the cowpea was sown, which was then incorporated into the soil during the formation of the beans in the pod. Before incorporating the cowpea biomass into the soil, the initial fertility status of the soil was assessed, and in the spring, the soil was sown with sorghum grain in two fertilization variants (N80P80K80 and N150P80K80). The soil chemical analyses carried out at the end of the vegetation cycle of the sorghum plant showed that by incorporating 52.4 t/ha green cowpea biomass (9.24 t/ha dry biomass), the quality properties of the soil were significantly improved, with annual increases of 0.17–0.29 g/kg in total nitrogen, 9.5–13.33 mg/kg in extractable phosphorus, 11.33–24 mg/kg in exchangeable potassium, and 0.08–0.12 g/kg in organic carbon, also depending on the sorghum fertilization system. It has also been shown to reduce the acidity of the soil pH and improve the water holding capacity of the soil.
Outcomes of a Community Cat Program Based on Sterilization of Owned, Semi-Owned and Unowned Cats in a Small Rural Town
Authors: Rand Jacquie , M. Saraswathy Abithaswathi , Verrinder Joy , Paterson Mandy B. A.
Published: October 2024

Free-roaming cats in urban and peri-urban areas and in rural towns pose significant challenges due to their nuisance behaviors, threats to wildlife, and potential disease transmission. Traditional management methods often involve trapping and euthanizing many of these cats, which is not only costly but also distressing for the shelter staff involved, and have failed to reduce numbers of impounded cats or cat-related complaints. Our study aimed to evaluate the effectiveness of a Community Cat Program that provided free sterilization, microchipping, and preventative veterinary care for all owned, semi-owned, and unowned cats in a small rural town with high cat impoundments in Ipswich, Queensland. The program was instigated by the Australian Pet Welfare Foundation in collaboration with the Royal Society for the Prevention of Cruelty to Animals, Queensland and the Animal Welfare League, Queensland, and it was funded by various organizations including Fondation Brigitte Bardot. Data from 2017 to 2023 were analyzed to assess the impact of the program on cat intake and outcomes in local shelters. A total of 308 cats were sterilized, representing 94 cats per 1000 residents over 3.4 years, and in the third year, this was associated with a 60% decrease in cat intake, an 85% reduction in numbers euthanized and 39% fewer cat-related calls to the local council. These findings suggest that high-intensity, targeted sterilization programs significantly decrease shelter intake and euthanasia, and thus alleviate the psychological burden on staff. These programs are a humane and effective alternative to traditional cat management, and benefit animal welfare as well as community wellbeing. This is aligned with a One Welfare approach, which enhances the well-being of animals, humans, and their environments. Legislative changes are required to support these programs to effectively decrease the numbers of free-roaming cats in cities and towns and the issues they cause, while protecting human wellbeing.
Performance Study of an MRI Motion-Compensated Reconstruction Program on Intel CPUs, AMD EPYC CPUs, and NVIDIA GPUs
Authors: Zeroual Mohamed Aziz , Isaieva Karyna , Vuissoz Pierre-André , Odille Freddy
Published: October 2024

Motion-compensated image reconstruction enables new clinical applications of Magnetic Resonance Imaging (MRI), but it relies on computationally intensive algorithms. This study focuses on the Generalized Reconstruction by Inversion of Coupled Systems (GRICS) program, applied to the reconstruction of 3D images in cases of non-rigid or rigid motion. It uses hybrid parallelization with the MPI (Message Passing Interface) and OpenMP (Open Multi-Processing). For clinical integration, the GRICS needs to efficiently harness the computational resources of compute nodes. We aim to improve the GRICS’s performance without any code modification. This work presents a performance study of GRICS on two CPU architectures: Intel Xeon Gold and AMD EPYC. The roofline model is used to study the software–hardware interaction and quantify the code’s performance. For CPU–GPU comparison purposes, we propose a preliminary MATLAB–GPU implementation of the GRICS’s reconstruction kernel. We establish the roofline model of the kernel on two NVIDIA GPU architectures: Quadro RTX 5000 and A100. After the performance study, we propose some optimization patterns for the code’s execution on CPUs, first considering only the OpenMP implementation using thread binding and affinity and appropriate architecture-compilation flags and then looking for the optimal combination of MPI processes and OpenMP threads in the case of the hybrid MPI–OpenMP implementation. The results show that the GRICS performed well on the AMD EPYC CPUs, with an architectural efficiency of 52%. The kernel’s execution was fast on the NVIDIA A100 GPU, but the roofline model reported low architectural efficiency and utilization.
The Impact of Post-Activation Performance Enhancement Protocols on Vertical Jumps: Systematic Review
Authors: Kasicki Krzysztof , Rydzik Łukasz , Ambroży Tadeusz , Spieszny Michał , Koteja Piotr
Published: October 2024

Background: The post-activation performance enhancement (PAPE) protocol is frequently utilized to enhance athletic performance through high-intensity stimuli. Despite its popularity, the effectiveness and mechanisms of PAPE remain unclear, particularly in the context of improving jump performance in sports. Methods: A systematic review was conducted following the PRISMA guidelines. A comprehensive search was conducted across databases including Scopus, Web of Science, PubMed, and EBSCO up to 31 March 2024. Results: Initially, 481 studies were identified, of which 8 were finally included, involving a varied number of participants, each focusing on the impact of PAPE on jump performance. These studies predominantly indicated that PAPE protocols can lead to short-term improvements in jump performance. Two publications demonstrated a positive effect on jump height, and one of them reported a questionable effect on jump height in the initial time period, while the remaining five publications showed a positive impact on jump performance. The average increase in height achieved based on the intervention from the publications was 4.03% (p < 0.05). Variability in effectiveness was noted depending on the type of exercises used in the activation phase, timing of performance post activation, and individual physiological characteristics. Conclusions: PAPE protocols can effectively enhance jump performance in athletic populations, although outcomes vary based on protocol specifics and individual differences. These findings support the need for individualized approaches in the application of PAPE protocols to optimize athletic performance.
EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection
Authors: Bilgen Yusuf , Kaya Mahmut
Published: October 2024

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.
Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum
Authors: Wang Jiaxue , Tao Hui , Wang Zhenlong , An Wei , Zhao Ya , Han Bing , Wang Jinquan , Sun Xiuzhu , Wang Xiumin
Published: October 2024

Cirsium japonicum, a traditional herb, exhibits significant anti-inflammatory activity. However, the main components and potential mechanisms of C. japonicum remain unclear. The aim of this study is to investigate the anti-inflammatory mechanism of Cirsium japonicum through network pharmacology and cellular experiments. The effective components of and potential targets for the anti-inflammatory activity of C. japonicum were identified using a traditional Chinese medicine systematic pharmacology database, the TCMSP analysis platform, and the GeneCards database. The drug–component–target–disease network diagram was constructed using Cytoscape 3.8.0 software, while the protein interaction network diagram was created using the STRING database and Cytoscape 3.8.0 software. Gene ontology (GO) enrichment and KEGG pathway enrichment analysis were carried out using the DAVID database. Molecular docking between key targets and active components was constructed with AutoDock 4.2.6 software to determine the best binding target. The results revealed that 14 active components of C. japonicum targeted 171 anti-inflammatory proteins. GO function enrichment analysis yielded 173 items, while KEGG pathway enrichment analysis identified 48 signaling pathways related to inflammation regulation. Molecular docking showed a strong affinity of sitosterol, stigmasterol, and other components with key targets such as peroxisome proliferator-activated receptor α recombinant protein (PPARA) and cyclooxygenase-2 (PTGS2). Vanillin, one active ingredient of C. japonicum, inhibited the release of lipopolysaccharide (LPS)-induced inflammatory factors in RAW264.7 cells. These findings suggest that C. japonicum may exert its anti-inflammatory effects by modulating the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signal pathway (PI3K-Akt) and apoptin signal pathway, highlighting the multi-component, multi-target, and multi-channel molecular mechanism underlying its anti-inflammatory properties. Finally, the anti-inflammatory effect of vanillin, an effective component of C. japonicum, was verified by cell experiments. This study provides a new understanding of the pharmacological mechanisms of C. japonicum in the treatment of inflammatory conditions.
Cellular Vehicle-to-Everything Automated Large-Scale Testing: A Software Architecture for Combined Scenarios
Authors: Han Qingwen , Zhou Miao , Zeng Lingqiu , Ye Lei , Tan Mingdeng , Xie Fukun
Published: October 2024

As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.
Condition Monitoring of Electrical Transformers Using the Internet of Things: A Systematic Literature Review
Authors: Msane Mzamo R. , Thango Bonginkosi A. , Ogudo Kingsley A.
Published: October 2024

The adoption of Internet of Things (IoT) technology for transformer condition monitoring is increasingly replacing traditional methods. This systematic review aims to evaluate the existing research on IoT frameworks used in transformer condition monitoring, providing insights into their effectiveness and research trends. This review seeks to identify the leading IoT frameworks employed in transformer condition monitoring; analyze the key research objectives, methods, and outcomes; and assess the global research distribution and technological tools used in this field. A systematic literature review was conducted by searching published databases using keywords related to “Internet of Things”, “transformers”, “condition monitoring”, and “fault diagnosis”. The search spanned publications released between 2014 and 2024, yielding 262 articles. Of these, 120 met the predefined review criteria and were included for further analysis. This review found that Arduino boards are the most used microcontrollers for monitoring and analyzing transformer operational parameters, with Arduino IDE 1.8 being the predominant software for programming. The primary research focus in the reviewed literature is the identification of transformer faults. The geographical distribution of research contributions shows that India leads with 65% of the studies, followed by China (11%) and Pakistan (5%). The findings indicate a strong global interest in developing IoT-based transformer condition monitoring systems, particularly in India. This review highlights the potential of IoT technologies to enhance transformer monitoring and diagnostics. The insights gained from this review can guide future research and the development of more advanced IoT frameworks for transformer condition monitoring.
Methods for Detection and Mapping of Methylated and Hydroxymethylated Cytosine in DNA
Authors: Kisil Olga , Sergeev Alexander , Bacheva Anna , Zvereva Maria
Published: October 2024

The chemical modifications of DNA are of pivotal importance in the epigenetic regulation of cellular processes. Although the function of 5-methylcytosine (5mC) has been extensively investigated, the significance of 5-hydroxymethylcytosine (5hmC) has only recently been acknowledged. Conventional methods for the detection of DNA methylation frequently lack the capacity to distinguish between 5mC and 5hmC, resulting in the combined reporting of both. The growing importance of 5hmC has prompted the development of a multitude of methods for the qualitative and quantitative analysis of 5hmC in recent years, thereby facilitating researchers’ understanding of the mechanisms underlying the onset and progression of numerous diseases. This review covers both established and novel methods for the detection of cytosine modifications, including 5mC, 5hmC, 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC), with a particular focus on those that allow for accurate mapping and detection, particularly with third-generation sequencing. The review aims to help researchers choose the most appropriate methods based on their specific research goals and budget.
An Effective Liposome-Based Nanodelivery System for Naphthalene Derivative Polyamines with Antitumor Activity
Authors: Pont Isabel , Felipe Rubén , Frías Juan C. , Chicote Javier U. , García-España Antonio , García-España Enrique , Albelda M. Teresa
Published: October 2024

This study focuses on the development of a novel liposome-based nanodelivery system designed to encapsulate polyamine-1, a compound with potential anti-tumor properties. The main objective of this work was to enhance the therapeutic and imaging potential of polyamine-1 by incorporating it into liposome-based nanoparticles, which were functionalized with a gadolinium complex for imaging purposes and a fluorescent phospholipid for tracking applications. These nanoparticles were characterized by measuring their size, shape, polydispersity index, zeta potential and encapsulation efficiency. In vitro experiments were conducted to evaluate the antitumor activity, specifically determining the cytotoxicity of both free and encapsulated polyamine-1 in cancerous and non-cancerous cell lines. Additionally, the study shows the enhanced signal intensity of gadolinium-loaded liposomes by T1-weighted MRI, highlighting their imaging potential. The experimental results suggest that this liposome-based nanodelivery system not only has therapeutic potential in targeted cancer therapy but also could be advantageous for diagnostic imaging, particularly in MRI applications.
Recent Advances in Nanozyme Sensors Based on Metal–Organic Frameworks and Covalent–Organic Frameworks
Authors: Cheng Xingliang , Liu Shuojiang , Hu Yuling
Published: October 2024

Nanozymes are nanomaterials that exhibit enzyme-like catalytic activity, which have drawn increasing attention on account of their unique superiorities including very high robustness, low cost, and ease of modification. Metal–organic frameworks (MOFs) and covalent–organic frameworks (COFs) have emerged as promising candidates for nanozymes due to their abundant catalytic activity centers, inherent porosity, and tunable chemical functionalities. In this review, we first compare the enzyme-mimicking activity centers and catalytic mechanisms between MOF and COF nanozymes, and then summarize the recent research on designing and modifying MOF and COF nanozymes with inherent catalytic activity. Moreover, typical examples of sensing applications based on these nanozymes are presented, as well as the translation of enzyme catalytic activity into a visible signal response. At last, a discussion of current challenges is presented, followed by some future prospects to provide guidance for designing nanozyme sensors based on MOFs and COFs for practical applications.
Exploring Electrophysiological Responses to Hypnosis in Patients with Fibromyalgia
Authors: Kumar Govindaiah Pradeep , Adarsh A. , Panda Rajanikant , Gosseries Olivia , Malaise Nicole , Salamun Irène , Tshibanda Luaba , Laureys Steven , Bonhomme Vincent , Faymonville Marie-Elisabeth , Vanhaudenhuyse Audrey , Bicego Aminata
Published: October 2024

Background/Objectives: Hypnosis shows great potential for managing patients suffering from fibromyalgia and chronic pain. Several studies have highlighted its efficacy in improving pain, quality of life, and reducing psychological distress. Despite its known feasibility and efficacy, the mechanisms of action remain poorly understood. Building on these insights, this innovative study aims to assess neural activity during hypnosis in fibromyalgia patients using high-density electroencephalography (EEG) and self-reported measures. Methods: Thirteen participants with fibromyalgia were included in this study. EEG recordings were done during resting state and hypnosis conditions. After both conditions, levels of pain, comfort, absorption, and dissociation were assessed using a numerical rating scale. Time perception was collected via an open-ended question. The study was prospectively registered in the ClinicalTrials.gov public registry (NCT04263324). Results: Neural oscillations showed increased theta power during hypnosis in the left parietal and occipital electrodes, increased beta power in the frontal and left temporal electrodes, and increased slow-gamma power in the frontal and left parietal electrodes. Functional connectivity using pairwise-phase consistency measures showed decreased connectivity in the frontal electrodes during hypnosis. Graph-based measures, the node strength, and the cluster coefficient were lower in frontal electrodes in the slow-gamma bands during hypnosis compared to resting state. Key findings indicate significant changes in neural oscillations and brain functional connectivity, suggesting potential electrophysiological markers of hypnosis in this patient population.
Equipment Sounds’ Event Localization and Detection Using Synthetic Multi-Channel Audio Signal to Support Collision Hazard Prevention
Authors: Elelu Kehinde , Le Tuyen , Le Chau
Published: October 2024

Construction workplaces often face unforeseen collision hazards due to a decline in auditory situational awareness among on-foot workers, leading to severe injuries and fatalities. Previous studies that used auditory signals to prevent collision hazards focused on employing a classical beamforming approach to determine equipment sounds’ Direction of Arrival (DOA). No existing frameworks implement a neural network-based approach for both equipment sound classification and localization. This paper presents an innovative framework for sound classification and localization using multichannel sound datasets artificially synthesized in a virtual three-dimensional space. The simulation synthesized 10,000 multi-channel datasets using just fourteen single sound source audiotapes. This training includes a two-staged convolutional recurrent neural network (CRNN), where the first stage learns multi-label sound event classes followed by the second stage to estimate their DOA. The proposed framework achieves a low average DOA error of 30 degrees and a high F-score of 0.98, demonstrating accurate localization and classification of equipment near workers’ positions on the site.
Cellular Automata-Based Experimental Study on the Evolution of Corrosion Damage in Bridge Cable Steel Wire
Authors: Zhou Liping , Yao Guowen , Zeng Guiping , He Zhiqiang , Gou Xuetong , He Xuanbo , Liu Mingxu
Published: October 2024

Cable-stayed bridges have become the preferred bridge type for large-span bridges due to their unique advantages, and the long-term performance of the cable under the extreme conditions has been facing great challenges. An accelerated corrosion test was carried out using in-service cable, and the evolution model of the etch pit was established based on cellular automata to study the evolution law of corrosion damage to steel wire. This study showed that with the increase in the number of dry-wet cycles in the electrified accelerated corrosion, the macro- and micromorphology of the steel wire showed more serious corrosion damage, the tensile strength decreased, the ductility index decreased, and the tensile strength of the steel wire after corrosion decreased by nearly 5%; the geometric dimension of the steel wire etch pits all met a right-skewed distribution with a broader range of etch pit depth, mainly consisting of shallow spherical etch pits and deep ellipsoidal etch pits. The length, width, and depth sizes were mainly distributed in the range of 0.005 mm to 0.015 mm, 0.005 mm to 0.02 mm, and 0 mm to 0.04 mm; at the early stage of corrosion, the etch pits were first developed along the longitudinal direction. As the corrosion process progressed, the iron matrix participated in the electrochemical reaction, leading to the rapid expansion of the etch pits’ dimensions. The stress concentration effect at the bottom of the etch pit caused the maximum stress to approach 1800 MPa, with a stress concentration coefficient of more than 3.0; when the cable anchorage system was located in the connecting sleeve and the threaded splice seam, where corrosion protection was prone to failure, the outer steel wire bore most of the corrosive effects, and the internal cable was less eroded by the corrosive medium.
Development of a Knowledge Base for Construction Risk Assessments Using BERT and Graph Models
Authors: Lee Wonjong , Lee Seulki
Published: October 2024

As a significant percentage of disasters and fatal accidents still occur in the construction sector, it is legally obligatory to conduct workplace risk assessments to avoid accidents and enhance safety. Identifying harmful and hazardous elements is crucial to discern the distinctive characteristics of potential accidents. However, conventional risk-assessment approaches, which rely on the skills and experience of safety managers, may overlook important factors, leading to inconsistencies in the procedures employed across different sites. Such unstructured safety knowledge reduces accessibility and utility, increases reliance on individual skills, and renders information management inefficient. Recently, the focus has shifted from efficient data storage to obtaining valuable knowledge tailored to specific use-cases. Knowledge-graph-based systems integrate and manage the relationships between knowledge entities, thereby enhancing the development of knowledge bases. Research on automatically extracting and managing predefined knowledge from various forms of data through natural language processing (NLP) is ongoing. This study proposes a novel method that uses NLP and graph models to automatically extract predefined knowledge from unstructured construction data and build an entity-relationship-based risk-assessment knowledge base. We developed an entity-name recognition and keyword-extraction engine that defines the core knowledge related to construction safety and risk assessments. This engine can automatically extract predefined knowledge from unstructured data by learning from NLP data. The extracted risk-assessment knowledsge was used to create a knowledge base, and its efficiency and effectiveness were validated through comparisons with existing methods. The results of this study are significant because they lay the foundation for an automatic knowledge-management system for construction safety and risk assessment, offering both practical and academic contributions to the field of construction safety.