bluuebunny/crossref_metadata_2025_split huggingface dataset:
Skin-inspired ultra-tough, self-healing anisotropic wood-based electronic skin for multidimensional sensing
Authors: Zhang Yongyue , Fu Zongying , Wu Tong , Ren Bohua , Chen Jiaxing , Xie Feifan , Leng Weiqi , Shi Jiangtao , Lu Yun 
Published: April 2024

Abstract

Skin is a flexible and stretchable sensor composed of anisotropic dermis (DM) and repairable epidermis (EM). It repairs itself when damaged and has a multi-dimensional sensing system that can assist the body in performing some complex multi-dimensional movements. Inspired by skin, we utilized natural wood as the skeleton and for the first time designed a low-cost, double-layer structure wood-based electronic skin (wood-eskin). Simple chemical treatments were employed to directionally regulate the ray structure of natural wood and to enhance its porosity. Subsequently, physical freeze-thawing was employed to facilitate the formation of an entangled network structure between the PVA solution and the wood nanofibers. Wood-eskin with multi-dimensional sensing was obtained by further introducing ions by immersing in salt solution. In comparison to the majority of contemporary bionic e-skins, wood-eskin exhibits superior mechanical properties. Additionally, it is capable of performing complex deformations, such as bending and knotting. After fracture, wood-eskin can also repair itself and recover ~94% of its mechanical properties. The oriented arrangement of cellulose nanofiber channels gives wood-eskin a multidimensional sensing capability that can be used to monitor more complex body movements. The low-cost, biodegradable wood-eskin exhibits similar functionality to human skin, and its potential for biomedical and bionic robotics applications is considerable.
Effect of Cervical Cancer Preventive Program Based on a Health Belief Model on the Knowledge and Beliefs of Women in Port Said City
Authors: Zohairy Shaymaa Aly , Elmaaty Gehad Mohammed Abu , Mohammed Maha Moussa , Abdelsalam Nahed Abdelazeem 
Published: April 2024

Abstract

Background:Cervical cancer is the leading cause of death worldwide, especially in developing countries. Cervical cancer is a highly preventable and curable cancer compared with other types of cancer, if detected at an early stage.Aim:The present study aims to assess the effect of cervical cancer preventive program based on a health belief model on the knowledge and beliefs of women in Port Said city.Method:A quasi-experimental research design was utilized in this study one group (pre & post-test) at five gynecological and family planning clinics in primary health care centers in Port Said city which affiliated by Egypt Health Care Authority. A multi- stage cluster sampling technique was used for 110 women were included in the study. Two tools were used to gather the required data consisted of; Tool (1) women’s knowledge questionnaire about cervical cancer (2) Health Belief Model Scale.Results: The study revealed that 89.1% of the studied women had satisfactory level of total knowledge and 83.6% of them had high level of total beliefs about cervical cancer prevention at post implementation of cervical cancer preventive program.Conclusion: It can be concluded that after implementation of the educational program, significant improvement with highly statistical differences were noticed in women's knowledge, and in all subscales of total women’s beliefs about cervical cancer.Trial Registration Number (TRN)The study protocol was approved by the Research Ethics Committee of the Faculty of Nursing, Port Said University {code number: NUR 13/2/2022 (10)}.
Three machine-learning models to predict dengue vectors Breteau Index based on meteorology and biotope in Fujian China
Authors: Ke Xuemei , Liu Hong , Yang Zimei , Wei Qiaochu , Ma Shannan , Wei Hongjie , Wu Sihan , Cao Chunxiang , Wang Xiaoyan , Chen Jiaona 
Published: April 2024

Abstract

Background: Dengue fever's rising prevalence in China underscores the need for improved surveillance tools. The Breteau Index (BI), critical for tracking dengue transmission, currently lacks timely and precise predictions, hindering effective response strategies. This study proposes a machine learning-enhanced BI predictive model to refine dengue forecasting in Fujian China.
Methods: Data Collection: In this study, data about the Breteau Index (BI), meteorological conditions, and biotope characteristics from 2015 to 2022 were systematically gathered from the Siming District in Xiamen. Model Development: A range of machine learning algorithms was employed for BI prediction, including Support Vector Regression (SVR), Step-down Linear Regression, Random Forest (RF), Decision Trees (DT), Logistic Regression Models, Deep Neural Networks (DNN), LASSO Logistic Models, and Generalized Additive Models (GAM). Evaluation Metrics: The effectiveness and fit of the predictive models were quantitatively evaluated using the Akaike Information Criterion (AIC)/Bayesian Information Criterion (BIC) and R-squared values, alongside autocorrelation analyses of residuals to ensure statistical integrity. External Validation: Models were further validated with BI data from other Fujian cities to test their generalizability.
Results: This study highlights the Deep Neural Network (DNN) model's superiority over alternative forecasting methods for the Breteau Index (BI), with significant determinants being stagnant water sources like unused containers and aquatic plant bonsais. Meteorological conditions, including precipitation(7-day lag), wind speed(3, 5, 7-day lags), sunshine(2-day lag), barometric pressure(3, 4-day lags), humidity, and temperature(7-day lag), were identified as critical influencers of BI. The analysis underscores the necessity of prompt waterlogged area management post-rainfall to mitigate dengue vector breeding, aligning with findings on the ecological preferences of Aedes albopictus.
Conclusions: In conclusion, the validated Deep Neural Network (DNN) model efficiently predicts the Breteau Index (BI) in Xiamen, indicating its utility in Fujian. Key strategies include controlling waterlogged containers to reduce Aedes aegypti habitats and promptly addressing weather-related BI fluctuations within three days post-rainstorm to lower dengue transmission risk.
Genome-wide identification of UGT gene family and their expression profiles analysis during fruit development in walnut (Juglans regia L.)
Authors: Shi Danhua , Yao Pei , Yang Jinyu , Li Gengyang , Shi Yanyu , Tian Jieyun , Zhang Xiaojun , Liu Qunlong 
Published: April 2024

Abstract

Walnut (Juglans regia L.) possess the ability to prevent coronary heart disease and promote cardiovascular health, which can be attributed to their rich content of polyphenols, particularly flavonoids. The biosynthesis of flavonoids is reliant on the catalytic activity of uridine diphosphate glycosyltransferase (UGT). However, the identification of UGTs in walnut has not been reported. In the current study, a total of 124 UGT genes containing the PSPG box were identified from the walnut genome. Based on phylogenetic analysis, the 124 UGT could be classified into 16 distinct groups, which exhibited an uneven distribution across the 16 chromosomes. Subcellular localization prediction analysis revealed that approximately 78.23% of walnut UGT proteins were predominantly localized in the cytoplasmic compartment. Furthermore, motif annotation confirmed that motifs 1, 2, and 3 represented conserved structural features within UGT proteins, while interestingly, around 56.5% of walnut UGT members lacked introns. In conjunction with transcriptome analysis and quantitative expression, approximately 39% of UGT genes in walnut exhibited high expression levels during early fruit development. Correlation analysis between UGT genes expression and phenolic content in walnut indicated that JrUGT6, JrUGT38, JrUGT39, JrUGT58, JrUGT69, JrUGT75, and JrUGT82 might be involved in phenolic biosynthesis in walnut. This comprehensive study provides a comprehensive overview of the UGT genes in walnut, serving as a valuable reference and theoretical foundation for further investigations into the biological functions of JrUGTs in flavonoid biosynthesis.
Cattle Hydatidosis occurrence, cyst characterization and associated risk factors at Wolaita Soddo Municipality Abattoir, Southern Ethiopia
Authors: Yirsa Tsedalu , Mulugeta Terefe , Berihun Abebe 
Published: April 2024

Abstract

Background
Hydatidosis is a zoonotic infection of many mammalian species produced by Echinococcus granulosus larvae which are distributed globally specifically in Ethiopia.
Methods
A cross-sectional study was conducted from February 2023 to August 2023 to determine the occurrence of cattle hydatidosis, cyst characterization and associated risk factors at Wolaita Soddo Municipality Abattoir, Southern Ethiopia. The antemortem investigation was intended to identify some of the associated risk factors for hydatid cysts in the tested animals. A post-mortem examination and laboratory methods were also used to determine the presence and characteristics of hydatid cysts in various organs.
Results
A total of 15% (60/400) had harboured hydatid cysts. No related risk variables (P ≥ 0.05) were found in hydatic cysts. In terms of organ distribution, the lung (20.5%) had the highest frequency of these cysts, followed by the liver (15.25%), kidney (4.75%), and spleen (3.5%). Out of the 176 hydatic cysts that were counted, the small-sized cysts (47.7%) were more observed than medium-sized (29.5%) and large-sized (22.7%) cysts. Furthermore, of these, more fertile cysts (40.9%) than infertility ones (59.1%) were seen. However, out of 72 fertile cysts that were tested for viability, more non-viable cysts (41.7%) were observed than viable cysts (58.3%).
Conclusions
Overall, even with the small size of the infection detected, hydatidosis seems to be supported by both a socioeconomic setting and a public health concern. Because of this, it continues to rank among the most significant illnesses at the study site, requiring significant preventative and control measures.
Gut Microbiota Shape Oligodendrocyte Response after Traumatic Brain Injury
Authors: Shumilov Kirill , Ni Allen , Garcia-Bonilla Maria , Celorrio Marta , Friess Stuart H. 
Published: April 2024

Abstract

White matter injury (WMI) is thought to be a major contributor to long-term cognitive dysfunctions after traumatic brain injury (TBI). This damage occurs partly due to apoptotic death of oligodendrocyte lineage cells (OLCs) after the injury, triggered directly by the trauma or in response to degenerating axons. Recent research suggests that the gut microbiota modulates the inflammatory response through the modulation of peripheral immune cell infiltration after TBI. Additionally, T-cells directly impact OLCs differentiation and proliferation. Therefore, we hypothesized that the gut microbiota plays a critical role in regulating the OLC response to WMI influencing T-cells differentiation and activation. Gut microbial depletion early after TBI chronically reduced re-myelination, acutely decreased OLCs proliferation, and was associated with increased myelin debris accumulation. Surprisingly, the absence of T-cells in gut microbiota depleted mice restored OLC proliferation and remyelination after TBI. OLCs co-cultured with T-cells derived from gut microbiota depleted mice resulted in impaired proliferation and increased expression of MHC-II compared with T cells from control-injured mice. Furthermore, MHC-II expression in OLCs appears to be linked to impaired proliferation under gut microbiota depletion and TBI conditions. Collectively our data indicates that depletion of the gut microbiota after TBI impaired remyelination, reduced OLCs proliferation with concomitantly increased OLC MHCII expression and required the presence of T cells. This data suggests that T cells are an important mechanistic link by which the gut microbiota modulate the oligodendrocyte response and white matter recovery after TBI.
Sustained inactivation of the Polycomb PRC1 complex induces DNA repair defects and genomic instability in epigenetic tumors
Authors: Rawal Chetan C. , Loubiere Vincent , Butova Nadejda L. , Garcia Juliette , Parreno Victoria , Martinez Anne-Marie , Cavalli Giacomo , Chiolo Irene 
Published: April 2024

Abstract

Cancer initiation and progression are typically associated with the accumulation of driver mutations and genomic instability. However, recent studies demonstrated that cancers can also be purely initiated by epigenetic alterations, without driver mutations. Specifically, a 24-hours transient down-regulation of polyhomeotic (ph-KD), a core component of the Polycomb complex PRC1, is sufficient to drive epigenetically initiated cancers (EICs) in Drosophila, which are proficient in DNA repair and are characterized by a stable genome. Whether genomic instability eventually occurs when PRC1 down-regulation is performed for extended periods of time remains unclear. Here we show that prolonged depletion of a PRC1 component, which mimics cancer initiating events, results in broad dysregulation of DNA replication and repair genes, along with the accumulation of DNA breaks, defective repair, and widespread genomic instability in the cancer tissue. A broad mis-regulation of H2AK118 ubiquitylation and to a lesser extent of H3K27 trimethylation also occurs, and might contribute to these phenotypes. Together, this study supports a model where DNA repair and replication defects amplify the tumorigenic transformation epigenetically induced by PRC1 loss, resulting in genomic instability and cancer progression.
Reliability Optimization of Smart Grid Based on Optimal Failure Rate Using Distributed Generation
Authors: Lekunze George Toh , Kenfack Pierre , Dandoussou Abraham 
Published: April 2024

Abstract

This paper evaluates the optimal budget of distributed generation (DG) units that could be used to substitute preventive maintenance in an electric power distribution system. Preventive maintenance is a tradition performed on power distribution system to reduce its failure rate and increase its reliability. In order to perform this, a yearly budget is allocated. As time goes on, this budget becomes significant. With the aim to safe cost, this research has investigated on how DG units could be used as a backup system whenever there is a failure in the grid while waiting for the fault to be remediated. A method has been proposed on the time length above which the DG units as backup systems become more economical over the yearly preventive maintenance. Results have shown that when this duration is below the lifespan of the DG units used, it is feasible and DG units become the cheapest option.
Learning curve analysis of laparoscopic low anterior resection using the ArtiSentialⓇ multi-articulating device: A multicenter pooled analysis (STARC trial)
Authors: Kim Seijong , Pyo Dae Hee , Lee Jae Im , Kim Chang Hyun , Oh Heung-Kwon , Lee Yoon Suk , Yoon Yong Sik , Bae Dong Hwan , Min Byung Soh , Huh Jung Wook 
Published: April 2024

Abstract

Purpose
Robotic surgery has shown promising outcomes in colorectal cancer treatment but is limited by its high cost. In response, the ArtiSential system, an articulated handheld laparoscopic instrument, has emerged as an alternative. This study aimed to assess the learning curve associated with ArtiSential during low anterior resection (LAR) of colorectal cancer.
Methods
A multicenter retrospective single-arm study enrolled 208 patients who underwent LAR with ArtiSential between January 2021 and October 2022. A cumulative sum (CUSUM) analysis was conducted for operation time. Three experienced surgeons, each with over 30 completed ArtiSential-assisted LAR procedures, were included in the analysis.
Results
The median operation time was 129 minutes; it was accompanied by minimal blood loss and no open conversions. Pathological analysis revealed favorable outcomes with high-quality total mesorectal excision and a circumferential resection margin. Surgeons A and B followed a conventional four-stage learning curve, achieving mastery after 25 and 24 cases, respectively. In contrast, Surgeon C demonstrated a unique two-stage learning curve, achieving mastery after 19 cases.
Conclusion
The CUSUM analysis suggests a relatively manageable learning curve, with the initial phase being completed after approximately 10 cases and proficiency attained after 20–30 cases. In conclusion, ArtiSential appears to be a promising tool for colorectal surgeons, offering a favorable learning curve and encouraging outcomes in LAR for rectal cancer.
DML-YOLOv8-SAR Image Object Detection Algorithm
Authors: Zhao Shuguang , Tao Ronghao , Jia Fengde 
Published: April 2024

Abstract

Given the challenges posed by noise and varying target scales in SAR images, conventional convolutional neural networks often underperform in SAR image detection. To address this, this paper introduces a novel approach. Firstly, a Res-Clo network is proposed for denoising SAR images as a preprocessing step to enhance detection accuracy. Subsequently, an improved network, DML-YOLOv8, is devised based on the YOLOv8 network. The enhancements in the proposed algorithm include several key modifications. Firstly, within the feature extraction layers, a designed MFB module is integrated to effectively broaden the network's receptive field. Next, deformable convolutions are introduced in the feature fusion layers to bolster the network's capability for multi-scale detection. Additionally, a novel loss function, RT-IOU, is designed in feature detection to enhance network inference speed. Finally, a specialized STD small target detection layer is designed to improve detection accuracy for small targets. In practical experiments, it has been shown that the detection method proposed in this paper effectively improves the detection performance of noisy SAR images, and also achieves satisfactory results in multi-scale detection.
Comparative proteomics and metabonomics analysis of different diapause stages revealed a new regulation mechanism of diapause in Loxostege sticticalis (Lepidoptera: Pyralidae)
Authors: Shao Lijun , Yue Fangzheng , Fan Jinfu , Su Qin , Liu Hairui , Zhang Quanyi , Xu Linbo 
Published: April 2024

Abstract

The Loxostege sticticalis Linnaeus, commonly referred to as the grassland pest, is a voracious migratory pest of global significance, annually inflicting substantial economic damage on the agricultural and pastoral sectors in the Northern Hemisphere. Diapause serves as a developmental adaptation mechanism in insects to cope with adverse environmental conditions, playing a crucial role in the population dynamics and infestation patterns of the grassland pest. This study focuses on the Loxostege sticticalis, employing photoperiod manipulation to obtain larvae in five distinct states of diapause: ND, PreD, D, CT, and RD. A combined metabolomic and proteomic analysis was conducted on larvae at various diapause stages. Proteomics identified a total of 5367 proteins, including 1179 differentially expressed proteins across the diapause phases. Through Gene ontology analysis, weighted gene co-expression network analysis, and GSEA, significant proteins related to carbohydrate, amino acid, and lipid metabolism, as well as insect development, were identified. Metabolomic time-series and KEGG enrichment analysis of the five diapause stages revealed 627 significantly different metabolites, including fructose, tryptophan, lysine, and glycerol. Annotations in the protein interaction network related to ribosomal subunits 40s and 60s, which are speculated to be regulated by histone acetyltransferases and deacetylases, compressing chromatin structure and hindering RNA polymerase accessibility, thereby reducing protein-related expression. These findings expand our understanding of the regulatory networks involved in diapause in Loxostege sticticalis and offer new insights into its control mechanisms.
Research on the mix proportion and on-site construction technology of a four-graded roller compacted concrete
Authors: Shi Yan , Zhou Shihua , Wu Cai , Huang Junzhou , Liang Yan , Wang Tianlei , Peng Sheng , Liang Yan 
Published: April 2024

Abstract

In order to improve construction and production efficiency, and fully leverage the technical and economic advantages of continuous pouring and rapid rise of roller compacted concrete, the four-graded roller compacted concrete was widely used in hydraulic engineering. Based on the characteristics and testing methods of four-graded roller compacted concrete, the multiple sets, the effects of mix proportion, maximum particle size of coarse aggregate, and compaction degree were analyzed. The differences in construction technology between three-graded roller compacted concrete and four-graded roller compacted concrete were compared, and the more suitable construction technology parameters for four-graded roller compacted concrete were obtained. When the water consumption and vibrating compacted (VC) value are within the range of 71kg/m³~70kg/m³ and 3.5s ~ 6s, respectively, the VC value and air content of four-graded roller compacted concrete are optimal. Choose 30%~32% for sand ratio. The combination ratio of natural coarse aggregates (extra large: large: medium: small) adopts 25: 30: 25: 20. The VC value of 3.5s is suitable. After 6 times of heavy rolling and vibration rolling, the surface has a good slurry state, with a relative compaction degree exceeding 98%. The vibration force is 395kN, and the working speed is 1.0 ~ 1.5km/h. The suggestions as follows: when the layer thickness is 0.4m, the four-graded roller compacted concrete should be rolled twice without vibration, six times with vibration, and two times without vibration; When the layer thickness is 0.5m, the four-graded roller compacted concrete should be rolled twice without vibration, eight times with vibration, and two times without vibration.
Development of a Pollution Index For Ports
Authors: Adegboye Omotayo Abayomi 
Published: April 2024

Abstract

The concept of a port pollution index is relatively new and as a result, there remains a need tointroduce the concept to several audiences- particularly port users and operators- and to better understand itsusage and benefits. It is in view of these that this study aimed to develop a port pollution index with Port Newcastle,Australia port as a case study. Amongst the different kinds of port pollution, water and air pollution were selectedfor consideration in this study because this two pollution types amount for the largest percentage of port pollution.Secondary data collection was the data collection method adopted for the study and the data were obtained fromcredible sources for the variables under consideration covering both summer and winter season. Collected datawere analyzed using Microsoft Excel and Index parameters were formulated. The pollution index of the port (PPI) isfound to be low with an index value of 26.73 during the winter period and 28.56 during the summer. P- Value of0.03 at a correlation of 0.999 shows a strong significant correlation for the port pollution index between the summerand winter season. While the water pollution index (WPI) analysis of the case study unveiled that the lower DOlevels and higher fecal coliforms markedly reduced the water quality of the port, air pollution index (AQI) analysis ofthe data set shows PM10 and PM2.5 as the major pollutants of the port under review with a minimum value of 71.2μg/m3and 58.3 μg/m3 respectively. Emissions from ship engines is the major source of pollution which determinesthe ambient air pollution while shipping activities are major factors responsible for pollution of port water, the portquality index of port Newcastle is however found to be good for both winter and summer seasonal period with thePPI higher during the winter and lower during the summer time. The index formulated by this research can helpport users determine the pollution level at a particular port. It can also help port managers determine and takecontrol measure at all time.