bluuebunny/crossref_metadata_2025 huggingface dataset:
Early-life microbiota-immune homeostasis
Authors: Reynolds Hayley M. , Bettini Matthew L.
Published: October 2023

As the prevalence of allergy and autoimmune disease in industrialized societies continues to rise, improving our understanding of the mechanistic roles behind microbiota-immune homeostasis has become critical for informing therapeutic interventions in cases of dysbiosis. Of particular importance, are alterations to intestinal microbiota occurring within the critical neonatal window, during which the immune system is highly vulnerable to environmental exposures. This review will highlight recent literature concerning mechanisms of early-life microbiota-immune homeostasis as well as discuss the potential for therapeutics in restoring dysbiosis in early life.
Resveratrol regulates macrophage recruitment and M1 macrophage polarization and prevents corneal allograft rejection in rats
Authors: Xu Chenjia , Guo Ruilin , Hou Chao , Ma Minglu , Dong Xiaojuan , Ouyang Chen , Wu Jing , Huang Ting
Published: October 2023

Introduction

Resveratrol is an immune modulator that can reduce M1 macrophage polarization in vitro. Reducing macrophage recruitment and M1 polarization can prevent corneal allograft rejection (CGR). In this study, rat corneal allograft rejection models were established to explore the effects of resveratrol on CGR and macrophages and the underlying mechanisms after corneal transplantation.

Methods

Corneal allograft models were established, and 100 mg/kg resveratrol was injected intraperitoneally. The corneal allografts were assessed clinically using the Holland rejection scoring system, anterior segment photography, and anterior segment optical coherence tomography. Corneal macrophages, pro-inflammatory cytokines, and corneal lymphatic vessels were detected using hematoxylin and eosin staining, immunofluorescence staining, and real-time quantitative polymerase chain reaction (qRT-PCR). Dendritic cells (DCs) in cervical lymph nodes were explored using flow cytometry. RNA sequencing experiments were conducted to identify the mechanisms through which resveratrol affected CGR. The results were verified using Simple Western analysis. Pro-inflammatory cytokines by macrophages in vitro were measured using qRT-PCR and enzyme-linked immunosorbent assays.

Results

Resveratrol significantly prolonged the survival of corneal grafts and reduced graft edema and central corneal thickness. Corneal macrophage recruitment and M1 macrophage polarization decreased significantly after corneal transplantation in the resveratrol group. Resveratrol also reduced pro-inflammatory cytokines in corneal grafts and suppressed the early generation of cornea lymphatic vessels and the recruitment of cornea inflammatory cells 14 days after surgery. Resveratrol decreased the proportion of DCs in ipsilateral cervical lymph nodes. The effect of resveratrol on CGR was related to the phosphatidylinositol 3-kinase/protein kinase-B (PI3K/Akt) pathway. Resveratrol reduced the secretion of pro-inflammatory cytokines by M1 macrophages in vitro.

Conclusion

Our findings suggest that resveratrol can reduce corneal macrophage recruitment and M1 macrophage polarization after corneal transplantation in rats and prevent CGR. The PI3K/Akt pathway may be an important mechanism that warrants further research.
Species-specificity of the secondary biosynthetic potential in Bacillus
Authors: Yin Qun-Jian , Ying Ti-Ti , Zhou Zhen-Yi , Hu Gang-Ao , Yang Cai-Ling , Hua Yi , Wang Hong , Wei Bin
Published: October 2023

Introduction

Although Bacillus species have produced a wide variety of structurally diverse and biologically active natural products, the secondary biosynthetic potential of Bacillus species is widely underestimated due to the limited number of biosynthetic gene clusters (BGCs) in this genus. The significant variation in the diversity and novelty of BGCs across different species within the Bacillus genus presents a major obstacle to the efficient discovery of novel natural products from Bacillus.

Methods

In this study, the number of each class of BGCs in all 6,378 high-quality Bacillus genomes was predicted using antiSMASH, the species-specificity of BGC distribution in Bacillus was investigated by Principal component analysis. Then the structural diversity and novelty of the predicted secondary metabolites in Bacillus species with specific BGC distributions were analyzed using molecular networking.

Results

Our results revealed a certain degree of species-specificity in the distribution of BGCs in Bacillus, which was mainly contributed by siderophore, type III polyketide synthase (T3PKS), and transAT-PKS BGCs. B. wiedmannii, B. thuringiensis, and B. cereus are rich in RiPP-like and siderophore BGCs, but lack T3PKS BGCs, while B. amyloliquefaciens and B. velezensis are abundant in transAT-PKS BGCs. These Bacillus species collectively encode 77,541 BGCs, with NRPS and RiPPs being the two most dominant types, which are further categorized into 4,291 GCFs. Remarkably, approximately 54.5% of GCFs and 93.8% of the predicted metabolite scaffolds are found exclusively in a single Bacillus species. Notably, B. cereus, B. thuringiensis, and B. velezensis exhibit the highest potential for producing species-specific NRPS and PKS bioinformatic natural products. Taking two species-specific NRPS gene clusters as examples, the potential of Bacillus to synthesize novel species-specific natural products is illustrated.

Conclusion

This study highlights the species-specificity of the secondary biosynthetic potential in Bacillus and provides valuable insights for the targeted discovery of novel natural products from this genus.
Changes in lipids and inflammation in adults with super-refractory status epilepticus on a ketogenic diet
Authors: Dickens Alex M. , Johnson Tory P. , Lamichhane Santosh , Kumar Anupama , Pardo Carlos A. , Gutierrez Erie G. , Haughey Norman , Cervenka Mackenzie C.
Published: October 2023

Introduction: This study aims to test the hypothesis that increased ketone body production resulting from a ketogenic diet (KD) will correlate with reductions in pro-inflammatory cytokines and lipid subspecies and improved clinical outcomes in adults treated with an adjunctive ketogenic diet for super-refractory status epilepticus (SRSE).

Methods: Adults (18 years or older) were treated with a 4:1 (fat: carbohydrate and protein) ratio of enteral KD as adjunctive therapy to pharmacologic seizure suppression in SRSE. Blood and urine samples and clinical measurements were collected at baseline (n = 10), after 1 week (n = 8), and after 2 weeks of KD (n = 5). In addition, urine acetoacetate, serum β-hydroxybutyrate, lipidomics, pro-inflammatory cytokines (IL-1β and IL-6), chemokines (CCL3, CCL4, and CXCL13), and clinical measurements were obtained at these three time points. Univariate and multivariate data analyses were performed to determine the correlation between ketone body production and circulating lipids, inflammatory biomarkers, and clinical outcomes.

Results: Changes in lipids included an increase in ceramides, mono-hexosylceramide, sphingomyelin, phosphocholine, and phosphoserines, and there was a significant reduction in pro-inflammatory mediators, IL-6 and CXCL13, seen at 1 and 2 weeks of KD. Higher blood β-hydroxybutyrate levels at baseline correlated with better clinical outcomes; however, ketone body production did not correlate with other variables during treatment. Higher chemokine CCL3 levels following treatment correlated with a longer stay in the intensive care unit and a higher modified Rankin Scale score (worse neurologic disability) at discharge and 6-month follow up.

Discussion: Adults receiving an adjunctive enteral ketogenic diet for super-refractory status epilepticus exhibit alterations in select pro-inflammatory cytokines and lipid species that may predict their response to treatment.
Effects of autonomic nervous system disorders on male infertility
Authors: Qiu Qixiang , Chen Jincong , Xu Nengquan , Zhou Xiaolong , Ye Chenlian , Liu Min , Liu Zhaoxia
Published: October 2023

The male reproductive functions are largely regulated by the autonomic nervous system. Male sexual behavior and fertility primarily depend on the normal function of the higher neural centers related to the autonomic nervous system, the hypothalamic–pituitary–gonadal axis, the autonomic nervous components within the spinal cord and spinal nerves, and certain somatic nerves in the pelvic floor. In this review article, we will summarize the role of the autonomic nervous system in regulating male reproductive capabilities and fertility, its impact on male infertility under abnormal conditions, including the role of drug-induced autonomic nervous dysfunctions on male infertility. The main purpose of this article was to provide an overview of the effects of autonomic nervous dysfunction on male reproductive function and shed light on the potential therapeutic target for male infertility.
Establishment and validation of a nomogram to select patients with metastatic sarcomatoid renal cell carcinoma suitable for cytoreductive radical nephrectomy
Authors: Zhou Yulin , Gu Yufeng , Tang Chaopeng , Dong Jie , Xu Song , Sheng Zhengcheng , Zhao Xiaodong , Hu Jun , Shen Tianyi , He Haowei , Yi Xiaoming , Zhou Wenquan , Qu Le , Ge Jingping , Han Conghui
Published: October 2023

Introduction

Metastatic renal cell carcinoma (mRCC) with sarcomatoid features has a poor prognosis. Cytoreductive radical nephrectomy (CRN) can improve prognosis, but patient selection is unclear. This study aimed to develop a prediction model for selecting patients suitable for CRN.

Materials and methods

Patients with a diagnosis of mRCC with sarcomatoid features in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were retrospectively reviewed. CRN benefit was defined as a survival time longer than the median overall survival (OS) in patients who did not receive CRN. A prediction nomogram was established and validated using the SEER cohort (training and internal validation) and an external validation cohort.

Results

Of 900 patients with sarcomatoid mRCC, 608 (67.6%) underwent CRN. OS was longer in the CRN group than in the non-CRN group (8 vs. 6 months, hazard ratio (HR) = 0.767, p = 0.0085). In the matched CRN group, 124 (57.7%) patients survived >6 months after the surgery and were considered to benefit from CRN. Age, T-stage, systematic therapy, metastatic site, and lymph nodes were identified as independent factors influencing OS after CRN, which were included in the prediction nomogram. The monogram performed well on the training set (area under the receiver operating characteristic (AUC) curve = 0.766, 95% confidence interval (CI): 0.687–0.845), internal validation set (AUC = 0.796, 95% CI: 0.684–0.908), and external validation set (AUC = 0.911, 95% CI: 0.831–0.991).

Conclusions

A nomogram was constructed and validated with good accuracy for selecting patients with sarcomatoid mRCC suitable for CRN.
Bibliometric analysis and visualization of research trends on HIV-1 capsid inhibitors (2000–2022)
Authors: Sun Lin , Zhang Tongchao , Xu Shujing , Zhang Xujie , Zhan Peng , Liu Xinyong
Published: October 2023

Background: Acquired immunodeficiency syndrome (AIDS) has seriously endangered human life and health, the main pathogenic agent is human immunodeficiency virus type 1 (HIV-1). The combination antiretroviral therapy (cART) has shown serious drug resistance and side effects, and the discovery of HIV-1 capsid inhibitors is an effective way to solve the problem. Recent studies have shown significant progress in the research of HIV-1 capsid inhibitors. However, there is still a lack of comprehensive overview of bibliometric analysis in this field. This study aimed to provide the research trends and hotspots of HIV-1 capsid inhibitors.

Method: Publications related to HIV-1 capsid inhibitors from 2000 to 2022 were searched on the Web of Science Core Collection (WoSCC) database and screened according to inclusion criteria. VOSviewer was conducted to evaluate the results.

Results: 96 publications from 25 countries were finally included, and the number of annual publications related to HIV-1 capsid inhibitors showed an increasing trend. The United States was the most productive country with the most publication number, H-index, and total citation number, as well as the widest international cooperation. The most popular journal in this field was Journal of Virology. Drexel University was the most productive institution, and Simon Cocklin participated in the most publications. Keywords co-occurrence analysis exhibited that studying the molecular mechanism of capsid protein, discovering drug candidates, and improving antiretroviral therapy are the main and hot topics in this field.

Conclusion: This is the first bibliometric study in the field of HIV-1 capsid inhibitors, which comprehensively analyzed the research trends and hotspots in this direction. This work is expected to provide the scientific community with new insights to promote the research of HIV-1 capsid inhibitors.
Xoo-YOLO: a detection method for wild rice bacterial blight in the field from the perspective of unmanned aerial vehicles
Authors: Pan Pan , Guo Wenlong , Zheng Xiaoming , Hu Lin , Zhou Guomin , Zhang Jianhua
Published: October 2023

Wild rice, a natural gene pool for rice germplasm innovation and variety improvement, holds immense value in rice breeding due to its disease-resistance genes. Traditional disease resistance identification in wild rice heavily relies on labor-intensive and subjective manual methods, posing significant challenges for large-scale identification. The fusion of unmanned aerial vehicles (UAVs) and deep learning is emerging as a novel trend in intelligent disease resistance identification. Detecting diseases in field conditions is critical in intelligent disease resistance identification. In pursuit of detecting bacterial blight in wild rice within natural field conditions, this study presents the Xoo-YOLO model, a modification of the YOLOv8 model tailored for this purpose. The Xoo-YOLO model incorporates the Large Selective Kernel Network (LSKNet) into its backbone network, allowing for more effective disease detection from the perspective of UAVs. This is achieved by dynamically adjusting its large spatial receptive field. Concurrently, the neck network receives enhancements by integrating the GSConv hybrid convolution module. This addition serves to reduce both the amount of calculation and parameters. To tackle the issue of disease appearing elongated and rotated when viewed from a UAV perspective, we incorporated a rotational angle (theta dimension) into the head layer's output. This enhancement enables precise detection of bacterial blight in any direction in wild rice. The experimental results highlight the effectiveness of our proposed Xoo-YOLO model, boasting a remarkable mean average precision (mAP) of 94.95%. This outperforms other models, underscoring its superiority. Our model strikes a harmonious balance between accuracy and speed in disease detection. It is a technical cornerstone, facilitating the intelligent identification of disease resistance in wild rice on a large scale.
Warming increases the differences among spring phenology models under future climate change
Authors: Mo Yunhua , Li Xiran , Guo Yahui , Fu Yongshuo
Published: October 2023

Phenological models are built upon an understanding of the influence of environmental factors on plant phenology, and serve as effective tools for predicting plant phenological changes. However, the differences in phenological model predictive performance under different climate change scenarios have been rarely studied. In this study, we parameterized thirteen spring phenology models, including six one-phase models and seven two-phase models, by combining phenological observations and meteorological data. Using climatic data from two Shared Socioeconomic Pathways (SSP) scenarios, namely SSP126 (high mitigation and low emission) and SSP585 (no mitigation and high emission), we predicted spring phenology in Germany from 2021 to 2100, and compared the impacts of dormancy phases and driving factors on model predictive performance. The results showed that the average correlation coefficient between the predicted start of growing season (SOS) by the 13 models and the observed values exceeded 0.72, with the highest reaching 0.80. All models outperformed the NULL model (Mean of SOS), and the M1 model (driven by photoperiod and forcing temperature) performed the best for all the tree species. In the SSP126 scenario, the average SOS advanced initially and then gradually shifted towards a delay starting around 2070. In the SSP585 scenario, the average SOS advanced gradually at a rate of approximately 0.14 days per year. Moreover, the standard deviation of the simulated SOS by the 13 spring phenology models exhibited a significant increase at a rate of 0.04 days per year. On average, two-phase models exhibited larger standard deviations than one-phase models after approximately 2050. Models driven solely by temperature showed larger standard deviations after 2060 compared to models driven by both temperature and photoperiod. Our findings suggest investigating the release mechanisms of endodormancy phase and incorporating new insights into future phenological models to better simulate the changes in plant phenology.
From process to product: writing engagement and performance of EFL learners under computer-generated feedback instruction
Authors: Shen Chen , Shi Penghai , Guo Jirong , Xu Suyun , Tian Jiwei
Published: October 2023

Artificial intelligence (AI) technology is gradually penetrating the domain of education, opening up many possibilities for teaching and learning. Many educators, faced with the burden of commenting on substantial student essays, have introduced automated writing evaluation (AWE) into second language (L2) writing considering its affordance of immediate scores and diagnostic information. However, students’ processing strategies and perceptions of such computer-generated feedback and its impact on student writing quality, particularly as mediated by language proficiency, remain under-explored. This study examines the impact of Pigai, a Chinese AWE system, on revision processes and writing products of 42 English as a foreign language (EFL) learners with varying language levels by analyzing feedback points, feedback uptake, text quality in complexity, accuracy, and fluency (CAF), and perceptions. The findings are as follows. When confronted with AWE instruction, the majority of student work focuses on correcting errors, but higher-level students exhibit an emphasis on language improvement beyond the surface level compared to lower-level students. According to CAF measures, automated feedback exerts greater effects on accuracy for unskilled learners and lexical complexity for skilled learners in the development of interlanguage. Learner profiles and perceptions of students at different levels are explored along four dimensions: writing quality, cognitive engagement, behavioral engagement, and affective engagement. Finally, the potential issues of such technology-based writing instruction are pointed out.
Revisiting the political biases of ChatGPT
Authors: Fujimoto Sasuke , Takemoto Kazuhiro
Published: October 2023

Although ChatGPT promises wide-ranging applications, there is a concern that it is politically biased; in particular, that it has a left-libertarian orientation. Nevertheless, following recent trends in attempts to reduce such biases, this study re-evaluated the political biases of ChatGPT using political orientation tests and the application programming interface. The effects of the languages used in the system as well as gender and race settings were evaluated. The results indicate that ChatGPT manifests less political bias than previously assumed; however, they did not entirely dismiss the political bias. The languages used in the system, and the gender and race settings may induce political biases. These findings enhance our understanding of the political biases of ChatGPT and may be useful for bias evaluation and designing the operational strategy of ChatGPT.
Genomic analyses of Asian honeybee from the Sansha Island in the South China Sea, suggest it’s evolutional origin and environmental adaption
Authors: Gao Jinglin , Tang Xiangyou , Zhao Shan , Tao Kunlin , Shi Xinyan , Song Huali , Yao Yuxin , Jiang Yan , Wang Tianbin , Li Xiang , Zhao Dongxiang , Xu Jinshan
Published: October 2023

Discovering new resources and enhancing our knowledge of distribution are crucial for the preservation and utilization of honeybee genetic resources. Our research focused on morphological and population genetic analysis, which revealed significant differences in the SanshaId group compared to others. Notably, the SanshaId group displayed smaller tongue length, averaging at 3.83 mm, and a larger fixation index, indicating a distinct level of subspecies differentiation (Fst = 0.2669). We therefore assumed that A.cerana sanshasis is a new ecotype and subspecies of A. cerana. Estimates of population history indicated that SanshaId population is most closely related to HainanId population, which differed from SanshaId population by about 0.57 Ma due to geological movements. We identified 131 high-frequency non-synonymous mutant genes in the SanshaId group compared to HainanId group. Among these genes, Cuticular genes related to tongue morphology were subject to evolutionary selection, and some genes related to glucose metabolism were highly expressed in the gut. Our results expand the understanding of the distribution range of Asian honeybee and provide a basis for understanding the population dynamics and evolutionary adaptation of A. cerana sanshasis in tropical island environments.
Automatic Myocardium Segmentation in Delayed-Enhancement MRI with Pathology-Specific Data Augmentation and Deep Learning Architectures
Authors: Mosquera-Rojas Gonzalo , Ouadah Cylia , Hadadi Azadeh , Lalande Alain , Leclerc Sarah
Published: October 2023

The extent of myocardial infarction (MI) can be evaluated thanks to delayed enhancement (DE) cardiac MRI. DE MRI is an imaging technique acquired several minutes after the injection of a contrast agent where MI appears with a bright signal. The automatic myocardium segmentation in DE MRI is quite challenging, especially when MI is present, since these areas usually showcase a heterogeneous aspect in terms of shape and intensity, thus obstructing the myocardium visibility. To overcome this issue, we propose an image processing-based data augmentation algorithm where diverse synthetic cases of MI were created in two different ways: fixed and adaptive. In the first one, the training set is enlarged by a specific factor, whereas in the second, the method receives feedback from the segmentation model during training and performs the augmentation exclusively on complex cases. The method performance was evaluated in single and multi-modality settings. In this latter, information from kinetic images (Cine MRI), which are acquired along DE MRI in the same examination, is also used, and the extracted features from both modalities are fused. The results show that applying the data augmentation in a fixed fashion on a multi-modality setting leads to a more consistent segmentation of the myocardium in DE MRI. The segmentation models, which were all UNet-based architectures, can better relate MI areas with the myocardium, thus increasing its overall robustness to pathology-specific local pattern perturbations.
Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods
Authors: Yousefi Mahsa , Martínez Ángeles
Published: October 2023

While first-order methods are popular for solving optimization problems arising in deep learning, they come with some acute deficiencies. To overcome these shortcomings, there has been recent interest in introducing second-order information through quasi-Newton methods that are able to construct Hessian approximations using only gradient information. In this work, we study the performance of stochastic quasi-Newton algorithms for training deep neural networks. We consider two well-known quasi-Newton updates, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (BFGS) and the symmetric rank one (SR1). This study fills a gap concerning the real performance of both updates in the minibatch setting and analyzes whether more efficient training can be obtained when using the more robust BFGS update or the cheaper SR1 formula, which—allowing for indefinite Hessian approximations—can potentially help to better navigate the pathological saddle points present in the non-convex loss functions found in deep learning. We present and discuss the results of an extensive experimental study that includes many aspects affecting performance, like batch normalization, the network architecture, the limited memory parameter or the batch size. Our results show that stochastic quasi-Newton algorithms are efficient and, in some instances, able to outperform the well-known first-order Adam optimizer, run with the optimal combination of its numerous hyperparameters, and the stochastic second-order trust-region STORM algorithm.
CSLSNet: A Compressed Domain Classification Model for Pest and Disease Images
Authors: Hua Jing , Zhu Tuan , Zou Fendong , Zou Jiawen , Tang Jianjun
Published: October 2023

The management of global food security is one of the major issues of concern to the international community today. Ensuring the stability of food sources and preventing crop pests and diseases are crucial in maintaining social stability and promoting economic development. In modern agriculture, computer vision has emerged as a tool to aid in pest and disease prevention. For instance, when calculating the overall fruit yield of fruit trees and identifying and categorising pests and diseases, traditional neural networks tend to preserve duplicate data during image prediction. Traditional neural networks store unnecessary information when predicting images, leading to more classification calculations and thus higher computing costs. By utilising the concept of deep compressed perception, classification, and other operations can be carried out on compressed data. In this paper, combining compressed sensing theory and traditional neural network techniques, a novel deep compressed sensing network model called CSLSNet is proposed. The model utilizes a parallel convolution and residual structure comprising of convolution, the LR module, and the LSR module. The model directly categorizes images in the compressed domain, leading to decreased computation and a reduction in the number of model parameters. By comparing experiments using different SR (sampling rates) and traditional image compression methods alongside existing network models, this paper demonstrates that our model achieves higher classification accuracy under the same experimental conditions. Also, in fewer training cycles, the loss trend of the proposed model rapidly converges, and the loss curve becomes smoother. The results show that, at a sampling rate of 0.5, our model, CSLSNet, achieves an accuracy of 90.08%. In contrast, other networks involved in the comparison, such as CSBNet and AlexNet, achieve only 84.81% and 86.5%, respectively.
Characterization of Diverse Pepper (Capsicum spp.) Germplasms Based on Agro-Morphological Traits and Phytochemical Contents
Authors: Moon Suyun , Ro Nayoung , Kim Junhong , Ko Ho-Cheol , Lee SuKyeung , Oh Hyeonseok , Kim Bichsaem , Lee Ho-Sun , Lee Gi-An
Published: October 2023

Pepper (Capsicum spp.) is one of the most important crops worldwide. The fruits of Capsicum species are known to contain high amounts of vitamins and carotenoids, and they have health-promoting properties. In this study, a total of 513 pepper accessions belonging to two Capsicum species, C. annuum and C. frutescens, were investigated for their morphological characteristics and contents of phytochemicals including carotenoids, β-carotene, vitamin C, capsaicinoids, and total soluble solids. The results revealed wide variations in morphological traits and phytochemical contents between the accessions and across species. In addition, the association of fruit color and orientation with phytochemical contents was evaluated; the results indicated that germplasm with yellow-colored and pendant-oriented fruits could be important due to their high vitamin C levels. Multivariate analysis of the agro-morphological and phytochemical parameters revealed that Capsicum germplasm were clearly distinguished according to species. Furthermore, cluster analysis showed that germplasms belonged to three groups, and six genotypes were determined as being good genetic resources with high health-promoting phytochemical contents. Especially, vitamin C content was positively correlated with fruit diameter, weight, and wall thickness. Our findings revealed morphological and phytochemical characteristics potentially useful for breeding programs.
Longitudinal Study of Subclinical Mastitis in Sheep in Greece: An Investigation into Incidence Risk, Associations with Milk Quality and Risk Factors of the Infection
Authors: Michael Charalambia K. , Lianou Daphne T. , Vasileiou Natalia G. C. , Mavrogianni Vasia S. , Petinaki Efthymia , Fthenakis George C.
Published: October 2023

The objectives of this work were (a) to describe the incidence risk of subclinical mastitis in dairy flocks throughout the milking period, (b) to present potential associations of subclinical mastitis with the quality of milk and (c) to identify risk factors for high-incidence risk of the infection. A longitudinal study was performed in 12 dairy sheep flocks in Greece. Mammary secretion samples from 240 ewes and bulk-tank milk samples were collected in four repeated visits over a period of six months for bacteriological, chemical and cytological examinations. Overall, the incidence risk of subclinical mastitis throughout the study period was 51.7%, and it varied among farms from 25.0% to 75.0%. The respective figure for staphylococcal subclinical mastitis was 48.8%. The incidence risk of recurrence of subclinical mastitis among ewes in the flocks was 35.4%. The most frequently identified bacteria from cases of subclinical mastitis were S. aureus and S. simulans; of the mastitis-causing staphylococcal isolates, 65.4% were biofilm-forming. Somatic cell counts in bulk-tank milk progressively increased as the lactation period advanced, with significant increases seen on the third and fourth visits to the farms. Somatic cell counts in bulk-tank milk correlated well with prevalence of subclinical mastitis in flocks. A significant inverse correlation and a significant positive correlation were seen for prevalence of subclinical mastitis versus total protein content or added water in bulk-tank milk. During multivariable analysis, younger age of newborns when taken away from the dam and consequently delayed start of milking of ewes, omission of anti-mastitis vaccination of ewes and lack of employed staff on the farms emerged to be significantly associated (positively) with increased incidence risk of subclinical mastitis.
Inflammation and Oxidative Stress Induced by Obesity, Gestational Diabetes, and Preeclampsia in Pregnancy: Role of High-Density Lipoproteins as Vectors for Bioactive Compounds
Authors: Jiménez-Osorio Angélica Saraí , Carreón-Torres Elizabeth , Correa-Solís Emmanuel , Ángel-García Julieta , Arias-Rico José , Jiménez-Garza Octavio , Morales-Castillejos Lizbeth , Díaz-Zuleta Hugo Alexander , Baltazar-Tellez Rosa María , Sánchez-Padilla María Luisa , Flores-Chávez Olga Rocío , Estrada-Luna Diego
Published: October 2023

Inflammation and oxidative stress are essential components in a myriad of pathogenic entities that lead to metabolic and chronic diseases. Moreover, inflammation in its different phases is necessary for the initiation and maintenance of a healthy pregnancy. Therefore, an equilibrium between a necessary/pathologic level of inflammation and oxidative stress during pregnancy is needed to avoid disease development. High-density lipoproteins (HDL) are important for a healthy pregnancy and a good neonatal outcome. Their role in fetal development during challenging situations is vital for maintaining the equilibrium. However, in certain conditions, such as obesity, diabetes, and other cardiovascular diseases, it has been observed that HDL loses its protective properties, becoming dysfunctional. Bioactive compounds have been widely studied as mediators of inflammation and oxidative stress in different diseases, but their mechanisms of action are still unknown. Nonetheless, these agents, which are obtained from functional foods, increase the concentration of HDL, TRC, and antioxidant activity. Therefore, this review first summarizes several mechanisms of HDL participation in the equilibrium between inflammation and oxidative stress. Second, it gives an insight into how HDL may act as a vector for bioactive compounds. Third, it describes the relationships between the inflammation process in pregnancy and HDL activity. Consequently, different databases were used, including MEDLINE, PubMed, and Scopus, where scientific articles published in the English language up to 2023 were identified.
Depth Evaluation of Tiny Defects on or near Surface Based on Convolutional Neural Network
Authors: Fei Qinnan , Cao Jiancheng , Xu Wanli , Jiang Linzhao , Zhang Jun , Ding Hui , Li Xiaohong , Yan Jingli
Published: October 2023

This paper proposes a method for the detection and depth assessment of tiny defects in or near surfaces by combining laser ultrasonics with convolutional neural networks (CNNs). The innovation in this study lies in several key aspects. Firstly, a comprehensive analysis of changes in ultrasonic signal characteristics caused by variations in defect depth is conducted in both the time and frequency domains, based on discrete frequency spectra and original A—scan signals. Continuous wavelet transform (CWT) is employed to obtain wavelet time–frequency maps, demonstrating the consistent characteristics of this image with crack depth variations. A crucial innovation in this research involves the targeted design and optimization of the model based on the characteristics of ultrasonic signals and dataset size. This includes aspects such as data preparation, CNN architecture construction, and hyperparameter selection. The model is tested using a random validation set, which effectively demonstrates the CNN model’s validity and high precision. The proposed method enables the recognition and depth assessment of tiny defects on or near surfaces.
A Real-Time Shipping Container Accident Inference System Monitoring the Alignment State of Shipping Containers in Edge Environments
Authors: Oh Se-Yeong , Jeong Junho , Kim Sang-Woo , Seo Young-Uk , Youn Joosang
Published: October 2023

Along with the recent development of artificial intelligence technology, convergence services that apply technology are undergoing active development in various industrial fields. In particular, artificial intelligence-based object recognition technologies are being widely applied to the development of intelligent analysis services based on image data and streaming video data. As such, in the port yard, these object recognition technologies are being used to develop port safety services in smart ports. Accidents are a frequent occurrence in port yards due to misaligned loading of ship containers. In order to prevent such accidents, various studies using artificial intelligence technology are underway. In this paper, we propose a real-time shipping container accident inference edge system that can analyze the ship container’s loading status from a safety point of view to prevent accidents in advance. The proposed system includes the collection of video data of the ship container, inferring the safety level of the alignment status of the ship container, and transmitting the inference results for the safety level. In this paper, the proposed inference model is implemented with YOLOv3, YOLOv4 and YOLOv7 networks and can be used in video monitoring to realize the accurate classification and positioning of three different safety levels (safe, caution, and danger) in real time. In the performance evaluation, the detection accuracy of the inference model implemented with the YOLOv4 network was greater than 0.95. Its performance was also significantly better than that of the inference model implemented with the YOLOv3 and YOLOv7 networks. Although it was slightly inferior to the YOLOv4 network in terms of the accuracy, the inference model implemented with the YOLOv3 network had a faster inference speed than the model implemented with the YOLOv4 and YOLOv7 networks. Because of the port safety scenario, in which the inference accuracy is more important than the inference speed, we applied the YOLOv4 algorithm to the inference model of the system.
Special Issue on Comprehensive Research in Earthquake Forecasting and Seismic Hazard Assessment
Authors: Zavyalov Alexey , Papadimitriou Eleftheria
Published: October 2023

Dear Colleagues, [...]
5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
Authors: Liu I-Hsien , Lee Meng-Huan , Huang Hsiao-Ching , Li Jung-Shian
Published: October 2023

New mobile network technologies, particularly 5G, have spurred a growth in smart healthcare networks. They enable real-time monitoring, personalized treatments, and more. However, these transformative capabilities have also uncovered potential vulnerabilities, emphasizing the urgency to safeguard patient data and healthcare services. This study analyzes the existing research on 5G-based smart healthcare network security with a specific emphasis on fake base station attacks. The research investigates potential security measures to mitigate the impact of fake base station attacks. And based on those findings, we propose a detection scheme to help combat fake base station threats effectively and to avoid the need to install individual apps on smart devices, providing a foothold for future efforts to develop and deploy better countermeasures. To ensure a secure and resilient ecosystem for 5G-based smart healthcare, continuous research and proactive measures are required. By staying vigilant and committed to research and development, we can protect patient privacy, ensure secure data transmission, and enhance the quality of services within smart healthcare networks and other mobile network applications alike.
Dynamic Analysis of a High-Performance Prosthetic Leg: Experimental Characterisation and Numerical Modelling
Authors: Barattini Claudia , Dimauro Luca , Vella Angelo Domenico , Vigliani Alessandro
Published: October 2023

In recent years, significant improvements in the design of leg blade prosthetics have been carried out. After several advances in material and topological optimisations, sport-purpose feet prosthetics have reached high-level performances, allowing athletes with limb loss to participate in various sport activities at a competitive level. Since the knowledge of prosthetic mechanical behaviour is crucial for its optimal design, specific studies are required to meet the anthropometric characteristics of the athlete. This research work is focused on investigating the dynamic behaviour of a running blade prosthetic and developing a validated prosthetic model, placing particular emphasis on the definition of suitable material properties. An experimental modal analysis is performed on the Cheetah Xcel, Össur lower limb prosthetic. In contrast with what has already been presented in the literature, a roving hummer test under free–free conditions is proposed here to avoid the uncertainties due to constraint conditions. For the first time, blade prosthetic dynamic characteristics in free–free conditions are presented. Additionally, a novel Finite Element model of the prosthetic is developed and tuned on the basis of the experimental results. The modal assurance criterion index is exploited to compare experimental and numerical mode shapes. Starting from frequency response functions, the first six mode shapes are experimentally identified in the frequency range up to 750 Hz, including both bending and torsion. As expected, the bending in the vertical plane constitutes the primary mode shape: this kind of flexion enhances energy storage, enabling athletes to achieve an optimal running gait. This study shows the dynamic modal behaviour of a lower limb prosthetic in free–free conditions and demonstrates that a traditional isotropic material is not suitable in describing its dynamic features. The development of a model that exploits orthotropic material properties improves the alignment between experimental and numerical outcomes. This result is in agreement with the material composition of the prosthetic, which consists of carbon fibre layers.
Security-Oriented Cyber-Physical Risk Assessment for Cyberattacks on Distribution System
Authors: Zhang Yuhang , Ni Ming
Published: October 2023

With the increasing deployment of advanced sensing and measurement devices, the modern distribution system is evolved into a cyber-physical power distribution system (CPPDS). Due to the extensive application of information and communication technology, CPPDS is prevalently exposed to a wide range of cybersecurity threats. In this paper, a novel security-oriented cyber-physical risk assessment method for CPPDS is proposed. Based on the information model composed of logical nodes, an attack graph of privilege promotion by exploiting the cyber vulnerabilities is constructed. The physical consequence caused by cyberattack is analyzed in detail. By using the Markov decision process (MDP) theory, the cyber-physical risk index (CPRI) is calculated. Furthermore, considering the allocation of the finite defense resource, the modified MDP approach with an attack–defense game is presented. The effectiveness of the proposed method is demonstrated with case studies on a four-feeder IEEE-RTBS test system.
New Molecular Mechanisms of Plant Response to Ammonium Nutrition
Authors: Di Dong-Wei
Published: October 2023

Ammonium (NH4+) and nitrate (NO3−) are two major inorganic nitrogen (N) forms for plants [...]
Special Issue on Electric Power Applications
Authors: Barrero Federico , Bermúdez Mario
Published: October 2023

The continuing trend toward greater electrification in consumer, commercial, industrial, and transportation applications promises a dynamic and increasingly important role for power electronics [...]
Experimental Study on Permeability Evolution of Sandstone during Triaxial Compression Damage
Authors: Wei Lide , Lin Zhinan , Long Haifeng , Ye Qiongyao
Published: October 2023

In order to investigate the mechanical properties and permeability characteristics of sandstone during damage evolution under hydromechanical condition, a series of coupled hydro-mechanical triaxial tests on sandstone specimens were conducted based on the Rock Top 50HT full-stress multi-field coupling triaxial test system. Variations in permeability as a function of confining pressure, seepage pressure gradient, and volumetric strain during damage evolution were obtained. The results show that: (1) When the confining pressure is constant and the specimen is gradually changed from a dry to a saturated state, the failure mode of sandstone changes from shear failure to single-slope shear failure. (2) There are four distinctive stages in the permeability evolution of sandstone: gradual decrease, steady development, gradual increase, and rapid growth. These stages correspond to the complete stress–strain curve under the respective working conditions. (3) Employing the Weibull distribution formula, this study investigates the evolution of fracture damage under varying working conditions and determines the permeability evolution relationships associated with damage variables. This exploration reveals an intrinsic link between permeability and damage variables. These findings enhance our understanding of the interplay between stress, deformation, permeability, and damage evolution in seepage-stress coupled sandstone. The results contribute valuable insights to the field of rock mechanics and hold implications for diverse geotechnical and engineering applications.
Inspired by Earthworms and Leeches: The Effects of Cylindrical Pit Arrays on the Performance of Piston-Cylinder Liner Friction Pairs
Authors: Gao Tianyu , Chen Hao , Tang Danna , Wang Yumo
Published: October 2023

To improve the friction and wear performance of the piston-cylinder liner friction pair, inspired by earthworms and leeches, 27 kinds of pistons with cylindrical pit arrays are designed and processed. Through a friction test, four superior textured pistons are optimized, and wear, life and thermal imaging tests are performed. Finite element analysis of the friction pair model is performed, and the friction and wear mechanisms are discussed. The results show that the pistons with cylindrical pit arrays have excellent friction and wear performance, less heat generation by friction, longer lives and less scratches on the cylinder liner. The temperature of the optimized textured pistons was reduced by around 5–10 °C. The wear amount of some textured pistons was reduced by over 50%, resulting in an improvement in their lifespan of at least 30% or more. The results of the finite element analysis indicate that the textured piston exhibited reduced deformation and favorable stress–strain distribution and satisfied the required contact pressure.
Effects of Micro-Scale Environmental Factors on the Quantity of Questing Black-Legged Ticks in Suburban New York
Authors: Di Chong , Sulkow Brian , Qiu Weigang , Sun Shipeng
Published: October 2023

The questing behaviors of blacklegged ticks (Ixodes scapularis) are largely regulated by environmental factors such as temperature, humidity, and vegetation. While this relationship is relatively clear at the macro- and meso-spatial scales, it is inadequately examined at the micro scale. Our field work in the New York City suburbs during 2017–2018 revealed significant local variations in the quantity of questing blacklegged ticks. The purpose of this study is to identify and test the environmental factors that impact the number of questing blacklegged ticks at the micro-spatial scale. In addition to the number of ticks, surface temperature, and relative humidity data collected in the field, geospatial technologies were leveraged to extract micro-scale spatial and environmental measures, including vegetation index, land cover, elevation, and ecotone, from high-resolution digital imagery and LiDAR data. Regression models were then built to identify the key factors that influence the spatiotemporal patterns of questing blacklegged ticks. The results largely align with the existing research but display characteristics of complexity such as multicollinearity, nonlinearity, and thresholds in relation to temperature, humidity, and vegetation composition at the micro scale, whereas mixed hardwood and dwarf shrubs tend to have higher numbers of questing ticks.