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O‐linked‐β‐N‐acetylglucosamine (O‐GlcNAcylation) is a distinctive posttranslational protein modification involving the coordinated action of O‐GlcNAc transferase and O‐GlcNAcase, primarily targeting serine or threonine residues in various proteins. This modification impacts protein functionality, influencing stability, protein–protein interactions, and localization. Its interaction with other modifications such as phosphorylation and ubiquitination is becoming increasingly evident. Dysregulation of O‐GlcNAcylation is associated with numerous human diseases, including diabetes, nervous system degeneration, and cancers. This review extensively explores the regulatory mechanisms of O‐GlcNAcylation, its effects on cellular physiology, and its role in the pathogenesis of diseases. It examines the implications of aberrant O‐GlcNAcylation in diabetes and tumorigenesis, highlighting novel insights into its potential role in cardiovascular diseases. The review also discusses the interplay of O‐GlcNAcylation with other protein modifications and its impact on cell growth and metabolism. By synthesizing current research, this review elucidates the multifaceted roles of O‐GlcNAcylation, providing a comprehensive reference for future studies. It underscores the potential of targeting the O‐GlcNAcylation cycle in developing novel therapeutic strategies for various pathologies. [ABSTRACT FROM AUTHOR]

Turbidity is an important parameter in monitoring water quality, and thus attracts significant attention. Changes in the various components and constituent elements of water directly affect turbidity measurements. The turbidity of water is generally quantified by measuring the absorbance or scattering characteristics of substances suspended in it. The complex composition of environmental water bodies complicates the determination of factors influencing their turbidity. Controlled experiments that quantitatively analyze the effect of a single factor on the measurement of turbidity in the laboratory are an important means to improve the accuracy of turbidity assessment. Properties of suspended materials in a water column that may affect its measured turbidity include the concentration of algae, particle size, and the color of soluble substances, etc. The laboratory experiments reported here used Chlorella as an example to investigate the effect of algal concentration on turbidity measurement. The results are as follows. When the turbidity is low (100 NTU), the average relative error between the theoretical and practical absorbance is about 37.52%, which decreases to 19.20% at 100–200 NTU and 5.16% at 200–400 NTU. The characteristic spectral bands sensitive to turbidity (680 nm) and Chlorella (240 nm) were selected, and the theoretical and practical turbidity results were analyzed. The average relative errors of mixed liquids of less than 100, 100–200, and 200–400 NTU are 65.07%, 34.18%, and 3.95%, respectively. Therefore, the concentration of Chlorella significantly affects the measured turbidity, and results in a more complex effect at low turbidity (<100 NTU). Combining the analysis of absorbance peak values and characteristic spectral bands, we can assess the turbidity changes in different components, and through calibration, information regarding the concentration and variation of different components in water bodies can be obtained. The results of this research could improve the accuracy of on-site measurement of the concentrations of different components suspended in water, and also facilitate the development of new turbidity sensors. [ABSTRACT FROM AUTHOR]

Kawasaki disease (KD) is an acute self‐limiting vasculitis with coronary complications, usually occurring in children. The incidence of KD in children is increasing year by year, mainly in East Asian countries, but relatively stably in Europe and America. Although studies on KD have been reported, the pathogenesis of KD is unknown. With the development of high‐throughput sequencing technology, growing number of regulatory noncoding RNAs (ncRNAs) including microRNA (miRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) have been identified to involved in KD. However, the role of ncRNAs in KD has not been comprehensively elucidated. Therefore, it is significative to study the regulatory role of ncRNA in KD, which might help to uncover new and effective therapeutic strategies for KD. In this review, we summarize recent studies on ncRNA in KD from the perspectives of immune disorders, inflammatory disorders, and endothelial dysfunction, and highlight the potential of ncRNAs as therapeutic targets for KD. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an $\alpha$ -cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options. [ABSTRACT FROM AUTHOR]

Broiler behavior is closely related to the breeding environment. Therefore, studying broiler behavior helps breeding farm workers to better carry out welfare breeding. In the breeding environment of yellow feather broilers, temperature, humidity, and ammonia concentration are the main factors that affect the behavior of the broilers. This study used a multichromatic aberration model to process the color images of yellow feather broilers to extract the activity feature of the broilers at different periods, utilized the Cb component of YCbCr color model and the b component of Lab color model to remove background litter in images, and employed the Q component of YIQ color model to remove the feeders and the drinkers from the image. The segmented images were constructed into an accumulator to generate a heat map of yellow feather broilers' activity. Then, the correlation between the activity and the temperature and humidity index (THI) and the correlation between the activity and ammonia concentration were explored. The experiment found that the activity of the broilers was significantly positively correlated with ammonia concentration (P < 0.05), indicating that the activity of yellow feather broilers increased with ammonia concentration ascending. Besides, the THI in the broiler chamber had a significant positive correlation with the ammonia data (P < 0.01), indicating that when the THI in the broiler chamber increases, the ammonia concentration rises. The research provides a direction for exploring the impact of THI and ammonia concentration on the performance of yellow feather broilers. At the same time, it provides a theoretical basis for the early warning and judgment of broiler breeding by farm workers in the future. [ABSTRACT FROM AUTHOR]

In an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need to be performed during maintenance breaks between any two consecutive missions under limited maintenance resources. In such case, selective maintenance is a widely used maintenance policy. As a typical discrete mathematics problem, selective maintenance has received widespread attention. In this work, a selective maintenance model considering human reliability for multi-component systems is investigated. Each maintenance worker can be in one of multiple discrete working levels due to their human error probability (HEP). The state of components after maintenance is assumed to be random and follow an identified probability distribution. To solve the problem, this paper proposes a human reliability model and a method to determine the state distribution of components after maintenance. The objective of selective maintenance scheduling is to find the maintenance action with the optimal reliability for each component in a maintenance break subject to constraints of time and cost. In place of an enumerative method, a genetic algorithm (GA) is employed to solve the complicated optimization problem taking human reliability into account. The results show the importance of considering human reliability in selective maintenance scheduling for an MSS. [ABSTRACT FROM AUTHOR]

A tidal creek represents a typical morphologic unit in an intertidal flat. The development and migration of a tidal creek can affect the mass transport, ecological environment, and geomorphologic evolution of the flat. By using field observations, this study links hydrodynamics, sediment transport processes with short-term changes in topography at a typical tidal creek system located at the Chongming Island of the Yangtze River estuary. Hydrodynamic and sediment transport associated with varying tidal cycles across both wet (flood) and dry seasons were measured through the field campaign. The wet and dry seasons represent higher and lower discharges of Yangtze River, respectively. The results indicated that most of the suspended sediment becomes entrained at the beginning of a flood tide. At a fixed point, 7.2 times of suspended sediments, which were entrained out of the creek in wet season, began to be transported along the creek compared to dry season. In the dry season, high flow velocity and shear stress conditions occurred in the tidal creek because the water level was below the top of the flat. In summary, the tidal creeks were found to serve as effective conduits for the transportation of sediments in the wet season, and the secondary flow enhanced the development of tidal meandering. Seasonal variations in creek morphological changes were also continuously monitored over two years at intervals of two months. The change of creek morphology varied from the high level to low level, and tidal meandering was strongly associated with flood and ebb tides. • Water flow and sediment transport were studied in a meso tidal creek system. • The tidal flat tended to receive sediment deposits from April to October, and to be eroded in the other months. • Tidal creek morphology was stable at the high creek level, and it meandered at the low level. • The results contribute to a better understanding of the development mechanism of tidal creeks. [ABSTRACT FROM AUTHOR]

Background: Myocardial infarction (MI) is characterized by irreversible cardiomyocyte death resulting from an inadequate supply of oxygenated blood to the myocardium. Recent studies have indicated that ferroptosis, a form of regulated cell death, exacerbates myocardial injury during MI. Concurrently, the upregulation of CD47 on the surface of damaged myocardium following MI impairs the clearance of dead cells by macrophages, thereby hindering efferocytosis. In this context, simultaneously inhibiting ferroptosis and enhancing efferocytosis may represent a promising strategy to mitigate myocardial damage post-MI., Methods: In this study, we engineered platelet membrane-coated hollow mesoporous silicon nanoparticles (HMSN) to serve as a drug delivery system, encapsulating ferroptosis inhibitor, Ferrostatin-1, along with an anti-CD47 antibody. We aimed to assess the potential of these nanoparticles (designated as Fer-aCD47@PHMSN) to specifically target the site of MI and evaluate their efficacy in reducing cardiomyocyte death and inflammation., Results: The platelet membrane coating on the nanoparticles significantly enhanced their ability to successfully target the site of myocardial infarction (MI). Our findings demonstrate that treatment with Fer-aCD47@PHMSN resulted in a 38.5% reduction in cardiomyocyte ferroptosis under hypoxia, indicated by decreased lipid peroxidation and increased in vitro. Additionally, Fer-aCD47@PHMSN improved cardiomyocyte efferocytosis by approximately 15% in vitro. In MI mice treated with Fer-aCD47@PHMSN, we observed a substantial reduction in cardiomyocyte death (nearly 30%), decreased inflammation, and significant improvement in cardiac function., Conclusion: Our results demonstrated that the cooperation between the two agents induced anti-ferroptosis effects and enhanced dead cardiomyocyte clearance by macrophage as well as anti-inflammation effects. Thus, our nanoparticle Fer-aCD47@PHMSN provides a new therapeutic strategy for targeted therapy of MI., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Song et al.)

Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade. Nevertheless, lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset. However, inconsistent standards on data collection, annotation, and partition are still restraining a fair and efficient comparison between different works. To this line, we introduced and benchmarked a first version of the Heart Sounds Shenzhen (HSS) corpus. Motivated and inspired by the previous works based on HSS, we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study. First, we segmented the heart sound recording into shorter recordings (10 s), which makes it more similar to the human auscultation case. Second, we redefined the classification tasks. Besides using the 3 class categories (normal, moderate, and mild/severe) adopted in HSS, we added a binary classification task in this study, i.e., normal and abnormal. In this work, we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies, which are reproducible by using open-source toolkits. Last but not least, we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable., Competing Interests: Author contributions: K.Q., Z.B., Z.Z., Q.D., and B.S. collectively wrote and revised the manuscript. K.Q., Z.B., T.K., F.D., W.J., and B.H. participated in the design and interpretation of the reported experiments or results. K.Q., Z.Z., F.D., Y.J., B.D., Z.D., and B.H. participated in the acquisition and analysis of data. K.Q., Z.B., F.D., M.S., J.S., and B.H. were primarily responsible for a particular, specialized role in the research. K.Q., B.H., and Y.Y. provided administrative, technical or supervisory support. Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Kun Qian et al.)

With the depressive psychiatric disorders becoming more common, people are gradually starting to take it seriously. Somatisation disorders, as a general mental disorder, are rarely accurately identified in clinical diagnosis for its specific nature. In the previous work, speech recognition technology has been successfully applied to the task of identifying somatisation disorders on the Shenzhen Somatisation Speech Corpus. Nevertheless, there is still a scarcity of labels for somatisation disorder speech database. The current mainstream approaches in the speech recognition heavily rely on the well labelled data. Compared to supervised learning, self-supervised learning is able to achieve the same or even better recognition results while reducing the reliance on labelled samples. Moreover, self-supervised learning can generate general representations without the need for human hand-crafted features depending on the different recognition tasks. To this end, we apply self-supervised learning pre-trained models to solve few-labelled somatisation disorder speech recognition. In this study, we compare and analyse the results of three self-supervised learning models (contrastive predictive coding, wav2vec and wav2vec 2.0). The best result of wav2vec 2.0 model achieves 77.0 % unweighted average recall and is significantly better than CPC (p < .005), performing better than the benchmark of the supervised learning model.Clinical relevance- This work proposed a self-supervised learning model to resolve the few-labelled SD speech data, which can be well used for helping psychiatrists with clinical assistant to diagnosis. With this model, psychiatrists no longer need to spend a lot of time labelling SD speech data.

Cardiovascular diseases (CVDs) are the leading cause of death globally. Heart sound signal analysis plays an important role in clinical detection and physical examination of CVDs. In recent years, auxiliary diagnosis technology of CVDs based on the detection of heart sound signals has become a research hotspot. The detection of abnormal heart sounds can provide important clinical information to help doctors diagnose and treat heart disease. We propose a new set of fractal features - fractal dimension (FD) - as the representation for classification and a Support Vector Machine (SVM) as the classification model. The whole process of the method includes cutting heart sounds, feature extraction, and classification of abnormal heart sounds. We compare the classification results of the heart sound waveform (time domain) and the spectrum (frequency domain) based on fractal features. Finally, according to the better classification results, we choose the fractal features that are most conducive for classification to obtain better classification performance. The features we propose outperform the widely used features significantly (p < .05 by one-tailed z-test) with a much lower dimension.Clinical relevance-The heart sound classification model based on fractal provides a new time-frequency analysis method for heart sound signals. A new effective mechanism is proposed to explore the relationship between the heart sound acoustic properties and the pathology of CVDs. As a non-invasive diagnostic method, this work could supply an idea for the preliminary screening of cardiac abnormalities through heart sounds.