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Trehalose is a naturally occurring, non-reducing disaccharide comprising two covalently-linked glucose molecules. It possesses unique physiochemical properties, which account for multiple biological roles in a variety of prokaryotic and eukaryotic organisms. In the past few decades, intensive research on trehalose has uncovered its functions, and extended its uses as a sweetener and stabilizer in the food, medical, pharmaceutical, and cosmetic industries. Further, increased dietary trehalose consumption has sparked research on how trehalose affects the gut microbiome. In addition to its role as a dietary sugar, trehalose has gained attention for its ability to modulate glucose homeostasis, and potentially as a therapeutic agent for diabetes. This review discusses the bioactive effects of dietary trehalose, highlighting its promise in future industrial and scientific contributions.

The detection of wind turbine blades (WTBs) damage is crucial for improving power generation efficiency and extending the lifespan of turbines. However, traditional detection methods often suffer from false positives and missed detections, and they do not adequately account for complex weather conditions such as fog and snow. Therefore, this study proposes a WTBs damage detection model based on an improved YOLOv8, named AUD-YOLO. Firstly, the ADown module is integrated into the YOLOv8 backbone to replace some conventional convolutional down-sampling operations, decreasing the parameter count while boosting the model’s capability to extract image features. Secondly, the model incorporates the UniRepLKNet large convolution kernel with the C2f module, enabling it to learn complex image features more comprehensively. Thirdly, a lightweight DySample dynamic up-sampler substitutes the nearest-neighbor interpolation up-sampling method in the original model, thereby obtaining richer semantic information. Experimental results show that the AUD-YOLO model demonstrates outstanding performance in detecting WTBs damage under complex and adverse weather conditions, achieving a 3% improvement in the mAP@0.5 metric and a 6.2% improvement in the mAP@0.5–0.95 metric compared to YOLOv8. Moreover, the model has only 2.5M parameters and 7.2 GFLOPs of computational complexity, this adaptation renders it appropriate for implementation in environments with constrained computational capacity, where precise detection is critical. Lastly, a mobile application named WTBs Damage Detection system is designed and developed, enabling mobile-based detection of WTBs damage.

Background Coronavirus disease (COVID-19) quickly spread around the world after its initial identification in Wuhan, China in 2019 and became a global public health crisis. COVID-19 related hospitalizations and deaths as important disease outcomes have been investigated by many studies while less attention has been given to the relationship between these two outcomes at a public health unit level. In this study, we aim to establish the relationship of counts of deaths and hospitalizations caused by COVID-19 over time across 34 public health units in Ontario, Canada, taking demographic, geographic, socio-economic, and vaccination variables into account. Methods We analyzed daily data of the 34 health units in Ontario between March 1, 2020 and June 30, 2022. Associations between numbers of COVID-19 related deaths and hospitalizations were explored over three subperiods according to the availability of vaccines and the dominance of the Omicron variant in Ontario. A generalized additive model (GAM) was fit in each subperiod. Heterogeneity across public health units was formulated via a random intercept in each of the models. Results Mean daily COVID-19 deaths increased quickly as daily hospitalizations increased, particularly when daily hospitalizations were less than 20. In all the subperiods, mean daily deaths of a public health unit was significantly associated with its population size and the proportion of confirmed cases in subjects over 60 years old. The proportion of fully vaccinated (2 doses of primary series) people in the 60 + age group was a significant factor after the availability of the COVID-19 vaccines. The deprivation index, a measure of poverty, had a significantly positive effect on COVID-19 mortality after the dominance of the Omicron variant in Ontario. Quantification of these effects was provided, including effects related to public health units. Conclusions The differences in COVID-19 mortality across health units decreased over time, after adjustment for other covariates. In the last subperiod when most public health protections were released and the Omicron variant dominated, the least advantaged group might suffer higher COVID-19 mortality. Interventions such as paid sick days and cleaner indoor air should be made available to counter lifting of health protections.

Solid polymer electrolytes (SPEs) are still being considered as a candidate to replace liquid electrolytes for high‐safety and flexible lithium batteries due to their superiorities including light‐weight, good flexibility, and shape versatility. However, inefficient ion transportation of linear polymer electrolytes is still the biggest challenge. To improve ion transport capacity, developing novel polymer electrolytes are supposed to be an effective strategy. Nonlinear topological structures such as hyperbranched, star‐shaped, comb‐like, and brush‐like types have highly branched features. Compared with linear polymer electrolytes, topological polymer electrolytes possess more functional groups, lower crystallization, glass transition temperature, and better solubility. Especially, a large number of functional groups are beneficial to dissociation of lithium salt for improving the ion conductivity. Furthermore, topological polymers have strong design ability to meet the requirements of comprehensive performances of SPEs. In this review, the recent development in topological polymer electrolytes is summarized and their design thought is analyzed. Outlooks are also provided for the development of future SPEs. It is expected that this review can raise a strong interest in the structural design of advanced polymer electrolyte, which can give inspirations for future research on novel SPEs and promote the development of next‐generation high‐safety flexible energy storage devices. The high designability, flexibility, and abundant functional groups of topological polymer electrolyte matrix are beneficial to enhancing ion conduction and battery performance. This review introduces development of different types of topological solid polymer electrolytes (SPEs), including hyperbranched, star‐shaped, comb‐like, and brush‐like SPEs. Besides, the review analyzes design thoughts of SPEs for promoting the progress of high‐safety flexible energy storage devices.

The type 2 bradykinin receptor (B2R) is a G protein-coupled receptor (GPCR) in the cardiovascular system, and the dysfunction of B2R leads to inflammation, hereditary angioedema, and pain. Bradykinin and kallidin are both endogenous peptide agonists of B2R, acting as vasodilators to protect the cardiovascular system. Here we determine two cryo-electron microscopy (cryo-EM) structures of human B2R-G q in complex with bradykinin and kallidin at 3.0 Å and 2.9 Å resolution, respectively. The ligand-binding pocket accommodates S-shaped peptides, with aspartic acids and glutamates as an anion trap. The phenylalanines at the tail of the peptides induce significant conformational changes in the toggle switch W283 6.48 , the conserved PIF, DRY, and NPxxY motifs, for the B2R activation. This further induces the extensive interactions of the intracellular loops ICL2/3 and helix 8 with G q proteins. Our structures elucidate the molecular mechanisms for the ligand binding, receptor activation, and G q proteins coupling of B2R. Type 2 bradykinin receptor (B2R) is essential in vasodilation and cardioprotection. Here the authors present two cryo-EM structures of human B2R-Gq in complex with bradykinin and kallidin to elucidate the mechanisms for ligand binding, receptor activation, and Gq proteins coupling.

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems.

Harnessing the power of artificial intelligence(AI) approaches to innovatively generating the vector graphics of fine-grained patterns has become an important task in image edge extraction, particularly on the domain of intangible cultural heritage (ICH) images where they are typically fine-grained and having the complex edges. With higher autonomy, the machine learning algorithms are able to accurately extract the image information, understand and convey the concept contained in it. In this paper, we take Qiang embroidery patterns as an example due to containing fine-grained patterns, which is more suitable for the study of image processing and pattern recognition techniques. We firstly adopt appropriate pre-processing methods, improved adaptive median filtering(IAMF) and non-local mean for the two different types of Qiang embroidery patterns to reduce image noise. Then, the Xception algorithm based on convolutional neural networks(CNNs) is used for edge detection and extraction to generate vector graphics of the patterns. Experimental results show that Qiang embroidery patterns, after denoising and edge extraction, can be clearly identified the shape characteristics of the patterns. Based on this approach, the images can be converted into vector graphics for the digital preservation and further artistic reinterpretation. The use of the Xception algorithm effectively solves the problem of extraction of Qiang embroidery in two-dimensional vectorial images. In addition, our proposed method provides a reliable practical reference for the preservation of other related ICH images.

The testes are the organs of gamete production and testosterone synthesis. Up to date, no model system is available for mammalian testicular development, and only few studies have characterized the mouse testis transcriptome from no more than three postnatal ages. To describe the transcriptome landscape of the developing mouse testis and identify the potential molecular mechanisms underlying testis maturation, we examined multiple RNA-seq data of mouse testes from 3-week-old (puberty) to 11-week-old (adult). Sperm cells appeared as expected in 5-week-old mouse testis, suggesting the proper sample collection. The principal components analysis revealed the genes from 3w to 4w clustered away from other timepoints, indicating they may be the important nodes for testicular development. The pairwise comparisons at two adjacent timepoints identified 7,612 differentially expressed genes (DEGs), resulting in 58 unique mRNA expression patterns. Enrichment analysis identified functions in tissue morphogenesis (3-4w), regulation of peptidase activity (4-5w), spermatogenesis (7-8w), and antigen processing (10-11w), suggesting distinct functions in different developmental periods. 50 hub genes and 10 gene cluster modules were identified in the testis maturation process by protein-protein interaction (PPI) network analysis, and the miRNA-lncRNA-mRNA, miRNA-circRNA-mRNA and miRNA-circRNA-lncRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed. The results suggest that testis maturation is a complex developmental process modulated by various molecules, and that some potential RNA-RNA interactions may be involved in specific developmental stages. In summary, this study provides an update on the molecular basis of testis development, which may help to understand the molecular mechanisms of mouse testis development and provide guidance for mouse reproduction.

The discrepancy between current CO2 emission trend and the targeted 1.5 °C warming requires the implementation of carbon dioxide removal (CDR) technologies. Among the engineered CDRs, enhanced weathering (EW) is expected to exhibit substantial potential for CO2 removal, owing to the availability of abundant reserves of ultramafic rocks and demonstration of worldwide liming practice. While the shrinking core model (SCM) has been commonly adopted in previous theoretical and experimental studies, there still lacks a comprehensive assessment on the impacts of model parameters, such as rock particle size, size distribution, weathering rate and time length on the weathering kinetics and the resultant CDR potential. Herein, this study incorporates particle size distribution of rock powder into the surface reaction-controlled SCM, and conducts sensitivity analysis on EW’s CDR potential quantitatively. Even fully powered by low-carbon energy in the optimistic case, the application of EW with olivine only achieves maximum CDR per unit of rock and energy consumption of 0.01 kg CO2 per kg rock and 19 g per kWh at size of 8 and 22 μm respectively, indicating the limitations of EW. The derived optimal application parameters with olivine powers within 3.7–79 μm provide valuable insights into the practical real-world applications to achieve net CO2 removal.

Background: The four subtypes of G protein-coupled receptors (GPCRs) regulated by histamine play critical roles in various physiological and pathological processes, such as allergy, gastric acid secretion, cognitive and sleep disorders, and inflammation. Previous experimental structures of histamine receptors (HRs) with agonists and antagonists exhibited multiple conformations for the ligands and G protein binding. However, the structural basis for HR regulation and signaling remains elusive. Methods: We determined the cryo-electron microscopy (cryo-EM) structure of the H4R-histamine-Gi complex at 2.9 Å resolution, and predicted the models for all four HRs in the ligand-free apo and G protein subtype binding states using AlphaFold3 (AF3). Results: By comparing our H4R structure with the experimental HR structures and the computational AF3 models, we elucidated the distinct histamine binding modes and G protein interfaces, and proposed the essential roles of Y6.51 and Q7.42 in receptor activation and the intracellular loop 2 (ICL2) in G protein bias. Conclusions: Our findings deciphered the molecular mechanisms underlying the regulation of different HRs, from the extracellular ligand-binding pockets and transmembrane motifs to the intracellular G protein coupling interfaces. These insights are expected to facilitate selective drug discovery targeting HRs for diverse therapeutic purposes.