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Objective: Obesity is a major risk factor for the morbidity and mortality of cardiovascular disease and predicts the development of hypertension, diabetes mellitus and other various diseases. Methods: A retrospective study evaluated predictors for higher total body weight loss following endoscopic sleeve gastroplasty (ESG). Adults (>18 years old) with BMI > 30 kg/m2 who underwent ESG from January 2019 to July 2022 were included. Patients under the age of 18 were excluded from the study. Results: This retrospective cohort included 76 patients, of whom 62 women (81.6%) and 14 were men (18.4%) with a mean age of 46.3 ± 10.4. The mean BMI baseline was 36.6 ± 4.21. Out of the included patients, 10% were lost to follow-up at 1 month, 33% at 3 months, 50% after 6 months, and only 30% met 12 months follow-up. During the follow-up period, no mortality was documented. Three major adverse events (3.9%) were documented (one mediastinal abscess, one lower gastrointestinal bleeding and one pulmonary embolism), all of them in female patients. Among the demographic clinical and laboratory data examined, smoking (N = 6, p < 0.001) was associated with successful ESG, which was determined as total body weight loss (TBWL) above 15%. The rest of the variables examined were not shown to be statistically significant to sleeve success. Overall, 65 of the 76 patients which were studied in this research had more than 5% TBWL, 42 patients had more than 10% TBWL, 21 patients had more than 15% TBWL and 7 patients lost more than 20% of their weight during 1 year of follow-up. Maximal TBWL was achieved 3 months following the procedure. During the first month following ESG, the average weight lost was 8.6% (N = 69); at 3 months, it was 12.3% (N = 48); at 6 months, it was 11.3% (N = 33); and at 12 months, it was 9.8% (N = 13). Smoking was associated with higher weight loss. Conclusions: The current study showed a positive correlation between ESG weight loss above 15% and smoking. Older patients (>50) gained weight earlier, within 3 months, and by 1 year of follow-up almost returned back to their original weight. Females sustained weight loss over 1 year of follow-up compared to males. Patients with lower BMI continued losing weight during the follow-up period (12 months). This study tries to summarize pre-procedural prediction of ESG success.

Objective: The aim of this study was to explore the associations of eating habits with self-rated health and life satisfaction in adolescents using a multiple-country sample. Methods: Cross-sectional data from the Health Behavior in School-Aged Children (HBSC) 2013/2014 wave was used in this study. A self-reported questionnaire was used to collect data on independent variables including breakfast on weekdays, breakfast at weekends, and consumption of fruits, vegetables, sweets, and soft dirks. Outcomes included self-rated health and life satisfaction. Regression models were used to assess the associations between the independent variables and the two outcomes, separately, after controlling for covariates. Results were presented using odds ratios (ORs) and 95% confidence intervals (CIs). Results: Of all the study participants (aged 11–15 years), 50.8% were girls. Compared with no consumption of breakfast on weekdays, eating breakfast for five days had 1.22 times greater likelihood for improved self-rated health (OR = 1.22, 95% CI: 1.19–1.25, p < 0.001). Participants who ate breakfast for both days (OR = 1.41, 95% CI: 1.36–1.46, p < 0.001) and one day (OR = 1.12, 95% CI = 1.08–1.17, p < 0.001) were more likely to experience improved self-rated health compared to never eating breakfast at weekends. Five or more days for fruit and vegetable consumption resulted in better self-rated health (all p < 0.001). Similar results were found in terms of the associations of breakfast, fruit, and vegetable consumption with life satisfaction. For example, a higher frequency of fruit intake was associated with enhanced self-rated health (e.g., OR for more than once daily = 1.42, 95% CI: 1.34–1.51, p < 0.001) compared to no fruit consumption. Similarly, a higher-frequency vegetable intake, such as more than once daily (OR = 1.33, 95% CI: 1.26–1.39, p < 0.001), was associated with improved self-rated health. Conclusions: Healthy eating habits, especially regular breakfast and a higher consumption of vegetables and fruit, are associated with better self-rated health and life satisfaction in school-aged children. Of note, the consumption of fruit would have the greatest impact on health and wellbeing outcomes. This study offers evidence that healthy eating habits can play a vital role in school-aged children’s health and wellbeing, highlighting the practical significance of educating adolescents to develop healthy eating habits.

A low-cost method for the simultaneous extraction of alkaloids and water-insoluble flavonoids and esters from Coptis chinensis Franch. (Abbrev. C. chinensis) was explored to provide a reference for the production of green plant-based preparations and traditional Chinese medicine formula granules. A combined extraction method with the deep eutectic solvents (DESs) of choline chloride and urea (molar mass ratio of 1:2) and organic solvent ethanol was used, supplemented by ultrasonic-assisted extraction (ultrasonic power: 150 W; ultrasonic temperature: 60 °C; treatment time: 15 min). The extraction efficiency of the 50% DES (choline chloride and urea) aqueous solution for berberine, palmatine, jatrorrhizine, and magnoflorine was found to be the highest and was superior to traditional ultrasonic extraction and water bath reflux extraction methods. Furthermore, the flavonoids and esters from C. chinensis residue were extracted using ethanol. The results from high-performance liquid chromatography and gas chromatography–mass spectrometry indicated a high extraction efficiency overall.

As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat 5/Thematic Mapper images to effectively and accurately identify small urban water bodies. The findings indicate that the trained U-net convolutional neural network (U-Net) water body extraction model and loss function combining Focal Loss and Dice Loss adopted in this study demonstrate high precision in identifying water bodies within the main urban area of Hangzhou, with an accuracy rate of 94.3%. Trends of decrease in water areas with a continuous increase in landscape fragmentation, particularly for the plain river network, were observed from 1985 to 2010, indicating a weaker connection between water bodies resulting from rapid urbanization. Large patches of water bodies, such as natural lakes and big rivers, located at divisions at the edge of the city are susceptible to disappearing during the rapid outward expansion. However, due to the limitations and strict control of development, water bodies, referring to as wetland, slender canals, and plain river networks, in the traditional center division of the city, are preserved well. Combined with the random forest classification method and the U-Net water body extraction model, land use changes from 1985 to 2010 are calculated. Reclamation along the Qiantang River accounts for the largest conversion area between water bodies and cultivated land, constituting more than 90% of the total land use change area, followed by the conversion of water bodies into construction land, particularly in the northeast of Xixi Wetland. Notably, the conversion of various land use types within Xixi Wetland into construction land plays a significant role in the rise of the carbon footprint.

The “Qixiadaxiangshui” pear (Pyrus bretschneideri Rehd.1) is a highly valued cultivar known for its crisp texture, abundant juice, and rich aroma. In this study, we reported the first complete chloroplast genome sequence of the “Qixiadaxiangshui” pear, which is 159,885 bp in length with a GC content of 36.58%. The genome exhibits a typical circular quadripartite structure, comprising a large single-copy region (LSC), a small single-copy region (SSC), and a pair of inverted repeat regions (IRs). A total of 131 genes were identified, including 84 protein-coding genes, 8 rRNA genes, and 37 tRNA genes. We also identified 209 simple sequence repeats (SSRs) and several mutation hotspots, such as ndhC-trnM-CAU and trnR-UCU-atpA, which can be applied in molecular identification and phylogenetic studies of Pyrus. Comparative genomic analysis showed high conservation among ten pear cultivars. Phylogenetic analysis indicated that the “Qixiadaxiangshui” pear is closely related to germplasm Dangshansuli, Wonwhang, and Yali, suggesting a recent common ancestor. These findings provided valuable insights into the genetic diversity and evolutionary dynamics of the Pyrus species and contribute to the conservation and breeding of pear germplasm resources.

(1) Background: This research aims to identify candidates for trimodality therapy (TMT) or radical cystectomy (RC) by using a predictive model. (2) Methods: Patients with nonmetastatic muscle-invasive bladder cancer (MIBC) in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. The clinical data of 2174 eligible patients were extracted and separated into RC and TMT groups. To control for confounding bias, propensity score matching (PSM) was carried out. A nomogram was established via multivariable logistic regression. The area under the receiver operating characteristic curve (AUC) and calibration curves were used to assess the nomogram’s prediction capacity. Decision curve analysis (DCA) was carried out to determine the nomogram’s clinical applicability. (3) Results: After being processed with PSM, the OS of the RC group was significantly longer compared with the TMT group (p < 0.001). This remarkable capacity for discrimination was exhibited in the training (AUC: 0.717) and validation (AUC: 0.774) sets. The calibration curves suggested acceptable uniformity. Excellent clinical utility was shown in the DCA curve. The RC and RC-Beneficial group survived significantly longer than the RC and TMT-Beneficial group (p < 0.001) or the TMT group (p < 0.001). However, no significant difference was found between the RC and TMT-Beneficial group and the TMT group (p = 0.321). (4) Conclusions: A predictive model with excellent discrimination and clinical application value was established to identify the optimal patients for TMT among nonmetastatic MIBC patients.

Brassica oleracea var. italica (broccoli), a member of the cabbage family, is abundant with many nutrients, including vitamins, potassium, fiber, minerals, and phytochemicals. Consequently, it has been used as a functional food additive to reduce oxidative stress and inflammatory responses. In the current study, the effects of sulforaphane-rich broccoli sprout extract (BSE) on the inflammatory response were investigated in vitro and in vivo. Comparative high-performance liquid chromatography analysis of sulforaphane content from different extracts revealed that 70% ethanolic BSE contained more sulforaphane than the other extracts. qPCR and enzyme immunoassay analyses revealed that BSE markedly reduced the expression of proinflammatory cytokines and mediators, including cyclooxygenase 2, interleukin (IL)-1β, IL-6, IL-1, inducible nitric oxide synthase (iNOS), and tumor necrosis factor-α (TNF-α), in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Pretreatment with BSE improved the survival rate and suppressed alanine aminotransferase and aspartate aminotransferase expression in LPS-induced endotoxemic mice, while proinflammatory cytokines such as IL-1β, TNF-α, IL-6, cyclooxygenase-2, and iNOS decreased dramatically in the LPS-induced liver injury model via BSE treatment. Additionally, F4/80 immunostaining showed that BSE suppressed hepatic macrophage infiltration in the liver after lipopolysaccharide injection. In conclusion, BSE may be a potential nutraceutical for preventing and regulating excessive immune responses in inflammatory disease.

Melatonin, a hormone secreted by the pineal gland of vertebrates, regulates sleep, blood pressure, and circadian and seasonal rhythms, and acts as an antioxidant and anti-inflammatory agent. We investigated the protective effects of melatonin against markers of D-galactose (D-Gal)-induced hepatocellular aging, including liver inflammation, hepatocyte structural damage, and non-alcoholic fatty liver. Mice were divided into four groups: phosphate-buffered saline (PBS, control), D-Gal (200 mg/kg/day), melatonin (20 mg/kg), and D-Gal (200 mg/kg) and melatonin (20 mg) cotreatment. The treatments were administered once daily for eight consecutive weeks. Melatonin treatment alleviated D-Gal-induced hepatocyte impairment. The AST level was significantly increased in the D-Gal-treated groups compared to that in the control group, while the ALT level was decreased compared to the melatonin and D-Gal cotreated group. Inflammatory genes, such as IL1-β, NF-κB, IL-6, TNFα, and iNOS, were significantly increased in the D-Gal aging model, whereas the expression levels of these genes were low in the D-Gal and melatonin cotreated group. Interestingly, the expression levels of hepatic steatosis-related genes, such as LXRα, C/EBPα, PPARα, ACC, ACOX1, and CPT-1, were markedly decreased in the D-Gal and melatonin cotreated group. These results suggest that melatonin suppresses hepatic steatosis and inflammation in a mouse model of D-Gal-induced aging.

Investigating the seeding mechanism of precision seeders is of great significance for improving the quality of cotton sowing operations. This paper designs a rotary type-hole cotton precision mulching dibbler. The main factors influencing the entry of cotton seeds into the seed wheel holes during the seeding process are then theoretically analyzed. Following this, an accurate discrete element model of coated cotton seeds is established and combined with a discrete element method (DEM) and multi-body dynamics (MBD)-coupled simulation model of the seed drill for seed picking and planting. Simulation experiments on the seeding performance of the precision dibbler were performed to study the influence of the seed wheel structure and motion parameters on the picking and planting performance under different speeds. The optimal parameter combination for the seed wheel is obtained through optimization experiments, and a precision dibbler is manufactured for bench testing. The bench test results are consistent with the simulation test results. At the precision dibbler rotation speed of 16 r/min, the qualified index reaches a maximum value of 93.28%, the skip sowing index increases with the precision dibbler rotation speed, and the re-sowing index decreases as the speed increases. These optimization results significantly improved seeding precision and efficiency and are of great significance for the reliability and effectiveness of cotton sowing operations.

The North China Plain (NCP) serves as a critical grain-producing region in China, playing a pivotal role in ensuring the nation’s food security. A comprehensive analysis of the carbon footprint (CF) related to the cultivation of major grain crops within this region and the proposal of strategies to reduce emissions through low-carbon production methods are crucial for advancing sustainable agricultural practices in China. This study employed the lifecycle assessment (LCA) method to estimate the CF of wheat, maize, and rice crops over a period from 2013 to 2022, based on statistical data collected from five key provinces and cities in the NCP: Beijing, Tianjin, Hebei, Shandong, and Henan. Additionally, the Logarithmic Mean Divisia Index (LMDI) model was utilized to analyze the influencing factors. The results indicated that the carbon footprints per unit area (CFA) of maize, wheat, and rice increased between 2013 and 2022. Rice had the highest carbon footprint per unit yield (CFY), averaging 1.1 kg CO2-eq kg−1, with significant fluctuations over time. In contrast, the CFY of wheat and maize remained relatively stable from 2013 to 2022. Fertilizers contributed the most to CF composition, accounting for 48.8%, 48.0%, and 25.9% of the total carbon inputs for wheat, maize, and rice, respectively. The electricity used for irrigation in rice production was 31.8%, which was much higher than that of wheat (6.8%) and maize (7.1%). The LMDI model showed that the labor effect was a common suppressing factor for the carbon emissions of maize, wheat, and rice in the NCP, while the agricultural structure effect and the economic development effect were common driving factors. By improving the efficiency of fertilizer and pesticide utilization, cultivating new varieties, increasing the mechanical operation efficiency, the irrigation efficiency, and policy support, the CF of grain crop production in the NCP can be effectively reduced. These efforts will contribute to the sustainable development of agricultural practices in the NCP and support China’s efforts to achieve its “double carbon” target.

In response to water scarcity in the Bashang area of northwest Hebei Province, a cold and arid region in north China, and to address the diminishing groundwater levels caused by pumping irrigation, this study investigated the impact of rainwater tank size and water supply on kidney beans production in greenhouses under various precipitation scenarios to determine the production potential and development strategies for regional precipitation resources. Under the background of average annual precipitation, kidney bean yield increased with increasing reservoir volume and shorter irrigation cycles. Under a 4-day irrigation cycle, the water demand satisfaction rate of kidney beans reached 100% water demand when the rainwater tank size was 15.7 m3. Against the wide variation in multi-year regional precipitation from 1992 to 2023, the annual effect of rainwater harvest was simulated using precipitation data collected 20 years with an 80% precipitation guarantee rate. The average minimum yield reduction rate obtained was 9.4%, and the corresponding minimum rainwater tank size was 29.5 m3. By superimposing the rainwater harvested in the shed and nonshed areas, the volume of the reservoir without yield reduction could be reduced to 20.0 m3. The sum of discharged and inventory water was much greater than the water scarcity in each water supply situation. Simulating and analyzing the effect of the relationship between rainwater tank size and water supply on rainwater harvesting in regional farmland by year provides important data affecting the construction of regional rainwater storage facilities and water supply efficiency. To achieve a high, stable yield of kidney beans grown in a greenhouse with shed film and shed area rainwater harvesting in north China, 2.6 m3 supplementary groundwater irrigation is still needed during the annual growing season.

Acanthopagrus latus and Rhabdosargus sarba are economically important marine species along the coast of China, with similar external morphological characteristics and living habits, with wide distribution and strong adaptability. To investigate the molecular mechanisms underlying the adaptive evolution of these two species, we conducted whole-genome resequencing of 10 individuals of both species from the coastal waters of Wuyu Island, Fujian, China, using high-throughput sequencing technology. We obtained SNP, InDel, CNV, and SV variation information and annotated these variations, constructing a genomic variation database for both species. By comparing the resequencing data with reference genomes, we identified 9,829,511 SNP loci in the population of A. latus and 34,051,056 SNP loci in the population of R. sarba. Using whole-genome SNP data, we employed Fst and ROD methods to identify candidate genomic regions under selection. Functional annotation and enrichment analysis using GO and KEGG databases revealed potential adaptive evolution in R. sarba associated with immune response, feeding, growth and development, and locomotion, while A. latus showed potential adaptive evolution associated with immune response, nervous system, growth and development, and metabolism.

Effective safety management is vital for ensuring construction safety. Traditional safety inspections in construction heavily rely on manual labor, which is both time-consuming and labor-intensive. Extensive research has been conducted integrating computer-vision technologies to facilitate intelligent surveillance and improve safety measures. However, existing research predominantly focuses on singular tasks, while construction environments necessitate comprehensive analysis. This study introduces a multi-task computer vision technology approach for the enhanced monitoring of construction safety. The process begins with the collection and processing of multi-source video surveillance data. Subsequently, YOLOv8, a deep learning-based computer vision model, is adapted to meet specific task requirements by modifying the head component of the framework. This adaptation enables efficient detection and segmentation of construction elements, as well as the estimation of person and machine poses. Moreover, a tracking algorithm integrates these capabilities to continuously monitor detected elements, thereby facilitating the proactive identification of unsafe practices on construction sites. This paper also presents a novel Integrated Excavator Pose (IEP) dataset designed to address the common challenges associated with different single datasets, thereby ensuring accurate detection and robust application in practical scenarios.

The efficient preparation of single-stranded DNA (ssDNA) rings, as a macromolecular construction approach with topological features, has aroused much interest due to the ssDNA rings’ numerous applications in biotechnology and DNA nanotechnology. However, an extra splint is essential for enzymatic circularization, and by-products of multimers are usually present at high concentrations. Here, we proposed a simple and robust strategy using permuted precursor (linear ssDNA) for circularization by forming an intramolecular dynamic nick using a part of the linear ssDNA substrate itself as the template. After the simulation of the secondary structure for desired circular ssDNA, the linear ssDNA substrate is designed to have its ends on the duplex part (≥5 bp). By using this permuted substrate with 5′-phosphate, the splint-free circularization is simply carried out by T4 DNA ligase. Very interestingly, formation of only several base pairs (2–4) flanking the nick is enough for ligation, although they form only instantaneously under ligation conditions. More significantly, the 5-bp intramolecular duplex part commonly exists in genomes or functional DNA, demonstrating the high generality of our approach. Our findings are also helpful for understanding the mechanism of enzymatic DNA ligation from the viewpoint of substrate binding.

Background: We compared the prognostic value of the C-reactive protein (CRP)-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), and modified Glasgow prognostic score (mGPS) with the Sequential Organ Failure Assessment (SOFA) score in an intensive care unit (ICUs). Methods: This study used the data of 53,877 adult patients admitted to an ICU between June 2013 and May 2022. Using the CAR, NLR, and mGPS values, as well as the SOFA score from the ICU, we conducted multivariable logistic regression analysis and used the receiver operating characteristic (ROC) curve to compare the predictive value for 28-day and 1-year mortality. Results: A total of 2419 patients (4.5%) died within 28 days, and 6209 (11.5%) patients died within 1 year. After an adjustment, all predictors were found to be independent risk factors for 28-day mortality (odds ratio [OR] 1.31, 95% confidence interval [CI] 1.29–1.33, p < 0.001 for the SOFA score; OR 1.05, 95% CI 1.03–1.07, p < 0.001 for CAR; OR 1.01, 95% CI 1.00–1.02, p < 0.001 for the NLR; and OR 1.19, 95% CI 1.08–1.30, p < 0.001 for the mGPS). This trend persisted for the 1-year mortality. In ROC curve analysis, the CAR showed better predictability than the NLR and mGPS. Furthermore, the predictive power of the CAR was significantly higher than that of the SOFA score for 1-year mortality. Conclusions: The CAR, NLR, and mGPS values at ICU admission were independent risk factors of mortality after ICU admission. The predictive value of CAR was higher than that of the SOFA score for 1-year mortality. CAR assessment at ICU admission may be a feasible predictor of long-term mortality.

With the development of the petroleum industry and advancements in technology, gas injection techniques have gradually matured and become an important means to enhance oil recovery in reservoir development. Gas channeling is a major challenge in the process of gas injection development. The presence of gas channeling can lead to a decrease in the swept volume of gas flooding, severely affecting the effectiveness of gas injection development. This paper focuses on low-permeability reservoirs, comprehensively analyzing the development characteristics of low-permeability reservoirs and the dynamic characteristics of gas flooding production. It selects and evaluates indicators for assessing the development degree of gas channeling and establishes a fuzzy comprehensive evaluation method for evaluating gas channeling in the gas injection development of low-permeability reservoirs. Based on the evaluation values derived from the fuzzy comprehensive evaluation, it classifies the development levels of gas channeling. Application in oilfield cases shows that the evaluation results of this method are generally consistent with the dynamic response of production data, with high evaluation accuracy. This provides strong support for implementing gas channeling prevention and control measures on site and improving the effectiveness of gas injection development.

A large amount of vegetable waste generated by farms is currently damaging the environment and public health. Anaerobic fermentation is a mature technology that significantly contributes to the recovery of energy and resources from tail vegetables and the control of environmental pollution. However, most vegetable wastes have not been utilized due to poor performance of biogas production, lack of optimal solid contents, and multiple other reasons. Herein, the anaerobic digestion biogas production performance of tail vegetables treated with different total solid (TS) content was studied using solanaceous and leafy vegetables as raw materials. Results showed that there was no acidification in all trials except for treatment with TS of 6%. The optimal TS for anaerobic fermentation of vegetable waste was determined to be around 20% in terms of methane production and biogas production efficiency. The cumulative methane production per unit of volatile solids (VSs) reached 241.7 mL CH4/g of VS, and the methane content was about 65% during the peak period of biogas production. Theoretically, the value of methane production based on anaerobic fermentation of tail vegetables is as high as 1.8 × 1013~4.6 × 1013 L in China. This research provides advice for screening specific and efficient parameters to promote the biogas production rate by tail vegetable anaerobic fermentation.

Plant leaves harbor a rich diversity of bacteria and fungi that, through their interactions with host plants, assume an influential role in plant physiological and metabolic processes. The unique phyllosphere environment of different plant species may shape and select distinct phyllosphere microbial communities. While most academic research has focused on the phyllosphere microorganisms within the same plant variety, there is relatively limited research on the phyllosphere microbial communities between different varieties. Populus L. is a typical tree species in temperate monsoon climates, widely distributed in northern China, and it constitutes a crucial component of China’s forestry resources. For the purpose of this study, we investigated the community structure and diversity of phyllosphere fungi and bacteria in different poplar varieties under identical growth conditions to elucidate the main factors contributing to differences in phyllosphere microbial communities among these varieties. Our findings revealed variations in nitrogen, phosphorus, starch, and soluble sugar contents among the three poplar species studied. Additionally, there were considerable disparities in both abundance and α diversity index of phyllosphere fungal and bacterial communities among these species. At the phylum level, Ascomycota and Basidiomycota have been identified as the dominant fungal communities; while Proteobacteria and Actinomycetes were dominant bacterial communities. The correlation analysis pointed out that chemical traits in the leaves, in particular the total phosphorus and the quantity of soluble sugar, had a significant correlation with the structure and diversity of the microbial community residing in the phyllosphere. Overall, our results demonstrate that even under identical site conditions, each poplar species harbors its own unique phyllosphere microbial community composition as well as distinct leaf characteristics—highlighting host plant diversity as a crucial factor driving differences in phyllosphere microbial composition.

This study aimed to develop a product that closely replicates the texture and appearance of tripe. The effect of three different proteins (soy protein isolate (SPI), pea protein isolate (PPI), and whey protein isolate (WPI)) at different protein levels and processing conditions (heating (90 °C, 1 h) followed by cooling (4 °C, 12 h) and heating (90 °C, 1h) followed by freezing (−18 °C, 12 h)) of konjac glucomannan (KGM) was analyzed. The optimal formulations for simulating tripe were screened by examining their similarity to real tripe in terms of texture, color, and sensory experience. The screened formulations were also subjected to a preliminary mechanistic investigation. The results show that all three proteins improved the gel’s textural properties to varying degrees. At the same concentration, the hardness and chewiness of the KGM/WPI composite gel were significantly higher than those of the other two KGM/protein composite gels, among which the composite gel obtained by adding 8% WPI and 5% KGM heating-frozen (FWK4) had the greatest hardness and chewiness of 4338.07 g and 2313.76, respectively, and the springiness differences in all of the composite gels were small. In addition, the addition of protein increased the whiteness of the hybrid gels, with WPI having the most significant effect on the whiteness of the composite gels (whiteness increased from 30.25 to 62.80 as the concentration of WPI increased from 0 to 10%). Freezing increased composite gel hardness and chewiness, but reduced gel springiness and whiteness. Cluster analysis showed that the composite gel obtained by heating–cooling 8% WPI and 5% KGM (WK4) was very similar to the real tripe in terms of chewiness and whiteness, and WK4 had the highest sensory scores for color, tissue morphology, tactile sensation, taste, and odor. The acceptability score in terms of tissue morphology reached 4.3. Meanwhile, the characterization results of WK4 indicate the presence of large junction areas in the gel network. Fourier transform infrared spectroscopy (FTIR) analysis, X-ray diffraction, and intermolecular force contributions indicated that the incorporation of WPI promoted integral interactions, and that hydrophobic interactions and disulfide bonding played a key role in the WK4 composite gel system. Moreover, scanning electron microscopy (SEM) also showed that the combination of WPI and konjac glucan resulted in a more compact gel structure. This study is informative for the development of the field of bionic tripe processing.

The ever-increasing presence of micropollutants necessitates the development of environmentally friendly bioremediation strategies. Inspired by the remarkable versatility and potent catalytic activities of microbial enzymes, researchers are exploring their application as biocatalysts for innovative environmental cleanup solutions. Microbial enzymes offer remarkable substrate specificity, biodegradability, and the capacity to degrade a wide array of pollutants, positioning them as powerful tools for bioremediation. However, practical applications are often hindered by limitations in enzyme stability and reusability. Enzyme immobilization techniques have emerged as transformative strategies, enhancing enzyme stability and reusability by anchoring them onto inert or activated supports. These improvements lead to more efficient pollutant degradation and cost-effective bioremediation processes. This review delves into the diverse immobilization methods, showcasing their success in degrading various environmental pollutants, including pharmaceuticals, dyes, pesticides, microplastics, and industrial chemicals. By highlighting the transformative potential of microbial immobilized enzyme biocatalysts, this review underscores their significance in achieving a cleaner and more sustainable future through the mitigation of micropollutant contamination. Additionally, future research directions in areas such as enzyme engineering and machine learning hold immense promise for further broadening the capabilities and optimizing the applications of immobilized enzymes in environmental cleanup.

Milk thermal treatment, such as pasteurization, high-temperature short-time processing, and the emerging ultra-short-time processing (

Atopic dermatitis (AD) is a common allergic inflammatory skin condition marked by severe itching, skin lichenification, and chronic inflammation. AD results from a complex immune response, primarily driven by T lymphocytes and environmental triggers, leading to a disrupted epidermal barrier function. Traditional treatments, such as topical corticosteroids, have limitations due to long-term side effects, highlighting the need for safer alternatives. Here, we aimed to show that Agrimonia coreana extract (ACext) can be used in treating AD-related dermatologic symptoms. ACext could inhibit CRAC (Calcium Release-Activated Calcium) channel activity, reducing Orai1/CRAC currents and decreasing intracellular calcium signaling. This inhibition was further confirmed by the reduced IL-2 levels and T cell proliferation upon ACext treatment. In a mouse model of AD, ACext significantly ameliorates symptoms, improves histological parameters, and enhances skin barrier function, demonstrating its potential for treating AD.

Following Einstein’s prediction that “Physics constitutes a logical system of thought” and “Nature is the realization of the simplest conceivable mathematical ideas”, this topical review outlines a formal extension of local realism limited by the speed of light to global realism with bipolar strings (GRBS) that unifies the principle of locality with quantum nonlocality. The related literature is critically reviewed to justify GRBS which is shown as a necessary and inevitable consequence of the Bell test and an equilibrium-based axiomatization of physics and quantum information science for brain–universe similarity and human-level intelligence. With definable causality in regularity and mind–light–matter unity for quantum superposition/entanglement, bipolar universal modus ponens (BUMP) in GRBS makes quantum emergence and submergence of spacetime logically ubiquitous in both the physical and mental worlds—an unexpected but long-sought simplification of quantum gravity with complete background independence. It is shown that GRBS forms a basis for quantum intelligence (QI)—a spacetime transcendent, quantum–digital compatible, analytical quantum computing paradigm where bipolar strings lead to bipolar entropy as a nonlinear bipolar dynamic and set–theoretic unification of order and disorder as well as linearity and nonlinearity for energy/information conservation, regeneration, and degeneration toward quantum cognition and quantum biology (QCQB) as well as information-conservational blackhole keypad compression and big bang data recovery. Subsequently, GRBS is justified as a real-world quantum gravity (RWQG) theory—a bipolar relativistic causal–logical reconceptualization and unification of string theory, loop quantum gravity, and M-theory—the three roads to quantum gravity. Based on GRBS, the following is posited: (1) life is a living bipolar superstring regulated by bipolar entropy; (2) thinking with consciousness and memory growth as a prerequisite for human-level intelligence is fundamentally mind–light–matter unitary QI logically equivalent to quantum emergence (entanglement) and submergence (collapse) of spacetime. These two posits lead to a positive answer to the question “If AI machine cannot think, can QI machine think?”. Causal–logical brain modeling (CLBM) for entangled machine thinking and imagination (EMTI) is proposed and graphically illustrated. The testability and falsifiability of GRBS are discussed.

Historical handwritten manuscripts pose challenges to automated recognition techniques due to their unique handwriting styles and cultural backgrounds. In order to solve the problems of complex text word misdetection, omission, and insufficient detection of wide-pitch curved text, this study proposes a high-precision text detection method based on improved YOLOv8s. Firstly, the Swin Transformer is used to replace C2f at the end of the backbone network to solve the shortcomings of fine-grained information loss and insufficient learning features in text word detection. Secondly, the Dysample (Dynamic Upsampling Operator) method is used to retain more detailed features of the target and overcome the shortcomings of information loss in traditional upsampling to realize the text detection task for dense targets. Then, the LSK (Large Selective Kernel) module is added to the detection head to dynamically adjust the feature extraction receptive field, which solves the cases of extreme aspect ratio words, unfocused small text, and complex shape text in text detection. Finally, in order to overcome the CIOU (Complete Intersection Over Union) loss in target box regression with unclear aspect ratio, insensitive to size change, and insufficient correlation between target coordinates, Gaussian Wasserstein Distance (GWD) is introduced to modify the regression loss to measure the similarity between the two bounding boxes in order to obtain high-quality bounding boxes. Compared with the State-of-the-Art methods, the proposed method achieves optimal performance in text detection, with the precision and mAP@0.5 reaching 86.3% and 82.4%, which are 8.1% and 6.7% higher than the original method, respectively. The advancement of each module is verified by ablation experiments. The experimental results show that the method proposed in this study can effectively realize complex text detection and provide a powerful technical means for historical manuscript reproduction.

Background: Cervical spine (C-spine) trauma usually results from blunt injuries and is traditionally managed by prehospital spinal immobilization using a cervical collar. We sought to examine if prehospital C-spine immobilization is associated with actual C-spine injuries and what factors are associated with the decision to immobilize the C-spine. Methods: We retrospectively analyzed blunt trauma patients treated by Israeli Defense Force (IDF) medical teams from 2015 to 2020. Children, penetrating injuries, and non-threatening injuries were excluded. Demographic data, injury characteristics, and prehospital information were collected from the IDF Trauma Registry’s electronic medical records and merged with corresponding hospital data from the Israeli National Trauma Registry. Results: Overall, 220 patients were included, with a mean age of 32 and a predominance of male patients (78%). Most injuries were due to motor vehicle collisions (77%). In total, 40% of the patients received a cervical collar. C-spine injuries were present in 8%, of which 50% were immobilized with a cervical collar. There were no significant differences in the incidences of C-spine injuries or disability outcomes with or without collar immobilization. The use of a collar was significantly associated with backboard immobilization (OR = 14.5, p < 0.001) and oxygen use (OR = 2.5, p = 0.032). Conclusions: Prehospital C-spine immobilization was not associated with C-spine injury or neurological disability incidences. C-spine immobilization by medical providers may be influenced by factors other than the suspected presence of a C-spine injury, such as the use of a backboard. Clear clinical guidelines for inexperienced medical providers are called for.

Introduction: Despite diagnostic and therapeutic advances, infective endocarditis (IE) is still associated with high mortality rates. Currently, there are no good prognostic tools for the risk assessment of patients with IE. The CHA2DS2-VASc score, used to estimate the risk of ischemic stroke in patients with non-valvular atrial fibrillation (AF), has been shown to be a powerful predictor of stroke and death in patients without known AF associated with other cardiovascular conditions. Objective: We aimed to evaluate the usefulness of the CHA2DS2-VASc score as a prognostic tool in a population of patients with IE. Methods: The Rabin Medical Center Endocarditis Team (RMCET) registry is a retrospective cohort of all patients evaluated at our center due to acute or sub-acute bacterial endocarditis. The CHA2DS2-VASc score was extracted for all patients. All-cause mortality was depicted for all patients. Results: The cohort included 330 patients with a mean age of 65.2 ± 14.7 years (70% men). During a median follow-up of 24 months [IQR 4.7–48.6], 121 (36.7%) patients died. The median CHA2DS2-VASc score was 3, and any score above 2 was associated with increased overall mortality (50.8% vs. 19.9%, p < 0.001). A multivariate model incorporating important confounders not included in the CHA2DS2-VASc model showed consistent results with a risk increase of 121% for the higher CHA2DS2-VASc score groups (HR 2.21 [CI 1.12–4.39], p = 0.023). Conclusions: IE currently has no good risk stratification models for clinical practice. The CHA2DS2-VASc score might serve as a simple and available tool to stratify risk among patients with IE.

Objectives: Tibial eminence fractures account for 2% to 5% of all knee injuries. Low-grade fractures, such as Type I, are typically treated conservatively, whereas high-grade fractures, such as Types III and IV, usually require surgical intervention. This paper describes a modified surgical arthroscopic technique, which employs pull-through triangle suture fixation for Type II and Type III arthroscopic intercondylar eminence avulsion fractures. In addition, we examined the efficacy and complication rate compared to the existing literature. Methods: Data were prospectively collected for knee arthroscopy surgeries and retrospectively analyzed with a minimum two-year follow-up. Twenty-three consecutive adults underwent arthroscopic treatment of displaced intercondylar Type II and Type III eminence fractures, as evidenced by clinical examination and imaging studies between May/2008 and May/2021. The patient’s knee evaluation was performed using clinical symptoms and physical examination, along with International Knee Documentation Committee (IKDC) questionnaire and Tegner Activity Score. Compared to the literature, post-hoc power was calculated based on the mean Tegner Activity Score in our analysis. Results: fifteen females and eight males (mean age 33.9 years, range 19–56 years) were enrolled. The average postoperative follow-up was 35.4 months (27–53). The post-hoc power was 95% confidence in terms of the Tegner Activity Score. The mean ± standard deviation postoperative Tegner Activity Score was 8.2 ± 1.7 (6.8–10.0). Fifteen patients were classified as IKDC A (normal), six as IKDC B (nearly normal), and two as IKDC C (abnormal). The mean IKDC subjective score was 72.7 ± 23 (23–100). Twenty-four patients achieved normal flexion degrees compared with the unaffected side, while one patient achieved a flexion of only 0–90°. The group’s mean flexion range of motion was 123 ± 16° (90–150°). Conclusion: This study presents a modified surgical arthroscopic suture fixation technique for tibial eminence fractures. The procedure is relatively simple and requires no more than basic arthroscopy equipment. The clinical and radiographic results indicate that this technique is safe, efficient, enables early initiation of rehabilitation, and has a lower complication rate in a variety of aspects compared with other fixation techniques used for tibial eminence fractures.

This study proposed a strategy to prepare metalized pellets for direct steelmaking by hydrogen cooling reduction (HCR) of iron ore pellets with a focus on the effect of H2 flow rate on the process. It was demonstrated that increasing H2 flow rate could effectively enhance the reduction performance of iron ore pellets. However, due to the influence of the countercurrent diffusion resistance of gas molecules, too high H2 flow rate no longer promoted the reduction of the pellets when the maximum reduction rate was reached. The reduction swelling index (RSI) of the pellets initially increased and then decreased with increasing H2 flow rate. This change was associated with the decreased content of Fe2SiO4 in the metalized pellets and the changes in porosity and iron particle size. The compressive strength (CS) decreased continuously, showing a sharp decline when the H2 flow rate reached 0.6 L/min. It was attributed to the significant increases in porosity and average pore size of the metalized pellets, with the presence of surface cracks. When the H2 flow rate was 0.8 L/min, the metalized pellets had the optimal performance, namely, reduction degree of 91.45%, metallization degree of 84.07%, total iron content of 80.67 wt%, RSI of 4.66%, and CS of 1265 N/p. The findings demonstrated the importance of controlling the H2 flow rate in the preparation of metallized pellets by HCR.

Accurately predicting wind speeds is of great significance in various engineering applications, such as the operation of high-speed trains. Machine learning models are effective in this field. However, existing studies generally provide deterministic predictions and utilize decomposition techniques in advance to enhance predictive performance, which may encounter data leakage and fail to capture the stochastic nature of wind data. This work proposes an advanced framework for the prediction and early warning of wind speeds by combining the optimized gated recurrent unit (GRU) and adaptive kernel density estimator (AKDE). Firstly, 12 samples (26,280 points each) were collected from an extensive open database. Three representative metaheuristic algorithms were then employed to optimize the parameters of diverse models, including extreme learning machines, a transformer model, and recurrent networks. The results yielded an optimal selection using the GRU and the crested porcupine optimizer. Afterwards, by using the AKDE, the joint probability density and cumulative distribution function of wind predictions and related predicting errors could be obtained. It was then applicable to calculate the conditional probability that actual wind speed exceeds the critical value, thereby providing probabilistic-based predictions in a multilevel manner. A comparison of the predictive performance of various methods and accuracy of subsequent decisions validated the proposed framework.

Steel infrastructure in tunnels significantly affects the accuracy of transient electromagnetic (TEM) data inversion. This study proposes and compares two methods to effectively mitigate this interference: the correction factor method and the prior information constraint method. The correction factor method constructs correction factors using interference data obtained in two ways. The first method, numerical simulation, is theoretically precise but relies heavily on accurate model parameters and requires significant computational resources. The second is conducted by measuring the interference data in the working space. While this approach is simple and operationally convenient, its effectiveness in correction is limited by the measured data quality. The a priori information constraint method improves the inversion by introducing a more accurate initial model. The inversion results of synthetic data indicate that the inversion based on the correction factor method and the prior information constraint method can effectively invert the anomaly. However, the delay effect remains a challenge for the correction factor method. Finally, we invert the field data measured in a mine tunnel by combining the interference processing methods.

The detection of trimethylamine (TMA) is critically important due to its toxic and flammable nature, which poses significant risks to human health and the environment. However, achieving high response, rapid kinetics, selectivity, and low operating temperatures in TMA sensing remains challenging. In this study, WS2/WO3 nanohybrids with flower-like hierarchical structures were synthesized via an in situ sulfurization process, utilizing varying amounts of thioacetamide to control the sulfurization state of WO3. These novel hierarchical WS2/WO3 nanohybrids exhibit remarkable selectivity towards TMA, as well as rapid response and recovery characteristics. Specially, the optimal WS2/WO3 sensor, composed of 5% WS2/WO3 nanohybrids, demonstrates exceptional TMA sensing performance, including a high response (19.45 at 10 ppm), good repeatability, reliable long-term stability, and a low theoretical detection limit (15.96 ppb). The superior sensing capabilities of the WS2/WO3 nanohybrids are attributed to the formation of p-n heterojunctions at the interface, the unique hierarchical structures, and the catalytic activity of WS2. Overall, this work provides a straightforward and versatile approach for synthesizing multifunctional nanomaterials by combining metal oxide micro-flowers with transition metal dichalcogenide nanoflakes for applications in monitoring TMA in complex environments.

Premature loss of root canal-treated primary teeth has long been a concern in dentistry. To address this, researchers developed a sodium iodide-based root canal-filling material as an alternative to traditional iodoform-based materials. The goal of this study was to improve the physicochemical properties of the sodium iodide-based material to meet clinical use standards. To resolve high solubility issues in the initial formulation, researchers adjusted component ratios and added new ingredients, resulting in a new paste called L5. This study compared L5 with L0 (identical composition minus lanolin) and Vitapex as controls, conducting physicochemical and antibacterial tests. Results showed that L5 met all ISO 6876 standards, demonstrated easier injection and irrigation properties than Vitapex, and exhibited comparable antibacterial efficacy to Vitapex, which is currently used clinically. The researchers conclude that if biological stability is further verified, L5 could potentially be presented as a new option for root canal-filling materials in primary teeth.

The objectives of this study were to explore the mechanism of stem mechanical strength in direct-seeded rice (DSR) as affected by paclobutrazol, especially its related endogenous hormone and cell wall component changes in culm tissue and response to the application of paclobutrazol. Field experiments were conducted in Changchun County, Jilin Province, China, by using two japonica rice varieties, Jiyujing and Jijing305, with soaking seeds in paclobutrazol at concentrations of (0 mg L−1, S0; 50 mg L−1; S1; 100 mg L−1; S2; 150 mg L−1, S3; 200 mg L−1, S4) in 2021 and 2022. The results suggest that the application of paclobutrazol increased the grain yield and reduced the lodging rate of DSR. Compared with the S0 treatments, soaking the seeds in paclobutrazol treatments rapidly shortened the length of the basal internode by decreasing the endogenous indole acetic acid (IAA) and gibberellin A3 (GA3) contents in culm tissue. The larger breaking strength (M) was attributed to a higher section modulus (SM) and bending stress (BS). The higher mechanical tissue thickness in culm tissue under paclobutrazol treatments, which was raised by higher endogenous zeatin and zeatin riboside (Z+ZR) content in culm tissue, increased the culm diameter, culm wall thickness, and section modulus (SM) of the internode. Compared with the S0 treatments, soaking the seeds in paclobutrazol treatments increased the cellulose content, lignin content, activities of lignin-related enzymes, and expression of key genes in lignin biosynthesis, as well as resulted in a higher bending stress (BS) to enhance the culm breaking strength (M).

The occurrence of space weather events, notably geomagnetic storms driven by various solar wind structures, can significantly alter Earth’s electromagnetic environment. In this study, we examined the interplanetary origins and statistical distribution of 384 geomagnetic storms (Dstmin ≤ −50 nT) that occurred from September 1996 to December 2023. We statistically analyzed the correlations between storm intensity and solar wind parameters (SWPs) across different subsets. The results indicate that (1) the solar activity level, indicated by the sunspot number (SSN), and the number of geomagnetic storms during the first four years of the 25th solar cycle were intermediate, compared to the first four years of the 23rd and 24th solar cycles. Specifically, ICME-related structures caused 80% of the strong storms (Dstmin ≤ −100 nT) and 34% of the moderate storms (−100 nT < Dstmin ≤ −50 nT) from 2020 to 2023. (2) The storm intensity correlated with the peak and/or time-integral values of the southward interplanetary magnetic field (IMF Bs), the dawn–dusk electric field (Ey), the Akasofu’s function (ε), and dynamic pressure (Psw) to varying extents. Strong storms exhibited higher correlation levels than moderate ones and ICME-related storms showed larger correlation levels compared to those driven by other sources. (3) Compared with the storms from 1996-09 to 2000-08, the storms that occurred from 2020 to 2023 had lower correlations with the peak values of the IMF Bs and Ey but higher correlations with the peak value of ε and the time-integral values of the IMF Bs, Ey, Psw, and ε. (4) Among the 174 events that featured continuous southward IMF during the storm’s main phase, the duration of southward IMF during about 66.7% of moderate storms and 51.5% of strong storms were less than 13 h. Continuous southward IMF resulted in more direct and efficient energy coupling, enhancing the correlation between the peak values of SWPs and storm intensity but weakening the relationships with the time-integral values of SWPs. Notably, when the southward IMF persisted for a longer duration (e.g., ∆t > 13 h), the continuous energy input further enhanced correlations with both peak and integral values of SWPs, leading to stronger overall correlations with storm intensity. This analysis sheds light on the intricate relationships between geomagnetic storms and their solar wind drivers, emphasizing the significant influence of ICME-related structures and the duration of southward IMF on storm intensity.

Typical satellite component detection is an application-valuable and challenging research field. Currently, there are many algorithms for detecting typical satellite components, but due to the limited storage space and computational resources in the space environment, these algorithms generally have the problem of excessive parameter count and computational load, which hinders their effective application in space environments. Furthermore, the scale of datasets used by these algorithms is not large enough to train the algorithm models well. To address the above issues, this paper first applies YOLOv8 to the detection of typical satellite components and proposes a Lightweight Typical Satellite Components Detection algorithm based on improved YOLOv8 (LTSCD-YOLO). Firstly, it adopts the lightweight network EfficientNet-B0 as the backbone network to reduce the model’s parameter count and computational load; secondly, it uses a Cross-Scale Feature-Fusion Module (CCFM) at the Neck to enhance the model’s adaptability to scale changes; then, it integrates Partial Convolution (PConv) into the C2f (Faster Implementation of CSP Bottleneck with two convolutions) module and Re-parameterized Convolution (RepConv) into the detection head to further achieve model lightweighting; finally, the Focal-Efficient Intersection over Union (Focal-EIoU) is used as the loss function to enhance the model’s detection accuracy and detection speed. Additionally, a larger-scale Typical Satellite Components Dataset (TSC-Dataset) is also constructed. Our experimental results show that LTSCD-YOLO can maintain high detection accuracy with minimal parameter count and computational load. Compared to YOLOv8s, LTSCD-YOLO improved the mean average precision (mAP50) by 1.50% on the TSC-Dataset, reaching 94.5%. Meanwhile, the model’s parameter count decreased by 78.46%, the computational load decreased by 65.97%, and the detection speed increased by 17.66%. This algorithm achieves a balance between accuracy and light weight, and its generalization ability has been validated on real images, making it effectively applicable to detection tasks of typical satellite components in space environments.

Presently, the practice of scientific crowdsourcing still suffers from user loss, platform operational inefficiency, and many other dilemmas, mainly because the process mechanism of realizing value co-creation through interaction between users and platforms has not yet been elaborated. To fill this gap, this study takes Kaggle as the research object and explores the realization process and internal mechanism of scientific crowdsourcing value co-creation from the perspective of knowledge flow. The results show that the operation process of Kaggle-based scientific crowdsourcing can be decomposed into five progressive evolutionary stages, including knowledge sharing, knowledge innovation, knowledge dissemination, knowledge application, and knowledge advantage formation. The knowledge flow activates a series of value co-creation activities of scientific crowdsourcing, forming a dynamic evolution and continuous optimization of the value co-creation process that includes the value proposition, value communication, value consensus, and all-win value. Institutional logic plays a key role as a catalyst in the value co-creation of scientific crowdsourcing, effectively facilitating the realization of value co-creation by controlling and guiding the flow of knowledge. The study unlocks the “gray box” from knowledge flow to value co-creation, providing new theoretical support and guidance for further enhancing the value co-creation capacity and accelerating the practice of scientific crowdsourcing.

(1) Background: Bovine viral diarrhea virus (BVDV) causes calf diarrhea, bovine respiratory syndrome, and cow abortion, resulting in substantial economic losses in the cattle industry. Owing to its persistent infection mechanism, BVDV is a major challenge in the treatment of cattle. (2) Methods: To determine how metformin (Met) inhibits the interaction between BVDV and host cells, we treated BVDV-infected cells with Met. We then performed an RNA sequencing (RNA-seq) analysis of Met-treated cells infected with BVDV to identify differentially expressed genes (DEGs). Consequently, the RNA-seq results were validated through real-time quantitative PCR (qPCR). (3) Results: Our analysis revealed 3169 DEGs in the Met-treated cells (Met group) vs. the negative controls (NC group) and 2510 DEGs in the BVDV-infected cells after pretreatment with Met (MetBVDV group) vs. the BVDV-infected cells (BVDV group). The DEGs were involved in MDBK interactions during BVDV infection, as indicated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The potential interactions of the DEGs were confirmed via a protein–protein interaction (PPI) network. Met treatment induced autophagy signaling activity and the expression of the autophagy-related genes ATG2A, ATG4B, ATG10, and ATG12 in BVDV-infected Met-pretreated cells. (4) Conclusions: We found that the host transcriptomic profile was affected by BVDV infection and Met pretreatment. These findings offer valuable new insights and provide support for future studies on the inhibition of BVDV replication by Met.

Quasi-static and dynamic tensile tests on aluminum-hydroxide-enhanced ethylene propylene diene monomer (EPDM) coatings were conducted using a universal testing machine and a Split Hopkinson Tension Bar (SHTB) over a strain rate range of 10−3 to 103 s−1. This comprehensive study explored the tensile performance of enhanced EPDM coatings in solid rocket motors. The results demonstrated a significant impact of strain rate on the mechanical properties of EPDM coatings. To capture the hyperelastic and viscoelastic characteristics of EPDM coatings at large strains, the Ogden hyperelastic model was used to replace the standard elastic component to develop an enhanced Zhu–Wang–Tang (ZWT) nonlinear viscoelastic constitutive model. The model parameters were fitted using a particle swarm optimization (PSO) algorithm. The improved constitutive model’s predictions closely matched the experimental data, accurately capturing stress–strain responses and inflection points. It effectively predicts the tensile behavior of aluminum-hydroxide-enhanced EPDM coatings within a 20% strain range and a wide strain rate range.

The ionospheric quasi-periodic wave is a type of typical and common electromagnetic wave phenomenon occurring in extremely low-frequency (ELF) and very low-frequency ranges (VLF). These emissions propagate in a distinct whistler-wave mode, with varying periodic modulations of the wave intensity over time scales from several seconds to a few minutes. We developed an automatic detection model for the QP waves in the ELF band recorded by the China Seismo-Electromagnetic Satellite. Based on the 827 QP wave events, which were collected through visual screening from the electromagnetic field observations, an automatic detection model based on the Transformer architecture was built. This model, comprising 34.27 million parameters, was trained and evaluated. It achieved mean average precision of 92.3% on the validation dataset, operating at a frame rate of 39.3 frames per second. Notably, after incorporating the proton cyclotron frequency constraint, the model displayed promising performance. Its lightweight design facilitates easy deployment on satellite equipment, significantly enhancing the feasibility of on-board detection.

The flowering period of rice significantly impacts variety adaptability and yield formation. Properly shortening the reproductive period of rice varieties can expand their ecological range without significant yield reduction. Targeted genome editing, like CRISPR/Cas9, is an ideal tool to fine-tune rice growth stages and boost yield synergistically. In this study, we developed a CRISPR/Cas9-mediated multiplex genome-editing vector containing five genes related to three traits, Hd2, Ghd7, and DTH8 (flowering-stage genes), along with the recessive rice blast resistance gene Pi21 and the aromatic gene BADH2. This vector was introduced into the high-quality japonica rice variety in Zhejiang province, Jiahe212 (JH212), resulting in 34 T0 plants with various effective mutations. Among the 17 mutant T1 lines, several displayed diverse flowering dates, but most exhibited undesirable agronomic traits. Notably, three homozygous mutant lines (JH-C15, JH-C18, and JH-C31) showed slightly earlier flowering dates without significant differences in yield-related traits compared to JH212. Through special Hyg and Cas marker selection of T2 plants, we identified seven, six, and two fragrant glutinous plants devoid of transgenic components. These single plants will serve as sib lines of JH212 and potential resources for breeding applications, including maintenance lines for indica–japonica interspecific three-line hybrid rice. In summary, our research lays the foundation for the creation of short-growth-period CMS (cytoplasmic male sterility, CMS) lines, and also provides materials and a theoretical basis for indica–japonica interspecific hybrid rice breeding with wider adaptability.

The 3D nanopatterned silica shells of diatoms have gained attention as drug delivery vehicles because of their high porosity, extensive surface area, and compatibility with living organisms. Tooth extraction may result in various complications, including impaired blood clotting, desiccation of the root canal, and infection. Therapeutic sponges that possess multiple properties, such as the ability to stop bleeding and kill bacteria, provide numerous advantages for the healing of the area where a tooth has been removed. This study involved the fabrication of a composite material with antibacterial and hemostatic properties for dental extraction sponges. We achieved this by utilizing the porous nature and hemostatic capabilities of diatom biosilica. The antibiotic used was doxycycline. The gelatin-based diatom biosilica composite with antibiotics had the ability to prevent bleeding and release the antibiotic over a longer time compared to gelatin sponge. These properties indicate its potential as a highly promising medical device for facilitating rapid healing following tooth extraction.

Reinforced concrete (RC) walls are extensively used in civil air defense engineering and are susceptible to low-speed impacts with high energy and massive weight during service. Therefore, it is crucial to consider the impact resistance of these walls and explore effective protection methods. The honeycomb structure, known for its energy-absorbing properties, has been widely utilized in aerospace, automotive, and maritime industries. However, there is a need for more research on applying honeycomb structures in energy-absorbing protection devices in civil engineering. This study proposes the use of honeycomb sandwich panels to protect civil air defense RC walls, creating a Honeycomb Sandwich Panel Composite RC wall (HSP-RC wall). Through pendulum impact experiments, we investigated the dynamic response of RC walls, both standard and HSP-RC walls with varying honeycomb parameters, under high-energy impacts. The goal was to evaluate the impact resistance of these RC walls, analyze the deformation differences among different HSP-RC walls, and examine the influence of honeycomb parameters on the impact protection effectiveness. The research results indicate that honeycomb sandwich panels can provide impact protection for RC walls by absorbing energy, and their protection effect is related to the parameters of the honeycomb core layer. This research result can be applied to RC structures that bear impact loads, achieving effective protection for RC structures.

This study presents an analytical approach for evaluating the reliability of anhydrite rock tunnels, focusing on their characteristic swelling behavior. Anhydrite rocks, prone to significant expansion upon moisture exposure, pose a challenge in tunnel construction, potentially leading to structural issues such as floor heave and lining damage. To address this, this research develops an elastic swelling analytical solution based on humidity stress field theory, enabling the assessment of time-dependent stress and deformation changes in anhydrite tunnels. The solution’s applicability is demonstrated through its application to the Lirang tunnel. The investigation into the effects of support pressure, swelling time, and reserved deformation on tunnel reliability reveals that circumferential stress at the tunnel wall increases by 13.94% and 21.86% for swelling periods of 30 and 365 days, respectively. Similarly, radial displacement escalates by 22.97% and 35.93% over these periods, highlighting the significant impact of swelling behavior. Using a spreadsheet-based First Order Reliability Method (FORM) for analysis, this study finds that the original design of the Lirang tunnel did not meet the desired reliability standards under swelling conditions. However, strategic adjustments in construction variables, such as increasing support pressure to 1.2 MPa or enhancing reserved deformation to 59 mm, elevated the tunnel’s reliability to meet safety requirements. This research provides a vital framework for assessing and enhancing the reliability of anhydrite rock tunnels, considering the long-term effects of swelling. It underscores the importance of incorporating swelling behavior in the design and construction of tunnels in anhydrite rock formations, offering valuable insights for optimizing tunnel stability in such challenging geological conditions.

Microbial synthesis offers a sustainable and eco-friendly approach for nanoparticle production. This study explores the biogenic synthesis of zinc oxide nanoparticles (ZnO-NPs) utilizing the actinomycete Saccharopolyspora hirsuta (Ess_amA6) isolated from Tapinoma simrothi. The biosynthesized ZnO-NPs were characterized using various techniques to confirm their formation and properties. UV–visible spectroscopy revealed a characteristic peak at 372 nm, indicative of ZnO-NPs. X-ray diffraction (XRD) analysis confirmed the crystalline structure of the ZnO-NPs as hexagonal wurtzite with a crystallite size of approximately 37.5 ± 13.60 nm. Transmission electron microscopy (TEM) analysis showed the presence of both spherical and roughly hexagonal ZnO nanoparticles in an agglomerated state with a diameter of approximately 44 nm. The biogenic ZnO-NPs exhibited promising biomedical potential. They demonstrated selective cytotoxic activity against human cancer cell lines, demonstrating higher efficacy against Hep-2 cells (IC50 = 73.01 µg/mL) compared to MCF-7 cells (IC50 = 112.74 µg/mL). Furthermore, the biosynthesized ZnO-NPs displayed broad-spectrum antimicrobial activity against both Pseudomonas aeruginosa and Staphylococcus aureus with clear zones of inhibition of 12.67 mm and 14.33 mm, respectively. The MIC and MBC values against P. aeruginosa and S. aureus ranged between 12.5 and 50 µg/mL. These findings suggest the potential of S. hirsuta-mediated ZnO-NPs as promising biocompatible nanomaterials with dual applications as antimicrobial and anticancer agents.

With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Prunus mume Siebold & Zucc (mei) is a horticulturally important fruit tree that undergoes anthesis in winter. Therefore, its flowering process is challenged by low-temperatures conditions. The transcription factor (TF) MYB21 is pivotal in regulating the flowering process, and particularly functions in petal expansion and filament elongation. However, the regulatory mechanism of PmMYB21 in mei remains unknown. To breed early-flowering cultivars, a deeper understanding of PmMYB21-regulated genes is essential. We employed DNA affinity purification sequencing (Dap-seq) to identify downstream genes bound by PmMYB21. The results revealed the promoter region is the primary binding region of PmMYB21, and the AGTTAGGTARR motif (motif1) is the predominant binding sequence type. Our analysis identified 8533 genes that are potentially bound by PmMYB21 with the motif1 sequence type, within the promoter region. These genes are involved in biological processes critical to flowering. Further refinement of candidate genes was achieved through Weighted Gene Co-expression Network Analysis (WGCNA), which identified the co-expressed genes of PmMYB21 during flowering activity. Integrating Dap-seq and WGCNA data, we narrowed down the candidate gene list to 54, with a focus on 4 MADS-box genes and 2 hormone signaling genes that are crucial to the flowering process under low-temperature conditions. This study offers valuable insights into the molecular underpinnings of PmMYB21’s role in the low-temperature flowering regulation of mei, paving the way for the development of new cultivars adapted to early blooming.

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health, combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management.

Healthy coral reefs provide diverse habitats for marine life, playing a crucial role in marine ecosystems. Coral health is under threat due to global climate change, ocean pollution, and other environmental stressors, leading to coral bleaching. Coral bleaching disrupts the symbiotic relationship between corals and algae, ultimately impacting the entire marine ecosystem. Processing complex underwater images manually is time-consuming and burdensome for marine experts. To rapidly locate and monitor coral health, deep neural networks are employed for identifying coral categories, which can facilitate the automated processing of extensive underwater imaging data. However, these classification networks may overlook critical classification criteria like color and texture. This paper proposes a multi-local perception network (ML-Net) for image classification of healthy and bleached corals. ML-Net focuses on local features of coral targets, leveraging valuable information for image classification. Specifically, the proposed multi-branch local adaptive block extracts image details through parallel convolution kernels. Then, the proposed multi-scale local fusion block integrates features of different scales vertically, enhancing the detailed information within the deep network. Residual structures in the shallow network transmit local information with more texture and color to the deep network. Both horizontal and vertical multi-scale fusion blocks in deep networks are used to capture and retain local details. We evaluated ML-Net using six evaluation metrics on the Bleached and Unbleached Corals Classification dataset. In particular, ML-Net achieves an ACC result of 86.35, which is 4.36 higher than ResNet and 8.5 higher than ConvNext. Experimental results demonstrate the effectiveness of the proposed modules for coral classification in underwater environments.

The quality of molten iron not only has a significant impact on the strength, toughness, smelting cost and service life of cast iron but also directly affects the satisfaction of users. The establishment of timely and accurate blast furnace molten iron quality prediction models is of great significance for the improvement of the production efficiency of blast furnace. In this paper, Si, S and P content in molten iron is taken as the important index to measure the quality of molten iron, and the 989 sets of production data from a No.1 blast furnace from August to October 2020 are selected as the experimental data source, predicting the quality of molten iron by the I-GWO-CNN-BiLSTM model. First of all, on the basis of the traditional data processing method, the missing data values are classified into correlation data, temporal data, periodic data and manual input data, and random forest, the Lagrangian interpolation method, the KNN algorithm and the SVD algorithm are used to complete them, so as to obtain a more practical data set. Secondly, CNN and BiLSTM models are integrated and I-GWO optimized hyperparameters are used to form the I-GWO-CNN-BiLSTM model, which is used to predict Si, S and P content in molten iron. Then, it is concluded that using the I-GWO-CNN-BiLSTM model to predict the molten iron quality can obtain high prediction accuracy, which can provide data support for the regulation of blast furnace parameters. Finally, the MCMC algorithm is used to analyze the influence of the input variables on the Si, S and P content in molten iron, which helps the steel staff control the quality of molten iron in a timely manner, which is conducive to the smooth running of blast furnace production.

In order to investigate the laws of the laser peening forming process and the effects of laser peening on the surface quality and tensile properties of 5083 aluminum alloy, experiments were conducted utilizing various laser peening paths, energies, and plate thicknesses. Subsequently, laser peening forming experiments were performed on S-shaped and different shapes of aluminum alloy substrates. The impact of different laser peening durations on surface morphology and tensile properties was then analyzed. Results indicated that the largest bending deformation perpendicular to the laser peening path reached 12.5 mm. In cases where the laser peening path was inclined relative to the horizontal direction, torsional deformations were observed in the aluminum alloy plate. For laser energy levels of 5 J, 6 J, and 7 J, deformation amounts were 3.8 mm, 4.9 mm, and 5.4 mm, respectively. Plates with thicknesses of 4 mm exhibited convex deformation, while those with 2 mm thickness showed concave deformation. Furthermore, following one and two laser peening cycles, the residual stresses in the alloy plates were −80 MPa and −107 MPa, the surface hardness increased by 16 HV and 31 HV, the roughness increased by 2.495 μm and 3.615 μm, and the tensile strength increased by 9.5 MPa and 18.5 MPa, respectively.