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Abstract. Recent advancements in the field of medical artificial intelligence (AI) have led to the widespread adoption of foundational and large language models. This review paper explores their applications within medical AI, introducing a novel classification framework that categorizes them as disease-specific, general-domain, and multi-modal models. The paper also addresses key challenges such as data acquisition and augmentation, including issues related to data volume, annotation, multi-modal fusion, and privacy concerns. Additionally, it discusses the evaluation, validation, limitations, and regulation of medical AI models, emphasizing their transformative potential in healthcare. The importance of continuous improvement, data security, standardized evaluations, and collaborative approaches is highlighted to ensure the responsible and effective integration of AI into clinical applications.

We investigated the challenge of unconstrained distributed optimization with a time-varying objective function, employing a prediction-correction approach. Our method introduced a backward Euler prediction step that used the differential information from consecutive moments to forecast the trajectory's future direction. This predicted value was then refined through an iterative correction process. Our analysis and experimental results demonstrated that this approach effectively addresses the optimization problem without requiring the computation of the Hessian matrix's inverse.

Abstract Background Existing data on maternal dietary patterns and birth weight remains limited and inconsistent, especially in non-Western populations. We aimed to examine the relationship between maternal dietary patterns and birth weight among a cohort of Chinese. Methods In this study, 4,184 mother-child pairs were included from the Iodine Status in Pregnancy and Offspring Health Cohort. Maternal diet during pregnancy was evaluated using a self-administered food frequency questionnaire with 69 food items. Principal component analysis was used to identify dietary patterns. Information on birth weight and gestational age was obtained through medical records. Adverse outcomes of birth weight were defined according to standard clinical cutoffs, including low birth weight, macrosomia, small for gestational age, and large for gestational age. Results Three maternal dietary patterns were identified: plant-based, animal-based, and processed food and beverage dietary patterns, which explained 23.7% variance in the diet. In the multivariate-adjusted model, women with higher adherence to the plant-based dietary patten had a significantly higher risk of macrosomia (middle tertile vs. low tertile: odds ratio (OR) 1.45, 95% CI 1.00-2.10; high tertile vs. low tertile: OR 1.55, 95% CI 1.03–2.34; P-trend = 0.039). For individual food groups, potato intake showed positive association with macrosomia (high tertile vs. low tertile: OR 1.72, 95% CI 1.20–2.47; P-trend = 0.002). Excluding potatoes from the plant-based dietary pattern attenuated its association with macrosomia risk. No significant associations was observed for the animal-based or processed food and beverage dietary pattern with birth weight outcomes. Conclusions Adherence to a plant-based diet high in carbohydrate intake was associated with higher macrosomia risk among Chinese women. Future studies are required to replicate these findings and explore the potential mechanisms involved.

Abstract Regenerating inflamed bone defects represents a severe clinical challenge due to the undesirable inflammatory microenvironment. The inflammatory stimulus poses a weighty threat to the regenerative capacity of endogenously derived mesenchymal stem cells (MSCs), which are mainly responsible for osteogenic differentiation, thereby resulting in compromised endogenous bone formation. Consequently, alleviating the biological characteristics of inflammatory-impaired MSCs is crucial for promoting inflamed bone regeneration. Nano-sized small extracellular vesicles (sEVs) have emerged as promising therapeutic tools to orchestrate MSCs fate due to their intrinsic biocompatibility and encapsulated bioactive contents. In the present study, we extracted sEVs from youthful and adult dental pulp MSCs and explored their ability to recover inflammation-compromised periodontal ligament stem cells (IPDLSCs). The results indicated that both types of sEVs were capable of facilitating IPDLSCs osteogenesis. However, young sEVs exhibited a more robust potential at a lower concentration compared to adult sEVs. Mechanically, young sEVs enhanced the expression of bone morphogenetic protein 4 (BMP4) via delivering the protein Biglycan, which correspondingly promoted the osteogenic capability of IPDLSCs. Collectively, our findings emphasized that young sEVs hold enormous potential to rescue the inherent function and regenerative competence of inflammation-impaired MSCs, shedding light on their promising therapeutic prospects for infected bone regeneration.

Abstract. Background:. In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. Methods:. We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on day 3 or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for the prediction of aneuploidy and consequent live-birth outcomes. Results:. These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709–0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. Conclusions:. Our study underscores the AI model’s ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.

Abstract Background The prevalence of sarcopenia is increasing in worldwide with accelerated aging process. The high dietary protein intakes are associated with improved muscle mass and strength especially in Asian countries. However, there are few researches on amino acid levels or mechanism exploration. We conducted a case-control study to explore the amino acid metabolic characteristics and potential mechanism of elderly women with sarcopenia using targeted amino acid metabolomics approach combined with an analysis of dietary intake. Methods For our case-control study, we recruited women (65–75 y) from a Shanghai community with 50 patients with sarcopenia and 50 healthy controls. The consensus updated by the Asian Working Group on Sarcopenia in 2019 was used to screening for sarcopenia and control groups. We collected fasting blood samples and evaluated dietary intake. We used the amino acid-targeted metabolomics by ultra performance liquid chromatography tandem mass spectrometry to identify metabolic differentials between the case and control groups and significantly enriched metabolic pathways. Results The case (sarcopenia) group had a lower intake of energy, protein, and high-quality protein (P

Background: Randomized controlled trials (RCTs) investigating the efficacy of bispectral index (BIS) to reduce intra-operative awareness (IOA) have reported conflicting results. The purpose of this meta-analysis is to consolidate results from RCTs to assess the efficacy of BIS in reducing IOA when compared to controls. Secondary outcomes included time to extubation, time to spontaneous and/or verbal eye opening, PACU discharge time, and utilization of inhaled anesthetics. Methods: RCTs which reported on one of the primary and/or secondary outcomes were included. Literature search utilized keywords “randomized control trial” and “intraoperative awareness.” Meta-analysis was performed using RevMan 5. Results: Twenty-seven RCTs were included in the study with a total of 35,585 patients, with 18,146 patients in the BIS and 17,439 in the control group. Eighteen of 14,062 patients (0.12%) and 42 of 16,765 (0.25%) reported definite IOA in the BIS and control group, respectively, with no statistically significant difference. BIS was effective in reducing the time to spontaneous eye opening by an average of 1.3 minutes and the time to extubation by an average of 1.97 minutes. There was no difference in PACU discharge times among the groups. There was a significant decrease in consumption of sevoflurane but no difference in desflurane and propofol compared to the control group. Conclusion: While BIS monitoring results in decreased incidence of intra-operative awareness by half, it was not statistically significant. BIS provides modest benefits with regard to reducing the time to extubation, the time to spontaneous eye opening, and consumption of sevoflurane. Level of evidence: I.

Ammonia is an important environmental pollutant that is toxic to most aquatic animals, and nutrient manipulation is an effective regulatory pathway. To assess the influence of taurine supplementation on growth, liver detoxification and anti-bacterial infection of yellow catfish subjected to ammonia exposure for a long time, three experiment diets with different taurine levels (0.50, 1.00, and 2.00 %) were formulated and fed to fish (7.28 ± 0.05) g under three ammonia levels (0.00, 25.00 and 50.00 mg/L total ammonia nitrogen) for 56 days. The results showed that hepatic glycogen and whole-body protein contents were reduced, suggesting that energy deficiency caused by ammonia poisoning plays an important role in growth inhibition; elevated serum alanine aminotransferase and aspartate aminotransferase levels suggest liver injury, which may be caused by oxidative stress (decreased activities of superoxide dismutase and catalase, and expressions of SOD and CAT) and inflammation (increased contents of tumor necrosis factor α, interleukin 1 and interleukin 8, and expressions of TNF α, IL 1 and IL8), analysis of tissue sections further confirmed this finding. Furthermore, after injection with Aeromonas hydrophila, cumulative mortality increased proportionally with the rise in ammonia levels, the observed decrease in serum complements C3, C4, immunoglobulin M, and antibody titer contents emerged as a crucial factor contributing to the decreased disease resistance. The exogenous taurine was found to alleviate the negative impact of the above-mentioned ammonia poisoning. This study provides valuable insights into the mechanism of ammonia-induced aquatic toxicology in fish.

Abstract A novel variant of rabbit haemorrhagic disease virus, designated RHDV2/b/GI.2, was first discovered in France in 2010. Subsequently, RHDV2 rapidly spread to Africa, North America, Australia, and Asia. RHDV2 outbreaks have resulted in significant economic losses in the global rabbit industry and disrupted the balance of natural ecosystems. Our study investigated the seasonal characteristics of RHDV2 outbreaks using seasonal indices. RHDV2 is prone to causing significant outbreaks within domestic and wild rabbit populations during the spring season and is more likely to induce outbreaks within wild rabbit populations during late autumn in the Southern Hemisphere. Furthermore, based on outbreak data for domestic and wild rabbits and environmental variables, our study established two MaxEnt models to explore the relationship between RHDV2 outbreaks and the environmental factors and conducted outbreak risk predictions for RHDV2 in global domestic and wild rabbit populations. Both models demonstrated good predictive performance, with AUC values of 0.960 and 0.974, respectively. Road density, isothermality, and population density were identified as important variables in the outbreak of RHDV2 in domestic rabbits, while road density, normalized difference vegetation index, and mean annual solar radiation were considered key variables in the outbreak of RHDV2 in wild rabbits. The environmental factors associated with RHDV2 outbreaks identified in our study and the outbreak risk prediction maps generated in our study will aid in the formulation of appropriate RHDV2 control measures to reduce the risk of morbidity in domestic and wild rabbits.

Team sports athletes may encounter significant stress, leading to competitive anxiety. The anxiety levels can be influenced by team behaviors and achievement goals. This study aims to investigate the relationship between team behaviors (i.e., perceptions of controlling coaching behavior and team cohesion) and competitive anxiety, and to examine the mediation effects of achievement goals (i.e., task-oriented and ego-oriented) on the relationship. A total of 298 team-handball players were involved in the study, ages ranging from 16 to 24 years old (M = 18.44, SD = 3.09). A cross-sectional research design was adopted, and structural equation modeling was utilized to analyze path coefficients and mediating effects. Findings indicated that perceptions of controlling coaching behaviors had significant positive predictions for state and somatic anxiety (β = 0.22, 0.29) and negative predictions for self-confidence (β = −0.19). Team cohesion had significant negative predictions for state anxiety (β = −0.31) and positive predictions for self-confidence (β = 0.58). In addition, ego-oriented goals play a positive mediating role in the relationship between team behaviors and competitive anxiety (β = 0.03–0.35), while task-oriented goals play a negative mediating role in the relationship between team behaviors and competitive anxiety (β = −0.18 - −0.03). In conclusion, team behaviors have a significant relationship with competitive anxiety, with achievement goals playing a mediating role among them. Therefore, to alleviate team sports athletes’ competitive anxiety, it is recommended to reduce coach control behaviors, enhance team cohesion, and employ psychological training methods (e.g., mindfulness or meditation) to strengthen athletes’ task-oriented goals.

Abstract Soft magnetic materials with flake geometry can provide shape anisotropy for breaking the Snoek limit, which is promising for achieving high-frequency ferromagnetic resonances and microwave absorption properties. Here, two-dimensional (2D) Fe3C microflakes with crystal orientation are obtained by solid-state phase transformation assisted by electrochemical dealloying. The shape anisotropy can be further regulated by manipulating the thickness of 2D Fe3C microflakes under different isothermally quenching temperatures. Thus, the resonant frequency is adjusted effectively from 9.47 and 11.56 GHz under isothermal quenching from 700 °C to 550 °C. The imaginary part of the complex permeability can reach 0.9 at 11.56 GHz, and the minimum reflection loss (RL min ) is −52.09 dB (15.85 GHz, 2.90 mm) with an effective absorption bandwidth (EAB≤−10 dB) of 2.55 GHz. This study provides insight into the preparation of high-frequency magnetic loss materials for obtaining high-performance microwave absorbers and achieves the preparation of functional materials from traditional structural materials.

Herein, a method of true-temperature inversion for a multi-wavelength pyrometer based on fractional-order particle-swarm optimization is proposed for difficult inversion problems with unknown emissivity. Fractional-order calculus has the inherent advantage of easily jumping out of local extreme values; here, it is introduced into the particle-swarm algorithm to invert the true temperature. An improved adaptive-adjustment mechanism is applied to automatically adjust the current velocity order of the particles and update their velocity and position values, increasing the accuracy of the true temperature values. The results of simulations using the proposed algorithm were compared with three algorithms using typical emissivity models: the internal penalty function algorithm, the optimization function (fmincon) algorithm, and the conventional particle-swarm optimization algorithm. The results show that the proposed algorithm has good accuracy for true-temperature inversion. Actual experimental results from a rocket-motor plume were used to demonstrate that the true-temperature inversion results of this algorithm are in good agreement with the theoretical true-temperature values.

The purpose of this study was to analyze the characteristics of occurrence and spread of highly pathogenic avian influenza H5N1 (HPAI-H5N1) globally, understand its spatiotemporal characteristics, investigate the risk factors influencing outbreaks, and identify high-risk areas for disease occurrence. We collected the data on global poultry HPAI-H5N1 outbreaks from January 2005 to April 2023, and conducted a thorough analysis of the spatial and temporal characteristics of the disease through time series decomposition and directional distribution analysis. Additionally, an ecological niche model was established to explore the major factors influencing the occurrence of HPAI-H5N1 and to pinpoint high-risk areas. Our findings revealed that HPAI-H5N1 outbreaks were cyclical, and seasonal, exhibiting a rising trend, with a predominant northwest-southeast transmission direction. The ecological niche model highlighted that species factors and economic trade factors are critical in influencing the outbreak of HPAI-H5N1. Variables such as chicken and duck density, population density, isothermality, and road density, contributed to importantly risk of outbreaks. High-risk areas for HPAI-H5N1 occurrence were primarily identified in Europe, West Africa, Southeast Asia, and Southeast China. This study provided valuable insights into the spatial and temporal distribution characteristics and risk factors of global poultry HPAI-H5N1 outbreaks. The identification of high-risk areas provides essential information that can be used to develop more effective prevention and control policies.

Urban expansion and fossil fuel dependence have led to energy and environmental concerns, highlighting the need for sustainable solutions. Rooftop photovoltaic (RPV) systems offer a viable solution for urban energy transition by utilizing idle rooftop space and meeting decentralized energy needs. However, due to limited information on building function attributes, detailed assessments of RPV potential at the city scale are still complicated. This study introduces a cost-effective framework that combines big Earth data and deep learning to evaluate RPV potential for various investment entities. We introduced a sample construction strategy that considers built environment and location awareness to improve the effectiveness and generalizability of the framework. Applied to Shanghai, our building function recognition model achieved 88.67%, 88.51%, and 67.18% for accuracy, weighted-F1, and kappa, respectively. We identified a potential installed capacity of 42 GW with annual electricity generation of 17 TWh for industrial and commercial, 16 TWh for residential, and 10 TWh for public RPVs. The levelized cost of electricity ranges from 0.32 to 0.41 CNY/kWh, demonstrating that both user-side and plant-side grid parity was achieved. This study supports sustainable development by providing detailed urban energy assessments and guiding local energy planning. The methods and findings may offer insights for similar studies globally.

Abstract Background Mosquitoes play an absolute role in the spread of epidemic arbovirus diseases. Worldwide, Aedes aegypti and Aedes albopictus are the main vectors responsible for the spread of these mosquito-borne diseases. Aedes vexans, a mosquito species native to China, also carries mosquito-borne viruses, such as dengue fever virus and Japanese encephalitis virus, but research on this mosquito has been inadequate. Mapping the potential distribution range of and monthly change in the distribution of these three Aedes species is of particular importance for mosquito surveillance, eradication and disease control. Methods Monitoring data were collected for the three Aedes species in China. Long-term temperature and precipitation data (2001–2021) and land cover data were used to represent various climate and environmental conditions. An ecological niche model was developed using a maximum entropy modeling method to predict the current optimum habitat areas for the three Aedes species and to obtain important variables influencing their monthly distribution. Results The distribution model for the three Aedes species performed well, with an area under the receiver operating characteristic curve value of 0.991 for Ae. aegypti, 0.928 for Ae. albopictus and 0.940 for Ae. vexans. Analysis of the distribution change and mapping of the optimum habitat range for each Aedes species for each month demonstrated that temperature, precipitation and construction land were important factors influencing the distribution of these three Aedes species. Conclusions In China, Aedes aegypti is mainly concentrated in a few tropical regions and along the Yunnan border; Aedes albopictus is widely distributed throughout most of the country, except for the arid and semi-arid regions of northwest China; and Aedes vexans is mainly found in the northern regions. Our results provide a basis for the timing and location of surveillance efforts for high-priority mosquitoes. Graphical abstract

BackgroundThe retail milk tea industry is in a period of rapid development, but there is little research on its nutrient content, which restricts the nutritional guidance of milk tea. ObjectiveTo determine the levels of nutrients in best-selling handcrafted milk tea in Shanghai and analyze the nutritional characteristics. MethodsIn 2018 and 2021, a total of 13 handcrafted milk tea brands with ≥3 branch stores in Shanghai were selected by searching for milk tea on Meituan and Ele.me food delivery platforms, and a total of 122 types of handcrafted milk tea products were collected from the top three sales [milk tea (including all sweetness levels available), milk cover tea, and fruit tea]. National standard methods were used to detect energy, protein, fat, carbohydrate, sugar, trans fatty acid, calcium, caffeine, and tea polyphenol. ResultsThe median energy of the milk tea samples was 310 kJ (per 100 g sample). The main sources of energy were carbohydrate and fat. The levels of energy, protein, and fat in milk cover tea and milk tea were significantly higher than those in fruit tea (P＜0.05), and there was no significant difference in carbohydrate among them. The total sugar, fructose, and glucose levels in milk tea were significantly lower than those in milk cover tea and fruit tea, and the lactose level in fruit tea was significantly lower than those in milk tea and milk cover tea (P＜0.05). Themedian trans fat acid level in milk cover tea was higher than that in milk tea (P＜0.05). The median levels of caffeine and tea polyphenol were higher in milk tea than in milk cover tea (P＜0.05). The levels of energy, carbohydrate, sucrose, total sugar, and calcium in milk tea were positively correlated with the number of ingredients added (0-3) (r=0.386, 0.371, 0.238, 0.698, 0.466, respectively, P < 0.05). The levels of energy, carbohydrate, and total sugar tended to increase with increasing sweetness (P

BackgroundChildren and adolescents drink sugar-sweetened beverages (SSBs) frequently. Research has confirmed that SSBs associate with weight gain and overweight or obesity. However, it is unclear whether high SSBs intake associates with abnormal changes in physical growth and glucolipid metabolism before causing adverse health outcomes such as overweight and obesity. Early identification of associated health risks of overconsumption of SSBs have important public health implications. ObjectiveTo investigate the differences in physical growth and glucolipid metabolism between different SSBs intake frequency groups in normal weight children and adolescents aged 6-17 years, and to evaluate the early effects of SSBs intake on physical growth and glycolipid metabolism before causing overweight and obesity, aiming to provide a scientific basis for the prevention and control of childhood overweight and obesity and related chronic diseases, and for the formulation of policies on the control of SSBs consumption. MethodsData were from the Shanghai Diet and Health Survey (SDHS) among primary and secondary school students. The participants were normal weight children and adolescents aged 6-17 years. Propensity scores were calculated according to energy intake and physical activity factors, after stratifying by age and gender. Participants were 1:1 matched with the closest propensity scores in the high-frequency (≥1 time·d−1) and the low-frequency (≤1 time·week−1) SSBs intake groups. The outcome indicators were physical measurements such as height, weight, percent of body fat, and waist circumference, and metabolic indicators such as fasting blood glucose, total triglycerides, total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Body mass index (BMI) was calculated. Food frequency questionnaire was used to collect SSBs consumption in the past three months through face-to-face interview. A paired t-test was used to compare the differences in physical and glycolipid metabolic indicators between the high-frequency intake group and the low-frequency intake group of SSBs. ResultsA total of 431 pairs were obtained. For children and adolescents in grades 6-9, overall height (difference=2.92 cm, P=0.002), weight (difference=2.53 kg, P=0.003), and waist circumference (difference=1.34 cm, P=0.035) were higher in those who consumed SSBs ≥1 time·d−1 than in those who consumed ≤1 time·week−1. For children and adolescents in grades 10-12, overall weight (difference=2.27 kg, P=0.041) was higher in those who consumed SSBs ≥1 time·d−1 than in those who consumed ≤1 time·week−1. Over 95% of the study subjects reported blood glucose and lipid test results within the normal range; but girls in grades 1-5 who consumed SSBs ≥1 time·d−1 had a higher total cholesterol (difference=0.20 mmol·L−1, P=0.027) and low-density lipoprotein cholesterol (difference=0.19 mmol·L−1, P=0.010) than those who consumed ≤1 time·week−1; boys in grades 6-9 who consumed SSBs ≥1 time·d−1 had a lower high-density lipoprotein cholesterol (difference=-0.10 mmol·L−1, P=0.039) than those who consumed ≤1 time·week−1. ConclusionHigh-frequency intake of SSBs may be associated with higher total cholesterol and low-density lipoprotein cholesterol in normal weight children and adolescents in grades 1-5, and higher weight in normal weight children and adolescents in grades 6-12. There is an urgent need to educate children and adolescents about nutritional health, enhance their ability to make healthy food and beverage choices, and take early interventions to control the intake of SSBs in children.

Recently, limiting the excessive intake of added sugar has gradually become an important topic in the field of nutrition. Sugar, sweetened dairy products, and sugar sweetened beverages (SSBs) are the three most important contributions of added sugar in China. For children, adolescents, and young adults, the issue of excessive intake of SSBs is not optimistic for now. Although the daily per capita intake of SSBs in China is currently at a low level on a global scale, the overall trend of SSBs production and consumption are rapidly increasing. Excessive intake of added sugar adversely affects glycolipid metabolism and multiple metabolic diseases, which is widely concerned and discussed. For the purpose of limiting added sugar, the gradually popularized use of sugar substitutes poses entirely new challenges. Multiple sugar-restriction strategies have been implemented and validated abroad. Given the rapid growth of consumption of added sugar and increasing evidence of related health hazards in China, there is a great need to improve the awareness of eating less added sugar for Chinese residents by multilevel strategies, and finally reduce the consumption of SSBs. In this article, the definition, history, and development of added sugar, main dietary sources, recommended limits, and both domestic and international consumption trends of added sugar were narrated. Then, the main health risks of added sugar and the global sugar-restriction strategies were briefly described. The article calls for raising awareness among Chinese residents of the health hazards of added sugar and limiting its excessive intake.

Identifying bridge damage using a movable test vehicle is highly regarded for its mobility, cost-effectiveness, and broad monitoring coverage. Previous studies have shown that the residual contact-point (CP) response between connected vehicles is free of the impact of vehicle self-vibrations and road roughness, making it particularly suitable for the indirect extraction of bridge modal properties. However, most experimental campaigns regarding contact-point (CP) responses focus on a single-axle testing vehicle within a non-moving state. This study aims to theoretically and experimentally identify bridge damage using the instantaneous amplitude squared (IAS) extracted from the residual CP response of a two-axle passing vehicle. First, the closed-form solution of the residual CP acceleration was derived for a two-axle vehicle interacting with a simply supported beam. The IAS index was constructed from the driving frequency of the residual CP acceleration. Then, numerical investigations using finite element simulation were conducted to validate using the IAS index for indirect bridge damage identification. The application scope of the approach under various vehicle speeds and road roughness grades was examined. Finally, a laboratory vehicle–bridge interaction system was tested to validate the approach. Numerical studies demonstrated that bridge damage could be directly determined by observing the IAS abnormalities, which were baseline-free. The IAS from the residual CP response outperformed the IAS from CP responses in identifying bridge damage. However, it was better to use the IAS when the vehicle speed was no greater than 2 m/s and the grade of the road surface roughness was not high. Laboratory tests showed that it was possible to identify bridge damage using the IAS extracted from the residual CP acceleration under perfect road surfaces. However, it fell short under rough road surfaces. Hence, further experiments are required to fully examine the capacity of the IAS for bridge damage identification in practical applications.

Introduction: Sports injuries are the most common cause of anterior cruciate ligament (ACL) ruptures. Previous research has demonstrated links between some of the COL27A1 and COL11A1 genetic variants and musculoskeletal soft tissue injuries. However, no previous research has investigated the COL27A1 rs1570460 and the COL11A1 rs3753841 in the context of ACL rupture in any population. Thus, our study aimed to assess the association between specific single nucleotide polymorphisms (SNPs), i.e., COL27A1 rs946053 and COL11A1 rs3753841, and the occurrence of ACL injury (ACL-I) in a cohort of Polish athletes. Methods: The study enrolled 233 athletes with ACL-I (161 males and 71 females) and 228 healthy control athletes (143 males and 85 females) with no prior ACL-I history. Genotyping was conducted to assess the presence of COL27A1 rs946053 and COL11A1 rs3753841 genetic variants. Statistical analyses were performed using the R programming environment and package, and an association between SNPs and ACL-I was tested in four genetic models: dominant, co-dominant recessive, and over-dominant. Results: All the analyzed polymorphisms conformed to Hardy-Weinberg equilibrium (HWE). The study revealed no significant differences between the ACL-I and control groups. Conclusions: Despite the absence of significant associations between the investigated SNPs and ACL-I in this study, our findings highlight the importance of continued research to unravel the precise genetic risk mechanisms and etiological factors contributing to ACL-I.

The excellent property that the intensity of autofocusing beams in the focal plane increases exponentially, makes them especially beneficial for biomedical treatment. We experimentally generate tunable (2+1)-dimensional circular parabolic umbilic beams (CPUBs) for the first time based on the high-dimensional diffraction catastrophe integral, which is determined by the potential function. Such CPUBs have circular and symmetrical intensity distributions in space through radial symmetry transformation. Due to the flexibility of high dimensionality, these light beams have rich light field structures and self-focusing property. Where, the maximum intensity suddenly increases by orders of magnitude in the focal plane. Unlike the classical circular Airy beams, CPUBs exhibit multiple self-focusing points along the optical axis and a needle-like structure during propagation, which can be affected by manipulating the control parameters. The rich properties provide a new perspective for exploring the novel physical mechanisms and phenomena in autofocusing beams. The experimental results verify the correctness of the numerical simulations. CPUBs greatly enrich the autofocusing beam family and will be advantageous for medical treatments, optical micromanipulation, and microscopic imaging.

Abstract Aims Heart failure with preserved ejection fraction (HFpEF) and non‐alcoholic fatty liver disease (NAFLD) are related conditions with an increasing incidence. The mechanism of their relationship remains undefined. Here, we aimed to explore the potential mechanisms, diagnostic markers, and therapeutic options for HFpEF and NAFLD. Methods and results HFpEF and NAFLD datasets were downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were screened for functional annotation. A protein–protein interaction network was constructed based on the STRING database, and hub genes were analysed using GeneMANIA annotation. ImmuCellAI (Immune Cell Abundance Identifier) was employed for analysis of immune infiltration. We also used validation datasets to validate the expression levels of hub genes and the correlation of immune cells. To screen for diagnostic biomarkers, we employed the least absolute shrinkage and selection operator and support vector machine‐recursive feature elimination. Drug signature database was used to predict potential therapeutic drugs. Our analyses identified a total of 33 DEGs. Inflammation and immune infiltration played important roles in the development of both diseases. The data showed a close relationship between chemokine signalling pathway, cytokine–cytokine receptor interaction, calcium signalling pathway, neuroactive ligand–receptor interaction, osteoclast differentiation, and cyclic guanosine monophosphate‐protein kinase G signalling pathway. We demonstrated that PRF1 (perforin 1) and IL2RB (interleukin‐2 receptor subunit beta) proteins were perturbed by the diseases and may be the hub genes. The analysis showed that miR‐375 may be a potential diagnostic marker for both diseases. Our drug prediction analysis showed that bosentan, eldecalcitol, ramipril, and probucol could be potential therapeutic options for the diseases. Conclusions Our findings revealed common pathogenesis, diagnostic markers, and therapeutic agents for HFpEF and NAFLD. There is need for further experimental studies to validate our findings.

In this paper, we analyze the average age of information (AoI) and the average peak AoI (PAoI) of a multiuser mobile edge computing (MEC) system where a base station (BS) generates and transmits computation-intensive packets to user equipments (UEs). In this MEC system, we focus on three computing schemes: (i) The local computing scheme where all computational tasks are computed by the local server at the UE, (ii) The edge computing scheme where all computational tasks are computed by the edge server at the BS, and (iii) The partial computing scheme where computational tasks are partially allocated at the edge server and the rest are computed by the local server. Considering exponentially distributed transmission time and computation time and adopting the first come first serve (FCFS) queuing policy, we derive closed-form expressions for the average AoI and average PAoI. To address the complexity of the average AoI expression, we derive simple upper and lower bounds on the average AoI, which allow us to explicitly examine the dependence of the optimal offloading decision on the MEC system parameters. Aided by simulation results, we verify our analysis and illustrate the impact of system parameters on the AoI performance.

Abstract Tensor decomposition is widely used to exploit the internal correlation in multi-way data analysis and process for communications and radar systems. As one of the main tensor decomposition methods, CANDECOMP/PARAFAC decomposition has advantages of uniqueness and interpretation properties which are significant in practical applications. However, traditional decomposition method is sensitive to both predefined rank and noise that results in inaccurate tensor decomposition. In this paper, we propose a improved algorithm called the Element-wise Average Alternating Direction Method of Multipliers by minimizing the sum of all factors’ trace norm and the noise variance. Our algorithm could overcome the dependence on predefined rank in traditional decomposition algorithms and alleviate the impact of noise. Moreover, this algorithm can be transferred to solve the problem of tensor completion conveniently. The simulation results show that our proposed algorithm could decompose the noisy tensor to the factors with above 90% similarity in various SNR and also interpolate the incomplete tensor with higher similar coefficient and lower relative reconstruction error when the missing rate is less than 0.5.

Abstract African swine fever (ASF) is a tick-borne infectious disease initially described in Shenyang province China in 2018 but is now currently present nationwide. ASF has high infectivity and mortality rates, which often results in transportation and trade bans, and high expenses to prevent and control the, hence causing huge economic losses and a huge negative impact on the Chinese pig farming industry. Ecological niche modeling has long been adopted in the epidemiology of infectious diseases, in particular vector-borne diseases. This study aimed to establish an ecological niche model combined with data from ASF incidence rates in China from August 2018 to December 2021 in order to predict areas for African swine fever virus (ASFV) distribution in China. The model was developed in R software using the biomod2 package and ensemble modeling techniques. Environmental and topographic variables included were mean diurnal range (°C), isothermality, mean temperature of wettest quarter (°C), precipitation seasonality (cv), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), normalized difference vegetation index, wind speed (m/s), solar radiation (kJ /day), and elevation/altitude (m). Contribution rates of the variables normalized difference vegetation index, mean temperature of wettest quarter, mean precipitation of coldest quarter, and mean precipitation of warmest quarter were, respectively, 47.61%, 28.85%, 10.85%, and 7.27% (according to CA), which accounted for over 80% of contribution rates related to variables. According to model prediction, most of areas revealed as suitable for ASF distribution are located in the southeast coast or central region of China, wherein environmental conditions are suitable for soft ticks’ survival. In contrast, areas unsuitable for ASFV distribution in China are associated with arid climate and poor vegetation, which are less conducive to soft ticks’ survival, hence to ASFV transmission. In addition, prediction spatial suitability for future ASFV distribution suggests narrower areas for ASFV spread. Thus, the ensemble model designed herein could be used to conceive more efficient prevention and control measure against ASF according to different geographical locations in China.

Introduction: The aim of the present study was to establish a simple method for the determination of riluzole in human plasma by ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and apply it for the determination of riluzole in amyotrophic lateral sclerosis (ALS) patients.Methods: Samples were prepared by protein precipitation and were then gradient-eluted on a column of ACQUITY UPLC® HSS T3 by using 0.1% formic acid acetonitrile and 0.1% formic acid water as the mobile phase. Detection was performed on a Xevo TQ-S tandem mass spectrometer in the multiple-reaction monitoring mode using positive electrospray ionization. Validation was performed in the range of 5–800 ng/mL.Results and discussion: Three batches of precision accuracy, selectivity, matrix effects, extraction recovery, and stability were also verified and met the requirements. The results showed that the method was reliable and successfully applied to the pharmacokinetics study of riluzole in Chinese amyotrophic lateral sclerosis patients. Meanwhile, in comparison with other prior published methods, our method has the advantages of simple sample preparation, relatively short running time, and small plasma sample consumption, which represented a high-throughput sample determination potential.

Abstract Enhancers are essential cis-acting regulatory elements that determine cell identity and tumor progression. Enhancer function is dependent on the physical interaction between the enhancer and its target promoter inside its local chromatin environment. Enhancer reprogramming is an important mechanism in cancer pathogenesis and can be driven by both cis and trans factors. Super enhancers are acquired at oncogenes in numerous cancer types and represent potential targets for cancer treatment. BET and CDK inhibitors act through mechanisms of enhancer function and have shown promising results in therapy for various types of cancer. Genome editing is another way to reprogram enhancers in cancer treatment. The relationship between enhancers and cancer has been revised by several authors in the past few years, which mainly focuses on the mechanisms by which enhancers can impact cancer. Here, we emphasize SE’s role in cancer pathogenesis and the new therapies involving epigenetic regulators (BETi and CDKi). We suggest that understanding mechanisms of activity would aid clinical success for these anti-cancer agents.

Abstract Background Recurrent bladder cancer is the most common type of urinary tract malignancy; nevertheless, the mechanistic basis for its recurrence is uncertain. Innovative technologies such as single‐cell transcriptomics and spatial transcriptomics (ST) offer new avenues for studying recurrent tumour progression at the single‐cell level while preserving spatial data. Method This study integrated single‐cell RNA (scRNA) sequencing and ST profiling to examine the tumour microenvironment (TME) of six bladder cancer tissues (three from primary tumours and three from recurrent tumours). Findings scRNA data‐based ST deconvolution analysis revealed a much higher tumour heterogeneity along with TME in recurrent tumours than in primary tumours. High‐resolution ST analysis further identified that while the overall natural killer/T cell and malignant cell count or the ratio of total cells was similar or even lower in the recurrent tumours, a higher interaction between epithelial and immune cells was detected. Moreover, the analysis of spatial communication reveals a marked increase in activity between cancer‐associated fibroblasts (CAFs) and malignant cells, as well as other immune cells in recurrent tumours. Interpretation We observed an enhanced interplay between CAFs and malignant cells in bladder recurrent tumours. These findings were first observed at the spatial level.

BackgroundAs a new type of medical service application for doctor-patient interaction, online health communities (OHCs) have alleviated the imbalance between the supply and demand of medical resources in different regions and the problems of “difficult and expensive access to medical care”, but also raised the concern of patients about the risk of disclosure of their health privacy information.MethodsIn this study, a dual-calculus model was developed to explore users' motivation and decision-making mechanism in disclosing privacy information in OHCs by combining risk calculus and privacy calculus theories.ResultsIn OHCs, users' trust in physicians and applications is a prerequisite for their willingness to disclose health information. Meanwhile, during the privacy calculation, users' perceived benefits in OHCs had a positive effect on both trust in doctors and trust in applications, while perceived risks had a negative effect on both trusts in doctors and trust in applications. Furthermore, in the risk calculation, the perceived threat assessment in OHCs had a significant positive effect on perceived risk, while the response assessment had a significant negative effect on perceived risk, and the effect of users' trust in physicians far exceeded the effect of trust in applications. Finally, users' trust in physicians/applications is a mediating effect between perceived benefits/risks and privacy disclosure intentions.ConclusionWe combine risk calculus and privacy calculus theories to construct a dual-calculus model, which divides trust into trust in physicians and trust in applications, in order to explore the intrinsic motivation and decision-making mechanism of users' participation in privacy disclosure in OHCs. On the one hand, this theoretically compensates for the fact that privacy computing often underestimates perceived risk, complements the research on trust in OHCs, and reveals the influencing factors and decision transmission mechanisms of user privacy disclosure in OHCs. On the other hand, it also provides guidance for developing reasonable privacy policies and health information protection mechanisms for platform developers of OHCs.

Global warming has accelerated the melting of Arctic sea ice, providing favorable conditions for Arctic shipping. Arctic ports are gaining prominence in shipping networks and international trade. Accessibility is a key indicator of port facilitation, and identifying trends in Arctic port accessibility holds significance for Arctic route planning and port development. To achieve this, this paper develops a modeling framework for assessing the accessibility of Arctic ports. First, we utilize the Coupled Model Intercomparison Project (CMIP6) model to predict sea ice conditions and quantify the navigation risk for open water (OW) vessels and Arc4 ice-class (Arc4) vessels during the summer months of 2030–2050. The A-star (A*) algorithm is then used to plan the vessel’s shortest route while avoiding high-risk waters. Finally, changes in the accessibility of Arctic ports are calculated by using an improved gravity model. The framework is applied for the quantitative analysis of the accessibility of Chinese coastal ports to Arctic ports. The results indicate that accessibility to Arctic ports will gradually increase for all Chinese ports in the future, with the port of Shanghai continuing to maintain its prominence under the trend of melting sea ice.

As an ecological index for plants, the diversity and structure of phyllosphere microbial communities play a crucial role in maintaining ecosystem stability and balance; they can affect plant biogeography and ecosystem function by influencing host fitness and function. The phyllosphere microbial communities reflect the immigration, survival, and growth of microbial colonists, which are influenced by various environmental factors and leaves’ physical and chemical properties. This study investigated the structure and diversity of phyllosphere fungal communities in three different Populus spp., namely—P. × euramaricana (BF3), P. nigra (N46), and P. alba × P. glandulosa (84K). Leaves’ chemical properties were also analyzed to identify the dominant factors affecting the phyllosphere fungal communities. N46 exhibited the highest contents of total nitrogen (Nt), total phosphorus (Pt), soluble sugar, and starch. Additionally, there were significant variations in the abundance, diversity, and composition of phyllosphere fungal communities among the three species: N46 had the highest Chao1 index and observed_species, while 84K had the highest Pielou_e index and Simpson index. Ascomycota and Basidiomycota are the dominant fungal communities at the phylum level. Results from typical correlation analyses indicate that the chemical properties of leaves, especially total phosphorus (Pt), total nitrogen (Nt), and starch content, significantly impact the structure and diversity of the phyllosphere microbial community. However, it is worth noting that even under the same stand conditions, plants from different species have distinct leaf characteristics, proving that the identity of the host species is the critical factor affecting the structure of the phyllosphere fungal community.

One of the most significant transcription factors in plants, WRKYs, are crucial for plant growth and stress response. In this study, we analyzed the physicochemical properties, evolutionary relationships, conservation structure, and expression of the WRKY gene family in S. baicalensis. The WRKY family has highly conserved structural domains, which have been classified into three major categories, I, II, and III, based on the number of WRKY structural domains and zinc finger structural features. SbWRKYs of the same subgroup are functionally similar and essentially contain the same motif. Additionally, different drought stress situations resulted in varying levels of SbWRKYs expression, with the majority of these factors being up-regulated in moderate drought stress settings, and fewer of them were up-regulated under severe drought stress conditions. Under moderate drought stress, the expression of key enzymes increased, while under severe drought stress, the expression of key enzymes decreased. Mild drought stress resulted in a 26.42% increase in baicalin accumulation, while severe drought stress led to a 22.88% decrease. The protein interaction analysis of key enzyme genes and SbWRKYs revealed that the expression of key enzyme genes affected the expression of SbWRKYs. We screened nine SbWRKYs with a significant relationship with baicalin accumulation, and SbWRKY8 and SbWRKY16 showed the highest correlation with the baicalin content. These findings offer a theoretical framework for more research on the roles of SbWRKYs and show that SbWRKYs can respond to drought stress in S. baicalensis.

The ubiquitous 5G-enable industrial Internet of Things interconnects a great number of intelligent sensors and actuators. Network management becomes challenging due to massive traffic data generated by industrial equipment. However, the conventional single traffic factor is insufficient for the increasingly complicated network engineering tasks due to the poor representation capability. Besides, the insecure equipment with open communication access easily brings irregular network fluctuations to network traffic which interferes with the primary traffic factor. The simple and interfered traffic factor decreases the network management efficiency and misleads the operators. Motivated by that, we construct a comprehensive tensor model representing multi-dimension traffic factors to describe the network traffic beneficial characteristics. Meanwhile, an adaptive and generic low-rank tensor recovery (AG-LRTR) algorithm in the tensor singular value decomposition (t-SVD) framework is proposed for denoising. For effective tensor recovery, the alternating direction method of multipliers is employed to theoretically solve the partial augmented Lagrangian function of our objective with a closed-form solution. Numerical experiments on both synthetic data and real-world traffic data in IIoT validate that our proposed algorithm outperforms other state-of-the-art of tensor recovery algorithms.

This paper uses “prolepsis,” a process of reaching into the past to inform present and future practices, to understand 12 English-as-a-second language (ESL) teachers’ practices of supporting English language learners (ELLs) through remote teaching during the COVID-19 pandemic from 2020-2021 in British Columbia and to envision some different current and future post-pandemic classroom literacies for diverse learners. Accounts of these ESL teachers’ synthetical moments of teaching and supporting ELLs during the pandemic suggest that they had to navigate “new” areas of teaching, including attending to students’ social-emotional learning (SEL), connecting with ELL parents, teaching and engaging students via technology-supported instruction, and co-teaching with mainstream teachers, on the basis of limited or no pre-pandemic experience. These insights suggest a need to widen the focus on ESL teachers’ knowledge and expertise in applied linguistics and instructional strategies to include classroom literacies in integrating SEL into ESL instruction, adopting interactive, student-driven instructional designs and practices afforded by multimodal technologies, maintaining multiple channels of communication with parents and students, and team-teaching with classroom teachers to provide tailored language support for ELLs.

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships' bunker fuel consumption and the accompanying emissions, including speed optimization, trim optimization, weather routing, and the virtual arrival policy. The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed, displacement/draft, trim, weather conditions, and sea conditions. Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection. To overcome this issue, this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution. Eleven widely-adopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company. The best datasets found reveal the benefits of fusing voyage report data and meteorological data, as well as the practically acceptable quality of voyage report data. Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG) present the best fit and generalization performances. Their R2 values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set, and 0.74 to 0.90 for the test set. Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day. These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis. However, important information about weather and sea conditions the ship sails through, such as waves, sea currents, and sea water temperature, is often absent from sensor data. This study addresses this issue by fusing sensor data and publicly accessible meteorological data, constructing nine datasets accordingly, and experimenting with widely adopted machine learning (ML) models to quantify the relationship between a ship's fuel consumption rate (ton/day, or ton/h) and its voyage-based factors (sailing speed, draft, trim, weather conditions, and sea conditions). The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification. The best ML models found are consistent with our previous studies, including Extremely randomized trees (ET), Gradient Tree Boosting (GB) and XGBoost (XG). Given the best dataset from data fusion, their R2 values over the training set are 0.999 or 1.000, and their R2 values over the test set are all above 0.966. Their fit errors with RMSE values are below 0.75 ton/day, and with MAT below 0.52 ton/day. These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis. The applicability of the selected datasets and ML models is also verified in a rolling horizon approach, resulting in a conjecture that a rolling horizon strategy of “5-month training + 1-month test/applicatoin” could work well in practice and sensor data of less than five months could be insufficient to train ML models.

When voyage report data is utilized as the main data source for ship fuel efficiency analysis, its information on weather and sea conditions is often regarded as unreliable. To solve this issue, this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources. These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models. Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments. For each ship, nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested. Experimental results revealed the benefits of fusing voyage report data, AIS data, and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate. Over the best datasets, the performances of several decision tree-based models are promising, including Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG). With the best datasets, their R2 values over the training sets are all above 0.96 and mostly reach the level of 0.99–1.00, while their R2 values over the test sets are in the range from 0.75 to 0.90. Fit errors of ET, AB, GB, and XG on daily bunker fuel consumption, measured by RMSE and MAE, are usually between 0.8 and 4.5 ton/day. These results are slightly better than our previous study, which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data, compared with the estimated geographical positions derived from the great circle route, in retrieving weather and sea conditions the ship sails through.

Introduction: Phosphatase and tensin (PTEN) is a multifunctional gene associated with the normal development and physiological function of various tissues including the kidney. However, its role in renal tubular reabsorption function has not been well elucidated. Methods: We generated a renal tubule-specific Pten knockout mouse model by crossing Ptenfl/fl mice with Ksp-Cre transgenic mice, evaluated the effect of Pten loss on renal tubular function, and investigated the underlying mechanisms. Results: Pten loss resulted in abnormal renal structure and function and water retention in multiple organs. Our results also demonstrated that aquaporin-2 AQP2, an important water channel protein, was upregulated and concentrated on the apical plasma membrane of collecting duct cells, which could be responsible for the impaired water balance in Pten loss mice. The regulation of Pten loss on AQP2 was mediated by Protein kinase B (AKT) activation. Discussion/Conclusions: Our results reveal a connection between PTEN gene inactivation and water retention, suggesting the importance of PTEN in normal kidney development and function.

Smoked cigarette butts are a non-biodegradable pollutant that has damaged the planet. However, carbon materials derived from cigarette butts have proven to be suitable for various applications. We synthesized cigarette butt-derived carbon via hydrothermal carbonization and chemical activation methods and then converted it to an electrode material for capacitive deionization. The fabricated material, SCC-750, exhibited a relatively high salt adsorption capacity of 10.27 mg g−1. The excellent CDI (capacitive deionization) performance is due to the high specific surface area of 3,093.10 m2 g−1 and a pore volume of 1.754 cm3 g−1. This work offers a new method to recycle harmful cigarette butts by converting them into promising electrode materials for capacitive deionization. Highlights Explore the influence of chemical activation temperature and compare the capacitive deionization performance of the carbon material derived from smoked cigarette butts and unsmoked cigarette butts.; Smoked cigarette butt-derived carbon at the activation temperature of 750 °C exhibits high specific surface area of 3,093.10 m2 g−1, high pore volume of 1.75 cm3 g−1, and reaches a high salt adsorption capacity 10.27 mg g−1.;

Purpose: In this study, proton nuclear magnetic resonance (1H NMR) spectroscopy was used to evaluate the effect of astaxanthin (ASTA) supplementation on changes in human plasma elicited by high-intensity exercise. Methods: Sixteen adult males were randomly divided into 2 groups (n = 8 per group), namely the control group C (placebo for 28 d, 4 weeks) and experimental group M (supplement medium dose ASTA: 12 mg/d for 28 d, 4 weeks). At 08:00 on the 29th day, fasting blood sampling was carried out on all the participants, and the samples were tested in the laboratory for the first time. Later, the participants performed acute exercise on a pedal-powered bicycle with full strength for 30 s × 3/3 min intervals (loading a weight of 0.075 kg/kg). Blood sampling was then respectively performed immediately, 1 h after the acute exercise, and 1 d after the acute exercise. Results: (1) The metabolites of the subjects of the two groups were found to be diverse at different time points, and 34 types of metabolites were screened from the two groups. (2) The metabolites with differences between the two groups 1 h after exercise were β-hydroxybutyrate, creatine, and glycerol. The levels of β-hydroxybutyric acid and glycerol in group M were significantly lower than those in group C, while the level of creatine was significantly higher. Compared with the resting state 1 h after exercise, the metabolites in common between the two groups were leucine (Leu), valine (Val), and citric acid (CA), and their levels were significantly decreased. (3) During the period between 1 h and 1 d after exercise, the different metabolites between the two groups were methionine (Met) and glycerol. The glycerol levels of group M were significantly lower than those of group C, while the levels of Met were significantly higher. The co-metabolites of the subjects in groups C and M 1 d after exercise were creatine, glucose, and glycerol, the levels of which were all significantly increased. Conclusions: (1) One hour after exercise, the consumption of creatine, amino acids, fatty acids, and CA was found to be obvious, and ASTA intake was conducive to their recovery. (2) After high-intensity exercise, changes occurred in the body’s energy metabolism that involved the metabolism of glucose, lipids, and proteins, and basic recovery was found 1 d after exercise. The findings of this study suggest that ASTA intake can accelerate metabolic recovery induced by physical exercise.

BackgroundPrevious studies have analyzed the associations between the circulating saturated fatty acids (SFAs) and gestational diabetes mellitus (GDM), but no consistent conclusions have been reached. The aim of this study was to evaluate whether plasma SFAs were in correlation with GDM risks in our in-house women cross-sectional study and to better define their associations on the clinical evidence available to date by a dose-response meta-analysis.MethodsWe carried out a cross-sectional study of 807 pregnant women in 2018–2019 (Shanghai, China). GDM was defined according to the criteria of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Gas chromatography was used to determine the plasma fatty acids (FAs) in the 24–28 gestational weeks. The SFAs levels of non-GDM and GDM participants were compared by Mann–Whitney test, and the association between SFAs and GDM was explored by multivariate logistic models. Further, the potential diagnostic value of plasma SFAs was evaluated using the method of receiver operating characteristic (ROC) analysis. For meta-analysis, five databases were systematically searched from inception to March 2022, and we included 25 relevant studies for calculating pooled standard mean differences (SMDs) and 95% CI to describe the differences in SFAs profiles between non-GDM and GDM women. Study-specific, multivariable-adjusted ORs and 95% CI were also pooled using a fixed-effect model or random-effects model according to the heterogeneity to evaluate the associations between circulating SFAs and GDM prevalence.ResultsIn our cross-sectional study, we found plasma proportion of palmitic acid (C16:0) was positively associated (aOR: 1.10 per 1% increase; 95% CI: 1.04, 1.17), while plasma stearic acid (C18:0) (aOR: 0.76 per 1% increase; 95% CI: 0.66, 0.89), arachidic acid (C20:0) (aOR: 0.92 per 0.1% increase; 95% CI: 0.87, 0.97), behenic acid (C22:0) (aOR: 0.94 per 0.1% increase; 95% CI: 0.92, 0.97), and lignoceric acid (C24:0) (aOR: 0.94 per 0.1% increase; 95% CI: 0.92, 0.97) were inversely associated with GDM. The area under the receiver operative characteristic curve increased from 0.7503 (the basic diagnostic model) to 0.8178 (p = 0.002) after adding total very-long-chain SFAs (VLcSFAs). A meta-analysis from 25 studies showed the circulating levels of three individual SFAs of GDM women were different from those of normal pregnant women. The summarized ORs for GDM was 1.593 (95% CI: 1.125, 2.255, p = 0.009), 0.652 (95% CI: 0.472, 0.901, p = 0.010) and 0.613 (95% CI: 0.449, 0.838, p = 0.002), respectively, comparing the highest vs. lowest quantile of the concentrations of C16:0, C22:0, and C24:0.ConclusionOur results, combined with the findings from meta-analysis, showed that women with GDM had a particular circulating SFA profile, characterized by higher levels of palmitic acid, and lower levels of VLcSFAs. Alterations in the chain lengths of blood SFA profile were shown to be associated with the occurrence of GDM.

Abstract Studies have suggested that handgrip strength might be a marker for cardiometabolic risk (CMR), but it has not been studied in Hispanic/Latino farmworker population. This study aimed to characterize absolute and relative handgrip strength in Hispanic/Latino farmworkers, and investigate the sex-specific association between handgrip strength and CMR factors. CMR factors and seated isometric absolute (the sum of both hands) and relative (absolute handgrip strength divided by body mass index) handgrip strengths were collected in 173 Hispanic/Latino farmworkers (mean age 35.1 ± 0.7 years; 49% female). The absolute and the relative handgrip strengths were 89.2 ± 1.8 kg, 3.3 ± 0.1 kg among males, and 56.5 ± 1.9 kg, 1.9 ± 0.1 kg among females, respectively. Age was correlated with absolute (r = − 0.17, p = 0.03) and relative handgrip strengths (r = − 0.28, p

Fusarium oxysporum, a common fungal pathogen that infects economic crops, causes Fusarium wilt disease to Saposhnikovia divaricata at an annual incidence rate of more than 15%. This study aimed to assess the potential of rhizospheric fungi as antifungal agents against Fusarium wilt of Saposhnikovia divaricata. In this study, 104 fungi were isolated from S. divaricata rhizospheric soil. Twelve rhizospheric strains that showed antagonistic activity against F. oxysporum, MR-16, MR-32, MR-38, etc., were screened out. Biocontrol activities of the twelve strains, especially MR-16, were subsequently characterized and evaluated. Strain MR-16 as potential stock for biocontrol had good antibiotic activity against F. oxysporum in vitro experiment. Based on the analysis of morphological properties and rDNA internal transcribed spacers (ITS), we identified an isolate MR-16 as Penicillium caperatum (GenBank No. OK287146.1), a new record of this species of China. The results of the in vitro antagonistic assay indicated that the conidial germination rate was significantly decreased, and the mycelia morphology of F. oxysporum induced change via the culture filtrate of P. caperatum MR-16, such as deformation and degradation. In an outdoor pot experiment, inoculation of S. divaricata plants with F. oxysporum created severe wilting symptoms; however, in inoculation trials, MR-16 effectively suppressed disease lesions, with a strong control efficacy of 60.76%. In addition, strain MR-16 could successfully colonize and form stable populations in the soil, and it showed a continuous positive growth-promoting effect on S. divaricata plants.

Light is essential for plants and plays a vital role in their growth and development. Light irradiation affects the physiological characteristics and synthesis of secondary metabolites in plants. As a semi-shade perennial plant, Panax ginseng C.A. Mey. is sensitive to changes in the light environment. Different light irradiations significantly affect the secondary metabolic processes of P. ginseng. However, few studies have investigated the changes in ginsenoside content in P. ginseng under different light irradiation conditions. In this study, 3-year-old P. ginseng was cultured under white (CK) light, blue (B) light, red (R) light, green (G) light, and natural light (NL) to explore the effects of light irradiation on the physiological characteristics and ginsenoside secondary metabolism of P. ginseng. The B and CK treatments significantly increased the photosynthetic level in P. ginseng leaves. The total saponin content under blue and red light treatments increased by 28.81% and 21.64%, respectively, compared with the CK treatment. Blue and red light improved the transcription levels of ginsenoside biosynthetic pathway genes. Blue light upregulated the expression of HMGR, SS, SE, DS, CYP716A52, and CYP716A47, and the expression of HMGR, SS, SE, DS, and CYP716A47 under red light treatment was significantly upregulated in P. ginseng roots. Principal component and correlation analyses revealed that the physiological and ecological processes of P. ginseng exhibited different responses to light irradiation. The total saponin content in the roots was positively correlated with the content of protopanaxatriol -type ginsenosides and water use efficiency in leaves. Our study indicates that light conditions can be improved by blue and red light or by blue and red film covering to facilitate the accumulation of saponin during the ecological cultivation of P. ginseng.

Aiming at utilizing artificial neural networks to enhance intelligent filtering for interfered wireless communication signal in harsh environments, a new method named convolutional neural filtering is designed and presented in this paper. This method is based on model-driven deep learning princeple, by analyzing the theoretical connection between the filter model and the convolutional neural layer, it attempts to use one-dimensional convolution kernels to learn a matched or bandpass filter. Moreover, the model introduces a kernel-wise attention mechanism between different convolution kernels to selectively emphasize informative filters. The results show that in terms of interference and noise suppression for received wireless signal, the filtering method has highlighted dynamic adaptability to variation of signals and interference, and it also reveals that the performance is affected by the initialization parameters and the number of convolution kernels. Based on this method an embeddable filtering unit fully based on neural network is provided, which can be easily integrated into a deep learning network targeting such as wireless signal detection and recognition applications, avoiding complex preprocessing for end-to-end wireless signal learning.

Pilot spoofing attack (PSA) is a kind of active eavesdropping, which launches the identical training sequence to manipulate the channel estimation outcome during the pilot training phase. The newly proposed intelligent reflecting surface (IRS) system is vulnerable to the PSA because it relies on the pilot-assisted channel estimation methods to obtain channel statement information (CSI). To combat PSA in IRS-assisted systems, we consider two significant challenges: 1) channel distribution information (CDI) is uncertain, which will make the PSA detection methods based on the statistic feature (SF) invalid; 2) the noise is uncertain when detecting PSA in practical scenarios, which would impair the popular energy-based detectors. In this paper, we design a three-step training (TST) scheme. The effective detector could achieve reliable detection performance by examining the transmitter's received signal power levels when PSA occurs. The proposed method does not require the prior knowledge of noise variance and could obtain the CSI of both legitimate and illegitimate dyadic backscatter channels. Theoretical analysis and numerical simulations are presented further to demonstrate the efficiency of our proposed TST scheme.

Background and Aims: Patients often undergo paracentesis prior to a transjugular intrahepatic portosystemic shunt (TIPS) procedure to improve respiratory mechanics. However, the effect of large volume paracentesis (LVP) on intraoperative hemodynamics and anesthetic management when it is performed immediately before the TIPS procedure is not well documented. Material and Methods: This is a retrospective study in patients undergoing the TIPS procedure between 2004 and 2017. Patients were divided into two groups based on the volume of preoperative paracentesis, namely, small volume paracentesis (SVP), defined as paracentesis volume less than 5 L and LVP, defined as paracentesis volume of at least 5 L. Patients' demographics and perioperative information were collected through chart review. The Wilcoxon signed-rank test, student's t-test, and Fisher's exact test were used when appropriate. Uni- and multivariate linear regression analyses were used to determine the predictive value of paracentesis volume in relation to intraoperative hemodynamics and management of hypotension. Results: Of 49 patients, 19 (39%) received LVP and the remainder received SVP. Baseline demographics were comparable between groups as were intraoperative hypotension and volume of infused crystalloid and colloid. However, vasopressor use (P = 0.02) and packed red blood cell transfusion (P = 0.01) were significantly higher in the large volume group. Paracentesis volume was an independent predictor of the phenylephrine dose (P = 0.0004), and of crystalloid (P = 0.05) and colloid (P = 0.009) volume administered after adjusting for age, sex, body mass index, alcohol use, hemoglobin, and model for end-stage liver disease score. Conclusion: The anesthetic management of patients who undergo LVP just prior to a TIPS procedure may require larger doses of vasopressors and colloids to prevent intraoperative hemodynamic instability during the TIPS placement but may be as well tolerated as SVP.

Urban research is progressively moving towards fine-grained simulation and requires more granular and accurate geospatial data. In comparison to building footprints, roof structure lines (RSLs) are finer-grained elements of building roofs that provide a more sophisticated data reference. However, generating high-quality and up-to-date RSLs is arduous owing to the high expense of data sources (e.g., digital surface models and light detection and ranging data) and the low robustness of conventional image processing approaches. While the current combination of high-resolution satellite imagery and deep learning methods enables the automatic generation of RSLs, it also introduces two distinct challenges. First, the high diversity of roof sizes, forms, and spatial distribution complicates the extraction of essential RSL features from satellite imagery using general deep learning methods. Second, the significant class imbalance issue between foreground objects (i.e., RSLs) and background context in satellite imagery makes it difficult for deep learning methods to concentrate on RSL locations. To overcome these challenges and effectively delineate RSLs from satellite imagery, this study designs Deep Roof Refiner—an end-to-end and detail-oriented deep learning network and proposes a synthetic strategy to enhance the network’s performance. The effectiveness of the proposed network is verified by quantitative and qualitative experiments, with the optimal dataset scale F1-score and optimal image scale F1-score of 60.89% and 63.48%, respectively. The proposed network significantly outperforms state-of-the-art deep learning methods and associated conventional research. The results indicate that the delineated RSLs can serve as a reliable data source for some urban building-based studies.