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The composition ratio of different capital sources in a company forms different financing structures among companies, and at the same time produces different financing costs and economic benefits, which have different impacts on the company’s financial performance. Corporate capital structures are becoming more and more important as the e-commerce sector develops quickly. Through in-depth analysis of the capital structure of Alibaba and related enterprises in the industry, this paper discusses the influence mechanism of different sources of capital and capital structure on financial performance. Research indicates that a sensible capital structure can enhance financial performance by lowering borrowing costs and optimizing the use of available money. On the contrary, improper capital structure may lead to increased financial risk and decreased profitability. This paper aims to provide useful reference for e-commerce corporations to optimize capital structure and enhance financial performance and promote the sustainable and healthy development of Alibaba and e-commerce industry.

Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL applications in the critical care setting. This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models. We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included. We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application. RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments.

In the era of big data, developing next-generation self-powered continuous energy harvesting systems is of great importance. Taking advantage of fallen leaves’ specific structural advantage gifted by nature, we propose a facile approach to convert fallen leaves into energy harvesters from ubiquitous moisture, based on surface treatments and asymmetric coating of hygroscopic iron hydrogels. Upon moisture absorption, a water gradient is established between areas with/without hydrogel coating, and maintained due to gel-like behaviors and leaf veins for water retention and diffusion restriction, thus forming electrical double layers over the leaf surface and showing capacitance-like behavior for energy charging and discharging. Besides, the specific leaf cell structures with small grooves enabled uniform carbon coatings instead of aggregations, and high electrical conductivity, resulting in 49 μA/cm 2 and 497 μW/cm 3 electrical output, achieving competitive performance with the state-of-art and potential for lower environmental impact compared to other types of energy harvesters. In this work, authors convert fallen leaves into energy harvesters using hygroscopic iron hydrogel, achieving continuous power generation from moisture. The device delivers high current density and power output with potential for lower environmental impact compared to alternative harvesters.

Innate immune receptors such as RIG-I, cGAS, and Toll-like receptors bind microbial fragments and alert the immune system to an infection. Each receptor type signals through a different adapter protein. These signals activate the protein kinase TBK1 and the transcription factor IRF3, which tells cells to secrete interferon proteins (IFNs) important for host defense. Liu et al. now report a common signaling mechanism used by all three types of innate immune receptor-adaptor protein pairs to activate IRF3 and generate IFNs. This is important because cells must regulate their IFN production carefully to avoid inflammation and autoimmunity. Science , this issue 10.1126/science.aaa2630 Diverse innate immune receptors use a common signaling mechanism to activate type I interferons. During virus infection, the adaptor proteins MAVS and STING transduce signals from the cytosolic nucleic acid sensors RIG-I and cGAS, respectively, to induce type I interferons (IFNs) and other antiviral molecules. Here we show that MAVS and STING harbor two conserved serine and threonine clusters that are phosphorylated by the kinases IKK and/or TBK1 in response to stimulation. Phosphorylated MAVS and STING then bind to a positively charged surface of interferon regulatory factor 3 (IRF3) and thereby recruit IRF3 for its phosphorylation and activation by TBK1. We further show that TRIF, an adaptor protein in Toll-like receptor signaling, activates IRF3 through a similar phosphorylation-dependent mechanism. These results reveal that phosphorylation of innate adaptor proteins is an essential and conserved mechanism that selectively recruits IRF3 to activate the type I IFN pathway.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, particularly among older adults. Identifying modifiable dietary factors associated with CVD prevalence is essential for prevention. This cross-sectional study analyzed 7,341 adults aged ≥ 65 years from NHANES 2007–2018. CVD was defined based on self-reported physician-diagnosed conditions, including coronary heart disease, heart failure, angina, heart attack, or stroke. A nutrient-wide association study (NWAS) evaluated associations between 56 dietary nutrients and CVD prevalence using multivariable logistic regression. Dose–response relationships were assessed using restricted cubic spline (RCS) models. Model discrimination was evaluated using receiver operating characteristic (ROC) analysis. Eight nutrients were significantly associated with CVD prevalence after adjusting for confounders and correcting for multiple comparisons: PUFAs 20:4 (OR 0.88, 95% CI 0.80–0.97, P  = 0.024), 22:5 (OR 0.72, 95% CI 0.56–0.92, P  = 0.019), and 22:6 (OR 0.92, 95% CI 0.87–0.99, P  = 0.032); total choline (OR 0.96, 95% CI 0.93–0.99, P  = 0.024); cholesterol (OR 0.89, 95% CI 0.82–0.96, P  = 0.037); protein (OR 0.82, 95% CI 0.76–0.89, P  = 0.040); vitamin A (OR 0.95, 95% CI 0.91–0.99, P  = 0.045); and total sugars (OR 0.97, 95% CI 0.96–0.98, P  = 0.049). Clustering analysis identified protective effects of fat-soluble vitamins and unsaturated fatty acids. The inclusion of these nutrients significantly improved model discrimination ( P  < 0.05). This study identified key dietary nutrients and clusters associated with CVD prevalence and characterized their dose–response relationships. Integrating these nutrients into predictive models enhances CVD risk discrimination, providing actionable dietary targets for prevention strategies in older adults.

The integration of digital technologies in home-based exercise (HBE) has emerged as a critical public health intervention, particularly following the COVID-19 pandemic. However, comprehensive understanding of how digital technologies influence HBE research evolution and practice remains limited. This systematic review aims to: (1) map the knowledge evolution of digital technology-supported HBE research, (2) analyze innovative applications and value propositions of digital technologies in HBE, (3) identify key determinants of HBE participation in the digital era, and (4) predict future development trends. Following PRISMA guidelines, we conducted a comprehensive search of the Web of Science database from 2000 to 2024, yielding 311 articles for bibliometric analysis using CiteSpace and VOSviewer. Results indicated that digital technology-supported HBE research has grown exponentially, peaking in 2022. We identified seven core research themes, including intelligent exercise training, AI-driven fall prevention, wearable device interventions, virtual reality rehabilitation, mobile health applications, cancer patient tracking systems, and remote diagnostics. These themes highlight innovative approaches to personalized training, remote monitoring, and immersive rehabilitation experiences, demonstrating significant value in enhancing exercise adherence and health outcomes. Key determinants of HBE participation were identified at personal (social support, professional guidance, self-efficacy) and technological (digital literacy, accessibility, user interface design, data privacy) levels. Digital technologies demonstrate substantial potential for enhancing HBE accessibility, improving adherence, and reducing public health burden. Future research should prioritize addressing digital equity and developing evidence-based implementation frameworks to ensure sustainable and inclusive digital health interventions.

Chronic administration of glucocorticoids are being linked to an increased risk of osteoporosis. 5-Hydroxymethylfurfural (5-HMF) is a natural compound that possesses various biological activities, including osteogenic regulation. In this research, we sought to investigate the potential of 5-HMF to exert protective effects against glucocorticoid-induced osteoporosis. In cellular investigations, we deployed Western Blot (WB), Real-Time Quantitative real-time PCR (qRT-PCR), Alkaline phosphatase (ALP) and Alizarin S-red (ARS) staining to scrutinize the expression of osteogenic differentiation markers within MC3T3-E1 pre-osteoblasts and BMSCs. We used dexamethasone to establish a mouse model for glucocorticoid-Induced osteoporosis and administered 5-HMF to evaluate its influence on bone density and architecture through the use of micro-CT and histological analysis. In addition, we employed network pharmacology to elucidate the potential pathways and targets of 5-HMF. Finally, we explored the effect of AKT knockout on the treatment of 5-HMF. We found that 5-HMF significantly enhanced VEGFR2 phosphorylation, reestablishing angiogenesis and activating the PI3K/AKT pathway in vitro and in vivo. 5-HMF also inhibited Dexamethasone -induced apoptosis by regulating Bax and Bcl-2 expression. MK2206, an AKT inhibitor, abrogated 5-HMF’s protective properties against GCs. These results indicate that 5-HMF counteracts the negative effects of GCs on osteoblasts and has good bone-promoting differentiation effects.

The recent thrust in utilizing atomically precise organic ligands protected gold clusters (Au clusters) as photosensitizer coupled with semiconductors for nano-catalysts has led to the claims of improved efficiency in photocatalysis. Nonetheless, the influence of photo-stability of organic ligands protected-Au clusters at the Au/semiconductor interface on the photocatalytic properties remains rather elusive. Taking Au clusters–TiO 2 composites as a prototype, we for the first time demonstrate the photo-induced transformation of small molecular-like Au clusters to larger metallic Au nanoparticles under different illumination conditions, which leads to the diverse photocatalytic reaction mechanism. This transformation process undergoes a diffusion/aggregation mechanism accompanied with the onslaught of Au clusters by active oxygen species and holes resulting from photo-excited TiO 2 and Au clusters. However, such Au clusters aggregation can be efficiently inhibited by tuning reaction conditions. This work would trigger rational structural design and fine condition control of organic ligands protected-metal clusters-semiconductor composites for diverse photocatalytic applications with long-term photo-stability.

Background The rapid development of digital health has reduced the time and cost of medical treatment, bringing efficient and economical benefits. However, older adults all over the world are deficient in digital health knowledge and skills to varying degrees. This study intends to investigate the current status and influencing factors of digital health literacy among community-dwelling older adults in Southwest China, so as to provide theoretical reference for global digital health researches and the construction of gerontological digital health service models. Methods A cross-sectional survey was conducted from September 2020 to April 2021 in Chongqing, China. 572 community-dwelling older adults (≥ 65 years) were surveyed by stratified sampling. Data on sociodemographic characteristics, Internet usage, attitude towards Internet health information and digital health literacy were collected. Wherein, the digital health literacy assessment adopted the Digital Health Literacy Assessment Scale for community-dwelling older adults, which was developed by the research group, proven to be with good internal consistency (0.941), split-half reliability (0.889), test–retest reliability (0.941), content validity (0.967), criterion validity (0.938) and construct validity. The influencing factors were explored by univariate analysis and multiple linear regression analysis. Results The average score of digital health literacy was 37.10 (SD 18.65). Univariate analysis showed that there were statistically significant differences in the comparison of digital health literacy according to 16 variables, such as different age and education levels. Multiple linear regression analysis showed that education level, marital status, self-rated health status, degree of health concerns, duration of Internet usage, time spent using the Internet per day, frequency of Internet usage, frequency of receiving guidance passively from family members, perceived usefulness, perceived ease of use and perceived reliability were positively correlated with digital health literacy, while age and perceived risk were negatively correlated with digital health literacy. Conclusion The overall digital health literacy of community-dwelling older adults in Southwest China is relatively low. In the future, health professionals should fully consider the diverse influencing factors of digital health literacy, assess individual differences and provide targeted intervention programs. Meanwhile, global public health authorities should integrate health resources effectively, and seek health service models for older adults in line with the development of the digital age to narrow the digital divide.

The growing concern over micropollutants in aquatic ecosystems motivates the development of electrochemical membrane reactors (EMRs) as a sustainable water treatment solution. Nevertheless, the intricate interplay among adsorption/desorption, electrochemical reactions, and byproduct formation within EMR complicates the understanding of their mechanisms. Herein, the degradation of micropollutants using an EMR equipped with carbon nanotube membrane are investigated, employing isotope-labeled steroid hormone micropollutant. The integration of high-performance liquid chromatography with a flow scintillator analyzer and liquid scintillation counting techniques allows to differentiate hormone removal by concurrent adsorption and degradation. Pre-adsorption of hormone is found not to limit its subsequent degradation, attributed to the rapid adsorption kinetics and effective mass transfer of EMR. This analytical approach facilitates determining the limiting factors affecting the hormone degradation under variable conditions. Increasing the voltage from 0.6 to 1.2 V causes the degradation dynamics to transition from being controlled by electron transfer rates to an adsorption-rate-limited regime. These findings unravels some underlying mechanisms of EMR, providing valuable insights for designing electrochemical strategies for micropollutant control. Pervasive micropollutants in aquatic environments pose significant threats to global water supply safety. Here, authors achieved permeate concentrations below the detection limit (2.5 ng/L) using a CNT-based electrochemical membrane, with the contributions of adsorption and degradation distinguished.