Assessment of cognitive dysfunction and its influencing factors after acute ischemic stroke.

OBJECTIVE: In order to provide a more accurate and effective basis for clinical diagnosis and treatment, patients with cognitive dysfunction after acute ischemic stroke (AIS) were evaluated and their influencing factors were analyzed. METHODS: A rigorous and systematic logistic regression analysis was conducted to comprehensively investigate the various influencing factors that contribute to cognitive dysfunction. RESULTS: Among them, the sex granulocyte/lymphocyte ratio (NLR), low-density lipoprotein cholesterol (LDL-C) level, and C-reactive protein (CRP) were also higher than those in the control group (p < 0.05). The scores of memory, orientation, visual and spatial function, abstract thinking and language in the control group were higher than those in the experimental group (p < 0.05). The results of multivariate logistic regression analysis showed that history of diabetes mellitus, high NLR, high LDL-C, high CRP, smoking and temporal lobe infarction were risk factors for cognitive dysfunction after AIS, while elevated BMI and love of exercise were protective factors for cognitive dysfunction after AIS. CONCLUSION: Patients with cognitive dysfunction had the highest incidence of temporal lobe infarction, and they scored lower than the control group on memory, orientation, visual and spatial function, abstract thinking, and language function. Multivariate logistic regression analysis showed that a history of diabetes mellitus, high NLR, high LDL-C, high CRP, smoking, and temporal lobe infarction were independent risk factors for cognitive dysfunction after acute ischemic stroke, while elevated BMI and a love of exercise were protective factors for cognitive dysfunction after acute ischemic stroke.

Simultaneous assessment of absorption and pharmacokinetic characteristics of four active flavonoids from Chimonanthus nitens Leaf Granules using LC-MS determination: in vivo and in vitro.

A sensitive UPLC-HRMS method was developed and validated for simultaneous quantification of four active flavonoids from Chimonanthus nitens Leaf Granules (CNLG) in biological matrix. The method was utilized in pharmacokinetic study of the four flavonoids in rats as well as other evaluation assays in vitro. It was revealed that rutin, nicotiflorin, and astragalin had poor oral bioavailability in rats possibly due to low intestinal permeability and metabolism in intestinal flora. Kaempferol underwent rapid glucuronidation and sulphation in rat plasma with medium permeability coefficient. The results provided valuable data for future research and development of CNLG flavonoids.

Comparative Analysis of Gut Bacterial Community Composition in Two Tropical Economic Sea Cucumbers under Different Seasons of Artificial Environment.

With the continuous rise of the sea cucumber aquaculture industry in China, the tropical sea cucumber aquaculture industry is also improving. However, research on the gut microorganisms of tropical sea cucumbers in captivity is scarce. In this study, high-throughput sequencing methods were used to analyze the gut microbial composition of Stichopus monotuberculatus and Holothuria scabra in the dry season and wet season of artificial environments. The results showed that 66 phyla were obtained in all samples, of which 59 phyla were obtained in the dry season, and 45 phyla were obtained in the wet season. The Tax4Fun analysis showed that certain gut bacterial communities affect the daily metabolism of two sea cucumber species and are involved in maintaining gut microecological balance in the gut of two sea cucumber species. In addition, compared with differences between species, PCoA and UPGMA clustering analysis showed the gut prokaryotes of the same sea cucumber species varied more in different seasons, indicating that the influence of environment was higher than the feeding choices of sea cucumbers under relatively closed conditions. These results revealed the gut bacterial community composition of S. monotuberculatus and H. scabra and the differences in gut bacterial structure between two sea cucumber species in different seasons were compared, which would provide the foundation for tropical sea cucumber aquaculture in the future.

A Lightweight Hybrid Model Using Multiscale Markov Transition Field for Real-Time Quality Assessment of Photoplethysmography Signals.

OBJECTIVE: The proliferation of wearable devices has escalated the standards for photoplethysmography (PPG) signal quality. This study introduces a lightweight model to address the imperative need for precise, real-time evaluation of PPG signal quality, followed by its deployment and validation utilizing our integrated upper computer and hardware system. METHODS: Multiscale Markov Transition Fields (MMTF) are employed to enrich the morphological information of the signals, serving as the input for our proposed hybrid model (HM). HM undergoes initial pre-training utilizing the MIMIC-III and UCI databases, followed by fine-tuning the Queensland dataset. Knowledge distillation (KD) then transfers the large-parameter model's knowledge to the lightweight hybrid model (LHM). LHM is subsequently deployed on the upper computer for real-time signal quality assessment. RESULTS: HM achieves impressive accuracies of 99.1% and 96.0% for binary and ternary classification, surpassing current state-of-the-art methods. LHM, with only 0.2 M parameters (0.44% of HM), maintains high accuracy despite a 2.6% drop. It achieves an inference speed of 0.023 s per image, meeting real-time display requirements. Furthermore, LHM attains a 97.7% accuracy on a self-created database. HM outperforms current methods in PPG signal quality accuracy, demonstrating the effectiveness of our approach. Additionally, LHM substantially reduces parameter count while maintaining high accuracy, enhancing efficiency and practicality for real-time applications. CONCLUSION: The proposed methodology demonstrates the capability to achieve high-precision and real-time assessment of PPG signal quality, and its practical validation has been successfully conducted during deployment. SIGNIFICANCE: This study contributes a convenient and accurate solution for the real-time evaluation of PPG signals, offering extensive application potential.

Source-based health risk assessment of heavy metal contamination in soil: a case study from a polymetallic mining region in Southeastern Hubei, Central China.

To conduct a precise health risk assessment of heavy metals (HMs) in soil, it is imperative to ascertain the primary sources of potential health risks. In this study, we conducted comprehensive measurements of HMs, specifically focusing on the accumulation of Cu, Cd, Sb, Zn, and Pb in local soil, which may pose threats to environmental quality. To achieve our objective, we employed a method that combines positive matrix factorization with a health risk assessment model to quantify the health risks associated with specific sources. The results obtained from the geo-accumulation index indicate that the majority of HMs found in the local soil are influenced by anthropogenic activities. Among these sources, local industrial-related activities contributed the largest proportion of HMs to the soil at 34.7%, followed by natural sources at 28.7%, mining and metallurgy-related activities at 28.2%, and traffic-related activities at 8.40%. Although the non-carcinogenic and carcinogenic risks associated with individual HMs were found to be below safety thresholds, the cumulative health risks stemming from total HMs exceeded safety limits for children. Moreover, the unacceptable health risks for children originating from industrial-related activities, natural sources, and mining and metallurgy-related activities were primarily concentrated in proximity to mining sites and industrial areas within the local region. This investigation furnishes valuable insights that can aid governmental authorities in formulating precise control policies to mitigate health threats posed by soils in polymetallic mining areas.

The methodological quality assessment of systematic reviews/meta-analyses of chronic prostatitis/chronic pelvic pain syndrome using AMSTAR2.

BACKGROUND: This study aimed to assess the methodological quality of the systematic reviews/meta-analyses (SRs/MAs) of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) using A Measurement Tool to Assess systematic Reviews (AMSTAR2) and to explore the potential influencing factors. METHODS: PubMed, EMBASE and Cochrane Library databases were searched for relevant studies. AMSTAR2 was used for evaluating the methodological quality of eligible SRs/MAs. Differences between methodological characteristics of SRs/MAs were compared using chi-square tests. The intra-class correlation coefficient (ICC) was used to assess reviewer agreement in the pre-experiment. Multivariate regression analysis was used to identify potential factors affecting methodological quality. RESULTS: A total of 45 SRs/MAs were included. After AMSTAR2 evaluation, only two (4.4%) of 45 SRs/MAs were moderate, three (6.7%) were rated as low quality, and the remainder 40 (88.9%) were rated as critically low quality. Among the 16 items of AMSTAR2, item 3 and item 10 had the poorest adherence. Item 4 received the most significant number of "Partial Yes" responses. Univariable analysis indicated that there were significant differences in methodological quality in SRs between different continents (P = 0.027) as well as between preregistered SRs and those that were not (P = 0.004). However, in multivariate analysis, there was no significant association between methodological quality and the following research characteristics: publication year, continent, whether reporting followed Preferred Reporting Items for Systematic Reviews (PRISMA), preregistration, funding support, randomized controlled trials (RCT) enrollment, whether SR was published in the Cochrane Database of Systematic Reviews (CDSR), and whether with meta-analysis. Additionally, subgroup analysis based on interventional SRs/MAs showed that continent was independently associated with the methodological quality of SRs/MAs of CP/CPPS via univariable and multivariate analysis. CONCLUSIONS: Our study demonstrates that the methodological quality of SRs/MAs of CP/CPPS was generally poor. SRs/MAs of CP/CPPS should adopt the AMSTAR2 to enhance their methodological quality.

Genomic analysis of almost 8,000 Salmonella genomes reveals drivers and landscape of antimicrobial resistance in China.

IMPORTANCE: We established the largest Salmonella genome database from China and presented the landscape and spatiotemporal dynamics of antimicrobial resistance genes. We also found that economic, climatic, and social factors can drive the rise of antimicrobial resistance. The Chinese local Salmonella genome database version 2 was released as an open-access database (https://nmdc.cn/clsgdbv2) and thus can assist surveillance studies across the globe. This database will help inform interventions for AMR, food safety, and public health.

Semi-Supervised Learning for Low-Cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram.

OBJECTIVE: Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can lead to a wide range of health issues if left untreated. This study aims to address the lack of research on personalized models for single-lead electrocardiogram (ECG)-based OSA detection, by proposing an automatic semi-supervised algorithm for automated low-cost personalization fine-tuning. METHODS: We utilize a convolutional neural network (CNN)-based auto-encoder (AE) with a modified training objective to detect anomalous region of OSA. An indicator based on model outputs is utilized as a benchmark measure to assign pseudo-labels with confidence to each sample. Finally, we perform validation of the semi-supervised algorithm on the same database and cross-database scenarios. RESULTS: By introducing semi-supervised personalization, the accuracy, AUC, and mean absolute error (MAE) of the general model (GM) of 35 subjects from the same database are improved from 86.3%, 0.915, and 5.178 to 90.3%, 0.948, and 2.593. Simultaneously, in the validation of 25 subjects from a cross-database, the accuracy, AUC, and MAE of the GM are enhanced from 75.6%, 0.800, and 9.149 to 84.3%, 0.881, and 3.509. CONCLUSION: The improved version of AE demonstrates excellent adaptability in identifying abnormal features in OSA, employing a data-driven approach to assign pseudo-labels for unknown data automatically. Additionally, leveraging the pseudo-labels through a semi-supervised fine-tuning strategy provides a solution to overcome the limitation of clinical annotations, facilitating low-cost implementation of personalized models. SIGNIFICANCE: The semi-supervised approach proposed in this article provides a high-performance and annotation-free solution for personalized adjustment of automatic OSA detection.

Reinforcement learning relieves the vaccination dilemma.

The main goal of this paper is to study how a decision-making rule for vaccination can affect epidemic spreading by exploiting the Bush-Mosteller (BM) model, one of the methodologies in reinforcement learning in artificial intelligence (AI), which can realize the systematic process of learning in humans, on complex networks. We consider the BM model with two stages-vaccination and epidemiological processes-and address two independent rules about fixed loss consideration and average payoff of neighbors to update agent's vaccination behavior for various stimuli, such as loss of payoffs and environments during the vaccination process. Higher sensitivity not only favors higher vaccination coverage rates but also delays the transition point in relative vaccination costs when transitioning from full vaccination (inoculation level 1) to incomplete vaccination (inoculation level less than 1). Extensive numerical simulations demonstrate that the vaccination dilemma can be overcome to some extent, and the distribution of the intended vaccination probabilities in both independent rules is either normal or skewed when different parameters are considered. Since AI is contributing to many fields, we expect that our BM-empowered learning can ultimately resolve the vaccination dilemma.

Exploring the effect of city size on carbon emissions: Evidence from 259 prefecture-level cities in China.

As a gathering place for human production activities, cities are the main places where energy consumption and carbon dioxide emissions occur. How to accurately measure city size and test the impact mechanism of city size on carbon emissions of different city levels is still controversial. This study uses the global nighttime light data to identify urban bright areas and built-up areas, and accordingly constructs the city size index of 259 prefecture level cities in China from 2003 to 2019. It avoids the problem of only considering the single index of population size or space size, and makes the measurement of city size more reasonable. We use a dynamic panel model to study the impact of city size on urban carbon emissions per capita, and discuss the heterogeneity of various cities under different population levels and economic development levels. The empirical results indicate that in the scale of cities in China showed a fluctuating growth trend in recent years. The city size index of most cities is clustered at medium and high values. The city size index of cities with different economic development levels and different population-scale levels shows obvious gradient differences but maintains an upward trend. The expansion of supercities (with a population of more than 5 million) introduces a drastic increase in carbon emissions. The carbon emissions growth caused by the expansion of cities that are classified as third tier and below is the smallest, while that caused by the expansion of cities that are classified as first-tier is the largest. The findings suggest differentiated emissions reduction suggestions for cities with different sizes.

Projected rapid growth in diabetes disease burden and economic burden in China: a spatio-temporal study from 2020 to 2030.

Background: This study projects the trend of disease burden and economic burden of diabetes in 33 Chinese provinces and nationally during 2020-2030 and investigates its spatial disparities. Methods: Time series prediction on the prevalence and disability-adjusted life-year (DALY) rates of diabetes was conducted using a Bayesian modelling approach in 2020-2030. The top-down method and the human capital method were used to predict the direct and indirect costs of diabetes for each Chinese province. Global and local spatial autocorrelation analyses were used to identify geographic clusters of low-or high-burden areas. Findings: Diabetes prevalence in Chinese adults aged 20-79 years was projected to increase from 8.2% to 9.7% during 2020-2030. During the same period, the total costs of diabetes would increase from $250.2 billion to $460.4 billion, corresponding to an annual growth rate of 6.32%. The total costs of diabetes as a percentage of GDP would increase from 1.58% to 1.69% in China during 2020-2030, suggesting a faster growth in the economic burden of diabetes than China's economic growth. Consistently, the per-capita economic burden of diabetes would increase from $231 to $414 in China during 2020-2030, with an annual growth rate of 6.02%. High disease and economic burden areas were aggregated in Northeast and/or North China. Interpretation: Our study projects a significant growth of disease and economic burden of diabetes in China during 2020-2030, with strong spatial aggregation in northern Chinese regions. The increase in the economic burden of diabetes will exceed that of GDP. Funding: National Natural Science Foundation of China, Outstanding Young Scholars Funding.

Dietary changes among pregnant individuals compared to pre-pandemic: A cross-sectional analysis of the Pregnancy during the COVID-19 Pandemic (PdP) study.

Introduction: Dietary changes are common in pregnancy and may affect pregnancy outcomes, yet these changes and the associated contributory factors during the COVID-19 pandemic have been understudied. We aimed to investigate the association between dietary change and socioeconomic variables, pre-pregnancy BMI, and mental health symptoms; the change in intake of seven food categories and their reasons; and the association between intake of these food categories and mental health symptoms. Materials and methods: In this cross-sectional analysis, we used data from the Pregnancy during the COVID-19 Pandemic (PdP) cohort study that collected data from pregnant Canadian individuals (n = 9,870, gestational age ≤ 35 weeks) on socioeconomic factors, pandemic-related hardships, pre-pregnancy body mass index (BMI), dietary changes compared to pre-pandemic and the reasons for these changes. We assessed depressive and anxiety symptoms using the Edinburgh Postpartum Depression Scale (EPDS) and Patient-Reported Outcomes Measurement Information System (PROMIS)-Anxiety, respectively. Results: 54.3% of the participants reported a change in their diet. Non-white ethnicity (OR = 1.33), job loss (OR = 1.29), clinically elevated depressive and anxiety symptoms (OR = 1.26 and 1.14, respectively), self-isolation (OR = 1.20), pre-pregnancy BMI (OR = 1.19), fear of COVID-19 (OR = 1.15), and pandemic phase at enrolment (OR = 0.90) significantly predicted dietary change. Most participants ate about the same amounts of dairy, meats and canned foods/dried goods as pre-pandemic (61.5, 61.7, and 60.2%, respectively), increased their intake of fresh vegetables/fruits and sweets/snacks (43.2 and 54.5%, respectively), and decreased fast-food and take-out/home delivery (53.2 and 43.1%, respectively). Changes in consumption of the food categories had a curvilinear association with mental health symptoms (except resilience) indicating greater symptoms with either decreased or increased intakes. Changes in craving, having more time for cooking/preparing foods, and being unable to go grocery shopping frequently (but not reduced affordability) were the main reasons driving these dietary changes. Conclusion: Some factors increase the odds of dietary change among pregnant individuals during the pandemic, with some changes toward a healthy and others toward an unhealthy diet. Given the importance of a healthy diet during gestation, identifying the risk and protective factors might be the first essential step in reducing the detrimental effects of unfavorable dietary changes during the pandemic on this vulnerable population.

Quantitative assessment of lumbar spine bone marrow in patients with different severity of CKD by IDEAL-IQ magnetic resonance sequence.

Background: Chronic kidney disease (CKD) has a significant negative impact on bone health. Bone marrow is an essential component of bone, mainly composed of trabecular bone and fat. The IDEAL-IQ sequence of MRI allows indirect quantification of trabecular bone mass by R2* and direct quantification of bone marrow fat content by FF map, respectively. Objective: Our objective was to explore the association of CKD severity with bone marrow using IDEAL-IQ and whether mineral and bone metabolism markers alter this association. Method: We recruited 68 CKD patients in this cross-sectional research (15 with CKD stages 3-4, 26 with stage 5, and 27 with stage 5d). All patients underwent lumbar spine IDEAL-IQ, BMD, and several bone metabolism markers (iPTH, 25-(OH)-VitD, calcium and phosphorus). Multiple linear regression analysis was used to examine the association of CKD severity with MRI measurements (R2* and FF). Results: More severe CKD was associated with a higher R2* value [CKD 5d versus 3-4: 30.077 s-1 (95% CI: 12.937, 47.217), P for trend < 0.001], and this association was attenuated when iPTH was introduced [CKD 5d versus 3-4: 19.660 s-1 (95% CI: 0.205, 39.114), P for trend = 0.042]. Furthermore, iPTH had an association with R2* value [iPTH (pg/mL): 0.033 s-1 (95% CI: 0.001, 0.064), P = 0.041]. Besides, FF was mainly affected by age and BMI, but not CKD. Conclusions: The bone marrow R2* value measured by IDEAL-IQ sequence is associated with CKD severity and iPTH. The R2* of IDEAL-IQ has the potential to reflect lumbar bone changes in patients with CKD.

Multiscale Price Lead-Lag Relationship between Steel Materials and Industry Chain Products Based on Network Analysis.

As two main steelmaking materials, iron ore and scrap steel have different price lead-lag relationships (PLRs) on midstream and downstream steel products in China. The relationships also differ as the time scale varies. In this study, we compare the price influences of two important steel materials on midstream and downstream steel products at different time scales. First, we utilize the maximal overlap discrete wavelet transform (MODWT) method to decompose the original steel materials and products price series into short-term, midterm, and long-term time scale series. Then, we introduce the cross-correlation and Podobnik test method to calculate and test the price lead-lag relationships (PLRs) between two steel materials and 16 steel products. Finally, we construct 12 price lead-lag relationship networks and choose network indicators to present the price influence of the two materials at different time scales. We find that first, most scrap steel and steel products prices fluctuate at the same time lag order, while iron ore leads most steel products price for one day. Second, products that exist in the downstream industry chain usually lead to iron ore. Third, as the time scale becomes longer, the lead relationships from steel materials to steel products become closer.

Occurrence, Distribution, and Risk Assessment of Antibiotics in the Aquatic Environment of the Karst Plateau Wetland of Yangtze River Basin, Southwestern China.

In this study, the occurrence, distribution, and ecological risk of 40 commonly used antibiotics, including 15 sulfonamides (SAs), 9 fluoroquinolones (FQs), 7 macrolides (MCs), 3 tetracyclines (TCs), 2 chloramphenicols (CAPs), and 4 other categories, in the aquatic environment of the karst plateau wetland Caohai of the Yangtze River basin in southwestern China are reported. In total, 27 antibiotics were detected, with the detection rate ranging from 5% to 100%. The total concentration at each site ranged from 21.8 ng/L to 954 ng/L, with the average concentration being 189 ng/L. FQs and MCs were the most predominant categories, contributing 29.3% and 25.0% of the total antibiotic burden. The five most commonly detected antibiotics were ciprofloxacin (CIP), oxytetracycline (OTC), acetyl sulfamethoxazole (ASMZ), norfloxacin (NOR), and florfenicol (FF). The spatial distribution of the total concentration at each site demonstrated a decreasing trend from the southeastern area upstream adjoining the main counties to the northwestern area downstream, indicating that human activities have a great impact. Meanwhile, the natural attenuation rates of different types of antibiotics in the direction of flow ranged from 17.6% to 100%, which implied the natural purification potential of the wetland for antibiotics. The cluster analysis results indicated that domestic sewage and wastewater from agriculture and animal husbandry were the main sources of contamination in the surrounding wetland. Risk quotients (RQs) assessment showed that most of the individuals were at low to medium risk and that the adverse risks posed by mixtures of antibiotics were higher than those posed by the individual antibiotics.

From metabolomic analysis to quality assessment and biosynthetic insight in traditional Chinese medicine: Mulberry tree as a case study.

INTRODUCTION: Ramulus Mori (RM, Sangzhi) and Cortex Mori (CM, Sangbaipi) both come from the Chinese medicinal plant mulberry tree. CM is usually used to relieve cough, while RM is usually used to treat pain. There are no studies on the quality control of RM and CM based on their analgesic and anti-inflammatory constituents associated with their traditional use. The chemical profiles of CM and RM were confusing. Some CM had similar profiles to RM, but some did not. OBJECTIVE: We aimed to reveal the chemical differences between RM and CM and to evaluate their quality. MATERIALS AND METHODS: Their chemical differences were studied using metabolomic analysis based on UHPLC-ESI-MS data. The contents of five quality marker candidates were determined by UHPLC-PDA. The analgesic activities of morusin and kuwanon C were assessed by an acetic acid-induced writhing test. RESULTS: CM was characterized by chemical diversity, whereas RM had good homogeneity. Four groups of CM were classified based on their chemicals. The chemical profiles of CM group 4 were more similar to that of RM. Eighteen putative features were identified based on an MS-Finder search and fragmentation rules. Content limits for four quality markers with anti-inflammatory or analgesic activities were proposed for RM. Furthermore, a possible biosynthetic relationship between kuwanon C, kuwanon G, and morusin was hypothesized based on the high Pearson coefficient between kuwanon G and morusin. CONCLUSION: The obtained results may be useful in the evaluation of RM and CM and afford insight into the biosynthetic pathway of Diels-Alder adducts in Morus.

Nitrous acid in the polluted coastal atmosphere of the South China Sea: Ship emissions, budgets, and impacts.

Nitrous acid (HONO) can significantly contribute to hydroxyl radicals (OH) and thus regulate atmospheric oxidation chemistry; however, ambient HONO sources are not well quantified and vary in different environments. In this study, we conducted comprehensive field observations at a coastal site in the South China Sea and performed chemical box modelling to demonstrate contrasting budgets and impacts of diurnal atmospheric HONO derived from the sea, coastline and continent. The ship emission ratio of HONO/nitrogen oxides (NOx) (1.21 ± 0.99%) was calculated from hundreds of night-time fresh plume measurements. Offshore marine air was frequently influenced by ship exhausts, and the sea acted as an HONO sink. Heterogeneous conversions of nitrogen dioxide (NO2) on underlying surfaces and photolysis of adsorbed nitric acid (HNO3(ads)) were the major HONO sources in coastal air, when heterogeneous NO2 conversions on the ground surface and the homogeneous NO + OH reaction dominated HONO formation in continental air. HONO photolysis was a significant source of reactive radicals (ROx = OH + HO2 + RO2) in these air masses. Atmospheric box model including only homogeneous HONO source of the NO + OH reactions significantly underpredicted the OH concentration and atmospheric oxidising capacity in coastal and continental air. This study provides new insights into the complex sources and significant impacts of HONO in the polluted coastal boundary layer.

Volunteer activity and depression among the elderly in China: A study on rural-urban differences.

ABSTRACT: Participation in volunteer activity has positive effects on health among elderly. Few studies have investigated the association between volunteer activity and depression among Chinese elderly. This study aimed to examine the association between volunteer activity and depression among the elderly in China regarding rural-urban differences.Totally 8255 subjects from the 2015 China Health and Retirement Longitudinal Study were selected in this study. Depression was assessed by 10-item Center for Epidemiologic Studies Depression Scale. Types and frequency of volunteer activity were measured in the questionnaire. Multiple linear regression analysis was used to explore the relationship between volunteer activity and depression of elderly.In our study, the urban elderly had lower depressive scores than rural elderly (6.7 ±â€Š5.8 vs. 9.1 ±â€Š6.7). After adjustment for all covariates, our results revealed that almost daily participation in formal volunteer activities was negatively associated with depression among urban elderly (B = -2.69, SE = 1.05, P = .010); almost daily caring for a sick or disabled adult was positively associated with depression among both urban and rural elderly (urban:B = 3.13, SE = 1.54, P = .043; rural:B = 2.56, SE = 1.18, P = .031).These findings suggested that there was a negative association between formal volunteer activity and depression among urban elderly, while there was a positive association between caring for a sick or disabled adult and depression among both urban and rural elderly. The government should take effective measures to encourage the elderly to participate in formal volunteer activities to prevent them from depression.

Spatial Threshold Effect of Industrial Land Use Efficiency on Industrial Carbon Emissions: A Case Study in China.

China's industry is still in the middle of industrialization. Land use activities are crucial to the growth of carbon emissions. However, few scholars focus on the influence mechanism between industrial land use efficiency (ILUE) and industrial carbon emissions. In this paper, the threshold model and the spatial Durbin model are used to investigate the spatial threshold effect of industrial land use efficiency on industrial carbon emission from 2003 to 2018. The results show that ILUE of China's provinces basically shows an improvement trend, with little difference in spatial distribution, showing a pattern of high in the eastern region and low in the western region. When economic development level (A) and technical level (T) are taken as the threshold variable, ILUE has a single threshold effect on industrial carbon emissions in the eastern region. In the central region, with a as the threshold variable, ILUE shows a double threshold effect on industrial carbon emission. Under the 0-1 geographical proximity weight matrix, the indirect spillover effect of ILUE on reducing regional carbon emissions is significant, and the indirect effect is even greater than that on regional carbon emissions. The spatial spillover effect is not significant in the eastern region. These findings have important practical significance for promoting regional industrial transformation and upgrading, optimizing land space and realizing high-quality economic development.

A metabolic model of Lipomyces starkeyi for predicting lipogenesis potential from diverse low-cost substrates.

BACKGROUND: Lipomyces starkeyi has been widely regarded as a promising oleaginous yeast with broad industrial application prospects because of its wide substrate spectrum, good adaption to fermentation inhibitors, excellent fatty acid composition for high-quality biodiesel, and negligible lipid remobilization. However, the currently low experimental lipid yield of L. starkeyi prohibits its commercial success. Metabolic model is extremely valuable to comprehend the complex biochemical processes and provide great guidance for strain modification to facilitate the lipid biosynthesis. RESULTS: A small-scale metabolic model of L. starkeyi NRRL Y-11557 was constructed based on the genome annotation information. The theoretical lipid yields of glucose, cellobiose, xylose, glycerol, and acetic acid were calculated according to the flux balance analysis (FBA). The optimal flux distribution of the lipid synthesis showed that pentose phosphate pathway (PPP) independently met the necessity of NADPH for lipid synthesis, resulting in the relatively low lipid yields. Several targets (NADP-dependent oxidoreductases) beneficial for oleaginicity of L. starkeyi with significantly higher theoretical lipid yields were compared and elucidated. The combined utilization of acetic acid and other carbon sources and a hypothetical reverse ß-oxidation (RBO) pathway showed outstanding potential for improving the theoretical lipid yield. CONCLUSIONS: The lipid biosynthesis potential of L. starkeyi can be significantly improved through appropriate modification of metabolic network, as well as combined utilization of carbon sources according to the metabolic model. The prediction and analysis provide valuable guidance to improve lipid production from various low-cost substrates.