Epidemiology, screening, and Co-management of type 2 diabetes mellitus and Metabolic-dysfunction associated steatotic liver disease.

Metabolic-dysfunction associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is increasingly recognized as a prevalent global burden. Type 2 diabetes mellitus (T2DM), another important metabolic disease, is considered a major contributor to the development of MASLD. MASLD and T2DM have a strong association with each other due to shared pathogenic mechanisms. The co-existence of the two diseases increases the risk of liver-related adverse outcomes, and imposes a heavier burden on extrahepatic outcomes, representing a substantial public health issue. Effective assessment and management of T2DM combined with MASLD necessitate a multidisciplinary approach. The emergence of numerous randomized clinical trials has shed light on the treatment of T2DM combined with MASLD. This review uncovered the epidemiology of the intertwined T2DM and MASLD, offer insights into the evaluation of hepatic fibrosis in T2DM patients, glucose monitoring in MASLD population, and provide comprehensive co-management strategies for addressing both diseases.

LiverRisk score: An accurate, cost-effective tool to predict fibrosis, liver-related, and diabetes-related mortality in the general population.

BACKGROUND: Noninvasive and early assessment of liver fibrosis is of great significance and is challenging. We aimed to evaluate the predictive performance and cost-effectiveness of the LiverRisk score for liver fibrosis and liver-related and diabetes-related mortality in the general population. METHODS: The general population from the NHANES 2017-March 2020, NHANES 1999-2018, and UK Biobank 2006-2010 were included in the cross-sectional cohort (n = 3,770), along with the NHANES follow-up cohort (n = 25,317) and the UK Biobank follow-up cohort (n = 17,259). The cost-effectiveness analysis was performed using TreeAge Pro software. Liver stiffness measurements ≥10 kPa were defined as compensated advanced chronic liver disease (cACLD). FINDINGS: Compared to conventional scores, the LiverRisk score had significantly better accuracy and calibration in predicting liver fibrosis, with an area under the receiver operating characteristic curve (AUC) of 0.76 (0.72-0.79) for cACLD. According to the updated thresholds of LiverRisk score (6 and 10), we reclassified the population into three groups: low, medium, and high risk. The AUCs of LiverRisk score for predicting liver-related and diabetes-related mortality at 5, 10, and 15 years were all above 0.8, with better performance than the Fibrosis-4 score. Furthermore, compared to the low-risk group, the medium-risk and high-risk groups in the two follow-up cohorts had a significantly higher risk of liver-related and diabetes-related mortality. Finally, the cost-effectiveness analysis showed that the incremental cost-effectiveness ratio for LiverRisk score compared to FIB-4 was USD $18,170 per additional quality-adjusted life-year (QALY) gained, below the willingness-to-pay threshold of $50,000/QALY. CONCLUSIONS: The LiverRisk score is an accurate, cost-effective tool to predict liver fibrosis and liver-related and diabetes-related mortality in the general population. FUNDING: The National Natural Science Foundation of China (nos. 82330060, 92059202, and 92359304); the Key Research and Development Program of Jiangsu Province (BE2023767a); the Fundamental Research Fund of Southeast University (3290002303A2); Changjiang Scholars Talent Cultivation Project of Zhongda Hospital of Southeast University (2023YJXYYRCPY03); and the Research Personnel Cultivation Program of Zhongda Hospital Southeast University (CZXM-GSP-RC125).

Prediction and the influencing factor study of colorectal cancer hospitalization costs in China based on machine learning-random forest and support vector regression: a retrospective study.

Aims: As people's standard of living improves, the incidence of colorectal cancer is increasing, and colorectal cancer hospitalization costs are relatively high. Therefore, predicting the cost of hospitalization for colorectal cancer patients can provide guidance for controlling healthcare costs and for the development of related policies. Methods: This study used the first page of medical record data on colorectal cancer inpatient cases of a tertiary first-class hospital in Shenzhen from 2018 to 2022. The impacting factors of hospitalization costs for colorectal cancer were analyzed. Random forest and support vector regression models were used to establish predictive models of the cost of hospitalization for colorectal cancer patients and to compare and evaluate. Results: In colorectal cancer inpatients, major procedures, length of stay, level of procedure, Charlson comorbidity index, age, and medical payment method were the important influencing factors. In terms of the test set, the R2 of the Random forest model was 0.833, the R2 of the Support vector regression model was 0.824; the root mean square error (RMSE) of the Random forest model was 0.029, and the RMSE of the Support vector regression model was 0.032. In the Random Forest model, the weight of the major procedure was the highest (0.286). Conclusion: Major procedures and length of stay have the greatest impacts on hospital costs for colorectal cancer patients. The random forest model is a better method to predict the hospitalization costs for colorectal cancer patients than the support vector regression.

Comprehensive evaluation of antibiotics pollution the Yangtze River basin, China: Emission, multimedia fate and risk assessment.

Antibiotics have attracted global attention because of their potential ecological and health risks. The emission, multimedia fate and risk of 18 selected antibiotics in the entire Yangtze River basin were evaluated by using a level Ⅳ fugacity model. High antibiotic emissions were found in the middle and lower reaches of the Yangtze River basin. The total antibiotic emissions in the Yangtze River basin exceeded 1600 tons per year between 2013 and 2021. The spatial distribution of antibiotics concentration was the upper Yangtze River > middle Yangtze River > lower Yangtze River, which is positively correlated with animal husbandry size in the basin. Temperature and precipitation increases may decrease the antibiotic concentrations in the environment. Transfer fluxes showed that source emission inputs, advection processes, and degradation fluxes contributed more to the total input and output. High ecological risks in the water environment were found in 2018, 2019, 2020, and 2021. The comprehensive health risk assessment through drinking water and fish consumption routes showed that a small part of the Yangtze River basin is at medium risk, and children have a relatively high degree of health risk. This study provides a scientific basis for the pollution control of antibiotics at the basin scale.

3D/2D Vessel Registration Based on Monte Carlo Tree Search and Manifold Regularization.

The augmented intra-operative real-time imaging in vascular interventional surgery, which is generally performed by projecting preoperative computed tomography angiography images onto intraoperative digital subtraction angiography (DSA) images, can compensate for the deficiencies of DSA-based navigation, such as lack of depth information and excessive use of toxic contrast agents. 3D/2D vessel registration is the critical step in image augmentation. A 3D/2D registration method based on vessel graph matching is proposed in this study. For rigid registration, the matching of vessel graphs can be decomposed into continuous states, thus 3D/2D vascular registration is formulated as a search tree problem. The Monte Carlo tree search method is applied to find the optimal vessel matching associated with the highest rigid registration score. For nonrigid registration, we propose a novel vessel deformation model based on manifold regularization. This model incorporates the smoothness constraint of vessel topology into the objective function. Furthermore, we derive simplified gradient formulas that enable fast registration. The proposed technique undergoes evaluation against seven rigid and three nonrigid methods using a variety of data - simulated, algorithmically generated, and manually annotated - across three vascular anatomies: the hepatic artery, coronary artery, and aorta. Our findings show the proposed method's resistance to pose variations, noise, and deformations, outperforming existing methods in terms of registration accuracy and computational efficiency. The proposed method demonstrates average registration errors of 2.14 mm and 0.34 mm for rigid and nonrigid registration, and an average computation time of 0.51 s.

PM2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS.

Assessing personal exposure risk from PM2.5 air pollution poses challenges due to the limited availability of high spatial resolution data for PM2.5 and population density. This study introduced a seasonal spatial-temporal method of modeling PM2.5 distribution characteristics at a 1-km grid level based on remote sensing data and Geographic Information Systems (GIS). The high-accuracy population density data and the relative exposure risk model were used to assess the relationship between exposure to PM2.5 air pollution and public health. The results indicated that the spatial-temporal PM2.5 concentration could be simulated by MODIS images and GIS method and could provide high spatial resolution data sources for exposure risk assessment. PM2.5 air pollution risks were most serious in spring and winter, and high risks of environmental health hazards were mostly concentrated in densely populated areas in Shanghai-Hangzhou Bay, China. Policies to control the total population and pollution discharge need follow the principle of adaptation to local conditions in high-risk areas. Air quality maintenance and ecological maintenance should be carried out in low-risk areas to reduce exposure risk and improve environmental health.

Assessment of causal direction between thyroid function and cardiometabolic health: a Mendelian randomization study.

BACKGROUND: Growing evidence have demonstrated that thyroid hormones have been involved in the processes of cardiovascular metabolism. However, the causal relationship of thyroid function and cardiometabolic health remains partly unknown. METHODS: The Mendelian randomization (MR) was used to test genetic, potentially causal relationships between instrumental variables and cardiometabolic traits. Genetic variants of free thyroxine (FT4) and thyrotropin (TSH) levels within the reference range were used as instrumental variables. Data for genetic associations with cardiometabolic diseases were acquired from the genome-wide association studies of the FinnGen, CARDIoGRAM and CARDIoGRAMplusC4D, CHARGE, and MEGASTROKE. This study was conducted using summary statistic data from large, previously described cohorts. Association between thyroid function and essential hypertension (EHTN), secondary hypertension (SHTN), hyperlipidemia (HPL), type 2 diabetes mellitus (T2DM), ischemic heart disease (IHD), myocardial infarction (MI), heart failure (HF), pulmonary heart disease (PHD), stroke, and non-rheumatic valve disease (NRVD) were examined. RESULTS: Genetically predicted FT4 levels were associated with SHTN (odds ratio = 0.48; 95% CI = 0.04-0.82,P = 0.027), HPL (odds ratio = 0.67; 95% CI = 0.18-0.88,P = 0.023), T2DM (odds ratio = 0.80; 95% CI = 0.42-0.86,P = 0.005), IHD (odds ratio = 0.85; 95% CI = 0.49-0.98,P = 0.039), NRVD (odds ratio = 0.75; 95% CI = 0.27-0.97,P = 0.039). Additionally, genetically predicted TSH levels were associated with HF (odds ratio = 0.82; 95% CI = 0.68-0.99,P = 0.042), PHD (odds ratio = 0.75; 95% CI = 0.32-0.82,P = 0.006), stroke (odds ratio = 0.95; 95% CI = 0.81-0.97,P = 0.007). However, genetically predicted thyroid function traits were not associated with EHTN and MI. CONCLUSIONS: Our study suggests FT4 and TSH are associated with cardiometabolic diseases, underscoring the importance of the pituitary-thyroid-cardiac axis in cardiometabolic health susceptibility.

[Multimedia Distribution Characteristics and Risk Assessment of 22 PPCPs in the Water Environment of Qingpu District, Yangtze River Delta Demonstration Area].

In this study, 50 surface water and sediment samples were collected from 25 sampling points in Qingpu District (including Taipu River basin, Jinze Reservoir, and Qingxi country park) in the Yangtze River Delta integration demonstration area, and 22 pharmaceuticals and personal care products (PPCPs) in the samples were analyzed using high-performance liquid chromatography tandem mass spectrometry (HPLC/MS-MS). The distribution characteristics, sources, and influencing factors of targeted PPCPs in the study area were studied in detail. The ecological and health risks of the target PPCPs were evaluated using the quotients method. The results showed that a total of 19 PPCPs were detected in the surface water and sediment samples from 25 sampling points in Qingpu District, with total concentrations ranging from 0.06 to 178.67 ng·L-1 and 0.07 to 37.68 ng·g-1, respectively. The average value of sulfachloropyridazine (SCP) in the surface water was the highest with a concentration of 129.54 ng·L-1, whereas the average value of sulfamethoxazole (SMX) in the sediment was the highest with a concentration of 70.62 ng·g-1. The spatial distribution of the total amount of PPCPs showed a trend of Qingxi country park > Jinze Reservoir > Taipu River basin. Principal component analysis showed that the main sources of pollution were animal antibiotics used in aquaculture and the discharge of domestic sewage. There was a significant correlation between lg Koc and lg Kd (P<0.05), indicating that the organic carbon plays an important role in the distribution of the target PPCPs in water and sediments. The ecological risk assessment results revealed that the fungicides (TCC and TCS) in the surface waters showed a moderate risk to aquatic organisms of different trophic levels. The Qingxi country park and Jinze Reservoir were the regions with the highest ecological risks of PPCPs in surface water and sediment. The health risk entropy (HQ) of people of all age groups exposed through drinking was less than 1; however, with the continuous emission and accumulation of PPCPs, the pollution control of PPCPs in the environment still requires further attention.

Data gaps towards health development goals, 47 low- and middle-income countries.

OBJECTIVE: To assess the availability and gaps in data for measuring progress towards health-related sustainable development goals and other targets in selected low- and middle-income countries. METHODS: We used 14 international population surveys to evaluate the health data systems in the 47 least developed countries over the years 2015-2020. We reviewed the survey instruments to determine whether they contained tools that could be used to measure 46 health-related indicators defined by the World Health Organization. We recorded the number of countries with data available on the indicators from these surveys. FINDINGS: Twenty-seven indicators were measurable by the surveys we identified. The two health emergency indicators were not measurable by current surveys. The percentage of countries that used surveys to collect data over 2015-2020 were lowest for tuberculosis (2/47; 4.3%), hepatitis B (3/47; 6.4%), human immunodeficiency virus (11/47; 23.4%), child development status and child abuse (both 13/47; 27.7%), compared with safe drinking water (37/47; 78.7%) and births attended by skilled health personnel (36/47; 76.6%). Nineteen countries collected data on 21 or more indicators over 2015-2020 while nine collected data on no indicators; over 2018-2020 these numbers reduced to six and 20, respectively. CONCLUSION: Examining selected international surveys provided a quick summary of health data available in the 47 least developed countries. We found major gaps in health data due to long survey cycles and lack of appropriate survey instruments. Novel indicators and survey instruments would be needed to track the fast-changing situation of health emergencies.

Multimedia fate model and risk assessment of typical antibiotics in the integrated demonstration zone of the Yangtze River Delta, China.

Due to the widespread consumption of antibiotics by humans and animals, antibiotic residues from human and animal excrements are released into the environment through domestic sewage and breeding wastewater, which ultimately affect the ecological environment and human health. In this study, the concentrations of 10 antibiotics in the air, water, soil, and sediment from 2013 to 2019 in Qingpu District of the integrated demonstration zone of the Yangtze River Delta were predicated by developing a dynamic Level IV fugacity model. The influence of seasonal environmental factors (e.g., temperature, rainfall) on the distribution and migration of antibiotics in multi-media was also explored. The simulation results show that the 10 antibiotics mainly existed in water and sediment. The concentrations of antibiotics in air, water, soil, and sediment were 0-7.629 × 10-14 ng/L, 1.187 × 10-10-16.793 ng/L, 1.042 × 10-14-3.500 × 10-11 ng/g and 8.015 × 10-12-14.188 ng/g, respectively. It was also found that the increase in temperature and rainfall can reduce the migration rate of some antibiotics into the water and sediment phases. The flux analysis of the cross-media migration and transformation of antibiotics in Qingpu District shows that advection was the prime input and output paths of antibiotics in the water. Moreover, the prime input and output paths of antibiotics in sediment were sedimentation from water to sediment and degradation. Sensitivity analysis shows that the characteristics of antibiotic emission, degradation rate, and Koc were the most influential parameters for target chemicals. The results of risk assessment based on Monte Carlo method reveal that the overall risk level of antibiotics in sediment was relatively risk-free, and the risk of antibiotics in water decreased in the order of tetracyclines > ß-lactams > fluoroquinolones > macrolides > sulfonamides.

SpeechSpiro: Lung Function Assessment from Speech Pattern as an Alternative to Spirometry for Mobile Health Tracking.

Respiratory illnesses are common in the United States and globally; people deal with these illnesses in various forms, such as asthma, chronic obstructive pulmonary diseases, or infectious respiratory diseases (e.g., coronavirus). The lung function of subjects affected by these illnesses degrades due to infection or inflammation in their respiratory airways. Typically, lung function is assessed using in-clinic medical equipment, and quite recently, via portable spirometry devices. Research has shown that the obstruction and restriction in the respiratory airways affect individuals' voice characteristics. Hence, audio features could play a role in predicting the lung function and severity of the obstruction. In this paper, we go beyond well-known voice audio features and create a hybrid deep learning model using CNN-LSTM to discover spatiotemporal patterns in speech and predict the lung function parameters with accuracy comparable to conventional devices. We validate the performance and generalizability of our method using the data collected from 201 subjects enrolled in two studies internally and in collaboration with a pulmonary hospital. SpeechSpiro measures lung function parameters (e.g., forced vital capacity) with a mean normalized RMSE of 12% and R2 score of up to 76% using 60-second phone audio recordings of individuals reading a passage.Clinical relevance - Speech-based spirometry has the potential to eliminate the need for an additional device to carry out the lung function assessment outside clinical settings; hence, it can enable continuous and mobile track of the individual's condition, healthy or with a respiratory illness, using a smartphone.

Assessment of causal association between thyroid function and lipid metabolism: a Mendelian randomization study.

BACKGROUND: Thyroid dysfunction is associated with cardiovascular diseases. However, the role of thyroid function in lipid metabolism remains partly unknown. The present study aimed to investigate the causal association between thyroid function and serum lipid metabolism via a genetic analysis termed Mendelian randomization (MR). METHODS: The MR approach uses a genetic variant as the instrumental variable in epidemiological studies to mimic a randomized controlled trial. A two-sample MR was performed to assess the causal association, using summary statistics from the Atrial Fibrillation Genetics Consortium (n = 537,409) and the Global Lipids Genetics Consortium (n = 188,577). The clinical measures of thyroid function include thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) levels, FT3:FT4 ratio and concentration of thyroid peroxidase antibodies (TPOAb). The serum lipid metabolism traits include total cholesterol (TC) and triglycerides, high-density lipoprotein, and low-density lipoprotein (LDL) levels. The MR estimate and MR inverse variance-weighted method were used to assess the association between thyroid function and serum lipid metabolism. RESULTS: The results demonstrated that increased TSH levels were significantly associated with higher TC (ß = 0.052, P = 0.002) and LDL (ß = 0.041, P = 0.018) levels. In addition, the FT3:FT4 ratio was significantly associated with TC (ß = 0.240, P = 0.033) and LDL (ß = 0.025, P = 0.027) levels. However, no significant differences were observed between genetically predicted FT4 and TPOAb and serum lipids. CONCLUSION: Taken together, the results of the present study suggest an association between thyroid function and serum lipid metabolism, highlighting the importance of the pituitary-thyroid-cardiac axis in dyslipidemia susceptibility.

Benchmark Status of Women Interventional Radiologists in China.

PURPOSE: To evaluate the current status of women interventional radiologists in China and discuss possible measures to boost their representation in this male-dominated field for a more diverse workplace environment in the future. MATERIALS AND METHODS: The list of Chinese interventional radiologists obtained from the Chinese College of Interventionalists was retrospectively reviewed. Key information was extracted from the database, including sex, chronologic trends of representation of women interventional radiologists, position, education level, geographic distribution, interventional radiology (IR) practice time, departmental affiliation, and hospital classification. RESULTS: Of the 13,855 entries, 7,324 (52.9%) were interventional radiologists having valid information. Among them, 684 (9.3%) were identified as women. The number of women interventional radiologists has continued to increase since the first woman registered in 1992. The average age of women interventional radiologists was 39.1 years ± 5.7 (range, 26-50). The majority of them were attending physicians (n = 280; 40.9%) with a bachelor's degree (n = 363; 53.1%). Most women interventional radiologists (n = 215, 31.4%) joined this specialty 5-9 years after becoming physicians, whereas 128 (18.7%) started practicing IR from the very beginning. A total of 42.4% of women interventional radiologists were from the departments of IR and cardiology. CONCLUSIONS: Although the total number shows an upward trend, women interventional radiologists are still underrepresented. Education level, geographic areas, and other socioeconomic factors may simultaneously influence the population size of women interventional radiologists in China.

A digital health profile & maturity assessment toolkit: cocreation and testing in the Pacific Islands.

INTRODUCTION: Countries need to determine their level of digital health capability maturity to assess and mobilize their knowledge, skills, and resources to systematically develop, implement, evaluate, scale up and maintain large-scale implementations of standards-based interoperable digital health tools. OBJECTIVE: Develop a Digital Health Profile and Maturity Assessment Toolkit (DHPMAT) to assist Pacific Island Countries (PICs) to harness digital tools to support national health priorities. MATERIALS AND METHODS: A literature review guided the development of the conceptual framework to underpin the DHPMAT. Key informants collaborated to collect key digital health features and indicators to inform their country's digital health maturity assessment. The DHPMAT was tested with country stakeholders at a Pacific Health Information Network workshop in 2019. RESULTS: A comprehensive list of indicators to describe country digital health profiles (DHP). A digital health maturity assessment tool that uses criteria codeveloped with country stakeholders to assess essential digital health foundations and quality improvement. DHPs created and maturity assessed and packaged into individualized DHPMATs for 13 PICs. PIC users perceived the DHPMAT as useful, especially the congruence with the 2017 WHO WPRO Regional Strategy but noted a "cognitive overload" from a plethora of complex digital health toolkits. CONCLUSIONS: The cocreation approach optimized currency, accuracy, and appropriateness of information in the DHP, understanding, and use of the DHPMAT to facilitate informed iterative discussion by PICs on their digital health maturity to harness digital tools to strengthen country health systems. The DHPMAT can rationalize the choice and use of existing tools and reduce cognitive overload.

Evaluation of a risk index for predicting short-term and long-term outcomes in patients with ST-elevation myocardial infarction.

OBJECTIVE: This study aimed to evaluate the usefulness of the admission risk index (RI) to predict short-term and long-term outcomes in a broad population with ST-elevation myocardial infarction (STEMI) using data from the Chinese Acute Myocardial Infarction Registry. BACKGROUND: The RI was developed as a simple tool to predict risk of death in STEMI patients. The performance in predicting short-term and long-term risk of death in Chinese patients receiving percutaneous coronary intervention and conservative treatment for STEMI remains unclear. METHODS: Age, heart rate (HR), and systolic blood pressure (SBP) were used to calculate RI using (HR×[age/10]2 )/SBP. We used the prediction tool to predict mortality over 12 months. RESULTS: The C-index of the admission RI for predicting in-hospital, 1-, 6-, and 12-months mortality were 0.78, 0.78, 0.78, and 0.77, respectively, compared with 0.75 of the Global Registry in Acute Coronary Events score. Based on the receiver operating characteristic curve analysis, the RI was categorized into quintiles for convenient clinical use, and it revealed a nearly 15-fold gradient of increasing mortality from 2.29 to 32.5% (p < .0001) while RI >34 had the highest mortality. By categorizing into five different risk groups, the short-term and long-term mortality of patients receiving different treatments could be distinguished. CONCLUSIONS: RI based on three routine variables and easily calculated by any medical practitioner is useful for predicting in-hospital and long-term mortality in patients with STEMI at the initial consultation with clinicians.

Ecosystem health monitoring in the Shanghai-Hangzhou Bay Metropolitan Area: A hidden Markov modeling approach.

Ecosystem health assessment is an important method for obtaining information on ecosystem conditions, and it plays a vital role in preserving and enhancing ecosystem health status. In addition, it provides useful information and knowledge for urban agglomeration development decision makers. However, ecological phenomena often vary considerably from one observation to the next, which makes it difficult to distinguish different status of the ecosystem health. In this study, hidden Markov model (HMM) was employed to simulate the internal-external correlations of ecosystem status through establishing the relationships between internal ecological health level and combination state of external observation. Based on the statistics and land use data in 2001, 2007 and 2013, the Vigor-Organization-Resilience (VOR) framework was employed to identify the ecosystem health in Shanghai-Hangzhou Bay Metropolitan (SHBM), in which the ecosystem health state was considered as a hidden state that could be estimated according to the conditions of vigor, organization and resilience. In addition, two parameter learning cases including mathematical statistics and extensible sequence method were employed to solve the iterative convergence problem of parameters in short-time series of ecosystem health simulation. Results show that HMM not only provides a comparable descriptive ability to that of the VOR model, but also can monitor ecosystem health at the optimal grid scale in SHBM. The combination of HMM and VOR greatly expands the spatiotemporal characteristics and provides a new research approach for the study of ecosystem health assessment of urban agglomerations.

A promising form-stable phase change material prepared using cost effective pinecone biochar as the matrix of palmitic acid for thermal energy storage.

A promising new form-stable phase change material (PA/PB) was fabricated using pinecone biochar (PB) as the supporting material of palmitic acid (PA). The biochar of PB with large surface area was produced by forest residue of pinecone, and it was cheap, environment friendly and easy to prepare. The PB was firstly utilized as the supporter of PA and the characterizations of PA/PB were analyzed by the BET, SEM, XRD, DSC, TGA, FT-IR and thermal conductivity tester. The results demonstrated that the PA was physically absorbed by the PB and the crystal structure of the PA was not destroyed. The results of DSC showed that the fusing and crystallization points of the form-stable phase change material with the maximum content of PA (PA/PB-4) were 59.25 °C and 59.13 °C, and its fusing and freezing latent heat were 84.74 kJ/kg and 83.81 kJ/kg, respectively. The results of TGA suggested that the thermal stability of the PA/PB-4 composite was excellent, which could be used for the applications of thermal energy storage. Furthermore, the thermal conductivity of PA/PB-4 was 0.3926 W/(m∙K), which was increased by 43.76% compared with that of the pure PA. Thus, the study results indicated that the PA/PB-4 had great potential for thermal energy storage applications.

An Assessment of Chinese Pathways to Implement the UN Sustainable Development Goal-11 (SDG-11)-A Case Study of the Yangtze River Delta Urban Agglomeration.

Urban sustainability is a crucial part of the United Nations (UN) Sustainable Development Goals (SDGs) and one of the core objectives of China's national strategy to promote new urbanization and achieve integration in the Yangtze River Delta (YRD). This paper mainly focused on the 11th SDG, which is a universal call to make cities and human settlements inclusive, safe, resilient and sustainable. The full permutation polygon synthetic indicator (FPPSI) method was applied to synthetically evaluate the sustainable level of 26 cities in the YRD urban agglomeration from 2007 to 2016. The results showed that: (1) the synthesis indicators were increasing year by year, which implied that the sustainable development of the YRD has shown obvious progress in recent years. However, each city faced its own challenges to achieving the sustainable development goals. The sustainability level for the majority of cities was restricted by obstacles such as the per capita green area, air quality and commercial housing sales area; (2) Among the 26 cities, small and medium-sized cities were subject to the traditional strong sustainability indicators while large and mega cities were more affected by weak sustainability indicators; (3) Spatial differences were found for the overall sustainable development level of the YRD. The diffusion and assembly effect among cities had not yet been formed; however, the strong spillover effect of developed cities might influence the ability of other cities to achieve sustainable development goals in many aspects of the environment, economy and society. The results suggest the need for a stronger focus on improving regional developing patterns and strengthening coordination in the process of achieving the sustainable development goal of urban agglomeration in the YRD. Furthermore, according to the conditions of different cities, integrated policies are required to address all aspects of sustainability and to avoid unintended consequences.

Emissions of non-CO2 greenhouse gases from livestock in China during 2000-2015: Magnitude, trends and spatiotemporal patterns.

Livestock production, an important source for non-CO2 greenhouse gases (GHGs) including methane (CH4) and nitrous oxide (N2O) in China, has changed remarkably over the past decades due to economic development and demand for livestock product. However, the variation of non-CO2 GHGs from China's livestock have not received sufficient attention in existing literature. Here, we examine the spatiotemporal patterns of emissions of CH4 and N2O from main livestock in China as well as their long-term trends during the period 2000-2015. Results suggest that the livestock sourced emissions of non-CO2 GHGs in China experienced three phases: a rapid increase from 2000 to 2006, followed by a sharp drop in 2007 and then a slow increase at a lower level from 2008 to 2015. The 2007 drop reflects the impact of macro-control policies on livestock development and extensive measures taken on livestock to control the flu outbreak that year, and the slower increase from 2008 to 2015 with respect to the period 2000-2006 reflects the changes in livestock categories and a general improvement in production efficiency. Spatiotemporal patterns demonstrate that traditional livestock provinces including Henan, Sichuan, Inner Mongolia, Shandong, Yunnan and Hunan stood out as top six provinces in emission of non-CO2 GHGs in 2015. On the other hand, provinces like Jiangxi, Hubei, Hunan, Yunnan, Inner Mongolia, Liaoning and Xinjiang, identified as the emerging provinces, demonstrate the highest growth rates over the last decades. We find that different livestock categories dominated the difference in pattern of non-CO2 GHG emissions in both provinces with high emissions and those with high growth rates. Mitigation measures and policies suggestions should not only focus on high non-CO2 GHG emissions provinces, but also pay attention to the emerging new sources.