Construction of a methodology framework to characterize dynamic full-sector land-use carbon emissions embodied in trade.

The globally massive land-use changes associated with unprecedented urbanization rate are leading to prodigious quantities of carbon emissions. Nonetheless, the dynamics of land-use carbon emissions, particularly driven by supply-chain activities across all relevant industrial sectors, remain largely unexplored, especially in non-agricultural sectors. Here, we constructed a novel methodological framework to quantify full-sector land-use carbon emissions in Shenzhen, China, an international megacity grappling with acute land resource scarcity. Then, we integrated this framework with multiregional input-output analysis to uncover the multi-scale embodied land-use emissions propelled by Shenzhen's supply-chain activities. Our results indicate a marked increase in Shenzhen's embodied carbon emissions, approximately two orders of magnitude greater than its physical emissions, tripling during 2005-2018. Remarkably, non-agriculture sectors contributed 81.3-90.5 % of physical and 46.6-58.4 % of embodied land-use emissions. The land-use changes occurred outside Shenzhen accounted for 6.5-13.3 % of Shenzhen's total embodied land-use emissions. The sectoral analysis revealed a transition from traditional manufacturing (e.g., metallurgy, chemical products, textiles, wood products) in 2010-2015 to high-tech sectors (e.g., electronic equipment and other manufacture) in 2015-2018. This shift was primarily attributed to concurrent industry transfer actions, leading to aggressive changes in land-use emission intensity discrepancies within and outside Shenzhen. This study provides a scientific basis for designing effective strategies to mitigate land-use carbon emissions associated with supply-chain activities.

An urban metabolism framework purpose to assess the dynamics of linear alkylbenzenes (LABs) discharge flows and driving forces at the city level in Guangdong province, China.

Linear alkylbenzenes (LABs) are a class of molecular markers derived from anthropogenic activities. A comprehensive understanding of the mechanism that determines their entry into anthroposphere, in terms of magnitude and pathway, is the prerequisite to establish effective mitigation measures. This study develops a methodology framework to analyze the source-sink interactions and driving factors of the direct and indirect LAB discharges from production and living activities in Guangdong Province, China from 2004 to 2017. Results indicated that the total LAB discharges of Guangdong into the environment were averaged at 2.9 kt yr-1, of which 61.9% originated from the Pearl River Delta (PRD) urban agglomeration. An average proportion of 76.0% was discharged into water bodies with the remaining released into land bodied. From 2014 to 2017, the LAB discharges increased by seven times, resulting from the steady increase of urban residential sources, while contribution from industrial sources continuously declined during the studied period. Meanwhile, the discharging hotspots expanded from Guangzhou city to other super-cities around it, including Shenzhen and Dongguan. The other cities exhibited a decreasing trend in discharges as a function of distance from these hotspot cities. The multisectoral sources of LABs differed considerably among cities, and the source contribution of each city changed significantly with progressive urbanization. The factor decomposition analysis indicated that LAB discharges in PRD cities primarily contributed by the pollutant concentration and reflected the treatment structure, while peripheral cities of the PRD mainly contributed by the per capita consumption and pollutant discharge per unit of GDP. Overall, our results provide a scientific database and supports for the regional co-remediation of anthropogenic pollution.

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically.

This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN.We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model's utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663-0.968), and the ROC of the DL model was 0.844 (95% CI:0.717-0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773-0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.

[Effect evaluation of graphical menu labeling in simulated ordering].

OBJECTIVE: To explore the feasibility of applying graphical menu labeling. METHODS: To design a radar chart menu label. From October 2020 to April 2021, convenience sampling was adopted to recruit 1407 research subjects(986 females and 421 males) through the online platform nationwide to complete the questionnaire and simulate ordering. The survey included basic information of the research subjects, their level of nutritional knowledge, and satisfaction with the graphic menu labels. The two simulated orderings were conducted using the regular menu and the menu with graphic nutritional information, respectively. Compare the nutrition scores of the two simulated orders, the selection ratio of each dish in each major category, the energy, fat, cholesterol and sodium content, and the amount of added oil and salt of the selected dishes. RESULTS: Compared with using the normal menu, the nutritional score of the simulated meal ordering increased from 15.57±2.65 to 16.73±3.24(P<0.05) using a menu with graphic nutrition labels, in which people with an income of less than 4000 yuan and a graduate degree or above increased the most. The proportion of dishes with higher nutritional value has increased among pork, fish, vegetables, and soy products. The energy, fat, cholesterol, sodium content, added oil and added salt of the selected dishes are decreased from 8455(7738, 9033) kcal, 658.6(598.1, 709.3) g, 1418(1238, 1665) mg, 17 430(15 695, 19 129)mg, 455(405, 502)g, 41.5(36.5, 47.0)g to 7415(6693, 8191)kcal, 562.54(504.0, 631.2)g, 1274(1076, 1549)mg, 17 185(14 574, 19 576.8)mg, 375(334, 437) g, 38.5(32.4, 43.6) g respectively(P<0.05). The satisfaction score of the graphic nutrition label is relatively high. CONCLUSION: Graphical menu labeling helps consumers to make healthier choices for catering food.

[Foodborne disease outbreaks in China from 2011 to 2020].

OBJECTIVE: To analyze the epidemiological characteristics of foodborne disease outbreaks in China and to provide references for formulating prevention strategies of foodborne diseases in China. METHODS: Collect the monitoring data reported in China's foodborne disease outbreak monitoring system from 2011 to 2020, and calculate relevant indicators. RESULTS: During 2011 and 2020 in 30 provinces(autonomous region, municipality), a total of 35 806 foodborne disease outbreaks were reported, which caused 266 968 illnesses. The western region had the largest number of reported incidents and the largest number of patients, Yunnan Province had the largest percentage of outbreaks(17.7%) and the largest percentage of cases(15.81%). Poisonous mushrooms and microorganisms are the main pathogenic factors. Poisonous mushrooms caused the largest percentage of foodborne disease outbreaks, accounting for 29.09% of the total. Microorganisms caused the largest percentage of cases, accounting for 35.69% of the total. Salmonella and Vibrio parahaemolyticus are the main pathogens. Catering service units were the main places of foodborne disease outbreaks, responsible for the largest percentage of outbreaks(49.31%) and cases(70.59%). CONCLUSION: From 2011 to 2020, the number of reported incidents and the number of patients in foodborne disease outbreaks in China showed an upward trend.

A novel clinical-radiomic nomogram for the crescent status in IgA nephropathy.

Objective: We used machine-learning (ML) models based on ultrasound radiomics to construct a nomogram for noninvasive evaluation of the crescent status in immunoglobulin A (IgA) nephropathy. Methods: Patients with IgA nephropathy diagnosed by renal biopsy (n=567) were divided into training (n=398) and test cohorts (n=169). Ultrasound radiomic features were extracted from ultrasound images. After selecting the most significant features using univariate analysis and the least absolute shrinkage and selection operator algorithm, three ML algorithms were assessed for final radiomic model establishment. Next, clinical, ultrasound radiomic, and combined clinical-radiomic models were compared for their ability to detect IgA crescents. The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis. Results: The average area under the curve (AUC) of the three ML radiomic models was 0.762. The logistic regression model performed best, with AUC values in the training and test cohorts of 0.838 and 0.81, respectively. Among the final models, the combined model based on clinical characteristics and the Rad score showed good discrimination, with AUC values in the training and test cohorts of 0.883 and 0.862, respectively. The decision curve analysis verified the clinical practicability of the combined nomogram. Conclusion: ML classifier based on ultrasound radiomics has a potential value for noninvasive diagnosis of IgA nephropathy with or without crescents. The nomogram constructed by combining ultrasound radiomic and clinical features can provide clinicians with more comprehensive and personalized image information, which is of great significance for selecting treatment strategies.

Noninvasive Evaluation of Lupus Nephritis Activity Using a Radiomics Machine Learning Model Based on Ultrasound.

Introduction: To explore whether ultrasonic radiomics extracted by machine learning method can noninvasively evaluate lupus nephritis (LN) activity. Materials and Methods: This retrospective study included 149 patients with LN diagnosed by renal biopsy. They were divided into a training cohort (n=104) and a test cohort (n=45). Ultrasonic radiomics features were extracted from the ultrasound images, and the radiomics features were constructed. Furthermore, five machine learning algorithms were compared to evaluate LN activity. The performance of the binary classification model was evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The average AUC of the five machine learning models was 79.4, of which the MLP model was the best. The AUC of the training group was 89.1, with an accuracy of 81.7%, a sensitivity of 83%, a specificity of 80.7%, a negative predictive value of 85.2%, and a positive predictive value of 78%. The AUC of the test group was 82.2, the accuracy was 73.3%, the sensitivity was 78.9%, the specificity was 69.2%, the negative predictive value was 81.8%, and the positive predictive value was 65.2%. Conclusion: Machine learning classifier based on ultrasonic radiomics has high accuracy for LN activity. The model can be used to noninvasively detect the activity of LN and can be an effective tool to assist the clinical decision-making process.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study.

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO2 emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO2 emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO2 increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO2 emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

Liquid-Metal-Mediated Electrocatalyst Support Engineering toward Enhanced Water Oxidation Reaction.

Functional and robust catalyst supports are vital in the catalysis field, and the development of universal and efficient catalyst support is essential but challenging. Traditional catalyst fabrication methods include the carbonization of ordered templates and high-temperature dehydration. All these methods involve complicated meso-structural disordering and allow little control over morphology. To this end, a eutectic GaInSn alloy (EGaInSn) was proposed and employed as an intermediate to fabricate low-dimensional ordered catalyst support materials. Owing to the lower Gibbs free energy of Ga2O3 compared to certain types of metals (e.g., Al, Mn, Ce, etc.), we found that a skinny layer of metal oxides could be formed and exfoliated into a two-dimensional nanosheet at the interface of liquid metal (LM) and water. As such, EGaInSn was herein employed as a reaction matrix to synthesize a range of two-dimensional catalyst supports with large specific surface areas and structural stability. As a proof-of-concept, Al2O3 and MnO were fabricated with the assistance of LM and were used as catalyst supports for loading Ru, demonstrating enhanced structural stability and overall electrocatalytic performance in the oxygen evolution reaction. This work opens an avenue for the development of functional support materials mediated by LM, which would play a substantial role in electrocatalytic reactions and beyond.

The carbon emissions related to the land-use changes from 2000 to 2015 in Shenzhen, China: Implication for exploring low-carbon development in megacities.

Megacities exploit enormous amounts of lands from outside of the city boundary. However, there is a large knowledge gap in the impact of socioeconomic activities associated land-use changes on carbon emissions of megacities during the urbanization. In the current work, we combined the material-flow analysis, environmental extended input-output model, and land matrix data to construct a hybrid network framework. Such a framework was used to estimate the carbon emissions driving from trade between sectors and associated land use changes during 2000-2015 in Shenzhen, China. Results indicated that the total carbon emissions of Shenzhen had a growth rate of 262.7% from 2000 to 2010 and a declining rate of 17.6% from 2010 to 2015. This pattern is associated with large declining rates in the overall energy and carbon intensities by 53.8% and 63.2% during the period of 2000-2015. Meanwhile, embodied carbon emissions of Shenzhen kept rising by approximately twofold, accompanied by the increasing trends in the land-use related carbon emissions both inside and outside of city boundary. The land uses per unit GDP showed a dramatical decline by 85.7% and with a large contribution of the transportation and industrial land, and this caused a gradual increase in overall land-use related emissions with average growth rate of 7.1%. In addition, the land-use change related carbon emissions of the transportation and industrial land had a cumulative growth of 85%. As for the embodied land-use related carbon emissions, the dominated contributor was the Agriculture sector which drove an average of 0.13 MtC yr-1 emissions via importing agricultural products from outside of Shenzhen. This study provides a scientific foundation for corporately mitigate carbon emissions between megacities and their surrounding regions.