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Little is known about the outcome for HIV-associated Hodgkin lymphoma (HIV-HL) as this is less common than HIV-negative lymphoma. Therefore, we performed a multi-center study to analyze the clinical characteristics and outcomes of HIV-HL patients in China. Nineteen cases of HIV-HL were diagnosed and treated at three center and including the sixth people's hospital of Zhengzhou, Peking union medical college hospital, and Chongqing university cancer hospital, between December 2013 and June 2022. Data on the clinical features, laboratory results, response, and prognosis were collected and analyzed. The median age at diagnosis was 43(22–74) years. All patients were infected with HIV through sexual transmission, with ten cases transmitted through man having sex with man (MSM) and nine cases transmitted through heterosexual transmission. Seven patients were diagnosed with lymphoma and found to be infected with HIV. Four cases were in stage III, and fifteen cases were in stage IV. After a median follow up of 46.8(4.0-112.9) months, 17 cases were alive after ABVD regimen chemotherapy combined with combination antiretroviral therapy (cART). The 5-year progression-free survival (PFS) and overall survival (OS) rate were 83.9% and 89.5%,respectively. HIV-HL exhibits an invasive process in clinical practice, and cART combined with ABVD regimen chemotherapy can achieve long-term survival for patients. [ABSTRACT FROM AUTHOR]

Accurate prediction of regional water evapotranspiration can greatly contribute to the rational utilization of regional water resources for the water resources saving. Crop evapotranspiration can be one of the most important indicators for the water evapotranspiration status of crops, in order to evaluate the soil water balance of farmland and the water management of farmland. However, the calculation of crop evapotranspiration requires a large amount of meteorological factor data. There is often redundant data with the low correlation with the evapotranspiration in meteorological data, which seriously affects the prediction accuracy and efficiency of the model. It is a high demand to extract the core environmental factors for the less-factor prediction model. The classification and regression tree (CART) can be used to make feasible for the large data sources in a short time. Then, Less factors can be extracted for the predictive analysis in a more scientific way. In this study, a less-factor water evapotranspiration model was constructed to efficiently and accurately predict the evapotranspiration data. The core factors were selected from multiple meteorological factors. 23 typical stations were selected in the nine agricultural regions, and then collected data on eight meteorological factors, such as precipitation, and sunshine hours. The meteorological factors were ranked in the order of importance using CART. The top 3-5 meteorological factors were then selected to predict the evapotranspiration using the extreme learning machine (ELM) model. At the same time, the ELM model was optimized using genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA). Three optimization algorithms (GA-ELM, PSO-ELM, SSA-ELM) were used to construct a less-factor hybrid optimization water evapotranspiration prediction model. Relative root mean square error (RMSE), coefficient of determination (R 2 ), mean absolute error (MAE), and NashSutcliffe coefficient (NSE) were used to evaluate the performance of the ELM and optimization model. The results showed that: 1) The main influencing factors were ranked in the order of the precipitation, sunshine duration, mean station pressure, daily maximum temperature, and average relative humidity using the importance ranking of the CART algorithm. 2) The PSOELM model presented the highest prediction accuracy among the three optimization algorithms. Specifically, the RMSE of evapotranspiration prediction for the 23 stations was ranged from 6.608 to 22.077 mm/d, the NSE of 0.824-0.998, R 2 of 0.908- 0.995, and MAE of 5.075-16.677 mm/d. In addition, the RF and SVR model with the strong generalization performance were selected to compare with the PSO-ELM model. The prediction performance of three models was slightly improved with the increase of the input factors, indicating the slight overall improvement. The three models shared the strong generalization and robustness. The meteorological factors were input into the prediction model, according to the order of importance. The meteorological factors ranked the 4th and 5th were relatively less important, where the prediction accuracy was slightly improved with the increase of the input parameters. The PSO-ELM model presented the highest prediction accuracy among the three models. 3) The ELM model performed better applicability in the Yunnan-Guizhou Plateau region, the Sichuan Basin, and the surrounding areas. The three optimization algorithms showed the better applicability in the South China and the YunnanGuizhou Plateau region, with the highest applicability of the PSO-ELM model. The findings can provide an important reference for the crop water demand calculation in nine major agricultural regions in China. [ABSTRACT FROM AUTHOR]

In recent years, China's resource and environmental problems have become increasingly severe, and the pressure on resource constraints has continued to increase. The Chinese government has delineated constraints for spatial planning based on the ecosystem, that is, a "red line for ecological protection", which aims to "control the ecological space" and optimize the ecological environment. The optimized verification of this red line can improve the accuracy of its boundary, ensure habitat integrity, and provide data and methods supporting the protection of the ecosystem. In this study, the problems of verifying the boundary of the ecological protection red line include five major categories and 24 minor categories, such as accuracy problems, current situation problems, contradictions with planning, boundary consistency problems, and integrity problems. Taking Fangshan District, Beijing, China, as an example, based on the results of delineating the ecological protection red line in Fangshan District, we integrated geographical monitoring, land use surveys, high-resolution remote sensing images, small forestry classes, protected areas, road networks, water networks, and 14 types of multisource data, such as architectural and planning data. On the basis of the idea of clustering, we granulated the types of plaques in the ecological protection red line into three categories, nature reserves, river wetlands, and other protection units, and we assessed multi-source data regarding the specific problems of various types of plaques in the ecological protection red line. We used an adaptive design, using the big data processing mode of red line plaque fragmentation, based on ArcGIS spatial data analysis and processing functions to optimize and verify the red line boundary. We obtained a mapping accuracy of 1:10000 for the ecological protection red line, which is a breakthrough in accuracy. This study provides a base map for ecological protection via the red line management, supervision, and comprehensive implementation of land and space planning. The research method is also applicable to research on optimizing the red line boundary for ecological protection in other regions of the country. [ABSTRACT FROM AUTHOR]

In view of the occupational health and safety (OHS) training on the safety of construction workers, and many complex factors. Through empirical investigation, this study proposes a set of multiple factor comprehensive analysis (CAMF) model to explore the effectiveness of OHS training and its main influencing factors. It has been found that training effectiveness is positively related to whether to receive OHS training or not and also positively related to training methods. It has also been found that the effectiveness of training is positively related to the importance of post responsibility. The results show that the construction industry needs to strengthen the OHS system training of grass-root workers, and the training should be classified and graded according to the workers' educational backgrounds, training methods, job satisfaction, and job responsibilities. In the safety training management of on-site workers, salary is the basic factor, and more attention should be paid to the factors such as workers' physical safety, training and learning, and future career development. The empirical results show that CAMF model has certain practical significance. The research results can provide paths and suggestions for improving the effectiveness of OHS training in China's construction industry, and provide some reference for other developing countries to carry out OHS training. [ABSTRACT FROM AUTHOR]

Plastic mulch is extensively applied in agricultural production in arid regions. It significantly influences the interactions between land and atmosphere by altering underlying surface characteristics. An accurate and timely extraction method for Plastic-Mulched Cropland (PMC) is required to understand land surface energy transfer processes, eco-hydrological cycle, the climate effect of PMC, and in the management of water resources. In this study, we proposed a Timely Plastic-mulched cropland Extraction Method (TPEM) from complex mixed surfaces with multi-source remote sensing data in the Shiyanghe River Basin (SRB), a typical representation of a complex and inhomogeneous arid region in the northwest of China. We defined TPEM in three phases; in the first phase, the spectral characteristic curves were drawn from ground object points labeled by visual interpretation with multi-source remote sensing data. In the second phase, a spectral characteristic analysis of the modified index was proposed to amplify the difference between PMC and non-PMC ground objects. Finally, the Classification and Regression Tree (CART) classifier was used to generate thresholds of indices as PMC extraction rules. The results showed that it can extract the boundary of PMC in large-scale farmland, distinguish PMC from ground objects in complex mixed surfaces, and separate the PMC from desert land that shares same spectral characteristics with PMC. The TPEM is verified to be efficient and robust, with an overall accuracy of 0.9234, quantity disagreement of 0.0541, and allocation disagreement of 0.0224, and outperformed two extensively used PMC extraction methods, especially for timely PMC extraction when satellite data only during the period that ground surface incomplete covered by plastic mulch is available. This study will provide us with an accurate and timely method to extract PMC, especially in the widely distributed complex mixed surfaces. [ABSTRACT FROM AUTHOR]

Data on hepatitis B virus (HBV) pregenomic (pgRNA) levels in HIV/HBV coinfected patients pre‐ and post‐combined antiretroviral therapy (cART) are limited. This study aimed to evaluate the distribution of HBV pgRNA levels in treatment‐naive coinfected patients and explore the changes that occur after the initiation of cART by examining patients from multicentre cohort studies performed in China. We included HIV/HBV coinfected subjects from the China AIDS Clinical Trial cohorts established from 2008 to 2014. Clinical and serological markers of HIV and HBV infection and biochemical data were acquired at baseline and after 96 and 240–480 weeks of cART. The correlations between HBV pgRNA and HBV DNA levels as well as HBsAg levels were calculated using Spearman's bivariate correlation analysis, and multivariate regression analysis was performed to determine factors associated with undetectable HBV pgRNA levels before cART and HBeAg loss after cART. A total of 132 HIV/HBV coinfected patients were enrolled, and 100 individuals were HBeAg‐negative. A total of 34.4% (32/93) of patients were positive for HBV pgRNA, and the median HBV pgRNA level was 4.92 (IQR: 4.21–6.12) log10 copies/mL before cART. The median HBV pgRNA level was significantly lower in HBeAg‐negative individuals than in HBeAg‐positive individuals (4.22 (IQR: 2.70–4.84) log10 copies/mL vs. 5.77 (IQR: 4.63–6.55) log10 copies/mL, p = 0.002). HBV pgRNA was moderately correlated with HBsAg (r = 0.594, p = 0.001), and positively associated with HBV DNA (r = 0.445, p = 0.011). The factors independently associated with undetectable HBV pgRNA level before cART were HBV DNA (OR: 5.61, 95% CI: 1.50–20.96, p = 0.01) and HBeAg status (OR: 5.95, 95% CI: 1.52–23.25, p = 0.01). A total of 87.5% (28/32) of patients were followed for a median duration of 138 (IQR: 54–240) weeks, and the HBV pgRNA levels became undetectable in seven patients. The 132 patients were observed for 695.5 person‐years, and no HBsAg loss occurred. Thirteen individuals achieved HBeAg loss, four patients had undetectable levels of HBV pgRNA pre‐cART, and the level of six individuals became undetectable during the 48‐week (IQR: 48–264) follow‐up period. HBeAg status was significantly associated with HBV pgRNA level in HIV/HBV coinfected patients pre‐ and post‐cART. Additionally, undetectable HBV pgRNA level may be associated with HBeAg loss after cART. [ABSTRACT FROM AUTHOR]

• The ISP method allows for gathering a large number of historical training samples. • The SRVI values of the same crop planted in neighboring years range from 0.91 and 1.2. • A distinct northward expansion trend is evident for all four major crops, in Heilongjiang Province, China. Mapping agricultural information such as cropping area and type is of great significance for land use and food security and a common method to retrieve such information is satellite imagery classification. The current image classification techniques require a statistically large number of training samples to obtain representative spectral signatures, ideally collected during the satellite overpass time/date. Although this prerequisite sounds simple and easy in principle, it is extremely challenging and sometimes impossible when classifying historical satellite images, as one simply cannot go back in time to collect training datasets or ground truthing. In this paper, we introduce an iterative and subsequent proximation (ISP) method to circumvent this problem for historical image classifications for historical crop mapping. This method assumes that the same crops grown two years apart have similar spectral properties within the same growing season, with limited variation. This allows the use of the crop information classified in any given year (n) to be used as training samples for the next year (n + 1) or the previous year (n-1). Iteratively repeating n-1, n-2... process leads to the mapping of historical cropping information without a priori ground truthing data. To demonstrate the ISP feasibility, we first used the historical Landsat time-series data to map four major stable crops (rice, maize, soybean, and wheat) in Hailun County, Heilongjiang Province, China, and then further expanded the ISP application to the entire Heilongjiang Province to map cropping areas from 1982 to 2020, using the classification and regression trees (CART) algorithm. The results were validated for those years when actual cropping measurement data were available and further verified with statistical data for other years. The results indicated that the proposed ISP method was appropriate for the historical mapping of four major crops, with a mapping accuracy of approximately 80 % when validated with field data, and correlation coefficients of 0.86, 0.85, 0.91, and 0.94, respectively when compared with historical statistical cropping data. The crop mapping results showed distinct trends of northward expansion in each of the four crops in Heilongjiang Province, which agreed well with previous studies. In conclusion, the ISP method is quick, easy, and convenient for historical crop mapping, which is important in understanding the agricultural production history. [ABSTRACT FROM AUTHOR]

China, the first country worldwide to approve a gene therapy in 2003, almost lost the advantage for a head start in cell and gene therapy (CGT) development due to a lack of clear and strict regulatory frameworks. The rapid advancements of CGTs' development worldwide as well as their therapeutic potential have triggered the government to conduct a spate of regulatory reforms to promote normative development of CGTs in China. Encouraged by policy support, the remarkable progress for CGTs in China has been observed over the past few years, thereby catapulting China back into the forefront of CGTs worldwide. This article aims to provide an overview of regulatory reforms, the current development landscape of CGTs, as well as key contributors and challenges for CGT development in China. [ABSTRACT FROM AUTHOR]

In large granaries, maize storage period could last 3–5 years in China. Free fatty acids (FFA) content is commonly used as a sensitivity indicator of grain quality changes during storage. Samples of the stored grain are taken manually and tested in the laboratory monthly for regular quality monitoring. Although substantial labor and time are consumed, the testing way lacks of real-time. Temperature is a main factor influencing the quality of stored grain. In this study, it was analyzed that the effective accumulated temperature (EAT) had a strong correlation coefficient with FFA. Classification and regression trees (CART) and model trees (ML) in machine learning (ML) methods were used to estimate FFA by EAT based on data collected from 11 large granaries in northeastern China. While the minimum number of samples for segmentation was set as 20, the two models both had the optimal performance. The two models were evaluated by mean absolute error (MAE), root mean square error (RMSE) and the coefficient of determination (R2). The MAE, RMSE and R2 of CART and MT are 1.296, 1.761, 0.759; 1.247, 1.821, and 0.741, respectively. As CART had the lower RMSE and the larger R2, the model performance of CART was better. The model provided a method to estimate the changes of FFA based on the exist temperature monitoring system in large granaries as a way of stored maize quality real-time monitoring. [ABSTRACT FROM AUTHOR]

[Display omitted] • Establishment of an object-oriented CART model for high-resolution identification of riverine wetlands. • Localization of a Pressure-State-Response model for riverine wetland ecosystem health assessment. • Urbanization tends to more fragmentation and less connectivity of riverine wetlands landscape. • Safeguarding existing wetlands should be paid more attention in riverine urban agglomerations. Riverine wetland is one of the important cityscapes along rivers, featuring powerful eco-hydrological regulations in safeguarding urban security and serving its quality. Over the past several decades, intensified climate change, together with the upgraded human activities, have deeply disturbed riverine wetland worldwide and caused variations in the amount and pattern, which might lead to negative effects on wetland ecosystem health (WEH), thus threaten the sustainability of the riverine urban agglomerations. To better understand the mechanism of the above effectiveness, the Riverine Urban Agglomerations along the Yellow River in China's Ningxia Hui Autonomous Region (RUAN) was taken as an example in the present study. First, by establishing an object-oriented remote sensing image classification system based on Classification and Regression Tree (CART), riverine wetland distribution in years of 2000, 2009 and 2018 were determined for a two-Stage (ST-I: 2000–2009, ST-II: 2009–2018) comparative research. Second, the transition matrix and landscape index were used to measure the spatiotemporal dynamics of the riverine wetland in the two stages. Third, a Pressure-State-Response (P-S-R) model, together with its index system, was constructed to comprehensively assess the WEH in there. Results revealed that: (1) Wetlands in RUAN are dominated by the artificial ones, presenting an overall increase in area during the statistical period, varying with different trends in the two stages. Conversion between the wetlands and the non-wetlands were found frequent during the urbanization, leading to remarkable changes in wetland patterns in space. (2) Rivers are important and basic in forming riverine landscape in RUAN, the natural wetlands increased during the statistical period. In general, wetland patches and diversity increased, shapes of that became homogenized. Accordingly, the aggregation of wetlands decreased and the fragmentation of that worsened. (3) Riverine wetlands in most of RUAN experienced an increase of external pressure and a deterioration of state, showing an overall degradation. Meanwhile, the WEH was determined more by the fundamental state of itself, the external disturbance seemed function less. Above findings confirmed the vulnerability of riverine wetlands during urbanization in an arid circumstance. It is worthy of strengthening the protection of existing wetlands and minimizing or eliminating the conversion, to ensure the WEH and serve the sustainability of riverine urban agglomerations. [ABSTRACT FROM AUTHOR]

Bathymetric mapping is integral to maintaining marine ecosystems, managing coastal zones, and safeguarding the environment. However, achieving accurate large-scale bathymetric maps remains a challenge in China, particularly in nearshore turbid waters. To address this gap, we leveraged seasonal Gaofen-6 (GF-6) data to conduct bathymetry mapping in the Yellow River Delta-Laizhou Bay area. In our study, we found that longer wavelengths, such as those in the red-edge2 and near-infrared (NIR) bands, exhibited superior performance in determining bathymetry. Moreover, specific band ratios derived from GF-6 data—such as Blue/NIR (BN), Violet/NIR (VN), Blue/Red-edge2 (BE), Violet/Red-edge2 (VE), Green/NIR (GN), and Green/Red-edge2 (GE)—showed promising outcomes, particularly in turbid nearshore waters. When comparing models, the random forest regression (RFR) model outperformed the classification and regression trees (CART) model in turbid nearshore areas, showing higher R2 values and lower RMSE. Notably, both models demonstrated higher accuracy in March compared to May and October. Incorporating the Normalized Difference Turbidity Index (NDTI) notably improved bathymetric results, especially in turbid sea regions. Furthermore, nearshore bathymetry proved highly susceptible to natural processes, seasonal variations, and human activities. The significant discrepancies in bathymetry among coastal areas emphasize the need for tailored management strategies to enhance coastal management and foster sustainable marine economic development. • The GF-6 data with different seasons were used to map nearshore bathymetry. • Longer wavelengths had better bathymetric performance in nearshore turbid waters. • The band ratios of BN, VN, BE, VE, GN and GE had good bathymetry performances. • Better bathymetry results were modelled with NDTI, especially in the turbid sea. • Adaptive strategies were proposed according to water depth and human activities. [ABSTRACT FROM AUTHOR]

Background: Strengthening the construction of community resilience and reducing disaster impacts are on the agenda of the Chinese government. The COVID-19 pandemic could alter the existing community resilience. This study aims to explore the dynamic change trends of community resilience in China and analyze the primary influencing factors of community resilience in the context of COVID-19, as well as construct Community Resilience Governance System Framework in China., Methods: A community advancing resilience toolkit (CART) was used to conduct surveys in Guangdong, Sichuan, and Heilongjiang provinces in China in 2015 and 2022, with community resilience data and information on disaster risk awareness and disaster risk reduction behaviors of residents collected. The qualitative (in-depth interview) data from staffs of government agencies and communities ( n  = 15) were pooled to explore Community Resilience Governance System Framework in China. Descriptive statistics analysis and t -tests were used to investigate the dynamic development of community resilience in China. Hierarchical regression analysis was performed to explore the main influencing factors of residential community resilience with such socio-demographic characteristics as gender and age being controlled., Results: The results indicate that community resilience in China has improved significantly, presenting differences with statistical significance ( p  < 0.05). In 2015, connection and caring achieved the highest score, while disaster management achieved the highest score in 2022, with resources and transformative potential ranking the lowest in their scores in both years. Generally, residents presented a high awareness of disaster risks. However, only a small proportion of residents that were surveyed had participated in any "community-organized epidemic prevention and control voluntary services" (34.9%). Analysis shows that core influencing factors of community resilience include: High sensitivity towards major epidemic-related information, particular attention to various kinds of epidemic prevention and control warning messages, participation in epidemic prevention and control voluntary services, and formulation of epidemic response plans. In this study, we have constructed Community Resilience Governance System Framework in China, which included community resilience risk awareness, community resilience governance bodies, community resilience mechanisms and systems., Conclusion: During the pandemic, community resilience in China underwent significant changes. However, community capital was, is, and will be a weak link to community resilience. It is suggested that multi-stages assessments of dynamic change trends of community resilience should be further performed to analyze acting points and core influencing factors of community resilience establishment at different stages., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yan, Liu, Zhang, Liu, Wang, Sun, Tang, Qi, Yu, Zhang and Ning.)

Background: Falls place a heavy burden on older adults and families, and there was little research on the relationship between falls and depressive symptoms among older adults in China. This study is designed to examine the association between falls and depressive symptoms in Chinese older adults., Methods: This study was based on 9,539 data sets from the China Health and Retirement Longitudinal Study (CHARLS) in 2018. The 10-item Center for Epidemiologic Studies-Depression Scale (CESD-10) was used to access depressive symptoms in older adults. A logistic regression model was used to calculate multivariate odds ratios (ORs) and 95% confidence intervals (CIs) for falls and depressive symptoms, adjusted for possible confounders. The Classification and regression tree (CART) demonstrates the prediction of the target variable values based on other variables., Results: In this study, 9,539 older people were selected: 60-69 years old accounted for 63.0%, 70-79 years old accounted for 29.7%, and 80 years old and above accounted for 7.3%. Male accounted for 49.7% and female for 50.3%. The rate of falls among older adults was 21.4%, and the rate of depressive symptoms was 33.9%. Adjusted ORs (OR = 1.37, 95% CI: 1.23, 1.53) showed a significant association between falls and depressive symptoms among older adults. Subgroup analysis revealed that this association was statistically significant across male (OR = 1.29, 95% CI: 1.23, 1.53) and female (OR = 1.42, 95% CI: 1.23, 1.64), 60-69 aged (OR = 1.38, 95% CI: 1.19, 1.60) and 70-79 aged (OR =1.42, 95% CI: 1.16, 1.74), rural (OR = 1.42, 95% CI: 1.25, 1.61), <15,000 CNY (OR = 1.35, 95% CI: 1.19, 1.54) and more than 25,000 CNY (OR = 1.42, 95% CI: 1.09, 1.85). Additionally, The CART model showed that the probability (73.0%) of falls was highest among older adults with depressive symptoms who self-rated poor health and female gender., Conclusions: This cross-sectional study demonstrated a significant association between falls and depressive symptoms in Chinese older adults. The findings provide some evidence and support for risk monitoring, screening for depressive symptoms, and early prevention in the high-risk older population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Feng, Chen, Li, Xue and Hao.)

This study evaluated epidemic temporal aspects of Japanese encephalitis (JE) and investigated the weather threshold of JE response across eight climate subtypes between 2005 and 2019 in Gansu Province, China. Epidemiological data were collected from the China Information System for Disease Control and Prevention (CISDCP). Three epidemic temporal indices [frequency index (α), duration index (β), and intensity index (γ)] were adopted for the comparison of epidemic features among different climate subtypes. In addition, the local indicators of spatial association (LISA) technique was used to detect the hot-spot areas. The category and regression tree (CART) model was used to detect the response threshold of weather variables in hot-spot areas across climate subtypes. Among eight climate subtypes in Gansu, in most hot-spot areas (i.e., high-high clusters), α, β, and γ were detected in the climate subtypes of subtropical winter dry (Cwa), temperate oceanic continental (Cwb), and continental winter dry (Dwa and Dwb). According to the CART analysis, a minimum monthly temperature is required for Japanese encephalitis virus (JEV) transmission, with different threshold values among the climatic subtypes. In temperate climate zones (Cwa and Cwb), this threshold is 19 °C at a 1-month lag. It is lower in continental winter dry climate zones: 18 °C in Dwa (snow climate, dry winter, and hot summer) and 16 °C in Dwb (snow climate, dry winter, and warm summer). Additionally, some areas of the areas with temperate arid (BWk and BSk) had the first JE cases. Further studies to detect whether the climate change influence the JEV's distribution in Gansu Province are needed., (© 2023. The Author(s).)

Background Many randomized studies have shown that epidermal growth factor receptor ( EGFR-) tyrosine kinase inhibitors ( TKIs) are apparently advantageous over standard chemotherapy in non-small-cell lung cancer ( NSCLC) patients with EGFR active mutation in front-line treatment. But which subgroup of advanced NSCLC could benefit from EGFR- TKIs in the second-or third-line setting remains elusive. To explore predictive factors of advanced NSCLC patients with the unknown status of EGFR mutation treated by gefitinib in the second-or third-line setting, we used classification and regression tree ( CART) analysis to screen which patients would benefit more. Methods One hundred and fifty-five patients with advanced NSCLC previously unsuccessfully treated with platinum-based chemotherapy were included in this study. Patients received gefitinib as part of the Expanded Access Program of the China Charity Federation between 2 March 2005 and 11 May 2011. Multivariate analysis of progression-free survival ( PFS) was performed using CART analysis. This method uses recursive partitioning to assess the effect of specific variables on PFS, thereby ultimately generating groups of patients with similar clinical features on PFS. Results The median PFS in patients with NSCLC who were treated with gefitinib after prior chemotherapy was 16 months (95% confidence interval [ CI] 13.44-18.56). CART was performed with an initial split on adenocarcinoma differentiation, and four terminal subgroups were formed. The median PFS of the four subsets ranged from 12 to 42 months. Conclusions Adenocarcinoma differentiation, brain metastasis and prior thoracic radiotherapy are predictors of PFS in previously treated NSCLC patients. CART can be used to identify homogeneous patient populations in clinical practice and future clinical trials. [ABSTRACT FROM AUTHOR]

Abstract: The present work describes the ability of two modeling methods, Classification and Regression Tree (CART) and Random Forest (RF), to predict endemic fish assemblages and species richness in the upper Yangtze River, and then to identify the determinant environmental factors contributing to the models. The models included 24 predictor variables and 2 response variables (fish assemblage and species richness) for a total of 46 site units. The predictive quality of the modeling approaches was judged with a leave-one-out validation procedure. There was an average success of 60.9% and 71.7% to assign each site unit to the correct assemblage of fish, and 73% and 84% to explain the variance in species richness, by using CART and RF models, respectively. RF proved to be better than CART in terms of accuracy and efficiency in ecological applications. In any case, the mixed models including both land cover and river characteristic variables were more powerful than either individual one in explaining the endemic fish distribution pattern in the upper Yangtze River. For instance, altitude, slope, length, discharge, runoff, farmland and alpine and sub-alpine meadow played important roles in driving the observed endemic fish assemblage structure, while farmland, slope grassland, discharge, runoff, altitude and drainage area in explaining the observed patterns of endemic species richness. Therefore, the various effects of human activity on natural aquatic ecosystems, in particular, the flow modification of the river and the land use changes may have a considerable effect on the endemic fish distribution patterns on a regional scale. [Copyright &y& Elsevier]

Spatial patterns of soil nutrient (N and P) were examined using geostatistical analysis in three different soil horizons (A, B, C) in a mixed forest in Beijing China. Soil nutrients (total nitrogen or TN, total phosphorous or TP, and available nitrogen or AN) were high near the surface and lower with depth. In general, the spatial correlation in soil nutrient content varied by depth. Soil nutrient content was also strongly influenced by vegetation type and elevation. These results consist of a set of simple statistical models that could provide information for the planning of the forest management and long-time research in this area. [ABSTRACT FROM AUTHOR]

Abstract: Recent enhanced urbanization and industrialization in China have greatly influenced soil Cu content. To better understand the magnitude of Cu contamination in soil, it is essential to understand its spatial distribution and estimate its values at unsampled points. However, Kriging often can not achieve satisfactory estimates when soil Cu data have weak spatial dependence. The proposed classification and regression tree method (CART) simulated Cu content using environmental variables, and it had no special data requirements. The Cu concentration classes estimated by CART had accuracy in attribution to the right classes of 80.5%, this is 29.3% better than ordinary Kriging method. Moreover, CART provides some insight into the sources of current soil Cu contents. In our study, low soil Cu accumulation was driven by terrain characteristic, agriculture land uses, and soil properties; while high Cu concentration resulted from industrial and agricultural land uses. [Copyright &y& Elsevier]

[Display omitted] • Weathering pedogenic process were main source of heavy metals in tea garden. • Organic matter and redox process control transfer of As, Pb, Cd and Hg. • Geochemical behaviors of Cr, Cu, Ni and Zn were related to pH and iron oxides. • Tea type, bedrock, soil texture and fertilizer affected metals transfer mainly. • High risk by Pb and Cd existed in area with granite/granodiorite and shale. As one of the most popular beverages around world, factors influencing transfer of heavy metals from soil to tea leaves is crucial to investigate and assess health risk through tea drinking. Parent material (PM), soil and tea samples from Anhui province, typical tea producing area in China were collected in this study. To find out distribution characteristics of heavy metals in tea and soil, and influencing factors for transfer process, variables of plantation factors, soil properties and geological background were taken into account. The results showed that weathering pedogenic process could be the main release source of heavy metals in soil under the acid environment for tea growth. More than 75% of soil Cd, Hg, Pb and Zn exceeded background. However heavy metals in tea samples were below the limits of China, WHO and EU standards. Soil organic matter and redox process influenced the distribution and transfer of As, Pb, Cd and Hg in soil and tea. While geochemical behaviours of Cr, Cu, Ni and Zn were mainly related to soil pH and iron oxides in tea garden. The method of classification and regression trees (CART) showed clones of tea type, bedrock type, soil texture, soil organic and fertilizer application were identified as the main factors influencing transfer factors of heavy metals from soil to tea. The specific types of tea grown in the soil with sandy clay and bedrock of granite/granodiorite and shale should be given more monitoring. The non-carcinogenic hazard quotients (HQ) and cancer risk (Risk) through tea drinking were primarily caused by Pb and Cd respectively. To reduce the potential health risk from tea, application of organic and/or compound fertilizer were thought to be the effective management strategy for tea plantation. [ABSTRACT FROM AUTHOR]

• A classification approach for urban metabolic types was presented in this study. • GDP per capita and climate types were important threshold variables for classifying urban resource consumption levels. • Different types of the features of urban metabolism were usually associated with specific urban resources. • The potential influence of climatic conditions should be considered in urban resource utilization strategies. Exploring resource utilization strategies for different types of cities is essential for achieving the sustainable development of cities. Urban metabolism is a systems-oriented approach to understanding the interrelationship between urban socioeconomic status and natural environment through resource utilization. Recognition and identification of urban metabolic types could help in formulating targeted urban resource utilization strategies. However, few studies have investigated the classification of urban metabolism. Therefore, this study combines material flow analysis with the classification and regression tree model to present a classification approach for urban metabolism that considers cities' socioeconomic, demographic, and climatic conditions and resource consumption. This approach was tested in Chinese prefecture-level cities. The results showed that gross domestic product (GDP) per capita, population, population density, and climate types were significantly correlated with cities' resource consumption indicators. GDP per capita and climate types were the most important threshold variables for classifying urban resource consumption levels. GDP per capita was the optimal split node for electricity, total energy, water, industrial minerals and metals, and total outputs, while climate types was the optimal split node for biomass, fossil fuels, and construction materials. The level of urban resource consumption in temperate climates was higher than in other climate types. Eight types of urban metabolism were identified in China that were closely related to urban socioeconomic conditions, scale grade, resource endowment, industrial level, and energy structure. The findings of this study provide a reference for determining the problem of urban critical resource consumption and formulating appropriate urban resource utilization strategies. [ABSTRACT FROM AUTHOR]

In this study, a statistical model, combining principal components analysis (PCA), stepwise-canonical discriminant analysis (stepwise-CDA), classification and regression tree (CART), partial least squares-discriminant analysis (PLS-DA) and an innovative multidimensional analysis (MDA), was build up to predict the geographical origin of edible porcini (Boletus sect. Boletus). To this purpose, the elemental signatures of 180 commercial and manually harvested samples from different Italian production areas, China and Poland, were chemometrically elaborated. PCA differentiated Italian products from Chinese and Polish mushrooms. Based on the fusion of PCA and hierarchical cluster analysis (HCA), MDA identified elements such as Na, Mn, Fe, Cu and Cd as powerful discriminating variables. Finally, highly accurate and trained stepwise-CDA, CART and PLS-DA models, were able to predict the geographical origin of a survey of commercial porcini, through few metals (Mg, Mn, and Fe). The provenance reported on the labelling of these products was confirmed. Nevertheless, both models revealed that a commercial sample, with a claimed Italian origin, consisted of Chinese mushrooms. Overall, the combination of standard and innovative chemometric techniques demonstrated to support a reliable authentication of the geographical origin of porcini, thus, protecting the Italian production from fraud. • A traceability system for protecting the Italian porcino was built up. • Porcini from China, Poland and Italy, including PGI mushrooms, were considered. • Samples were screened by ICP-MS and elaborated by multivariate statistics. • A restricted pool of traceability markers was determined. • The traceability model was applied on commercial porcini to reveal potential fraud. [ABSTRACT FROM AUTHOR]

Understanding water intake variation is crucial for assessing human exposure to water pollutants. The correlation between water intake and demographic factors warrants further exploration. A cross-sectional study was conducted to estimate urban water consumption and its associated factors among adults in Wuxi, China, in 2015. The water consumption information was obtained by a 24-h self-report diary over seven consecutive days. A classification and regression tree (CART) analysis was applied to detect how water consumption varied with the demographic variables. Finally, a total of 1188 adults (18-87 years old) were included. The results demonstrated that the median water consumption of the adults was 1525 mL/day in summer and 1217 mL/day in winter. The results of the CART analysis demonstrated that body mass index (BMI) and age were the leading factors that were associated with water consumption in summer and winter, respectively. The water consumption threshold of BMI for men differed from women (23 kg/m 2 vs. 18 kg/m 2 ) in summer, and the threshold of age for men was also different from women (43 years vs. 21 years) in winter. In conclusion, the findings are useful for accurately assessing human exposure to water pollutants and identifying the high-risk subgroups.

Background: Dehydration due to insufficient fluid intake (IFI) is detrimental to health. This cross-sectional study aimed to assess the fluid intake of urban adults in Wuxi, China, and to identify potential risk factors contributing to IFI., Methods: Adults were selected from the urban area of Wuxi, China, using a multiple-stage random sampling method. The fluid intake information was obtained with a 24-h self-reported diary over seven consecutive days in both summer and winter of 2015. A classification and regression tree (CART) analysis was conducted to detect the potential risk factors associated with IFI. CART is a machine-learning algorithm that portions the data into subsets by threshold., Results: A total of 584 adults aged 18-87 years were included. The results showed that the median (P25-P75) values of daily fluid intake of the participants were 1100 (800-1550) mL in summer and 1000 (750-1300) mL in winter. Women had a higher prevalence of IFI than men in both summer (odds ratio (OR) = 2.683, 95% confidence interval (CI): 1.830-3.934) and winter (OR = 2.636, 95% CI: 1.677-4.142). The results of CART analysis showed that, in summer, BMI < 25 kg/m 2 (probability: 64.2%) and age < 64 years (probability: 67.4%) were main risk factors of IFI for men, and BMI < 29 kg/m 2 (probability: 81.6%) and living in C Community (probability: 86.7%) were main risk factors for women. In winter, age < 40 years (probability: 81.8%) and BMI < 20 kg/m 2 (probability: 94.5%) were identified as main risk factors of IFI for men and women, respectively., Conclusions: Most of the participants living in the study site had IFI. The fluid consumption varied by gender, age, location, and BMI. The findings could be useful for the implementation and optimization of intervention programs by identifying the individuals who may at greater risk of dehydration.

Avian influenza A (H7N9) is a serve zoonosis with a high mortality rate. Timely and effective diagnosis and early warning is crucial for the clinical treatment of H7N9 patients. The previous studies indicated that thrombocytopenia was associated with the prognosis of influenza cases, but the related evidence of platelet change within the course of the disease remains largely insufficient. A total of 130 laboratory-confirmed H7N9 cases and their corresponding medical records from August 2013 to March 2015 were collected from 23 hospitals of 13 cities in Guangdong, China. The results indicated that there was a significant difference between the outcome of H7N9 cases and their average platelet count (PC) including maximum, minimum, range, admission and discharge/death of the PC value. Furthermore, we built a classification and regression tree (CART) model to predict the fatality rate which varied with average PC. There was a 7% chance for a mortality from H7N9 if PC was over 207.0 × 10^9/L, while there was a 46.3% chance of a mortality from H7N9 when PC was between 123.9 × 10^9/L and 207.0 × 10^9/L, and 81.3% chance of a mortality from H7N9 when PC was less than 123.9 × 10^9/L. This study demonstrates that using platelet count to predict the fatality of H7N9 is significant, and lower platelet counts of H7N9 patients were associated with higher risk of mortality of H7N9 patients, which may need to be taken into consideration when planning clinical treatment.

SP110 is a promising anti-Mycobacterium tuberculosis (MTB) gene. To investigate the effects of SP110 and its associated genes, i.e., MYBBP1A and RELA, on pathological progression of MTB infection, an association study with 424 patients of fresh pulmonary tuberculosis (PTB) and 424 healthy controls was performed. Moreover, classification and regression tree and multifactor dimensionality reduction were employed to explore the effects of gene-gene interactions on cavitary PTB. The results indicated that both the heterozygous genotype GC and homozygous genotype CC in rs3809849 had significant effects on the risk of PTB (OR 1.42, 95 % CI 1.06-1.92, p 0.019; OR 1.55, 95 % CI 1.04-2.33, p = 0.033, respectively), and heterozygous genotype CT in rs9061 also had similar effects (OR 1.43, 95 % CI 1.07-1.90, p = 0.014). The rs3809849 and rs9905742 in MYBBP1A were also significantly associated with cavitary PTB (p = 0.00046 and 0.039, respectively), while rs9061 in SP110 had no such association (p = 0.06931) except its significant association with non-cavitary PTB (p = 0.0093). The interaction of MYBBP1A and RELA had significant effect on cavitary PTB (OR 4.24, 95 % CI 1.44-12.49, p = 0.005). These suggest that MYBBP1A instead of SP110 may be a genetic risk factor for cavitary PTB and play important effects on its whole progress.

Background Currently available tools for identifying individuals at high risk of type 2 diabetes can be invasive, costly and time consuming. This study aims to develop and validate a self-assessment tool for identifying individuals at high risk of type 2 diabetes in the Chinese general population. Methods A cross-sectional survey was conducted from 2000 to 2001 in a nationally representative sample of 15?540 Chinese adults aged 35-74 years. The diabetes risk level (DRL) was assessed by classification and regression tree (CART) analysis using four predictors: age, body mass index, waist-hip ratio (WHR) and waist circumference (WC). Results The significant predictors for type 2 diabetes were WHR and age for women and WC and age for men. The categories generated by CART analysis stratified women into eight DRL and men into five DRL. The prevalence of type 2 diabetes increased with the increase in DRL in both women and men. A DRL of 6 or greater predicted type 2 diabetes status with a sensitivity of 0.61 (95% CI 0.55 to 0.67), a specificity of 0.71 (95% CI 0.70 to 0.73) in women, and a DRL of 3 or greater predicted type 2 diabetes status with a sensitivity of 0.59 (95% CI 0.52 to 0.65) and a specificity of 0.63 (95% CI 0.62 to 0.65) in men. Conclusions This study demonstrates that application of the DRL has identified a substantial proportion of individuals with type 2 diabetes in the Chinese general population. It suggests that there is a great potential for applying the self-assessment tool in healthcare-limited settings. [ABSTRACT FROM AUTHOR]