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Changes in the water–energy–food (WEF) nexus lead to changes in carbon emissions, but the mechanisms of change are often unclear owing to large-scale land use changes. This constrains cross-sectoral low-carbon adaptive socio-ecological system management. This research used an improved WEF nexus index (WEFNI) and land-use carbon emission assessment method to estimate the spatio-temporal variability of provincial WEF carbon emissions in China from 2011 to 2020. The matching values of water–carbon (W-C) and energy–carbon (E–C) were used to assess multi-scale impacts of land-use change on WEF carbon emissions. Finally, the proportion of WEF carbon emissions contributing to values obtained from satellite monitoring and industry inventories were compared separately. Our results show: (1) WEF nexus in nine provinces showed decoupling from socio-economic development trends. (2) Developed regions with larger carbon emissions also have higher energy and water use efficiency. (3) Spatio-temporal distribution patterns of carbon emissions and CO 2 concentrations were consistent, and the carbon emissions accounted for 5.19%–6.02% of the total industry emission inventory. (4) The expansion of construction land and increased demand for resources in the WEF system are responsible for increasing carbon emissions. This study provides a scientific reference for decision-makers to optimize WEF carbon emissions and plan land use under the constraints of water–energy–food management, which is consistent with the Chinese Government's policies relating to tackling climate change, protecting the environment, and ensuring resource security. [Display omitted] • Improved WEFNI and land-use assessment reveal China's provincial carbon emissions. • Multi-scale impacts of land-use change on WEF carbon emissions assessed. • WEF-related carbon emissions decoupled from socio-economic trends. • High water and energy utilization regions exhibit elevated carbon emissions. • WEF carbon emissions accounted for 5.19%–6.02% of China's total emissions. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal dynamics of atmospheric carbon dioxide (CO 2) is crucial for achieving carbon neutrality and safeguarding planetary well-being. Satellite-based carbon monitoring is increasingly instrumental in studying atmospheric CO 2 changes, offering a broad spatial view. However, current research lacks a comprehensive exploration of both day-to-day variations in atmospheric CO 2 at a finer-resolution grid and its coupled relationship with particulate pollution, owing to a substantial number of missing values in satellite-retrieved CO 2 data. To address these issues, this study presents an enhanced regression-based machine learning model, reconstructing full-coverage daily atmospheric CO 2 concentrations in China from 2015 to 2020 at a 0.01° spatial resolution. Utilizing spatiotemporal high-resolution column-averaged dry-air mole fraction of CO 2 (XCO 2) data from the Orbiting Carbon Observatory 2 (OCO-2) as the dependent variable and multi-source environmental factors as independent variables, we achieved overall, spatial, and temporal cross-validation R2 [RMSE] results of 0.98 [0.74 ppm], 0.95 [1.15 ppm], and 0.93 [1.44 ppm], respectively. The resulting full-coverage estimates revealed significant spatial heterogeneity over time, with the highest CO 2 levels primarily in spring in the North China Plain, and the lowest in summer in northern China and autumn in the Qinghai-Tibet Plateau and the south. National weekend averages of mean symmetrized residuals in XCO 2 surpassed weekday averages. Within megacities such as Shanghai and Wuhan, industry-intensive areas exhibited higher XCO 2 values compared to other urban areas within the same cities. Despite spatial and seasonal fluctuations, the country-average XCO 2 increased from 2015 to 2020 but has decelerated recently. A moderate correlation was observed between the spatial pattern of long-term CO 2 concentrations and particulate aerosols, viewed from the total atmospheric column perspective, with some regional discrepancies. The present modeling method advances carbon monitoring techniques, and the predictive data foster an enriched understanding of the carbon cycle, climate change, and sustainable development. • A full-coverage dataset of daily 1-km atmospheric CO 2 from 2015 to 2020 is generated. • The resulting dataset achieves high modeling accuracy (CV R2 = 0.98, RMSE = 0.74 ppm). • Atmospheric CO 2 varies significantly within and between cities, with seasonal fluctuations. • The national CO 2 increased but with spatial, seasonal, and interannual fluctuations. • Atmospheric CO 2 is correlated well with aerosol loads but varies by seasons and regions. [ABSTRACT FROM AUTHOR]

Despite the availability of numerous satellite-based machine-learning methods for supplementing air quality monitoring data, models estimating ground-level PM 2.5 concentrations have proven unsatisfactory at a daily scale during periods without PM 2.5 observations, with validated R2 values for daily estimates in the literature typically below 0.55. In this study, we introduce a novel ensemble learning-based hindcast modeling method that incorporates information on predictors in periods without PM 2.5 observations into the modeling process to enhance the reliability of daily estimates for those unmonitored historical years. Our proposed method constructs the annually varying relationship between PM 2.5 and the predictors during periods without PM 2.5 observations, differing from previous hindcast modeling procedures where the PM 2.5 -predictors relationship established was assumed to be the same between periods with and without PM 2.5 observations. Using PM 2.5 heavily polluted northern China from 2013 to 2020 as an example, our history-informed machine-learning hindcast model outperformed the state-of-the-art method by a large margin, improving the leave-one-year-out cross-validation (CV) R2 [ root-mean-square error (RMSE)] from 0.56 [34.66 μg/m3] to 0.63 [31.79 μg/m3] a daily scale, while achieving a comparable sample-based 10-fold CV R2 [(RMSE] of 0.90 [16.85 μg/m3]. In addition, incorporating satellite aerosol optical depth (AOD) into the hindcast modeling further improved the historical PM 2.5 -predictors relationship and provided more reliable estimates during periods without PM 2.5 observations, increasing the leave-one-year-out CV R2 [RMSE] from 0.61 [30.31 μg/m3] for the model without AOD to 0.65 [28.56 μg/m3]. Consequently, our proposed hindcast modeling framework enables the production of more reliable PM 2.5 estimates during periods without PM 2.5 observations and holds promise for application to other regions and air pollutants with longer predictor data compared to the response variable data. • Enhanced the reliability of satellite-based PM 2.5 estimates in years without PM 2.5 observations. • The proposed model incorporated information on predictors in those unobserved years. • The proposed method outperformed the state-of-the-art method by a large margin. • Filling AOD gaps is recommended for PM 2.5 modeling in periods without PM 2.5 observations. [ABSTRACT FROM AUTHOR]

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM 2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM 2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM 2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM 2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM 2.5 concentration greater than 80 μg/m3 in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM 2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM 2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated. Image 1 • PM 2.5 exposure levels and health outcomes are dynamically assessed. • Satellite-station-based PM 2.5 concentrations with fine resolution are used. • LBS data from social media are used to track population distribution. • Five exposure models employing different datasets are compared. An improved PM 2.5 exposure assessment method is developed using satellite-ground-integrated PM 2.5 concentrations and LBS-based dynamic population maps. [ABSTRACT FROM AUTHOR]

Abstract With China's rapid economic growth, particle pollution, especially fine particulate matter (PM 2.5), which is known to have adverse health impacts, has become an increasingly serious issue. Satellite aerosol optical depth (AOD), an important physical property of aerosol particles, can serve as a proxy for investigating particle pollution because it can provide observations with comprehensive spatial and temporal coverage compared with ground-level measurements. This study used an improved 14-year high-resolution AOD dataset to examine the spatial characteristics and temporal dynamics of the dominant pollutants in China from 2003 to 2016 using advanced statistical methods. The improved AOD dataset combines the Moderate Resolution Imaging Spectroradiometer (MODIS) 3-km dark target AOD and 10-km deep blue AOD datasets, which enables a comparison of aerosol loading between eastern and western China. Pixel-based analysis indicates a significant difference between eastern and western China: high AOD values were generally observed in the east with a notable decrease, while low aerosol loadings were found in western China with no distinct change. The most particle polluted areas were the North China Plain, Hubei-Hunan region, Sichuan Basin, and Guangxi-Guangdong region in eastern China and western Qinghai and Tarim Basin in western China, with changes in the national AOD average center shifting to the northwest from 2013 to 2016. The impact factor analysis based on geographically weighted regression indicates that the effect of topography on the spatial characteristics of AOD is negative and more important in eastern China, which has low elevations. Built-up areas significantly exacerbate air pollution in the areas between eastern and western China, and there is no apparent AOD–vegetation relation dominates the country. This study thus provides a comprehensive understanding of the spatiotemporal variations of particle concentrations and can facilitate environmental management, policies to alleviate particle pollution, and health risk assessment studies. Graphical abstract Unlabelled Image Highlights • Significant decrease in AOD was mainly occurred in eastern China from 2003 to 2016. • No distinct change was found in areas west of Heihe-Tengchong Line. • The national average center of AOD shifted to the northwest from 2003 to 2016. • The effects of landscape factors on AOD vary with different surface settings. • Topography predominates the spatial variation of aerosol loading in China. [ABSTRACT FROM AUTHOR]

Abstract: China has performed crop residue burning (CRB) for a long time and has suffered from resultant environmental pollution. High temporal resolution has not been fully discussed in attempts to address the temporal and spatial impact of CRB in China on air quality. Our study used the MOD14A1 product of the MODerate resolution Imaging Spectrometer (MODIS) to extract the daily CRB for China during the period from 2014 to 2016, and the daily aerosol optical depth (AOD) provided by MODIS Collection 6 was obtained to simultaneously reflect the air pollution. First, the study area was classified into five subregions. A temporal analysis was conducted on the daily variation in the number of CRB events and the regional mean value of AOD, the spatial contribution ratio of CRB on aerosol pollution was then calculated, and finally, a temporal and spatial Pearson correlation was calculated to find the spatially varying relationship between CRB and aerosol. The results suggest the following: (1) CRB possesses seasonal characteristics that are associated with the harvest time or sowing time of major crops in the region. (2) The impact of CRB on aerosol was delayed by 1–6 days. (3) High contribution ratios (70%–90%) occurred in northeast China on a large scale; even when the impact of the CRB on aerosol pollution in the Huang-Huai-Hai river basin occurred on a large scale, the value was merely approximately 30%. Relatively low contributions of CRB have been found in other places, whereas the contribution of CRB was severe in some places with high-density populations. (4) Temporal-spatial correlation provided an accurate index to reflect the correlation of CRB and aerosol in a specific location, which suggests that, in places with large scale and dense CRB, CRB tends to have a high positive correlation with aerosol pollution for each day. Graphical abstract Image 1 Highlights • This study concerns the spatial and temporal variation of crop residual burning (CRB) for the whole China, and the major sources of CRB have been clarified. • A 1–6 day's hysteresis has been found in the daily temporal analysis when the CRB makes an impact on aerosol pollution. • Evident aerosol increase in the CRB seasons has been found in the regions. Major sources and contribution to aerosol pollution of CRB have been declared for various regions. Moreover, the temporal delaying impact of CRB on aerosol pollution was found. [ABSTRACT FROM AUTHOR]

Metal-organic framework (MOF)-based membranes have been considered a promising candidate for the separation membrane due to their high porosity and controlled pore structure. However, the fabrication of a robust MOF-based membrane is commonly subject to the limitations of MOF particle agglomeration and interface defects. Herein, we propose a facile and effective approach for the preparation of a robust zeolite imidazole framework-8 (ZIF-8)/protein hybrid membrane by introducing a phase-transited lysozyme (PTL) layer to mediate the growth of ZIF-8. The PTL layer was self-assembly on the ultrafiltration membrane and then ZIF-8 was in-situ growth to form a uniform ZIF-8/PTL hybrid layer. The prepared ZIF-8/PTL membrane possessed water permeance up to 39.5 L m−2 h−1 bar−1 and high rejections (>99.0%) for heavy metal ions. Furthermore, the fabricated ZIF-8/PTL hybrid membrane was verified to have excellent antimicrobial activity and antifouling performance. These results indicate that as a simple and robust approach, PTL modification can be employed to construct new interfaces for mediating MOF growth, and the prepared MOF-based membranes have great potential for water treatment. [Display omitted] • A phase-transited lysozyme (PTL) layer was used to mediate the growth of ZIF-8. • The PTL layer enhanced the stability of ZIF-8 membrane. • ZIF-8/PTL membrane exhibits superior antibacterial activity. • ZIF-8/PTL membrane shows excellent separation and antifouling performances. [ABSTRACT FROM AUTHOR]

This paper presents the non-stationarity and autocorrelation (with a Moran’s I index score of 0.75) of the MODIS-retrieved aerosol optical depth (AOD) of the Wuhan agglomeration (WHA) in Central China, using geographically weighted regression (GWR) to identify the spatial relationships between AOD and its impact factors. In addition to the socio-economic factors, i.e., GDP and population, vegetation cover, elevation, land-use density and landscape metrics are also considered. Faced with the rapid process of urbanization and the impact of land-use change on AOD, which has been confirmed in previous studies, we propose an AOD prediction method, combining a land-use change simulation model, a cellular automata and Markov chain (CA-Markov) model, and spatial relationships built by GWR to represent the spatial distribution of AOD in 2030. The results suggest that the GWR model is able to address the spatially varying relationships, with an R-squared value, corrected Akaike’s information criterion (AICc), and standard residual better than those of the ordinary least squares (OLS) model. Land-use simulation, with an accuracy of 89.76%, indicates that an increase in the built-up area and a decrease in the forest area will be the major trends of land-use change and will lead to increased AOD. The AOD simulation results indicate that the most developed areas, i.e., the cities of Wuhan and Huangshi, will be the AOD increase hot spots in the WHA. This study provides an alternative method to identify the varying spatial relationships between AOD and its impact factors. A spatial prediction method for AOD is developed from the perspective of land-use change, which will help land-use planners in decision making. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is important for high-efficiency utilization of wind energy. Correspondingly, numerous researchers have always focused on the development of reliable forecasting models of wind speed, which is often noisy, unstable and irregular. Current approaches could adapt to various wind speed data. However, many of these usually ignore the importance of the selection of the modeling sample, which often results in poor forecasting performance. In this study, a hybrid forecasting system is proposed that contains three modules: data preprocessing, data clustering, and forecasting modules. In this system, the decomposing technique is applied to reduce the influence of noise within the raw data series to obtain a more stable sequence that is conducive to extract traits from the original data. To extract the characteristic of similarity within wind speed data, a kernel-based fuzzy c-means clustering algorithm is used in data clustering module. In the forecasting module, a sample with a highly similar fluctuation pattern is selected as training dataset, and which could reduce the training requirement of model to improve the forecasting accuracy. The experimental results indicate that the developed system outperforms the discussed traditional forecasting models with respect to forecasting accuracy. [ABSTRACT FROM AUTHOR]

Ground fine particulate matter (PM2.5) concentrations at high spatial resolution are substantially required for determining the population exposure to PM2.5 over densely populated urban areas. However, most studies for China have generated PM2.5 estimations at a coarse resolution (≥10 km) due to the limitation of satellite aerosol optical depth (AOD) product in spatial resolution. In this study, the 3 km AOD data fused using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOD products were employed to estimate the ground PM2.5 concentrations over the Beijing-Tianjin-Hebei (BTH) region of China from January 2013 to December 2015. An improved geographically and temporally weighted regression (iGTWR) model incorporating seasonal characteristics within the data was developed, which achieved comparable performance to the standard GTWR model for the days with paired PM 2.5 - AOD samples (Cross-validation (CV) R 2  = 0.82) and showed better predictive power for the days without PM 2.5 - AOD pairs (the R 2 increased from 0.24 to 0.46 in CV). Both iGTWR and GTWR (CV R 2  = 0.84) significantly outperformed the daily geographically weighted regression model (CV R 2  = 0.66). Also, the fused 3 km AODs improved data availability and presented more spatial gradients, thereby enhancing model performance compared with the MODIS original 3/10 km AOD product. As a result, ground PM2.5 concentrations at higher resolution were well represented, allowing, e.g., short-term pollution events and long-term PM2.5 trend to be identified, which, in turn, indicated that concerns about air pollution in the BTH region are justified despite its decreasing trend from 2013 to 2015. [ABSTRACT FROM AUTHOR]

The use of satellite-retrieved aerosol optical depth (AOD) data and statistical modeling provides a promising approach to estimating PM 2.5 concentrations for a large region. However, few studies have conducted high spatial resolution assessments of ground-level PM 2.5 for China at the national scale, due to the limitations of high-resolution AOD products. To generate high-resolution PM 2.5 for the entirety of mainland China, a combined 3 km AOD dataset was produced by blending the newly released 3 km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOD data with the 10 km-resolution MODIS Deep Blue AOD data. Using this dataset, surface PM 2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an interior point algorithm (IPA)-based efficient mechanism for selecting optimal parameter values, was developed to estimate a large set of daily PM 2.5 concentrations. Comparisons with the popular spatiotemporal models (daily geographically weighted regression and two-stage model) indicated that the proposed GTWR model, with an R 2 of 0.80 in cross-validation (CV), performs notably better than the two other models, which have an R 2 in CV of 0.71 and 0.72, respectively. The use of the combined 3-km high resolution AOD data was found not only to present detailed particle gradients, but also to enhance model performance (CV R 2 is only 0.32 for the non-combined AOD data). Furthermore, the GTWR's ability to predict PM 2.5 for days without PM 2.5 -AOD paired samples and to generate historical PM 2.5 estimates was demonstrated. As a result, fine-scale PM 2.5 maps over China were generated, and several PM 2.5 hotspots were identified. Therefore, it becomes possible to generate daily high-resolution PM 2.5 estimates over a large area using GTWR, due to its synergy of spatial and temporal dimensions in the data and its ability to extend the temporal coverage of PM 2.5 observations. [ABSTRACT FROM AUTHOR]

rRNA operon (rrn) copy number is a functional trait that reflects the ecological strategies of bacteria. Here, we estimated the average rrn copy number of soil bacterial communities in postglacial ecosystems of Mount Gongga using the 16S rRNA gene sequencing data and the ancestral trait reconstruction method. We found that the community rrn copy number decreased with soil depth and ecosystem succession. More than 90 % of the variations were explained by soil pH and resource availability, indicating that this trait was under strong selection pressure of environmental factors. Thus, community rrn copy number may reveal the changes in bacterial community from the perspective of adaptive strategies, which is a complement to the taxonomic approach. • Community rRNA operon (rrn) copy number of soil bacteria decreased with soil depth and ecosystem succession. • Soil pH and resource availability explained >90 % of the variation in community rrn copy number. • Community rrn copy number revealed the changes in bacterial community from the perspective of adaptive strategies. [ABSTRACT FROM AUTHOR]

The recently released Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 introduced a fine scale aerosol optical depth (AOD) distribution, the 3 km product, which is expected to perform well in analyzing aerosols and identifying local air pollution, especially in the severely polluted atmosphere of China. However, few detailed evaluations of regional variations have been conducted. In this paper, we evaluate MODIS 3 km and 10 km AOD products for China against ground-based measurements and compare their performance with respect to spatial and temporal variations. The ground validations indicate that the two products are generally correlated well to ground-based observations. Spatially, the 3 km product slightly outperform the 10 km product in well-developed areas of southern China. Temporally, both products perform worse during spring and summer. Atmospheric clouds and underlying surface are two key factors that influence the accuracy and number of retrievals for both products. The comparison analysis reveals the newly introduced AOD product clearly shows good relationships with the coarse resolution retrievals in spatial and temporal variation but significant differences regarding details. The 3 km AOD product provides better aerosol gradients, more retrievals in bare areas of western China and some spikes of diurnal variation in cloudy days. Seasonal comparisons show the 3 km AOD product is higher than the 10 km product in all seasons, especially during spring and summer. Although the 3 km product for China generally performs slightly worse than the 10 km product, the added information of the MODIS 3 km AOD product shows potential for studying local aerosol characterization, and may facilitate studies of air pollution. [ABSTRACT FROM AUTHOR]

Air quality in China, especially the concentration of particles suspended in the atmosphere, is increasingly affecting the country's climate, the health of communities and even policy-makers. Satellite-derived aerosol optical depth (AOD) data provide an alternative means of analysing fine-scale aerosol variations over the entire of China, thus overcoming the limitations of the sparse network of ground-level measurements. This study used moderate resolution imaging spectrometer data at 550 nm to investigate the variation in and factors affecting AOD over a 3-km grid for the entire of China, and five typical regions in particular. Spatial and temporal data from 2002 onwards were used. The high aerosol loadings were usually located in the economically and industrially developed areas of eastern and southern China, especially over the five typical regions, whereas the low aerosol loadings were located in the rural and less developed areas of western and northeastern China. A notable transition dominated the long-term overall trend in the AOD: an upward tendency (+0.0003) pre-2008 followed by a downward tendency (−0.0005) post-2008. The seasonally averaged AOD reached its maximum in spring (AOD of about 0.41), followed by summer (0.37), winter (0.34) and autumn (0.26). AOD was negatively associated with terrain and positively associated with socio-economic activities over the entire country, consistent with the regional correlations. However, the effect of vegetation on AOD exhibited large spatial and temporal heterogeneity, as indicated by the weak relationship between AOD and the Normalized Difference Vegetation Index. The multiple linear regression results indicated that of the 10 indices, elevation and population were the main factors influencing aerosol variation. [ABSTRACT FROM AUTHOR]

Abstract: Background: Telomere is protected by its G-quadruplex, T-loop structure, telomerase, and binding protein complex. Protein POT1 (protection of telomeres 1) is one subunit of telomere binding protein complex Shelterin. POT1 acts as a regulator of telomerase-dependent telomere length, and it can help telomere to form D-loop structure to stabilize telomere. POT1 protects telomere ends from ATR-dependent DNA damage response as well. Methods: Extensive methods were used, including CD, EMSA, ITC, PCR stop assay, luciferase reporter assay, quantitative real-time PCR, Western blot, chromatin immunoprecipitation (Ch-IP), cloning, expression and purification of proteins. Results: We found a new G-rich 30-base-pair long sequence (P-pot1 G18) located from −165 to −136 base pairs upstream of the translation starting site of protein POT1. This sequence in the promoter region of pot1 gene formed G-quadruplex resulting in down-regulation of pot1 gene transcription. This G-rich sequence is close to a binding site “TCCC” for transcription factor hnRNP K (heterogeneous nuclear ribonucleoprotein K), and its conversion to G-quadruplex prevented the access of hnRNP K to this binding site. The binding of hnRNP K could up-regulate pot1 gene transcription. TMPyP4 (meso-tetra(N-methyl-4-pyridyl)porphine) has been widely used as G-quadruplex binding ligand, which stabilized the G-quadruplex in vitro and in cellulo, resulting in down-regulation of pot1 gene transcription. Conclusions: This G-quadruplex might become a potentially new drug target for antitumor agents. General significance: Our results first demonstrated that G-quadruplex formation can affect the binding of transcription factor to its nearby binding site, and thus making additional influence to gene transcription. [Copyright &y& Elsevier]

[Display omitted] • Spatiotemporal dynamics of PM 2.5 -attributable premature mortality are analyzed. • Contribution of drivers to PM 2.5 -attributable premature mortality is decomposed. • PM 2.5 -attributable premature mortality in central China decreased in recent 6 years. • The mortality reduction is due to baseline IR but weakened by IHD and LC IR changes. • More effort on reducing PM 2.5 together with other actions are strongly recommended. Ambient PM 2.5 was reported to be related to numerous negative health outcomes, leading to adverse public health impacts in many countries such as China. Despite the apparent reduction in PM 2.5 levels over China due to its emission control policies in recent years, the health burdens were not reduced as much as expected. This calls for a comprehensive analysis to explain the reasons behind to provide a useful reference for formulating effective emission control strategies. Taking central China as an example due to its large population and high levels of PM 2.5 , this study quantified the spatiotemporal dynamics of premature mortality associated with PM 2.5 pollution in central China for each year during 2003–2018 and applied a decomposition analysis to dissect the contribution of various driving factors including ambient PM 2.5 level, demographic distribution and baseline incidence rate of four diseases related to air pollution. Results show significant spatiotemporal variations in PM 2.5 -attributed health impact in central China, including Henan, Hubei, and Hunan provinces. Five Henan cities had the largest PM 2.5 -attributable premature mortality (∼8–12 K premature mortalities), while three Hubei cities and one Hebei city had the least chronic PM 2.5 -related all-cause mortality numbers (<1 K mortalities). Throughout the study period, the PM 2.5 -caused premature mortality decreased by 54 K, in which changes in PM 2.5 levels and baseline incidence rates of stroke and chronic obstructive pulmonary disease contributed to the positive effect, whereas demographic changes and baseline incidence rate change of ischemic heart disease and lung cancer brought a countervailing effect. Our findings suggest more dynamic and comprehensive policies and measures that take into account spatiotemporal variations of health burden for effective alleviation of the health impact of PM 2.5 pollution in the country. [ABSTRACT FROM AUTHOR]

ABSTRACT: Within the last ten years, the development of innovative minimally invasive breast biopsy technologies has made a significant impact on the diagnostic evaluation of impalpable breast lesions. The Mammotome minimally invasive biopsy system is a diagnostic tool used under stereotactic or ultrasound guidance. The minimally invasive biopsies for 143 impalpable breast lesions in 86 patients were performed using the B-ultrasound-guided 8-gauge vacuum-assisted Mammotome system at Jinan Military General Hospital. One hundred and forty-three breast lesions in 86 patients were excised completely using this method. The average biopsy procedure time was 17min (range, 3–45min). Clinically, suspicious lesions were satisfactorily confirmed by this system. One hundred and twenty-one lesions were diagnosed as fibroadenoma, nineteen lesions as breast adenosis, one lesion as invasive breast cancer, and two lesions as ductal carcinoma in situ. Skin ecchymosis was found in two patients. One hundred and thirty-two impalpable lesions were completely excised, as demonstrated by the follow-up ultrasound examination. The results suggest that the B-ultrasound-guided Mammotome system may be an efficient tool for the diagnosis and treatment of impalpable breast lesions. [Copyright &y& Elsevier]

Abstract: Purpose: To evaluate the feasibility of internal mammary lymphatic chain dissection by thoracoscopic surgery. Material and methods: Thirty-two breast cancer women were involved in this pilot study, who were with lesion located at central area or internal quadrant, or with cN2 or cN3 ALNs, or with T4 tumor, or indicating IMNs drainage by preoperative lymphoscintigraphy, but without distant metastasis. Subareolar injection and peritumoral injection of 5ml methylene blue were done before operation. Double cavity intubation was used to permit collapse of the homolateral lung and set-up the endoscopic operation space. Trocars were introduced through three thoracic incisions of less than 15mm at the third, the fifth and the seventh intercostal spaces along the midaxillary line. Dissection of internal mammary lymphatic chain was performed by a thoracoscopic grasper and ultrasonically activated scalpel. Results: Thoracoscopic internal mammary lymphatic chain dissection was successfully finished in 28 patients. The procedure time was 30–70min (45.2±9.6min). A total of 128 internal mammary nodes were removed. Among the 28 patients, 11 had internal mammary node metastasis. Only one patient had internal mammary node metastasis without axillary node metastasis. All internal mammary nodes were located at the first rib to the fourth intercostal space. Thoracoscopic internal mammary lymphatic chain dissection in four patients was impossible because of unfavorable local anatomic conditions. There was no damage on great vessels or the lungs, nor pulmonary atelectasis or pulmonary infection. Conclusions: Thoracoscopic internal mammary lymphatic chain dissection is feasible, and it is easy to perform without serious additional complications. It may improve nodal staging of breast cancer with internal mammary node. [Copyright &y& Elsevier]

• Generated daily 1-km PM 2.5 estimates across China in historical years of 2000–2018. • Proposed a more advanced statistical method with adaptive model structures. • Improved coverage of daily MAIAC 1-km AOD by a customized fusion method. • Estimates gained higher precision by a strict validation and comparison method. • Conducted a multi-scale spatiotemporal trend and PM 2.5 exposure analysis. Exposure to ambient PM 2.5 (fine particulate matter) can cause adverse effects on human health. China has been experiencing dramatic changes in air pollution over the past two decades. Statistically deriving ground-level PM 2.5 from satellite aerosol optical depth (AOD) has been an emerging attempt to provide such PM 2.5 data for environmental monitoring and PM 2.5 -related epidemiologic study. However, current countrywide datasets in China have generally lower accuracies with lower spatiotemporal resolutions because surface PM 2.5 level was rarely recorded in historical years (i.e., preceding 2013). This study aimed to reconstruct daily ambient PM 2.5 concentrations from 2000 to 2018 over China at a fine scale of 1 km using advanced satellite datasets and ground measurements. Taking advantage of the newly released Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1-km AOD dataset, we developed a novel statistical strategy by establishing an advanced spatiotemporal model relying on adaptive model structures with linear and non-linear predictors. The estimates in historical years were validated against surface observations using a strict leave-one-year-out cross-validation (CV) technique. The overall daily leave-one-year-out CV R2 and root-mean-square-deviation values were 0.59 and 27.18 μg/m3, respectively. The resultant monthly (R2 = 0.74) and yearly (0.77) mean predictions were highly consistent with surface measurements. The national PM 2.5 levels experienced a rapid increase in 2001–2007 and significantly declined between 2013 and 2018. Most of the discernable decreasing trends occurred in eastern and southern areas, while air quality in western China changed slightly in the recent two decades. Our model can deliver reliable historical PM 2.5 estimates in China at a finer spatiotemporal resolution than previous approaches, which could advance epidemiologic studies on the health impacts of both short- and long-term exposure to PM 2.5 at both a large and a fine scale in China. [ABSTRACT FROM AUTHOR]

Summary: Fine needle aspiration cytology (FNAC) has been used extensively for the diagnosis of breast lesions over the past 15 years. More recently, large gauge needle biopsy such as core biopsy has been used and the clinical value of FNAC has therefore been questioned. To answer this clinical question, we performed an 11-year study in which 1238 aspirates from patients with a breast lump were involved. In total, 1071 breast carcinomas were diagnosed with postoperative histological diagnosis. One thousand and forty-six of these 1071 breast carcinomas were definitely diagnosed by FNAC. Only one breast carcinoma identified by FNAC was not finally verified by histological diagnosis postoperatively. The diagnostic sensitivity, diagnostic specificity, overall accuracy, and the pseudo-negative and pseudo-positive results of FNAC for diagnosing breast carcinoma are 97.72%, 99.4%, 97.94%, 2.28%, and 0.6%, respectively. The method is rapid, accurate, and essentially complication-free, particularly in patients for neoadjuvant chemotherapy or endocrine therapy. The results suggest that FNAC is still a useful and reliable method for the preoperative diagnosis of breast carcinoma. [Copyright &y& Elsevier]