Explore the vast range of titles available.

To analyze the global stroke burden attributable to household air pollution (HAP) using Global Burden of Disease (GBD) 2021 data, examine its spatiotemporal patterns from 1990-2021, and project future trends through 2040. Retrospective analysis using GBD data with future projections. Analysis of age-standardized rates, deaths, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) due to HAP-related stroke across 204 countries. The linear regression model examined global time trends. Cluster analysis investigated the patterns of disease burden changes across GBD regions. The Nordpred model projected trends up to 2040. In 2021, HAP caused 1.23 million stroke deaths and 26.78 million DALYs globally. From 1990-2021, age-standardized death rates (EAPC = -0.37), DALY rates (EAPC = -0.20), and YLL rates (EAPC = -0.30) decreased, while YLD rates increased (EAPC = 1.11). Males showed a higher burden than females, with peak rates in the 80-84 age group. Middle-SDI regions had the highest age-standardized rates, with East Asia recording the largest absolute numbers. South and East Asia showed significant increases, while Western Europe, Eastern Europe, and High-income North America showed the greatest declines. Predictions using the Nordpred model indicated rising absolute numbers of deaths (to 1.79 million), DALYs (to 34.76 million), YLDs (to 3.29 million), and YLLs (to 31.39 million), with males consistently bearing a higher burden, though ASRs are expected to decline for both sexes. Our findings suggest that regions with higher economic development and greater adoption of clean energy are associated with lower HAP-related stroke burden, possibly through improvements in indoor air quality. The observed regional and gender disparities emphasize the need for targeted interventions, particularly in less developed regions and among high-risk groups such as men and older adults.

Reducing the lag in the accuracy assessment of multi-temporal land-cover products has been a hot research topic. By identifying the changed strata, the annual accuracy in multi-temporal products can be quickly evaluated. However, there are still two limitations in the accuracy assessment of multi-temporal products. Firstly, the setting of the parameters (e.g., the total sample size, allocation of samples in the changed strata, etc.) in the fundamental sampling design is not based on specific setting criteria. Therefore, this evaluation method is not always applicable when the product or research area changes. Secondly, the accuracy evaluation of multi-temporal products does not consider the influence of misclassification. This can lead to an overestimation of the accuracy of changed strata in single-year evaluations. In this paper, we describe how the total sample and the assignment of samples in every stratum can be adjusted according to the characteristics of the land-cover product, which improves the applicability of the evaluation. The samples in the changed strata that propagate misclassification are essentially pixels that have not undergone any land-cover change. Therefore, in order to eliminate the propagation of this inter-annual classification error, the misclassified samples are reclassified as unchanged strata. This method was used in the multi-temporal ESA CCI land-cover product. The experimental results indicate that the single-year accuracy, considering classification error, is closer to the traditional evaluation accuracy of single-temporal data. For the categories with a small ratio of unchanged strata samples to changed strata samples, the accuracy improvement, after eliminating the classification errors, is more obvious. For the urban class, in particular, the misclassification affects its estimated accuracy by 9.72%.

Background Long non-coding RNA (lncRNA) closely associates with numerous biological processes, and with many diseases. Therefore, lncRNA-disease association prediction helps obtain relevant biological information and understand pathogenesis, and thus better diagnose preventable diseases. Results Herein, we offer the LDAF_GAN method for predicting lncRNA-associated disease based on association filtering and generative adversarial networks. Experimentation used two types of data: lncRNA-disease associated data without lncRNA sequence features, and fused lncRNA sequence features. LDAF_GAN uses a generator and discriminator, and differs from the original GAN by the addition of a filtering operation and negative sampling. Filtering allows the generator output to filter out unassociated diseases before being fed into the discriminator. Thus, the results generated by the model focuses only on lncRNAs associated with disease. Negative sampling takes a portion of disease terms with 0 from the association matrix as negative samples, which are assumed to be unassociated with lncRNA. A regular term is added to the loss function to avoid producing a vector with all values of 1, which can fool the discriminator. Thus, the model requires that generated positive samples are close to 1, and negative samples are close to 0. The model achieved a superior fitting effect; LDAF_GAN had superior performance in predicting fivefold cross-validations on the two datasets with AUC values of 0.9265 and 0.9278, respectively. In the case study, LDAF_GAN predicted disease association for six lncRNAs-H19, MALAT1, XIST, ZFAS1, UCA1, and ZEB1-AS1-and with the top ten predictions of 100%, 80%, 90%, 90%, 100%, and 90%, respectively, which were reported by previous studies. Conclusion LDAF_GAN efficiently predicts the potential association of existing lncRNAs and the potential association of new lncRNAs with diseases. The results of fivefold cross-validation, tenfold cross-validation, and case studies suggest that the model has great predictive potential for lncRNA-disease association prediction.

The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the “change” strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes.

Using a shape model (SM) is a typical method to determine the phenological phases of crops with long-time-series satellite remote sensing data. The average AGDD-based shape model (AAGDD-SM) takes temperature into account compared to SM, however, the commonly used daily average temperature is not sufficient to determine the exact AGDD owing to the possibly significant changes in temperatures throughout the day. In this paper, a modified shape model was proposed for the better estimation of phenological dates and it is incorporated into the continuous AGDD (CAGDD) which was calculated based on temperatures from a continuous 24 h within a day, different from the calendar day or the average AGDD indicators. In this study, the CAGDD replaced the abscissa of the NDVI growth curve over a 5-year period (2014 to 2018, excluding 2015) for a test site of early rice in Jiangxi province of China. Four key phenological phases, including the reviving, tillering, heading and anthesis phases, were selected and determined with reference to the field-observed phenological data. The results show that compared with the AAGDD-SM, the method proposed in this paper has basically improved the prediction of each phenological period. For those cases where the average temperature is lower than the minimum temperatures (K1) but the effective accumulated temperature is not zero, more accurate AGDD can be calculated according to the method in this paper.

Over the recent years, deep convolutional neural networks based models have been absolutely attractive in image denoising field due to their favorable performance. However, many existing deep neural network based image denoising models lack flexibility for spatially variant or real-world noise, which restricts the application of these models in real denoising scenes. In this paper, we propose a flexible and effective U-shaped network (FEUNet), which is effective in a wide range of noise levels, and can deal with spatially variant noise. The adjustable noise level map is used as the input of the FEUNet to enhance its flexibility. The U-Net is utilized to enhance the effectiveness of the proposed model. Experimental results have verified that the proposed FEUNet can obtain competitive denoising performances on many denoising tasks compared with the state-of-the-art denoising methods, which makes the proposed FEUNet well suited for the practical image denoising tasks.

Global and local land-cover mapping products provide important data on land surface. However, the accuracy of land-cover products is the key issue for their further scientific application. There has been neglect of the relationship between inclusion probability and spatial heterogeneity in traditional spatially balanced sampling. The aim of this paper was to propose an improved spatially balanced sampling method using landscape pattern-based inclusion probability. Compared with other global land-cover datasets, Globeland30 has the advantages of high resolution and high classification accuracy. A two-stage stratified spatially balanced sampling scheme was designed and applied to the regional validation of GlobeLand30 in China. In this paper, the whole area was divided into three parts: the Tibetan Plateau region, the Northwest China region, and the East China region. The results show that 7242 sample points were selected, and the overall accuracy of GlobeLand30-2010 in China was found to be 80.46%, which is close to the third-party assessment accuracy of GlobeLand30. This method improves the representativeness of samples, reduces the classification error of remote sensing, and provides better guidance for biodiversity and sustainable development of environment.

A simple and sensitive method based on liquid–liquid extraction (LLE) and liquid chromatography electrospray ionization tandem mass spectrometry (LC–ESI–MS/MS), was developed and validated for the simultaneous determination of 8-hydroxy-2′-deoxyguanosine and twelve cosmetic phenols, including five benzophenone (BP)-type ultraviolet (UV) filters, five parabens, bisphenol A (BPA) and triclosan (TCS) in human urine. Urine samples were treated by a combination of enzymatic deconjugation with β-glucuronidase/arylsulfatase and extraction with ethyl acetate. The efficiencies of different extraction procedures were investigated. Both chromatographic and mass spectrometric conditions were optimized. As a result, under the optimized conditions, the limits of detection (LODs) for the analytes in the range from 0.01 to 0.23 μg/L were achieved. The intra-day and inter-day precisions were within the range of 0.8–6.1% and 1.8–9.5% (relative standard deviation, RSD), respectively. The correlation coefficients (R2) and recoveries for the analytes were ranged from 0.991–0.999 and 80.0–108%, respectively. The developed method initially combines the simultaneous analysis of urinary 8-hydroxy-2′-deoxyguanosine, BP-type UV filters, parabens, BPA and TCS, and was successfully applied to urine samples from children in South China.

The origin of tumor-infiltrating Tregs, critical mediators of tumor immunosuppression, is unclear. Here, we show that tumor-infiltrating naive CD4＋ T cells and Tregs in human breast cancer have overlapping TCR repertoires, while hardly overlap with circulating Tregs, suggesting that intratumoral Tregs mainly develop from naive T cells in situ rather than from recruited Tregs. Furthermore, the abundance of naive CD4＋ T cells and Tregs is closely correlated, both indicating poor prognosis for breast cancer patients. Naive CD4＋ T cells adhere to tumor slices in proportion to the abundance of CCL18-producing macrophages. Moreover, adoptively transferred human naive CD4＋ T cells infil- trate human breast cancer orthotopic xenografts in a CCL18-dependent manner. In human breast cancer xenografts in humanized mice, blocking the recruitment of naive CD4＋ T cells into tumor by knocking down the expression of PITPNM3, a CCL18 receptor, significantly reduces intratumoral Tregs and inhibits tumor progression. These findings suggest that breast tumor-infiltrating Tregs arise from chemotaxis of circulating naive CD4＋ T cells that differentiate into Tregs in situ. Inhibiting naive CD4＋ T cell recruitment into tumors by interfering with PITPNM3 recognition of CCL18 may be an attractive strategy for anticancer immunotherapy.