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Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.

Hepatocellular carcinoma (HCC) is a common liver malignancy with high morbidity and poor prognosis. Long non-coding RNAs (lncRNAs) are involved in crucial biological processes of tumorigenesis and progression, and play four major regulatory roles, namely signal, decoy, guide, and scaffold, to regulate gene expression. Through these processes, lncRNAs can target microRNAs (miRNAs) to form lncRNA and miRNA networks, which regulate cancer cell proliferation, metastasis, drug resistance, and the tumor microenvironment. Here, we summarize the multifaceted functions of lncRNA and miRNA networks in the pathogenesis of HCC, the potential use of diagnostic or prognostic biomarkers, and novel therapeutic targets in HCC. This review also highlights the regulatory effects of lncRNA and miRNA networks in the tumor microenvironment of HCC.

Cholangiocarcinoma (CCA), an aggressive malignancy, is typically diagnosed at an advanced stage. It is associated with dismal 5-year postoperative survival rates, generating an urgent need for prognostic and diagnostic biomarkers. MicroRNAs (miRNAs) are a class of non-coding RNAs that are associated with cancer regulation, including modulation of cell cycle progression, apoptosis, metastasis, angiogenesis, autophagy, therapy resistance, and epithelial–mesenchymal transition. Several miRNAs have been found to be dysregulated in CCA and are associated with CCA-related risk factors. Accumulating studies have indicated that the expression of altered miRNAs could act as oncogenic or suppressor miRNAs in the development and progression of CCA and contribute to clinical diagnosis and prognosis prediction as potential biomarkers. Furthermore, miRNAs and their target genes also contribute to targeted therapy development and aid in the determination of drug resistance mechanisms. This review aims to summarize the roles of miRNAs in the pathogenesis of CCA, their potential use as biomarkers of diagnosis and prognosis, and their utilization as novel therapeutic targets in CCA.

Metal halide perovskite solar cells have demonstrated a high power conversion efficiency (PCE), and further enhancement of the PCE requires a reduction of the bandgap-voltage offset (WOC) and the non-radiative recombination photovoltage loss (ΔVOC,nr). Here, we report an effective approach for reducing the photovoltage loss through the simultaneous passivation of internal bulk defects and dimensionally graded two-dimensional perovskite interface defects. Through this dimensionally graded perovskite formation approach, an open-circuit voltage (VOC) of 1.24 V was obtained with a champion PCE of 21.54% in a 1.63 eV perovskite system (maximum VOC = 1.25 V, WOC = 0.38 V and ΔVOC,nr = 0.10 V); we further decreased the WOC to 0.326 V in a 1.53 eV perovskite system with a VOC of 1.21 V and a PCE of 23.78% (certified 23.09%). This approach is equally effective in achieving a low WOC (ΔVOC,nr) in 1.56 eV and 1.73 eV perovskite solar cell systems, and further leads to the substantially improved operational stability of perovskite solar cells.The use of a dimensionally graded 2D perovskite interface and passivation results in perovskite solar cells with very low photovoltage loss.

Rapid global climate change is posing a substantial threat to biodiversity. The assessment of population vulnerability and adaptive capacity under climate change is crucial for informing conservation and mitigation strategies. Here we generate a chromosome-scale genome assembly and re-sequence genomes of 230 individuals collected from 24 populations for Populus koreana , a pioneer and keystone tree species in temperate forests of East Asia. We integrate population genomics and environmental variables to reveal a set of climate-associated single-nucleotide polymorphisms, insertion/deletions and structural variations, especially numerous adaptive non-coding variants distributed across the genome. We incorporate these variants into an environmental modeling scheme to predict a highly spatiotemporal shift of this species in response to future climate change. We further identify the most vulnerable populations that need conservation priority and many candidate genes and variants that may be useful for forest tree breeding with special aims. Our findings highlight the importance of integrating genomic and environmental data to predict adaptive capacity of a key forest to rapid climate change in the future. Assessment of population vulnerability and adaptive capacity under climate change is crucial for informing conservation strategies. Sang et al. assemble a reference genome for Populus koreana and combine population genomics and modelling to predict spatiotemporal responses to climate change.

Acute myocardial infarction (AMI) induces blood leukocytosis, which correlates inversely with patient survival. The molecular mechanisms leading to leukocytosis in the infarcted heart remain poorly understood. Using an AMI mouse model, we identified gasdermin D (GSDMD) in activated leukocytes early in AMI. We demonstrated that GSDMD is required for enhanced early mobilization of neutrophils to the infarcted heart. Loss of GSDMD resulted in attenuated IL-1β release from neutrophils and subsequent decreased neutrophils and monocytes in the infarcted heart. Knockout of GSDMD in mice significantly reduced infarct size, improved cardiac function, and increased post-AMI survival. Through a series of bone marrow transplantation studies and leukocyte depletion experiments, we further clarified that excessive bone marrow-derived and GSDMD-dependent early neutrophil production and mobilization (24 hours after AMI) contributed to the detrimental immunopathology after AMI. Pharmacological inhibition of GSDMD also conferred cardioprotection after AMI through a reduction in scar size and enhancement of heart function. Our study provides mechanistic insights into molecular regulation of neutrophil generation and mobilization after AMI, and supports GSDMD as a new target for improved ventricular remodeling and reduced heart failure after AMI.

ObjectiveTo describe the prevalence and variations of non-alcoholic fatty liver disease/non-alcoholic steatohepatitis (NAFLD/NASH) among children and adolescents (CADs) and young adults (YADs).DesignA population-based observational study.SettingAnnual cases and prevalence of NAFLD/NASH from 1990 to 2017, by sex, region and country were collected from the Global Burden of Disease database.Main outcome measuresThe estimated annual percentage change, which was calculated by a regression line, was used to quantify the temporal trends in NAFLD/NASH burden among young people at the global, regional and national levels.ResultsGlobally, NAFLD/NASH incidence increased from 19.34 million in 1990 to 29.49 million in 2017 among CADs, with an annual increase of 1.35%. Additionally, in YADs, the number of cases and NAFLD/NASH prevalence significantly increased during this period, independent of sex and region. The greatest NAFLD/NASH increase was in North Africa and the Middle East. Almost all countries showed an increasing trend from 1990 to 2017, with the most pronounced increase observed in the developed regions.ConclusionsThe epidemiology of NAFLD/NASH in young people has changed considerably over the last three decades. Both the prevalence and number of cases have increased irrespective of sex, age and region. This phenomenon can result in a predictable increase in chronic liver disease burden in the near future. Understanding the prevalence of NAFLD/NASH and its variations is of paramount importance to develop strategies to implement public health policy.

HighlightsThe synergistic “anchor-capture” mechanism of polar groups on Zn stripping/plating process is firstly proposed.The amino group firmly anchors on Zn surface and the carboxyl group strongly captures Zn2+, constructing a robust anode–electrolyte interface and inducing uniform Zn deposition.The ultra-stable cycle lifespan of Zn–Zn symmetric cell (over 2800 h) and high utilization rate of Zn anode (the depth of discharge up to 68% for 200 h) are achieved under the proposal of synergistic “anchor-capture.”While the rechargeable aqueous zinc-ion batteries (AZIBs) have been recognized as one of the most viable batteries for scale-up application, the instability on Zn anode–electrolyte interface bottleneck the further development dramatically. Herein, we utilize the amino acid glycine (Gly) as an electrolyte additive to stabilize the Zn anode–electrolyte interface. The unique interfacial chemistry is facilitated by the synergistic “anchor-capture” effect of polar groups in Gly molecule, manifested by simultaneously coupling the amino to anchor on the surface of Zn anode and the carboxyl to capture Zn2+ in the local region. As such, this robust anode–electrolyte interface inhibits the disordered migration of Zn2+, and effectively suppresses both side reactions and dendrite growth. The reversibility of Zn anode achieves a significant improvement with an average Coulombic efficiency of 99.22% at 1 mA cm−2 and 0.5 mAh cm−2 over 500 cycles. Even at a high Zn utilization rate (depth of discharge, DODZn) of 68%, a steady cycle life up to 200 h is obtained for ultrathin Zn foils (20 μm). The superior rate capability and long-term cycle stability of Zn–MnO2 full cells further prove the effectiveness of Gly in stabilizing Zn anode. This work sheds light on additive designing from the specific roles of polar groups for AZIBs.

The development of digital economy affects environmental pollution emission and green sustainable development. However, the relationship between digital economy and industrial wastewater discharge has rarely been examined. This study establishes the urban digital economy evaluation index system, measures the digital economy indexes of 281 prefecture-level cities in China from 2011 to 2016, and examines the impact effect of digital economy development on industrial wastewater discharge using the system generalized method of moment method and the intermediary effects model. The empirical results indicate that the digital economy reduces the industrial wastewater discharge. As evidence shows, the digital economy significantly promotes the upgrading of industrial structure, which is an important factor affecting the industrial wastewater discharge. Additionally, the inhibiting effect of digital economy on industrial wastewater discharge is more significant in big cities. This study provides a scientific base and guidance for reducing environmental pollution emissions and promoting the development of digital economy.