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AbstractObjectiveTo evaluate the associations between prediabetes and the risk of all cause mortality and incident cardiovascular disease in the general population and in patients with a history of atherosclerotic cardiovascular disease.DesignUpdated meta-analysis.Data sourcesElectronic databases (PubMed, Embase, and Google Scholar) up to 25 April 2020.Review methodsProspective cohort studies or post hoc analysis of clinical trials were included for analysis if they reported adjusted relative risks, odds ratios, or hazard ratios of all cause mortality or cardiovascular disease for prediabetes compared with normoglycaemia. Data were extracted independently by two investigators. Random effects models were used to calculate the relative risks and 95% confidence intervals. The primary outcomes were all cause mortality and composite cardiovascular disease. The secondary outcomes were the risk of coronary heart disease and stroke.ResultsA total of 129 studies were included, involving 10 069 955 individuals for analysis. In the general population, prediabetes was associated with an increased risk of all cause mortality (relative risk 1.13, 95% confidence interval 1.10 to 1.17), composite cardiovascular disease (1.15, 1.11 to 1.18), coronary heart disease (1.16, 1.11 to 1.21), and stroke (1.14, 1.08 to 1.20) in a median follow-up time of 9.8 years. Compared with normoglycaemia, the absolute risk difference in prediabetes for all cause mortality, composite cardiovascular disease, coronary heart disease, and stroke was 7.36 (95% confidence interval 9.59 to 12.51), 8.75 (6.41 to 10.49), 6.59 (4.53 to 8.65), and 3.68 (2.10 to 5.26) per 10 000 person years, respectively. Impaired glucose tolerance carried a higher risk of all cause mortality, coronary heart disease, and stroke than impaired fasting glucose. In patients with atherosclerotic cardiovascular disease, prediabetes was associated with an increased risk of all cause mortality (relative risk 1.36, 95% confidence interval 1.21 to 1.54), composite cardiovascular disease (1.37, 1.23 to 1.53), and coronary heart disease (1.15, 1.02 to 1.29) in a median follow-up time of 3.2 years, but no difference was seen for the risk of stroke (1.05, 0.81 to 1.36). Compared with normoglycaemia, in patients with atherosclerotic cardiovascular disease, the absolute risk difference in prediabetes for all cause mortality, composite cardiovascular disease, coronary heart disease, and stroke was 66.19 (95% confidence interval 38.60 to 99.25), 189.77 (117.97 to 271.84), 40.62 (5.42 to 78.53), and 8.54 (32.43 to 61.45) per 10 000 person years, respectively. No significant heterogeneity was found for the risk of all outcomes seen for the different definitions of prediabetes in patients with atherosclerotic cardiovascular disease (all P>0.10).ConclusionsResults indicated that prediabetes was associated with an increased risk of all cause mortality and cardiovascular disease in the general population and in patients with atherosclerotic cardiovascular disease. Screening and appropriate management of prediabetes might contribute to primary and secondary prevention of cardiovascular disease.

Accurate detection of rice pests in field is a key problem in field pest control. U-Net can effectively extract local image features, and Transformer is good at dealing with long-distance dependencies. A Cross-Attention TransU-Net (CATransU-Net) model is constructed for paddy pest detection by combining U-Net and Transformer. It consists of encoder, decoder, dual Transformer-attention module (DTA) and cross-attention skip-connection (CASC), where dilated residual Inception (DRI) in encoder is adopted to extract the multiscale features, DTA is added into the bottleneck of the model to efficiently learn nonlocal interactions between encoder features, and CASC instead of skip-connection between encoder/decoder is designed to model the multi-resolution feature representation. Compared with U-Net and Transformer, CATransU-Net can extract multiscale features through DRI and DTA, and enhance feature representation to generate high-resolution insect images through CASC and decoder. The experimental results on the large-scale multiclass IP102 and AgriPest benchmark datasets verify that CATransU-Net is effective for rice pest extraction with precision of 93.51%, about 2% more than other methods, especially 9.36% more than U-Net. The proposed method can be applied to the field rice pest detection system. Code is available at https://github.com/chenchenchen23123121da/CATransU-Net .

Snow grain size is important in albedo calculation, mass balance, and climate research. Critically, in situ measurements of snow grain size on the Tibetan Plateau remain scarce. As a broad, continuous, and multiscale measurement method, remote sensing has become the primary means of sourcing data for calculating snow grain size, and the Asymptotic Radiative Transfer (ART) model is the most popular retrieval model. In this research, three-band data from MODIS and point data from the ICESat/GLAS L2A campaign were adopted to retrieve snow grain size based on the ART model. Snow grain size data from 2003 to 2024 were obtained using the Snow Grain Size and Pollution (SGSP) algorithm, and point snow grain size data from September 2003 to November 2003 were acquired using a 1-band algorithm. Cross-validation showed a stronger correlation between snow grain sizes retrieved using different methods in stable snow-covered areas. The correlation coefficients in the three areas are around 0.8. For other areas, especially those affected by seasonal snows, the snow grain sizes that retried by two methods have a lower correlation. Affected by global warming and the Karakoram anomaly, the trends in snow grain size in glaciers near the Karakoram ranges differ from those in other regions. Point-to-point cross-validation showed consistency between the MODIS and ICESat/GLAS retrieval results, offering a new way of estimating snow grain size.

Due to the difficulty in distinguishing transparent and white foreign fibers from seed cotton in RGB images and in order to improve the recognition ability of deep learning (DL) algorithms for white, transparent, and multi-class mixed foreign fibers with different sizes in seed cotton, this paper proposes a method of combining hyperspectral imaging technology with a multi-module joint hierarchical residue network (MJHResNet). Firstly, a series of preprocessing methods are performed on the hyperspectral image (HSI) to reduce the interference of noise. Secondly, a double-hierarchical residual (DHR) structure is designed, which can not only obtain multi-scale information, but also avoid gradient vanishing to some extent. After that, a squeeze-and-excitation network (SENet) is integrated to reduce redundant information, improve the expression of model features, and improve the accuracy of foreign fiber identification in seed cotton. Finally, by analyzing the experimental results with advanced classifiers, this method has significant advantages. The average accuracy is 98.71% and the overall accuracy is 99.28%. This method has great potential for application in the field of foreign fiber identification in seed cotton.

To enhance the sustainability and high-temperature resistance of tunnel lining materials, this study prepared an ultra-high-performance shotcrete (UHPS) incorporating polypropylene fibres (PPF) and used waste glass aggregate as a substitute raw material. The effects of PPF content (0–0.4%) and high-temperature exposure (20–800 °C) on the residual mechanical properties, flexural toughness, and self-sensing capability of UHPS were systematically investigated. At 20 °C, adding PPF reduces strengths; relative to 0% PPF, 0.4% PPF lowers compressive, splitting-tensile, and flexural strengths by 13.28%, 23.04%, and 29.28%, respectively. In addition, PPF reduces flexural toughness indices and self-sensing capability at room temperature, with more pronounced reductions at higher fibre dosages. However, under elevated temperatures, PPF significantly suppresses the risk of spalling and enhances the residual mechanical properties of UHPS, with the optimal effect observed at 0.3% PPF. As the temperature increases, the mechanical strength of UHPS first increases and then decreases, while flexural toughness continuously deteriorates. Moreover, high temperatures weaken both pressure-sensitive and bending-sensitive properties, with self-sensing capability showing a marked decline above 400 °C. Microscopic analysis indicates that the decomposition of hydration products, fibre oxidation, and crack propagation are the main mechanisms underlying performance degradation at high temperatures.

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park.

To promote the recycling of waste glass and satisfy the demands of environmental sustainability for ultrahigh performance concrete (UHPC), in this study, glass sand was employed to partially or entirely replace machine-made sand, and steel fibres were incorporated to fabricate ultrahigh performance shotcrete (UHPS). The effects of glass sand and steel fibres on the mechanical and electrical properties of composite materials were analysed in this study. Furthermore, alkali‒silica reaction (ASR) tests and microstructural analyses were conducted. The results indicate that at higher steel fibre contents, the incorporation of glass sand does not reduce the compressive strength of the UHPS and that glass sand has no significant effect on the split tensile or flexural strength. When the steel fibre content is 2% and the replacement ratio of glass sand reaches 100%, the mechanical properties of the UHPS reach their maximum. The addition of glass sand negatively affects the electrical properties, whereas the use of steel fibres improves them. The results of the alkali‒silica reaction tests confirm that the use of glass sand does not induce harmful expansion reactions. The study revealed the trends in the mechanical and electrical properties of concrete from a microstructural perspective and provided explanations for the alkali‒silica reaction outcomes. This study provides technical support for the application of UHPS to tunnel linings.

Sleep and wakefulness are basic behavioral states that require coordination between several brain regions, and they involve multiple neurochemical systems, including neuropeptides. Neuropeptides are a group of peptides produced by neurons and neuroendocrine cells of the central nervous system. Like traditional neurotransmitters, neuropeptides can bind to specific surface receptors and subsequently regulate neuronal activities. For example, orexin is a crucial component for the maintenance of wakefulness and the suppression of rapid eye movement (REM) sleep. In addition to orexin, melanin-concentrating hormone, and galanin may promote REM sleep. These results suggest that neuropeptides play an important role in sleep–wake regulation. These neuropeptides can be divided into three categories according to their effects on sleep–wake behaviors in rodents and humans. (i) Galanin, melanin-concentrating hormone, and vasoactive intestinal polypeptide are sleep-promoting peptides. It is also noticeable that vasoactive intestinal polypeptide particularly increases REM sleep. (ii) Orexin and neuropeptide S have been shown to induce wakefulness. (iii) Neuropeptide Y and substance P may have a bidirectional function as they can produce both arousal and sleep-inducing effects. This review will introduce the distribution of various neuropeptides in the brain and summarize the roles of different neuropeptides in sleep–wake regulation. We aim to lay the foundation for future studies to uncover the mechanisms that underlie the initiation, maintenance, and end of sleep–wake states.

The multifunctionality and sustainability of ecosystems are strongly dependent on their ability to withstand and recover from disturbances—that is, ecosystem resilience (ER). However, the dynamics and attributes of ER remain largely unknown, especially in China, where climatic and anthropogenic pressures are high. In this study, we evaluated spatiotemporal patterns of ER in China from 2001 to 2020 using solar-induced chlorophyll fluorescence. We estimated the relative independent importance of climate change, CO 2 , and anthropogenic factors on changes in ER signals. The results showed that more than half of the ecosystems in the study area have experienced ER gain followed by ER loss during the past two decades. Before breakpoints (BPs), climate change explained 58.29% of the ER change associated with increasing precipitation. After BPs, 65.10% of the ER change was most affected by CO 2 , and drought from rising temperature further deteriorated ER loss. We highlight that relationships between changes in ER and climate are spatially heterogeneous and suggest increased negative radiative effects of CO 2 , associated with global warming, on ecosystem stability due to the saturated canopy photosynthesis. These findings have crucial implications for future climate change mitigation, carbon peak, and carbon neutrality targets.

Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level. Whole-slide images (WSI) and digital pathology are valuable approaches for the analysis of tumours and their microenvironments. Here, the authors present scMTOP, a framework to characterise tumour ecosystems and intercellular relationships at the single-cell level from WSIs, which they apply to breast cancer samples.