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The stochastic block model (SBM), a popular framework for studying community detection in networks, is limited by the assumption that all nodes in the same community are statistically equivalent and have equal expected degrees. The degree-corrected stochastic block model (DCSBM) is a natural extension of SBM that allows for degree heterogeneity within communities. To find the communities under DCSBM, this paper proposes a convexified modularity maximization approach, which is based on a convex programming relaxation of the classical (generalized) modularity maximization formulation, followed by a novel doubly-weighted ℓ₁-norm k-medoids procedure. We establish nonasymptotic theoretical guarantees for approximate and perfect clustering, both of which build on a new degree-corrected density gap condition. Our approximate clustering results are insensitive to the minimum degree, and hold even in sparse regime with bounded average degrees. In the special case of SBM, our theoretical guarantees match the best-known results of computationally feasible algorithms. Numerically, we provide an efficient implementation of our algorithm, which is applied to both synthetic and realworld networks. Experiment results show that our method enjoys competitive performance compared to the state of the art in the literature.

The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.

Biological membranes are not only essential barriers that separate cellular and subcellular structures, but also perform other critical functions such as the initiation and propagation of intra- and intercellular signals. Each membrane-delineated organelle has a tightly regulated and custom-made membrane lipid composition that is critical for its normal function. The endoplasmic reticulum (ER) consists of a dynamic membrane network that is required for the synthesis and modification of proteins and lipids. The accumulation of unfolded proteins in the ER lumen activates an adaptive stress response known as the unfolded protein response (UPR-ER). Interestingly, recent findings show that lipid perturbation is also a direct activator of the UPR-ER, independent of protein misfolding. Here, we review proteostasis-independent UPR-ER activation in the genetically tractable model organism Caenorhabditis elegans. We review the current knowledge on the membrane lipid composition of the ER, its impact on organelle function and UPR-ER activation, and its potential role in human metabolic diseases. Further, we summarize the bi-directional interplay between lipid metabolism and the UPR-ER. We discuss recent progress identifying the different respective mechanisms by which disturbed proteostasis and lipid bilayer stress activate the UPR-ER. Finally, we consider how genetic and metabolic disturbances may disrupt ER homeostasis and activate the UPR and discuss how using -omics-type analyses will lead to more comprehensive insights into these processes.

Evapotranspiration (ET) is one of the components in the water cycle and the surface energy balance systems. It is fundamental information for agriculture, water resource management, and climate change research. This study presents a scheme for regional actual evapotranspiration estimation using multi-source satellite data to compute key land and meteorological variables characterizing land surface, soil, vegetation, and the atmospheric boundary layer. The algorithms are validated using ground observations from the Heihe River Basin of northwest China. Monthly data estimates at a resolution of 1 km from the proposed algorithms compared well with ground observation data, with a root mean square error (RMSE) of 0.80 mm and a mean relative error (MRE) of −7.11%. The overall deviation between the average yearly ET derived from the proposed algorithms and ground-based water balance measurements was 9.44% for a small watershed and 1% for the entire basin. This study demonstrates that both accuracy and spatial depiction of actual evapotranspiration estimation can be significantly improved by using multi-source satellite data to measure the required land surface and meteorological variables. This reduces dependence on spatial interpolation of ground-derived meteorological variables which can be problematic, especially in data-sparse regions, and allows the production of region-wide ET datasets.

With the rapid development of artificial intelligence technology, intelligent question and answer systems such as Chat Assistant are increasingly used in daily life. However, as a special category of user group, the cognitive fitness and satisfaction assessment of elderly people to such intelligent systems have not been sufficiently studied yet. The purpose of this study is to explore the cognitive fitness and satisfaction of older adults with Chat Assistant Q&A results, in order to provide a basis for improving the design of these systems and enhancing the user experience of older adults. To this end, this paper provides an in-depth study of older adults’ use of technology by designing and distributing a questionnaire. Analytical methods such as the ACSI indicator model and Structural Equation Modeling (SEM) were used to explore the relationship between satisfaction and various factors, thereby providing valuable guidance to help improve and optimize the Chat Assistant system.

Understanding the correlation between cognitive ability and risk preferences is crucial for promoting technological progress and innovation in China. This study aims to investigate this relationship using survey data from the China Family Panel Studies. The results reveal a significant positive correlation between cognitive ability and risk tolerance. These findings have important implications for understanding the relationship between cognitive ability and risk preferences, as well as for informing policy decisions in areas such as financial investments and entrepreneurship incentives.

Background Time-restricted eating (TRE), a feasible form of intermittent fasting, has been proven to benefit metabolic health in animal models and humans. To our knowledge, specific guidance on the appropriate period for eating during TRE has not yet been promoted. Therefore, to compare and assess the relative effectiveness estimates and rankings of TRE with different eating windows on human metabolic health, we conducted a systematic review and network meta-analysis (NMA). Method PubMed, EMBASE and the Cochrane Library were searched for randomized controlled trials that compared different eating windows on human metabolic health for adults. A Bayesian NMA was used to compare direct and indirect effects to determine the best different eating windows, and scientific evidence using GRADE. Results Twenty-seven RCTs comparing TRE with different eating windows on human metabolic health were reviewed, and all were included in the NMA. Compared with the normal diet group (non-TRE), the TRE group has certain benefits in reducing weight and fasting insulin. In terms of reducing fasting insulin, the 18:6 group (eating time = 6 h) was better than the 14:10 group (eating time = 10 h) and 16:8 group (eating time = 8 h) (P < 0.05); The < 6 group (eating time < 6 h) was better than the 14:10 group (P < 0.05). In terms of reducing fasting glucose, the < 6 group was better than the 14:10 group (P < 0.05). There were no statistical variations in weight, HDL, TG, and LDL across the different modes of TRE (P > 0.05). Conclusions Our research showed that no particular metabolic advantages of various eating windows were found. Therefore, our results suggested that different eating windows could promote similar benefits for metabolic parameters.

Bamboo forest is a unique forest landscape that is mainly composed of herbal plants. It has a stronger capability to increase terrestrial carbon sinks than woody forests in the same environment, thus playing a special role in absorbing atmospheric CO2. Accurate and timely bamboo forest maps are necessary to better understand and quantify their contribution to the carbon and hydrological cycles. Previous studies have reported that the unique phenology pattern of bamboo forests, i.e., the on- and off-year cycle, can be detected with time-series high spatial resolution remote sensing (RS) images. Nevertheless, this information has not yet been applied in large-scale bamboo mapping. In this study, we innovatively incorporate newly designed phenology features reflecting the aforementioned on- and off-year cycles into a typical end-to-end classification workflow, including two features describing growing efficiency during the green-up season and two features describing the difference between annual peak greenness. Additionally, two horizonal morphology features and one tree height feature were employed, simultaneously. An experiment in southeast China was carried out to test the method’s performance, in which seven categories were focused. A total of 987 field samples were used for training and validation (70% and 30%, respectively). The results show that combining the time-series features based on spectral bands and vegetation indices and newly designed phenology and morphology patterns can differentiate bamboo forests from other vegetation categories. Based on these features, the classification results exhibit a reasonable spatial distribution and a satisfactory overall accuracy (0.89). The detected bamboo area proportion in 82 counties agrees with the statistics from China’s Third National Land Survey, which was produced based on high resolution images from commercial satellites and human interpretation (correlation coefficient = 0.69, and root mean squared error = 5.1%). This study demonstrates that the new scheme incorporating phenology features helps to map bamboo forests accurately while reducing the sample size requirement.

Glomerular endothelial cells (GECs) are pivotal in developing glomerular sclerosis disorders. The advancement of focal segmental glomerulosclerosis (FSGS) is intimately tied to disruptions in lipid metabolism. Sphingosine-1-phosphate (S1P), a molecule transported by high-density lipoproteins (HDL), exhibits protective effects on vascular endothelial cells by upregulating phosphorylated endothelial nitric oxide synthase (p-eNOS) and enhancing nitric oxide (NO) production. Nevertheless, the abundance of S1P within HDL in individuals with FSGS and minimal change disease (MCD) is yet to be elucidated, and its defensive role in GECs necessitates empirical confirmation. A total of 14 FSGS patients, 16 MCD patients, and 16 healthy controls (NC) were included in the study, with FSGS and MCD confirmed by renal biopsy. After blood sample collection, HDL was isolated and categorized into intact HDL, phospholipid-depleted HDL(apo-HDL), phospholipid-remained HDL(phoHDL), and recombinant HDL (rHDL). Various HDL samples, comprising intact, apo-HDL, pho-HDL and rHDL, were co-cultivated with human renal glomerular endothelial cells (HRGECs). Western blotting was utilized to quantify p-eNOS levels and assess PI3K-AKT pathway activation. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyzed S1P concentrations, while real-time quantitative PCR evaluated the expression of enzymes involved in S1P metabolism. Fluorescence labeling methods measured NO levels, and an immunofluorescence colocalization assay investigated Sphingosine-1-phosphate receptor 1 (S1PR1) expression in GECs across distinct kidney tissue groups. The HDL from FSGS patients demonstrated a significantly enhanced ability to promote p-eNOS expression and NO release in HRGECs compared to MCD patients and healthy controls. Additionally, the synthesis activity of S1P in renal tissues of FSGS patients was markedly higher than that observed in MCD patients and healthy controls, suggesting that S1P may play a crucial protective role in the progression of FSGS. Immunofluorescence staining showed that compared with MCD and NC, the expression of S1PR1 in GECs of FSGS patients was significantly decreased. Recombinant HDL with added S1P promoted the increase of p-eNOS in HRGECs. Knockdown of S1PR1 using siRNA reduced the expression of p-eNOS and NO release. The mechanism underlying the regulation of p-eNOS expression by rHDL was associated with the PI3K-AKT signaling pathway. The enhanced presence of S1P on HDL could serve as a diagnostic marker to differentiate FSGS from MCD. Incorporating S1P into HDL enhances glomerular endothelial cell function, suggesting that the S1P/S1PR pathway might offer a promising therapeutic avenue for FSGS.

In response to escalating global environmental challenges, mitigating carbon emissions in the construction sector has emerged as a critical strategy for addressing climate change. As reported by the United Nations Environment Programme (UNEP) and the International Energy Agency (IEA), the construction industry remains a major contributor to global greenhouse gas emissions. This study investigates the influencing factors and optimization pathways for embodied carbon emissions during the materialization phase of prefabricated buildings. Through longitudinal field research at a large-scale precast component factory in western China, key carbon emission factors were identified using Min–Max normalization and Principal-Components Analysis (PCA). A cloud entropy–based evaluation model was further developed to quantify the emission weights of 32 factors. The results reveal the existence of ‘leveraging effects’ among emission factors, wherein certain low-weight factors exert disproportionate influence on systemic carbon reduction because of their cascading impacts on other variables. Prioritizing factors with greater leveraging potential is imperative for the formulation of effective emission reduction policies. This study leverages NK model simulations (10,000 iterations), to predict the reduction potential of each factor and identifies four indicators with the most significant leveraging effects. Strategic recommendations are proposed that emphasize a synergistic approach that integrates direct emission control and indirect cascading optimization. These findings provide actionable insights for achieving systemic carbon reduction in prefabricated building systems.