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Understanding how future climate scenarios impact land use/cover (LUC) and carbon storage (CS) is crucial for achieving carbon neutrality. However, research often overlooks the spatiotemporal impacts of future climate and socioeconomic changes on CS. This study integrates system dynamic (SD), patch-generating land use simulation (PLUS), the integrated valuation of ecosystem services and tradeoffs (InVEST) model, and the geographical detector to assess the LUC and CS evolution in the source of the Yellow River (SYR) from 2020 to 2060. Utilizing carbon density and LUC data, we explored the influence of natural and socioeconomic factors on CS under five shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) scenarios. Our findings demonstrate that: (1) Ecological land, including woodland, grassland, and wetland, expanded more under SSP126 compared to SSP245, with SSP345, SSP460, and SSP585 showing a trend of degradation tied to deeper economic contribution. (2) By 2060, CS in terrestrial ecosystem under SSP126, SSP245, SSP345, SSP460, and SSP585 were 702.33 × 106 t, 700.33 × 106 t, 697.22 × 106 t, 696.03 × 106 t, and 691.21 × 106 t, respectively. This represents changes of 3.69 × 106 t, 1.69 × 106 t, −1.49 × 106 t, −2.68 × 106 t, and −7.43 × 106 t compared to 2020. (3) Soil type predominantly influenced the spatial differentiation of CS, with significant interactions with precipitation. This research provides new insights into land redistribution, economic strategies, and achieving carbon neutrality.

Abstract Sleep, locomotor and social activities are essential animal behaviors, but their reciprocal relationships and underlying mechanisms remain poorly understood. Here, we elicit information from a cutting-edge large-language model (LLM), generative pre-trained transformer (GPT) 3.5, which interprets 10.2–13.8% of Drosophila genes known to regulate the 3 behaviors. We develop an instrument for simultaneous video tracking of multiple moving objects, and conduct a genome-wide screen. We have identified 758 fly genes that regulate sleep and activities, including mre11 which regulates sleep only in the presence of conspecifics, and NELF-B which regulates sleep regardless of whether conspecifics are present. Based on LLM-reasoning, an educated signal web is modeled for understanding of potential relationships between its components, presenting comprehensive molecular signatures that control sleep, locomotor and social activities. This LLM-aided strategy may also be helpful for addressing other complex scientific questions.

Abstract Background The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index has been recommended as an ideal indicator of body fat and exhibited significant correlation with cardiometabolic risk factors. However, whether the CUN-BAE index correlates with incident diabetes in Asian populations is unknown. Therefore, this longitudinal study was designed to evaluate the association between baseline CUN-BAE index and type 2 diabetes mellitus (T2DM). Methods This retrospective longitudinal study involved 15,464 participants of 18–79 years of age in the NAGALA (NAfld in the Gifu Area Longitudinal Analysis) study over the period of 2004–2015. Cox proportional hazards regression was performed to test the relationship between the baseline CUN-BAE index and diabetes incidence. Further stratification analysis was conducted to ensure that the results were robust. The diagnostic utility of the CUN-BAE index was tested by the receiver operating characteristic (ROC) curve. Results Over the course of an average follow-up of 5.4 years, 373 (2.41%) participants developed diabetes. A higher diabetes incidence was associated with higher CUN-BAE quartiles (P for trend

Organic radicals have potential applications in a variety of fields, including energy conversion. Here, the authors report Eu-based coordination polymers that enable the stabilization of organic radicals up to 350 °C; these systems can be used to enhance solar steam generation.

Abstract Background The incidence of renal cell carcinoma (RCC) has increased in recent years. Metastatic RCC is common and remains a major cause of mortality. A regulatory role for circular RNAs (circRNAs) in the occurrence and progression of RCC has been identified, but their function, molecular mechanisms, and potential clinical applications remain poorly understood. Methods High‐throughput RNA sequencing was used to explore the differential expression of circRNAs and their related pathways in RCC patients. Transwell and CCK‐8 assays were used to assess the function of hsa_circ_0057105 in RCC cells. The clinical relevance of hsa_circ_0057105 was evaluated in a cohort of RCC patients. The hsa_circ_0057105 regulatory axis was defined using RNA pull‐down, luciferase reporter assays, and fluorescence in situ hybridization assays, and the in vivo effect of hsa_circ_0057105 was validated using animal experiments. Results Single‐sample gene set enrichment analysis and correlation analysis of RNA‐seq data showed that hsa_circ_0057105 was potentially oncogenic and may serve to regulate epithelial‐mesenchymal transition (EMT) activation in RCC. Hsa_circ_0057105 expression was associated with advanced TNM stages and was an independent prognostic factor for poor RCC patient survival. Phenotypic studies show that hsa_circ_0057105 can enhance the migration and invasion abilities of RCC cells. Further, hsa_circ_0057105 was shown to inhibit the expression of miR‐577, a miRNA that regulated the expression of both COL1A1, which induced EMT activation, and VDAC2, which modulated ferroptosis sensitivity. The dual regulatory roles of hsa_circ_0057105 on EMT and ferroptosis sensitivity were verified using rescue experiments. Animal studies confirmed that hsa_circ_0057105 increased the metastatic ability and ferroptosis sensitivity of RCC cells in vivo. Conclusions In RCC, hsa_circ_0057105 regulates COL1A1 and VDAC2 expression through its sponge effect on miR‐577, acting like a ‘double‐edged sword’. These findings provide new insight into the relationship between EMT and ferroptosis in RCC and provide potential biomarkers for RCC surveillance and treatment.

Abstract High lymphocyte infiltration and immunosuppression characterize the tumor microenvironment (TME) in renal cell carcinoma (RCC). There is an urgent need to elucidate how tumor cells escape the immune attack and to develop novel therapeutic targets to enhance the efficacy of immune checkpoint blockade (ICB) in RCC. Overactivated IFN‐γ‐induced JAK/STAT signaling involves in such TME, but the underlying mechanisms remain elusive. Here, EH domain‐binding protein 1‐like protein 1 (EHBP1L1) is identified as a crucial mediator of IFN‐γ/JAK1/STAT1/PD‐L1 signaling in RCC. EHBP1L1 is highly expressed in RCC, and high EHBP1L1 expression levels are correlated with poor prognosis and resistance to ICB. EHBP1L1 depletion significantly inhibits tumor growth, which is attributed to enhanced CD8+ T cell‐mediated antitumor immunity. Mechanistically, EHBP1L1 interacts with and stabilizes JAK1. By competing with SOCS1, EHBP1L1 protects JAK1 from proteasomal degradation, which leads to elevated JAK1 protein levels and JAK1/STAT1/PD‐L1 signaling activity, thereby forming an immunosuppressive TME. Furthermore, the combination of EHBP1L1 inhibition and ICB reprograms the immunosuppressive TME and prevents tumor immune evasion, thus significantly reinforcing the therapeutic efficacy of ICB in RCC patient‐derived xenograft (PDX) models. These findings reveal the vital role of EHBP1L1 in immune evasion in RCC, which may be a potential complement for ICB therapy.

Abstract Neuromorphic system based on artificial synaptic devices is considered as a potential candidate to realize the in‐memory computing and parallel processing of data for overcoming the von Neumann bottleneck. However, to fully imitate the complicated functions of the biological neural networks at the hardware level is still a challenging task. In this work, a multi‐terminal MoS2 synaptic transistor is developed, which not only simulates various biological synaptic behaviors, including paired pulse facilitation (PPF), excitatory/inhibitory post‐synaptic current (EPSC/IPSC), spike‐rate‐dependent plasticity (SRDP), and spike‐timing‐dependent plasticity (STDP), but also can independently mimic the parallel signal processing and transmissions in biological multipolar neurons. By combining the multi‐terminal MoS2 synaptic transistor with the micro‐structured polydimethylsiloxane (PDMS) pressure sensors, an intelligent tactile recognition system is built up, which can realize the spatiotemporal recognition of touch position. Furthermore, with sensor selection, the spatiotemporal modulation of synaptic plasticity and the human learning and forgetting behaviors to the knowledge with different difficulty degrees can be mimicked. This work provides a novel interconnection scheme for simulating signal transmission and processing among neurons, showing broad application prospects of the multi‐terminal MoS2 synaptic transistor in intelligent human–computer interaction and bionic neuromorphic perception systems.

Investigating the spatiotemporal variation characteristics of aerosol optical depth (AOD) and its driving factors is essential for assessing atmospheric environmental quality and alleviating air pollution. Based on a 22-year high-resolution AOD dataset, the spatiotemporal variations of AOD in mainland China and ten national urban agglomerations were explored based on the Mann–Kendall trend test and Theil–Sen median method. Random forest (RF) and multiscale geographically weighted regression (MGWR) were combined to identify the main driving factors of AOD in urban agglomerations and to reveal the spatial heterogeneity of influencing factors. The results showed that areas with high annual average AOD concentrations were mainly concentrated in the Chengdu–Chongqing, Central Plains, Shandong Peninsula, and Middle Yangtze River urban agglomerations. Southern Beijing–Tianjin–Hebei and its surrounding areas revealed the highest AOD pollution during summer, whereas the worst pollution during the remaining three seasons occurred in the Chengdu–Chongqing urban agglomeration. Temporally, except for the Ha-Chang and Mid-Southern Liaoning urban agglomerations, where the average annual AOD increased, the other urban agglomerations showed a decreasing trend. Among them, the Central Plains, Middle Yangtze River, Guanzhong Plain, and Yangtze River Delta urban agglomerations all exhibited a decline greater than 20%. According to the spatial trends, most urban agglomerations encompassed much larger areas of decreasing AOD values than areas of increasing AOD values, indicating that the air quality in most areas has recently improved. RF analysis revealed that PM2.5 was the dominant factor in most urban clusters, followed by meteorological factors. MGWR results show that the influencing factors have different spatial scale effects on AOD in urban agglomerations. The socioeconomic factors and PM2.5 showed strong spatial non-stationarity with regard to the spatial distribution of AOD. This study can provide a comprehensive understanding of AOD differences among urban agglomerations, and it has important theoretical and practical implications for improving the ecological environment and promoting sustainable development.

ABSTRACT: Objective: To identify novel biomarkers and therapeutic targets for primary melanoma using network-based microarray data analysis. Methods: Eligible microarray datasets from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs). The protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify hub genes and pathways that might affect the survival of melanoma patients. Immunohistochemistry results obtained from the Human Protein Atlas (HPA) database confirmed the protein expression levels of hub genes. The Cancer Genome Atlas (TCGA) database was used to further verify the gene expression levels and conduct survival analysis. Results: Three microarray datasets (GSE3189, GSE15605, and GSE46517) containing 122 melanoma and 30 normal skin tissue samples were included. A total of 262 common differentially expressed genes (cDEGs) were identified based on three statistical approaches (Fisher's method, the random effects model (REM), and vote counting) with strict criteria. Of these, two upregulated genes, centromere protein F (CENPF) and pituitary tumor-transforming gene 1 (PTTG1), were selected as hub genes. HPA and TCGA database analyses confirmed that CENPF and PTTG1 were overexpressed in melanoma. Survival analysis showed that high expression levels of CENPF were significantly correlated with decreased overall survival (OS) (P=0.028). Conclusion: The expression level of CENPF was significantly upregulated in melanoma and correlated with decreased OS. Thus, CENPF may represent a novel biomarker and therapeutic target for melanoma patients.

Long-time series global fractional vegetation cover (FVC) products have received widespread international publication, and they supply the essential data required for eco-monitoring and simulation study, assisting in the understanding of global warming and preservation of ecosystem stability. However, due to the insufficiency of high-precision FVC ground-measured data, the accuracy of these FVC products in some regions (such as the Qinghai–Tibet Plateau) is still unknown, which brings a certain impact on eco-environment monitoring and simulation. Here, based on current international mainstream FVC products (including GEOV1 and GEOV2 at Copernicus Global Land Services, GLASS from Beijing Normal University, and MuSyQ from National Earth System Science Data Center), the study of the dynamic change of vegetation cover and its influence factors were conducted in the three-rivers source region, one of the core regions on the Qinghai–Tibet Plateau, via the methods of trend analysis and partial correlation analysis, respectively. Our results found that: (1) The discrepancy in the eco-environment assessment results caused by the inconsistency of FVC products is reflected in the statistical value and the spatial distribution. (2) About 70% of alpine grassland in the three-rivers source region changing trend is controversial. (3) The limiting or driving factors of the alpine grassland change explained via different FVC products were significantly discrepant. Thus, before conducting these studies in the future, the uncertainties of the FVC products utilized should be validated first to acquire the fitness of the FVC products if the accuracy information of these products is unavailable within the study area. In addition, more high-precision FVC ground-measured data should be collected, helping us to validate FVC product uncertainty.

Analysis and prediction of urban ecological risk are crucial means for resolving the dichotomy between ecological preservation and economic development, thereby enhancing regional ecological security and fostering sustainable development. This study uses Nanning, a Chinese landscape garden city, as an example. Based on spatial granularity and extent perspectives, using 30 m land use data, the optimal scale for an ecological risk assessment (ERA) and prediction is confirmed. This study also explores the patterns of spatial and temporal changes in ecological risk in Nanning on the optimal scale. At the same time, the Patch-generating Land Use Simulation model is used to predict Nanning’s ecological risk in 2036 under two scenarios and to propose ecological conservation recommendations in light of the study results. The study results show that: a spatial granularity of 120 m and a spatial extent of 7 km are the best scales for ERA and prediction in Nanning. Although the spatial distribution of ecological risk levels is obviously different, the overall ecological risk is relatively low, and under the scenario of ecological protection in 2036, the area of high ecological risk in Nanning is small. The results can provide theoretical support for ERA and the prediction of landscape cities and ecological civilization construction.

To address the problem of winding turn-to-turn breakdown faults in 35 kV dry-type transformers in wind farms under overvoltage conditions, this paper establishes a simulation model based on the structural dimensions and material parameters of the transformer windings. The winding distribution parameters were calculated using the finite element method. The transient processes inside the high-voltage coil were calculated by constructing a multi-conductor transmission line model (MTL) that took into account the influence of the secondary winding. The voltage distribution of the winding was analysed for both lightning shock and extra-fast transient overvoltage conditions. The simulation results show that the maximum overvoltage between turns of the transformer winding under lightning shock is 5.282 kV; the maximum overvoltage between turns of the winding under very fast transient overvoltage is 11.6 kV, which occurs between the first 2–3 layers of the section, close to the insulation breakdown margin. On this basis, the transformer winding structure was optimised and the maximum inter-turn overvoltage after optimisation was 9.104 kV, reducing the likelihood of insulation breakdown by 24.1%. Finally, the accuracy of the winding structure optimisation simulation study was verified by testing the transformer’s impulse voltage before and after optimisation, providing a reference for the stable operation of 35 kV dry-type transformers in wind farm practical applications.

Fractional vegetation cover (FVC) products provide essential data support for ecological environmental monitoring and simulation studies. However, the lack of validation efforts of FVC products limits their applications. Based on Sentinel-2 data and intensive multi-scale measured FVC data, the accuracies of two FVC products (GEOV3 and MuSyQ) in alpine grassland ecosystems were validated through direct validation and multi-scale validation. Based on the heterogeneity of the underlying surface (HUS) of the monitoring plots, the impact of the HUS of the monitoring plots on the product validation was analyzed. The results showed that: (1) the measured data directly validated that the GEOV3 FVC product performed better than the MuSyQ FVC product; (2) the multi-scale validation method based on high-resolution reference FVC map of Sentienl-2 satellite images validated the accuracy of these two FVC products, which was higher than the accuracy directly validated by FVC measured data, leading to overestimation of the validation results; and (3) the HUS of the monitored plots has a significant impact on the FVC product validation. By quantifying the HUS of the monitored plots and removing the heterogeneous monitoring plots, the uncertainty of the validation results can be reduced. It is necessary to consider the impact of validation methods and the HUS on the validation results in future product validation.

Polymerizations promoted by organic catalysts can produce polymeric materials without any metallic residues contamination. Here the authors show a fluorinated alcohol based catalytic system for polypeptide synthesis from α-amino acid N-carboxyanhydride, fulfilling cocatalyst and metal free conditions with high rate and selectivity.

The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1/2 as data sources, this study designed four schemes using convolutional neural network (CNN) and random forest (RF) classifiers, respectively, to demonstrate the potential of high-resolution images in LCZ mapping and evaluate the optimal combination of different data sources and classifiers. The results showed that the combination of GF-6 and CNN (S3) was considered the best LCZ classification scheme for urban areas, with OA and kappa coefficients of 85.9% and 0.842, respectively. The accuracy of urban building categories is above 80%, and the F1 score for each category is the highest, except for LCZ1 and LCZ5, where there is a small amount of confusion. The Sentinel-1/2-based RF classifier (S2) was second only to S3 and superior to the combination of GF-6 and random forest (S1), with OA and kappa coefficients of 64.4% and 0.612, respectively. The Sentinel-1/2 and CNN (S4) combination has the worst classification result, with an OA of only 39.9%. The LCZ classification map based on S3 shows that the urban building categories in Xi’an are mainly distributed within the second ring, while heavy industrial buildings have started to appear in the third ring. The urban periphery is mainly vegetated and bare land. In conclusion, CNN has the best application effect in the LCZ mapping task of high-resolution remote sensing images. In contrast, the random forest algorithm has better robustness in the band-abundant Sentinel data.