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Building height and footprint are two fundamental urban morphological features required by urban climate modelling. Although some statistical methods have been proposed to estimate average building height and footprint from publicly available satellite imagery, they often involve tedious feature engineering which makes it hard to achieve efficient knowledge discovery in a changing urban environment with ever-increasing earth observations. In this work, we develop a deep-learning-based (DL) Python package – SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery) to extract such information. Multi-task deep-learning (MTDL) models are proposed to automatically learn feature representation shared by building height and footprint prediction. Besides, we integrate digital elevation model (DEM) information into developed models to inform models of terrain-induced effects on the backscattering displayed by Sentinel-1 imagery. We set conventional machine-learning-based (ML) models and single-task deep-learning (STDL) models as benchmarks and select 46 cities worldwide to evaluate developed models’ patch-level prediction skills and city-level spatial transferability at four resolutions (100, 250, 500 and 1000 m). Patch-level results of 43 cities show that DL models successfully produce discriminative feature representation and improve the coefficient of determination (R2) of building height and footprint prediction more than ML models by 0.27–0.63 and 0.11–0.49, respectively. Moreover, stratified error assessment reveals that DL models effectively mitigate the severe systematic underestimation of ML models in the high-value domain: for the 100 m case, DL models reduce the root mean square error (RMSE) of building height higher than 40 m and building footprint larger than 0.25 by 31 m and 0.1, respectively, which demonstrates the superiority of DL models on refined 3D building information extraction in highly urbanized areas. For the evaluation of spatial transferability, when compared with an existing state-of-the-art product, DL models can achieve similar improvement on the overall performance and high-value prediction. Furthermore, within the DL family, comparison in building height prediction between STDL and MTDL models reveals that MTDL models achieve higher accuracy in all cases and smaller bias uncertainty for the prediction in the high-value domain at the refined scale, which proves the effectiveness of multi-task learning (MTL) on building height estimation.

When the urban heat island (UHI) effect coincides with a heat wave (HW), thermal stress in cities is exacerbated. Understanding the surface energy balance (SEB) responses to HWs is critical for improving predictions of the synergies between UHIs and HWs. This study evaluates observed SEB characteristics in four cities (Beijing, Łódź, London and Swindon), along with their ambient meteorological conditions, for both HW and background summer climate scenarios. Using the Analytical Objective Hysteresis Model (AnOHM), particular emphasis is on the heat storage. The results demonstrate that in London and Swindon the amount of daytime heat storage and its fraction relative to the net all-wave radiation increase under HWs. Results further demonstrate that such increases are strongly tied to lower wind speeds. The effects of different UHI mitigation measures on heat storage are assessed using AnOHM. Results reveal that use of reflective materials and maintaining higher soil moisture availability can offset the adverse effects of increased heat storage.

Accurate high-resolution estimates of precipitation are vital to improving the understanding of basin-scale hydrology in mountainous areas. The traditional interpolation methods or satellite-based remote sensing products are known to have limitations in capturing the spatial variability of precipitation in mountainous areas. In this study, we develop a fusion framework to improve the annual precipitation estimation in mountainous areas by jointly utilizing the satellite-based precipitation, gauge measured precipitation, and vegetation index. The development consists of vegetation data merging, vegetation response establishment, and precipitation remapping. The framework is then applied to the mountainous areas of the Nu River basin for precipitation estimation. The results demonstrate the reliability of the framework in reproducing the high-resolution precipitation regime and capturing its high spatial variability in the Nu River basin. In addition, the framework can significantly reduce the errors in precipitation estimates as compared with the inverse distance weighted (IDW) method and the TRMM (Tropical Rainfall Measuring Mission) precipitation product.

Urban-induced thermal stress can threaten human health, especially during heat waves (HWs). The growth of cities further exacerbates this effect. Here, weather research and forecasting (WRF) with an urban canopy model (UCM) is used to assess the effects of megacities and their growth on the thermal regime of proximal cities during heat waves. Analysis of the heat fluxes shows that advection impacts cities downwind. Results indicate that as urban areas change size (50%–100% and 100–150% of their current size), the local 2 m temperature increases by 2.7 and 1.7 °C, and the 2 m specific humidity decreases by 2.1 and 1.4 g kg−1, respectively. A small city downwind is impacted with a 0.3–0.4 °C increase in 2 m temperature. Green roof is a potential mitigation strategy for these regions (i.e., beyond the megacity). With 50% green roofs in an urban area, a 0.5 °C decrease in 2 m temperature and 0.6 g kg−1 increase in specific humidity is simulated. Urbanization upwind of a megacity will contribute to regional climate change.

This study investigates spatial characteristics of annual and decadal precipitation in the Huaihe River basin. Daily precipitation data, obtained from meteorological gauges, are analyzed for a 51-year period, from 1961 to 2011. Precipitation is analyzed in grids (5 km2) with respect to temporal variability. The spatial distribution and intensity of annual rainfall (mm/10 year), determined by the linear regression method, reveals a slight increase of 3 mm/10 year over the basin. However, the trend did not present a significant change at 95% significance level in the most of basin. Precipitation is mostly increasing for each ten-year periods during the total 51 years. The annual precipitation randomicity was calculated from the non-uniform coefficient Cv (coefficient of variation) test and showed a significant non-uniform spatial distribution, indicating that randomicity of annual rainfall was the moderate variability. The Pettitt test determined that the abrupt change points occurred mainly in 1965, 1975 and 2002. Wavelet analysis showed that cyclic variations appeared almost every 5 to 10 years, accounting for 36% of the basin area. Meanwhile, these cycles tended to be delimited by the abrupt change points. This study aims to provide insights for water resources management, mitigation of climate change effects and water supply in the Huaihe River basin and surrounding watersheds.

Anthropogenic heat is an important component of the urban energy budgets that can affect land surface and atmospheric boundary layer processes. Representation of anthropogenic heat in numerical climate modeling systems is therefore important when simulating urban meteorology and climate and has the potential to improve weather forecasts, climate process studies, and energy demand analysis. Here, spatiotemporally dynamic anthropogenic heat data estimated by the Building Effects Parameterization and Building Energy Model (BEP-BEM) are incorporated into the Weather Research and Forecasting (WRF) Model system to investigate its impact on simulation of summertime rainfall in Beijing, China. Simulations of four local rainfall events with and without anthropogenic heat indicate that anthropogenic heat leads to increased rainfall over the urban area. For all four events, anthropogenic heat emission increases sensible heat flux, enhances mixing and turbulent energy transport, lifts PBL height, increases dry static energy, and destabilizes the atmosphere in urban areas through thermal perturbation and strong upward motion during the prestorm period, resulting in enhanced convergence during the major rainfall period. Intensified rainfall leads to greater atmospheric dry-down during the storm and a higher poststorm LCL.

Short-video dependence (SVD) has become a significant mental health issue around the world. The lack of scientific tools to assess SVD hampers further advancement in this area. This study aims to develop and validate a scientific tool to measure SVD levels, ensuring a scientifically determined cutoff point. We initially interviewed 115 highly engaged short-video users aged 15 to 63 years. Based on the summary of the interview and references to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for behavioral addictions, we proposed the first version of the short-video dependence scale (SVDS). We then screened the items through item analysis (second version) and extracted common factors using exploratory factor analysis (third version) and confirmatory factor analysis (final version). Convergent validity was tested with other scales (Chinese Internet Addiction Scale [CIAS] and DSM-5). Finally, we tested the validity of the final version in 16,038 subjects and set the diagnostic cutoff point through latent profile analysis and receiver operating characteristic curve analysis. The final version of the SVDS contained 20 items and 4 dimensions, which showed strong structural validity (Kaiser-Meyer-Olkin value=0.94) and internal consistency (Cronbach α=.93), and good convergent validity (r =0.61 and r =0.68), sensitivity (0.77, 0.83, 0.87, and 0.62 for each of the 4 dimensions), and specificity (0.75, 0.87, 0.80, and 0.79 for each of the 4 dimensions). Additionally, an SVDS score of 58 was determined as the best cutoff score, and latent profile analysis identified a 5-class model for SVD. We developed a tool to measure SVD levels and established a threshold to differentiate dependent users from highly engaged nondependent users. The findings provide opportunities for further research on the impacts of short-video use.

Background Glucocorticoids (GCs) overuse is associated with decreased bone mass and osseous vasculature destruction, leading to severe osteoporosis. Platelet lysates (PL) as a pool of growth factors (GFs) were widely used in local bone repair by its potent pro-regeneration and pro-angiogenesis. However, it is still seldom applied for treating systemic osteopathia due to the lack of a suitable delivery strategy. The non-targeted distribution of GFs might cause tumorigenesis in other organs. Results In this study, PL-derived exosomes (PL-exo) were isolated to enrich the platelet-derived GFs, followed by conjugating with alendronate (ALN) grafted PEGylated phospholipid (DSPE-PEG-ALN) to establish a bone-targeting PL-exo (PL-exo-ALN). The in vitro hydroxyapatite binding affinity and in vivo bone targeting aggregation of PL-exo were significantly enhanced after ALN modification. Besides directly modulating the osteogenic and angiogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) and endothelial progenitor cells (EPCs), respectively, PL-exo-ALN also facilitate their coupling under GCs’ stimulation. Additionally, intravenous injection of PL-exo-ALN could successfully rescue GCs induced osteoporosis (GIOP) in vivo. Conclusions PL-exo-ALN may be utilized as a novel nanoplatform for precise infusion of GFs to bone sites and exerts promising therapeutic potential for GIOP. Graphical Abstract

The growing world population and shrinkage of arable land demand yield improvement of rice, one of the most important staple crops. To elucidate the genetic basis of yield and uncover its associated loci in rice, we resequenced the core recombinant inbred lines of Liang–You–Pei–Jiu , the widely cultivated super hybrid rice, and constructed a high-resolution linkage map. We detected 43 yield-associated quantitative trait loci, of which 20 are unique. Based on the high-density physical map, the genome sequences of paternal variety 93–11 and maternal cultivar PA64s of Liang–You–Pei–Jiu were significantly improved. The large recombinant inbred line population combined with plentiful high-quality single nucleotide polymorphisms and insertions/deletions between parental genomes allowed us to fine-map two quantitative trait loci, qSN8 and qSPB1 , and to identify days to heading8 and lax panicle1 as candidate genes, respectively. The quantitative trait locus qSN8 was further confirmed to be days to heading8 by a complementation test. Our study provided an ideal platform for molecular breeding by targeting and dissecting yield-associated loci in rice.

Objectives Although internet gaming disorder (IGD) has become a serious mental health issue throughout the world, current treatment strategies face significant challenges. Mindfulness meditation (MM) is both versatile and widely accepted and has been proposed as an approach for treating IGD. However, its effects on IGD and the potential neural mechanisms underlying MM remain unknown. Methods Sixty-two participants with IGD were included, with 31 assigned to the MM group and 31 to the progressive muscle relaxation (PMR) group, all of them completed the entire experimental procedure, including the pre-test, eight MM training sessions (or progressive muscle relaxation), and the post-test. Two hundred and ten cortical and 36 subcortical subregions were arranged into eight brain networks according to Yeo’s functional template. The brain network features (intra- and inter-modular segregations) were compared between the different groups and tests. Results MM was found to reduce both addiction severity and gaming cravings in IGD subjects. In terms of brain networks, MM increased modular segregation in the subcortical network (SCN), especially between frontoparietal network (FPN)-SCN, and dorsal attentional network (DAN)-SCN, while also increasing connections in nodes in default mode networks (DMN), especially between FPN-DMN and DAN-DMN. Significant correlations between behavioral and network features were found. PMR also generated similar effects but inferior to MM. Conclusions MM was found decreased gaming craving and addiction severity in treating IGD. The process was associated with enhanced top-down control functions. The findings have implications for both clinicians and researchers. Preregistration The protocol of the trial has been pre-registered at the Chinese clinical trial registry ( www.chictr.org.cn ; ChiCTR2300075869).