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Shifts in plant phenology associated with climate change have received unprecedented attention from land managers, policymakers, and the scientific community, with important implications for the structure and function of the biosphere. Eddy covariance (EC) has been used to measure carbon exchange between the land surface and atmosphere, which provides an opportunity to describe the seasonality of ecosystem processes. Using BASE flux products provided by the AmeriFlux network and a non-linear modeling approach, this study developed a vegetation carbon phenology (VCP) dataset, which we termed VCPNET (version 1.0). VCPNET was developed with fine spatial and temporal scales for a total of 87 EC sites (2820 site-years of data) across North America, including nine vegetation types. We extracted phenology metrics by site to provide long-term VCP metrics across these ecosystems. Analyses revealed significant differences in photosynthetic capacity and respiration rate-derived VCP metrics across the landscape, as well as differences associated with whether daily cumulative or half-hourly maximum measurements were used. Daily carbon fluxes may be more consistent in simulating phenology behavior than 30-min maximum fluxes across all vegetation types, except in sites classified as Grassland. Overall vegetation types, the seasonality of photosynthetic capacity may exert a larger control on the net carbon uptake of the ecosystem compared to the respiration rate. Thus, model-estimated photosynthesis-activated seasons may better represent true phenology, particularly the development of above- and below-ground plant biomass in tree-covered areas, which is crucial in forecasting ecosystem carbon sequestration. In addition, a nonlinear model can robustly capture day-to-day variation in carbon flux; however, precise simulation of phenological patterns with multiple peaks, typically observed in disturbed ecosystems, must be addressed during model optimization. The cross-ecosystem phenology metrics provided by VCPNET could be utilized for regional-global analysis and calibration of satellite-based vegetation indices, which accurately scale land surface phenology. Our findings provide a prospective tool for a deeper understanding of ecosystem function, allowing a direction and foundation for optimizing phenology models and algorithms and ultimately contributing to developing new ecological theories and practices in response to climate change.

Abstract Zinc bromine flow batteries (ZBFBs) are well suited for stationary energy storage due to their attractive features of high energy density and low cost. Nevertheless, the ZBFBs suffer from low power density and limited efficiency owing to the relatively severe polarization of the Br2/Br− redox couple. Herein, a three‐dimensional (3D) hierarchical composite electrode based on core‐shell Ni/NiO heterostructures anchored on graphite felt (Ni/NiO@GF) is designed to promote the kinetics of the Br2/Br− couple, so as to improve the power density and efficiency of the ZBFB. In this design, the highly conductive carbon felt and Ni cores provide a composite electrode with a 3D electron transporting framework to guarantee excellent electronic conductivity, while the NiO shells possess great absorption ability to Br2 and brilliant catalytic activity for the Br2/Br− redox reaction to reduce the electrochemical polarization. As a result, an enhanced ZBFB with Ni/NiO@GF electrode shows an outstanding energy efficiency of 86% at 20 mA cm−2 and can be operated at a current density of up to 160 mA cm−2 with a respectable energy efficiency of 67%. These results exhibit a promising strategy to fabricate catalytic electrodes for high‐performance ZBFBs.

The Green–Ampt (GA) model is a widely used analytical method to calculate the depth of the wetting front during intense rainfall. However, it neglects the existence of the transition layer and the seepage parallel to the slope surface. Therefore, a modified stratified Green–Ampt (MSGA) model is proposed. A process to assess the stability of the finite slope during a rainfall event is demonstrated by combining the MSGA model and the limit equilibrium method. In the case of the Liangshuijing landslide, the factor of safety presents a negative correlation with the depth of the wetting front. The factor of safety obtained by the stratified Green–Ampt (SGA) model is smaller than that calculated by the MSGA model, and the gap between the factor of safety based on the two methods widens with time. The moving speed of the wetting front accelerates with the increase in the length of the slope surface, and the size effect becomes apparent when the length is short. In the initial stage of infiltration, the effect of the seepage parallel to the slope surface is small. The effect of the seepage cannot be neglected at the latter stage. The result calculated by the MSGA model agrees well with the measured result in the test.

Knowledge on spatial distribution, provenance and delivery mode of surface sediment aids in interpretation of nearby sediment records for paleoenvironmental reconstruction. Such knowledge, however, remains largely unknown for the modern Ross Sea, Antarctica: a key region for understanding the dynamical behavior of Antarctic Ice Sheet over geological past. In this study, we address this gap by analyzing the grain-size distribution, coarse fraction (>250 μm) lithology, and clay mineralogy of a set of surface sediment samples covering the whole Ross Sea continental shelf. Our data reveals that the sediments were mostly delivered by icebergs and bottom currents. Iceberg delivery was significantly controlled by factors such as water depth, proximity to the iceberg sources, and invasion of the Modified Circumpolar Deep Water. Bottom current activity was stronger in the Western Ross Sea (WRS) than in the Eastern Ross Sea (ERS), controlled by the formation and transport of Dense Shelf Water. Three major sorts of coarse fraction were identified, including the quartz-rich Iceberg Rafted Detritus (IRD) originating from West Antarctic glaciers and primarily distributed in the ERS, the mafic rocks-rich IRD from the Ferrar Group as well as the McMurdo Volcanic Group and mainly found in the WRS, and deformed silt traced back to the grounding zone of the David Glacier-Dragalski Ice Tongue system. The distribution of clay minerals is dominated by a distinct binary mixing pattern. Smectite and kaolinite are mainly present in the ERS, derived from beneath the West Antarctic Ice Sheet. Higher illite and chlorite contents were found offshore of the Southern Victoria Land, derived from the East Antarctic craton. Overall, these results demonstrate that the glaciers draining into Ross Sea from both the East and West Antarctic Ice Sheets are highly dynamical in the context of modern climate conditions, with implications for potential contribution to future sea level rise.

Background: No evidence exists on the impact of bivalirudin in patients with the acute coronary syndrome undergoing rotational atherectomy. This study aimed to evaluate the impact of bivalirudin on patients with acute coronary syndrome undergoing rotational atherectomy. Methods: This was a retrospective cohort study conducted in our hospital between January 2017 and December 2019. The study included patients with acute coronary syndrome undergoing rotational atherectomy. Furthermore, 2 cohorts were included in this study (bivalirudin cohort and control cohort unfractionated heparin). The primary end-point was in-hospital net adverse clinical events. The secondary endpoint was all-cause mortality at 23 months. Results: The study included 157 patients with 33 (21.0%) in the bivalirudin cohort and 124 (79.0%) in the control cohort. Net adverse clinical events during hospitalization in the bivalirudin cohort were higher than that in the control cohort [9 (27.3%) vs. 14 (11.3%), P = .021]. However, there was no significant difference in all-cause mortality at 23 months between the 2 cohorts [25 (20.2%) vs. 10 (30.3%), P =.214]. After adjusting for potential confounders, the usage of bivalirudin was not associated with net adverse clinical event (odds ratio = 0.90; 95% CI: 0.18-4.45; P =.890), and the hazard ratio for all-cause mortality at 23 months was 1.01 (95% CI: 0.33-3.15; P =.983). Conclusion: Bivalirudin appears to exhibit a similar impact as unfractionated heparin on patients with acute coronary syndrome undergoing rotational atherectomy in real-life setting.

The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting.

On-year and off-year phenomena is an unique spatiotemporal characteristics of Moso bamboo forests (MBFs), the combination of multiple sources data to assess the impact of on-/off-year phenomena and human activities is a new perspective to better understand MBFs. There is an urgent need to explore how to characterize the spatial variability of MBFs, whether there is a pattern in the spatial distribution of MBFs boundary lines, and whether the causes of the boundary lines are directly related to human activities. In this paper, Anji County, with a large MBF area, was selected as the study area. Based on Sentinel-2 time-series data, topography and impermeability data, the on-year/off-year status and boundary lines of MBFs were determined, and the Integrated Moso bamboo On-off year Differentiation Index (IMODI) was proposed to evaluate spatial differentiation. Spatial heterogeneity, the shape pattern of boundary lines, and the impact of human activities on the MBFs were quantitatively analyzed, and 631.5 km2 of MBFs and 140 on-off boundaries were accurately obtained. The on-year and off-year MBFs and boundaries were mainly distributed in mountainous areas, especially at elevations of 100–400 m and on slopes of 10–35°. The patterns in elevation can be categorized as stable, single-change, ascending-descending and descending-ascending patterns. The patterns in slope can be summarized as stable, single-change, and trapezoidal patterns. The newly proposed IMODI shows the best performance in reflecting the spatial differentiation of MBFs, and the on-year and off-year phenomenon of MBFs was closely related to human activities because the boundary lines were mostly concentrated within 2 km of villages. The results of this paper provide a new perspective to understand landscape of MBFs and the integrated management of carbon sequestration and sinks.

In 2002 and 2020–2022, KH-9 HEXAGON mapping camera system (MCS) and panoramic camera system (PCS) images were made available to the public, respectively. Although great efforts have been made by the scientific community to develop applications that utilize KH-9 HEXAGON images, little attention has been paid to de-noising and contrast enhancement of these images particularly over urban landscapes. This paper focuses on developing a de-noising and contrast enhancement pipeline for KH-9 HEXAGON MCS and PCS over urban regions. The proposed approach employs first a wavelet transform trained using a suite of ‘degree of over-smoothing’ metrics (DOSM) for image de-noising. These metrics are sensitive to structure, texture, edges and local homogeneity of image objects. Then the de-noised image is subjected to the multi-resolution Top-hat to optimize the contrast. This method incorporates a range of shapes and neighborhoods at multiple scales. The method was applied to a KH-9 HEXAGON MCS image (acquired in 1975) and PCS image (acquired in 1974) representing a complex urban landscape, to support comprehensive evaluation under a range of settings. Performance was assessed against three state-of-the-art benchmark approaches: residual learning (deep learning), blind deconvolution and spatial filtering. To evaluate the performance of the proposed pipeline against the benchmarks, we employed the saturation image edge difference standard-deviation, co-occurrence metrics and the semivariogram. Additionally, the potential applications of pre-processed results were demonstrated using change detection, identification reference points and stereo images. The proposed method not only improved the quality of the KH-9 image across the different urban landscape types, but also preserved the original spatial characteristics of the image in comparison with the benchmark methods. At a time when understanding the nature of our changing planet is paramount, the proposed pipeline should be of great benefit to investigators wishing to use KH program images to extend their historical or time-series analyses further back in time.

In this paper, a swarm trajectory-planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path-searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. The simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory-planning method for a network of ASVs.

Transition metal phosphides (TMPs) have been widely studied for water decomposition for their monocatalytic property for anodic or cathodic reactions. However, their bifunctional catalytic activity still remains a major challenge. Herein, hexagonal nickel-cobalt bimetallic phosphide nanoneedles with 1–3 μm length and 15–30 nm diameter supported on NF (NixCo2−xP NDs/NF) with adjusted electron structure have been successfully prepared. The overall alkaline water electrolyzer composed of the optimal anode (Ni0.67Co1.33P NDs/NF) and cathode (Ni1.01Co0.99P NDs/NF) provide 100 mA cm−2 at 1.62 V. Gibbs Free Energy for reaction paths proves that the active site in the hydrogen evolution reaction (HER) is Ni and the oxygen evolution reaction (OER) is Co in NixCo2−xP, respectively. In the HER process, Co-doping can result in an apparent accumulation of charge around Ni active sites in favor of promoting HER activity of Ni sites, and ΔGH* of 0.19 eV is achieved. In the OER process, the abundant electron transfer around Co-active sites results in the excellent ability to adsorb and desorb *O and *OOH intermediates and an effectively reduced ∆GRDS of 0.37 eV. This research explains the regulation of electronic structure change on the active sites of bimetallic materials and provides an effective way to design a stable and effective electrocatalytic decomposition of alkaline water.

Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed data for mapping China’s ISA distribution at 30-m spatial resolution. The integration of Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were used to extract initial ISA with spatial resolution of 250 m using a thresholding approach. The Landsat-derived NDVI and Modified Normalized Difference Water Index (MNDWI) were used to remove vegetation and water areas from the mixed pixels that existed in the initial ISA data. The spectral signatures of these ISA data were further extracted from Landsat multispectral images and used to refine the ISA data using expert knowledge. The results indicate that the integration of multi-source data can successfully map ISA distribution with 30-m spatial resolution in China with producer’s and user’s accuracies of 83.1 and 91.9%, respectively. These ISA data are valuable for better management of urban landscapes and for use as an input in other studies such as socioeconomic and environmental modeling.

Abstract Background Spartina alterniflora is an invasive plant on the coast of China that replaces native vegetation and has a serious negative impact on local ecosystems. Monitoring the spatial distribution of S. alterniflora and its changes over time can reveal its expansion mechanism, which is crucial for the management of coastal ecosystems. The purpose of this study was to map the distribution of S. alterniflora in Zhejiang Province from 1985 to 2015 using a time series of Landsat TM/OLI images and analyze the temporal and spatial patterns of expansion of this species. Results After analyzing the distribution of coastal vegetation, the vegetation index was calculated based on Landsat images for 4 years (1985, 1995, 2005 and 2015). According to a threshold determined based on expert knowledge, the distribution of S. alterniflora in Zhejiang Province was extracted, and the temporal and spatial changes in the distribution of S. alterniflora were analyzed. The classification accuracy was 90.3%. S. alterniflora has expanded rapidly in recent decades after being introduced into southern Zhejiang. Between 1985 and 2015, S. alterniflora increased its area of distribution by 10,000 hm2, and it replaced native vegetation to become the most abundant halophyte in tidal flats. Overall, S. alterniflora expanded from south to north over the decades of the study, and the fastest expansion rate was 463.64 hm2/year, which occurred between 1995 and 2005. S. alterniflora was widely distributed in the tidal flats of bays and estuaries and expanded outward as sediment accumulated. Conclusions This study reveals the changes over time in S. alterniflora cover in Zhejiang and can contribute to the control and management of this invasive plant.

Abstract Anaerobic digestion (AD) of livestock and poultry manure to produce biogas can not only improve environmental problems, but also effectively utilize waste. In order to maximize the resource utilization of livestock and poultry manure, the AD experiments of mixing cow dung, deer manure, and mushroom fungus were carried out. The changes of AD biogas production (daily and cumulative biogas production), CH4 content, volatile fatty acids (VFAs), pH at different temperatures (20, 35, and 50°C), and mixing ratios were studied, and the optimum reaction conditions were discussed. Results show that with the increase in temperature the biogas production rate and the cumulative biogas production at each proportions increase. At 20°C, the amount of biogas generated is very small. Under different proportions of raw materials, the cumulative biogas production first increased and then decreased. The best biogas production is at a temperature of 50°C and a proportion of cow dung, deer manure, and mushroom fungus is 0.3:0.4:0.3. The unit total solid biogas production rate reached 0.23 L·g−1. The mixed fermentation of livestock manure and mushroom fungus made up for the inadequacy of its separate fermentation performance and was also an effective method to improve the biogas rate and comprehensive utilization rate of livestock manure. The results of this study are of great significance in promoting the utilization of livestock and poultry manure.

The identification of seed vigor is of great significance to improve the seed germination rate, increase crop yield, and ensure product quality. In this study, based on a hyperspectral data acquisition system and an improved feature extraction algorithm, an identification model of the germination characteristics for corn seeds was constructed. In this research, hyperspectral data acquisition and the standard corn seed germination test for Zhengdan 958 were carried out. By integrating the hyperspectral data in the spectral range of 386.7–1016.7 nm and the first derivative information of the spectral data, the root length prediction for corn seeds was successfully completed. The data regression model and prediction relationship between the spectral characteristics and seedling root length were established by principal component regression, partial least squares, and support vector regression. The first derivative information of the hyperspectral data was obtained by comparing the prediction model results with the original spectral data, which was preprocessed by Savitzky–Golay smoothing, multiplicative scatter correction, standard normal variate, and curve fitting. The results showed that the prediction model based on the first-order differential spectral data showed better performance than the one based on the spectral data obtained by other processing algorithms. By comparing the prediction results using different data characteristics and regression models, it was found that the hyperspectral method can effectively predict the root length of the seed, with the coefficient of determination reaching 0.8319.

Background: Proteomic studies investigating novel molecular markers of coronary artery calcification (CAC) are scarce.This study compared the protein expression in the serum of patients with severe CAC and non-CAC. Methods: The serum from 30 patients with severe CAC and 30 matched-controls were screened by data-independent acquisition(DIA)-based proteomic technology. Bioinformatics analysis tools were used to analyze the underlying molecular mechanisms of the differentially expressed proteins. Candidate proteins were further validated by an enzyme-linked immunosorbent assay (ELISA) in an independent cohort. A receiver operating characteristic (ROC) curve was used to estimate the diagnostic power of the candidate proteins. Results: Among the 110 identified proteins, the expression of 81 was significantly upregulated, whereas 29 proteins were downregulated (fold change ≥1.5; p < 0.05) between patients with and without CAC. Bioinformatics analysis indicated that the differential proteins are involved in complement and coagulation cascades, platelet activation, regulation of actin cytoskeleton, or glycolysis/gluconeogenesis pathways. Further verification showed that serum levels of complement C5 (C5), fibrinogen gamma (FGG), pyruvate kinase isoform M2 (PKM2), and tropomyosin 4 (TPM4) were consistent with the proteomic findings, which could allow discrimination between CAC and non-CAC patients. Conclusions: This study revealed that high serum levels of serum C5, FGG, PKM2, and TPM4 proteins were linked to severe CAC. These proteins may be developed as biomarkers to predict coronary calcification.

The contents of cellulose and hemicellulose (C and H) in corn stover (CS) have an important influence on its biochemical transformation and utilization. To rapidly detect the C and H contents in CS by near-infrared spectroscopy (NIRS), the characteristic wavelength selection algorithms of backward partial least squares (BIPLS), competitive adaptive reweighted sampling (CARS), BIPLS combined with CARS, BIPLS combined with a genetic simulated annealing algorithm (GSA), and CARS combined with a GSA were used to select the wavelength variables (WVs) for C and H, and the corresponding regression correction models were established. The results showed that five wavelength selection algorithms could effectively eliminate irrelevant redundant WVs, and their modeling performance was significantly superior to that of the full spectrum. Through comparison and analysis, it was found that CARS combined with GSA had the best comprehensive performance; the predictive root mean squared errors of the C and H regression model were 0.786% and 0.893%, and the residual predictive deviations were 3.815 and 12.435, respectively. The wavelength selection algorithm could effectively improve the accuracy of the quantitative analysis of C and H contents in CS by NIRS, providing theoretical support for the research and development of related online detection equipment.

Objective ST-segment elevation myocardial infarction (STEMI) is the most serious type of acute coronary syndrome. This study aimed to investigate the efficacy and safety of bivalirudin application during primary percutaneous coronary intervention (PPCI) in older patients with acute STEMI. Methods A total of 672 older patients with STEMI (>75 years) who underwent PPCI were studied. The primary endpoints were 30-day net adverse clinical events (NACEs) post-emergency percutaneous coronary intervention, including major adverse cardiac and cerebrovascular events (MACCEs) and Bleeding Academic Research Consortium grades 2 to 5 (BARC 2–5) bleeding events. Results The incidence of NACEs and BARC 2–5 bleeding events in the bivalirudin group was significantly lower than that in the unfractionated heparin group. Multivariate Cox regression analysis showed that bivalirudin significantly reduced 30-day NACEs (odds ratio: 0.700, 95% confidence interval: 0.492–0.995) and BARC 2–5 bleeding events (odds ratio: 0.561, 95% confidence interval: 0.343–0.918). At 1-year follow-up, these results were similar. Conclusions Bivalirudin can be safely and effectively used during PPCI in older patients with STEMI. Bivalirudin reduces the risks of NACEs and bleeding within 30 days after PPCI, without increasing the risks of MACCEs and stent thrombosis compared with heparin.

Tea plantations are widely distributed in the southern provinces of China and have expanded rapidly in recent years due to their high economic value. This expansion has caused ecological problems such as soil erosion, and it is therefore urgent to clarify the spatial distribution and area of tea plantations. In this study, we developed a simple method to accurately map tea plantations based on their unique phenological characteristics observed from VENμS high-spatiotemporal-resolution multispectral imagery. The normalized difference vegetation index (NDVI) and red—green ratio index (RGRI) of time series were calculated using 40 VENμS images taken in 2018 to evaluate the phenology of tea plantations. The unique phenological period of tea plantations in northern Zhejiang is from April to May, with obvious deep pruning, which is very different from the phenological period of other vegetation. During this period, the RGRI values of tea plantations were much higher than those of other vegetation such as broadleaf forest and bamboo forest. Therefore, it is possible to identify tea plantations from the vegetation in images acquired during their phenological period. This method was applied to tea plantation mapping in northern Zhejiang. The NDVI value of the winter image was used to extract a vegetation coverage map, and spatial intersection analysis combined with maps of tea plantation phenological information was performed to obtain a tea plantation distribution map. The resulting tea plantation map had a high accuracy, with a 94% producer accuracy and 95.9% user accuracy. The method was also applied to Sentinel-2 images at the regional scale, and the obtained tea plantation distribution map had an accuracy of 88.7%, indicating the good applicability of the method.

Bamboo forests, due to rapid growth and short harvest rotation, play an important role in carbon cycling and local economic development. Accurate estimation of bamboo forest aboveground biomass (AGB) has garnered increasing attention during the past two decades. However, remote sensing-based AGB estimation for bamboo forests is challenging due to poor understanding of the mechanisms between bamboo forest growth characteristics and remote sensing data. The objective of this research is to examine the remote sensing characteristics of on-year and off-year bamboo forests at different dates and their AGB estimation performance. This research used multiple Sentinel-2 data to explore AGB estimation of bamboo forests in Zhejiang Province, China, by taking into account the unique characteristics of on-year and off-year bamboo forest growth features. Combining field survey data and Sentinel-2 spectral responses (spectral bands and vegetation indices) and textural images, random forest was used to identify key variables for AGB estimation. The results show that (1) the on-year and off-year bamboo forests have considerably different spectral signatures, especially in the wavelengths between red edge 2 and near-infrared wavelength (NIR2) (740⁻865 nm), making it possible to separate on-year and off-year bamboo forests; (2) on-year bamboo forests have similar spectral signatures although AGB increases from as small as 40 Mgha−1 to as high as 90 Mgha−1, implying that optical sensor data cannot effectively model on-year bamboo AGB; (3) off-year bamboo AGB has significant relationships with red and shortwave infrared (SWIR) spectral bands in the April image and with red edge 2 in the July image, but the AGB saturation problem yields poor estimation accuracy; (4) stratification considerably improved off-year bamboo AGB estimation but not on-year, non-stratification using the April image is recommended; and (5) Sentinel-2 data cannot solve the bamboo AGB data saturation problem when AGB is greater than 70 Mgha−1, similar to other optical sensor data such as Landsat. More research should be conducted in the future to integrate multiple sources—remotely sensed data (e.g., lidar, optical sensor data) and ancillary data (e.g., soil, topography)—into AGB modeling to improve the estimation. The use of very high spatial resolution images that can effectively extract tree density information may improve bamboo AGB estimation and yield new insights.

Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles (Shanghai and Shenzhen in China, and Ho Chi Minh City in Vietnam) and inland metropoles (Ulaanbaatar in Mongolia, Lanzhou in China, and Vientiane in Laos) with various developing stages and physical conditions for examining the spatiotemporal patterns of urban expansions in the past 25 years (1990–2015). Multitemporal Landsat images with 30 m spatial resolution were used to develop urban impervious surface area (ISA) distributions and examine their dynamic changes. The impacts of elevation, slope, and rivers on spatial patterns of urban expansion were examined. This research indicates that ISA is an important variable for examining urban expansion. Coastal metropoles had much faster urbanization rates than inland metropoles. The spatial patterns of urban ISA distribution and expansion are greatly influenced by physical conditions; that is, ISA is mainly distributed in the areas with slopes of less than 10 degrees. Rivers are important geographical factors constraining urban expansion, especially in developing stages, while bridges across the rivers promote urban expansion patterns and rates. The relationships of spatial patterns of urban ISA distribution and dynamics with physical conditions provide scientific data for urban planning, management, and sustainability.

Analysis of urban distribution and its expansion using remote sensing data has received increasing attention in the past three decades, but little research has examined spatial patterns of urban distribution and expansion with buffer zones in different directions. This research selected Hangzhou metropolis as a case study to analyze spatial patterns and dynamic changes based on time-series urban impervious surface area (ISA) datasets. ISA was developed from Landsat imagery between 1991 and 2014 using a hybrid approach consisting of linear spectral mixture analysis, decision tree classifiers, and post-processing. The spatial patterns of ISA distribution and its dynamic changes in eight directions—east, southeast, south, southwest, west, northwest, north, and northeast—at the temporal scale were analyzed with a buffer zone-based approach. This research indicated that ISA can be extracted from Landsat imagery with both producer and user accuracies of over 90%. ISA in Hangzhou metropolis increased from 146 km2 in 1991 to 868 km2 in 2014. Annual ISA growth rates were between 15.6 km2 and 48.8 km2 with the lowest growth rate in 1994–2000 and the highest growth rate in 2005–2010. Urban ISA increase before 2000 was mainly due to infilling within the urban landscape, and, after 2005, due to urban expansion in the urban-rural interfaces. Urban expansion in this study area has different characteristics in various directions that are influenced by topographic factors and urban development policies.

Abstract Protein kinases are key signaling nodes that regulate fundamental biological and disease processes. Illuminating kinase signaling from multiple angles can provide deeper insights into disease mechanisms and improve therapeutic targeting. While fluorescent biosensors are powerful tools for visualizing live-cell kinase activity dynamics in real time, new molecular tools are needed that enable recording of transient signaling activities for post hoc analysis and targeted manipulation. Here, we develop a light-gated kinase activity coupled transcriptional integrator (KINACT) that converts dynamic kinase signals into “permanent” fluorescent marks. KINACT enables robust monitoring of kinase activity across scales, accurately recording subcellular PKA activity, highlighting PKA activity distribution in 3D cultures, and identifying PKA activators and inhibitors in high-throughput screens. We further leverage the ability of KINACT to drive signaling effector expression to allow feedback manipulation of the balance of Gαs R201C-induced PKA and ERK activation and dissect the mechanisms of oncogenic G protein signaling.

Street space is a crucial component of public space, serving as a site for a variety of human activities. However, prior studies have primarily focused on the traffic function of street space, neglecting other functional types, such as residential and commercial. To address this gap, this study proposes a classification method for street space by integrating taxi trajectory and street view imagery. The dynamic travel features of the residents are extracted from taxi trajectory data and the multi-level physical environment features of the streets are constructed based on street view imagery data. Then, the street space with same urban functions is assigned to the same cluster based on the dynamic travel features of the residents, the multi-level physical environment features of streets and K-means method. The proposed method is empirically applied to the Futian District of Shenzhen to demonstrate its validity, and the street space is successfully classified into five types, namely public, traffic, commercial, residential, and mixed commercial and residential. This work contributes to the evaluation of street space quality, facilitating scientific planning and efficient governance of street space.

Abstract Glioma, with its heterogeneous microenvironments and genetic subtypes, presents substantial challenges for treatment prediction and development. We integrated 3D bioprinting and multi-algorithm machine learning as a novel approach to enhance the assessment and understanding of glioma treatment responses and microenvironment characteristics. The bioprinted patient-derived glioma tissues successfully recapitulated molecular properties and drug responses of native tumors. We then developed GlioML, a machine learning workflow incorporating nine distinct algorithms and a weighted ensemble model that generated robust gene expression-based predictors, each reflecting the diverse action mechanisms of various compounds and drugs. The ensemble model superseded the performance of all individual algorithms across diverse in vitro systems, including sphere cultures, complex 3D bioprinted multicellular models, and 3D patient-derived tissues. By integrating bioprinting, the evaluative scope of the treatment expanded to T cell-related therapy and anti-angiogenesis targeted therapy. We identified promising compounds and drugs for glioma treatment and revealed distinct immunosuppressive or angiogenic myeloid-infiltrated tumor microenvironments. These insights pave the way for enhanced therapeutic development for glioma and potentially for other cancers, highlighting the broad application potential of this integrative and translational approach.