Search

Peripheral nerve injury (PNI) seriously affects the health and life of patients, and is an urgent clinical problem that needs to be resolved. Nerve implants prepared from various biomaterials have played a positive role in PNI, but the effect should be further improved and thus new biomaterials is urgently needed. Ovalbumin (OVA) contains a variety of bioactive components, low immunogenicity, tolerance, antimicrobial activity, non-toxicity and biodegradability, and has the ability to promote wound healing, cell growth and antimicrobial properties. However, there are few studies on the application of OVA in neural tissue engineering. In this study, OVA implants with different spatial structures (membrane, fiber, and lyophilized scaffolds) were constructed by casting, electrospinning, and freeze-drying methods, respectively. The results showed that the OVA implants had excellent physicochemical properties and were biocompatible without significant toxicity, and can promote vascularization, show good histocompatibility, without excessive inflammatory response and immunogenicity. The in vitro results showed that OVA implants could promote the proliferation and migration of Schwann cells, while the in vivo results confirmed that OVA implants (the E5/70% and 20 kV 20 μL/min groups) could effectively regulate the growth of blood vessels, reduce the inflammatory response and promote the repair of subcutaneous nerve injury. Further on, the high-throughput sequencing results showed that the OVA implants up-regulated differential expression of genes related to biological processes such as tumor necrosis factor-α (TNF-α), phosphatidylinositide 3-kinases/protein kinase B (PI3K-Akt) signaling pathway, axon guidance, cellular adhesion junctions, and nerve regeneration in Schwann cells. The present study is expected to provide new design concepts and theoretical accumulation for the development of a new generation of nerve regeneration implantable biomaterials.

ABSTRACTJiangxi Province boasts the second-highest forest coverage in China. Its forests play a crucial role in providing essential ecosystem services and maintaining the ecological health of the region. High-resolution and high-precision forest mapping are significant in the timely and accurate monitoring of dynamic forest changes to support sustainable forest management. This study used Sentinel-2 images from four seasons in the Google Earth Engine (GEE) platform to map forest distribution. Moreover, the classification results were compared and analyzed using different classification algorithms and feature-variable combinations. Based on the overall accuracy, the optimal image seasonality, feature combinations and classification algorithms were selected, and the forest maps of Jiangxi Province were mapped from 2019 to 2021. The accuracy evaluation showed that the winter image classification results had the highest accuracy (above 0.88). The red edge bands carried by Sentinel-2 could effectively improve the classification accuracy. The Random Forest classifier is the optimal classification algorithm for forest mapping in Jiangxi Province. The forest mapping obtained can be used for ecological health assessment and ecosystem function. The study provides a scientific basis for accurate and timely extraction of forest cover and can serve as a valuable resource for forest management planning and future research.

Numerous studies have conducted in-depth research on the biological and chemical properties of tissue-engineered neural graft (TENG) on peripheral nerve regeneration, while the physical properties of the graft also display a significant impact on the regeneration of the injured nerve. Among them, the elasticity properties of TENG show a significant impact on the adhesion, proliferation, migration and bio-functionality of nerve cells in peripheral nerve regeneration. This review summarizes the latest research progress on elastic biomaterials for peripheral nervous system (PNS), including categories of elastic biomaterials, preparation methods and the effect of elasticity on the growth behavior of nerve cells, etc. In addition, the effect of the elastic substrate on the elasticity of the cell itself is also briefly described. Finally, we analyze and discuss the underlying mechanism by which elastic substrate affects nerve cell behavior.

Abstract Rodent dorsal root ganglion (DRG) is widely used for studying axonal injury. Extensive studies have explored genome-wide profiles on rodent DRGs under peripheral nerve insults. However, systematic integration and exploration of these data still be limited. Herein, we re-analyzed 21 RNA-seq datasets and presented a web-based resource (DRGProfile). We identified 53 evolutionarily conserved injury response genes, including well-known injury genes (Atf3, Npy and Gal) and less-studied transcriptional factors (Arid5a, Csrnp1, Zfp367). Notably, we identified species-preference injury response candidates (e.g. Gpr151, Lipn, Anxa10 in mice; Crisp3, Csrp3, Vip, Hamp in rats). Temporal profile analysis reveals expression patterns of genes related to pre-regenerative and regenerating states. Finally, we found a large sex difference in response to sciatic nerve injury, and identified four male-specific markers (Uty, Eif2s3y, Kdm5d, Ddx3y) expressed in DRG. Our study provides a comprehensive integrated landscape for expression change in DRG upon injury which will greatly contribute to the neuroscience community.

On the scientific basis, the carbon sink estimation of wetland was summarized up, that is stock-difference method and gain–loss method as a fundamental approach. The major issues when those methods were applied in that estimation had been raised up for attention to be paid. Based upon the science achievements, the research approaches and policy foundation for market trading about the wetland carbon sink had been analyzed for that sink sustainability. It was concluded that the decomposition reduction of wetland plant material and rewetting and suitable restoration of wetland are essential for its sink conservation. Nowadays, China has already set the wetland conservation for its carbon sink sustainability as a part of the national goal in the carbon peaking road-map, further elaboration on the policymaking should be taken for the wetland sink conclusive into the voluntary market and as an offset of the partly compulsory reduction of emission.

Sustainable development policies and spatial planning for maintaining greenery are crucial for all major cities in the world, and the measurement of green space indicators in planning practice needs to evolve in response to the demands of the times and technological drivers. This study explores an informal urban green space indicator, the green view index (GVI), which uses the visual perception of an observer to measure the quality of urban space by simulating the pedestrian perspective of the road in street-view image data and then calculating the proportion of vegetation in the road landscape. The GVI is different from macro indicators, such as public recreational green space, forest coverage, and green space rate, which are derived from planning data or remote sensing data in traditional urban planning; it starts from the bottom-up perception of individual residents and is more relevant to their subjective demands. At present, most international cities have made outstanding achievements in controlling public recreational green space, forest coverage, green space rates, and other macrolevel indicators of urban spatial quality; however, with the promotion of the concept of “human-oriented” urban planning, the potential restoration of urban spatial quality at the microlevel is gradually being recognized. To ensure the efficiency and reliability of this study, inspired by computer vision techniques and related GVI studies, a research method based on chromaticity was built to identify the proportions of green vegetation in street view images, and the credibility was improved by eliminating unreliable data. By using this method, we could evaluate a city at an overall scale instead of the previous block scale. The final research result showed that Shenzhen is friendly to human visual senses, and the GVI of the streets in developed areas is generally higher than that in developing areas. The geostatistical analysis of the green viewpoint data provides a more intuitive guide for researchers and planners, and it is believed to inform the planning and design of environmentally friendly, smart, and sustainable future cities.

Magnesium alloy is considered as one of the ideal cardiovascular stent materials owing to its good mechanical properties and biodegradability. However, the in vivo rapid degradation rate and the insufficient biocompatibility restrict its clinical applications. In this study, the magnesium alloy (AZ31B) was modified by combining the surface chemical treatment and in-situ self-assembly of 16-phosphonyl-hexadecanoic acid, followed by the immobilization of chitosan-functionalized graphene oxide (GOCS). Heparin (Hep) and GOCS were alternatively immobilized on the GOCS-modified surface through layer by layer (LBL) to construct the GOCS/Hep bioactive multilayer coating, and the corrosion resistance and biocompatibility were extensively explored. The results showed that the GOCS/Hep bioactive multilayer coating can endow magnesium alloys with an excellent in vitro corrosion resistance. The GOCS/Hep multilayer coating can significantly reduce the hemolysis rate and the platelet adhesion and activation, resulting in an excellent blood compatibility. In addition, the multilayer coating can not only enhance the adhesion and proliferation of the endothelial cells, but also promote the vascular endothelial growth factor (VEGF) and nitric oxide (NO) expression of the attached endothelial cells on the surfaces. Therefore, the method of the present study can be used to simultaneously control the corrosion resistance and improve the biocompatibility of the magnesium alloys, which is expected to promote the application of magnesium alloys in biomaterials or medical devices, especially cardiovascular stent.

The surface topographies of artificial implants including surface roughness, surface groove size and orientation, and surface pore size and distribution have a great influence on the adhesion, migration, proliferation, and differentiation of nerve cells in the nerve regeneration process. Optimizing the surface topographies of biomaterials can be a key strategy for achieving excellent cell performance in various applications such as nerve tissue engineering. In this review, we offer a comprehensive summary of the surface topographies of nerve implants and their effects on nerve cell behavior. This review also emphasizes the latest work progress of the layered structure of the natural extracellular matrix that can be imitated by the material surface topology. Finally, the future development of surface topographies on nerve regeneration was prospectively remarked.

Biofunctionalization of artificial nerve implants by incorporation of specific bioactive factors has greatly enhanced the success of grafting procedures for peripheral nerve regeneration. However, most studies on novel biofunctionalized implants have emphasized the promotion of neuronal and axonal repair over vascularization, a process critical for long-term functional restoration. We constructed a dual-biofunctionalized chitosan/collagen composite scaffold with Ile-Lys-Val-Ala-Val (IKVAV) and vascular endothelial growth factor (VEGF) by combining solution blending, in situ lyophilization, and surface biomodification. Immobilization of VEGF and IKVAV on the scaffolds was confirmed both qualitatively by staining and quantitatively by ELISA. Various single- and dual-biofunctionalized scaffolds were compared for the promotion of endothelial cell (EC) and Schwann cell (SC) proliferation as well as the induction of angiogenic and neuroregeneration-associated genes by these cells in culture. The efficacy of these scaffolds for vascularization was evaluated by implantation in chicken embryos, while functional repair capacity in vivo was assessed in rats subjected to a 10 mm sciatic nerve injury. Dual-biofunctionalized scaffolds supported robust EC and SC proliferation and upregulated the expression levels of multiple genes and proteins related to neuroregeneration and vascularization. Dual-biofunctionalized scaffolds demonstrated superior vascularization induction in embryos and greater promotion of vascularization, myelination, and functional recovery in rats. These findings support the clinical potential of VEGF/IKVAV dual-biofunctionalized chitosan/collagen composite scaffolds for facilitating peripheral nerve regeneration, making it an attractive candidate for repairing critical nerve defect. The study may provide a critical experimental and theoretical basis for the development and design of new artificial nerve implants with excellent biological performance.

Abstract Background Shenzhen has rapidly grown into a megacity in the recent decades. It is a challenging task for the Shenzhen government to provide sufficient healthcare services. The spatial configuration of healthcare services can influence the convenience for the consumers to obtain healthcare services. Spatial accessibility has been widely adopted as a scientific measurement for evaluating the rationality of the spatial configuration of healthcare services. Methods The multi-modal two-step floating catchment area (2SFCA) method is an important advance in the field of healthcare accessibility modelling, which enables the simultaneous assessment of spatial accessibility via multiple transport modes. This study further develops the multi-modal 2SFCA method by introducing online map APIs to improve the estimation of travel time by public transit or by car respectively. Results As the results show, the distribution of healthcare accessibility by multi-modal 2SFCA shows significant spatial disparity. Moreover, by dividing the multi-modal accessibility into car-mode and transit-mode accessibility, this study discovers that the transit-mode subgroup is disadvantaged in the competition for healthcare services with the car-mode subgroup. The disparity in transit-mode accessibility is the main reason of the uneven pattern of healthcare accessibility in Shenzhen. Conclusions The findings suggest improving the public transit conditions for accessing healthcare services to reduce the disparity of healthcare accessibility. More healthcare services should be allocated in the eastern and western Shenzhen, especially sub-districts in Dapeng District and western Bao’an District. As these findings cannot be drawn by the traditional single-modal 2SFCA method, the advantage of the multi-modal 2SFCA method is significant to both healthcare studies and healthcare system planning.

Abstract Background The two-step floating catchment area (2SFCA) method, which is one of the most widely used methods for measuring healthcare spatial accessibility, defines the catchment area of each facility as the area within a certain distance from the facility. However, in some cases, the service utilization behavior is constrained by administrative boundaries, therefore the definition of catchment area within a certain distance may be inappropriate. Methods In this study, we aim to propose a modification of the 2SFCA method for measuring spatial accessibility to healthcare services in a system constrained by administrative boundaries. The proposed method defines the catchment areas of healthcare facilities within certain administrative units. The method is applied in a case study of the healthcare services in Yanqing District of Beijing, China. Three types of healthcare facilities, including general hospitals, community healthcare centers and stations, are included. Results Based on the sensitivity analysis of the distance-decay parameter β, result of the β = 1 scenario is relatively appropriate and is utilized for further analysis. The difference between spatial accessibility with or without constraint of administrative boundary is relatively significant. The results of the proposed model show that the village-level spatial accessibility to healthcare services shows a significant disparity, and the uneven distribution of general hospitals is the main cause. Conclusions The constraint of administrative boundary has a significant impact on healthcare accessibility, which verifies the validity of the modification proposed by this study in empirical studies. The empirical results also lead to policy recommendations to improve healthcare equity in the study area. At the town-level, the improvement of equity in healthcare accessibility could be achieved in two ways. First, the sizes of community healthcare centers in towns with small accessibility scores should be expanded. Second, new general hospitals can be built in the eastern part of Yanqing District. Within each town, to improve the equity in healthcare accessibility, community healthcare stations should be expanded or newly built in the periphery villages.

Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable. The results showed that at the interval scale, the land transition rate increased from the first to the second period for all of the upper, middle, and lower reaches. The exchange component was larger than the quantity and shift component, and the gross change was 4.1 times larger than the net change. The size of cultivated land decreased during both intervals. The artificial surfaces gains were active for all three reaches and had strong evidence. A hypothetical error in 93% of the 2000 data and 58% of the 2010 data can explain deviations from uniform transition given woodland gain during 2000–2010 and 2010–2020. Ecological restoration projects such as Grain for Green implemented in 2000 in the upper reaches resulted in the woodland increase.

As a newly emerging satellite form of data, solar-induced chlorophyll fluorescence (SIF) provides a direct measurement of photosynthetic activity. The potential of SIF for drought assessment in different grassland ecosystems is not yet clear. In this study, the correlations between spaceborne SIF and nine drought indices were evaluated. Standardized precipitation evapotranspiration index (SPEI) at a 1, 3, 6, 9, 12 month scale, Palmer drought severity index (PDSI), soil moisture, temperature condition index (TCI), and vapor pressure deficit (VPD) were evaluated. The relationships between different grassland types and different seasons were compared, and the driving forces affecting the sensitivity of SIF to drought were explored. We found that the correlations between SIF and drought indices were different for temperate grasslands and alpine grasslands. The correlation coefficients between SIF and soil moisture were the highest (the mean value was 0.72 for temperate grasslands and 0.69 for alpine grasslands), followed by SPEI and PDSI at a three month scale, and the correlation coefficient between SIF and TCI was the lowest (the mean value was 0.38 for both temperate and alpine grasslands). Spaceborne SIF is more effective for drought monitoring during the peak period of the growing season (July and August). Temperature and radiation are important factors affecting the sensitivity of SIF to drought. The results from this study demonstrated the importance of SIF in drought monitoring especially for temperate grasslands in the peak growing season.

In this work, a smartphone-based electrochemical detection system was designed and developed for rapid and real-time detection of dopamine (DA). The system included a screen-printed electrode (SPE) used as a sensor, a hand-held electrochemical potentiostat and a smart phone with a specially designed app. During the detection period, the SPEs modified with poly(3,4-ethylenedioxythiophene) (PEDOT), chitosan (CS) and graphene (G) were used to convert and amplify the electrochemical reaction signals. The electrochemical potentiostat was used to generate excitation electrical signals and collect the electrical signals converted from the sensor. The smartphone—connected to the detector via Bluetooth-was used to control the detector for tests, further process the uploaded data, and plot graphs in real time. Experimental results showed that the self-designed sensing system could be employed for highly selective detection of DA in the presence of interfering substances such as ascorbic acid (AA) and uric acid (UA). CV was carried out to characterize the electrochemical properties of the modified SPEs and the electrochemical behaviors of DA on the modified SPEs. Finally, according to the analysis of DPV responses of DA, the system could detect DA with a detection sensitivity of 0.52 ± 0.01 μA/μM and a limit of detection of 0.29 μM in the linear range of DA concentrations from 0.05 to 70 μM.

Climate warming has induced significant changes in permafrost and seasonally frozen ground (SFG) in the Tibetan Plateau, which have complex influences on local environments. A better understanding of the impacts of soil freeze/thaw (F/T) dynamics on soil water transfer and vegetation growth is important to explore related eco-hydrological influences. We investigated soil F/T dynamics and their impacts with in-situ and satellite-based observations. Our results showed the contrasting F/T dynamics between SFG and permafrost areas. In permafrost areas, soil froze downward from the ground surface and upward from the active layer bottom with a distinct freezing zero-curtain, and minimum soil moisture occurred in the intermediate layer, but the thawing process was unidirectional. However, the vertical F/T directions were contrary in SFG areas, where soil moisture generally increased with depth and the thawing zero-curtain was distinct. The spring onset showed a positive correlation with thaw onset in permafrost areas, but such a correlation was variable in SFG areas likely depending on soil-moisture level. Our results implied that the different soil-moisture patterns and the varying vegetation response might be related to the spatially contrasting F/T dynamics, which may have different impacts on soil water transfer, and further affect the zero-curtain and vegetation phenology.

Gross primary production (GPP) plays an important role in the net ecosystem exchange of CO2 between the atmosphere and terrestrial ecosystems. It is particularly important to monitor GPP in Southeast Asia because of increasing rates of tropical forest degradation and deforestation in the region in recent decades. The newly available, improved, third generation Normalized Difference Vegetation Index (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) group provides a long temporal dataset, from July 1981 to December 2011, for terrestrial carbon cycle and climate response research. However, GIMMS NDVI3g-based GPP estimates are not yet available. We applied the GLOPEM-CEVSA model, which integrates an ecosystem process model and a production efficiency model, to estimate GPP in Southeast Asia based on three independent results of the fraction of photosynthetically active radiation absorbed by vegetation (FPAR) from GIMMS NDVI3g (GPPNDVI3g), GIMMS NDVI1g (GPPNDVI1g), and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2 FPAR product (GPPMOD15). The GPP results were validated using ground data from eddy flux towers located in different forest biomes, and comparisons were made among the three GPPs as well as the MOD17A2 GPP products (GPPMOD17). Based on validation with flux tower derived GPP estimates the results show that GPPNDVI3g is more accurate than GPPNDVI1g and is comparable in accuracy with GPPMOD15. In addition, GPPNDVI3g and GPPMOD15 have good spatial-temporal consistency. Our results indicate that GIMMS NDVI3g is an effective dataset for regional GPP simulation in Southeast Asia, capable of accurately tracking the variation and trends in long-term terrestrial ecosystem GPP dynamics.

Leaf area index (LAI) is a key surface parameter that describes the structure of vegetation and plays an important role in Earth system process modeling. In this paper, a new set of LAI products (MERSI GLOBCARBON LAI) has been developed based on the GLOBCARBON LAI algorithm and one year of FY-3A/MERSI land surface reflectance data. MERSI GLOBCARBON LAI has been inter-compared and validated over mainland China against MODIS land surface reflectance (LSR) derived LAI (using the same algorithm) and field LAI measurements. MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI show continuous and smooth LAI distributions at the start and end of the growing season. For most areas in China, the two LAI products agree well. The temporal variation in MERSI GLOBCARBON LAI and MODIS GLOBCARBON LAI consistently follows the growing season. The largest LAI difference occurs during July, when MERSI shows a much higher frequency of retrievals than does MODIS. Through validation of LAI retrievals with field measurements, our study demonstrates that LAI derived from MERSI and MODIS land surface reflectance products have comparable accuracy. MODIS top-of-atmosphere simple ratio (MODIS TOA SR) is related to MERSI TOA SR with linear correlation coefficients greater than 0.6. After atmospheric correction, the correlation coefficient increases from 0.69 to 0.75 over cropland and from 0.82 to 0.93 over grassland. However, atmospheric correction can still give rise to substantial differences in the reflectance data between the two sensors. Furthermore, different land cover types and different terrain relief have contrasting influences on the atmospheric correction, and these influences reduce the agreement between the two LAI products. This study shows the great potential of FY-3A/MERSI data for global LAI retrieval.

Accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. Forest AGB estimation has been conducted with a variety of data sources and prediction methods, but many uncertainties still exist. In this study, six prediction methods, including Gaussian processes, stepwise linear regression, nonlinear regression using a logistic model, partial least squares regression, random forest, and support vector machines were used to estimate forest AGB in Jiangxi Province, China, by combining Geoscience Laser Altimeter System (GLAS) data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, and field measurements. We compared the effect of three factors (prediction methods, sample sizes of field measurements, and cross-validation settings) on the predictive quality of the methods. The results showed that the prediction methods had the most considerable effect on the prediction quality. In most cases, random forest produced more accurate estimates than the other methods. The sample sizes had an obvious effect on accuracy, especially for the random forest model. The accuracy increased with increasing sample sizes. The random forest algorithm with a large number of field measurements, was the most precise (coefficient of determination (R2) = 0.73, root mean square error (RMSE) = 23.58 Mg/ha). Increasing the number of folds within the cross-validation settings improved the R2 values. However, no apparent change occurred in RMSE for different numbers of folds. Finally, the wall-to-wall forest AGB map over the study area was generated using the random forest model.

Accurate and prior identification of local severe storm systems in pre-convection environments using geostationary satellite imagery measurements is a challenging task. Methodologies for “convective initiation„ identification have already been developed and explored for operational nowcasting applications; however, warning of such convective systems using the new generation of geostationary satellite imagery measurements in pre-convection environments is still not well studied. In this investigation, the Random Forest (RF) machine learning algorithm is used to develop a predictive statistical model for tracking and identifying three different types of convective storm systems (weak, medium, and severe) over East Asia by combining spatially-temporally collocated Himawari-8 (H08) measurements and Numerical Weather Prediction (NWP) forecast data. The Global Precipitation Measurement (GPM) gridded product is used as a benchmark to train the predictive models based on a sample-balance technique which can adjust or balance the samples of three different convection types to avoid over-fitting any type of dataset. Variables such as brightness temperatures (BTs) from H08 water vapor absorption bands (6.2 μm, 6.9 μm and 7.3 μm) and Total Precipitable Water (TPW) from NWP show relatively high ranks in the predictive model training. These sensitive variables are closely associated with convectively dominated precipitation areas, indicating the importance of predictors from both H08 and NWP data. The final optimal RF model is achieved with an accuracy of 0.79 for classification of all convective storm systems, while the Probability of Detection (POD) of this model for severe and medium convections can reach 0.66 and 0.70, respectively. Two typical sudden convective storm cases in the warm season of 2018 tracked by this algorithm are described, and results indicate that the H08 and NWP based statistical model using the RF algorithm is capable of capturing local burst convective storm systems about 1⁻2 h earlier than the outbreak of heavy rainfall.

Despite fundamental market reforms, the Chinese financial system has remained a mixed system. From the perspectives of the mainstream doctrines of financial liberalization, this system is easily judged to be entailing serious allocative inefficiencies. Nevertheless, from alternative theoretical perspectives, the system might have been conducive to promoting productive efficiency. This paper argues that the actual experience does seem to indicate that, hitherto, the gains in productive efficiency have more than compensated for the losses in allocative efficiency. This judgement helps to make sense of the Chinese anomaly that a seemingly inefficient financial system has co-existed with the outstanding performance of financial deepening and economic development over the past three decades.

Despite fundamental market reforms, the Chinese financial system has remained a mixed system. From the perspectives of the mainstream doctrines of financial liberalization, this system is easily judged to be entailing serious allocative inefficiencies. Nevertheless, from alternative theoretical perspectives, the system might have been conducive to promoting productive efficiency. This paper argues that the actual experience does seem to indicate that, hitherto, the gains in productive efficiency have more than compensated for the losses in allocative efficiency. This judgement helps to make sense of the Chinese anomaly that a seemingly inefficient financial system has co-existed with the outstanding performance of financial deepening and economic development over the past three decades. JEL Codes: G21, N65, O16

In ecologically vulnerable regions with intricate land use dynamics, such as ecotones, frequent and intense land use transitions unfold. Therefore, the precise and timely mapping of land use becomes imperative. With that goal, by using principal component analysis, we integrated Sentinel-1 and Sentinel-2 data, using an object-oriented methodology to craft a 10-meter-resolution land use map for the forest–grassland ecological zone of the Greater Khingan Mountains spanning the years 2019 to 2021. Our research reveals a substantial enhancement in classification accuracy achieved through the integration of synthetic aperture radar–optical remote sensing data. Notably, our products outperformed other land use/land cover data sets, excelling particularly in delineating intricate riverine wetlands. The 10-meter land use product stands as a pivotal guide, offering indispensable support for sustainable development, ecological assessment, and conservation endeavors in the Greater Khingan Mountains region. [ABSTRACT FROM AUTHOR]

Biocompatible and morphable hydrogels capable of multimode reprogrammable, and adaptive shape changes are potentially useful for diverse biomedical applications. However, existing morphable systems often rely on complicated structural designs involving cumbersome and energy-intensive fabrication processes. Here, we report a simple electric-field-activated protein network migration strategy to reversibly program silk-protein hydrogels with controllable and reprogrammable complex shape transformations. The application of a low electric field enables the convergence of net negatively charged protein cross-linking networks toward the anode (isoelectric point plane) due to the pH gradient generated in the process, facilitating the formation of a gradient network structure and systems suitable for three-dimensional shape change. These tunable protein networks can be reprogrammed or permanently fixed by control of the polymorphic transitions. We show that these morphing hydrogels are capable of conformally interfacing with biological tissues by programming the shape changes and a bimorph structure consisting of aligned carbon nanotube multilayers and the silk hydrogels was assembled to illustrate utility as an implantable bioelectronic device for localized low-voltage electrical stimulation of the sciatic nerve in a rabbit. [ABSTRACT FROM AUTHOR]