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The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model's reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study's outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management. [ABSTRACT FROM AUTHOR]

The rapid increase in reactive nitrogen (N) released into the environment by human activities has notably altered the structure and composition of forest soil microbial communities (SMCs), profoundly impacting the N cycle in terrestrial ecosystems. However, the response of soil microorganisms to nitrogen addition in different seasons is not clear. This study delved into how SMCs in a subtropical region of central Yunnan, China, specifically in an evergreen broad-leaved forest (EBLF), respond to N deposition during both the dry and wet seasons. Through high-throughput sequencing, we assessed the composition and structure of SMCs under varying N addition treatments across seasons, examining their interplay with soil chemical properties, enzyme activities, and community responses. The findings revealed significant outcomes following four years of N addition in the subtropical EBLF: (1) Significant changes were observed due to the interaction between N addition and seasonal changes. Soil pH significantly decreased, indicating increased soil acidification, particularly in the dry season. Increased moisture in the wet season mitigated soil acidification. (2) In the dry season, N addition led to a decrease in microbial richness and diversity. In the wet season, N addition increased microbial richness and diversity, alleviating the downward trend observed in the dry season. (3) N addition significantly impacted the composition of soil bacterial and fungal communities. Dominant fungal genera in the wet season were particularly sensitive to N addition. (4) Seasonal changes and N addition altered soil microbial community structures. Soil chemical properties and enzyme activities significantly influenced the microbial community structure. However, due to differences in soil moisture, the key environmental factors that regulate microbial communities have changed significantly during the dry and wet seasons. This study serves as a foundation for understanding how N deposition impacts SMCs in EBLF ecosystems in subtropical regions, offering valuable insights for the scientific management of forest ecological resources amidst global change trends. [ABSTRACT FROM AUTHOR]

Glycosphingolipids (GSLs) are essential components of cell membranes, particularly enriched in the nervous system. Altered molecular distributions of GSLs are increasingly associated with human diseases, emphasizing the significance of lipidomic profiling. Traditional GSL analysis methods are hampered by matrix effect from phospholipids and the difficulty in distinguishing structural isomers. Herein, we introduce a highly sensitive workflow that harnesses magnetic TiO2 nanoparticle-based selective enrichment, charge-tagging Paternò–Büchi reaction, and liquid chromatography-tandem mass spectrometry. This approach enables mapping over 300 distinct GSLs in brain tissues by defining sugar types, long chain bases, N-acyl chains, and the locations of desaturation and hydroxylation. Relative quantitation of GSLs across multiple structural levels provides evidence of dysregulated gene and protein expressions of FA2H and CerS2 in human glioma tissue. Based on the structural features of GSLs, our method accurately differentiates human glioma with/without isocitrate dehydrogenase genetic mutation, and normal brain tissue. The molecular profiling of glycosphingolipids (GSLs) is hindered by the coexistence of abundant phospholipids and diverse isomers. The authors introduce a highly sensitive workflow that maps out the structural atlas of neutral GSLs, previously deemed a "dark space" within the lipidome. [ABSTRACT FROM AUTHOR]

This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments. [ABSTRACT FROM AUTHOR]

Tissue engineered heart valves (TEHVs) demonstrates the potential for tissue growth and remodel, offering particular benefit for pediatric patients. A significant challenge in designing functional TEHV lies in replicating the anisotropic mechanical properties of native valve leaflets. To establish a biomimetic TEHV model, we employed melt-electrowriting (MEW) technology to fabricate an anisotropic PCL scaffold. By integrating the anisotropic MEW-PCL scaffold with bioactive hydrogels (GelMA/ChsMA), we successfully crafted an elastic scaffold with tunable mechanical properties closely mirroring the structure and mechanical characteristics of natural heart valves. This scaffold not only supports the growth of valvular interstitial cells (VICs) within a 3D culture but also fosters the remodeling of extracellular matrix of VICs. The in vitro experiments demonstrated that the introduction of ChsMA improved the hemocompatibility and endothelialization of TEHV scaffold. The in vivo experiments revealed that, compared to their non-hydrogel counterparts, the PCL-GelMA/ChsMA scaffold, when implanted into SD rats, significantly suppressed immune reactions and calcification. In comparison with the PCL scaffold, the PCL-GelMA/ChsMA scaffold exhibited higher bioactivity and superior biocompatibility. The amalgamation of MEW technology and biomimetic design approaches provides a new paradigm for manufacturing scaffolds with highly controllable microstructures, biocompatibility, and anisotropic mechanical properties required for the fabrication of TEHVs. [ABSTRACT FROM AUTHOR]

As a toxic Volatile Organic Pollutant (TVOC), formaldehyde has a toxic effect on microorganisms, consequently inhibiting the biochemical process of formaldehyde wastewater treatment. Therefore, the selective degradation of formaldehyde is of great significance in achieving high-efficiency and low-cost formaldehyde wastewater treatment. This study constructed a heterogeneous Fe-ZSM-5/H2O2 Fenton system f or the selective degradation of target compounds. By immobilizing Fe3+ onto the surface of a ZSM-5 molecular sieve, Fe-ZSM-5 was prepared successfully. XRD, BET and FT-IR spectral studies showed that Fe-ZSM-5 was mainly composed of micropores. The influences of different variables on formaldehyde-selective heterogeneous Fenton degradation performance were studied. The 93.7% formaldehyde degradation and 98.2% selectivity of formaldehyde compared with glucose were demonstrated in the optimized Fenton system after 360 min. Notably, the resultant selective Fenton oxidation system had a wide range of pH suitability, from 3.0 to 10.0. Also, the Fe-ZSM-5 was used in five consecutive cycles without a significant drop in formaldehyde degradation efficiency. The use of reactive oxygen species scavengers indicated that the hydroxyl radical was the primary active species responsible for degrading formaldehyde. Furthermore, great degradation performance was acquired with high concentrations of formaldehyde for this system, and the degradation efficiency was more than 95.0%. [ABSTRACT FROM AUTHOR]

This paper aims to investigate a new method that uses buoyant gas mixed with air to control the floating height of scientific balloons. Firstly, the static characteristics and thermophysical properties of mixed-gas balloons are analyzed. Subsequently, the inflation model and the thermal-dynamic coupled model are established. Furthermore, based on theoretical research, a GUI program is compiled to simulate the ascent of mixed-gas balloons. Finally, flight tests are conducted. As the balloon volume expands to the maximum, the vertical velocity begins to decay and eventually oscillates around 0 m/s, which is consistent with the simulation. In addition, there is a noticeable shift in which the balloon starts to float after climbing to the target altitude, and the difference values between the test and the simulation are less than 350 m. Moreover, the trajectory results are similar to the prediction, and the errors of the end position are less than 2.5 km in horizontal distance. Consequently, this paper provides guidance for balloon-designated ceiling height technology which can allow a single balloon system to be used for tests at multiple heights. [ABSTRACT FROM AUTHOR]

Space solar cells, have been providing a consistent supply of energy for various spacecraft for decades. Currently, the third‐generation solar cells, such as perovskite solar cells (PSCs) and organic solar cells, have demonstrated significant potential for space applications. However, their real performance in space environments is not yet clear. To assess crucial performance parameters, solar cells need to be calibrated under the standard space spectral irradiance before deployment in space. This is known as AM0 calibration. This review article focuses on the calibration techniques and methods for space solar cells. The topics covered include space environment and standard testing condition for solar cells, the measurements of key parameters for traditional solar cells, advancements in PSCs and organic solar cells for space applications, and a comprehensive review of diverse calibration methods. The discussion spans from near‐ground calibration to near‐space calibration and space calibration, encompassing approaches, key technologies, developmental milestones, and the respective merits of each calibration methods, especially focus on high altitude balloon calibration. Finally, the article addresses the challenges and future trends of space solar cell calibration based on the aforementioned discussions. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection is used to recognize unusual patterns or anomalies in hyperspectral data. Currently, many spectral–spatial detection methods have been proposed with a cascaded manner; however, they often neglect the complementary characteristics between the spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral–spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with an entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce the spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets covering ocean and airport scenes prove that the proposed SSIF produces superior detection results over other state-of-the-art detection techniques. [ABSTRACT FROM AUTHOR]

Forest fires in the southern region of China pose significant threats to ecological balance, human safety, and socio-economic stability. Forecast zoning the occurrence of these fires is crucial for timely and effective response measures. This study employs the random forest algorithm and geospatial analysis, including kernel density and standard deviation ellipse methods, to predict forest fire occurrences. Historical fire data analysis reveals noteworthy findings: (i) Decreasing Trend in Forest Fires: The annual forest fire count in the southern region exhibits a decreasing trend from 2001 to 2019, indicating a gradual reduction in fire incidence. Spatial autocorrelation in fire point distribution is notably observed. (ii) Excellent Performance of Prediction Model: The constructed forest fire prediction model demonstrates outstanding performance metrics, achieving high accuracy, precision, recall, F1-scores, and AUC on the testing dataset. (iii) Seasonal Variations in High-Risk Areas: The probability of high-risk areas for forest fires in the southern region shows seasonal variations across different months. Notably, March to May sees increased risk in Guangxi, Guangdong, Hunan, and Fujian. June to August concentrates risk in Hunan and Jiangxi. September to November and December to February have distinct risk zones. These findings offer detailed insights into the seasonal variations of fire risk, providing a scientific basis for the prevention and control of forest fires in the southern region of China. [ABSTRACT FROM AUTHOR]

Papillary thyroid cancer (PTC) is a prevalent malignancy worldwide. Spleen tyrosine kinase (SYK) is a crucial enzyme that participates in various biological processes, including cancer progression. This study aims to uncover the biological function of SYK in PTC. SYK expression patterns in PTC were evaluated using quantitative real time polymerase chain reaction (qRT‐PCR), immunohistochemistry (IHC), and western blot. Cell function assays were performed to assess the effects of SYK on PTC. Bioinformatics analysis was conducted to identify intriguing microRNA (miRNA) and circular RNA (circRNA). Dual‐Luciferase Reporter or RNA immunoprecipitation assays were used to investigate the correlation among SYK, miR‐377‐3p, and hsa_circ_0006417. SYK was upregulated in PTC. Overexpression of SYK exhibited a positive correlation with tumor size, lymph node metastasis, and unfavorable disease‐free survival. Functional assays revealed that SYK exerted tumorigenic effect on PTC cells through mTOR/4E‐BP1 pathway. Mechanistically, hsa_circ_0006417 and miR‐377‐3p regulated SYK expression, offering modulating its tumor‐promoting effects. Collectively, SYK acts as an oncogene in PTC through mTOR/4E‐BP1 pathway, which is regulated by the hsa_circ_0006417/miR‐377‐3p axis, thereby providing a potential alternative for PTC treatment. [ABSTRACT FROM AUTHOR]

Building energy consumption is the main urban energy consumption component, which mainly serves people-centered work and living energy demands. Based on the physical requirements of humans in urban buildings and to determine the comfortable body temperature in each season, this paper establishes a sizing optimization model of building-type integrated energy systems (IES) for sustainable development, where the cooling and heating loads required to maintain indoor somatosensory body comfort temperature are calculated. Depending on the external energy price, internal power balance, and other constraints, the model develops an optimal sizing and capacity-planning method of energy conversion and storage unit in a building-type IES with PV generation. The operating principle is described as follows: the PV generation is fully consumed, a gas engine satisfies the electric and thermal base load requirements, and the power system and a heat pump supply the remaining loads. The gas price, peak-valley electricity price gap, and heat-to-power ratio of gas engines are considered important factors for the overall techno-economic analysis. The developed method is applied to optimize the economic performance of building-type IES and verified by practical examples. The results show that using the complementary characteristics of different energy conversion units is important to the overall IES cost. A 300 kW building photovoltaic system can reduce the gas engine capacity from 936.7 kW to 854.7 kW, and the annual cost can be approximately reduced from 7.82 million to 7.50 million RMB yuan. [ABSTRACT FROM AUTHOR]

Surface cracks are commonly observed in coatings and films. When structures with coatings are subject to stretching, opening mode cracks are likely to form on the surface, which may further lead to other forms of damage, such as interfacial delamination and substrate damage. Possible crack forms include cracks extending towards the interface and channeling across the film. In this paper, a two-dimensional numerical model is proposed to obtain the structural strain energy at arbitrary crack lengths for bilayer structures under uniaxial tension. The energy release rate and structural stress intensity factors can be obtained accordingly, and the effects of geometry and material features on fracture characteristics are investigated, with most crack patterns being confirmed as unstable. The proposed model can also facilitate the analysis of the stress distribution in periodic crack patterns of films. The results from the numerical model are compared with those obtained by the finite element method (FEM), and the accuracy of the theoretical results is demonstrated. [ABSTRACT FROM AUTHOR]

Dramatic alterations in epigenetic landscapes are known to impact genome accessibility and transcription. Extensive evidence demonstrates that senescent cells undergo significant changes in chromatin structure; however, the mechanisms underlying the crosstalk between epigenetic parameters and gene expression profiles have not been fully elucidated. In the present study, we delineate the genome-wide redistribution of accessible chromatin regions that lead to broad transcriptome effects during senescence. We report that distinct senescence-activated accessibility regions (SAAs) are always distributed in H3K27ac-occupied enhancer regions, where they are responsible for elevated flanking senescence-associated secretory phenotype (SASP) expression and aberrant cellular signaling relevant to SASP secretion. Mechanistically, a single transcription factor, TEAD4, moves away from H3K27ac-labled SAAs to allow for prominent chromatin accessibility reconstruction during senescence. The enhanced SAAs signal driven by TEAD4 suppression subsequently induces a robust increase in the expression of adjacent SASP genes and the secretion of downstream factors, which contribute to the progression of senescence. Our findings illustrate a dynamic landscape of chromatin accessibility following senescence entry, and further reveal an insightful function for TEAD4 in regulating the broad chromatin state that modulates the overall transcriptional program of SASP genes. [ABSTRACT FROM AUTHOR]

Ultrasonic testing (UT) is increasingly combined with machine learning (ML) techniques for intelligently identifying damage. Extracting significant features from UT data is essential for efficient defect characterization. Moreover, the hidden physics behind ML is unexplained, reducing the generalization capability and versatility of ML methods in UT. In this paper, a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efficiency of UT. Firstly, multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space. Subsequently, a feature selection method based on model interpretable strategy (FS-MIS) is innovatively developed by integrating Shapley additive explanation (SHAP), filter method, embedded method and wrapper method. The most effective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively. The proposed framework is validated by identifying and locating side-drilled holes (SDHs) with 0.5λ central distance and different depths. An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments. The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival (ToAs) of the scattered waves emitted by adjacent SDHs. The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67% with an average error of 0.25%, significantly improving the time resolution of UT signals. On this basis, the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets. The imaging resolution is enhanced to 0.5λ by implementing the total focusing method (TFM). The relative errors of hole depths and central distance are no more than 0.51% and 3.57%, respectively. Finally, the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques. [ABSTRACT FROM AUTHOR]

Southwestern China is an area heavily affected by forest fires, having a complex combination of fire sources and a high degree of human interference. The region is characterized by karst topography and a mixture of agricultural and forested areas, as well as diverse and dynamic mountainous terrain. Analyzing the driving factors behind forest fire occurrences in this area and conducting fire risk zoning are of significant importance in terms of implementing effective forest fire management. The Light Gradient Boosting Machine (LightGBM) model offers advantages in terms of efficiency, low memory usage, accuracy, scalability, and robustness, making it a powerful predictive algorithm that can handle large-scale data and complex problems. In this study, we used nearly 20 years of forest fire data in Southwestern China as the data source. Using mathematical statistics and kernel density analysis, we studied the spatiotemporal distribution characteristics of forest fires in Southwestern China. Considering 16 variables, including climate, vegetation, human factors, and topography, we employed the LightGBM model to predict and zone forest fire occurrences in Southwestern China. The results indicated the following conclusions: (i) Forest fires in Southwestern China are primarily concentrated in certain areas of Sichuan Province (such as Liangshan Yi Autonomous Prefecture and Panzhihua City), Guizhou Province (such as Qiannan Buyi and Miao Autonomous Prefecture), Yunnan Province (such as Puer City, Xishuangbanna Dai Autonomous Prefecture, and Wenshan Zhuang and Miao Autonomous Prefecture), and Chongqing Municipality. (ii) In terms of seasonality, forest fires are most frequent during the spring and winter, followed by the autumn and summer. (iii) The LightGBM forest fire prediction model yielded good results, having a training set accuracy of 83.088080%, a precision of 81.272437%, a recall of 88.760399%, an F1 score of 84.851539%, and an AUC of 91.317430%. The testing set accuracy was 79.987694%, precision was 78.541074%, recall was 85.978470%, F1 score was 82.091662%, and AUC was 87.977684%. These findings demonstrate the effectiveness of the LightGBM model in predicting forest fires in Southwest China, providing valuable insights regarding forest fire management and prevention efforts in the area. [ABSTRACT FROM AUTHOR]

A lipidome comprises thousands of lipid species, many of which are isomers and isobars. Liquid chromatography-tandem mass spectrometry (LC-MS/MS), although widely used for lipidomic profiling, faces challenges in differentiating lipid isomers. Herein, we address this issue by leveraging the orthogonal separation capabilities of hydrophilic interaction liquid chromatography (HILIC) and trapped ion mobility spectrometry (TIMS). We further integrate isomer-resolved MS/MS methods onto HILIC-TIMS, which enable pinpointing double bond locations in phospholipids and sn-positions in phosphatidylcholine. This system profiles phospholipids at multiple structural levels with short analysis time (<10 min per LC run), high sensitivity (nM detection limit), and wide coverage, while data analysis is streamlined using a home-developed software, LipidNovelist. Notably, compared to our previous report, the system doubles the coverage of phospholipids in bovine liver and reveals uncanonical desaturation pathways in RAW 264.7 macrophages. Relative quantitation of the double bond location isomers of phospholipids and the sn-position isomers of phosphatidylcholine enables the phenotyping of human bladder cancer tissue relative to normal control, which would be otherwise indistinguishable by traditional profiling methods. Our research offers a comprehensive solution for lipidomic profiling and highlights the critical role of isomer analysis in studying lipid metabolism in both healthy and diseased states. The existence of large number of isomers poses challenges for lipidomic analysis. The authors integrate hydrophilic interaction liquid chromatography, trapped ion mobility, and isomer-resolved MS/MS into a single system, enabling deep profiling of phospholipidomes at fast speed and wide coverage. [ABSTRACT FROM AUTHOR]

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development. [ABSTRACT FROM AUTHOR]

Xiong'an New Area is defined as the future city of China, and the regulation of water resources is an important part of the scientific development of the city. Baiyang Lake, the main supplying water for the city, is selected as the study area, and the water quality extraction of four typical river sections is taken as the research objective. The GaiaSky-mini2-VN hyperspectral imaging system was executed on the UAV to obtain the river hyperspectral data for four winter periods. Synchronously, water samples of COD, PI, AN, TP, and TN were collected on the ground, and the in situ data under the same coordinate were obtained. A total of 2 algorithms of band difference and band ratio are established, and the relatively optimal model is obtained based on 18 spectral transformations. The conclusion of the strength of water quality parameters' content along the four regions is obtained. This study revealed four types of river self-purification, namely, uniform type, enhanced type, jitter type, and weakened type, which provided the scientific basis for water source traceability evaluation, water pollution source area analysis, and water environment comprehensive treatment. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI), heart failure, cardiomyopathy, myocarditis, and myocardial ischemia-reperfusion injury (I/R) are the most common heart diseases, yet there is currently no effective therapy due to their complex pathogenesis. Cardiomyocytes (CMs), fibroblasts (FBs), endothelial cells (ECs), and immune cells are the primary cell types involved in heart disorders, and, thus, targeting a specific cell type for the treatment of heart disease may be more effective. The same interleukin may have various effects on different kinds of cell types in heart disease, yet the exact role of interleukins and their pathophysiological pathways on primary cell types remain largely unexplored. This review will focus on the pathophysiological effects of various interleukins including the IL-1 family (IL-1, IL-18, IL-33, IL-37), IL-2, IL-4, the IL-6 family (IL-6 and IL-11), IL-8, IL-10, IL-17 on primary cell types in common heart disease, which may contribute to the more precise and effective treatment of heart disease. [ABSTRACT FROM AUTHOR]

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH3N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH3N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources. [ABSTRACT FROM AUTHOR]

Mass spectrometry imaging (MSI) of lipids in biological tissues is useful for correlating molecular distribution with pathological results, which could provide useful information for both biological research and disease diagnosis. It is well understood that the lipidome could not be clearly deciphered without tandem mass spectrometry analysis, but this is challenging to achieve in MSI due to the limitation in sample amount at each image spot. Here we develop a multiplexed MS2 imaging (MS2I) method that can provide MS2 images for 10 lipid species or more for each sampling spot, providing spatial structural lipidomic information. Coupling with on‐tissue photochemical derivatization, imaging of 20 phospholipid C=C location isomers is also realized, showing enhanced molecular images with high definition in structure for mouse brain and human liver cancer tissue sections. Spatially mapped t‐distributed stochastic neighbor embedding has also been adopted to visualize the tumor margin with enhancement by structural lipidomic information. [ABSTRACT FROM AUTHOR]

Mass spectrometry imaging (MSI) of lipids in biological tissues is useful for correlating molecular distribution with pathological results, which could provide useful information for both biological research and disease diagnosis. It is well understood that the lipidome could not be clearly deciphered without tandem mass spectrometry analysis, but this is challenging to achieve in MSI due to the limitation in sample amount at each image spot. Here we develop a multiplexed MS2 imaging (MS2I) method that can provide MS2 images for 10 lipid species or more for each sampling spot, providing spatial structural lipidomic information. Coupling with on‐tissue photochemical derivatization, imaging of 20 phospholipid C=C location isomers is also realized, showing enhanced molecular images with high definition in structure for mouse brain and human liver cancer tissue sections. Spatially mapped t‐distributed stochastic neighbor embedding has also been adopted to visualize the tumor margin with enhancement by structural lipidomic information. [ABSTRACT FROM AUTHOR]

Purpose: This prospective study was aimed to investigate the potential utility of [18F]fibroblast activation protein inhibitor (FAPI) PET/CT for evaluating focal liver lesions (FLLs) with [18F]FDG non-avidity. Methods: From January 2021 to March 2022, this prospective study included 80 FLLs that were not avid on [18F]FDG PET/CT from 37 patients, then underwent [18F]FAPI PET/CT. All patients with FLL(s) with biopsy-proof or follow-up confirmation were categorized into four subgroups (20 hepatocellular carcinomas [HCCs]/5 non-HCC malignancies/4 inflammatory FLLs/8 benign noninflammatory FLLs). The diagnostic value of [18F]FAPI for detecting liver malignancy was determined by visual evaluation. Differences in the maximum standardized uptake value (SUVmax) and lesion-to-background ratio (LBR) obtained from [18F]FAPI PET/CT among the four subgroups were analyzed by semiquantitative analysis. Results: Among the thirty-seven enrolled participants (34 males; median age 57 years, range 48–67 years), on visual evaluation, the sensitivity, specificity, and accuracy of [18F]FAPI PET for detecting liver malignancy in the patient-based analysis were 96.0% (24/25), 58.3% (7/12), and 83.8% (31/37), respectively. On semiquantitative analysis, the SUVmax and LBR of [18F]FAPI PET in liver malignancy (33 HCC lesions; 19 non-HCC malignant lesions) were significantly higher than those in 11 benign noninflammatory FLLs [HCC: SUVmax: 6.4 vs. 4.5, P = 0.017; LBR: 5.1 vs. 1.5, P = 0.003; non-HCC: SUVmax: 5.5 vs. 4.5, P = 0.008; LBR: 4.4 vs. 1.5, P = 0.042]. Notably, there was no significant difference in the SUVmax of [18F]FAPI PET between 33 HCC lesions and 17 inflammatory FLLs (6.4 vs. 8.2, P = 0.37), but the LBR of [18F]FAPI PET in HCC were significantly lower than that in inflammatory FLLs (5.1 vs. 9.1, P = 0.003). Conclusions: [18F]FAPI PET/CT shows high sensitivity in detecting HCC and non-HCC malignancy with [18F]FDG non-avidity. [18F]FAPI might be a promising radiopharmaceutical for the differential diagnosis of benign noninflammatory FLLs and liver malignancy with [18F]FDG non-avidity. [ABSTRACT FROM AUTHOR]

The alterations in vegetation cover in the Three North Protection Forest Project area influence its ecological and environmental management. It helps to study soil erosion, environmental change, and ecosystem protection to provide basic data support. Based on the Google Earth Engine cloud platform, this paper selects MODIS 3Q1 data from 2000–2020 and uses the image element dichotomous model to estimate the fractional vegetation cover (FVC) of the Three North Protection Forest Project area, evaluates the stability and temporal and spatial variation of FVC, investigates the coupling relationship between the FVC and temperature and rainfall through evaluation indexes such as the slope of inter-annual variation and partial correlation coefficient, and then analyzes the influence of land use changes on the FVC. The results show that the fractional vegetation cover of the Three North Protection Forest Project area as a whole has increased significantly over the past 20 years, the medium and high vegetation coverage areas have reached 36.4%, the high vegetation FVC has increased from 20.66% in 2000 to 21.59% in 2020, and the vegetation activity is increasing. The vegetation cover is significantly affected by the topographic effect, with the vegetation cover improving relatively well at slopes between 5–13° and elevations ranging from 2000–2500 m. The overall positive correlation between vegetation and temperature and vegetation and precipitation in the study area was 54.08% and 70.04%, respectively, and precipitation was the key factor influencing vegetation growth in the research region. Human activities have a stronger effect on vegetation construction than destruction, and this study contributes to the evaluation of the benefits of the Three North Protection Forest Project and the understanding of human influence on environmental change. [ABSTRACT FROM AUTHOR]

Traditional microchannel needs to face the flow-reversal difficulty in high heat fluxes due to limited space. It results in large pressure and temperature fluctuation. Porous microchannels arouse more interest to provide a new solution to this problem. Flow boiling experiments in porous microchannels with PFA were investigated. Porous microchannels were sintered by 10 μm (or 30 μm) spherical copper particles with pore-forming agent (Na2CO3, 60–90 μm). Porous microchannels were composed of 23 parallel porous microchannels with 600 μm in width and 1200 μm in depth.The addition of PFA (pore-forming agent) could increase the sample porosity. For Q10 series, sample porosities increase from 20.4% to 52.9% with the PFA percentage change from 0% to 40%, while for the Q30 series they increase from 26.6% to 47.5%. Experimental results showed the boiling heat transfer coefficient (HTC) reached the maximum at the moderate porosity for both Q10 and Q30 series. Too large or too small porosity would degrade boiling heat transfer performance. It demonstrated that there existed an optimal range of PFA content for sintered microchannels. PFA content has a minor effect on the average pressure drop and would not cause the rapid increase in flow resistance. Visual observation disclosed that the sample porosity would affect the pressure instability significantly. The sample with moderate porosity showed periodic pressure fluctuation and could establish rhythmical boiling. Particle size also exerted a certain influence on the boiling heat transfer performance. Q30 series could achieve higher HTC and CHF (Critical heat flux) than Q10 series. This is attributed to the larger ratio of layer-thickness-to-particle-size (δ/d) for Q10-series samples. [ABSTRACT FROM AUTHOR]

Maintaining the balance between power station operation and environmental carrying capacity in the process of cooling water discharge into coastal waters is an essential issue to be considered. Earth observations with airborne and sea surface sensors can efficiently estimate distribution characteristics of extensive sea surface temperature compared with traditional numerical and physical simulations. Data acquisition timing windows for those sensors are designed according to tidal data. The airborne thermal infrared data (Thermal Airborne Spectrographic Imager, TASI) is preprocessed by algorithms of atmospheric correction, geometric correction, strip brightness gradient removal, and noise reduction, and then the seawater temperature is inversed in association with sea surface synchronous temperature measurement data (Sea-Bird Electronics, SBE). Verification analyses suggested a satisfied accuracy of less than about 0.2 °C error between the predicted and the measured values in general. Multiple factors influence seawater temperature, i.e., meteorology, ocean current, runoff, water depth, seawater convection, and eddy current; tidal activity is not the only one. Environmental background temperature in different seasons is the governing factor affecting the diffusion effect of seawater temperature drainage according to analyses of the covariances and correlation coefficients of eight tidal states. The present study presents an efficient and quick seawater temperature monitoring technique owing to industrial warm drainage to sea by means of a complete set of seawater temperature inversion algorithms with multi-source thermal infrared hyperspectral data. [ABSTRACT FROM AUTHOR]

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations'sustainable development goals. [ABSTRACT FROM AUTHOR]

Introduction: Pulmonary infections are frequent in immunocompromised hosts (ICH), and microbial detection is difficult. As a new method, next‐generation sequencing (NGS) may offer a solution. Objectives: This study aimed to assess the impact of NGS‐assisted pathogenic detection on the diagnosis, treatment, and outcomes of ICH complicated by pulmonary infection and radiographic evidence of bilateral diffuse lesions. Methods: This study enrolled 356 patients with ICH complicated by pulmonary infection that were admitted to Zhongshan Hospital, Fudan University, from November 17, 2017, to November 23, 2018, including 102 and 254 in the NGS and non‐NGS groups, respectively. Clinical characteristics, detection time, rough positive rate, effective positive rate, impact on anti‐infective treatment plan, 30‐day/60‐day mortality, and in‐hospital mortality were compared. Results: NGS‐assisted pathogenic detection reduced detection time (28.2 h [interquartile range (IQR) 25.9–29.83 h] vs. 50.50 h [IQR 47.90–90.91 h], P < 0.001), increased positive rate, rate of mixed infection detected, effective positive rate, and proportion of antibiotic treatment modification (45.28% vs. 89.22%, 4.72% vs. 51.96%, 21.65% vs. 64.71%, 16.54% vs. 46.08%, P < 0.001). The NGS group had a significantly lower 60‐day mortality rate (18.63% vs. 33.07%, P = 0.007). The difference in the Kaplan–Meier survival curve was significant (P = 0.029). After multivariate logistic regression, NGS‐assisted pathogenic detection remained a significant predictor of survival (OR 0.189, confidence interval [CI], 0.068–0.526). Conclusion: NGS‐assisted pathogenic detection may improve detection efficiency and is associated with better clinical outcomes in these patients. [ABSTRACT FROM AUTHOR]

Large-scale monitoring of water quality parameters (WQPs) is one of the most critical issues for protecting and managing water resources. However, monitoring optically inactive WQPs, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP) in inland waters, is still challenging. This study constructed retrieval models to explore the spatiotemporal evolution of TN, AN, and TP by Landsat 8 images, water quality sampling, and five machine learning algorithms (support vector regression, SVR; random forest regression, RFR; artificial neural networks, ANN; regression tree, RT; and gradient boosting machine, GBM) in the Nandu River downstream (NRD), a tropical river in China. The results indicated that these models can effectively monitor TN, AN, and TP concentrations at in situ sites. In particular, TN by RFR as well as AN and TP by ANN had better accuracy, in which the R2 value ranged between 0.44 and 0.67, and the RMSE was 0.03–0.33 mg/L in the testing dataset. The spatial distribution of TN, AN, and TP was seasonal in NRD from 2013–2022. TN and AN should be paid more attention to in normal wet seasons of urban and agricultural zones, respectively. TP, however, should be focus on in the normal season of agricultural zones. Temporally, AN decreased significantly in the normal and wet seasons while the others showed little change. These results could provide a large-scale spatial overview of the water quality, find the sensitive areas and periods of water pollution, and assist in identifying and controlling the non-point source pollution in the NRD. This study demonstrated that multispectral remote sensing and machine learning algorithms have great potential for monitoring optically inactive WQPs in tropical large-scale inland rivers. [ABSTRACT FROM AUTHOR]

A flash flood disaster is one of the most destructive natural disasters. With the increase in extreme rainfall events, more and more areas will be threatened by flash floods. The flash flood susceptibility assessment is the basis of flash flood risk assessment and is also an important step in flash flood disaster management. Based on Citespace analysis tools, this study made a bibliometric and visualized analysis of 305 documents collected in the core collection of Web of Science in the past 15 years, including the analysis of the number of publications and citation frequency, influence analysis, keyword analysis, author co-citation analysis, and institutional co-operation analysis. This paper summarizes the current research status and future development trend of flash flood susceptibility assessment from five key research subfields, including assessment scale, assessment unit, assessment index, assessment model, and model assessment method, discusses the analysis of the application of remote sensing and GIS in flash flood susceptibility assessment, discusses the problems encountered in the current research of the five subfields, and provides suggestions for flash flood hazard control. [ABSTRACT FROM AUTHOR]

To build a multi-energy cloud platform with the distributed generation, energy storage, micro-grid, flexible load, electric vehicle piles for high efficiency application is of great significance. In order to manage the resources for dispatching and trading in the cloud platform, this paper solves three problems. Firstly, to present the cloud platform planning method. The modelling and linear optimization algorithm for the prosumer's self-balanced to minimize the cost with trading quantity and random bidding price are proposed. Secondly, the key technologies to realize the information collection and interaction, and data model management are summarized on the basis of the demonstration project, faced to be urgently solved. Thirdly, P2P trade for small and medium scaled communities affected by grid's time-of-use tariff for prosumers are discussed. The MATLAB simulation with the bidding price following uniformly distributed sampling is taken to analyze the consumers' benefits and behaviors. The feasibility, methodology, technology and trade mode are analyzed based on the policy background, platform, optimization algorithm and simulation. The P2P trade is a new topic, that needs more work to do in the future real project. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 continues to spread worldwide. Given the urgent need for effective treatments, many clinical trials are ongoing through repurposing approved drugs. However, clinical data regarding the cardiotoxicity of these drugs are limited. Human pluripotent stem cell‐derived cardiomyocytes (hCMs) represent a powerful tool for assessing drug‐induced cardiotoxicity. Here, by using hCMs, it is demonstrated that four antiviral drugs, namely, apilimod, remdesivir, ritonavir, and lopinavir, exhibit cardiotoxicity in terms of inducing cell death, sarcomere disarray, and dysregulation of calcium handling and contraction, at clinically relevant concentrations. Human engineered heart tissue (hEHT) model is used to further evaluate the cardiotoxic effects of these drugs and it is found that they weaken hEHT contractile function. RNA‐seq analysis reveals that the expression of genes that regulate cardiomyocyte function, such as sarcomere organization (TNNT2, MYH6) and ion homeostasis (ATP2A2, HCN4), is significantly altered after drug treatments. Using high‐throughput screening of approved drugs, it is found that ceftiofur hydrochloride, astaxanthin, and quetiapine fumarate can ameliorate the cardiotoxicity of remdesivir, with astaxanthin being the most prominent one. These results warrant caution and careful monitoring when prescribing these therapies in patients and provide drug candidates to limit remdesivir‐induced cardiotoxicity. [ABSTRACT FROM AUTHOR]

In addition, although no significant difference in sarcomere structure was observed between EPSC-CMs and iPSC-CMs, the EPSC-CM-derived engineered heart tissue (EHT) (Fig. These authors contributed equally: Li Li, Zhongjun Wan Dear Editor, The availability of functional human cardiomyocytes is essential for cardiac disease modeling, drug screening, and cell therapy, whereas donor human cardiomyocytes are incredibly scarce. However, whether EPSCs can efficiently generate other lineages such as cardiomyocytes and how the EPSC-derived cardiomyocytes (EPSC-CMs) function compared with ESC/iPSC-CMs have not been studied yet. [Extracted from the article]

Due to the successful holding of the Winter Olympics, China's enthusiasm for ice and snow sports has soared, and its enthusiasm for ice and snow tourism has also been further stimulated. At present, most of China's ice and snow tourism (IST) attractions are concentrated in the three eastern provinces. At the same time, due to the increasingly harsh environment, the natural landscape of IST is also shrinking. Therefore, this paper aims at studying the strategies of IST to optimize the ecological environment and economic growth from the perspective of sustainable development. First of all, this paper makes a certain introduction to the current situation of IST. After that, the current IST resources in China were analyzed, and the three northeastern provinces were taken as the research objects, and the distribution location, traffic conditions, and development trends of IST resources were studied in detail. Then, the autoregressive model is used to fit the tourism resources and tourist growth with the economic growth, and combined with the trend shown by the economic growth model, a strategy for the sustainable development of IST is proposed. The experimental results of this paper prove that the regional system attribute of IST resources and economic growth model is between 3 and 5, and the average is about 3.6, which shows that the growth of tourism resources has a positive correlation with economic growth. Therefore, from the perspective of sustainable development, the economic growth of the IST industry should also focus on the protection and humidity development of natural ice and snow landscapes. [ABSTRACT FROM AUTHOR]

The research on node importance identification for temporal networks has attracted much attention. In this work, combined with the multi-layer coupled network analysis method, an optimized supra-adjacency matrix (OSAM) modeling method was proposed. In the process of constructing an optimized super adjacency matrix, the intra-layer relationship matrixes were improved by introducing the edge weight. The inter-layer relationship matrixes were formed by improved similarly and the inter-layer relationship is directional by using the characteristics of directed graphs. The model established by the OSAM method accurately expresses the structure of the temporal network and considers the influence of intra- and inter-layer relationships on the importance of nodes. In addition, an index was calculated by the average of the sum of the eigenvector centrality indices for a node in each layer and the node importance sorted list was obtained from this index to express the global importance of nodes in temporal networks. The experimental results on three real temporal network datasets Enron, Emaildept3, and Workspace showed that compared with the SAM and the SSAM methods, the OSAM method has a faster message propagation rate and larger message coverage and better SIR and NDCG@10 indicators. [ABSTRACT FROM AUTHOR]

In this study, vanadium dioxide thin films were fabricated on Al2O3 (0001) substrates by sol-gel method and subsequent annealing process. The effects of annealing temperature on the structure, chemical state, surface morphology and electrical properties of the films were systematically studied. The results displayed that with the annealing temperature increasing up to 520 °C, the composition evolution experienced four processes: the reduction of V5+ to V4+ (or V3+), the comproportionation of V5+/V3+ to V4+, the oxidation of V3+ to V4+, and the secondary reduction of V5+ to V3+. Accompanied with the secondary reduction at 520 °C, the disappearance of grain boundaries was observed on the film surface. Furthermore, as the annealing temperature increased from 420 °C to 470 °C and then 520 °C, the prepared films showed good phase transition property with the resistance change up to nearly four orders of magnitude, and the phase transition temperatures were 62.8 °C, 61.8 °C and 61.1 °C respectively, showing a slow downward trend. The results not only revealed the mutual transformation of various vanadium oxides during the annealing treatment, but also supplied some clues for optimizing the parameters for VO2 film preparation with high quality. Highlights: VO2 films were prepared on Al2O3 (0001) by sol-gel method and subsequent annealing process. The composition evolution image with annealing temperature is proposed in terms of XPS results. Accompanied by the secondary reduction process, the grain boundaries disappear synchronously. Both the Tc and the hysteresis width can be reduced by higher annealing temperature. [ABSTRACT FROM AUTHOR]

Monitoring the lake waterbody area in the Qinghai–Tibet Plateau (QTP) is significant in dealing with global climate change. The latest released Landsat-9 data, which has higher radiation resolution and can be complemented with other Landsat data to improve imaging temporal resolution, have great potential for applications in lake area extraction. However, no study is published on identifying waterbodies and lakes in large-scale plateau scenes based on Landsat-9 data. Therefore, we relied on the Google Earth Engine (GEE) platform and selected ten waterbody extraction algorithms to evaluate the quantitative evaluation of waterbody and lake area extraction results on the QTP and explore the usability of Landsat-9 images in the relationship between the extraction accuracy and the algorithm. The results show that the random forest (RF) algorithm performs best in all models. The overall accuracy of waterbody extraction is 95.84%, and the average lake waterbody area extraction error is 1.505%. Among the traditional threshold segmentation waterbody extraction algorithms, the overall accuracy of the NDWI waterbody extraction method is 89.89%, and the average error of lake waterbody area extraction is 3.501%, which is the highest performance model in this kind of algorithm. The linear regression coefficients of NDVI and reflectance of Landsat-8 and Landsat-9 data are close to 1, and R2 is more significant than 0.91. At the same time, the overall accuracy difference of water extraction between the two data is not better than 1.1%. This study proves that Landsat-9 and Landsat-8 data have great consistency, which can be used for collaborative analysis to identify plateau waterbodies more efficiently. With the development of cloud computing technologies, such as Gee, more complex models, such as RF, can be selected to improve the extraction accuracy of the waterbody and lake area in large-scale research. [ABSTRACT FROM AUTHOR]

An unprecedented number of COVID-19 vaccination campaign are under way worldwide. The spike protein of SARS-CoV-2, which majorly binds to the host receptor angiotensin converting enzyme 2 (ACE2) for cell entry, is used by most of the vaccine as antigen. ACE2 is highly expressed in the heart and has been reported to be protective in multiple organs. Interaction of spike with ACE2 is known to reduce ACE2 expression and affect ACE2-mediated signal transduction. However, whether a spike-encoding vaccine will aggravate myocardial damage after a heart attack via affecting ACE2 remains unclear. Here, we demonstrate that cardiac ACE2 is up-regulated and protective after myocardial ischemia/reperfusion (I/R). Infecting human cardiac cells or engineered heart tissues with a spike-based adenovirus type-5 vectored COVID-19 vaccine (AdSpike) does not affect their survival and function, whether subjected to hypoxia-reoxygenation injury or not. Furthermore, AdSpike vaccination does not aggravate heart damage in wild-type or humanized ACE2 mice after I/R injury, even at a dose that is ten-fold higher as used in human. This study represents the first systematic evaluation of the safety of a leading COVID-19 vaccine under a disease context and may provide important information to ensure maximal protection from COVID-19 in patients with or at risk of heart diseases. The safety of spike-based adenovirus type-5 vectored COVID-19 vaccine is evaluated, showing it neither affects the survival and function of human cardiac cells and engineered heart tissues nor aggravates heart damage in mice after ischemic injury. [ABSTRACT FROM AUTHOR]

Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mosquito control is still the main prevention and control measure for numerous mosquito‐borne diseases causing millions of deaths each year. New strategies for mosquito control are in demand. Proteases play an important role in mosquito physiology, therefore this study explored the inhibition of a serpin (serine protease inhibitor) in mosquitoes and its effect on reproductive capacity. RESULTS: A factor Xa inhibitor homolog (named Pipiserpin) was amplified and identified in Culex pipiens pallens mosquitoes. We expressed a recombinant Pipiserpin protein in vitro against which a mouse antiserum was generated. We found that female mosquitoes expressed more Pipiserpin protein than male mosquitoes. After mating, female mosquitoes were fed with blood mixed with different amounts of antisera and results showed that consumption of Pipiserpin impeded ovary development and decreased eggs hatching rates compared to that of the pre‐immune serum group. CONCLUSION: We identified a Culex mosquito factor Xa inhibitor, Pipiserpin, which affects female reproductive potential. Our results suggest that Pipiserpin may be a novel target for mosquito population control. The conclusions from our study on Cx. pipiens pallens might serve as a reference for the development of control measures for other mosquitoes as well. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen's trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October. [ABSTRACT FROM AUTHOR]