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Abstract Background Blood and urine biomarkers have been associated with urologic tumors, but their causal relationship with urologic tumors is unclear. Methods We performed a bidirectional Mendelian randomization (MR) analysis of the association between 35 blood and urine biomarkers and urological tumors in our discovery cohort. A Bayesian weighting approach was used to validate the positive results identified in the discovery cohort, and steiger filtering analysis was used to distinguish causality from reverse causality. Bayesian colocalization analysis was used to analyze which single nucleotide polymorphisms (SNPs) specifically co-located between the positive blood and urine biomarkers and the disease phenotypes were driven, and MR-positive results from the discovery cohort and the validation cohort were combined using the MR-meta method. Results Several blood and urine biomarkers were found to be significantly and causally associated with urologic cancers. Notably, calcium (OR: 1.34, 95%CI 1.10–1.63, P = 0.0040) and sex hormone-binding globulin (SHBG) (OR: 0.81, 95%CI 0.70–0.95, P = 0.0092) were associated with bladder cancer; gamma glutamyl transferase (GGT) (OR: 0.91, 95%CI 0.83–0.99, P = 0.0209), lipoprotein A (Lp(a)) (OR: 1.12, 95%CI 1.01–1.24, P = 0.0399), and insulinlike growth factor 1 (IGF 1) (OR: 1.10, 95%CI 1.01–1.20, P = 0.0220) were linked to prostate cancer (PCa); non albumin protein (OR: 0.78, 95%CI 0.65–0.93, P = 0.0065) and total protein (OR: 0.80, 95%CI 0.64–0.99, P = 0.0380) were linked to renal cancer; and apolipoprotein A (Apo-A) (OR: 0.56, 95%CI 0.32–0.98, P = 0.0426) and urate (OR:1.89, 95%CI 1.03–3.47, P = 0.0399) were associated with renal pelvis cancer. These associations were validated in an independent cohort, with GGT, IGF 1, and Lp(a) being consistently linked to PCa. Conclusion This study identified significant biomarkers associated with urological cancers in blood and urine. These include GGT, IGF 1, and Lp(a), which are strong biomarkers for PCa. In addition, the findings of this study provide evidence for a handful of risk and protective factors for the development of urologic cancers.

This study examined the potential health risks posed by the operation of 96 waste-to-energy (WtE) plants in 30 cities in the Bohai Rim of China. Utilizing a sophisticated simulation approach, the Weather Research and Forecasting (WRF) model coupled with the California Puff (CALPUFF) model, we obtained the spatial distribution of pollutants emitted by WtE plants in the atmosphere. Hazard indices (HI) and cancer risks (CR) were calculated for each plant using the United States Environmental Protection Agency's recommended methodologies. The results indicated that both HIs and CRs were generally low, with values below the accepted threshold of 1.0 and 1.0 × 10−6, respectively. Specifically, the average HI and CR values for the entire study area were 2.95 × 10−3 and 3.43 × 10−7, respectively. However, some variability in these values was observed depending on the location and type of WtE plant. A thorough analysis of various parameters, such as waste composition, moisture content, and operating conditions, was conducted to identify the factors that influence the health risks associated with incineration. The findings suggest that proper waste sorting and categorization, increased cost of construction, and elevated height of chimneys are effective strategies for reducing the health risks associated with incineration. Overall, this study provides valuable insights into the potential health risks associated with WtE plants in the Bohai Rim region of China. The findings can serve as useful guidelines for law enforcement wings and industry professionals seeking to minimize the risks associated with municipal solid waste (MSW) management and promote sustainable development.

With the rapid development of intelligent and informationized air battlefields, intelligent air combat has increasingly become key to affecting the outcome of a battlefield. In conventional multi-aircraft air combat, there are issues of low efficiency in intelligent decision-making, difficulty in meeting the needs of complex air combat environments, and unreasonable target allocation. In response to the problems in conventional multi-aircraft air combat, we introduce a long short-term memory–proximal policy optimization algorithm (LSTM–PPO). Using the long short-term memory network to extract features and perceive the situation of the state, an intelligent agent trains the normalized and feature-fused state information residual network and value network, chooses the optimal action through the proximal policy optimization strategy based on the current situation, and embeds a reward function containing expert knowledge during the training process to solve the problem of sparse rewards. Meanwhile, a target allocation algorithm based on threat value calculation is presented. Using angle, speed, and height threat values as the basis for target allocation, the ID of the target aircraft with the highest threat value on the battlefield is calculated in real-time. When the strategy network outputs an action of attack, it conducts target allocation. To confirm the effectiveness of the algorithm, we carried out 4v4 multi-aircraft air combat experiments in a digital twin simulation environment built by our research group. The red team consists of reinforcement learning agents based on LSTM–PPO algorithm, whereas the blue team comprises a finite state machine composed of expert knowledge bases. After more than 1200 rounds of aerial confrontation, the algorithm has been converged, and the win rate of the red team has reached 82%. Furthermore, we assessed the performance of four other mainstream reinforcement learning algorithms in 4v4 air combat experiments under the same experimental conditions. It is shown that the deep Q-network (DQN) and soft actor-critic (SAC) algorithms have difficulties in dealing with high-dimensional continuous action spaces and multiagent collaboration. The multi-agent deep deterministic policy gradient algorithm (MADDPG) employs a multi-agent strategy and cooperative training, so it exhibits a significantly higher win rate than the DQN and SAC algorithms. The multi-agent proximal policy optimization (MAPPO) algorithm has a relatively high failure rate and is not stable enough to deal with enemy aircraft’s strategies in some cases. The LSTM–PPO algorithm shows a significantly higher win rate than other mainstream reinforcement learning algorithms in multi-aircraft collaborative air combat, which confirms the effectiveness of the LSTM–PPO algorithm in dealing with high-dimensional continuous action spaces and multi-aircraft collaborative operations.

A cavity-based X-ray free-electron laser (CBXFEL) is a possible future direction in the development of fully coherent X-ray sources. CBXFELs consist of a low-emittance electron source, a magnet system with several undulators and chicanes, and an X-ray cavity. The X-ray cavity stores and circulates X-ray pulses for repeated FEL interactions with electron pulses until the FEL reaches saturation. CBXFEL cavities require low-loss wavefront-preserving optical components: near-100%-reflectivity X-ray diamond Bragg-reflecting crystals, outcoupling devices such as thin diamond membranes or X-ray gratings, and aberration-free focusing elements. In the framework of the collaborative CBXFEL research and development project of Argonne National Laboratory, SLAC National Accelerator Laboratory and SPring-8, we report here the design, manufacturing and characterization of X-ray optical components for the CBXFEL cavity, which include high-reflectivity diamond crystal mirrors, a diamond drumhead crystal with thin membranes, beryllium refractive lenses and channel-cut Si monochromators. All the designed optical components have been fully characterized at the Advanced Photon Source to demonstrate their suitability for the CBXFEL cavity application.

Abstract Road defect detection is critical step for road maintenance periodic inspection. Current methodologies exhibit drawbacks such as low detection accuracy, slow detection speed, and the inability to support edge deployment and real-time detection. To solve this issue, we introduce an improved YOLOv8 road defect detection model. Firstly, we designed the EMA Faster Block structure using partial convolution to replace the Bottleneck structure in the YOLOv8 C2f module, and the enhanced C2f module was labeled as C2f-Faster-EMA. Secondly, we improved the model speed by introducing SimSPPF instead of SPPF. Finally, for the head, Detect-Dyhead, chosen to replace the original head, significantly improves the representation ability of heads without introducing any GFLOPs. Experimental results on the road defect detection dataset show that the improved model in this paper outperforms the original YOLOv8, with a 5.8% increase in average accuracy (mAP@0.5), and notable reductions of 22.33% in model size, 23.03% in parameter size, and 21.68% in computational complexity.

Abstract Waste activated sludge (WAS) is attracting attention for its energy and resource potential. Anaerobic digestion (AD) can be used to efficiently recover energy resources and stabilize pollutants in WAS, with biochar emerging as an ideal additive to boost this process. However, the low abundance of surface functional groups and small pore structure of raw biochar may limit its performance in the AD of WAS. These limitations can be overcome by using functional biochar. In this review, the preparation and modification methods for functional biochar and its role in the AD of WAS are systematically summarized and discussed. Notably, the role of functional biochar in electron transfer, buffering effects, microbial immobilization, alleviation of inhibitory effects, and functional microbial modulation are analyzed. Additionally, the impacts of biochar on digestate utilization, pollutant removal, and carbon fixation are discussed. Special attention is given to functional biochar for its ability to promote circular economy and carbon neutrality. Finally, future perspectives on the use of functional biochar in the AD of WAS are presented. The aim of this review is to provide insights into the application of functional biochar in the AD of WAS from the perspective of performance improvement, resource utilization, and circular economy. Graphical Abstract

This study utilized multisource remote sensing data and advanced technology to investigate the potential driving factors of urban heat island (UHI) effects from the perspective of local climate zones (LCZs), including natural, social, and urban three-dimensional (3-D) structural factors. Using MODIS land surface temperature remote sensing data products and supplementary datasets, the simplified urban-extent algorithm was employed to identify UHI areas and quantify UHI Intensity (UHII). The stepwise multiple linear regression method and SHapley Additive exPlanations-explained eXtreme gradient boosting machine learning method were then applied to attribute UHII to 15 selected driving factors across 17 LCZ types in 369 Chinese cities. The findings indicate that large UHI areas are predominantly associated with low-rise LCZ types, where compact building arrangements intensify UHII, and increased building heights exacerbate this effect. During daytime, the UHI effects are largely driven by urban 3-D structures, particularly within LCZ 1-6 areas. Conversely, at night, the UHI effect is more significantly impacted by natural environmental factors. These insights offer a robust scientific foundation for urban planners to craft LCZ-specific strategies aimed at fostering the development of sustainable cities and communities.

Abstract The senescence-associated secretory phenotype (SASP) is a generic term for the secretion of cytokines, such as pro-inflammatory factors and proteases. It is a crucial feature of senescent cells. SASP factors induce tissue remodeling and immune cell recruitment. Previous studies have focused on the beneficial role of SASP during embryonic development, wound healing, tissue healing in general, immunoregulation properties, and cancer. However, some recent studies have identified several negative effects of SASP on fracture healing. Senolytics is a drug that selectively eliminates senescent cells. Senolytics can inhibit the function of senescent cells and SASP, which has been found to have positive effects on a variety of aging-related diseases. At the same time, recent data suggest that removing senescent cells may promote fracture healing. Here, we reviewed the latest research progress about SASP and illustrated the inflammatory response and the influence of SASP on fracture healing. This review aims to understand the role of SASP in fracture healing, aiming to provide an important clinical prevention and treatment strategy for fracture. Clinical trials of some senolytics agents are underway and are expected to clarify the effectiveness of their targeted therapy in the clinic in the future. Meanwhile, the adverse effects of this treatment method still need further study.

The advent of next-generation synchrotron radiation sources and X-ray free-electron lasers calls for high-quality Bragg-diffraction crystal optics to preserve the X-ray beam coherence and wavefront. This requirement brings new challenges in characterizing crystals in Bragg diffraction in terms of Bragg-plane height errors and wavefront phase distortions. Here, a quantitative methodology to characterize crystal optics using a state-of-the-art at-wavelength wavefront sensing technique and statistical analysis is proposed. The method was tested at the 1-BM-B optics testing beamline at the Advanced Photon Source for measuring silicon and diamond crystals in a self-referencing single-crystal mode and an absolute double-crystal mode. The phase error sensitivity of the technique is demonstrated to be at the λ/100 level required by most applications, such as the characterization of diamond crystals for cavity-based X-ray free-electron lasers.

Abstract Background Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths worldwide, primarily due to its propensity for metastasis. Patients diagnosed with localized primary cancer have higher survival rates than those with metastasis. Thus, it is imperative to discover biomarkers for the early detection of NSCLC and the timely prediction of tumor metastasis to improve patient outcomes. Methods Here, we utilized an integrated approach to isolate and characterize plasma exosomes from NSCLC patients as well as healthy individuals. We then conducted proteomics analysis and parallel reaction monitoring to identify and validate the top-ranked proteins of plasma exosomes. Results Our study revealed that the proteome in exosomes from NSCLC patients with metastasis was distinctly different from that from healthy individuals. The former had larger diameters and lower concentrations of exosomes than the latter. Furthermore, among the 1220 identified exosomal proteins, we identified two distinct panels of biomarkers. The first panel of biomarkers (FGB, FGG, and VWF) showed potential for early NSCLC diagnosis and demonstrated a direct correlation with the survival duration of NSCLC patients. The second panel of biomarkers (CFHR5, C9, and MBL2) emerged as potential biomarkers for assessing NSCLC metastasis, of which CFHR5 alone was significantly associated with the overall survival of NSCLC patients. Conclusions These findings underscore the potential of plasma exosomal biomarkers for early NSCLC diagnosis and metastasis prediction. Notably, CFHR5 stands out as a promising prognostic indicator for NSCLC patients. The clinical utility of exosomal biomarkers offers the potential to enhance the management of NSCLC.

In adverse foggy weather conditions, images captured are adversely affected by natural environmental factors, resulting in reduced image contrast and diminished visibility. Traditional image dehazing methods typically rely on prior knowledge, but their efficacy diminishes in practical, complex environments. Deep learning methods have shown promise in single-image dehazing tasks, but often struggle to fully leverage depth and edge information, leading to blurred edges and incomplete dehazing effects. To address these challenges, this paper proposes a deep-guided bilateral grid feature fusion dehazing network. This network extracts depth information through a dedicated module, derives bilateral grid features via Unet, employs depth information to guide the sampling of bilateral grid features, reconstructs features using a dedicated module, and finally estimates dehazed images through two layers of convolutional layers and residual connections with the original images. The experimental results demonstrate the effectiveness of the proposed method on public datasets, successfully removing fog while preserving image details.

Magnetometer towfish can be utilized for underwater and aerial towed magnetic field measurements. Although they lack propulsion systems, significant magnetic interference persists, necessitating magnetic interference compensation. However, as towfish possess no maneuverability, they cannot employ calibration methods akin to those of airborne platforms for determining magnetic compensation parameters. This article proposed a ground calibration method of magnetic compensation parameters for magnetometer towfish and designed a ground magnetic interference calibration device to impose certain constraints on the magnetic field environment. Based on the equivalence analysis with the aeromagnetic compensation parameter calibration scheme, a ground calibration method tailored for magnetometer towfish was designed and subsequently subjected to ground calibration experiments and dynamic noise validation in actual environments. Experimental results reveal that the method possesses remarkable efficacy for calibrating magnetic compensation parameters of towfish by using towed magnetic field measurement devices, yielding a quality akin to that of aircraft platforms. The improvement ratio for magnetic compensation calibration exceeds 16.

The worldwide elderly population is on the rise, and aging is a major osteoporosis risk factor. Senescent cells accumulation can have a detrimental effect the body as we age. The senescence-associated secretory phenotype (SASP), an essential cellular senescence hallmark, is an important mechanism connecting cellular senescence to osteoporosis. This review describes in detail the characteristics of SASPs and their regulatory agencies, and shed fresh light on how SASPs from different senescent cells contribute to osteoporosis development. Furthermore, we summarized various innovative therapy techniques that target SASPs to lower the burden of osteoporosis in the elderly and discussed the potential challenges of SASPs-based therapy for osteoporosis as a new clinical trial.

BackgroundChina has entered an aging society and will likely become the fastest-aging country in the world. The demand for aging care services has greatly increased. In recent years, the model of mutual aid for aging care has met the needs of older adults, especially those in rural areas. In this case, how much time should be spent on mutual aid for the older adult in terms of time and how much influence the size of the group has in terms of space are two very important questions when studying mutual aid for aging care.MethodsAn overlapping generations model is built in this article, which includes representative agents, representative enterprises, the endowment insurance system constructed by the government, and the behavior of representative agents in mutual aid for aging care under the background of active aging.ResultsIn the base case, the optimal proportion of time to participate in the mutual aid group is 9.31%, and the optimal proportion of time is influenced by the benchmark time of care and the size of the care group. With the increase in the benchmark time of care, the optimal proportion of time increases correspondingly, but the increase is decreasing. With the increase in the size of the care group, the optimal proportion of time decreases, but after the size reaches 4 or 5, the impact becomes very small. When parents' psychological preference coefficient changes from 0.1 to 0.9, that is, when parents change from introverted to extroverted personalities, the optimal proportion of time and parents' utility will also change.ConclusionFor children who usually take care of their parents for a long benchmark time, the optimal time to participate in the mutual aid group based on personal utility maximization is also long. Second, as the size of the group increases, the time for representative agents to participate in the mutual aid group gradually decreases. In addition, the model of mutual aid for aging care is highly correlated with parents' personalities, and extrovert parents benefit more from this model.

Abstract This paper studies the extraction of information from unstructured text data of medical literature and electronic medical records in the field of medicine, and proposes a TCM-KR method of knowledge reasoning based on electronic medical records to enhance association rules, and carries out a study on association characteristics in the field of the electronic medical record. This method abstracts the word bag representation mode of text semantics from the unstructured data representation and integrates the correlation information of the knowledge graph of the medicine domain. The method based on a graph convolutional network was used to predict the unknown associations' relations between viscera, channel tropism, and channel distribution. The experimental results show that the TCM-KR method can efficiently infer a large amount of high-quality triple knowledge from the unstructured text data of medicine, and predict the correlation characteristics of Syndromes-Viscera, Chinese medicinal-Channel tropism, Acupoints-Channel distribution in treating lumbar intervertebral disc prolapse and provide a dedicated machine learning model and guidance for clinical diagnosis and treatment.

This study proposes a numerical model to simulate the thermal characteristics of stratospheric airships (SA). This model introduces a simple yet accurate method for the selection of the optical bands for target detection based on multispectral features. The key to this model is the selection of the spectral bands in which a significant radiation difference exists between the target and its surrounding background. Using the signal-to-noise ratio (SNR), detection probability, and false alarm rate as model evaluation metrics, and based on the analysis of the radiation differences between the target and atmospheric background, it is found that there are two narrow bands possible for SA detection: one with a central wavelength of 8.83 µm and a bandwidth of 0.35 µm and the other with a central wavelength of 11.51 µm and a bandwidth of 0.34 µm. Further numerical simulations and theoretical analyses under varying space environments demonstrate that considerable detection potential can be achieved; thus, the presented optical detection model is useful for the night detection of SA targets.

Timely and accurate acquisition of spatial distribution and changes in cropland is of significant importance for food security and ecological preservation. Most studies that monitor long-term changes in cropland tend to overlook the rationality in the process of cropland evolution, and there are conflicts between the interannual cropland data, so they cannot be used to analyze land use change. This study focuses on the rationality of annual identification results for cropland, considering the long-term evolution and short-term variations influenced by natural environmental changes and human activities. An approach for annual monitoring of cropland based on long time series and deep learning is also proposed. We acquired imagery related to cropland’s vegetation lush period (VLP) and vegetation differential period (VDP) from Landsat images on the Google Earth Engine (GEE) platform and used the ResUNet-a structural model for training. Finally, a long-time-series cropland correction algorithm based on LandTrendr is introduced, and interannual cropland maps of Guangdong Province from 1991 to 2020 were generated. Evaluating the cropland monitoring results in Guangdong Province every five years, we found an overall accuracy of 0.91–0.93 and a kappa coefficient of 0.80–0.83. Our results demonstrate good consistency with agricultural statistical data. Over the past 30 years, the total cropland area in Guangdong Province has undergone three phases: a decrease, significant decrease, and stabilization. Significant regional variations have also been observed. Our approach can be applied to long-time-series interannual cropland monitoring in the southern regions of China, providing valuable data support for the further implementation of cropland protection.

Abstract Hair is the most important but onerous step for depicting dynamic 3D virtual characters. The photorealistic hair animation requires high‐quality simulation and rendering models. These models are based on complex calculations of mechanics and optics. Because of the huge time budget, it is difficult to apply in the interactive scene. A promising solution to overcome the time budget is the reduced model that struggles to reduce the computation of physical details by various interpolation methods. However, current reduced models compromise too much reality. This research intends to achieve photorealistic hair animation in a fast way. Building a deep learning‐based framework to synthesize photorealistic hair is aimed at. Furthermore, this research also presents a pipeline for hair merging into the scene. This new framework enables the model to significantly improve the appearances of hair animation while adding little computation overhead.

Currently, urban heat island (UHI) has evolved beyond being a homogeneous climate and environmental issue, it has emerged as a critical threat to the sustainable development of urban environment. This study highlights substantial spatio-temporal expansions of UHIs in China between 2005 and 2020. Notably, the UHI intensity (UHII) exhibited a more pronounced increase during nighttime than daytime, with land surface temperature rises of 0.46 °C and 0.17 °C, respectively. Analysis of 48 representative cities revealed that although most cities experienced a faster UHII growth rate in new UHIs compared with pre-existing ones, the significant initial disparity hinders the new UHIs in 2010 and 2015 from catching up with the UHII of pre-existing UHIs in the short term. However, by 2025, it is projected that the daytime UHII of new UHIs in 2015 will surpass that of new UHIs in 2010 in 21 cities, while the same trend is expected in 31 cities during nighttime. Furthermore, spatial gradient analysis unveiled individual variations in UHII within different buffer rings. These findings provide valuable insights into the spatial expansion and dynamics of UHIs in China, contributing to the understanding of UHI effects. Moreover, these findings can inform urban renewal strategies aimed at mitigating UHI's adverse impacts, enhancing urban resilience, and fostering the development of sustainable and livable cities.

Adaptive X-ray mirrors are being adopted on high-coherent-flux synchrotron and X-ray free-electron laser beamlines where dynamic phase control and aberration compensation are necessary to preserve wavefront quality from source to sample, yet challenging to achieve. Additional difficulties arise from the inability to continuously probe the wavefront in this context, which demands methods of control that require little to no feedback. In this work, a data-driven approach to the control of adaptive X-ray optics with piezo-bimorph actuators is demonstrated. This approach approximates the non-linear system dynamics with a discrete-time model using random mirror shapes and interferometric measurements as training data. For mirrors of this type, prior states and voltage inputs affect the shape-change trajectory, and therefore must be included in the model. Without the need for assumed physical models of the mirror's behavior, the generality of the neural network structure accommodates drift, creep and hysteresis, and enables a control algorithm that achieves shape control and stability below 2 nm RMS. Using a prototype mirror and ex situ metrology, it is shown that the accuracy of our trained model enables open-loop shape control across a diverse set of states and that the control algorithm achieves shape error magnitudes that fall within diffraction-limited performance.

In climate change adaptation and mitigation, including the reduction of negative impacts associated with urban heat environment, it is essential to quantify the contributions of natural and anthropogenic factors. Using remotely sensed land surface temperature, emissivity, land use types, and nightlight data for 364 Chinese cities, we proposed an urban land surface heat environment change attribution algorithm based on land use types, attributing the change of urban land surface heat environment to natural factors, land use change, and other anthropogenic factors at urban scale. From 2005 to 2020, summer daytime land surface temperature decreased and increased in 40.93% and 59.07% of these cities, respectively. Natural factors made a larger contribution than land use change and other anthropogenic factors to urban land surface temperature changes in 79.67% of cities; in 60.44% of cities, other anthropogenic factors other than land use change and natural factors experienced the highest contribution intensities. Three factors were spatially heterogeneous. Urban land surface temperatures were influenced by background natural climate endowment and human social development values, increasing with population density (up to 2,000 people·km−2) and annual precipitation (up to 800 mm·year−1). These results have important implications for the detection and attribution of urban-scale climate change and will be useful in designing management plans to optimize land use configuration, lead in climate actions, and carry out collaborative mitigation and adaptation strategies to achieve sustainable development.

The reasonable application of the random sampling consistent algorithm can be used to eliminate the theoretically possible error points and then complete the initial aiming pairing. The interior points obtained after this algorithm are used to estimate the relative adjacent poses of the new robot in the operation of the least squares method, so as to make certain improvements to the ICP (iterative closest point) algorithm, making it useful for accurate aiming pairing. Finally, by using the optimization method of G20, the posture of the new mobile robot relative to the adjacent position is optimized to a certain extent. In order to reasonably plan and design the landscape of mountain villages, this paper proposes the corresponding solution by analyzing the multidimensional value and landscape design data of traditional mountain villages, which not only needs to adapt to modern economic development but also needs to live in harmony with the natural ecological environment. Taking a village as a case for analysis, the village houses, roads, services, greening, ecology, and other aspects were reconstructed, and then, the detailed landscape design of the multimedia multidimensional resource village was analyzed and implemented.

Land salinization is a global environmental problem, and how to manage saline soils and promote healthy ecosystems has become a major challenge. China-Singapore Tianjin Eco-City is located in coastal land reclamation areas, so salinization is severe in this region. In this study, geostatistical methods, the ordinary kriging method, and principal component analysis were used. Vertical sampling was performed over three layers (0–20 cm, 20–40 cm, and 40–60 cm) at 184 locations within the study area to produce a total of 542 soil samples. It was found that areas with soluble salt contents greater than 3000 mg/kg account for over 90% of the study area, and high soluble salt content in surface layer soils is the dominant factor in soil salinization. Na+, Cl−, and SO42- are the primary control factors that determine the coefficient of variation of the soils’ soluble salt content. Total salinity and Na+, Cl−, SO42-, K+, and Mg2+ reflect on the salinization of the soils, while effective phosphorus, available potassium, and soil organic carbon reflect on the state of soil nutrition. Based on our results, we proposed site-specific and scientific soil remediation and greening measures.

Understanding the cooling effect (CE) of urban parks is vital to mitigate the urban heat islands. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) land surface temperature data (LST) data were used to construct diurnal thermal variations to explore the CE of urban parks in the fifth ring regions of Beijing. Local climate zone (LCZ) classification was used to distinguish different parks for examining the internal CE of parks. New subclasses of LCZ (e.g., LCZAG, LCZBG, LCZCG) were created based on the basic subclasses, such as LCZA (mainly consist of dense trees), LCZB (scattered tress with dense bush), LCZC (scattered bush), and LCZG (water). The external CE of parks was evaluated in different directions, and the impacts of buildings around parks on the external CE were also analyzed. The results showed that urban parks tended to heat up slower than the whole urban regions during the daytime, and water releasing energy dominated the mean LST of parks at night. Large parks meeting the minimum area (40,000 m2) of LCZ had better CE inside each park during the daytime. Three hundred thirty four large parks were divided into six types of LCZ. The strength of the CE decrease with the increases of distance, and seemed to exist at a distance of up to 150–200 m. Such impacts of parks were insignificant at night. In addition, parks containing certain dense trees and water had better CE inside and out. The height and density of buildings had a relatively obvious impact on the CE of parks. This study can provide insight into the impacts of urban parks on the urban thermal environment and promote the CE of urban parks in future urban planning.

Soft robots have shown great potential to enable safe interactions with unknown environments due to their inherent compliance and variable stiffness. However, without knowledge of potential contacts, a soft robot could exhibit rigid behaviors in a goal-reaching task and collide into obstacles. In this paper, we introduce a Sliding Mode Augmented by Reactive Transitioning (SMART) controller to detect the contact events, adjust the robot’s desired trajectory, and reject estimated disturbances in a goal reaching task. We employ a sliding mode controller to track the desired trajectory with a nonlinear disturbance observer (NDOB) to estimate the lumped disturbance, and a switching algorithm to adjust the desired robot trajectories. The proposed controller is validated on a pneumatic-driven fabric soft robot whose dynamics is described by a new extended rigid-arm model to fit the actuator design. A stability analysis of the proposed controller is also presented. Experimental results show that, despite modeling uncertainties, the robot can detect obstacles, adjust the reference trajectories to maintain compliance, and recover to track the original desired path once the obstacle is removed. Without force sensors, the proposed model-based controller can adjust the robot’s stiffness based on the estimated disturbance to achieve goal reaching and compliant interaction with unknown obstacles.

Neurostimulant drugs or magnetic/electrical stimulation techniques have shown limited effects on learning capabilities of healthy subjects. The authors show that, without introducing an exogeneous gene, mid-infrared light can modulate firing activity of neurons in vivo and accelerate learning in mice.

In the process of studying the spatiotemporal cause mechanism of urban heat island (UHI) effects, the classification method used will directly affect the robustness of urban surface heat classification. Applying five commonly used standard classification methods, we divided Beijing's urban surface temperatures in the summer of 2020 into five levels. We then compared the reliability of the five classification methods in resolving 12-period data and the seasonal average temperature in UHI patches, based on two indicators: UHI area and UHI intensity. The actual land-use composition of the UHI patches obtained with traditional methods was applied to confirm our results. The mean-standard deviation method and natural breaks (Jenks) method were more robust with regard to UHI classification and 12-period data reliability. For the UHI area index, the mean-standard deviation method produced the smallest total area of UHI patches for summer days and nights. For the UHI intensity index, the quantile method, mean-standard deviation method, and natural breaks (Jenks) method were associated with smaller errors. Considering the composition of land-use types in UHI patches, the mean-standard deviation method, and natural breaks (Jenks) method were more rigorous. Thus, our research results provide guidance for method selection when classifying UHI.

Abstract Background In the stabilization of femoral fractures in mono- and polytrauma, clinical practice has shown better care through intramedullary nailing. However, the reason why this is the case is not fully understood. In addition to concomitant injuries, the immunological aspect is increasingly coming to the fore. Neutrophil granulocytes (PMNL), in particular next to other immunological cell types, seem to be associated with the fracture healing processes. For this reason, the early phase after fracture (up to 72 h after trauma) near the fracture zone in muscle tissue was investigated in a pig model. Material and methods A mono- and polytrauma pig model (sole femur fracture or blunt thoracic trauma, hemorrhagic shock, liver laceration, and femur fracture) was used to demonstrate the immunological situation through muscle biopsies and their analysis by histology and qRT-PCR during a 72 h follow-up phase. Two stabilization methods were used (intramedullary nail vs. external fixator) and compared with a nontraumatized sham group. Results Monotrauma shows higher PMNL numbers in muscle tissue compared with polytrauma (15.52 ± 5.39 mono vs. 8.23 ± 3.36 poly; p = 0.013), regardless of the treatment strategy. In contrast, polytrauma shows a longer lasting invasion of PMNL (24 h vs. 72 h). At 24 h in the case of monotrauma, the fracture treated with external fixation shows more PMNL than the fracture treated with intramedullary nailing (p = 0.026). This difference cannot be determined in polytrauma probably caused by a generalized immune response. Both monotrauma and polytrauma show a delayed PMNL increase in the muscle tissue of the uninjured side. The use of intramedullary nailing in monotrauma resulted in a significant increase in IL-6 (2 h after trauma) and IL-8 (24 and 48 h after trauma) transcription. Conclusion The reduction of PMNL invasion into the nearby muscle tissue of a monotrauma femur fracture stabilized by intramedullary nailing supports the advantages found in everyday clinical practice and therefore underlines the usage of nailing. For the polytrauma situation, the fixation seems to play a minor role, possibly due to a generalized immune reaction.

Traditional electrical grid as the largest and most complex industry in the world is currently under fundamental development. According to the IEEE Std1547.4, large distribution systems can be clustered into a number of micro-grids (MGs) to facilitate the control and operation infrastructure in future distribution systems. A distribution network with distributed energy resources serving a group of loads, can be operated as a MG, in grid-connected or isolated grid modes. Most recently, the concept of MGs has become an important subject in smart grid area, demanding a systematic method for their optimal planning. The prosperous development of the micro-grid concept implies the definition of an appropriate regulation for its integration on distribution systems. The MG operators are responsible for a reliable energy supply to their consumers. MG formation represents a suitable solution for uninterrupted supplying of critical loads, in the absence of network.In this study, a systematic approach is presented for optimal construction of micro-grid. The problem is solved in two stages. In the first stage, the network is designed as an integrated and active distribution network considering the importance of reliability by using Backward–forward load flow. Next, the distribution network is designed to be divided in to several MGs in order to minimize the cost of electricity generation, improve reliability and voltage profile. In the presented study, stochastic models are considered for representing generation availability related to wind energy and solar energy. The generated power of distributed generation resources such as wind turbines and photovoltaic, have been modeled per hours of the day and the amount of important network parameters are obtained from the results of probabilistic load flow. Practical and important factors such as problem solving space reduction, load controllability, load priority, bus voltage limitation, line capacity limitation and the possibility of forming larger micro-grids by connecting switches between the obtained initial MGs from the first stage are considered in the proposed method.The proposed methodology is implemented for a standard 69-bus distribution system by applying the Imperialist Competitive Algorithm (ICA) in MATLAB software. Results verify the usefulness of the proposed approach in transforming an existing radial distribution network into several autonomous MG.

As an important parameter in Land surface system research, surface soil moisture (SSM) links the surface water and groundwater that plays a key role in water resources, agricultural management and global warming studies. Remote sensing techniques provide a direct and convenient means to estimate SSM on a regional scale. In this study, the performance of the normalized land surface temperature-vegetation index (LST-VI) model was evaluated using the in situ soil moisture measurements at Hetao irrigation region of Inner Mongolia that is a representative semi-arid area with relatively uniform underlying surface. The model was used to estimate soil moisture from HJ-1B and Landsat 8 images on clear days in 2014–2017. The overall SSM estimation accuracy was high, and the average RMSE was approximately 0.04 m3/m3. Moreover, a systematic sensitivity analysis was conducted for the input parameters and other impact factors. The results demonstrated that the model could credibly monitor the regional surface soil water content.

Determining the land surface temperature (LST) variations in urban surfaces using high spatiotemporal resolution thermal infrared data is necessary for urban microthermal environmental modeling. An unmanned aerial vehicle (UAV) equipped with a thermal infrared imager was used to monitor the high-precision spatiotemporal variation of LSTs for six typical urban surfaces, including water, tree canopy, grass, brick pavement, asphalt pavement, and marble pavement, in the Jiangning district, Nanjing. An estimation of sensible heat release was further conducted to quantitatively describe the fine characteristics of the LSTs of these surfaces. The factors influencing LST were investigated by correlating LSTs with the meteorological factors. The results showed that there were significant differences in LSTs and sensible heat release for six typical urban surfaces in sunny and cloudy days, respectively. On sunny days, the diurnal variations in different surfaces fluctuated substantially, which was not evident on cloudy days. The artificial surfaces always represented as the main source of sensible heat release. Solar radiation and air temperature, among the meteorological factors, were positively correlated with the LSTs of the surfaces. The air humidity was negatively correlated with the LSTs and played a role in cooling the land surface.

Objective: Osteosarcoma is the most common malignancy in the skeletal system; studies showed an important role of miRNAs in tumorigenesis, indicating miRNAs as possible therapeutic molecules. This study found abnormal hsa-miR-557 expression levels in osteosarcoma and tried to explore the potential function and the mechanism.Methods: Differential expression genes of osteosarcoma were analyzed using GSE28423 from the GEO database. Survival analysis of miRNAs was conducted with data obtained from the TARGET-OS database. STRING and miRDIP were used to predict target genes of hsa-miR-557; KRAS was then verified using dual-luciferase reporter assay. Expression of genes was detected by qPCR, and levels of proteins were detected by Western blot. The proliferation ability of cells was detected by CCK-8 and cell cycle analysis. Tumor formation assay in nude mice was used to detect the influence of osteosarcoma by hsa-miR-557 in vivo.Results: Analysis from the GEO and TARGET databases found 12 miRNAs that are significantly related to the osteosarcoma prognosis, 7 downregulated (hsa-miR-140-3p, hsa-miR-564, hsa-miR-765, hsa-miR-1224-5p, hsa-miR-95, hsa-miR-940, and hsa-miR-557) and 5 upregulated (hsa-miR-362-3p, hsa-miR-149, hsa-miR-96, hsa-miR-744, and hsa-miR-769-5p). CCK-8 analysis and cell cycle analysis found that hsa-miR-557 could significantly inhibit the proliferation of osteosarcoma cells. The tumor formation assay in nude mice showed that tumor sizes and weights were inhibited by hsa-miR-557 transfection. Further studies also proved that hsa-miR-557 could target the 3′UTR of KRAS and modulate phosphorylation of downstream proteins.Conclusion: This study showed that hsa-miR-557 could inhibit osteosarcoma growth both in vivo and in vitro, by modulating KRAS expression.

In order to alleviate the pressure of the basic endowment insurance system, China has implemented the policy of individual income tax-deferred commercial endowment insurance (IITDCEI) and actively explored the development path of individual supplementary endowment insurance. At present, the same tax rules are adopted for different income groups. Considering that people's income gap is large, different income groups enjoy different degrees of tax preference under this policy, which may cause social injustice and increase the gap between the rich and the poor. Based on this, we propose a new optimization scheme by adjusting the tax rate and the maximum premium limit of the insurance amount so as to coordinate the interests of low-, medium-, and high-income subjects and balance the degree of tax preference among the three subjects. At the same time, we also change the two parameters of the predetermined interest rate and retirement age, and compare the changes of the tax preference under the old and new schemes, and provide corresponding countermeasures for the implementation of the IITDCEI policy.

Accelerated urbanization and frequent heatwave events pose significant threats to human health. Analyses of the differences in air and land surface temperature (LST) under extreme climates can aid in understanding human-nature ecosystem coupling and the required adaptations to climate change. In this study, we quantified differences in urban and rural temperatures in China under heatwave (CHW) and non-heatwave periods (NHW) conditions and the influence of meteorological factors on these differences. Based on impervious surface data, 2421 urban and rural stations were dynamically classified from 2008 to 2017. Heatwaves were identified using relative thresholds, and differences were explored using meteorological data and MODIS LST data. For LST, urban–rural temperature difference (U-R _Tempdiff ) was highest during the day, whereas air temperature peaks occurred at night, under both NHW and CHW conditions. During CHWs, the daytime U-R _Tempdiff was greater for LST than for air temperature, reaching 4.24 ± 3.38 °C. At night, U-R _Tempdiff was slightly lower (1.04 ± 1.41 °C). The proportion of air U-R _Tempdiff contributed by rural air temperature was significantly higher during CHW nights than during NHW nights, whereas the proportion of land surface and air U-R _Tempdiff remained relatively stable during daytime. Spatially, the daytime temperature difference in the north decreased with latitude, whereas the difference in the south was lower. Under CHWs, urbanization had a stronger effect on LST than on air temperature, with a slightly smaller difference (0.01 °C yr ^−1 ) during the day and a slightly larger difference (0.03 °C yr ^−1 ) at night. The contribution of urbanization to LST was higher than that to air temperature, particularly during the day (16.34%). The effects of wind speed and precipitation on the average air urban–rural temperature difference was greater than those of LST under CHW, accounting for 16.13%, with the effects of wind speed being more significant. These results show that a comprehensive perspective is needed to understand the risks associated with a temperature rise risk under extreme climate conditions and to formulate effective mitigation measures that will they improve human thermal comfort under climate change.

A calm astronomical site means a site where astronomical observation would be less likely to be interfered with by optical turbulence. Previous turbulence measurements at a few sites in Antarctica have demonstrated very calm atmospheric conditions here. So far, to realize a wide range of measurements of the turbulence conditions above the Antarctic plateau will be a great hardship. Thus, in this study, the numerical weather model outputs provided by the Antarctic Mesoscale Prediction System (AMPS) have been used. Based on the AMPS outputs, the boundary layer height and the atmospheric Richardson number were obtained, from which the turbulence conditions above the Antarctic plateau have been evaluated. Finally, a statistical conclusion evaluating the total atmospheric turbulence above the whole Antarctic continent for an entire year is first reported. We find some sites (or regions) have a calmer atmosphere than Dome A; this is of great instructional significance for planning the next generation of ground-based optical astronomical telescopes.

Cropland abandonment is one of the most widespread types of land-use change in Southern China. Quickly and accurately monitoring spatial-temporal patterns of cropland abandonment is crucial for food security and a good ecological balance. There are still enormous challenges in the long-term monitoring of abandoned cropland in cloud and rain-prone and cropland-fragmented regions. In this study, we developed an approach to automatically obtain Landsat imagery for two key phenological periods, rather than as a time series, and mapped annual land cover from 1989 to 2021 based on the random forest classifier. We also proposed an algorithm for pixel-based, long-term annual land cover correction based on prior knowledge and natural laws, and generated cropland abandonment maps for Guangdong Province over the past 30 years. This work was implemented in Google Earth Engine. Accuracy assessment of the annual cropland abandonment maps for every five years during study period revealed an overall accuracy of 92–95%, producer (user) accuracy of 90–96% (73–87%), and Kappa coefficients of 0.81–0.88. In recent decades, the cropland abandonment area was relatively stable, at around 50 × 104 ha, while the abandonment rate gradually increased with a decrease in the cultivated area after 2000. The Landsat-based cropland abandonment monitoring method can be implemented in regions such as southern China, and will support food security and strategies for maintaining ecological balance.

Graph neural network has been widely used in various fields in recent years. However, the appearance of an adversarial attack makes the reliability of the existing neural networks challenging in application. Premeditated attackers, can make very small perturbations to the data to fool the neural network to produce wrong results. These incorrect results can lead to disastrous consequences. So, how to defend against adversarial attacks has become an urgent research topic. Many researchers have tried to improve the model robustness directly or by using adversarial training to reduce the negative impact of an adversarial attack. However, the majority of the defense strategies currently in use are inextricably linked to the model-training process, which incurs significant running and memory space costs. We offer a lightweight and easy-to-implement approach that is based on graph transformation. Extensive experiments demonstrate that our approach has a similar defense effect (with accuracy rate returns of nearly 80%) as existing methods and only uses 10% of their run time when defending against adversarial attacks on GCN (graph convolutional neural networks).

Signed graph neural networks learn low-dimensional representations for nodes in signed networks with positive and negative links, which helps with many downstream tasks like link prediction. However, most existing signed graph neural networks ignore individual characteristics of nodes and thus limit the ability to learn the underlying structure of real signed graphs. To address this limitation, a deep graph neural network framework SiNP to learn Signed network embedding with Node Polarity is proposed. To be more explicit, a node-signed property metric mechanism is developed to encode the individual characteristics of the nodes. In addition, a graph convolution layer is added so that both positive and negative information from neighboring nodes can be combined. The final embedding of nodes is produced by concatenating the outcomes of these two portions. Finally, extensive experiments have been conducted on four significant real-world signed network datasets to demonstrate the efficiency and superiority of the proposed method in comparison to the state-of-the-art.

A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13° N, 113.26° E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, –11.96% and –0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale.

Urban green space (UGS) can be regarded as an effective approach to mitigate urban heat island (UHI) effects. Many studies have investigated the impacts of composition and configuration of UGS on land surface temperature (LST), while little attention has been paid to the impacts among different urban blocks. Thus, taking 1835 urban blocks in Beijing as samples, including low-rise point (LRP), low-rise street (LRS), low-rise block (LRB), mid-rise point (MRP), mid-rise street (MRS), mid-rise block (MRB), high-rise point (HRP), high-rise street (HRS) and high-rise block (HRB), this study investigated the impacts of UGS on LST among different urban blocks. The results showed that UGS serves as cold islands among different urban blocks. Percentage of landscape (PLAND) of UGS in all types of urban blocks, edge density (ED) of UGS in MRS, area-weighted fractal dimension index (FRAC_AM) of UGS in HRS and HRB show significantly negative impacts on LST, while aggregation index (AI) of UGS in LRP shows significantly positive impacts. The findings suggest that both composition and configuration of UGS can affect LST among different urban blocks and rational allocation of UGS would be effective for mitigating UHI effects.

This study focused on medical care in a single country (China) and in regions with different economic backgrounds and different economic development levels to determine the effect of healthcare expenditure on short- and long-term economic growth. The study supported some interesting conclusions: (1) For most areas of China, increasing healthcare expenditure has a negative impact on economic growth in the short term but promotes growth in the long run; (2) Under different levels of economic development within China, there is significant heterogeneity in the interaction between healthcare expenditure and economic growth; (3) The negative effects of healthcare expenditure on short-term economic growth are greater during periods of economic turbulence than during times of stability; and (4) Healthcare expenditure has a negative effect on underdeveloped areas through the accumulation of material capital, while in economically developed areas, this channel has the opposite effect. To improve the quality of medical and health protection and the quality of life and welfare, China needs to consider the development characteristics of different economic zones and establish a multilevel, systematic and diversified medical and health protection system.

Dynamic polymer materials with reversible covalent bonds can reorganize their macromolecular architectures, and thus produce a macroscopic response to the environment. Here, the authors show dynamic covalent polyureas formed by a polyaddition reaction between pyrazoles and diisocyanates at ambient temperature in the absence of a catalyst.

Purpose: To compare the clinical efficacy of ultrasound-guided percutaneous microwave ablation (MWA) and parathyroidectomy for primary hyperparathyroidism. Methods: In an observational retrospective study, we compared the outcomes in patients with primary hyperparathyroidism who underwent ultrasound-guided MWA with the outcomes in those who underwent surgical resection (SR). The primary outcome was cure rate. Secondary outcomes were the rate of complications, and the difference of the treatment parameters of two treatment methods. Propensity-score matching was used to assemble a cohort of patients with similar baseline characteristics. Results: Among 108 patients who met inclusion criteria, 28 patients who underwent ultrasound-guided MWA (MWA group) and 28 who underwent SR (SR group) had similar propensity scores and were included in the analyses. There was no significant difference in the cure rate between MWA group and SR group (82.1% vs. 89.3%, p = .705). Patients who underwent MWA had significantly less estimated blood loss and shorter surgical time than those who underwent SR (p

Accurate mapping of built-up land is essential for urbanization monitoring and ecosystem research. At present, remote sensing is one of the primary means used for real-time and accurate surveying and mapping of built-up land, due to the long time series and multi-information advantages of existing remote sensing images and the ability to obtain highly precise year-by-year built-up land maps. In this study, we obtained feature-enhanced data regarding built-up land from Landsat images and phenology-based algorithms and proposed a method that combines the use of the Google Earth Engine (GEE) and deep learning approaches. The Res-UNet++ structural model was improved for built-up land mapping in Guangdong from 1991 to 2020. Experiments show that overall accuracy of built-up land map in the study area in 2020 was 0.99, the kappa coefficient was 0.96, user accuracy of built-up land was 0.98, and producer accuracy was 0.901. The trained model can be applied to other years with good results. The overall accuracy (OA) of the assessment results every five years was above 0.97, and the kappa coefficient was above 0.90. From 1991 to 2020, built-up land in Guangdong has expanded significantly, the area of built-up land has increased by 71%, and the proportion of built-up land has increased by 3.91%. Our findings indicate that the combined approach of GEE and deep learning algorithms can be developed into a large-scale, long time-series of remote sensing classification techniques framework that can be useful for future land-use mapping research.

The aerosol extinction coefficient (AEC) characterises the attenuation of the light propagating in a turbid medium with suspended particles. Therefore, it is of great significance to carry out AEC prediction research using state-of-art neural network (NN) methods. The attention mechanism (AM) has become an indispensable part of NNs that focuses on input weight assignment. Traditional AM is used in time steps to help generate the outputs. To select important features of meteorological parameters (MP) that are helpful for forecasting, in this study, we apply AM to features instead of time steps. Then we propose a bidirectional long short-term memory (BiLSTM) NN based on AM to predict the AEC. The proposed method can remember information twice (i.e., forward and backward), which can provide more context for AEC forecasting. Finally, an in situ measured MP dataset is applied in the proposed model, which presents Maoming coastal area’s atmospheric conditions in November 2020. The experimental results show that the model proposed in this paper has higher accuracy compared with traditional NN, providing a novel solution to the AEC prediction problem for the current studies of marine aerosol.

Lung cancer is one of the most common human cancers both in incidence and mortality, with prognosis particularly poor in metastatic cases. Metastasis in lung cancer is a multifarious process driven by a complex regulatory landscape involving many mechanisms, genes, and proteins. Membrane proteins play a crucial role in the metastatic journey both inside tumor cells and the extra-cellular matrix and are a viable area of research focus with the potential to uncover biomarkers and drug targets. In this work we performed membrane proteome analysis of highly and poorly metastatic lung cells which integrated genomic, proteomic, and transcriptional data. A total of 1,762 membrane proteins were identified, and within this set, there were 163 proteins with significant changes between the two cell lines. We applied the Tied Diffusion through Interacting Events method to integrate the differentially expressed disease-related microRNAs and functionally dys-regulated membrane protein information to further explore the role of key membrane proteins and microRNAs in multi-omics context. Has-miR-137 was revealed as a key gene involved in the activity of membrane proteins by targeting MET and PXN, affecting membrane proteins through protein–protein interaction mechanism. Furthermore, we found that the membrane proteins CDH2, EGFR, ITGA3, ITGA5, ITGB1, and CALR may have significant effect on cancer prognosis and outcomes, which were further validated in vitro. Our study provides multi-omics-based network method of integrating microRNAs and membrane proteome information, and uncovers a differential molecular signatures of highly and poorly metastatic lung cancer cells; these molecules may serve as potential targets for giant-cell lung metastasis treatment and prognosis.

Air quality in China is characterized by significant spatial and temporal differences, which are directly related to local meteorological conditions. This study used air quality monitoring data, namely, the air pollution index (API) and air quality index (AQI) between 2005 and 2018, together with meteorological data and identified key meteorological factors that affected the spatial and temporal variation of air quality using a random forest algorithm. The spatial and temporal differences in the threshold values of different meteorological factors affecting the concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 were identified. The AQI has the advantages of facilitating higher index values than the API. The air quality showed an improvement from 2005 to 2018. Wind direction and precipitation were the most important meteorological factors affecting the air quality in northern and southern China, respectively, which to some extent reflected the causes and degradation mechanisms of air pollution in the two regions. There were significant spatial and temporal differences in the effects of meteorological factors on the concentrations of different pollutants. The influence of atmospheric pressure on pollutant concentration differed between the east and west. Precipitation and relative humidity in most cities had significant impacts on PM2.5 and PM10. The influence of relative humidity was most significant for SO2 and it also had a great influence on O3, while wind speed had a great influence on NO2. The results of the study confirm the meteorological sensitivity of air quality and provide support for the implementation of regional air pollution prevention and control initiatives.

Tourism development activities affect the structure and functions of ecosystems directly triggering changes in the thermal environment of tourism destinations and raising a need for sustainable development of the tourism industry. Using the 2005–2015 moderate resolution imaging spectroradiometer (MODIS) data on the land surface temperature combined with the land use data, the urban thermal environment contribution index (CI) of prefecture-level cities and ecosystem types corresponding to the study area in Sichuan-Chongqing region were quantitatively calculated under various seasonal and diurnal conditions in terms of the scales of administrative divisions and ecosystem types. The characteristics of the roles played by different cities and ecosystem types to contribute to the thermal environment of the metropolitan region were summarized, and the differences and changes in the corresponding contribution intensity of various ecosystem types were measured. The results indicate the following: (1) Different cities play different roles as the sources and sinks with respect to the thermal environment in the daytime and nighttime. Based on the diurnal differences of the contribution indices, cities can be divided into three types: the day-night heat source type, the day-sink and night-source type, and the day-night heat sink type. (2) The farmland and the grassland ecosystems are the most important source and sink landscapes in the thermal environment of the Sichuan-Chongqing Region, respectively. (3) The region is affected by the spatial arrangement of the internal ecosystems and its own development conditions, and, consequently, there are significant temporal-spatial variations and role transitions between heat source and heat sink regarding the contribution of different ecosystem types to the thermal environment of individual cities. It is important to scientifically regulate the thermal environment effect on tourism destinations and maintain the comfort and sustainable development through identifying the source and sink ecosystems of the thermal environment, controlling the quantity and spatial arrangement of the heat source ecosystems, and fully enabling the cooling effect of the heat sink ecosystems.

In the process of rapid urbanization, urban heat island (UHI) effect has been showing more and more significant impacts on human well-being. Therefore, a more detailed understanding of the impact of three-dimensional (3D) building morphology on UHI effect across a continuum of spatial scales will be necessary to guide and improve the human settlement.This study selected 31 provincial capital cities of mainland China to analyze the impacts of the 3D building morphology, including the number, area, height, volume, and the surface area of the buildings, on the land surface temperature (LST). By exploring how the influence of 3D building morphology on LST changes with the increase of spatial scale (between 0 and 600 m radii), this study finally recognized which 3D building morphology index is the most significant index affecting LST in different cities, and which spatial scale these 3D building morphology indexes have the most significant impact on LST. The results showed that the building area is the most important 3D building morphology parameter affecting the LST, while the building height has the slightest influence on the LST. These effects are more significant in the spatial scale of 150 m–540 m, and the spatial scale increases with the increase of building areas in developed cities. These results highlight the necessity of considering fine-grained management in the governance and alleviating of the urban thermal environment through urban planning and urban renewal strategies.