Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid ...development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.
Redox flow batteries are promising for large-scale energy storage, but some long-standing problems such as safety issues, system cost and cycling stability must be resolved. Here we demonstrate a ...type of redox flow battery that is based on all-polymer particulate slurry electrolytes. Micro-sized and uniformly dispersed all-polymer particulate suspensions are utilized as redox-active materials in redox flow batteries, breaking through the solubility limit and facilitating the application of insoluble redox-active materials. Expensive ion-exchange membranes are replaced by commercial dialysis membranes, which can simultaneously realize the rapid shuttling of H
ions and cut off the migration of redox-active particulates across the separator via size exclusion. In result, the all-polymer particulate slurry redox flow batteries exhibit a highly reversible multi-electron redox process, rapid electrochemical kinetics and ultra-stable long-term cycling capability.
Respirable silica dust is a common hazard faced by occupational workers and prolonged exposure to this dust can lead to pulmonary inflammation, fibrosis and, in severe cases, silicosis. However, the ...underlying mechanism by which silica exposure causes these physical disorders is not yet understood. In this study, we aimed to shed light on this mechanism by establishing in vitro and in vivo silica exposure models from the perspective of macrophages. Our results showed that compared to the control group, silica exposure resulted in an upregulation of the pulmonary expression of P2X7 and Pannexin-1, but this effect was suppressed by treatment with MCC950, a specific inhibitor of NLRP3. Our in vitro studies showed that silica exposure induced mitochondrial depolarization in macrophages, which led to a reduction of intracellular ATP and an influx of Ca2+. Furthermore, we found that creating an extracellular high potassium environment by adding KCl to the macrophage medium inhibited the expression of pyroptotic biomarkers and pro-inflammatory cytokines such as NLRP3 and IL-1β. Treatment with BBG, a P2X7 antagonist, also effectively inhibited the expression of P2X7, NLRP3, and IL-1β. On the other hand, treatment with FCF, a Pannexin-1 inhibitor, suppressed the expression of Pannexin-1 but had no effect on the expression of pyroptotic biomarkers such as P2X7, NLRP3, and IL-1β. In conclusion, our findings suggest that silica exposure triggers the opening of P2X7 ion channels, resulting in intracellular K+ efflux, extracellular Ca2+ influx, and the assembly of the NLRP3 inflammasome, ultimately leading to macrophage pyroptosis and pulmonary inflammation.
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•Exposure to different sizes of silica particle induces cellular toxicity.•Death through pyroptosis is a dominant manner for silica-induced inflammation.•P2X7‐gated cation exchange is a crucial segment in initiating macrophage pyroptosis.
Enantioselective Michael addition of β‐dicarbonyl compounds toward nitroalkenes were realized by using an immobilized, N‐terminal‐guanidinylated peptide, ...H2N−C(=NH)−Trp−Trp−(Leu−Leu−Aib)3−PEG−PS‐resin (Aib=2‐aminoisobutyric acid), as catalyst. Viable nucleophiles were acetylacetone, dimethyl malonate, and β‐ketoesters. The electrophiles include the β‐nitrostryrenes with various substituents on the benzene ring and the aliphatic nitroalkenes. Sterically congested α‐ethoxycarbonyl‐β‐nitrostryrene also successfully reacted. The resin‐supported catalyst could be recycled for five times.
Network emulation is an essential method to test network architecture, protocol and application software during a network’s entire life-cycle. Compared with simulation and test-bed methods, network ...emulation possesses the advantages of accuracy and cost-efficiency. However, legacy network emulators are typically restricted in scalability, agility, and extensibility, which builds barriers to prevent them from being widely used. In this paper, we introduce the currently prevalent cloud computing and the related technologies including resource virtualization, NFV (network functional virtualization), SDN (software-defined networking), traffic control and flow steering to the network emulation domain. We design and implement an innovative cloud-based network emulation platform, aiming at providing users Network Emulation as a Service (NEaaS), which can be conveniently deployed on both public and private clouds. In order to emulate networks of much larger scale, and to reduce the hardware cost of the proposed platform, a representative light-weighted virtualization technology, namely Docker container is adopted as a supplement to virtual machine (VM) to emulate networking nodes in a hybrid manner. We carried out a comprehensive performance evaluation with in-depth discussions for this emulation platform. It turns out, our platform can significantly outperform legacy network emulators regarding to scalability, agility, and extensibility in large scale emulation scenarios, with much lower costs. Finally, a case study of applying the proposed platform to emulate a typical space-ground integrated network (SGIN) is given, which illustrates the platform’s effectivity and efficiency.
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•FITC was successfully coupled onto SiNPs based on the Stöber method.•SiNP penetrated the trophoblast membrane and induced biological dysfunction in vitro.•Uterine accumulation of ...SiNP triggered acute inflammation in vivo.•SiNP-induced reproductive toxicity was mediated by the PPARγ signaling pathway.
Previous studies have demonstrated that silica nanoparticle (SiNP) exposure induces pulmonary and cardiovascular diseases, yet their transportation and degradation in vivo have not been fully elucidated. From the perspective of reproduction, this study was implemented to examine the uterine accumulation of SiNP and explore its reproductive toxicity and pathogenic mechanisms. First, we coupled FITC onto SiNPs and intratracheally instilled them into pregnant mice on the fifth gestational day, and the toxic effect of SiNP was evaluated in vitro and in vivo. It was found that SiNP penetrated the trophoblast membrane, leading to apoptosis and suppression of cell proliferation, tube formation, and invasion in a dose-dependent manner. Mechanistically, SiNP dysregulated the expression of Scd1, Slc27a1, and Cpt1a, and induced over synthesis and efflux obstruction of fatty acid through the PPARγ signaling pathway. The downregulation of Caspase-3 triggered apoptosis of trophoblast, which was causally associated with intracellular fatty acid accumulation as revealed by the correlation analysis. Besides, SiNP induced uterine inflammation in vivo, which aggravated with the observation prolongation within 24 h. Overall, SiNPs were visualized by coupling with FITC, and the uterine accumulation of SiNP induced fatty acid metabolic disorder, biological dysfunction, and trophoblast apoptosis, which were mediated in part by the PPARγ signaling pathway. These findings would contribute to understanding the environmental impacts of SiNP better, as well as the development of control measures for environmental pollution.
In 2013, the government of Zhejiang Province put forward a strategic project named “Five Water Cohabitation” (FWC) by integrating five water treatments: “sewage treatment,” “flood prevention,” ...“drainage system improvement,” “water supply guarantee,” and “water saving promotion.” It has been eight years since the project was proposed and launched. The primary purpose of the present study is to investigate the performance and significant effects of the project on the sustainable development of agriculture. This study investigates the project’s implementation from four aspects: environmental sustainability, resource sustainability, social sustainability, and economic sustainability. Furthermore, the difference-in-differences approach is applied to verify the treatment effect. Liaoning Province is chosen as the control group because it is also the traditionally agricultural province, and it has not implemented any large-scale water management projects. This study selects six sustainable variables, i.e., per capita GDP, urban-rural disparity, total water resources, domestic waste clearance, urbanization level, and health security level. The results show that the FWC project positively affects the sustainable development of agriculture for Zhejiang Province in economic sustainability, ecological sustainability, and social sustainability.
With the popularity of smart manufacturing, data-driven fault diagnosis methods for rolling bearings have been extensively studied in recent years. Existing rolling bearing fault diagnosis method has ...problems such as low precision and poor generalization ability when diagnosing multi-working condition bearings. In actual industrial scenarios, bearings usually operate under different operating conditions, causing differences in the probability distribution of the vibration data. Considering existing problem, this article proposes a diagnostic method of Inception ResNet Network (TL-IResnet) based on feature transfer learning. First, we utilize the Inception network to derive multiple scales of features from the original vibration signal. This enhances the capacity for feature expression in the model, and addresses the over-fitting issue in the deep model. Then the residual network is used to carry out deep learning on the fused multi-scale features to improve the residual network's ability to pay attention to important information, the self-attention mechanism is integrated into the residual network, and a new residual network structure is proposed. Finally, the maximum mean difference (MMD) is employed in output layer to measure the degree to which the probability distribution differs between the source and target domains to enhance the ability of model to transfer knowledge and complete the task of diagnosing the bearing of a machine. TL-IResnet is evaluated using the bearing dataset from Case Western Reserve University (CWRU) and the gearbox dataset from Southeast University. Experimental results demonstrate that TL-IResnet has a strong capacity to generalize information in addition to a high degree of accuracy under different conditions of operation, and has certain advantages over existing fault diagnosis methods.
Changes in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies ...have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change.
Plain Language Summary
Vegetation greenness changes year to year in response to climate variability and reflects the stability of ecosystems. How the interannual variability (IAV) of vegetation greenness has changed in the past decades, however, remained uncertain with recent studies reporting conflicting IAV trends using different satellite remote sensing products. Here, we investigated the greenness IAV trends of global vegetation using multiple mainstream satellite remote sensing products. We found that the changes in greenness IAV are conflicting on half of the global vegetated surface, while the differences in background climate, greening trends and nitrogen deposition rates account for either positive or negative trends in greenness IAV on the remaining half of the vegetated surface.
Key Points
On half of the global vegetated surface, the changes in the vegetation greenness interannual variability (IAV) are conflicting
22.20% and 28.20% of the global vegetated surface show significant positive and negative trends of vegetation greenness IAV, respectively
Warmer and drier places lead to greater greenness IAV whereas greater greening trend and higher nitrogen deposition make IAV smaller
Frequent freeze-thaw phenomena, together with widely used deicing salt and intense acid precipitation, often occur in northeastern China, causing damage to various aspects of plants, such as the ...permeability of biological membranes, osmotic adjustment, and photosystems. Aiming to explore the resistance of alfalfa to freezing-thawing (F), acid precipitation (A) and deicing salt (D), this study used Medicago sativa cv. Dongmu-70 as the experimental material, and the contents of malondialdehyde (MDA), soluble protein, soluble sugars, proline and chlorophyll were evaluated.
As the temperature decreased, the MDA content in the seedlings of the group under combined stress (A-D-F) increased and was significantly higher than that of group F (by 69.48 ~ 136.40%). Compared with those in the control (CK) group, osmotic substances such as soluble sugars and proline in the treatment groups were higher, while the soluble protein content was lower. The chlorophyll contents in the seedlings of the treatment groups were lower than those of the CK group; however, the chlorophyll content displayed a non-significant change during the free-thaw cycle.
Injury to the permeability of the biological membranes and photosystems of alfalfa results from stress. Moreover, alfalfa maintains osmotic balance by adaptively increasing the potential of osmotic substances such as soluble sugars and proline. Furthermore, the influence of stress from freezing-thawing and deicing salt is highly substantial, but the combined stresses of acid precipitation with the two factors mentioned above had little effect on the plants.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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