Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature representations. ...Nevertheless, their ability in modeling relations between the samples remains limited. Beyond the limitations of grid sampling, graph convolutional networks (GCNs) have been recently proposed and successfully applied in irregular (or nongrid) data representation and analysis. In this article, we thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification. Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a huge computational cost, particularly in large-scale remote sensing (RS) problems. To this end, we develop a new minibatch GCN (called miniGCN hereinafter), which allows to train large-scale GCNs in a minibatch fashion. More significantly, our miniGCN is capable of inferring out-of-sample data without retraining networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of HS features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since miniGCNs can perform batchwise network training (enabling the combination of CNNs and GCNs), we explore three fusion strategies: additive fusion, elementwise multiplicative fusion, and concatenation fusion to measure the obtained performance gain. Extensive experiments, conducted on three HS data sets, demonstrate the advantages of miniGCNs over GCNs and the superiority of the tested fusion strategies with regard to the single CNN or GCN models. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_GCN for the sake of reproducibility.
Garden waste is one of the main components of urban solid waste which affects the urban environment. In this study, garden waste of
(SS),
(BY),
(LS),
(YS),
(GH) and
(
)
(CB) was pyrolyzed at 300 °C, ...500 °C, 700 °C to obtain different types of biochar, coded as SSB300, SSB500, SSB700, BYB300, etc., which were tested for their Cr (VI) adsorption capacity. The results demonstrated that the removal efficiency of Cr by biochar pyrolyzed from multiple raw materials at different temperatures was variable, and the pH had a great influence on the adsorption capacity and removal efficiency. GHB700 had the best removal efficiency (89.44%) at a pH of 2 of the solution containing Cr (VI). The pseudo second-order kinetics model showed that Cr (VI) adsorption by biochar was chemisorption. The Langmuir model showed that the adsorption capacity of SSB300 was the largest (51.39 mg·g
), BYB500 was 40.91 mg·g
, GHB700, CBB700, LSB700, YSB700 were 36.85 mg·g
, 36.54 mg·g
, 34.53 mg·g
and 32.66 mg·g
, respectively. This research, for the first time, used a variety of garden wastes to prepare biochar, and explored the corresponding raw material and pyrolysis temperature for the treatment of Cr (VI). It is hoped to provide a theoretical basis for the research and utilization of garden wastes and the production and application of biochar.
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity are readily available ...nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much research on multimodal RS data fusion has made tremendous progress in recent years, yet these developed traditional algorithms inevitably meet the performance bottleneck due to the lack of the ability to comprehensively analyze and interpret strongly heterogeneous data. Hence, this non-negligible limitation further arouses an intense demand for an alternative tool with powerful processing competence. Deep learning (DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in numerous computer vision tasks owing to its impressive ability in data representation and reconstruction. Naturally, it has been successfully applied to the field of multimodal RS data fusion, yielding great improvement compared with traditional methods. This survey aims to present a systematic overview in DL-based multimodal RS data fusion. More specifically, some essential knowledge about this topic is first given. Subsequently, a literature survey is conducted to analyze the trends of this field. Some prevalent sub-fields in the multimodal RS data fusion are then reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic aperture radar-optical, and RS-Geospatial Big Data fusion. Furthermore, We collect and summarize some valuable resources for the sake of the development in multimodal RS data fusion. Finally, the remaining challenges and potential future directions are highlighted.
•A systematic review of deep learning-based multimodal remote sensing data fusion.•Statistical analysis of relevant literature is conducted.•Seven prevalent sub-fields in multimodal remote sensing data fusion are detailed.•Some available resources, including tutorials, datasets, and codes, are provided.•Deep learning yields great achievements in multimodal remote sensing data fusion.
•Interactions between lake, catchment and Yangtze River were investigated.•Yangtze River effect was the primary factor affecting recession period of Poyang Lake.•Modifications to the River from the ...Three Gorges Dam caused seasonal dryness of the Lake.•Hydraulic engineering between the Lake and River would resolve water supply crisis.
Changes in lake hydrological regimes and the associated impacts on water supplies and ecosystems are internationally recognized issues. During the past decade, the persistent dryness of Poyang Lake (the largest freshwater lake in China) has caused water supply and irrigation crises for the 12.4 million inhabitants of the region. There is conjecture as to whether this dryness is caused by climate variability and/or human activities. This study examines long-term datasets of catchment inflow and Lake outflow, and employs a physically-based hydrodynamic model to explore catchment and Yangtze River controls on the Lake’s hydrology. Lake water levels fell to their lowest during 2001–2010 relative to previous decades. The average Lake size and volume reduced by 154km2 and 11×108m3 during the same period, compared to those for the preceding period (1970–2000). Model simulations demonstrated that the drainage effect of the Yangtze River was the primary causal factor. Modeling also revealed that, compared to climate variability impacts on the Lake catchment, modifications to Yangtze River flows from the Three Gorges Dam have had a much greater impact on the seasonal (September–October) dryness of the Lake. Yangtze River effects are attenuated in the Lake with distance from the River, but nonetheless propagate some 100km to the Lake’s upstream limit. Proposals to build additional dams in the upper Yangtze River and its tributaries are expected to impose significant challenges for the management of Poyang Lake. Hydraulic engineering to modify the flow regime between the Lake and the Yangtze River would somewhat resolve the seasonal dryness of the Lake, but will likely introduce other issues in terms of water quality and aquatic ecosystem health, requiring considerable further research.
•The bed erosion of the northern outlet channel was significant in Poyang Lake.•A hydrodynamic model was used to quantify the influence of the bathymetric changes.•The water levels decreased and the ...reduction was enhanced due to lower water levels.•The rising and recession rates of the water level and the outflow increased.
The hydrological regime of a lake is largely dependent on its bathymetry. A dramatic water level reduction has occurred in Poyang Lake in recent years, coinciding with significant bed erosion. Few studies have focused on the influence of bathymetric changes on the hydrological regime in such a complex river-lake floodplain system. This study combined hydrological data and a physically based hydrodynamic model to quantify the influence of the bathymetric changes (1998–2010) on the water level spatiotemporal distribution in Poyang Lake, based on a dry year (2006), a wet year (2010) and an average year (2000–2010). The following conclusions can be drawn from the results of this study: (1) The bed erosion of the northern outlet channel averaged 3 m, resulting in a decrease in the water level by 1.2–2 m in the northern channels (the most significantly influenced areas) and approximately 0.3 m in the central lake areas during low-level periods. The water levels below 16 m and 14 m were significantly affected during the rising period and recession period, respectively. The water level reduction was enhanced due to lower water levels. (2) The water surface profiles adjusted, and the rising and recession rates of the water level increased by 0.5–3.1 cm/d at the lake outlet. The bathymetric influence extended across the entire lake due to the emptying effect, resulting in a change in the water level distribution. The average annual outflow increased by 6.8%. (3) The bathymetric changes contributed approximately 14.4% to the extreme low water level in autumn 2006 and enhanced the drought in the dry season. This study quantified the impact of the bathymetric changes on the lake water levels, thereby providing a better understanding of the potential effects of continued sand mining operations and providing scientific explanations for the considerable variations in the hydrological regimes of Poyang Lake. Moreover, this study attempts to provide a reference for the assessment of similarly dramatic bathymetric changes in complex floodplain lakes.
Sepsis is a life‐threatening organ dysfunction syndrome, and liver is a susceptible target organ in sepsis, because the activation of inflammatory pathways contributes to septic liver injury. ...Oxidative stress has been documented to participate in septic liver injury, because it not only directly induces oxidative genotoxicity, but also exacerbates inflammatory pathways to potentiate damage of liver. Therefore, to ameliorate oxidative stress is promising for protecting liver in sepsis. Wogonin is the compound extracted from the medicinal plant Scutellaria baicalensis Geogi and was found to exert therapeutic effects in multiple inflammatory diseases via alleviation of oxidative stress. However, whether wogonin is able to mitigate septic liver injury remains unknown. Herein, we firstly proved that wogonin treatment could improve survival of mice with lipopolysaccharide (LPS)‐ or caecal ligation and puncture (CLP)‐induced sepsis, together with restoration of reduced body temperature and respiratory rate, and suppression of several pro‐inflammatory cytokines in circulation. Then, we found that wogonin effectively alleviated liver injury via potentiation of the anti‐oxidative capacity. To be specific, wogonin activated Nrf2 thereby promoting expressions of anti‐oxidative enzymes including NQO‐1, GST, HO‐1, SOD1 and SOD2 in hepatocytes. Moreover, wogonin‐induced Nrf2 activation could suppress NF‐κB‐regulated up‐regulation of pro‐inflammatory cytokines. Ultimately, we provided in vivo evidence that wogonin activated Nrf2 signalling, potentiated anti‐oxidative enzymes and inhibited NF‐κB‐regulated pro‐inflammatory signalling. Taken together, this study demonstrates that wogonin can be the potential therapeutic agent for alleviating liver injury in sepsis by simultaneously ameliorating oxidative stress and inflammatory response through the activation of Nrf2.
Due to advances in remote sensing satellite imaging and image processing technologies and their wide applications, intelligent remote sensing satellites are facing an opportunity for rapid ...development. The key technologies, standards, and laws of intelligent remote sensing satellites are also experiencing a series of new challenges. Novel concepts and key technologies in the intelligent hyperspectral remote sensing satellite system have been proposed since 2011. The aim of these intelligent remote sensing satellites is to provide real-time, accurate, and personalized remote sensing information services. This article reviews the current developments in new-generation intelligent remote sensing satellite systems, with a focus on intelligent remote sensing satellite platforms, imaging payloads, onboard processing systems, and other key technological chains. The technological breakthroughs and current defects of intelligence-oriented designs are also analyzed. Intelligent remote sensing satellites collect personalized remote sensing data and information, with real-time data features and information interaction between remote sensing satellites or between satellites and the ground. Such developments will expand the use of remote sensing applications beyond government departments and industrial users to a massive number of individual users. However, this extension faces challenges regarding privacy protection, societal values, and laws regarding the sharing and distribution of data and information.
STING （also known as MITA） mediates the innate antiviral signaling and ubiquitination of STING is key to its function. However, the deubiquitination process of STING is unclear. Here we report that ...USP18 recruits USP20 to deconjugate K48-1inked ubiquitination chains from STING and promotes the stability of STING and the expression of type I IFNs and proinflammatory cytokines after DNA virus infection. USP18 deficiency or knockdown of USP20 resulted in enhanced K48-1inked ubiquitination and accelerated degradation of STING, and impaired activation of IRF3 and NF-κB as well as induction of downstream genes after infection with DNA virus HSV-1 or transfeetion of various DNA ligands. In addition, Uspl8-/- mice were more susceptible to HSV-1 infection compared with the wildtype littermates. USP18 did not deubiquitinate STING in vitro but facilitated USP20 to catalyze deubiquitination of STING in a manner independent of the enzymatic activity of USP18. In addition, reconstitution of STING into Uspl8-/- MEFs restored HSV-1-induced expression of downstream genes and cellular antiviral responses. Our findings thus uncover previously uncharacterized roles of USPI8 and USP20 in mediating virus-triggered signaling and contribute to the understanding of the complicated regulatory system of the innate antiviral responses.
Diabetic nephropathy (DN) is characterized by sterile inflammation with continuous injury and loss of renal inherent parenchyma cells. Podocyte is an essential early injury target in DN. The injury ...and loss of podocytes are closely associated with proteinuria, the early symptom of renal injury in DN. However, the exact mechanism for podocyte injury and death in DN remains ambiguous. In this study we investigated whether pyroptosis, a newly discovered cell death pathway was involved in DN. Diabetic mice were generated by high-fat diet/STZ injections. We showed that the expression levels of caspase-11 and cleavage of gasdermin D (GSDMD-N) in podocytes were significantly elevated, accompanied by reduced expression of podocyte makers nephrin and podocin, loss and fusion in podocyte foot processes, increased inflammatory cytokines NF-κB, IL-1β, and IL-18, macrophage infiltration, glomerular matrix expansion and increased urinary albumin to creatinine ratio (UACR). All these changes in diabetic mice were blunted by knockout of caspase-11 or GSDMD. Cultured human and mouse podocytes were treated with high glucose (30 mM), which significantly increased the expression levels of caspase-11 or caspase-4 (the homolog of caspase-11 in human), GSDMD-N, NF-κB, IL-1β, and IL-18, and decreased the expression of nephrin and podocin. Either caspase-4 or GSDMD knockdown by siRNA significantly blunted these changes. In summary, our results demonstrate that caspase-11/4 and GSDMD-mediated pyroptosis is activated and involved in podocyte loss under hyperglycemia condition and the development of DN.
Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical ...industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.
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