Pyroptotic Patterns in Blood Leukocytes Predict Disease Severity and Outcome in COVID-19 Patients

The global coronavirus disease 2019 (COVID-19) pandemic has lasted for over 2 years now and has already caused millions of deaths. In COVID-19, leukocyte pyroptosis has been previously associated with both beneficial and detrimental effects, so its role in the development of this disease remains controversial. Using transcriptomic data (GSE157103) of blood leukocytes from 126 acute respiratory distress syndrome patients (ARDS) with or without COVID-19, we found that COVID-19 patients present with enhanced leukocyte pyroptosis. Based on unsupervised clustering, we divided 100 COVID-19 patients into two clusters (PYRcluster1 and PYRcluster2) according to the expression of 35 pyroptosis-related genes. The results revealed distinct pyroptotic patterns associated with different leukocytes in these PYRclusters. PYRcluster1 patients were in a hyperinflammatory state and had a worse prognosis than PYRcluster2 patients. The hyperinflammation of PYRcluster1 was validated by the results of gene set enrichment analysis (GSEA) of proteomic data (MSV000085703). These differences in pyroptosis between the two PYRclusters were confirmed by the PYRscore. To improve the clinical treatment of COVID-19 patients, we used least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model based on differentially expressed genes between PYRclusters (PYRsafescore), which can be applied as an effective prognosis tool. Lastly, we explored the upstream transcription factors of different pyroptotic patterns, thereby identifying 112 compounds with potential therapeutic value in public databases

Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee

As an invasive plant species, kudzu has been spreading rapidly in the Southeastern United States in recent years. Accurate mapping of kudzu is critical for effective invasion control and management. However, the remote detection of kudzu distribution using multispectral images is challenging because of the mixed reflectance and potential misclassification with other vegetation. We propose a three-step classification process to map kudzu in Knox County, Tennessee, using multispectral Sentinel-2 images and the integration of spectral unmixing analysis and phenological characteristics. This classification includes an initial linear unmixing process to produce an overestimated kudzu map, a phenological-based masking to reduce misclassification, and a nonlinear unmixing process to refine the classification. The initial linear unmixing provides high producer’s accuracy (PA) but low user’s accuracy (UA) due to misclassification with grasslands. The phenological-based masking increases the accuracy of the kudzu classification and reduces the domain for further processing. The nonlinear unmixing further refines the kudzu classification via the selection of an appropriate nonlinear model. The final kudzu classification for Knox County reaches relatively high accuracy, with UA, PA, Jaccard, and Kappa index values of 0.858, 0.907, 0.789, and 0.725, respectively. Our proposed method has potential for continuous monitoring of kudzu in large areas

Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee

As an invasive plant species, kudzu has been spreading rapidly in the Southeastern United States in recent years. Accurate mapping of kudzu is critical for effective invasion control and management. However, the remote detection of kudzu distribution using multispectral images is challenging because of the mixed reflectance and potential misclassification with other vegetation. We propose a three-step classification process to map kudzu in Knox County, Tennessee, using multispectral Sentinel-2 images and the integration of spectral unmixing analysis and phenological characteristics. This classification includes an initial linear unmixing process to produce an overestimated kudzu map, a phenological-based masking to reduce misclassification, and a nonlinear unmixing process to refine the classification. The initial linear unmixing provides high producer’s accuracy (PA) but low user’s accuracy (UA) due to misclassification with grasslands. The phenological-based masking increases the accuracy of the kudzu classification and reduces the domain for further processing. The nonlinear unmixing further refines the kudzu classification via the selection of an appropriate nonlinear model. The final kudzu classification for Knox County reaches relatively high accuracy, with UA, PA, Jaccard, and Kappa index values of 0.858, 0.907, 0.789, and 0.725, respectively. Our proposed method has potential for continuous monitoring of kudzu in large areas

Pattern of Turbidity Change in the Middle Reaches of the Yarlung Zangbo River, Southern Tibetan Plateau, from 2007 to 2017

Turbidity is an important indicator of riverine conditions, especially in a fragile environment such as the Tibetan Plateau. Remote sensing, with the advantages of large-scale observations, has been widely applied to monitor turbidity change in lakes and rivers; however, few studies have focused on turbidity change of rivers on the Tibetan Plateau. We investigated the pattern of turbidity change in the middle reaches of the Yarlung Zangbo River, southern Tibetan Plateau, based on multispectral satellite imagery and in situ measurements. We developed empirical models from in situ measured water leaving reflectance and turbidity, and applied the best performed s-curve models on satellite imagery from Sentinel-2, Landsat 8, and Landsat 5 to derive turbidity change in 2007–2017. Our results revealed an overall decreasing spatial trend from the upper to lower streams. Seasonal variations were observed with high turbidity from July to September and low turbidity from October to May. Annual turbidity showed a temporally slightly declining trend from 2007 to 2017. The pattern of turbidity change is affected by the confluence of tributaries and the changes in precipitation and vegetation along the river. These findings provide important insights into the responses of riverine turbidity to climate and environmental changes on the Tibetan Plateau

Progress in Imidazolium Ionic Liquids Assisted Fabrication of Carbon Nanotube and Graphene Polymer Composites

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Comprehensive evaluation of the effectiveness on metals recovery and decontamination from MSWI fly ash by an integrating hydrometallurgical process in Guangzhou

Municipal solid waste incineration (MSWI) fly ash generally contains substantial toxic elements which can be easily released into the environment, giving rise to serious environmental contaminations. In order to dispose of these harmful fly ashes safely and feasibly, an advanced and reliable strategy is needed. This work presented an integrated method designed for recycling of valuable copper (Cu) and zinc (Zn) through hydrochloric acid leaching and sequential extraction (using LIX 860N-I and Cyanex 572 for Cu and Zn as extractants, respectively) and clean-up of cadmium (Cd) and lead (Pb) in consequential waste effluent by adsorption with a versatile material – bundle-like hydroxyapatite (B-HAP). The method was applied in the pilot scale tests with recovery yields of 95% and 61% for Cu and Zn, respectively. Additionally, satisfied removal efficiencies of Cd and Pb (over 95% for both) were realized, reaching the acceptable emission level for Cd and Pb in China. A scenario based on the latest MSW data in 2018 in Guangzhou was assessed through the integrated pilot experiment. The evaluation demonstrates a reduction of a $ 20.8 million cost; over 48.2 k GJ of energy consumption and 5800 tons of CO2 emission can be reduced in 2018, comparing to that landfilled in hazardous waste sites, which reveals great benefits. The valuable metal recovery in combination with decontamination of toxic elements/substances as a complete and combined process gives a promising fly ash treatment strategy in future