Efficient dealkalization of red mud and recovery of valuable metals by a sulfur-oxidizing bacterium

Red mud (RM) is a highly alkaline polymetallic waste generated via the Bayer process during alumina production. It contains metals that are critical for a sustainable development of modern society. Due to a shortage of global resources of many metals, efficient large-scale processing of RM has been receiving increasing attention from both researchers and industry. This study investigated the solubilization of metals from RM, together with RM dealkalization, via sulfur (S(0)) oxidation catalyzed by the moderately thermophilic bacterium Sulfobacillus thermosulfidooxidans. Optimization of the bioleaching process was conducted in shake flasks and 5-L bioreactors, with varying S(0):RM mass ratios and aeration rates. The ICP analysis was used to monitor the concentrations of dissolved elements from RM, and solid residues were analyzed for surface morphology, phase composition, and Na distribution using the SEM, XRD, and STXM techniques, respectively. The results show that highest metal recoveries (89% of Al, 84% of Ce, and 91% of Y) were achieved with the S(0):RM mass ratio of 2:1 and aeration rate of 1 L/min. Additionally, effective dealkalization of RM was achieved under the above conditions, based on the high rates (>95%) of Na, K, and Ca dissolution. This study proves the feasibility of using bacterially catalyzed S(0) oxidation to simultaneously dealkalize RM and efficiently extract valuable metals from the amassing industrial waste

Neural network fusion: a novel CT-MR Aortic Aneurysm image segmentation method.

Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease. In this work, we present methods on using deep neural network models to perform such multi-modal medical image segmentation. As CT image and MR image of the abdominal area cannot be well registered due to non-affine deformations, a naive approach is to train CT and MR segmentation network separately. However, such approach is time-consuming and resource-inefficient. We propose a new approach to fuse the high-level part of the CT and MR network together, hypothesizing that neurons recognizing the high level concepts of Aortic Aneurysm can be shared across multiple modalities. Such network is able to be trained end-to-end with non-registered CT and MR image using shorter training time. Moreover network fusion allows a shared representation of Aorta in both CT and MR images to be learnt. Through experiments we discovered that for parts of Aorta showing similar aneurysm conditions, their neural presentations in neural network has shorter distances. Such distances on the feature level is helpful for registering CT and MR image

Role of NRP1 in Bladder Cancer Pathogenesis and Progression

Bladder urothelial carcinoma (BC) is a fatal invasive malignancy and the most common malignancy of the urinary system. In the current study, we investigated the function and mechanisms of Neuropilin-1 (NRP1), the co-receptor for vascular endothelial growth factor, in BC pathogenesis and progression. The expression of NRP1 was evaluated using data extracted from GEO and HPA databases and examined in BC cell lines. The effect on proliferation, apoptosis, angiogenesis, migration, and invasion of BC cells were validated after NRP1 knockdown. After identifying differentially expressed genes (DEGs) induced by NRP1 silencing, GO/KEGG and IPA® bioinformatics analyses were performed and specific predicted pathways and targets were confirmed in vitro. Additionally, the co-expressed genes and ceRNA network were predicted using data downloaded from CCLE and TCGA databases, respectively. High expression of NRP1 was observed in BC tissues and cells. NRP1 knockdown promoted apoptosis and suppressed proliferation, angiogenesis, migration, and invasion of BC cells. Additionally, after NRP1 silencing the activity of MAPK signaling and molecular mechanisms of cancer pathways were predicted by KEGG and IPA® pathway analysis and validated using western blot in BC cells. NRP1 knockdown also affected various biological functions, including antiviral response, immune response, cell cycle, proliferation and migration of cells, and neovascularisation. Furthermore, the main upstream molecule of the DEGs induced by NRP1 knockdown may be NUPR1, and NRP1 was also the downstream target of NUPR1 and essential for regulation of FOXP3 expression to activate neovascularisation. DCBLD2 was positively regulated by NRP1, and PPAR signaling was significantly associated with low NRP1 expression. We also found that NRP1 was a predicted target of miR-204, miR-143, miR-145, and miR-195 in BC development. Our data provide evidence for the biological function and molecular aetiology of NRP1 in BC and for the first time demonstrated an association between NRP1 and NUPR1, FOXP3, and DCBLD2. Specifically, downregulation of NRP1 contributes to BC progression, which is associated with activation of MAPK signaling and molecular mechanisms involved in cancer pathways. Therefore, NRP1 may serve as a target for new therapeutic strategies to treat BC and other cancers

Near-real-time monitoring of global CO₂ emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO₂) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO₂ emissions (−1551 Mt CO₂) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (−1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic’s effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially

The fruits of Xanthium sibiricum Patr: A review on phytochemistry, pharmacological activities, and toxicity

In recent years, drug development and research have gradually shifted from chemical synthesis to biopharmaceutical and natural drugs. Natural medicines, such as traditional Chinese medicine, have been among the first studied because of their long medicinal history, simplicity, and the relatively low cost of research. Among them, Xanthii Fructus (XF) is famous for the treatment of sinusitis. In this article, the achievements of research on XF from 1953 to 2020 are systematically reviewed, focusing on the aspects of chemical constituents, pharmacological effects, clinical applications, toxicity and side effects, and processing methods. To date, there have been significant advances in both the phytochemistry and pharmacology of XF. Some traditional uses have been validated and clarified in modern pharmacological studies. However, its mechanism of action in the treatment of allergic diseases has not been satisfactorily explained. Further in vitro and in vivo studies are required to rationally develop new drugs and to elucidate the therapeutic potential of XF. A comprehensive evaluation of XF and an understanding of network pharmacology are also needed. © 2020 World Journal of Traditional Chinese Medicine | Published by Wolters Kluwer ‑ Medknow

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Design of New Test Function Model Based on Multi-objective Optimization Method

Space partitioning method, as a new algorism, has been applied to planning and decision-making of investment portfolio more and more often. But currently there are so few testing function for this algorism, which has greatly restrained its further development and application. An innovative test function model is designed in this paper and is used to test the algorism. It is proved that for evaluation of space partitioning method in certain applications, this test function has fairly obvious advantage