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•Novel organo-clays are renowned organics adsorbents with wide availability and cost-effectiveness.•Synthesis and characterization of various gemini surfactants for organo-clays are ...described.•Adsorption performances and mechanisms are compared on tunable organo-Mts, organo-Vts and organo-SiNSs.•Potential precursors and modifiers are proposed for promising organo-clays.
Novel organo-clays functionalized with gemini surfactants are one of the most prevalent adsorbents covering a vast range of organic pollutants. The adsorption performances, mechanisms and regenerabilities of organo-clays are closely associated with their interlayer structure, resulting from variations of modifier and/or the precursor. To this end, a comprehensive view of organo-clay adsorbents is described from two angles: preparation and adsorption. The synthetic routes of gemini surfactants (abbr. geminis) tuning the structure and morphology of organo-clays have been summarized, with the geminis examining on different layer charged precursors (vermiculite, montmorillonite and silica nanosheets with the layer charge decreasing). A systematic summarization, comparison and future prospection about the fate of novel organo-clays is achieved, including functionalization by gemini surfactants varying in alkyl chains, spacers, head groups and functional groups, adsorption and desorption towards specific pollutants, and novel characterization methods. The adsorption isotherms, kinetics, thermodynamics as well as additional interactions are compared and overviewed significantly. Furthermore, the limitations, differences and directions of organo-clay adsorbents are discussed.
The conversion of unactivated carbon-hydrogen (C-H) bonds to carbon-nitrogen (C-N) bonds is a highly valued transformation. Existing strategies typically accomplish such reactions at only a single ...C-H site because the first derivatization diminishes the reactivity of surrounding C-H bonds. Here, we show that alkylated arenes can undergo vicinal C-H diamination reactions to form 1,2-diamine derivatives through an electrophotocatalytic strategy, using acetonitrile as both solvent and nitrogen source. The reaction is catalyzed by a trisaminocyclopropenium (TAC) ion, which undergoes anodic oxidation to furnish a stable radical dication while the cathodic reaction reduces protons to molecular hydrogen. Irradiation of the TAC radical dication (wavelength of maximum absorption of 450 to 550 nanometers) with a white-light compact fluorescent light generates a strongly oxidizing photoexcited intermediate. Depending on the electrolyte used, either 3,4-dihydroimidazole or aziridine products are obtained.
Metabolic Reprogramming in COVID-19 Shen, Tao; Wang, Tingting
International journal of molecular sciences,
11/2021, Letnik:
22, Številka:
21
Journal Article
Recenzirano
Odprti dostop
Plenty of research has revealed virus induced alternations in metabolic pathways, which is known as metabolic reprogramming. Studies focusing on COVID-19 have uncovered significant changes in ...metabolism, resulting in the perspective that COVID-19 is a metabolic disease. Reprogramming of amino acid, glucose, cholesterol and fatty acid is distinctive characteristic of COVID-19 infection. These metabolic changes in COVID-19 have a critical role not only in producing energy and virus constituent elements, but also in regulating immune response, offering new insights into COVID-19 pathophysiology. Remarkably, metabolic reprogramming provides great opportunities for developing novel biomarkers and therapeutic agents for COVID-19 infection. Such novel agents are expected to be effective adjuvant therapies. In this review, we integrate present studies about major metabolic reprogramming in COVID-19, as well as the possibility of targeting reprogrammed metabolism to combat virus infection.
Clustering is a long-standing important research problem, however, remains challenging when handling large-scale image data from diverse sources. In this paper, we present a novel Binary Multi-View ...Clustering (BMVC) framework, which can dexterously manipulate multi-view image data and easily scale to large data. To achieve this goal, we formulate BMVC by two key components: compact collaborative discrete representation learning and binary clustering structure learning, in a joint learning framework. Specifically, BMVC collaboratively encodes the multi-view image descriptors into a compact common binary code space by considering their complementary information; the collaborative binary representations are meanwhile clustered by a binary matrix factorization model, such that the cluster structures are optimized in the Hamming space by pure, extremely fast bit-operations. For efficiency, the code balance constraints are imposed on both binary data representations and cluster centroids. Finally, the resulting optimization problem is solved by an alternating optimization scheme with guaranteed fast convergence. Extensive experiments on four large-scale multi-view image datasets demonstrate that the proposed method enjoys the significant reduction in both computation and memory footprint, while observing superior (in most cases) or very competitive performance, in comparison with state-of-the-art clustering methods.
A Survey on Learning to Hash Wang, Jingdong; Zhang, Ting; Song, Jingkuan ...
IEEE transactions on pattern analysis and machine intelligence,
04/2018, Letnik:
40, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Nearest neighbor search is a problem of finding the data points from the database such that the distances from them to the query point are the smallest. Learning to hash is one of the major solutions ...to this problem and has been widely studied recently. In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization, and discuss their relations. We separate quantization from pairwise similarity preserving as the objective function is very different though quantization, as we show, can be derived from preserving the pairwise similarities. In addition, we present the evaluation protocols, and the general performance analysis, and point out that the quantization algorithms perform superiorly in terms of search accuracy, search time cost, and space cost. Finally, we introduce a few emerging topics.
Managing excess lead iodideIn hybrid perovskite solar cells, the formation of lead iodide (PbI2) can provide some passivation effects but can lead to device instability and hysteresis in ...current–density changes with voltage. Zhao et al. show that doping with rubidium chloride (RbCl) can create a passive inactive (PbI2)2RbCl phase that stabilizes the perovskite phase and lowers its bandgap. Devices exhibited 25.6% certified power efficiency and maintained 80% of that efficiency after 500 hours of operation at 85°C. —PDS
Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to ...distinguish between foreground and background or to keep track of the foreground in complex scenarios. The common practice of introducing motion information, such as optical flow, can lead to overreliance on optical flow estimation. To address these challenges, we propose an encoder-decoder-based hierarchical co-attention propagation network (HCPN) capable of tracking and segmenting objects. Specifically, our model is built upon multiple collaborative evolutions of the parallel co-attention module (PCM) and the cross co-attention module (CCM). PCM captures common foreground regions among adjacent appearance and motion features, while CCM further exploits and fuses cross-modal motion features returned by PCM. Our method is progressively trained to achieve hierarchical spatio-temporal feature propagation across the entire video. Experimental results demonstrate that our HCPN outperforms all previous methods on public benchmarks, showcasing its effectiveness for ZS-VOS. Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HCPN.
Aging is a gradual and irreversible pathophysiological process. It presents with declines in tissue and cell functions and significant increases in the risks of various aging-related diseases, ...including neurodegenerative diseases, cardiovascular diseases, metabolic diseases, musculoskeletal diseases, and immune system diseases. Although the development of modern medicine has promoted human health and greatly extended life expectancy, with the aging of society, a variety of chronic diseases have gradually become the most important causes of disability and death in elderly individuals. Current research on aging focuses on elucidating how various endogenous and exogenous stresses (such as genomic instability, telomere dysfunction, epigenetic alterations, loss of proteostasis, compromise of autophagy, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, deregulated nutrient sensing) participate in the regulation of aging. Furthermore, thorough research on the pathogenesis of aging to identify interventions that promote health and longevity (such as caloric restriction, microbiota transplantation, and nutritional intervention) and clinical treatment methods for aging-related diseases (depletion of senescent cells, stem cell therapy, antioxidative and anti-inflammatory treatments, and hormone replacement therapy) could decrease the incidence and development of aging-related diseases and in turn promote healthy aging and longevity.
Hashing methods for efficient image retrieval aim at learning hash functions that map similar images to semantically correlated binary codes in the Hamming space with similarity well preserved. The ...traditional hashing methods usually represent image content by hand-crafted features. Deep hashing methods based on deep neural network (DNN) architectures can generate more effective image features and obtain better retrieval performance. However, the underlying data structure is hardly captured by existing DNN models. Moreover, the similarity (either visually or semantically) between pairwise images is ambiguous, even uncertain, to be measured in the existing deep hashing methods. In this article, we propose a novel hashing method termed deep fuzzy hashing network (DFHN) to overcome the shortcomings of existing deep hashing approaches. Our DFHN method combines the fuzzy logic technique and the DNN to learn more effective binary codes, which can leverage fuzzy rules to model the uncertainties underlying the data. Derived from fuzzy logic theory, the generalized hamming distance is devised in the convolutional layers and fully connected layers in our DFHN to model their outputs, which come from an efficient xor operation on given inputs and weights. Extensive experiments show that our DFHN method obtains competitive retrieval accuracy with highly efficient training speed compared with several state-of-the-art deep hashing approaches on two large-scale image datasets: CIFAR-10 and NUS-WIDE.
A novel and direct metal-free nitro-carbocyclization of activated alkenes leading to valuable nitro-containing oxindoles via cascade C-N and C-C bond formation has been developed. The mechanistic ...study indicates that the initial NO and NO2 radical addition and the following C-H functionalization processes are involved in this transformation.
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