At present, natural polymers have found applications in clothing, paper, medicine, fuel, etc. The advancement in science and technology has paved the way for nanotechnology, which combines the unique ...properties of these abundant polymers on a nanoscale to produce novel nanomaterials. Using mechanical or chemical treatment, cellulosic materials can be converted into cellulose nanofibers and nanocrystals which have excellent capabilities compared to microscale native cellulose fibers, especially in water purification applications. This review summarizes the most recent processing methods for nanocellulose and techniques employed for rational surface chemical modification. It also critically assesses the applications of cellulose-based nanomaterials with respect to their functionalization for removal of pollutants such as heavy metals, organic compounds and pharmaceutical residues from water.
Graphical abstract
Gears are the most commonly used components in mechanical transmission systems. Their failures may cause transmission system breakdown and result in economic loss. Identification of different gear ...crack levels is important to prevent any unexpected gear failure because gear cracks lead to gear tooth breakage. Signal processing based methods mainly require expertize to explain gear fault signatures which is usually not easy to be achieved by ordinary users. In order to automatically identify different gear crack levels, intelligent gear crack identification methods should be developed. The previous case studies experimentally proved that K-nearest neighbors based methods exhibit high prediction accuracies for identification of 3 different gear crack levels under different motor speeds and loads. In this short communication, to further enhance prediction accuracies of existing K-nearest neighbors based methods and extend identification of 3 different gear crack levels to identification of 5 different gear crack levels, redundant statistical features are constructed by using Daubechies 44 (db44) binary wavelet packet transform at different wavelet decomposition levels, prior to the use of a K-nearest neighbors method. The dimensionality of redundant statistical features is 620, which provides richer gear fault signatures. Since many of these statistical features are redundant and highly correlated with each other, dimensionality reduction of redundant statistical features is conducted to obtain new significant statistical features. At last, the K-nearest neighbors method is used to identify 5 different gear crack levels under different motor speeds and loads. A case study including 3 experiments is investigated to demonstrate that the developed method provides higher prediction accuracies than the existing K-nearest neighbors based methods for recognizing different gear crack levels under different motor speeds and loads. Based on the new significant statistical features, some other popular statistical models including linear discriminant analysis, quadratic discriminant analysis, classification and regression tree and naive Bayes classifier, are compared with the developed method. The results show that the developed method has the highest prediction accuracies among these statistical models. Additionally, selection of the number of new significant features and parameter selection of K-nearest neighbors are thoroughly investigated.
•620 redundant statistical features are extracted.•New significant features are generated from 620 redundant features.•The developed method is better than other 4 K-nearest neighbors based methods.•Selection of the number of new significant features is investigated.•Parameter selection of K-nearest neighbors is investigated.
Conspectus Fluorescent sensing has emerged as a powerful tool for detecting various analytes and visualizing numerous biological processes by virtue of its superb sensitivity, rapidness, excellent ...temporal resolution, easy operation, and low cost. Of particular interest is activity-based sensing (ABS), a burgeoning sensing approach that is actualized on the basis of dynamic molecular reactivity rather than conventional lock-and-key molecular recognition. ABS has been recognized to possess some distinct advantages, such as high specificity, extraordinary sensitivity, and accurate signal outputs. A majority of ABS sensors are constructed by modifying conventional fluorogens, which are strongly emissive when molecularly dissolved in solvents but experience emission quenching upon aggregate formation or concentration increase. The aggregation-caused quenching (ACQ) phenomenon leads to a limited amount of labeling of the analyte with the sensor and low photobleaching resistance, which could impede practical applications of the ABS protocol. As an anti-ACQ phenomenon, aggregation-induced emission (AIE) provides a straightforward solution to the ACQ problem. Thanks to their intrinsic advantages, including high photobleaching threshold, high signal-to-noise ratio, fluorescence turn-on nature, and large Stokes shift, AIE-active luminogens (AIEgens) represent a class of extraordinary fluorogen alternatives for the ABS protocol. The use of AIEgen-involved ABS can integrate the advantages of AIEgens and ABS, and additionally, the AIE process offers some unique properties to the ABS approach. For instance, in some cases of water-soluble AIEgen-involved ABS, chemical reaction not only leads to a chang in the emission color of the AIEgens but also causes solubility variations, which could result in specific “light-up” signaling. In this Account, the basic concepts and mechanistic insights of the ABS approach involving the AIE principle are briefly summarized, and then we highlight the new breakthroughs, seminal studies, and trends in the area that have been most recently reported by our group. This emerging sensing protocol has been successfully utilized for detecting an array of targets including ions, small molecules, biomacromolecules, and microenvironments, all of which closely relate to human health, medical, and public concerns. These detections are smoothly achieved on the basis of various reactions (e.g., hydrolysis, boronate cleavage, dephosphorylation, addition, cyclization, and rearrangement reactions) through different sensing principles. In these studies, the AIEgen-involved ABS strategy generally shows good biocompatibility, high selectivity, excellent reliability and high signal contrast, strongly indicating its great potential for high-tech innovations in the sensing field, among which bioprobing is of particular interest. With this Account, we hope to spark new ideas and inspire new endeavors in this emerging research area, further promoting state-of-the-art developments in the field of sensing.
Whereas noble metal compounds have long been central in catalysis, Earth-abundant metal-based catalysts have in the same time remained undeveloped. Yet the efficacy of Earth-abundant metal catalysts ...was already shown at the very beginning of the 20th century with the Fe-catalyzed Haber-Bosch process of ammonia synthesis and later in the Fischer-Tropsch reaction. Nanoscience has revolutionized the world of catalysis since it was observed that very small Au nanoparticles (NPs) and other noble metal NPs are extraordinarily efficient. Therefore the development of Earth-abundant metals NPs is more recent, but it has appeared necessary due to their "greenness". This review highlights catalysis by NPs of Earth-abundant transition metals that include Mn, Fe, Co, Ni, Cu, early transition metals (Ti, V, Cr, Zr, Nb and W) and their nanocomposites with emphasis on basic principles and literature reported during the last 5 years. A very large spectrum of catalytic reactions has been successfully disclosed, and catalysis has been examined for each metal starting with zero-valent metal NPs followed by oxides and other nanocomposites. The last section highlights the catalytic activities of bi- and trimetallic NPs. Indeed this later family is very promising and simultaneously benefits from increased stability, efficiency and selectivity, compared to monometallic NPs, due to synergistic substrate activation.
This review presents the recent remarkable developments of efficient Earth-abundant transition-metal nanocatalysts.
Abstract
Background
The duration of humoral and T and B cell response after the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains unclear.
Methods
We performed a ...cross-sectional study to assess the virus-specific antibody and memory T and B cell responses in coronavirus disease 2019 (COVID-19) patients up to 343 days after infection. Neutralizing antibodies and antibodies against the receptor-binding domain, spike, and nucleoprotein of SARS-CoV-2 were measured. Virus-specific memory T and B cell responses were analyzed.
Results
We enrolled 59 patients with COVID-19, including 38 moderate, 16 mild, and 5 asymptomatic patients; 31 (52.5%) were men and 28 (47.5%) were women. The median age was 41 years (interquartile range, 30–55). The median day from symptom onset to enrollment was 317 days (range 257 to 343 days). We found that approximately 90% of patients still have detectable immunoglobulin (Ig)G antibodies against spike and nucleocapsid proteins and neutralizing antibodies against pseudovirus, whereas ~60% of patients had detectable IgG antibodies against receptor-binding domain and surrogate virus-neutralizing antibodies. The SARS-CoV-2-specific IgG+ memory B cell and interferon-γ-secreting T cell responses were detectable in more than 70% of patients.
Conclusions
Severe acute respiratory syndrome coronavirus 2-specific immune memory response persists in most patients approximately 1 year after infection, which provides a promising sign for prevention from reinfection and vaccination strategy.
SARS-CoV-2-specific antibody and memory T and B cell responses were detectable in most patients approximately 1 year after infection, indicating that durable immunity against secondary COVID-19 disease is possible in most individuals.
•The first comprehensive survey on deep-learning-based trackers.•Review existing deep visual trackers from three different perspectives.•Large-scale benchmark evaluations of deep visual ...trackers.•Summarize cutting-edge research works and discuss future directions•Provide useful insights and conclusions for deep visual trackers.
Recently, deep learning has achieved great success in visual tracking. The goal of this paper is to review the state-of-the-art tracking methods based on deep learning. First, we introduce the background of deep visual tracking, including the fundamental concepts of visual tracking and related deep learning algorithms. Second, we categorize the existing deep-learning-based trackers into three classes according to network structure, network function and network training. For each categorize, we explain its analysis of the network perspective and analyze papers in different categories. Then, we conduct extensive experiments to compare the representative methods on the popular OTB-100, TC-128 and VOT2015 benchmarks. Based on our observations, we conclude that: (1) The usage of the convolutional neural network (CNN) model could significantly improve the tracking performance. (2) The trackers using the convolutional neural network (CNN) model to distinguish the tracked object from its surrounding background could get more accurate results, while using the CNN model for template matching is usually faster. (3) The trackers with deep features perform much better than those with low-level hand-crafted features. (4) Deep features from different convolutional layers have different characteristics and the effective combination of them usually results in a more robust tracker. (5) The deep visual trackers using end-to-end networks usually perform better than the trackers merely using feature extraction networks. (6) For visual tracking, the most suitable network training method is to per-train networks with video information and online fine-tune them with subsequent observations. Finally, we summarize our manuscript and highlight our insights, and point out the further trends for deep visual tracking.
The gut microbiota regulates the biological processes of organisms acting like 'another' genome, affecting the health and disease of the host. MicroRNAs, as important physiological regulators, have ...been found to be involved in health and disease. Recently, the gut microbiota has been reported to affect host health by regulating host miRNAs. For example, Fusobacterium nucleatum could aggravate chemoresistance of colorectal cancer by decreasing the expression of miR-18a* and miR-4802. What's more, miRNAs can shape the gut microbiota composition, ultimately affecting the host's physiology and disease. miR-515-5p and miR-1226-5p could promote the growth of Fusobacterium nucleatum (Fn) and Escherichia coli (E.coli), which have been reported to drive colorectal cancer. Here, we will review current findings of the interactions between the gut microbiota and microRNAs and discuss how the gut microbiota-microRNA interactions affect host pathophysiology including intestinal, neurological, cardiovascular, and immune health and diseases.
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