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.
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.
Gut microbiota, an integral part of the human body, comprise bacteria, fungi, archaea, and protozoa. There is consensus that the disruption of the gut microbiota (termed “gut dysbiosis”) is ...influenced by host genetics, diet, antibiotics, and inflammation, and it is closely linked to the pathogenesis of inflammatory diseases, such as obesity and inflammatory bowel disease (IBD). Macrophages are the key players in the maintenance of tissue homeostasis by eliminating invading pathogens and exhibit extreme plasticity of their phenotypes, such as M1 or M2, which have been demonstrated to exert pro- and anti-inflammatory functions. Microbiota-derived metabolites, short-chain fatty acids (SCFAs) and Gram-negative bacterial lipopolysaccharides (LPS), exert anti-inflammatory or pro-inflammatory effects by acting on macrophages. Understanding the role of macrophages in gut microbiota-inflammation interactions might provide us a novel method for preventing and treating inflammatory diseases. In this review, we summarize the recent research on the relationship between gut microbiota and inflammation and discuss the important role of macrophages in this context.
Instead of using organic solvents, a deep eutectic solvent (DES) composed of tetrabutylammonium bromide and imidazole (Bu4NBr/Im) was employed as a solvent for the first time to synthesize covalent ...organic frameworks (COFs). Due to the low vapor pressure of the Bu4NBr/Im‐based DES, a new carboxyl‐functionalized COF (TpPa‐COOH) was synthesized under environmental pressure. The as‐synthesized TpPa‐COOH has open channels, and the DES can be removed completely from the pores. The dye adsorption performance of TpPa‐COOH was examined for three organic dyes with similar molecular sizes: one anionic dye (eosin B, EB) and two cationic dyes (methylene blue, MB and safranine T, ST). TpPa‐COOH showed an excellent selective adsorption effect on MB and ST. The electronegative keto form in TpPa‐COOH might help to form electrostatic and π–π interactions between the π‐stacking frameworks of TpPa‐COOH and the positive plane MB and ST molecules. The adsorption isotherms of MB and ST on TpPa‐COOH were further investigated in detail, and the equilibrium adsorption was well modeled by using a Langmuir isotherm model. Together with hydrogen bonding, TpPa‐COOH showed higher adsorption capacity for ST than for MB (1135 vs. 410 mg g−1). These results could provide a guidance for the green synthesis of adsorbents in removing organic dyes from wastewater.
Waste not: A deep eutectic solvent has been used as a green solvent for the first time to synthesize a new carboxyl‐functionalized covalent organic framework, TpPa‐COOH, under environmental pressure. The as‐synthesized TpPa‐COOH exhibited good crystallinity, with ordered, open channels. Significantly, its high adsorption capacity could be used to remove cationic organic dyes from wastewater.
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