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Big multimedia data is heterogeneous in essence, that is, the data may be a mixture of video, audio, text, and images. This is due to the prevalence of novel applications in recent years, such as social media, video sharing, and location based services (LBS), etc. In many multimedia applications, for example, video/image tagging and multimedia recommendation, text classification techniques have been used extensively to facilitate multimedia data processing. In this paper, we give a comprehensive review on feature selection techniques for text classification. We begin by introducing some popular representation schemes for documents, and similarity measures used in text classification. Then, we review the most popular text classifiers, including Nearest Neighbor (NN) method, Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Neural Networks. Next, we survey four feature selection models, namely the filter, wrapper, embedded and hybrid, discussing pros and cons of the state-of-the-art feature selection approaches. Finally, we conclude the paper and give a brief introduction to some interesting feature selection work that does not belong to the four models.

A novel β-coronavirus (2019-nCoV) caused severe and even fetal pneumonia explored in a seafood market of Wuhan city, Hubei province, China, and rapidly spread to other provinces of China and other countries. The 2019-nCoV was different from SARS-CoV, but shared the same host receptor the human angiotensin-converting enzyme 2 (ACE2). The natural host of 2019-nCoV may be the bat Rhinolophus affinis as 2019-nCoV showed 96.2% of whole-genome identity to BatCoV RaTG13. The person-to-person transmission routes of 2019-nCoV included direct transmission, such as cough, sneeze, droplet inhalation transmission, and contact transmission, such as the contact with oral, nasal, and eye mucous membranes. 2019-nCoV can also be transmitted through the saliva, and the fetal–oral routes may also be a potential person-to-person transmission route. The participants in dental practice expose to tremendous risk of 2019-nCoV infection due to the face-to-face communication and the exposure to saliva, blood, and other body fluids, and the handling of sharp instruments. Dental professionals play great roles in preventing the transmission of 2019-nCoV. Here we recommend the infection control measures during dental practice to block the person-to-person transmission routes in dental clinics and hospitals.

Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

Oral bacteria directly affect the disease status of dental caries and periodontal diseases. The dynamic oral microbiota cooperates with the host to reflect the information and status of immunity and metabolism through two-way communication along the oral cavity and the systemic organs. The oral cavity is one of the most important interaction windows between the human body and the environment. The microenvironment at different sites in the oral cavity has different microbial compositions and is regulated by complex signaling, hosts, and external environmental factors. These processes may affect or reflect human health because certain health states seem to be related to the composition of oral bacteria, and the destruction of the microbial community is related to systemic diseases. In this review, we discussed emerging and exciting evidence of complex and important connections between the oral microbes and multiple human systemic diseases, and the possible contribution of the oral microorganisms to systemic diseases. This review aims to enhance the interest to oral microbes on the whole human body, and also improve clinician’s understanding of the role of oral microbes in systemic diseases. Microbial research in dentistry potentially enhances our knowledge of the pathogenic mechanisms of oral diseases, and at the same time, continuous advances in this frontier field may lead to a tangible impact on human health.

Lysine acetylation is a frequently occurring post-translational modification (PTM), emerging as an important metabolic regulatory mechanism in prokaryotes. This process is achieved enzymatically by the protein acetyltransferase (KAT) to specifically transfer the acetyl group, or non-enzymatically by direct intermediates (acetyl phosphate or acetyl-CoA). Although lysine acetylation modification of glucosyltransferases (Gtfs), the important virulence factor in Streptococcus mutans , was reported in our previous study, the KAT has not been identified. Here, we believe that the KAT ActG can acetylate Gtfs in the enzymatic mechanism. By overexpressing 15 KATs in S . mutans , the synthesized water-insoluble extracellular polysaccharides (EPS) and biofilm biomass were measured, and KAT ( actG ) was identified. The in-frame deletion mutant of actG was constructed to validate the function of actG . The results showed that actG could negatively regulate the water-insoluble EPS synthesis and biofilm formation. We used mass spectrometry (MS) to identify GtfB and GtfC as the possible substrates of ActG. This was also demonstrated by in vitro acetylation assays, indicating that ActG could increase the acetylation levels of GtfB and GtfC enzymatically and decrease their activities. We further found that the expression level of actG in part explained the virulence differences in clinically isolated strains. Moreover, overexpression of actG in S . mutans attenuated its cariogenicity in the rat caries model. Taken together, our study demonstrated that the KAT ActG could induce the acetylation of GtfB and GtfC enzymatically in S . mutans , providing insights into the function of lysine acetylation in bacterial virulence and pathogenicity.

It is necessary to detect multi-type farmland obstacles in real time and accurately for unmanned agricultural vehicles. An improved YOLOv5s algorithm based on the K-Means clustering algorithm and CIoU Loss function was proposed to improve detection precision and speed up real-time detection. The K-Means clustering algorithm was used in order to generate anchor box scales to accelerate the convergence speed of model training. The CIoU Loss function, combining the three geometric measures of overlap area, center distance and aspect ratio, was adopted to reduce the occurrence of missed and false detection and improve detection precision. The experimental results showed that the inference time of a single image was reduced by 75% with the improved YOLOv5s algorithm; compared with that of the Faster R-CNN algorithm, real-time performance was effectively improved. Furthermore, the mAP value of the improved algorithm was increased by 5.80% compared with that of the original YOLOv5s, which indicates that using the CIoU Loss function had an obvious effect on reducing the missed detection and false detection of the original YOLOv5s. Moreover, the detection of small target obstacles of the improved algorithm was better than that of the Faster R-CNN.

Animal viruses are parasites of animal cells that have characteristics such as heredity and replication. Viruses can be divided into non-enveloped and enveloped viruses if a lipid bilayer membrane surrounds them or not. All the membrane proteins of enveloped viruses that function in attachment to target cells or membrane fusion are modified by glycosylation. Glycosylation is one of the most common post-translational modifications of proteins and plays an important role in many biological behaviors, such as protein folding and stabilization, virus attachment to target cell receptors and inhibition of antibody neutralization. Glycans of the host receptors can also regulate the attachment of the viruses and then influence the virus entry. With the development of glycosylation research technology, the research and development of novel virus vaccines and antiviral drugs based on glycan have received increasing attention. Here, we review the effects of host glycans and viral proteins on biological behaviors of viruses, and the opportunities for prevention and treatment of viral infectious diseases.

Pneumonia, such as acute lung injury (ALI), has been a type of lethal disease that is generally caused by uncontrolled inflammatory response and excessive generation of reactive oxygen species (ROS). Herein, we report Fe-curcumin-based nanoparticles (Fe-Cur NPs) with nanozyme functionalities in guiding the intracellular ROS scavenging and meanwhile exhibiting anti-inflammation efficacy for curing ALI. The nanoparticles are noncytotoxic when directing these biological activities. Mechanism studies for the anti-inflammation aspects of Fe-Cur NPs were systematically carried out, in which the infected cells and tissues were alleviated through downregulating levels of several important inflammatory cytokines (such as TNF-α, IL-1β, and IL-6), decreasing the intracellular Ca2+ release, inhibiting NLRP3 inflammasomes, and suppressing NF-κB signaling pathways. In addition, we performed both the intratracheal and intravenous injection of Fe-Cur NPs in mice experiencing ALI and, importantly, found that the accumulation of such nanozymes was enhanced in lung tissue (better than free curcumin drugs), demonstrating its promising therapeutic efficiency in two different administration methods. We showed that the inflammation reduction of Fe-Cur NPs was effective in animal experiments and that ROS scavenging was also effectively achieved in lung tissue. Finally, we revealed that Fe-Cur NPs can decrease the level of macrophage cells (CD11bloF4/80hi) and CD3+CD45+ T cells in mice, which could help suppress the inflammation cytokine storm caused by ALI. Overall, this work has developed the strategy of using Fe-Cur NPs as nanozymes to scavenge intracellular ROS and as an anti-inflammation nanodrugs to synergistically cure ALI, which may serve as a promising therapeutic agent in the clinical treatment of this deadly disease. Fe-Cur NP nanozymes were designed to attenuate ALI by clearing intracellular ROS and alleviating inflammation synergistically. Relevant cytokines, inflammasomes, and signaling pathways were studied.

A harmonic reducer is an important component of industrial robots. In practical applications, it is difficult to obtain enough anomaly data from error cases for the supervised training of models. Whether the information contained in regular features is sensitive to anomaly detection is unknown. In this paper, we propose an anomaly detection frame for a harmonic reducer with only healthy data. We considered an auto-encoder trained using only healthy features, such as feature mapping, in which the difference between the output and the input constitutes a new high-dimensional feature space that retained information relevant only to anomalies. Compared to the original feature space, this space was more sensitive to abnormal data. The mapped features were then fed into the OCSVM to preserve the feature details of the abnormal information. The effectiveness of this method was validated by multiple sets of data collecting from harmonic reducers. Three different residual calculations and four different AE models were used, showing that the method outperforms an AE or an OCSVM alone. It is also verified that the method outperforms other typical anomaly detection methods.

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors.