Recently, silver nanoparticles (AgNPs) have been widely used in various applications as antimicrobial agents, anticancer, diagnostics, biomarkers, cell labels, and drug delivery systems for the treatment of various diseases. Microorganisms generally acquire resistance to antibiotics through the course of antibacterial therapy. Multi-drug resistance (MDR) has become a growing problem in the treatment of infectious diseases, and the widespread use of broad-spectrum antibiotics has resulted in the development of antibiotic resistance by numerous human and animal bacterial pathogens. As a result, an increasing number of microorganisms are resistant to multiple antibiotics causing continuing economic losses in dairy farming. Therefore, there is an urgent need for the development of alternative, cost-effective, and efficient antimicrobial agents that overcome antimicrobial resistance. Here, AgNPs synthesized using the bio-molecule quercetin were characterized using various analytical techniques. The synthesized AgNPs were highly spherical in shape and had an average size of 11 nm. We evaluated the efficacy of synthesized AgNPs against two MDR pathogenic bacteria, namely, Pseudomonas aeruginosa and Staphylococcus aureus, which were isolated from milk samples produced by mastitis-infected goats. The minimum inhibitory concentrations (MICs) of AgNPs against P. aeruginosa and S. aureus were found to be 1 and 2 μg/mL, respectively. Our findings suggest that AgNPs exert antibacterial effects in a dose- and time-dependent manner. Results from the present study demonstrate that the antibacterial activity of AgNPs is due to the generation of reactive oxygen species (ROS), malondialdehyde (MDA), and leakage of proteins and sugars in bacterial cells. Results of the present study showed that AgNP-treated bacteria had significantly lower lactate dehydrogenase activity (LDH) and lower adenosine triphosphate (ATP) levels compared to the control. Furthermore, AgNP-treated bacteria showed downregulated expression of glutathione (GSH), upregulation of glutathione S-transferase (GST), and downregulation of both superoxide dismutase (SOD) and catalase (CAT). These physiological and biochemical measurements were consistently observed in AgNP-treated bacteria, thereby suggesting that AgNPs can induce bacterial cell death. Thus, the above results represent conclusive findings on the mechanism of action of AgNPs against different types of bacteria. This study also demonstrates the promising use of nanoparticles as antibacterial agents for use in the biotechnology and biomedical industry. Furthermore, this study is the first to propose the mode of action of AgNPs against MDR pathogens isolated from goats infected with subclinical mastitis.

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm.

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Due to the significant reduction in computational cost and storage, hashing techniques have gained increasing interests in facilitating large-scale cross-view retrieval tasks. Most cross-view hashing methods are developed by assuming that data from different views are well paired, e.g., text-image pairs. In real-world applications, however, this fully-paired multiview setting may not be practical. The more practical yet challenging semi-paired cross-view retrieval problem, where pairwise correspondences are only partially provided, has less been studied. In this paper, we propose an unsupervised hashing method for semi-paired cross-view retrieval, dubbed semi-paired discrete hashing (SPDH). In specific, SPDH explores the underlying structure of the constructed common latent subspace, where both paired and unpaired samples are well aligned. To effectively preserve the similarities of semi-paired data in the latent subspace, we construct the cross-view similarity graph with the help of anchor data pairs. SPDH jointly learns the latent features and hash codes with a factorization-based coding scheme. For the formulated objective function, we devise an efficient alternating optimization algorithm, where the key binary code learning problem is solved in a bit-by-bit manner with each bit generated with a closed-form solution. The proposed method is extensively evaluated on four benchmark datasets with both fully-paired and semi-paired settings and the results demonstrate the superiority of SPDH over several other state-of-the-art methods in term of both accuracy and scalability.