Pili in Probiotic Bacteria

The ability to adhere to intestinal epithelial tissue and mucosal surfaces is a key criterion in selecting probiotics. Adhesion is considered to be a prerequisite for successful colonization and survival in the gastrointestinal tract to provide persistent beneficial effects to the host. Bacteria express a multitude of surface components that mediate adherence. Pili or fimbriae are surface adhesive components implicated in initiating bacterial adhesion and mediating interaction with the host. These nonflagellar proteinaceous fiber appendages were identified and explored over several decades in pathogenic bacteria, and many distinct types are known. However, the presence of pili in probiotics and/or commensalic bacteria has only recently been recognized. Thus knowledge about pili in probiotics is relatively limited, but structural and functional data have begun to emerge. Availability of these data in the future would enable us to understand the pili-mediated adhesion strategies of probiotics. This knowledge could be utilized to develop antiadhesion-based therapies against bacterial infections as well as probiotic designs for beneficial effects. This chapter will briefly summarize the current knowledge of pili in probiotics with emphasis on members of lactobacilli and bifidobacteria

Purification, crystallization, and preliminary X-ray diffraction analysis of SpaD, a backbone-pilin subunit encoded by the fimbrial spaFED operon in Lactobacillus rhamnosus GG

Peer reviewe

Bent conformation of a backbone pilin N-terminal domain supports a three-stage pilus assembly mechanism

Journal editors’ pick of their favorite papers from the first year of publishing.Peer reviewe

Deep Learning Based Hate Speech Detection on Twitter

There have been growing worries about the effects of the widespread use of hate speech and harsh language on social media sites like Twitter. Effective strategies for recognising and reducing such dangerous material are necessary for resolving this problem. In this research, we give a detailed analysis of four deep learning models for identifying hate speech and inflammatory language on Twitter: the Long Short-Term Memory (LSTM), the Recurrent Neural Network (RNN), the Bidirectional LSTM (Bi-LSTM), and the Gated Recurrent Unit (GRU). We downloaded a large dataset from Kaggle that was curated for hate speech identification and used it in our experiment. We built each model after preprocessing and tokenization, then tweaked their hyperparameters for maximum efficiency. The models' abilities to detect hate speech were evaluated using standard measures including accuracy, precision, recall, and Fl-score. Our findings show that there is a wide range of effectiveness amongst models in terms of identifying hate speech and inflammatory language on Twitter. In terms of accuracy and Fl-scores, the Bi-LSTM and GRU models were superior to the LSTM and RNN. The results of this study imply that using bidirectional and gated processes may increase the models' capability of understanding the interdependencies and contexts of tweets, and hence, their classification accuracy