Milk Derived Antimicrobial Bioactive Peptides: A Review

In recent decades, bioactive peptides have attracted increasing interest as health promoting functional foods. A variety of naturally formed bioactive peptides have been found in fermented dairy products such as yogurt, sour milk, and cheese. Initially these peptides are inactive within the sequence of the parent protein molecule and can be generated by gastrointestinal digestion of milk, fermentation of milk with proteolytic starter cultures, and/or hydrolysis by proteolytic enzymes. Milk derived peptides exert a number of health beneficial activities, even upon oral administration. Bioactive peptides have a great impact on major body systems including the digestive, nervous, endocrine, cardiovascular, diabetes type II, obesity, and immune systems. Antimicrobial peptides are also an important ingredient of innate immunity, especially at mucosal surfaces such as lungs and small intestine that are constantly exposed to a range of potential pathogens. Therefore, it plays an important role in boosting natural immune protection by reducing the risk of chronic diseases. Bioactive peptides are considered as potent drugs with well-defined pharmacological residues and also used to formulate health-enhancing nutraceuticals.     

A partial Granger causality based method for analysis of parameter interactions in bioreactors

In this article, we utilize the concept of partial Granger causality to study the penicillin production process under several operating conditions. We propose a graph-theoretic template (causal network) based method for intelligent process monitoring. We validate our results with the aid of existing knowledge and available literature. The proposed method is quite general and can be extended to analyze several physical, chemical or biological systems.     

Importance of Deoxyribose Degradation Assay for Evaluating Hydroxyl Radical Scavenging Activity of Punica Extract

Punica granatum L. (Family: Punicaceae), commonly known as pomegranate (Anar) has been used traditionally as medicine to treat a number of diseases and disorders. Various parts of the plant and their active constituents are known to possess diverse biological activity. However, little is known about the antioxidant potential of pomegranate fruit peel, which is otherwise considered as waste. Therefore, the concentration-dependent hydroxyl radical scavenging ability of Punica granatum seed and peel extracts (alcoholic and aqueous) using deoxyribose degradation assay were analyzed and compared. The hydroxyl radical scavenging activity at different concentrations of extract (10 to 250 μg/mL) both in presence and/or absence of ascorbic acid and ethylenediamine tetra acetic acid was determined. It was observed that a higher concentration of extract suppresses scavenging activity and lower promotes antioxidant property. Based on the observations, it may be inferred that pomegranate extract, especially from the spent/waste prt, has a strong antioxidant property as assessed by its property of scavenging hydroxyl radical formation.     

Experimental analysis of the thermal properties of fused quartz-reinforced Al-6061alloy matrix composites using RSM approach

Journal of Mechanical Science and Technology - This study investigates the thermal property of fused quartz-reinforced aluminum 6061 alloy metal matrix composites for three different chill material...     

Enhanced near-infrared periocular recognition through collaborative rendering of hand crafted and deep features

Periocular recognition leverage from larger feature region and lesser user cooperation, when compared against the traditional iris recognition. Moreover, in the current scenario of Covid-19, where majority of people cover their faces with masks, potential of recognizing faces gets reduced by a large extent, calling for wide applicability of periocular recognition. In view of these facts, this paper targets towards enhanced representation of near-infrared periocular images, by combined use of hand-crafted and deep features. The hand-crafted features are extracted through partitioning of periocular image followed by obtaining the local statistical properties pertaining to each partition. Whereas, deep features are extracted through the popular convolutional neural network (CNN) ResNet-101 model. The extensive set of experiments performed with a benchmark periocular database validates the promising performance of the proposed method. Additionally, investigation of cross-spectral matching framework and comparison with state-of-the-art, reveal that combination of both types of features employed could prove to be extremely effective.

     

IoT Based Design of Air Quality Monitoring System Web Server for Android Platform

Wireless Personal Communications - The web technology is rapidly increased in various fields. This paper aims to create a server on cloud platform to store data and process the information...     

An innovative image encryption scheme based on chaotic map and Vigenère scheme

Multimedia Tools and Applications - Confidentiality of images during data communication is a prime concern of many researchers, as a result, several mechanisms have been proposed for image...     

Like-minded communities: bringing the familiarity and similarity together

Community detection in social networks is a well-studied problem. A community in social network is commonly defined as a group of people whose interactions within the group are more than outside the group. It is believed that people’s behavior can be linked to the behavior of their social neighborhood. While shared characteristics of communities have been used to validate the communities found, to the best of authors’ knowledge, it is not demonstrated in the literature that communities found using social interaction data are like-minded, i.e., they behave similarly in terms of their interest in items (e.g., movie, products). In this paper, we experimentally demonstrate, on a social networking movie rating dataset, that people who are interested in an item are socially better connected than the overall graph. Motivated by this fact, we propose a method for finding communities wherein like-mindedness is an explicit objective. We find small tight groups with many shared interests using a frequent item set mining approach and use these as building blocks for the core of these like-minded communities. We show that these communities have higher similarity in their interests compared to communities found using only the interaction information. We also compare our method against a baseline where the weight of edges are defined based on similarity in interests between nodes and show that our approach achieves far higher level of like-mindedness amongst the communities compared to this baseline as well.