Potential of bacteriocinogenic Lactococcus lactis subsp. lactis inhabiting low pH vegetables to produce nisin variants

Antimicrobial behavior of lactic acid bacteria (LAB) has been explored since many years to assess their ability to produce bacteriocin, a natural preservative, to increase the shelf life of food. This study aims to characterize bacteriocin producing strains of lactic acid bacteria isolated from acidic to slightly acidic raw vegetables including tomato, bell pepper and green chili and to investigate their potential to inhibit food related bacteria. Among twenty nine LAB screened for antimicrobial activity, three exhibited antagonism against closely related bacterial isolates which was influenced by varying temperature and pH. They were identified up to strain level as Lactococcus lactis subsp. lactis TI-4, L. lactis subsp. lactis CE-2 and L. lactis subsp. lactis PI-2 based on 16S rRNA gene sequence. Their spectrum of inhibition was observed against food associated strains of Bacillus subtilis and Staphylococcus aureus. Moreover, L. lactis subsp. lactis PI-2 selected on the basis of higher antimicrobial activity was further evaluated for bacteriocin production which was detected as nisin A and nisin Z. These findings suggest the possible use of L. lactis strains of vegetable origin as protective cultures in slightly acidic as well as slightly alkaline food by the bio-preservative action of bacteriocins.     

Characterisation of normal corn starch using asymmetrical flow field‐flow fractionation

The apparent average molar masses (M_w,app), apparent average radii of gyration (R_g,app), diffusion co‐efficients (D_T), and hydrodynamic radii (R_h) of normal corn (maize) starch and fractions were determined using asymmetrical flow field‐flow fractionation coupled with multi‐angle light scattering and refractive index detectors (AF4/MALS/RI). AM‐type (Fraction A) and AP‐type (Fraction B) were chemically separated from normal corn starch. Normal corn starch and Fractions (A–B) were dissolved in 1 M KSCN using a high pressure microwave vessel. The effect of varying cross flow rates at a fixed channel flow rate upon the M_w,app and R_g,app distributions of normal corn starch and Fractions (A–B) were investigated. The average M_w,app of normal corn starch, Fractions (A) and Fraction (B) were 41 × 10⁶, 1.4 × 10⁶ and 39 × 10⁶ g/mol, respectively, with R_g,app values of 129, 60 and 129 nm, respectively.     

Eco-friendly food products as source of natural colorant for wool yarn dyeing

ABSTRACT

Food products as a source of natural colorants are gaining widespread popularity around the globe due to their ayurvedic and eco-friendly nature. Current study is aimed with the utilization of food products (Saffron and madder) as a source of dye for woolen yarn. It is found that, for Saffron, irradiation of yarn for 6 min gives high color strength by dyeing for 45 min in the presence of 2% of salts using un-irradiated dye bath of 1 pH, whereas for madder, 4 min is optimal irradiation time for extract to dye irradiated yarn for 25 min in the presence of 6% of salts using the irradiated dye bath of pH 1. The mordanting carried out at optimal conditions show that using both extracts, woolen yarn dyed at optimal conditions has given good color characteristics. It is concluded that microwave radiation has not only improved natural dyeing process but also make the process more eco-friendly and sustainable as well.     

N-Body Simulation Inspired by Metaheuristics Optimization

The N-body problem in classical physics, is the calculation of force of gravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists and mathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been the major hurdles in solving this problem for large and complex galaxies. Advent of high performance computational machines have mitigated the problem to much extent, but still for large number of bodies it consumes huge amount of resources and days for computation. Conventional algorithms have been able to reduce the computational complexity from to by splitting the space into a tree or mesh network, researchers are still looking for improvements. In this research work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. The results are compared with that of conventional algorithms. The outcomes show about 50% time saving with almost no loss in accuracy. The proposed approach being a metaheuristics optimization technique, attempts to find optimal solution to the problem, searching the entire space in a unique and efficient manner in a very limited amount of time.     

Innovative Fungal Disease Diagnosis System Using Convolutional NeuralNetwork

Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections. The outer layer of your body is called the integumentary system. Your skin, hair, nails, and glands are all part of it. These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun. The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect. Heat, light, damage, and illness are all protected by it. Fungi-caused infections are found in almost every part of the natural world. When an invasive fungus takes over a body region and overwhelms the immune system, it causes fungal infections in people. Another primary goal of this study was to create a Convolutional Neural Network (CNN)-based technique for detecting and classifying various types of fungal diseases. There are numerous fungal illnesses, but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis (IFDD) system of Candidiasis and Tinea Infections. This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings. To identify and categorize fungal infections, deep machine learning techniques are utilized. A CNN architecture was created, and it produced a promising outcome to improve the proposed system accuracy. The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards. Our CNN-Based can detect fungal diseases through medical images; earmarked IFDD system has a predictive performance of 99.6% accuracy.