NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION

For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function

Environmental Impact of Microbial Pesticides

Biological control constitutes an important component of integrated pest management (IPM). However, the non-availability o f efficient biocontrol agents is one of the major constraints in adopting IPM practices. Microbial control, which makes use o f naturally occurring microbes to control insect pests, pathogens, and weeds, is less harmful to nontarget organisms and the environment than the chemical pesticides. Microbials are promising alternatives to chemical pesticides and have opened up new vistas in insect pest management to aid promotion o f safe, eco-friendly pest management. The use o f microbial pesticides in pest management is quite limited because o f lack of appropriate formulations and the availability of quality products to the farmers. Since 2006, the registration o f the microbial pesticides for commercial purposes has been made mandatory in India. It warrants information on toxicological results against mammals and eco-toxicity data on nontargets such as fishes, birds, earthworms, honeybees, and silkworm. The data is to be generated with technical product and the formulation of every strain intended for commercialization. It is also mandatory to generate data on the safety of the formulation to natural enemies along with data on the bioefficacy and phytotoxicity to the crop. Fourteen primary microbial pesticide products and their formulations have been registered in India by 2009. There are 478 products o f the 14 microbial pesticides registered in India. There are 184 products for the management of plant pathogen

Synthesis and characterization of novel positively charged gold nanoparticle for the detection of food contaminants

The suggested work emphasizes the benefits of employing positively charged gold nanoparticles to detect negatively charged E.coli cells by electrostatic attraction without the need of any identification elements. As a result, a simple, label-free colorimetric biosensor for detecting E.coli, a well-known food contaminant, was developed. Initially, the Taguchi approach was used to synthesis small, stable, and positively charged Au NPs in orthogonal array L8 by optimizing 5 critical parameters such as pH, capping agents, temperature, Sonication duration, and Au/LE ratio. Based on the Maximum wavelength (λ max) values and ANOVA, Trail 3 (T3) a Polyallyl amine hydrochloride capped Au NPs (PAH-Au NPs) along with D-Mannose was chosen to be employed in detecting E.coli. It functions as a colorimetric sensor in both sol and paper coated forms. Based on the sensing technique, we discovered that the LOD in sol is (1*105CFU/mL), which is significantly higher than the sensor based on paper (1*102 CFU/mL)

A rapid colorimetric dual sensor for the detection of mercury and lead ions in water using cysteine capped silver nanoparticles

Thiol groups containing amino acids like cystine and l-cysteine help in the chelation of poisonous heavy metals in biological systems therein making them eliminated easily in urine. Based on this phenomenon, cysteine-coated silver nanoparticles could be helpful as potential label-free sensors systems for detecting heavy metals. In this study, the benefit of using silver nanoparticles with cysteine capping (Cys-AgNPs) for colorimetric detection of individual and simultaneous sensing of Lead(Pb2+) and Mercury( Hg2+) in water samples was reported. Cys-AgNPs were characterized using spectroscopy, microscopy, X-ray diffractometer analysis and the photophysical properties of the particles towards Hg2+ and Pb2+ ions were investigated through absorption spectroscopy and colorimetric tests. Upon exposure of Hg2+ ions to Cys-AgNPs, a hypsochromic shift in the surface plasmon resonance (SPR) band with decreased intensity was detected. In contrast, it was a bathochromic shift with an increase in absorbance intensity observed with Pb2+ ions. Cys-AgNPs are highly sensitive toward Hg2+ and Pb2+ ions individually with clear color changes in the solution visible to the naked eye. The limit of detection (LOD) was found to be 45.39 × 10−9 M (for Hg2+ ions) and 49.39 × 10−9 M (for Pb2+ ions). Furthermore, dual ion sensing ability was also assessed by Cys-AgNPs by varying Hg2+ and Pb2+ ions individually in water samples. The LOD for constant Hg2+ and varying Pb2+ was found to be 43 × 10−9 M while it is 37.95 × 10−9 M for constant Pb2+ and varying Hg2+. These results determine that the Cys-AgNPs system has potential application as a colorimetric sensor for the detection of individual or mixed Hg2+ and Pb2+ ions in water samples quickly