ISOLATION AND SCREENING OF ALKALI TOLERANT TRICHODERMA SPP AS BIOCONTROL AGENT FOR ALKALINE AGRICULTURAL SOIL

Objective: The main aim of the study was to screen twenty isolates of Trichoderma spp isolated from high alkaline agricultural soil and non agricultural soil and to test them invitro for their pH levels tolerance and biomass production. Methods: All the twenty Trichoderma spp isolates were assessed for growth and biomass yield at different alkaline pH 9, 10, 11 on nutrient medium PCA and PCB. The growth were measured in terms of mycelial weight (cell biomass) and sporulation. The most tolerant isolates were further studied for their biocontrol activity against phytopathogen isolated from same location. Results: Significant variations in the growth of the isolates were recorded with increasing pH. Different isolates of same species also varies in their growth and biomass yield at pH 7, 9, 10, and 11. The isolates were screened for highest growth at pH 11 in terms of biomass yield. 12 isolates showed prominent biomass yield at pH 10.0, but only 5 isolates were able to grow prominently at pH 11 Conclusion: The study concluded that Trichoderma spp isolated from alkali soil with a high pH of more than 11 was only able to grow under high pH range of 9, 10, 11 and also exhibited maximum antagnostic activity for the pathogens studied

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