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High altitude pulmonary edema (HAPE) is experienced by non-acclimatized sea level individuals on exposure to high altitude hypoxic conditions. Available evidence suggests that genetic factors and perturbed mitochondrial redox status may play an important role in HAPE pathophysiology. However, the precise mechanism has not been fully understood. In the present study, sequencing of mitochondrial DNA (mtDNA) from HAPE subjects and acclimatized controls was performed to identify pathogenic mutations and to determine their role in HAPE. Hypobaric hypoxia induced oxidative stress and metabolic alterations were also assessed in HAPE subjects. mtDNA copy number, mitochondrial oxidative phosphorylation (mtOXPHOS) activity, mitochondrial biogenesis were measured to determine mitochondrial functions. The data revealed that the mutations in Complex I genes affects the secondary structure of protein in HAPE subjects. Further, increased oxidative stress during hypobaric hypoxia, reduced mitochondrial biogenesis and mtOXPHOS activity induced metabolic reprogramming appears to contribute to mitochondrial dysfunctions in HAPE individuals. Haplogroup analysis suggests that mtDNA haplogroup H2a2a1 has potential contribution in the pathobiology of HAPE in lowlanders. This study also suggests contribution of altered mitochondrial functions in HAPE susceptibility.

In today’s world Images play very important role to get the information about any object. The real examples are in medical industry For M.R.I and C.T Scans, in forecasting for analyzing the satellite images and in tourism industries to select the better image among the various images of place. Sometimes images are not clear so we are unable to extract information. To solve this problem there is a concept Image Processing. Images are used to extract information about any object to generate the reports and data for the further processing. The main motive to write this paper is to detect more edges with good intensity, good contrast and less noise in an image which is enforced using PYTHON and the results obtained are studied and thereby mentioned, highlighting the techniques performance. So, in this dissertation some existing techniques are used and new improved algorithm is applied on the image and results of proposed algorithm are comparing with existing method. Performed experiments showed that improved algorithm is better as compare to the normal Laplacian edge detection and existing method; we can say this by examining images with some parameters like MSE, PSNR, standard deviation and MEAN values. The algorithm is demonstrated through the segmentation of colored or grayscale images. The correctness of proposed method is estimated and a comparative analysis against techniques Laplacian and existing method [2] and improved algorithm is presented.