Articles

In the 200 years since Joseph Priestley first discovered that green plants replenish dephlogistated air by releasing oxygen during illumination, there has not been a more active period than the last few years for investigations of the process of photosynthetic O2 evolution. This period has been highlighted by major advances in our understanding of the molecular components essential for this process, as well as the application of new probes of the catalytic site. This spurt of activity is founded upon a broad base of knowledge. The material deals with the organization and function of the metal sites implicated in the catalysis of water oxidation during photosynthesis and how they interact with the constituents of Photosystem-II. The following topic deals with the O2 evolution and role of manganese in O2 evolution. Photosystem II is a membrane protein located in the thylakoid membrane of oxygen photosynthetic organism like green plants, green algae and cyano bacteria and possesses a number of redox active component which are enable to catalyze the oxidation of water and reduction of plastoquinone .So it performs a series of light induced electron transfer reactions leading to the splitting of water into protons and molecular oxygen, and also water is the source of the electrons that are finally used to convert carbon di-oxide to carbohydrate. The product of PS II, namely chemical energy and molecular oxygen. So it is supplies the oxygen we breath, it maintained the ozone layer needed to protect us from UV radiation and of course it provides the reducing equivalents necessary to fix carbon di-oxide to organic molecule that create biomass, food, and fuel. For these reason it is truly the engine of life and its appearance about 2.5 billion years ago represented the BIG BANG of evolution. The light induced oxidation of water is catalysed by a Mn4Ca cluster, located in the luminal surface of PS II which is denoted as oxygen evolving complex.

Pneumonia is an infection of the lungs caused by bacteria, viruses, fungi, or parasites, leading to the accumulation of pus in the air sacs, which can affect one or both lungs. This serious illness poses a global health threat, with early diagnosis being a key challenge. Traditionally, it is diagnosed by medical professionals using chest x-rays. In this study, a collection of x-ray and CT-Scan images is employed to enable automated pneumonia detection. As the condition progresses, patients experience increasing difficulty breathing. Machine learning methods offer potential for faster and more accurate diagnosis by applying computer vision techniques for automatic detection in medical imaging.