The term Phylogentics is the study of evolutionary relationship between different species, organisms or genes. These relationships are depicted as branched, tree like diagrams that provide insight into the events that occurred during the evolution process. These trees may also have a root which is known as the common ancestor. Building the "Tree of Life" has been the prime objective of many researchers, until it was proved that the tree of life cannot be represented by a single? tree. Many evolutionary events cannot be represented with the help of a simple tree, hence phylogenetic networks came into picture. Phylogenetic networks can be classified into different categories. In this paper, an algorithm (ReTF) has been proposed which would improve the results of the current phylogenetic network reconstruction algorithms. The idea behind ReTF is rearranging the input sequences in a way that the new arrangement gives a better tree, since the order of input sequences affects the outcomes of phylogenetic network.
Conversational agents have become extraordinarily popular over the last few years, with accelerated adoption due to COVID-19. Even though a lot of work has been done to devise a real-time agent very few of them focus on dynamic responses. The challenges for automatic medical diagnosis not only include issues for topic transition coherency and question understanding but also issues regarding the context of medical knowledge and symptoms of disease relations. In this paper, we propose a conversational agent that not only generates answers to specific medical questions but also makes more natural and human-like conversations and can adapt to the context and evolve over time. We propose an End-to-End knowledge-routed Relational Dialogue System that would incorporate a rich medical knowledge graph into the topic transition in dialogue management, and make it accommodative with NLU (Natural Language Understanding) and NLG (Natural Language Generation). A knowledge-routed graph for topic decision-making is used, which helps to identify relationships between symptoms and symptom-disease pairs. However, there are constraints on the extent of questions that knowledge graphs can address independently. To overcome these, we have used a fine-tuned GPT-3 model. While knowledge graphs organize data as interconnected entities, GPT-3 generates human-like text using learned patterns from large datasets. This approach enhances responses to intricate queries.
BACKGROUND: ABO and Rh blood groups are most important blood groups in human beings.The frequency of four main blood group systems varies in population throughout the world and even in different parts of country.Objective if this study was to identify distribution of ABO and Rh blood group system.MATERIALS AND METHODS: Blood samples from 10680 tribals were collected in Jhabua district of Madhya Pradesh during the month of June 2012.Among 10680 tribals, 5670 were male.Blood groups were done in tribals belonging to Bhil, Bhilala & Katthiwar tribes.For the blood grouping of the patients, 5cc of clotted blood was collected & transported to Department Pathology, Peoples College of medical sciences, Bhopal.RESULTS: A total 10680 samples were analyzed, out of which 5670 (53%) samples were of male.The frequency of blood group B in our population was 36.9 %; n= 3950 (35.37%B Rh positive and 1.61% B Rh negative) and frequency of blood group B remain highest in our study group.The frequency of blood group O in our population was 31.8%;n=3400 (30.43% O Rh positive and 1.4% O Rh negative) followed by blood group A was 24.3%; n=2600 (23.15%A Rh positive and 1.18%A Rh negative) and blood group AB was 6.8%; n=730 (6.63% AB Rh positive and 0.2% AB Rh negative) The overall phenotypic frequencies of ABO blood groups were B>O>A>AB.Rh (D) positive were 95.59%; n=10210 and Rh (D) negative were 4.41%; n=470.DISCUSSION: B positive blood group is significantly high in our population.Every transfusion center should have a record of frequency of blood group system in their population.It helps in inventory management.Knowledge of blood group distribution is important for clinical studies, for reliable geographical information and for forensic studies in the population.
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