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Abstract Nemipterus randalli, commonly known as Randall’s threadfin bream, is a commercially important marine finfish. Understanding its genetic structure is critical to effective management and conservation efforts. Previous investigations on population structure in this species were limited by geographic coverage. In this study, we utilized the mitochondrial Cytochrome b gene and nuclear Ribosomal protein gene intron Rp S7 sequences to investigate the population genetic structure, demography and genetic diversity of N. randalli along Indian waters. Our results revealed high haplotype diversity but low nucleotide diversity. AMOVA revealed that the variation among the population was highly significant. Hierarchical AMOVA provided further evidence of significant genetic differentiation between the west and east coasts, which was corroborated by the Bayesian tree and the median-joining network diagram. The mtDNA sequences revealed significant genetic structure between populations based on fixation index analysis following the isolation-by-distance model. Furthermore, the neutrality test and mismatch analysis suggest that N. randalli populations may have experienced a population expansion. However, nuclear marker RpS7, showed a high level of polymorphism, which obscured the population structuring observed with the mitochondrial marker. Consequently, concordant results were not obtained when comparing the mitochondrial and nuclear DNA sequences. The strong genetic differentiation between the east and west coast observed using mitochondrial marker could be attributed to a combination of geographic and environmental factors. These findings lay the groundwork for developing effective conservation and management strategies for N. randalli, considering its genetic structure.

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]