Classification of Atrial Fibrillation using Random Forest Algorithm

The electrocardiogram is indicates the electrical activity of the heart and it can be used to detect cardiac arrhythmias. In the present work, we exhibited a methodology to classify Atrial Fibrillation (AF), Normal rhythm, and Other abnormal ECG rhythms using a machine learning algorithm by analyzing single-lead ECG signals of short duration. First, the events of ECG signals will be detected, after that morphological features and HRV features are extracted. Finally, these features are applied to the Random Forest classifier to perform classification. The Physionet challenge 2017 dataset with more than 8500 ECG recordings is used to train our model. The proposed methodology yields an F1 score of 0.86, 0.97, and 0.83 respectively in classifying AF, normal, other rhythms, and an accuracy of 0.91 after performing a 5-fold cross-validation

A Machine learning Classification approach for detection of Covid 19 using CT images

Coronavirus disease 2019 popularly known as COVID 19 was first found in Wuhan, China in December 2019. World Health Organization declared Covid 19 as a transmission disease. The symptoms were cough, loss of taste, fever, tiredness, respiratory problem. These symptoms were likely to show within 11 –14 days. The RT-PCR and rapid antigen biochemical tests were done for the detection of COVID 19. In addition to biochemical tests, X-Ray and Computed Tomography (CT) images are used for the minute details of the severity of the disease. To enhance efficiency and accuracy of analysis/detection of COVID images and to reduce of doctors' time for analysis could be addressed through Artificial Intelligence. The dataset from Kaggle was utilized to analyze. The statistical and GLCM features were extracted from CT images for the classification of COVID and NON-COVID instances in this study. CT images were used to extract statistical and GLCM features for categorization. In the proposed/prototype model, we achieved the classification accuracy of 91%, and 94.5% using SVM and Random Forest respectively

Alternative patterns of sex chromosome differentiation in Aedes aegypti (L).

BACKGROUND: Some populations of West African Aedes aegypti, the dengue and zika vector, are reproductively incompatible; our earlier study showed that divergence and rearrangements of genes on chromosome 1, which bears the sex locus (M), may be involved. We also previously described a proposed cryptic subspecies SenAae (PK10, Senegal) that had many more high inter-sex FST genes on chromosome 1 than did Ae.aegypti aegypti (Aaa, Pai Lom, Thailand). The current work more thoroughly explores the significance of those findings. RESULTS: Intersex standardized variance (FST) of single nucleotide polymorphisms (SNPs) was characterized from genomic exome capture libraries of both sexes in representative natural populations of Aaa and SenAae. Our goal was to identify SNPs that varied in frequency between males and females, and most were expected to occur on chromosome 1. Use of the assembled AaegL4 reference alleviated the previous problem of unmapped genes. Because the M locus gene nix was not captured and not present in AaegL4, the male-determining locus, per se, was not explored. Sex-associated genes were those with FST values ≥ 0.100 and/or with increased expected heterozygosity (H exp , one-sided T-test, p < 0.05) in males. There were 85 genes common to both collections with high inter-sex FST values; all genes but one were located on chromosome 1. Aaa showed the expected cluster of high inter-sex FST genes proximal to the M locus, whereas SenAae had inter-sex FST genes along the length of chromosome 1. In addition, the Aaa M-locus proximal region showed increased H exp levels in males, whereas SenAae did not. In SenAae, chromosomal rearrangements and subsequent suppressed recombination may have accelerated X-Y differentiation. CONCLUSIONS: The evidence presented here is consistent with differential evolution of proto-Y chromosomes in Aaa and SenAae

Poly-β-hydroxybutyrate administration during early life: effects on performance, immunity and microbial community of European sea bass yolk-sac larvae

The reliable production of marine fish larvae is one of the major bottlenecks in aquaculture due to high mortalities mainly caused by infectious diseases. To evaluate if the compound poly-β-hydroxybutyrate (PHB) might be a suitable immunoprophylactic measure in fish larviculture, its capacity to improve immunity and performance in European sea bass (Dicentrarchus labrax) yolk-sac larvae was explored. PHB was applied from mouth opening onwards to stimulate the developing larval immune system at the earliest possible point in time. Larval survival, growth, microbiota composition, gene expression profiles and disease resistance were assessed. PHB administration improved larval survival and, furthermore, altered the larva-associated microbiota composition. The bacterial challenge test using pathogenic Vibrio anguillarum revealed that the larval disease resistance was not influenced by PHB. The expression profiles of 26 genes involved e.g. in the immune response showed that PHB affected the expression of the antimicrobial peptides ferritin (fer) and dicentracin (dic), however, the response to PHB was inconsistent and weaker than previously demonstrated for sea bass post-larvae. Hence, the present study highlights the need for more research focusing on the immunostimulation of different early developmental stages for gaining a more comprehensive picture and advancing a sustainable production of high quality fry

The dominant Anopheles vectors of human malaria in the Asia-Pacific region: occurrence data, distribution maps and bionomic précis

<p>Abstract</p> <p>Background</p> <p>The final article in a series of three publications examining the global distribution of 41 dominant vector species (DVS) of malaria is presented here. The first publication examined the DVS from the Americas, with the second covering those species present in Africa, Europe and the Middle East. Here we discuss the 19 DVS of the Asian-Pacific region. This region experiences a high diversity of vector species, many occurring sympatrically, which, combined with the occurrence of a high number of species complexes and suspected species complexes, and behavioural plasticity of many of these major vectors, adds a level of entomological complexity not comparable elsewhere globally. To try and untangle the intricacy of the vectors of this region and to increase the effectiveness of vector control interventions, an understanding of the contemporary distribution of each species, combined with a synthesis of the current knowledge of their behaviour and ecology is needed.</p> <p>Results</p> <p>Expert opinion (EO) range maps, created with the most up-to-date expert knowledge of each DVS distribution, were combined with a contemporary database of occurrence data and a suite of open access, environmental and climatic variables. Using the Boosted Regression Tree (BRT) modelling method, distribution maps of each DVS were produced. The occurrence data were abstracted from the formal, published literature, plus other relevant sources, resulting in the collation of DVS occurrence at 10116 locations across 31 countries, of which 8853 were successfully geo-referenced and 7430 were resolved to spatial areas that could be included in the BRT model. A detailed summary of the information on the bionomics of each species and species complex is also presented.</p> <p>Conclusions</p> <p>This article concludes a project aimed to establish the contemporary global distribution of the DVS of malaria. The three articles produced are intended as a detailed reference for scientists continuing research into the aspects of taxonomy, biology and ecology relevant to species-specific vector control. This research is particularly relevant to help unravel the complicated taxonomic status, ecology and epidemiology of the vectors of the Asia-Pacific region. All the occurrence data, predictive maps and EO-shape files generated during the production of these publications will be made available in the public domain. We hope that this will encourage data sharing to improve future iterations of the distribution maps.</p