Detection of Mutations in folp1, rpoB and gyrA genes of M. leprae by PCR- direct sequencing – A rapid tool for screening drug resistance in leprosy

Introduction: Conventional Mouse foot-pad (MFP) assay for screening drug resistance in M. leprae is cumbersome and time-consuming (approximately 6 to 12 months). Molecular targets for different anti-leprosy drugs have been well defined. Molecular tools for rapid detection of drug resistance in M. leprae have been standardised. A study to compare molecular methods with MFP assay in determining the drug susceptibility of M. leprae was carried out. Methods: Forty Bacteriological Index (BI) positive patients of leprosy with clinical features of relapse (25), new cases (11) and defaulters (4) were included in the study. A skin biopsy was done and the samples were processed using both MFP assay and Molecular method. PCR assays were carried out to amplify, 388 bp of folP1gene for dapsone resistance, 305 bp of rpoB gene for rifampicin resistance and 342 bp of gyrA gene for ofloxacin resistance, followed by direct DNA sequencing. Results: Significant growth in the MFP test was obtained in only 28 out of 40 biopsies processed (70%). Ten of these isolates were dapsone resistant; one isolate showed combined resistance against dapsone, rifampicin and clofazimine. Amplification for all three genes was successful in all the 40 (100%) samples. Among folP1 products sequenced, six isolates showed mutations at 53 (or) 55 amino acid positions. Those strains which showed high-level resistance with two log growth

The State of School Infrastructure in the Assembly Constituencies of Rural India: Analysis of 11 Census Indicators from Pre-Primary to Higher Education

In India, assembly constituencies (ACs), represented by elected officials, are the primary geopolitical units for state-level policy development. However, data on social indicators are traditionally reported and analyzed at the district level, and are rarely available for ACs. Here, we combine village-level data from the 2011 Indian Census and AC shapefiles to systematically derive AC-level estimates for the first time. We apply this methodology to describe the distribution of 11 education infrastructures—ranging from pre-primary school to senior secondary school—across rural villages in 3773 ACs. We found high variability in access to higher education infrastructures and low variability in access to lower education variables. For 40.3% (25th percentile) to 79.7% (75th percentile) of villages in an AC, the nearest government senior secondary school was >5 km away, whereas the nearest government primary school was >5 km away in just 0% (25th percentile) to 1.9% (75th percentile) of villages in an AC. The states of Manipur, Arunachal Pradesh, and Bihar showed the greatest within-state variation in access to education infrastructures. We present a novel analysis of access to education infrastructure to inform AC-level policy, and demonstrate how geospatial and Census data can be leveraged to derive AC-level estimates for any population health and development indicators collected in the Census at the village level

Human Evaluation of Models Built for Interpretability

Recent years have seen a boom in interest in interpretable machine learning systems built on models that can be understood, at least to some degree, by domain experts. However, exactly what kinds of models are truly human-interpretable remains poorly understood. This work advances our understanding of precisely which factors make models interpretable in the context of decision sets, a specific class of logic-based model. We conduct carefully controlled human-subject experiments in two domains across three tasks based on human-simulatability through which we identify specific types of complexity that affect performance more heavily than others–trends that are consistent across tasks and domains. These results can inform the choice of regularizers during optimization to learn more interpretable models, and their consistency suggests that there may exist common design principles for interpretable machine learning systems