Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.

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ED can present with a wide spectrum of symptomatology. It can present as mass abdomen, intestinal obstruction or even can mimic as hydrocoele. High index of suspicion is therefore required. Ultimate aim of treatment is excision of cyst with preservation of vascularity of native gut.

The incidence, distribution, and variation of simple sequence repeats (SSRs) in viruses is instrumental in understanding the functional and evolutionary aspects of repeat sequences. Full-length genome sequences retrieved from NCBI were used for extraction and analysis of repeat sequences using IMEx software. We have also developed two MATLAB-based tools for extraction of gene locations from GenBank in tabular format and simulation of this data with SSR incidence data. Present study encompassing 147 Mycobacteriophage genomes revealed 25,284 SSRs and 1,127 compound SSRs (cSSRs) through IMEx. Mono- to hexa-nucleotide motifs were present. The SSR count per genome ranged from 78 (M100) to 342 (M58) while cSSRs incidence ranged from 1 (M138) to 17 (M28, M73). Though cSSRs were present in all the genomes, their frequency and SSR to cSSR conversion percentage varied from 1.08 (M138 with 93 SSRs) to 8.33 (M116 with 96 SSRs). In terms of localization, the SSRs were predominantly localized to coding regions (∼78%). Interestingly, genomes of around 50 kb contained a similar number of SSRs/cSSRs to that in a 110 kb genome, suggesting functional relevance for SSRs which was substantiated by variation in motif constitution between species with different host range. The three species with broad host range (M97, M100, M116) have around 90% of their mono-nucleotide repeat motifs composed of G or C and only M16 has both A and T mononucleotide motifs. Around 20% of the di-nucleotide repeat motifs in the genomes exhibiting a broad host range were CT/TC, which were either absent or represented to a much lesser extent in the other genomes.

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