Prevalence of exclusive breastfeeding and its determinants in first 6 months of life: A prospective study

Background: Exclusive breastfeeding for first 6 months of life is recommended under Infant and Young Child Feeding practices in India. The objective of present study was to estimate the prevalence of exclusive breastfeeding during first 6 months of life of babies and to identify factors that interfere with the practice in the study area. Methods: A prospective cohort of 462 women who delivered at maternity unit of Government Medical College & Hospital, Rajkot, which is a tertiary care centre for the district, was studied. Data collection was done at hospital as well as during home visits of babies at 1, 3 and 6 months. Factors related to cessation of breastfeeding were analyzed using univariate, bivariate and multivariate analysis. Results: All 462 mothers reported breastfeeding their newborns. Prevalence of exclusive breastfeeding reported at 3 months was 97% which declined to 62% by 6 months of age of infants. Bivariate analysis revealed no significant association between interruption of exclusive breastfeeding before 6 months of age and various demographic, socioeconomic, maternal and infant characteristics. Multivariate analysis by logistic regression demonstrated no association between discontinuation of exclusive breastfeeding and socioeconomic status, maternal education and maternal age, number of antenatal visits, maternal employment and initiation of breastfeeding after delivery. Conclusion: Exclusive breastfeeding prevalence rate found higher than at national level indicating better feeding practices in these part of India. Also, factors classically considered as supportive for breastfeeding had shown no association with breastfeeding pattern in present study

CO2 Emissions reduction strategies and economic development of India

This paper examines the consequences of alternative CO2 emission reduction strategies on economic development and, in particular, the implications for the poor by empirically implementing an economy-wide model for India over a 35-year time horizon. A multi-sectoral, inter-temporal model in the activity analysis framework is used for this purpose. The model with specific technological alternatives, endogenous income distribution, truly dynamic behaviour and covering the whole economy is an integrated top-down bottom-up model. The results show that CO2 emission reduction imposes costs in terms of lower GDP and higher poverty. Cumulative emission reduction targets are, however, preferable to annual reduction targets and that a dynamically optimum strategy can help reduce the burden of emission reductions. The scenarios involving compensation for the loss in welfare are not very encouraging as they require large capital inflows. Contrasted with these, scenarios involving tradable emission quota give India an incentive to be carbon efficient. It becomes a net seller for the first 25 years and because of reduction in carbon intensity it would demand less in later years when it becomes a net buyer. The results suggest that for India, and other developing countries, the window of opportunity to sell carbon quotas is the next two decades or so.

A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data

We propose a matrix factorization technique that decomposes the resting state fMRI (rs-fMRI) correlation matrices for a patient population into a sparse set of representative subnetworks, as modeled by rank one outer products. The subnetworks are combined using patient specific non-negative coefficients; these coefficients are also used to model, and subsequently predict the clinical severity of a given patient via a linear regression. Our generative-discriminative framework is able to exploit the structure of rs-fMRI correlation matrices to capture group level effects, while simultaneously accounting for patient variability. We employ ten fold cross validation to demonstrate the predictive power of our model on a cohort of fifty eight patients diagnosed with Autism Spectrum Disorder. Our method outperforms classical semi-supervised frameworks, which perform dimensionality reduction on the correlation features followed by non-linear regression to predict the clinical scores