Homi Bhabha National Institute

About: Homi Bhabha National Institute is a education organization based out in Mumbai, Maharashtra, India. It is known for research contribution in the topics: Density functional theory & Population. The organization has 4496 authors who have published 6764 publications receiving 43272 citations.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Catalytic carbon dioxide hydrogenation to methanol: A review of recent studies

Abstract: Methanol demand is continuously increasing in the chemical and energy industries. It is commercially produced from synthesis gas (CO + CO 2 + H 2 ) using CuO/ZnO/Al 2 O 3 catalysts. Today, much effort is being put on the development of technologies for its production from carbon dioxide (CO 2 ). In this way, the Greenhouse effect may be mitigated. Over the years, several useful works on CO 2 hydrogenation to methanol have been reported in the literature. In this article, we present a comprehensive overview of all the recent studies published during the past decade. Various aspects on this reaction system (such as thermodynamic considerations, innovations in catalysts, influences of reaction variables, overall catalyst performance, reaction mechanism and kinetics, and recent technological advances) are described in detail. The major challenges confronting methanol production from CO 2 are considered. By now, such a discussion is still missing, and we intend to close this gap in this paper.