Whole Tumor Segmentation from Brain MR images using Multi-view 2D Convolutional Neural Network.

In this paper, a study is reported on the popular BraTS dataset for segmentation of brain tumor. The BraTS 2019 dataset is used that comprises four MR modalities along with the ground-truth for 259 high grade glioma (HGG) and 76 low grade glioma (LGG) patient data. We have employed U-Net architecture based 2D convolutional neural network (CNN) for each of the orthogonal planes (sagittal, coronal and axial) and fused their predictions.

Use and validation of text mining and cluster algorithms to derive insights from Corona Virus Disease-2019 (COVID-19) medical literature.

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) late last year has not only led to the world-wide coronavirus disease 2019 (COVID-19) pandemic but also a deluge of biomedical literature. Following the release of the COVID-19 open research dataset (CORD-19) comprising over 200,000 scholarly articles, we a multi-disciplinary team of data scientists, clinicians, medical researchers and software engineers developed an innovative natural language processing (NLP) platform that combines an advanced search engine with a biomedical named entity recognition extraction package. In particular, the platform was developed to extract information relating to clinical risk factors for COVID-19 by presenting the results in a cluster format to support knowledge discovery.

BRAHMA: Population specific T1, T2, and FLAIR weighted brain templates and their impact in structural and functional imaging studies.

Differences in brain morphology across population groups necessitate creation of population-specific Magnetic Resonance Imaging (MRI) brain templates for interpretation of neuroimaging data. Variations in the neuroanatomy in a genetically heterogeneous population make the development of a population-specific brain template for the Indian subcontinent imperative. A dataset of high-resolution 3D T1, T2-weighted, and FLAIR images acquired from a group of 113 volunteers (M/F - 56/57, mean age-28.

An efficient synthesis of 1,8-dioxo-octahydro-xanthene derivatives promoted by a room temperature ionic liquid at ambient conditions under ultrasound irradiation.

The condensation reaction involving an aldehyde and diketone was efficiently promoted by the Ionic liquid, [Hbim]BF(4) (IL) as a reaction medium with methanol as co-solvent at ambient temperature under ultrasonic irradiation to afford the corresponding 1,8-dioxo-octahydro-xanthene (xanthene) derivatives in excellent yields. The advantages of this method include among others the use of a recyclable, non-volatile ionic liquid, which promotes this protocol under ambient temperature without the requirement of any added catalyst. The reaction times and yields are compared with p-TSA catalyzed synthesis of xanthenes under thermal conditions, which is also reported for the first time under our reaction conditions.