Background: The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer’s disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the “gold standard” is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods. Purpose: The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms. Methods: The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years. Conclusion: Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

HaptenDB is available on http://www.imtech.res.in/raghava/haptendb/ and http://bioinformatics.uams.edu/raghava/haptendb/ (Mirror site).

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

A B S T R A C TOsmolytes are known to be an important factor for the stabilization and proficient functioning of proteins. However, the stabilization mechanism of proteins by the interaction of osmolytes is still not well explored. Here, we performed in silico 3D structure modelling of rice catalase-A (CatA) protein and its molecular interaction with sucrose. Further, in planta was conducted to see the effects of sucrose on catalase activity in rice grown in saline sodic soil at different time intervals. The molecular docking experiments results showed that sucrose can be ligated with CatA, protein forming hydrogen bond with precise amino acid residues like, R49, R89, P309, F311, Y335 and T338. The interaction also comprises the contribution of hydrophobic amino acid residues like V50, V51, H52, L123, A310, Q339 and R342. The planta in vitro catalase activity assay showed that plants treated with sucrose significantly affect the catalase activity in rice. Results revealed that maximum catalase activity was recorded in plants treated with 150 and 200 ppm of sucrose after 15 days of sucrose application. However, minimum activity was recorded in control plants. We believe that our study will provides an advanced understanding of catalase activity in plants exposed to osmotic stress.

No abstract