Cadmium Induced Toxicity

Cadmium is a highly toxic metal that can disrupt a number of biological systems. The cadmium is responsible for the generation of reactive oxygen species, glutathione depletion, lipid peroxidation, proteins cross-liking, and DNA damage; ultimately result in oxidant induced cell death. Cadmium stimulates and binds to various biological components such as proteins and non-protein sulfhydryl groups, macromolecules and metallothionein. Cadmium intoxication can lead to kidney, bone, and pulmonary damage, damage to the lungs, liver, and kidneys in animals and humans in cadmium-exposed conditions.This review gives detail about mechanism and diseases resulting from cadmium induced toxicity

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. BárbaraAvelar-Pereira 9 , EthanRoy2 , Valerie J.Sydnor3,4,5, JasonD.Yeatman1,2, The Fibr Community Science Consortium*, TheodoreD.Satterthwaite3,4,5,88 & Ariel Roke

A system verilog approach for verification of memory controller

Memory performance has become the major bottleneck to improve the overall performance of the computer system. By using memory controller, there is effective control of data between processor and memory. In this paper, a memory controller for interfacing Synchronous Static Random Access Memory (SSRAM), Synchronous Dynamic Random Access Memory (SDRAM), Read Only Memory (ROM) and FLASH which is Electrically Erasable Programmable Read-Only Memory is designed and a coverage driven Constraint random verification environment is built for the designed memory controller. Verification plays an important role in any design flow as it is done before silicon development. It is done at time of product development for quality checking and bug fixing in design

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018