A weight of evidence review on the mode of action, adversity, and the human relevance of xylene's observed thyroid effects in rats.

Xylene substances have wide industrial and consumer uses and are currently undergoing dossier and substance evaluation under Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) for further toxicological testing including consideration of an additional neurotoxicological testing cohort to an extended one-generation reproduction toxicity (EOGRT) study. New repeated dose study data on xylenes identify the thyroid as a potential target tissue, and therefore a weight of evidence review is provided to investigate whether or not xylene-mediated changes on the hypothalamus-pituitary-thyroid (HPT) axis are secondary to liver enzymatic induction and are of a magnitude that is relevant for neurological human health concerns. Multiple published studies confirm xylene-mediated increases in liver weight, hepatocellular hypertrophy, and liver enzymatic induction the oral or inhalation routes, including an increase in uridine 5'-diphospho-glucuronosyltransferase (UDP-GT) activity, the key step in thyroid hormone metabolism in rodents.

Fused Multi-Domains and Adaptive Variational Mode Decomposition ECG Feature Extraction for Lightweight Bio-Inspired Key Generation and Encryption.

Security is one of the increasingly significant issues given advancements in technology that harness data from multiple devices such as the internet of medical devices. While protecting data from unauthorized user access, several techniques are used including fingerprints, passwords, and others. One of the techniques that has attracted much attention is the use of human features, which has proven to be most effective because of the difficulties in impersonating human-related features.

K-Means Based Bee Colony Optimization for Clustering in Heterogeneous Sensor Network.

In Wireless Sensor Networks (WSNs), an efficient clustering technique is critical in optimizing the energy level of networked sensors and prolonging the network lifetime. While the traditional bee colony optimization technique has been widely used as a clustering technique in WSN, it mostly suffers from energy efficiency and network performance. This study proposes a Bee Colony Optimization that synergistically combines K-mean algorithms (referred to as K-BCO) for efficient clustering in heterogeneous sensor networks.