Systematic Review of Post-Traumatic Parkinsonism, an Emerging Parkinsonian Disorder Among Survivors of Traumatic Brain Injury.

This systematic review focuses on an increasing subset of traumatic brain injury (TBI) survivors who develop post-traumatic parkinsonism (PTP), characterized by slowness of movement (bradykinesia), rigidity (stiffness), postural instability, and resting tremors caused by obstruction or damage to deep brain structures of the basal ganglia. PTP is rare, and one hypothesis to explain PTP rarity is that TBIs severe enough to affect deep brain structures are often lethal; however, with increasing survivability of TBIs, these numbers are expected to increase. The goal of this review is to raise awareness of an expected global increase of a subgroup of TBI patients who are treatment responsive and report therapeutic results aiding providers in diagnosing, educating, and treating PTP patients.

Gulf War toxicant-induced reductions in dendritic arbors and spine densities of dentate granule cells are improved by treatment with a Nrf2 activator.

Gulf War Illness (GWI) is a chronic multi-symptom disorder affecting approximately 30 % of Veterans deployed to the Persian Gulf from 1990 to 91. GWI encompasses a wide spectrum of symptoms which frequently include neurological problems such as learning and memory impairments, mood disorders, and an increased incidence of neurodegenerative disorders. Combined exposure to both reversible and irreversible acetylcholinesterase (AChE) inhibitors has been identified as a likely risk factor for GWI.

Repetitive mild TBI causes pTau aggregation in nigra without altering preexisting fibril induced Parkinson's-like pathology burden.

Population studies have shown that traumatic brain injury (TBI) is associated with an increased risk for Parkinson's disease (PD) and among U.S. Veterans with a history of TBI this risk is 56% higher.

Speaker Recognition Using Constrained Convolutional Neural Networks in Emotional Speech.

Speaker recognition is an important classification task, which can be solved using several approaches. Although building a speaker recognition model on a closed set of speakers under neutral speaking conditions is a well-researched task and there are solutions that provide excellent performance, the classification accuracy of developed models significantly decreases when applying them to emotional speech or in the presence of interference. Furthermore, deep models may require a large number of parameters, so constrained solutions are desirable in order to implement them on edge devices in the Internet of Things systems for real-time detection.