22 research outputs found
Effects of sleep deprivation on neural functioning: an integrative review
Sleep deprivation has a broad variety of effects on human performance and neural functioning that manifest themselves at different levels of description. On a macroscopic level, sleep deprivation mainly affects executive functions, especially in novel tasks. Macroscopic and mesoscopic effects of sleep deprivation on brain activity include reduced cortical responsiveness to incoming stimuli, reflecting reduced attention. On a microscopic level, sleep deprivation is associated with increased levels of adenosine, a neuromodulator that has a general inhibitory effect on neural activity. The inhibition of cholinergic nuclei appears particularly relevant, as the associated decrease in cortical acetylcholine seems to cause effects of sleep deprivation on macroscopic brain activity. In general, however, the relationships between the neural effects of sleep deprivation across observation scales are poorly understood and uncovering these relationships should be a primary target in future research
Interpersonal problems and personality disorder in Korean criminal offenders with schizophrenia
P.1.a.015 No interaction of the 5-HTTLPR genetic polymorphism on body image dissatisfaction by neuroticism in a young adult Korean population
P.1.a.004 Polymorphisms and haplotypes of the DRD2 gene are associated with negative symptom treatment response to amisulpride
P.1.i.01l A standardisation of the Korean version of reaction inventory to measure anger
P.1.e.014 Study on auditory hallucination and delusion using single photon emission computer tomography in schizophrenia
Analysis of opportunistic two‐path successive relaying in consideration of inter‐relay interference
Real-Time Optimized Error Protection Assignment for Scalable Image and Video over Wireless Channels
A new error protection assignment scheme with applications to real-time wireless multimedia transmission is presented. The proposed scheme exploits the structure of scalable sources to ensure optimal assignment. This novel approach recasts the nonlinear optimization problem into a linear one, and then further remodels it into a discrete programming problem, thereby reducing the computational complexity dramatically. Furthermore, the proposed algorithm does not impose any requirement on the convexity of the source; i.e., it can equally be applied on a convex or nonconvex source. Results show that the described method facilitates a significant complexity reduction with respect to existing schemes, while exhibiting almost equivalent performance
