Two-qutrit Entanglement Witnesses and Gell-Mann Matrices

The Gell-Mann $\lambda$ matrices for Lie algebra su(3) are the natural basis for the Hilbert space of Hermitian operators acting on the states of a three-level system(qutrit). So the construction of EWs for two-qutrit states by using these matrices may be an interesting problem. In this paper, several two-qutrit EWs are constructed based on the Gell-Mann matrices by using the linear programming (LP) method exactly or approximately. The decomposability and non-decomposability of constructed EWs are also discussed and it is shown that the $\lambda$-diagonal EWs presented in this paper are all decomposable but there exist non-decomposable ones among $\lambda$-non-diagonal EWs.Comment: 25 page

Objective identification and analysis of physiological and behavioral signs of schizophrenia

BACKGROUND: A patient\u27s physical activity is often used by psychiatrists to contribute to the diagnostic process for mental disorders. Typically, it is based mostly on self-reports or observations, and hardly ever upon actigraphy. Other signals related to physiology are rarely used, despite the fact that the autonomic nervous system is often affected by mental disorders. AIM: This study attempted to fuse physiological and physical activity data and discover features that are predictive for schizophrenia. METHOD: Continuous simultaneous heart rate (HR) and physical activity recordings were made on 16 individuals with schizophrenia and 19 healthy controls. Statistical characteristics of the recorded data were analyzed, as well as non-linear rest-activity measures and disorganization measures. RESULTS: Four most predictive features for schizophrenia were identified, namely, the standard deviation and mode of locomotor activity, dynamics of Multiscale Entropy change over scales of HR signal and the mean HR. A classifier trained on these features provided a cross-validation accuracy of 95.3% (AUC = 0.99) for differentiating between schizophrenia patients and controls, compared to 78.5 and 85.5% accuracy (AUC = 0.85 and AUC = 0.90) using only the HR or locomotor activity features. CONCLUSION: Physiological and physical activity signals provide complimentary information for assessment of mental health

The efficacy of time-based short-course acyclovir therapy in treatment of post-herpetic pain

Introduction: Various treatments have been used to manage post-herpetic neuralgia (PHN). Safe and effective therapies to prevent PHN are needed. Methodology: A clinical trial involving 152 patients diagnosed with acute herpes Zoster (HZ) was conducted to determine whether short-course acyclovir therapy (800 mg five times a day for four days) can alleviate HZ-associated pain and prevent post-herpetic neuralgia (PHN). The patients were divided into two groups: Group 1 had a rash with a duration of less than 72 hours and Group 2 had a rash with a duration of more than 72 hours. To assess PHN, the patients categorized and assessed the severity of their symptoms using a four-point verbal rating scale (VRS). Results: By the fourth week, 134 out of 152 patients (88.2) had complete pain response (CPR). Of these, 68 patients (89.5) were from Group 1 and 66 from Group 2 (86.8). After four weeks, the mean VRS scores had changed significantly in both groups compared to the scores at the beginning of study (p = 0.001), but there was no difference between the two groups (0.88 ± 0.66 Vs. 0.94 ± 0.72; p = 0.66) After three months no differences were observed in the treatment results between the two groups (0.51 ± 0.13 Vs.0.54 ± 0.19; p = 0.77). Conclusion: Short-course acyclovir therapy is an effective treatment for zoster and its efficacy in patients with a rash duration of more than 72 hours is similar to that in patients with rash duration of less than 72 hours. © 2010 Rasi et al

Deriving a multi-subject functional-connectivity atlas to inform connectome estimation

MICCAI 2014 preprintInternational audienceThe estimation of functional connectivity structure from functional neuroimaging data is an important step toward understanding the mechanisms of various brain diseases and building relevant biomarkers. Yet, such inferences have to deal with the low signal-to-noise ratio and the paucity of the data. With at our disposal a steadily growing volume of publicly available neuroimaging data, it is however possible to improve the estimation procedures involved in connectome mapping. In this work, we propose a novel learning scheme for functional connectivity based on sparse Gaussian graphical models that aims at minimizing the bias induced by the regularization used in the estimation, by carefully separating the estimation of the model support from the coefficients. Moreover, our strategy makes it possible to include new data with a limited computational cost. We illustrate the physiological relevance of the learned prior, that can be identified as a functional connectivity atlas, based on an experiment on 46 subjects of the Human Connectome Dataset

Variance and Autocorrelation of the Spontaneous Slow Brain Activity

Slow (<0.1 Hz) oscillatory activity in the human brain, as measured by functional magnetic imaging, has been used to identify neural networks and their dysfunction in specific brain diseases. Its intrinsic properties may also be useful to investigate brain functions. We investigated the two functional maps: variance and first order autocorrelation coefficient (r1). These two maps had distinct spatial distributions and the values were significantly different among the subdivisions of the precuneus and posterior cingulate cortex that were identified in functional connectivity (FC) studies. The results reinforce the functional segregation of these subdivisions and indicate that the intrinsic properties of the slow brain activity have physiological relevance. Further, we propose a sample size (degree of freedom) correction when assessing the statistical significance of FC strength with r1 values, which enables a better understanding of the network changes related to various brain diseases

Pattern Classification of Large-Scale Functional Brain Networks: Identification of Informative Neuroimaging Markers for Epilepsy

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders

Adaptation of cortical activity to sustained pressure stimulation on the fingertip

Background Tactile adaptation is a phenomenon of the sensory system that results in temporal desensitization after an exposure to sustained or repetitive tactile stimuli. Previous studies reported psychophysical and physiological adaptation where perceived intensity and mechanoreceptive afferent signals exponentially decreased during tactile adaptation. Along with these studies, we hypothesized that somatosensory cortical activity in the human brain also exponentially decreased during tactile adaptation. The present neuroimaging study specifically investigated temporal changes in the human cortical responses to sustained pressure stimuli mediated by slow-adapting type I afferents. Methods We applied pressure stimulation for up to 15 s to the right index fingertip in 21 healthy participants and acquired functional magnetic resonance imaging (fMRI) data using a 3T MRI system. We analyzed cortical responses in terms of the degrees of cortical activation and inter-regional connectivity during sustained pressure stimulation. Results Our results revealed that the degrees of activation in the contralateral primary and secondary somatosensory cortices exponentially decreased over time and that intra- and inter-hemispheric inter-regional functional connectivity over the regions associated with tactile perception also linearly decreased or increased over time, during pressure stimulation. Conclusion These results indicate that cortical activity dynamically adapts to sustained pressure stimulation mediated by SA-I afferents, involving changes in the degrees of activation on the cortical regions for tactile perception as well as in inter-regional functional connectivity among them. We speculate that these adaptive cortical activity may represent an efficient cortical processing of tactile information.open

Particle release from implantoplasty of dental implants and impact on cells

Abstract: Background: With increasing numbers of dental implants placed annually, complications such as peri-implantitis and the subsequent periprosthetic osteolysis are becoming a major concern. Implantoplasty, a commonly used treatment of peri-implantitis, aims to remove plaque from exposed implants and reduce future microbial adhesion and colonisation by mechanically modifying the implant surface topography, delaying re-infection/colonisation of the site. This in vitro study aims to investigate the release of particles from dental implants and their effects on human gingival fibroblasts (HGFs), following an in vitro mock implantoplasty procedure with a diamond burr. Materials and methods: Commercially available implants made from grade 4 (commercially pure, CP) titanium (G4) and grade 5 Ti-6Al-4 V titanium (G5) alloy implants were investigated. Implant particle compositions were quantified by inductively coupled plasma optical emission spectrometer (ICP-OES) following acid digestion. HGFs were cultured in presence of implant particles, and viability was determined using a metabolic activity assay. Results: Microparticles and nanoparticles were released from both G4 and G5 implants following the mock implantoplasty procedure. A small amount of vanadium ions were released from G5 particles following immersion in both simulated body fluid and cell culture medium, resulting in significantly reduced viability of HGFs after 10 days of culture. Conclusion: There is a need for careful evaluation of the materials used in dental implants and the potential risks of the individual constituents of any alloy. The potential cytotoxicity of G5 titanium alloy particles should be considered when choosing a device for dental implants. Additionally, regardless of implant material, the implantoplasty procedure can release nanometre-sized particles, the full systemic effect of which is not fully understood. As such, authors do not recommend implantoplasty for the treatment of peri-implantitis