Eigenlogic: a Quantum View for Multiple-Valued and Fuzzy Systems

We propose a matrix model for two- and many-valued logic using families of observables in Hilbert space, the eigenvalues give the truth values of logical propositions where the atomic input proposition cases are represented by the respective eigenvectors. For binary logic using the truth values {0,1} logical observables are pairwise commuting projectors. For the truth values {+1,-1} the operator system is formally equivalent to that of a composite spin 1/2 system, the logical observables being isometries belonging to the Pauli group. Also in this approach fuzzy logic arises naturally when considering non-eigenvectors. The fuzzy membership function is obtained by the quantum mean value of the logical projector observable and turns out to be a probability measure in agreement with recent quantum cognition models. The analogy of many-valued logic with quantum angular momentum is then established. Logical observables for three-value logic are formulated as functions of the Lz observable of the orbital angular momentum l=1. The representative 3-valued 2-argument logical observables for the Min and Max connectives are explicitly obtained.Comment: 11 pages, 2 table

Validation of the Persian version of the Instagram Addiction Scale among Iranian students

Background: The increasing use of social networking sites (SNSs) globally has brought about significant changes in individuals' daily lives and interpersonal relationships. Instagram is one of the most popular SNSs and has played an important role in these changes. While most individuals' use of Instagram has beneficial effects, there have been some studies suggesting that Instagram use can be addictive for a small minority of users. Therefore, valid and reliable tools are needed to investigate this phenomenon. Method: The present study tested the psychometric properties of the 15-item Persian version of the Instagram Addiction Scale (IAS) among Iranian students. Confirmatory factor analysis and convergent validity were used to evaluate scale validity, and Cronbach's alpha and test-retest methods were used to evaluate the reliability. The sample comprised 660 students, including 476 women (72.1%) and 184 men (27.9%). The mean age of the total sample was 23.7 years. Results: Cronbach's alpha coefficients were 0.87 for the whole scale, 0.74 for the social effect subscale and 0.84 for the compulsion subscale. Correlation coefficients obtained from divergent validity with psychological well-being and life satisfaction scales were significant. Conclusion: The findings suggest that the Persian IAS is a reliable and valid instrument for assessing the risk of Instagram addiction among Iranian students

Relationship between Activity in Human Primary Motor Cortex during Action Observation and the Mirror Neuron System

The attenuation of the beta cortical oscillations during action observation has been interpreted as evidence of a mirror neuron system (MNS) in humans. Here we investigated the modulation of beta cortical oscillations with the viewpoint of an observed action. We asked subjects to observe videos of an actor making a variety of arm movements. We show that when subjects were observing arm movements there was a significant modulation of beta oscillations overlying left and right sensorimotor cortices. This pattern of attenuation was driven by the side of the screen on which the observed movement occurred and not by the hand that was observed moving. These results are discussed in terms of the firing patterns of mirror neurons in F5 which have been reported to have similar properties

Paricalcitol reduces oxidative stress and inflammation in hemodialysis patients

Background: Treatment with selective vitamin D receptor activators such as paricalcitol have been shown to exert an anti-inflammatory effect in patients on hemodialysis, in addition to their action on mineral metabolism and independently of parathyroid hormone (PTH) levels. The objective of this study was to evaluate the additional antioxidant capacity of paricalcitol in a clinical setting. Methods: The study included 19 patients with renal disease on hemodialysis, of whom peripheral blood was obtained for analysis at baseline and three months after starting intravenous paricalcitol treatment. The following oxidizing and inflammatory markers were quantified: malondialdehyde (MDA), nitrites and carbonyl groups, indoleamine 2,3-dioxygenase (IDO), tumor necrosis factor alfa (TNF-α), interleukin-6 (IL-6), interleukin-18 (IL-18) and C-reactive protein (CRP). Of the antioxidants and anti-inflammatory markers, superoxide dismutase (SOD), catalase, reduced glutathione (GSH), thioredoxin, and interleukin-10 (IL-10) levels were obtained. Results: Baseline levels of oxidation markers MDA, nitric oxide and protein carbonyl groups significantly decreased after three months on paricalcitol treatment, while levels of GSH, thioredoxin, catalase and SOD activity significantly increased. After paricalcitol treatment, levels of the inflammatory markers CRP, TNF-α, IL-6 and IL-18 were significantly reduced in serum and the level of anti-inflammatory cytokine IL-10 was increased. Conclusions: In renal patients undergoing hemodialysis, paricalcitol treatment significantly reduces oxidative stress and inflammation, two well known factors leading to cardiovascular damageBackground: Treatment with selective vitamin D receptor activators such as paricalcitol have been shown to exert an anti-inflammatory effect in patients on hemodialysis, in addition to their action on mineral metabolism and independently of parathyroid hormone (PTH) levels. The objective of this study was to evaluate the additional antioxidant capacity of paricalcitol in a clinical setting. Methods: The study included 19 patients with renal disease on hemodialysis, of whom peripheral blood was obtained for analysis at baseline and three months after starting intravenous paricalcitol treatment. The following oxidizing and inflammatory markers were quantified: malondialdehyde (MDA), nitrites and carbonyl groups, indoleamine 2,3-dioxygenase (IDO), tumor necrosis factor alfa (TNF-α), interleukin-6 (IL-6), interleukin-18 (IL-18) and C-reactive protein (CRP). Of the antioxidants and anti-inflammatory markers, superoxide dismutase (SOD), catalase, reduced glutathione (GSH), thioredoxin, and interleukin-10 (IL-10) levels were obtained. Results: Baseline levels of oxidation markers MDA, nitric oxide and protein carbonyl groups significantly decreased after three months on paricalcitol treatment, while levels of GSH, thioredoxin, catalase and SOD activity significantly increased. After paricalcitol treatment, levels of the inflammatory markers CRP, TNF-α, IL-6 and IL-18 were significantly reduced in serum and the level of anti-inflammatory cytokine IL-10 was increased. Conclusions: In renal patients undergoing hemodialysis, paricalcitol treatment significantly reduces oxidative stress and inflammation, two well known factors leading to cardiovascular damage

The requirements and challenges in preventing of road traffic injury in Iran. A qualitative study

<p>Abstract</p> <p>Background</p> <p>Road traffic injuries (RTIs) are a major public health problem, especially in low- and middle-income countries. Among middle-income countries, Iran has one of the highest mortality rates from RTIs. Action is critical to combat this major public health problem. Stakeholders involved in RTI control are of key importance and their perceptions of barriers and facilitators are a vital source of knowledge. The aim of this study was to explore barriers to the prevention of RTIs and provide appropriate suggestions for prevention, based on the perceptions of stakeholders, victims and road-users as regards RTIs.</p> <p>Methods</p> <p>Thirty-eight semi-structured interviews were conducted with informants in the field of RTI prevention including: police officers; public health professionals; experts from the road administrators; representatives from the General Governor, the car industry, firefighters; experts from Emergency Medical Service and the Red Crescent; and some motorcyclists and car drivers as well as victims of RTIs. A qualitative approach using grounded theory method was employed to analyze the material gathered.</p> <p>Results</p> <p>The core variable was identified as "The lack of a system approach to road-user safety". The following barriers in relation to RTI prevention were identified as: human factors; transportation system; and organizational coordination. Suggestions for improvement included education (for the general public and targeted group training), more effective legislation, more rigorous law enforcement, improved engineering in road infrastructure, and an integrated organization to supervise and coordinate preventive activities.</p> <p>Conclusion</p> <p>The major barriers identified in this study were human factors and efforts to change human behaviour were suggested by means of public education campaigns and stricter law enforcement. However, the lack of a system approach to RTI prevention was also an important concern. There is an urgent need for both an integrated system to coordinate RTI activities and prevention and a major change in stakeholders' attitudes towards RTI prevention. The focus of all activities should take place on road users' safety.</p

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care