3D Textured Model Encryption via 3D Lu Chaotic Mapping

In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be popularized just as images and videos today. The security and privacy of these 3D contents should be taken into consideration. 3D contents contain surface models and solid models. The surface models include point clouds, meshes and textured models. Previous work mainly focus on encryption of solid models, point clouds and meshes. This work focuses on the most complicated 3D textured model. We propose a 3D Lu chaotic mapping based encryption method of 3D textured model. We encrypt the vertexes, the polygons and the textures of 3D models separately using the 3D Lu chaotic mapping. Then the encrypted vertices, edges and texture maps are composited together to form the final encrypted 3D textured model. The experimental results reveal that our method can encrypt and decrypt 3D textured models correctly. In addition, our method can resistant several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI

Clinical and course indicators of bipolar disorder type I with and without opioid dependence

BACKGROUND: The existing evidence about the clinical situations of the bipolar patients with opioid dependence is scarce. The present study was carried out to compare the clinical features and course of the bipolar disorder type I regarding the two subgroups of opioid dependent and non-dependent. METHODS: There were 178 adult patients with bipolar disorder type I consecutively referred to the Iran Hospital of Psychiatry, Tehran, Iran, from January 2008 to January 2009 who enrolled in the study. The Persian Structured Clinical Interview for DSM-IV axis I disorders (SCID-I), HDRS-17, and Y-MRS were administered for all patients. Other clinical information was gathered through the face-to-face interviews with the probands and the hospital records. The T test, Chi square test and logistic regression were used to analyze the data. RESULTS: The mean age of probands were 33.6 (± 11.1) years old and they were mostly male. Among the evaluated indices, the factors gender, anxiety disorders comorbidity, non-adherence, and positive family history were different significantly and independently from the other studied factors between opioid dependent and non-dependent bipolar patients. CONCLUSIONS: Despite some differences, the opioid dependent and non-dependent bipolar patients did not have any significant difference regarding most of the examined clinical and course indices

Association between anxiety and depression with dialysis adequacy in patients on maintenance hemodialysis

Background: Depression and anxiety are common among hemodialysis patients and affect their treatment outcomes. Dialysis adequacy also affects the hemodialysis patients' survival rates. Objectives: This study aimed to evaluate the correlation between anxiety and depression with dialysis adequacy. PatientsandMethods: In this cross-sectional study, 127 hemodialysis patients (73 males, 57.5) with themeanage of 55.7-17.5 were enrolled. Demographic and recent laboratory data were collected using self-administered questionnaires and by reviewing medical records. Dialysis adequacy measures including the Kt/V and urea reduction rate (URR) were calculated using standard formulas. The Hospital Anxiety and Depression Scale (HADS) was used to diagnose depression and anxiety. Independent sample t-test and Chisquare test were used to compare the values in different groups. Pearson correlations and linear regression were used to analyze the data using SPSS version 21. Results: The prevalence rates of depression and anxiety (HADS score �8) were 31.5 and 41.7, respectively. The prevalence of both conditions was significantly higher inwomenthan inmen(P < 0.05). Themeanvalues of Kt/V andURRwere not different in patients with and without depression or anxiety. The anxiety scores were correlated with age (P = 0.007, r = -0.24) and parathyroid hormone (P = 0.04, r = -0.19). Younger age and lower parathyroid hormone were the only factors that predicted higher scores of anxiety in linear regression. The Kt/V or URR were not significantly correlated with depression and anxiety scores. Conclusions: Depression and anxiety are common among hemodialysis patients. There are no statistically significant correlation between depression and anxiety and dialysis adequacy. © 2016, Mazandaran University of Medical Sciences

Robust Multimodal Representation Learning with Evolutionary Adversarial Attention Networks

Multimodal representation learning is beneficial for many multimedia-oriented applications such as social image recognition and visual question answering. The different modalities of the same instance (e.g., a social image and its corresponding description) are usually correlational and complementary. Most existing approaches for multimodal representation learning are not effective to model the deep correlation between different modalities. Moreover, it is difficult for these approaches to deal with the noise within social images. In this paper, we propose a deep learning-based approach named Evolutionary Adversarial Attention Networks (EAAN), which combines the attention mechanism with adversarial networks through evolutionary training, for robust multimodal representation learning. Specifically, a two-branch visual-textual attention model is proposed to correlate visual and textual content for joint representation. Then adversarial networks are employed to impose regularization upon the representation by matching its posterior distribution to the given priors. Finally, the attention model and adversarial networks are integrated into an evolutionary training framework for robust multimodal representation learning. Extensive experiments have been conducted on four real-world datasets, including PASCAL, MIR, CLEF, and NUS-WIDE. Substantial performance improvements on the tasks of image classification and tag recommendation demonstrate the superiority of the proposed approach

Spiritual care for cancer patients in Iran

Background: Studies have shown that a return to spirituality is a major coping response in cancer patients so that therapists can adopt a holistic approach by addressing spirituality in their patient care. The present study was conducted to develop a guideline in the spiritual field for healthcare providers who serve cancer patients in Iran. Materials and Methods: Relevant statements were extracted from scientific documents that through study questions were reviewed and modified by a consensus panel. Results: The statements were arranged in six areas, including spiritual needs assessment, spiritual care candidates, the main components of spiritual care, spiritual care providers, the settings of spiritual care and the resources and facilities for spiritual care. Conclusions: In addition to the development and preparation of these guidelines, health policy-makers should also seek to motivate and train health service providers to offer these services and facilitate their provision and help with widespread implementation

Repetitive transcranial magnetic stimulation in resistant visual hallucinations in a woman with schizophrenia: A case report

A 29-year-old woman with schizophrenia introduced for application of repetitive transcranial magnetic stimulation for refractory visual hallucinations. Following inhibitory rTMS on visual cortex she reported significant reduction in severity and simplification of complexity of hallucinations, which lasted for three months. rTMS can be considered as a possibly potent treatment for visual hallucinations. © 2016, Mazandaran University of Medical Sciences

High frequency of bipolar disorder comorbidity in medical inpatients

Objective: Bipolar disorder is a severe, disabling, and recurring disorder. Some studies have shown that the frequency of bipolar disorder in patients with medical diseases is higher than healthy controls. The aim of this study was to investigate the frequency of bipolar disorders in medically ill patients hospitalized in Iranian general hospitals. Method: In this cross sectional study, 697 inpatients (342 men, 49.1) from different wards of 3 general hospitals, with the mean age of 39.3+-10, were enrolled in the study using nonprobability sampling. Demographic questionnaire, Mood Disorder Questionnaire (MDQ) and Bipolar Spectrum Diagnostic Scale (BSDS) were used. Inclusion criteria were as follow: informed consent, age 18-65 years, ability to speak Persian, and having at least middle school education. Results: The frequency of bipolar disorder was 12.1 and 20.8 based on BSDS and MDQ, respectively. The results of both tests were positive in 7.9 of hospitalized patients. The frequency of bipolar mood disorder was significantly higher in single patients and in those with comorbidity of alcohol and substance use disorders. Conclusion: Considering the high frequency of bipolar mood disorders in hospitalized medically ill patients and its probable effects on compliance and prognosis, early screening, diagnosis, and treatment of bipolar mood disorders is important in these patients. © 2019 Tehran University of Medical Sciences. All rights reserved

The relationship between maternal awareness, socioeconomic situation of families and metabolic control in children with type 1 diabetes miletus in an Iranian population

Background: Type 1 diabetes mellitus (T1DM) is one of the most common chronic pediatric conditions, with potentially life-threatening sequels. However, good metabolic control can protect the patients against sequels. Objectives: The aim of this study was to examine the relationship between awareness of the mothers about this disease on improving diabetic children metabolic control and also, to examine the relationship between socioeconomic situations of families and control of diabetes in this group of patients. Patients and Methods: This is a cross-sectional descriptive analytic study on 80 diabetic children and their mothers, who were registered in the diabetes association of Iran, for outpatient control of disease. Diabetes knowledge was measured by Michigan diabetes knowledge test and glycemic control was assessed by glycosylated hemoglobin (HbA1c). To assess the socio-economic status of a diabetic child's family, educational level, occupational and marital status of parents were asked and the socioeconomic status (SES) was evaluated with Hollingshed four-factor index of SES. Results: Mothers' mean knowledge score was 17.72, children's mean HbA1c was 7.77 and mean of SES was 27.89. There was no significant correlation between children's HbA1c and mother's SES. Also, there was an inverse linear relationship between mothers' knowledge score and children's HbA1c and there was a direct linear relationship between the mothers' knowledge score and SES. Conclusions: Finally, based on the results obtained in this study, it can be concluded that the awareness of mothers of T1DM children has a good impact on blood sugar control, whereas the SES of families has no direct effect on blood sugar control. Additionally, SES can indirectly impact on the consciousness of mothers and lead to the reduction of HbA1c. © 2015, Iranian Society of Pediatrics

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research