Accuracy of the Arabic HCL - 32 and MDQ in detecting patients with bipolar disorder.

BACKGROUND: Studies about the two most used and validated instruments for the early detection of Bipolar Disorder (BD), the 32 - item Hypomania Checklist (HCL - 32) and the Mood Disorder Questionnaire (MDQ), are scarce in non-Western countries. This study aimed to explore the reliability, factor structure, and criterion validity of their Arabic versions in a sample of Tunisian patients diagnosed with mood disorders. METHODS: The sample included 59 patients with BD, 86 with unipolar Major Depressive Disorder (MDD) and 281 controls. Confirmatory factor analysis was applied to show that a single global score was an appropriate summary measure of the screeners in the sample. Receiver Operating Characteristic analysis was used to assess the capacity of the translated screeners to distinguish patients with BD from those with MDD and controls. RESULTS: Reliability was good for both tools in all samples. The bifactor implementation of the most reported two-factor model had the best fit for both screeners. Both were able to distinguish patients diagnosed with BD from putatively healthy controls, and equally able to distinguish patients diagnosed with BD from patients with MDD. CONCLUSION: Both screeners work best in excluding the presence of BD in patients with MDD, which is an advantage in deciding whether or not to prescribe an antidepressant.

An Electromagnetic Time-Reversal Imaging Algorithm for Moisture Detection in Polymer Foam in an Industrial Microwave Drying System.

Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A decomposition of the time-reversal operator (DORT) algorithm with a proper Green's function of multilayered media is utilized to localize the moisture location. The derived Green's function can be applied to the media with low or high contrast layers. It is shown that the time-reversal imaging (TRI) with the proposed Green's function can be applied to the multilayered media with a moderately rough surface. Moreover, a single frequency TRI is proposed to decrease the measurement time. Numerical results for different moisture scenarios are presented to demonstrate the efficacy of the proposed method. The developed method is then tested on the experimental data for different moisture scenarios from our developed MWT experimental prototype. Image reconstruction results show promising capabilities of the TRI algorithm in estimating the moisture location in the polymer foam.

Supporting Visualization Analysis in Industrial Process Tomography by Using Augmented Reality-A Case Study of an Industrial Microwave Drying System.

Industrial process tomography (IPT) based process control is an advisable approach in industrial heating processes for improving system efficiency and quality. When using it, appropriate dataflow pipelines and visualizations are key for domain users to implement precise data acquisition and analysis. In this article, we propose a complete data processing and visualizing workflow regarding a specific case-microwave tomography (MWT) controlled industrial microwave drying system. Furthermore, we present the up-to-date augmented reality (AR) technique to support the corresponding data visualization and on-site analysis. As a pioneering study of using AR to benefit IPT systems, the proposed AR module provides straightforward and comprehensible visualizations pertaining to the process data to the related users. Inside the dataflow of the case, a time reversal imaging algorithm, a post-imaging segmentation, and a volumetric visualization module are included. For the time reversal algorithm, we exhaustively introduce each step for MWT image reconstruction and then present the simulated results. For the post-imaging segmentation, an automatic tomographic segmentation algorithm is utilized to reveal the significant information contained in the reconstructed images. For volumetric visualization, the 3D generated information is displayed. Finally, the proposed AR system is integrated with the on-going process data, including reconstructed, segmented, and volumetric images, which are used for facilitating interactive on-site data analysis for domain users. The central part of the AR system is implemented by a mobile app that is currently supported on iOS/Android platforms.

Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System.

The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from the MWT sensors and real-time image reconstruction of the process. Thus, a limited number of sensors are chosen for the MWT and are placed only on top of the polymer foam to enable fast data acquisition. For real-time estimation, we present a neural network-based reconstruction scheme to estimate moisture distribution in a polymer foam. Training data for the neural network is generated using a physics-based electromagnetic scattering model and a parametric model for moisture sample generation. Numerical data for different moisture scenarios are considered to validate and test the performance of the network. Further, the trained network performance is evaluated with data from our developed prototype of the MWT sensor array. The experimental results show that the network has good accuracy and generalization capabilities.

The Factor Structure of the Mood Disorder Questionnaire in Tunisian Patients.

BACKGROUND: The Mood Disorder Questionnaire (MDQ) is a frequently used screening tool for the early detection of Bipolar Disorder (BD), which is often unrecognized or misdiagnosed at its onset. In this study, data from Tunisia has been used to evaluate the psychometric properties of the Arabic MDQ. METHODS: The sample included 151 patients with a current major depressive episode. The Arabic adapted version of the Structured Clinical Interview for DSM-IV-TR was used to formulate a diagnosis, yielding 62 patients with BD and 89 with unipolar Major Depressive Disorder (MDD). Principal component analysis with parallel analysis was used to establish the spontaneous distribution of the 13 core items of the MDQ. Confirmatory Factor Analysis (CFA) was used to check the available factor models. Receiver Operating Characteristic (ROC) analysis was used to assess the capacity of the MDQ to distinguish patients with BD from those with MDD. RESULTS: Cronbach's α in the sample was 0.80 (95%CI: 0.75 to 0.85). Ordinal α was 0.88. Parallel analysis suggested two main components, which explained 59% of variance in the data. CFA found a good fit for the existing unidimensional, the two-factor, and the three-factor models. ROC analysis showed that at a threshold of 7, the MDQ was able to distinguish patients with BD from those with MDD with extraordinary negative predictive value (0.92) and a positive diagnostic likelihood ratio of 3.8. CONCLUSION: The Arabic version of the MDQ showed good measurement properties in terms of reliability, factorial validity and discriminative properties.

Ibn Imran's 10th century Treatise on Melancholy.

Some see current views of mental illness, such as depression, as merely contemporary social constructions, with madness seen as a modernist break from medieval and ancient concepts. In contrast to these assumptions, here we describe one of the earliest texts on melancholia and mania, by Ibn Imran, an Arab physician of the 10th century.