Une plate-forme sans fil pour electrochimique spectroscopie d'impédance

Avec l’émergence soutenue de capteurs et de dispositifs électrochimiques innovants, la spectroscopie d'impédance électrochimique est devenue l'un des outils les plus importants pour la caractérisation et la modélisation de la matière ionique et de l'interfaçage des capteurs. La capacité de détecter automatiquement, à l’aide de dispositifs électrochimiques peu couteux, les caractéristiques physiques et chimiques de la matière ionique ouvre une gamme d’application très variée pour la compréhension et l’optimisation des procédés ou interviennent les processus électrochimiques. Cette thèse décrit le développement d’une plate-forme microélectronique miniaturisée, connectée, multiplexée, et à faible coût pour la spectroscopie d'impédance diélectrique (SID) conçue pour les mesures électrochimiques in-situ et adaptée aux architectures de réseau sans fil. La plate-forme développée durant ce travail de maitrise a été testée et validée au sein d’une maille ZigBee et a été en mesure d'interfacer jusqu'à trois capteurs SID en même temps et de relayer l'information à travers le net Zigbee pour l'analyse de données et le stockage. Le système a été construit à partir de composants microélectroniques disponibles commercialement et bénéficie des avantages d'une calibration système on-the-fly qui effectue la calibration du capteur de manière aisée. Dans ce mémoire de maitrise, nous rapportons la modélisation et la caractérisation de senseurs électrochimiques de nitrate; notamment nous décrivons la conception microélectronique, la réponse d'impédance de Nyquist, la sensibilité et la précision de la mesure électrochimique, et les résultats de tests de la plate-forme pour les applications de spectroscopie d'impédance relatives à la détection du nitrate, de la détection de la qualité de l'eau, et des senseurs tactiles.The emergence of the various applications of electrochemical sensors and devices, electrochemical impedance spectroscopy became one of the most important tools for characterizing and modeling of the material and interfacing the sensors. The ability to sense in an automatic manner enables a wide variety of processes to be better understood and optimized cost-effectively. This thesis describes the development of a low-cost, miniaturized, multiplexed, and connected platform for dielectric impedance spectroscopy (DIS) designed for in-situ measurements and adapted to wireless network architectures. The platform has been tested and used as a DIS sensor node on a ZigBee mesh and was able to interface up to three DIS sensors at the same time and relay the information through the Zigbee net for data analysis and storage. The system was built from commercial microelectronics components and benefits from an on-the-fly calibration system that makes sensor calibration easy. The thesis reports characterizing and modeling of two electro-chemical devices (i.e. nitrate sensor and optically-transparent electrically-conductive glasses) and also describes the microelectronics design, the Nyquist impedance response, the measurement sensitivity and accuracy, and the testing of the platform for in-situ dielectric impedance spectroscopy applications pertaining to fertilizer sensing, water quality sensing, and touch sensing

Karyological studies of four agamid lizards from Semnane province of Iran

Iran possesses about 241 species of reptiles, which 55 species of them (22.8%) are endemic to Iran. Agamidae is the important family of reptile in Iran with 22 species, which is poor in terms of chromosomal studies. In this paper, karyological survey was made for four species of the family Agamidae by bone marrow cell preparations. Karyotype of male and female of Laudakia caucasia (2n=34) was consisted of 6 pairs macro and 11pairs of microchromosomrs. Karyotype of Laudakia nupta nupta (2n=36) was including of 6 pairs of macro and 12 pairs of microchromosomes. Karyotype of Phrynocephalus scutellatus (2n=46) was consisted of 22 macro and 24 microchromosomes, which is reported here for the first time. Also, new cytotype of Traplus agilis agilis (2n=49) is reported here for the first time. Karyotype of this species was consisted of 21 large acrocentric and 28 microchromosomes, which one of the acrocentric chromosomes may be a sex chromosome

Tropisetron suppresses colitis-associated cancer in a mouse model in the remission stage

Patients with inflammatory bowel disease (IBD) have a high risk for development of colitis-associated cancer (CAC). Serotonin is a neurotransmitter produced by enterochromaffin cells of the intestine. Serotonin and its receptors, mainly 5-HT3 receptor, are overexpressed in IBD and promote development of CAC through production of inflammatory cytokines. In the present study, we demonstrated the in vivo activity of tropisetron, a 5-HT3 receptor antagonist, against experimental CAC. CAC was induced by azoxymethane (AOM)/dextran sodium sulfate (DDS) in BALB/c mice. The histopathology of colon tissue was performed. Beta-catenin and Cox-2 expression was evaluated by immunohistochemistry as well as quantitative reverse transcription-PCR (qRT-PCR). Alterations in the expression of 5-HT3 receptor and inflammatory-associated genes such as Il-1β, Tnf-α, Tlr4 and Myd88 were determined by qRT-PCR. Our results showed that tumor development in tropisetron-treated CAC group was significantly lower than the controls. The qRT-PCR analysis demonstrated that the expression of 5-HT3 receptor was significantly increased following CAC induction. In addition, tropisetron reduced expression of β-catenin and Cox-2 in the CAC experimental group. The levels of Il-1β, Tnf-α, Tlr4 and Myd88 were significantly decreased upon tropisetron treatment in the AOM/DSS group. Taken together, our data show that tropisetron inhibits development of CAC probably by attenuation of inflammatory reactions in the colitis

Unsupervised automatic tracking of thermal changes in human body

An automated system for detecting and tracking of the thermal fluctuation in human body is addressed. It applies HSV based k-means clustering which initialized and controlled the points which lie on the ROI boundary. Afterward a particle filter tracked the targeted ROI in the thermal video stream. There were six subjects have voluntarily participated on these experiments. For simulating the hot spots occur during the some medical tests a controllable heater utilized close to the subjects body. The results indicated promising accuracy of the proposed approach for tracking the hot spots. However, there were some approximations (e.g. the transmittance of the atmosphere and emissivity of the fabric) which can be neglected because of independency of the proposed approach for these parameters. The approach can track the heating spots efficiently considering the movement in the subjects which provided a confidence of considerable robustness against motion-artifact usually occurs in the medical tests

Mapping catquest scores onto EQ-5D utility values in patients with cataract disease

Background: Mapping from non-performance-based measures onto generic performance-based measures provides an appropriate solution to derive utilities to be used in economic evaluations. Objectives: This study aimed to create a model through which EQ-5D utilities for cataracts can be obtained from scores on the disease-specific Catquest measure. Patients and Methods: One hundred ninety-nine observations from 103 patients who self-administered the EQ-5D, the Catquest and questions on demographic and clinical characteristics were included in the analysis. Data was divided into estimation and validation datasets. To predict EQ-5D utilities, multiple regression analysis, using the Ordinary Least Square (OLS) and the censored least absolute deviation (CLAD), was performed. Catquest scores, age, gender, and performing surgery were included as explanatory variables. An estimation dataset was used to derive the coefficients, and these coefficients were then validated using a validation dataset. Based on the explanatory power, the consistency, the simplicity, the mean absolute error (MAE) and the correlations between observed and fitted utilities, the most appropriate model was selected. Results: The mean EQ-5D and Catquest scores of the total sample were 0.631 and 15.8, respectively. Age and surgery showed no significant effect for either method. Removing age and surgery, model II was built and given an R2of 0.697, an MAE of 0.1176 for the OLS and an R2of 0.614, and an MAE of 0.1153 for the CLAD method. In the validation stage, the CLAD revealed better prediction ability, with an MAE of 0.198 versus an MAE of 0.209 for the OLS. ICC and Bland-Altman analysis put the CLAD as a preferred method with the following equation: Utilities (EQ-5D) = 0.988 - 0.0281 × Catquest (PD) + 0.102 × gender (male = 1). Conclusions: Based on these results, a mapping function was obtained which appears to be valuable in predicting EQ-5D utilities from Catquest scores. This function gives an appropriate solution to estimate utilities when primary EQ-5D data is not available. Although the model represents good consistency and predictive ability, further examination of obtained function is required with large samples. © 2016, Iranian Red Crescent Medical Journal

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Statistical Methods for Efficient Digital Electronic Hardware Design

RÉSUMÉ: Avec la complexité croissante de nombreuses nouvelles applications qui servent notre société moderne, il est essentiel de concevoir des plates-formes informatiques efficaces. Cependant, la conception de matériel efficace est un problème complexe à objectifs multiples et qui est influencée par de nombreux paramètres. Étant donné le nombre élevé de paramètres et objectifs qui sont impliqués dans ce processus de conception, la synthèse de toutes les combinaisons possibles n’est pas une méthode viable pour trouver la solution optimale. Ainsi, les chercheurs souhaitent trouver des methodes plus efficaces pour modéliser le matériel et effectuer une exploration de l’espace de conception. Cette recherche vise à optimiser le processus de conception matérielle d’algorithmes à calcul intensif utilisant l’intelligence artificielle. Pour atteindre cet objectif, diverses techniques d’intelligence artificielle, telles que l’apprentissage actif, l’apprentissage par renforcement et l’apprentissage automatique statistique, sont envisagées pour résoudre ce problème difficile. Cette thèse propose d’utiliser des algorithmes de recherche méta-heuristiques intelligents tels que l’optimisation Grey Wolf (GWO) en conjonction avec l’optimisation Bayésienne (BO) pour effectuer l’exploration de l’espace de conception matérielle. Nous montrons que nous pouvons réduire davantage l’effort de conception en utilisant un modèle de substitution créé sur la base de notre méthode hybride GWO-BO proposée. Le modèle de substitution est une abstraction utile pour détecter les interdépendances fonctionnelles et physiques dans le système afin de prédire avec précision ses performances (par exemple, le débit ou la latence). Nous évaluons notre méthodologie et montrons qu’elle peut produire des résultats compétitifs pour trouver les meilleurs paramètres de conception qui maximisent les performances du système. De plus, nous proposons une approche d’apprentissage actif basée sur un modèle pour réaliser la modélisation des performances du matériel. Notre méthode proposée utilise des modèles Bayésiens pour caractériser divers aspects des performances matérielles. Nous utilisons également des techniques d’apprentissage par transfert et d’amorçage de régression Gaussienne en conjonction avec un apprentissage actif pour créer des modèles plus précis. Notre méthode de modélisation statistique proposée fournit des modèles matérielles suffisamment précis pour effectuer simultanément l’exploration de l’espace de conception et la prédiction des performances. Nous utilisons notre méthode proposée pour effectuer l’exploration de l’espace de conception et la prédiction des performances pour diverses configurations matérielles, telles que la conception de micro-architecture et les noyaux OpenCL pour les FPGAs. Nos expériences montrent que le nombre d’échantillons nécessaires pour créer des modèles de performance diminue considérablement tout en maintenant le pouvoir prédictif de nos modèles statistiques proposés. Par exemple, la méthode proposée nécessite 65 % d’échantillons en moins pour créer le modèle de prédiction des performances. De plus, Dans le cadre de l’exploration de l’espace de conception, notre méthode proposée peut trouver les meilleurs paramètres de conception en explorant aussi peu que 50 échantillons. ABSTRACT: With the rising complexity of numerous novel applications that serve our modern society comes the essential need to design efficient computing platforms. Designing efficient hardware is, however, a complex multi-objective problem that deals with multiple parameters and their interactions. Since many parameters and objectives are involved in hardware design, synthesizing all possible combinations is not a feasible method to find the optimal solution. Thus, researchers are interested in finding more intelligent approaches to model the hardware and perform design space exploration. This research aims to optimize the hardware design procedure of compute-intensive algorithms using artificial intelligence. To achieve this goal, various artificial intelligence techniques, such as active learning, reinforcement learning, and statistical machine learning, are envisioned to solve this challenging problem. We propose using intelligent meta-heuristic search algorithms such as GreyWolf Optimization (GWO) in conjunction with Bayesian Optimization (BO) to perform hardware design space exploration. We show that we can further reduce the design effort using a surrogate model created based on our proposed hybrid GWO-BO method. The surrogate model is a useful abstraction to detect functional and physical inter dependencies in the system to predict its performance (e.g. throughput or latency) accurately. We evaluate our methodology and show that it can produce competitive results to find the best design parameters that maximize the system’s performance. Additionally, we propose a model-based active learning approach to accomplish performance modeling of hardware. Our proposed method uses Bayesian models to characterize various aspects of hardware performance. We also use transfer learning and Gaussian regression bootstrapping techniques in conjunction with active learning to create more accurate models. Our proposed statistical modeling method provides hardware models that are sufficiently accurate to perform design space exploration and performance prediction simultaneously. We use our proposed method to perform design space exploration and performance prediction for various hardware setups, such as micro-architecture design and OpenCL kernels for FPGA targets. Our experiments show that the number of samples required to create performance models significantly reduces while maintaining the predictive power of our proposed statistical models. For instance, the proposed method needs 65% fewer samples to create the model in the performance prediction setting. Moreover, our proposed method can find the best design parameter settings in the design space exploration setting by exploring as few as 50 samples

Evaluating the diagnosis and treatment of neuro-Behçet’s disease with cascade sign appearance in brainstem: a case report

This study aimed to investigate a case of neuro-Behçet’s disease misdiagnosed as acute ischemic stroke or multiple sclerosis (MS). The studied case was a 29-year-old man with subacute onset of hemiparesis, dysarthria, and ataxia who was diagnosed as acute ischemic stroke at first. Due to no significant improvement, the patient was managed as an MS case, but he did not experience any improvements again. We noticed a history of oral and genital aphthous and cascade sign appearance in his brain MRI. Then, Behçet’s disease with secondary parenchymal involvement of brainstem was confirmed. The patient received infliximab, which resulted in clinico-radiological recovery. Practical Implications. Given the prevalence of Behçet’s disease in the Middle East, the possibility of its diagnosis should be considered in patients with atypical history or imaging for ischemic stroke or MS

Pulmonary Arteriovenous Malformation Surgery in a Pregnant Woman: A Case Report

Background and purpose: Pulmonary arteriovenous malformations (PAVM) are lung lesions that affect most of the periphery of the lower lobes, and are manifested by dyspnea, hemoptysis, and hemothorax. Pregnancy is one of the conditions in which these malformations appear due to hemodynamic changes and hormonal factors such as increased levels of estrogen. This paper presents the case of a pregnant woman at 28 weeks of gestation who received first-aid treatment for symptoms of respiratory distress and hemothorax. Then, she was transferred to the operating room and thoracotomy was performed and the vascular lesion was resected. After that, the patient had stable hemodynamic conditions and was transferred to the ward. Our patient was eligible for thoracotomy due to pulmonary manifestations of the disease. In dealing with these situations, immediate counseling and collaboration of gynecologists with thoracic surgeons can save the lives of mother and her fetus