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Systems developed to work with computational intelligence have become very efficient, and in some cases obtain more accurate results than evaluations by humans. Hence, this work proposes a new online approach based on deep learning tools according to the concept of transfer learning to generate a computational intelligence framework for use with the Internet of Health Things (IoHT) devices. This framework allows the user to add their images and perform platform training almost as easily as creating folders and placing files in regular cloud storage services. The trials carried out with the tool showed that even people with no programming and image processing knowledge were able to set up projects in a few minutes. The proposed approach is validated using three medical databases, which include cerebral vascular accident images for stroke type classification, lung nodule images for malignant classification, and skin images for the classification of melanocytic lesions. The results show the efficiency and reliability of the framework, which reached 91.6% Accuracy in the stroke images and lung nodules databases, and 92% Accuracy in the skin images databases. This prove the immense contribution that this work can bring to assist medical professionals in analyzing complex examinations quickly and accurately, allowing a large medical examination database through a consolidated collaborative IoT platform.

Localization is one of the most challenging requirements needed for a mobile robots. Successful localization represents success in meeting the other principal requirements, such as perception and navigation. This article proposes a new approach for the localization of autonomous mobile robots using a Kinect sensor and the concept of Transfer Learning linked to Convolutional Neural Networks (CNNs). The images acquired from the sensor are applied in a mosaic form, which consists of the three color channels provided along with the depth information of the environment. Topological maps were used for indoor environment localization. The proposed computer vision system employed the Bayesian Classifier, k-Nearest Neighbor, Random Forest, Multi-layer Perceptron, and Support Vector Machine as the classifiers. The main focus of this work is the use of a unique configuration of RGB-D images transformed into a mosaic image, combined with the descriptive power of CNNs, in order to estimate the location of a mobile robot in an indoor environments. The results show that the proposed approach proved to be a convincing method for the tasks of localization and supporting the navigation of mobile robots; the results achieved 100% in Accuracy and in the F1-Score. The values obtained for the processing times were also suitable for a computer vision system, with 31.885ms, 0.040s, and 0.044s for the extraction, training, and classification stages, respectively. All these results were from the RGB-D mosaic images. The data demonstrated the relevance of the proposed work, providing a successful localization and, consequently, a successful navigation for mobile robots.

Unmanned Aerial Vehicles are constantly being using in professional activities that require higher precision in navigating and positioning the aircraft during operation. Advanced location technologies such as Global Navigation Satellite System and Real-Time Kinematic are widely used, however, they depend on an area with transmission coverage. In this approach, this article presents a visual navigation methodology based on topological maps. We compared the performance of consolidated classifiers such as Bayesian classifier, k-nearest neighbor, Multilayer Perceptron, Optimal Path Forest and Support Vector Machines (SVM). They are evaluated with attributes returned by last generation resource extractors such as Fourier, Gray Level Co-Occurrence and Local Binary Patterns (LBP). After analyzing the results we found that the combination of LBP and SVM obtained the best values in the evaluation metrics considered, among them, 99.99% Specificity and 99.98% Precision in the navigation process. SVM reached 5.49787 s in combination with LBP completes the training in 5.49787 s. Concerning the testing time, SVM achieving 80.91 ms in association with LBP.

BACKGROUND: Inadequate sleep duration has been suggested as a chronic stressor associated with changes in telomere length (TL). This study aimed to explore the association between sleep duration and TL using the INMA birth cohort study data. METHODS: A total of 1014 children were included in this study (cross-sectional: 686; longitudinal: 872). Sleep duration (h/day) was reported by caregivers at 4 years and classified into tertiles (7-10 h/day; >10-11 h/day; >11-14 h/day). Leucocyte TL at 4 and 7-9 years were measured using quantitative PCR methods. Multiple robust linear regression models, through log-level regression models, were used to report the % of difference among tertiles of sleep duration. RESULTS: In comparison to children who slept between >10 and 11 h/day, those in the highest category (more than 11 h/day) had 8.5% (95% CI: 3.56-13.6) longer telomeres at 4 years. Longitudinal analysis showed no significant association between sleep duration at 4 years and TL at 7-9 years. CONCLUSION: Children who slept more hours per day had longer TL at 4 years independently of a wide range of confounder factors. Environmental conditions, such as sleep duration, might have a major impact on TL during the first years of life. IMPACT: Telomere length was longer in children with longer sleep duration (>11 h/day) independently of a wide range of confounder factors at age 4 and remained consistent by sex. Sleep routines are encouraged to promote positive child development, like the number of hours of sleep duration. Considering the complex biology of telomere length, future studies still need to elucidate which biological pathways might explain the association between sleep duration and telomere length.

A person affected by Alzheimer's disease (AD) gradually loses the ability to perform activities of daily living and becomes dependent on caregivers, thereby having a negative impact on the caregivers' quality of life. There is evidence that suggests that interventions aimed at caregivers, such as mindfulness, may be effective at reducing this burden and emotional issues, such as depression and anxiety, and improving their quality of life. However, there is a lack of consistency in the findings and conclusions remain tentative. In addition, as neuropsychiatric symptoms (NPSs) of AD are major determinants of the caregiver's burden, these interventions should examine the relationship between these symptoms and caregiver outcomes. Importantly, to improve the design of therapeutic interventions for caregivers and complement the treatment of AD, aspects related to occupational performance and the participation of people with AD and their caregivers should also be considered. Therefore, this study will aim to examine first, the effects of a mindfulness-based program designed for caregivers on NPSs of AD and caregivers' anxiety and depression; second, the effects of this program on patients' functional capacity, cognitive performance, executive functions, and quality of life, and on caregivers' burden, quality of life, occupational balance, executive functions, psychological wellbeing, and self-compassion. We believe that the findings of this study will have significant implications for future healthcare strategies focused on improving the quality of life and wellbeing of caregivers.

The use of standardized assessment tools is essential for the occupational therapy intervention process to ensure clinical practice is appropriate and of high quality. This study aimed to explore which assessment tools Spanish occupational therapists use in their clinical practice. An ad hoc online questionnaire of 19 open and closed questions was used to collect information on assessment tools, socio-demographics, and academic background. The assessment tools were classified according to the main domains of occupational therapy using the last edition of the American Occupational Therapy Association framework. The survey was completed by 73 Spanish occupational therapists, of whom 86.3% were women; the median age was 31 years, and more than 60% treated people suffering from neurological and neurodegenerative disorders or worked in geriatric medicine. Of 147 assessment tools identified, those designed to assess activities of daily living, body functions, process skills, and motor skills were the instruments most frequently used. Specifically, the Barthel Index, Lawton Instrumental ADL Scale, Functional Independence Measure, Sensory Profile 2, and Mini-Examen Cognoscitivo were the tools most employed by Spanish occupational therapists. However, more than one third of the participants used assessment tools without knowing whether they were validated in the Spanish context and/or a specific target population. To our knowledge, this is the first time a study has examined the use and type of assessment tools in Spanish occupational therapists. Our results may constitute a positive step forward for developing strategies for enhancing evidence-based assessment in occupational therapy practice.

In Mobile Robotics, localization is a primordial task, as it makes possible the navigation of the robot, thus enabling it to carry out its activities. From the emergence of the Internet of Things (IoT), there was a different approach of interacting objects with each other, as well as between objects and humans. Based on the context presented, this article evidences the utilization of IoT for the development of a system aimed for localization mobile robots employing Convolutional Neural Networks (CNN) in the process of feature extraction of the images, according to the concept of Transfer Learning. The mechanism uses the topological mapping method to orient themselves in the exploration environment considered. The effectiveness of the approach is demonstrated by parameters such as Accuracy, F1-score and time of processing. The IoT system confers the centralization of processing, reducing costs and allowing reuse of the robot’s idle computing power. Combined with this benefit, CNN still achieves 100% Accuracy and F1-Score, proving to be an effective technique for the required activity. With this, the proposed approach demonstrates to be efficient for the use in the task of locating mobile robots.

Cerebral vascular accidents (CVA) affect about 16 million people worldwide annually. CVA, also know as stroke, is a serious global health problem, and can cause significant physical limitations to those affected. Computed tomography is the most appropriate procedure to diagnose and evaluate the dimensions and magnitude of a stroke. Thus, in this article we present an Internet of Things (IoT) framework for the classification of stroke from CT images applying Convolutional Neural Networks (CNN) in order to identifying a healthy brain, an ischemic stroke or a hemorrhagic stroke. Following the Transfer Learning concept CNN was combined with different consolidated Machine Learning methods such as Bayesian Classifier, Multilayer Perceptron, k-Nearest Neighbor, Random Forest and Support Vector Machines. Our approach contributes to the automation of the diagnostic process by a competent method that is able to obtain information imperceptible to the human eye, and thus it contributes to a more precise diagnosis. In addition, with the advent of IoT, a highly efficient and flexible new instrument emerges to address issues related to health care services and specifically in our approach can provide remote diagnoses and monitoring of patients. The approach was validated by analyzing the parameters Accuracy, F1-Score, Recall, Precision and processing time. The results showed that CNN obtained 100% Accuracy, F1-Score, Recall and Precision in combination with most of the classifiers tested. The shortest training and test times were 0.015 s and 0.001 s, respectively, both in combination with the Bayesian Classifier. Thus, our proposed approach demonstrates efficiency and reliability to detect strokes.

he search for optimization of mechanisms and systems is constant and quite desirable in several areas and, therefore, has been gaining more and more emphasis in scientific research. In this paper, two methods for solving singularities, which can occur while a robot arm is moving through a given trajectory, are presented in order to optimize the trajectory tracking control. One method is based on a local genetic algorithm and the other on a global genetic algorithm. Polynomial trajectories up to 3rd degree were used to evaluate the performance of each method by analyzing the trajectory error, number of singularities and computation cost. From the results, it can be concluded that the method based on the global genetic algorithm had the best performance, once it followed the 3rd degree trajectory with the lowest error, number of singularities and computation cost. The results presented are related to the simulation considering a 6-DOF robotic arm.

The acquisition of scientific competencies for the application of evidence-based practice (EBP) is considered an essential part of healthcare education programs in order to improve clinical effectiveness. An examination of scientific skills in occupational therapists may be helpful in understanding their current practice as well as being useful in providing a basis for applying suitable approaches to the development and implementation of EBP. Hence, this study was designed with a double main objective: (1) to describe the level of acquisition of scientific skills and academic achievement in Spanish-speaking occupational therapists; (2) to examine the factors associated with these skills. The screening for Scientific Skills in Occupational Therapists (HAbilidades Cientificas en Terapeutas Ocupacionales), the HACTO-Screen, is an online cross-sectional survey divided into five sections: sociodemographic data, academic and professional background; assessment of scientific skills; research training and development needs; experience as a researcher and/or academic. A total sample of 1159 occupational therapists finally participated. Main associations will be analyzed using multiple linear and/or Poisson regression models with/without robust variance. Our findings will provide valuable insights on the research skills and associated factors in a large sample of Spanish-speaking occupational therapists. The results will also be helpful to enhance research training and research career development in occupational therapy in order to promote the use of EBP.

Background: The relationship between children's sleep and health has been widely examined; however, research focused on the link between sleep and sensory reactivity in children without medical conditions is relatively new and based on studies with small samples. Hence, we aimed at exploring the association between sleep duration and quality and prevalence of sensory reactivity in a population-based sample of children aged 3-7. Methods: We examined data on 579 school-age children from the InProS project, a cross-sectional population-based study. Children's sleep duration was classified as

Nutrition is a significant determinant of health, the resolution of many nutritional issues, initially requires an anthropometry examination. Body measures provide data for studying the relationship between diet, nutritional status, and health. Manual and automatic methods can perform body measurements. The manual method usually uses an anthropometric tape. However, the automatic process uses the equipment of Dual-energy X-ray absorptiometry (DXA). Our work presents a new approach to calculate body measures using 2D Camera Images, applying Digital Image Processing, Convolution Neural Networks, and Machine Learning techniques. The dataset used contains 38 exams, for each exam, has four digital images and the dimensions of body measurements, performed by a specialist. The methods used in this work for segmentation were Dense Human Pose Estimation - CNN with the Bayesian, K-Nearest Neighbors, Support Vector Machine, Decision Threes, Adaptive Boosting, Random Forest, Multilayer Perceptron and Expectation-Maximization classifiers. The approach with Dense Human Pose Estimation and Expectation-Maximization reached the best results, with mean squared error (MSE) always bellow 4.606 ± 3.412 cm when compared with specialist measures.

The identification of different textile fabrics is a task commonly learned in practice and, therefore, is considered a very strenuous and costly form of learning, causing annoyance to the individual who performs it. Based on this context, this paper proposes a new method for classifying textile fabrics, based on the development of a computer vision system using Convolutional Neural Network (CNN). CNN works as a feature extractor by incorporating the concept of Transfer Learning. Using Transfer Learning allows a pre-trained CNN model to be reused for a new problem. In order to highlight the high performance of CNN, an analysis is performed with feature extractors established in the literature. Parameters such as Accuracy, F1-Score, and processing time are considered to evaluate the efficiency of the proposed approach. For the classification were used Bayesian Classifier, Multi-layer Perceptron (MLP), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM). The results show that the best combination is the CNN architecture DenseNet201 with SVM (RBF), obtaining an accuracy of 94% and F1-Score of 94.2%.

We assessed the association between body mass index (BMI) and sensory processing in 445 Spanish children aged 3-7 from the InProS project. Child sensory processing was measured using the short sensory profile (SSP); an atypical sensory performance was defined as an SSP total score

Sensory processing difficulties may have potential detrimental consequences on the physical, social and intellectual development of children. It includes serious disturbances affecting emotional regulation, motor performance, social behavior and daily life functioning, among others factors. Since these symptoms are more frequent among children with disabilities, most research has been carried out in clinical populations. However, recent studies have suggested that sensory problems may be prevalent in around 20% of children without clinical conditions. To date, epidemiological research on sensory dysfunctions in normally developing children is lacking; therefore, it is unknown whether or not sensory processing difficulties are significant factors that affect child's development. Hence, this study has a double general purpose: (1) to determine the sensory profile of school-aged children; (2) to examine the associations between atypical sensory processing patterns and socio-demographic, health and lifestyle features of these children and their parents. The Infancia y Procesamiento Sensorial (InProS, Childhood and Sensory Processing in English) project is a population-based cross-sectional study of Spanish children aged 3-7 years. Data were gathered from different ad hoc questionnaires and several standardized tests. We propose an objective and reliable methodology using statistical and research procedures to describe and determine associations with sensory processing outcomes. We believe that this project will contribute to filling the gap in epidemiological research on sensory issues by providing more convincing evidence. Nevertheless, the potential results should be corroborated in other larger samples.

Lung cancer accounts for more than 1.5 million deaths worldwide, and it corresponded to 26% of all deaths due to cancer in 2017. However, lung computer-aided diagnosis systems developed to identify lung cancer at early stages are increasing survival rates. This study explores the performance of deep transfer learning from non-medical images on lung nodule malignancy classification tasks in order to improve such systems. Initially, the 1018 chest computed tomography (CT) examinations and medical annotations from the LIDC/IDRI were processed. Then, several convolutional neural networks (VGG16, VGG19, MobileNet, Xception, InceptionV3, ResNet50, InceptionResNetV2, DenseNet169, DenseNet201, NASNetMobile and NASNetLarge) were built, trained on the ImageNet dataset, converted into feature extractors and applied on the LIDC/IDRI nodule images. Following this, each set of deep features was submitted to 10-fold cross-validations with naive Bayes, multilayer perceptron, support vector machine (SVM), K-nearest neighbors KNN and random forest classifiers. Finally, the evaluation metrics accuracy (ACC), area under the curve (AUC), true positive rate (TPR), precision (PPV) and F1-score of each cross-validation average result were computed and compared. The results showed that the deep feature extractor based on the ResNet50 and the SVM RBF classifier, achieved an AUC metric of 93.1% (the highest value not only among the evaluated combinations, but also among the related works in the literature evaluated), a TPR of 85.38%, an ACC of 88.41%, a PPV of 73.48% and an F1-score of 78.83%. Based on these results, deep transfer learning proves to be a relevant strategy to extract representative features from lung nodule CT images.

Induction motors are reported as the horse power in industries. Due to its importance, researchers have been studied how to predict its faults in order to improve reliability. Condition health monitoring plays an important role in this field, since it is possible to predict failures by analyzing its operational data. This paper proposes the usage of vibration signals, combined with Higher-Order Statistics (HOS) and machine learning methods to detect broken bars in a squirrel-cage three-phase induction motor. The Multi-Layer Perceptron and Optimum-Path Forest have presented as promising approaches for faults classifications in an induction motor.

In later years, research in mobile robotic areas have been experiencing a growth in interest due to its vast application area. In an unknown environment, the robot's location and movement are essential for its operation. In addition, machine learning techniques, along with signal or image processing, have been applied to map the environment, locate and move the mobile robot. This article proposes a low cost and efficient approach for mobile robot localization. It uses a omnidirectional sonar with machine learning and image processing. The feature extractors used in this paper were: Structural Co-occurrence Matrix (SCM), Statistical Moments, Central Moments, Hu Moments and Gray Level Co-occurrence Matrix (GLCM). The classifiers used in this study were: Bayes classifier, k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Optimum Path Forest (OPF) and Support Vector Machines (SVM). The results showed that the best accuracy was achieved with Central Moments as feature extractor and OPF as classifier, achieving 96.61% and with a test time of 100us.

Chronic kidney diseases cause over a million deaths worldwide every year. One of the techniques used to diagnose the diseases is renal scintigraphy. However, the way that is processed can vary depending on hospitals and doctors, compromising the reproducibility of the method. In this context, we propose an approach to process the exam using computer vision and machine learning to classify the stage of chronic kidney disease. An analysis of different features extraction methods, such as Gray-Level Co-occurrence Matrix, Structural Co-occurrence Matrix, Local Binary Patters (LBP), Hu's Moments and Zernike's Moments in combination with machine learning methods, such as Bayes, Multi-layer Perceptron, k-Nearest Neighbors, Random Forest and Support Vector Machines (SVM), was performed. The best result was obtained by combining LBP feature extractor with SVM classifier. This combination achieved accuracy of 92.00% and F1-score of 91.00%, indicating that the proposed method is adequate to classify chronic kidney disease in two stages, being a high risk of developing end-stage renal failure and other outcomes, and otherwise.

With the advancement of Unmanned Aerial Vehicles (UAVs) and their application in the most diverse areas, the demand for increasingly better precision in their positioning and navigation task has arisen. Based on this context, this article proposes a new approach for localization and navigation UAVs using topological maps and Convolutional Neural Networks (CNN). CNN is used as features extractor, according to the concept of Transfer Learning. The use of topological maps helps to guide the vehicle through the exploration environment. To evaluate the performance of the approach, parameters such as Accuracy, F1-Score, and processing time are considered. For the classification were used Bayesian Classifier, k-Nearest Neighbor (kNN), Multi-layer Perceptron (MLP), Optimum-Path Forest (OPF) and Support Vector Machine (SVM). The results show that CNN achieved 99.97% Accuracy and also F1-Score in combination with most of the considered classifiers, proving the effectiveness of our approach.

Among illnesses, heart diseases are accounted for as one of the most responsible for deaths. Precise and fast diagnoses increase the patient’s chances to receive treatment time. A non-invasive and low-cost way to diagnose it is by using Electrocardiogram (ECG). In this paper, we propose a way to diagnosis two types of heart arrhythmia, by using the ECG record as an image. To access the performance of our system, five feature extraction methods well-known in literature are used along with five different classifiers are tested. We were able to identify heart disorders with over 96.00% of accuracy, using a vanilla neural-network, Multilayer Perceptron (MLP), and Local Binary Patterns (LBP) from ECG images. This investigation has shown promising results from a medical point-of-view.

Location represents a crucial issue for mobile robotics since it is directly related to the performance of the robot in the execution of the activity for which it was designated. In this paper, a new navigation and localization approach for mobile robots in topological maps using classification with reject option in attributes obtained from a Structural Co-occurrence Matrix (SCM) in omnidirectional images is proposed. A study among several feature extractors and classifiers consolidated in the literature is performed and characteristics such as robustness and efficiency related to the hit rate and processing time are highlighted since these parameters are essential in recognition systems aimed at real world applications. According to the proposed approach, SCM produced relevant results and stood out among other extractors, reaching an average accuracy of 99% and the shortest extraction time, with 0.1518s, consolidating itself as a suitable method in the location and navigation task.

In this paper, we propose a new localization and navigation approach for mobile robots using topological maps and classification with reject option applying convolutional neural networks (CNN) for feature extraction in omnidirectional images. The use of CNN as feature extractor is based on the concept of Transfer Learning. Reject option is used to improve the task of the classifiers, querying information from the topological map. With the objective of evidencing the high performance of the technique considered, an analysis is made between several feature extractors and classifiers, established in the literature. Parameters such as processing time and accuracy are calculated to prove the credibility and effectiveness of the approach, since these properties are fundamental in the analysis of embedded systems. Considering the proposed approach, CNN stands out among the other feature extractors, as it generated the best results in extraction time and accuracy. It obtained an average accuracy of 99.86% and an extraction time of 0.1517s, proving to be a relevant method for the localization and navigation activities.

Lung cancer corresponds to 26% of all deaths due to cancer in 2017, accounting more than 1.5 million deaths globally. Considering this challenging situation, several computeraided diagnosis systems have been developed to detect lung cancer at early stages, which increases the patients’ survival rate. Motivated by the success of deep learning in natural and medical image classification tasks, the proposed approach aims to explore the performance of deep transfer learning for lung nodules malignancy classification. For this, convolutional neural networks (CNN), such as VGG16, VGG19, MobileNet, Xception, InceptionV3, ResNet50, Inception-ResNet-V2, DenseNet169, DenseNet201, NASNetMobile and NASNetLarge, were used as features extractors to process the Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI). Next, the deep features returned were classified using Naive Bayes, MultiLayer Perceptron (MLP), Support Vector Machine (SVM), Near Neighbors (KNN) and Random Forest (RF) classifiers. Additionally, to compare the classifiers performance with themselves and with other ones in literature, the evaluation metrics Accuracy (ACC), Area Under the Curve (AUC), True Positive Rate (TPR), Precision (PPV), and F1-Score were computed. Finally, the best combination of deep extractor and classifier was CNN-ResNet50 with SVM-RBF, which achieved ACC of 88.41% and AUC of 93.19%. These results are equivalent to related works, even just using a CNN pre-trained on non-medical images. For this reason, deep transfer learning proved to be a relevant strategy to extract representative imaging biomarkers for lung nodule malignancy classification in chest CT images.

Floods in estuaries are associated to different hazard sources, such as high tide levels, storm surges and large fresh water discharges. In adjacent urban areas, the impact of estuarine high water levels is often amplified due to inadequate drainage conditions. As requested by the European Directive on Floods, flood risk assessment and management is becoming a priority as the effects of climate change and the growth of exposed population will increase inundation vulnerability. An integrated approach for flood risk assessment was developed, in which inundation for different climate scenarios and return periods is evaluated through estuarine and urban drainage numerical modelling. Exposed elements assessment, including population, infrastructures and environmental resources, supports territorial vulnerability mapping, and risk is analysed considering different spatial scales (regional and local) and territorial topologies (urban and interface). Results will contribute to improve the understanding of estuarine inundation processes and to develop adequate flood risk management guidelines. 313-316pp DHA/NEC 2014 24 a 26 de junho

BackgroundProton magnetic resonance spectroscopy (H-1-MRS) is a sensitive, noninvasive imaging technique capable of measuring brain metabolites in vivo. Chronic exposure to endogenous hypercortisolism in Cushing's syndrome (CS) is associated with negative effects on memory and hippocampal volumes, even after biochemical cure. ObjectiveTo investigate metabolites in the hippocampi of CS patients and controls, using H-1-MRS. Patients and methodsEighteen right-handed cured CS patients (age 448125years, 12638years of education) and 18 right-handed healthy controls, matched for age (400 +/- 119) and years of education (144 +/- 38), underwent 3-Tesla magnetic resonance imaging (3T MRI) and H-1-MRS including the head of each hippocampus. Concentrations of Glu (Glutamate), Glx (Glutamate+Glutamine), NAA (N-Acetyl-aspartate), total NAA (N-Acetyl-aspartate+N-Acetyl-aspartyl-Glutamate), Cho (Glycerophosphocholine and Phosphocholine compounds), Cr (Creatine) and MI (mionositol) were measured (mmol/l). Hippocampal volumes (mm(3)) were additionally calculated using an automated procedure (FreeSurfer). ResultsCS patients had lower NAA than controls in the left and right hippocampus (52 +/- 10 vs 61 +/- 07, P

A double-blind crossover study on the effects of deanol and lithium carbonate was conducted on a sample of 29 chronic schizophrenic patients with tardive dyskinesia. In addition to his usual treatment

From 2007-2017, pediatric emergency department (ED) visits for mental health concerns increased by 66% in Canada, with repeat visits accounting for a significant proportion of all visits. Our objective was to examine patient and visit characteristics associated with repeat visits to a tertiary care pediatric ED for mental health concerns.Data were obtained from the administrative records of McMaster Children's Hospital ED for mental health-related visits from February 2013-December 2017. Data on 9,018 ED visits made by 4,976 unique patients were included in this study. Logistic regression analysis was used to examine characteristics associated with repeat visit within six months of index presentation.22% (n=1,088) of individuals returned to the ED for a mental health concern within six months following their index visit. A repeat visit within six months was associated with female sex (OR=1.19,We found that approximately one in five patients presenting to the ED for a mental health concern have a repeat visit within six months, consistent with previous studies. This study provides support for previously identified risk factors for repeat visits and offers information on interactions between patient sex and diagnosis.De 2007 à 2017, les visites au service d’urgence (SU) pédiatrique pour des problèmes de santé mentale ont augmenté de 66 % au Canada, les visites répétées représentant une proportion significative de toutes les visites. Notre objectif était d’examiner les caractéristiques des patients et des visites associées aux visites répétées à un SU pédiatrique de soins tertiaires pour les problèmes de santé mentale.Les données ont été obtenues des dossiers administratifs du SU de l’Hôpital pour enfants McMaster pour les visites liées à la santé mentale de février 2013 à décembre 2017. Les données de 9 018 visites au SU faites par 4 976 patients uniques ont été incluses dans l’étude. L’analyse de régression logistique a servi à examiner les caractéristiques associées aux visites répétées en dedans de six mois de la première visite.22 % (n = 1 088) des personnes sont revenues au SU pour un problème de santé mentale en dedans de six mois de leur première visite. Une visite répétée en dedans de six mois était associée au sexe féminin (RC = 1,19,Nous avons constaté qu’environ un patient sur cinq qui se présente au SU pour un problème de santé mentale a une visite répétée en dedans de six mois, conformément aux études précédentes. Cette étude apporte un appui aux facteurs de risque précédemment identifiés pour les visites répétées, et offre de l’information sur les interactions entre le sexe des patients et le diagnostic.

Meat productions from sixteen local Portuguese cattle breeds represent high economic and cultural value for local populations. Among these, Minhota is one of the most important on meat aptitude located on the northwest of the country. This breed is used for high-quality meat. This study describes the influence of slaughter age, corresponding veal (6 months) and beef (9 months) and sex, reared in a traditional production system, on intramuscular fatty acid composition of the longissimus muscle and nutritional quality. Samples of the longissimus thoracis (LT) muscle were analysed for fatty acid composition of intramuscular fat from 41 Minhota breed animals. Unweaned veal (22) and beef (19), corresponding to males (n=25) and females (n=16) were analysed. Both groups of animals were reared in a traditional production system. The meat from the Minhota breed is a valuable source of polyunsaturated fatty acid (PUFA), particularly the C-20 and C-22 n-3 fatty acids in the human diet. A low n-6:n-3 ratio show that fat in this breed has a good nutritional value. The differences in fat composition from veal and beef could be attributed to the fact that maternal suckling is more important in the youngest animals. FE1B-06B2-126F | José Pedro Pinto de Araújo N/A

Bisaro pigs in a hoop barn with outdoor Access - feeding wih traditional and innovative feed

To measure the association of perfectionism with obsessive-compulsive and depressive symptoms in children and adolescents with obsessive-compulsive disorder (OCD).Sample consisted of 94 youth (44 boys, 49 girls; mean age = 13.2 y, SD = 2.5 y) with a DSM-IV diagnosis of OCD. Perfectionism beliefs were measured with both the Adaptive/Maladaptive Perfectionism Scale (AMPS) and a 14-item version of the Child and Adolescents Perfectionism Scale (CAPS-14). Using a hierarchical linear-regression model, we measured the association of perfectionist beliefs with severity of OCD and depressive symptoms.Both AMPS and CAPS-14 scores were associated with the severity of OCD symptoms in our sample. In addition, CAPS-14 scores were associated with the severity of depressive symptoms, even when OCD symptoms were taken into account.Our findings lend further support to the hypothesis that perfectionism in youth with OCD is associated with variation in the severity of OCD and depressive symptoms.Mesurer l’association du perfectionnisme avec les symptômes obsessionnels-compulsifs et dépressifs chez les enfants et les adolescents souffrant du trouble obsessionnel-compulsif (TOC).L’échantillon consistait en 94 adolescents (44 garçons, 49 filles; âge moyen = 13,2 ans, ET = 2,5 ans) ayant reçu un diagnostic de TOVC du DSMIV. Les croyances perfectionnistes ont été mesurées à l’aide de l’échelle de perfectionnisme adapté/mésadapté (AMPS) et d’une version en 14 items de l’échelle de perfectionnisme des enfants et des adolescents (CAPS-14). Un modèle hiérarchique de régression linéaire a servi à mesurer l’association des croyances perfectionnistes avec la gravité du TOC et des symptômes dépressifs.Les scores à l’AMPS et à la CAPS-14 étaient associés à la gravité des symptômes du TOC dans notre échantillon. En outre, les scores à la CAPS-14 étaient associés à la gravité des symptômes dépressifs, même quand les symptômes du TOC étaient pris en compte.Nos résultats renforcent l’hypothèse selon laquelle le perfectionnisme chez les adolescents souffrant du TOC est associé à une variation de la gravité du TOC et des symptômes dépressifs.