Exercise training prevents oxidative stress and ubiquitin-proteasome system overactivity and reverse skeletal muscle atrophy in heart failure

Background: Heart failure (HF) is known to lead to skeletal muscle atrophy and dysfunction. However, intracellular mechanisms underlying HF-induced myopathy are not fully understood. We hypothesized that HF would increase oxidative stress and ubiquitin-proteasome system (UPS) activation in skeletal muscle of sympathetic hyperactivity mouse model. We also tested the hypothesis that aerobic exercise training (AET) would reestablish UPS activation in mice and human HF. Methods/Principal Findings: Time-course evaluation of plantaris muscle cross-sectional area, lipid hydroperoxidation, protein carbonylation and chymotrypsin-like proteasome activity was performed in a mouse model of sympathetic hyperactivity-induced HF. At the 7th month of age, HF mice displayed skeletal muscle atrophy, increased oxidative stress and UPS overactivation. Moderate-intensity AET restored lipid hydroperoxides and carbonylated protein levels paralleled by reduced E3 ligases mRNA levels, and reestablished chymotrypsin-like proteasome activity and plantaris trophicity. In human HF (patients randomized to sedentary or moderate-intensity AET protocol), skeletal muscle chymotrypsin-like proteasome activity was also increased and AET restored it to healthy control subjects' levels. Conclusions: Collectively, our data provide evidence that AET effectively counteracts redox imbalance and UPS overactivation, preventing skeletal myopathy and exercise intolerance in sympathetic hyperactivity-induced HF in mice. Of particular interest, AET attenuates skeletal muscle proteasome activity paralleled by improved aerobic capacity in HF patients, which is not achieved by drug treatment itself. Altogether these findings strengthen the clinical relevance of AET in the treatment of HF.

Effects of aerobic exercise training on cardiac renin-angiotensin system in an obese Zucker rat strain

Objective: Obesity and renin angiotensin system (RAS) hyperactivity are profoundly involved in cardiovascular diseases, however aerobic exercise training (EXT) can prevent obesity and cardiac RAS activation. The study hypothesis was to investigate whether obesity and its association with EXT alter the systemic and cardiac RAS components in an obese Zucker rat strain. Methods: The rats were divided into the following groups: Lean Zucker rats (LZR); lean Zucker rats plus EXT (LZR+EXT); obese Zucker rats (OZR) and obese Zucker rats plus EXT (OZR+EXT). EXT consisted of 10 weeks of 60-min swimming sessions, 5 days/week. At the end of the training protocol heart rate (HR), systolic blood pressure (SBP), cardiac hypertrophy (CH) and function, local and systemic components of RAS were evaluated. Also, systemic glucose, triglycerides, total cholesterol and its LDL and HDL fractions were measured. Results: The resting HR decreased (, 12%) for both LZR+EXT and OZR+EXT. However, only the LZR+EXT reached significance (p, 0.05), while a tendency was found for OZR versus OZR+EXT (p = 0.07). In addition, exercise reduced (57%) triglycerides and (61%) LDL in the OZR+EXT. The systemic angiotensin I-converting enzyme (ACE) activity did not differ regardless of obesity and EXT, however, the OZR and OZR+EXT showed (66%) and (42%), respectively, less angiotensin II (Ang II) plasma concentration when compared with LZR. Furthermore, the results showed that EXT in the OZR prevented increase in CH, cardiac ACE activity, Ang II and AT2 receptor caused by obesity. In addition, exercise augmented cardiac ACE2 in both training groups. Conclusion: Despite the unchanged ACE and lower systemic Ang II levels in obesity, the cardiac RAS was increased in OZR and EXT in obese Zucker rats reduced some of the cardiac RAS components and prevented obesity-related CH. These results show that EXT prevented the heart RAS hyperactivity and cardiac maladaptive morphological alterations in obese Zucker rats.

Laboratorial training of examiners for using a visual caries detection system in epidemiological surveys

Background: In epidemiological surveys, a good reliability among the examiners regarding the caries detection method is essential. However, training and calibrating those examiners is an arduous task because it involves several patients who are examined many times. To facilitate this step, we aimed to propose a laboratory methodology to simulate the examinations performed to detect caries lesions using the International Caries Detection and Assessment System (ICDAS) in epidemiological surveys. Methods: A benchmark examiner conducted all training sessions. A total of 67 exfoliated primary teeth, varying from sound to extensive cavitated, were set in seven arch models to simulate complete mouths in primary dentition. Sixteen examiners (graduate students) evaluated all surfaces of the teeth under illumination using buccal mirrors and ball-ended probe in two occasions, using only coronal primary caries scores of the ICDAS. As reference standard, two different examiners assessed the proximal surfaces by direct visual inspection, classifying them in sound, with non-cavitated or with cavitated lesions. After, teeth were sectioned in the bucco-lingual direction, and the examiners assessed the sections in stereomicroscope, classifying the occlusal and smooth surfaces according to lesion depth. Inter-examiner reproducibility was evaluated using weighted kappa. Sensitivities and specificities were calculated at two thresholds: all lesions and advanced lesions (cavitated lesions in proximal surfaces and lesions reaching the dentine in occlusal and smooth surfaces). Conclusion: The methodology purposed for training and calibration of several examiners designated for epidemiological surveys of dental caries in preschool children using the ICDAS is feasible, permitting the assessment of reliability and accuracy of the examiners previously to the survey´s development.

Intelligent tutoring system for distance education

This study aims to develop and implement a tool called intelligent tutoring system in an online course to help a formative evaluation in order to improve student learning. According to Bloom et al. (1971,117) formative evaluation is a systematic evaluation to improve the process of teaching and learning. The intelligent tutoring system may provide a timely and high quality feedback that not only informs the correctness of the solution to the problem, but also informs students about the accuracy of the response relative to their current knowledge about the solution. Constructive and supportive feedback should be given to students to reveal the right and wrong answers immediately after taking the test. Feedback about the right answers is a form to reinforce positive behaviors. Identifying possible errors and relating them to the instructional material may help student to strengthen the content under consideration. The remedial suggestion should be given in each answer with detaileddescription with regards the materials and instructional procedures before taking next step. The main idea is to inform students about what they have learned and what they still have to learn. The open-source LMS called Moodle was extended to accomplish the formative evaluation, high-quality feedback, and the communal knowledge presented here with a short online financial math course that is being offered at a large University in Brazil. The preliminary results shows that the intelligent tutoring system using high quality feedback helped students to improve their knowledge about the solution to the problems based on the errors of their past cohorts. The results and suggestion for future work are presented and discussed.

Training-related modulations of the autonomic nervous system in endurance athletes: is female gender cardioprotective?

The risk of sudden death is increased in athletes with a male predominance. Regular physical activity increases vagal tone, and may protect against exercise-induced ventricular arrhythmias. We investigated training-related modulations of the autonomic nervous system in female and male endurance athletes. Runners of a 10-mile race were invited. Of 873 applicants, 68 female and 70 male athletes were randomly selected and stratified according to their average weekly training hours in a low (≤4 h) and high (>4 h) volume training group. Analysis of heart rate variability was performed over 24 h. Spectral components (high frequency [HF] and low frequency [LF] power in normalized units) were analyzed for hourly 5 min segments and averaged for day- and nighttime. One hundred and fourteen athletes (50 % female, mean age 42 ± 7 years) were included. No significant gender difference was observed for training volume and 10-mile race time. Over the 24-h period, female athletes exhibited a higher HF and lower LF power for each hourly time-point. Female gender and endurance training hours were independent predictors of a higher HF and lower LF power. In female athletes, higher training hours were associated with a higher HF and lower LF power during nighttime. In male athletes, the same was true during daytime. In conclusion, female and male athletes showed a different circadian pattern of the training-related increase in markers of vagal tone. For a comparable amount of training volume, female athletes maintained their higher markers of vagal tone, possibly indicating a superior protection against exercise-induced ventricular arrhythmias.

Intelligent Virtual Patients for Training Clinical Skills

The article presents the design process of intelligent virtual human patients that are used for the enhancement of clinical skills. The description covers the development from conceptualization and character creation to technical components and the application in clinical research and training. The aim is to create believable social interactions with virtual agents that help the clinician to develop skills in symptom and ability assessment, diagnosis, interview techniques and interpersonal communication. The virtual patient fulfills the requirements of a standardized patient producing consistent, reliable and valid interactions in portraying symptoms and behaviour related to a specific clinical condition.

Assessment of anastomotic leakage: A Novel System for the Training of Surgeons to Perform a Tubular Anastomosis and Objectively Evaluate Anastomotic Leak

Introduction: Dehiscence of the suture line of an anastomosis can lead to reoperation, temporary or permanent stoma, and even sepsis or death. Few techniques for the laboratory training of tubular anastomosis use ex-vivo animal tissues. We describe a novel model that can be used in the laboratory for the training of anastomosis in tubular tissues and objectively assess any anastomotic leak. [See PDF for complete abstract]

The Texas driver responsibility program: A preliminary analysis of the impact on impaired driving and trauma system funding

Objective. In 2003, the State of Texas instituted the Driver Responsibility Program (TDRP), a program consisting of a driving infraction point system coupled with a series of graded fines and annual surcharges for specific traffic violations such as driving while intoxicated (DWI). Approximately half of the revenues generated are earmarked to be disbursed to the state's trauma system to cover uncompensated trauma care costs. This study examined initial program implementation, the impact of trauma system funding, and initial impact on impaired driving knowledge, attitudes and behaviors. A model for targeted media campaigns to improve the program's deterrence effects was developed. ^ Methods. Data from two independent driver survey samples (conducted in 1999 and 2005), department of public safety records, state health department data and a state auditor's report were used to evaluate the program's initial implementation, impact and outcome with respect to drivers' impaired driving knowledge, attitudes and behavior (based on constructs of social cognitive theory) and hospital uncompensated trauma care funding. Survey results were used to develop a regression model of high risk drivers who should be targeted to improve program outcome with respect to deterring impaired driving. ^ Results. Low driver compliance with fee payment (28%) and program implementation problems were associated with lower surcharge revenues in the first two years ($59.5 million versus $525 million predicted). Program revenue distribution to trauma hospitals was associated with a 16% increase in designated trauma centers. Survey data demonstrated that only 28% of drivers are aware of the TDRP and that there has been no initial impact on impaired driving behavior. Logistical regression modeling suggested that target media campaigns highlighting the likelihood of DWI detection by law enforcement and the increased surcharges associated with the TDRP are required to deter impaired driving. ^ Conclusions. Although the TDRP raised nearly $60 million in surcharge revenue for the Texas trauma system over the first two years, this study did not find evidence of a change in impaired driving knowledge, attitudes or behaviors from 1999 to 2005. Further research is required to measure whether the program is associated with decreased alcohol-related traffic fatalities. ^

TALISMAN+: Intelligent System for Follow-Up and Promotion of Personal Autonomy

The TALISMAN+ project, financed by the Spanish Ministry of Science and Innovation, aims to research and demonstrate innovative solutions transferable to society which offer services and products based on information and communication technologies in order to promote personal autonomy in prevention and monitoring scenarios. It will solve critical interoperability problems among systems and emerging technologies in a context where heterogeneity brings about accessibility barriers not yet overcome and demanded by the scientific, technological or social-health settings.

Vision-based active safety system for automatic stopping

ntelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate – obtained via a vision-based system – and the wheel-locking probability – obtained via the vehicle’s CAN bus – that generates a safe braking action. The system has been tested in a real car – a convertible Citroën C3 Pluriel – equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle’s original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver.

On-line learning of a fuzzy controller for a precise vehicle cruise control system

Usually, vehicle applications require the use of artificial intelligent techniques to implement control methods, due to noise provided by sensors or the impossibility of full knowledge about dynamics of the vehicle (engine state, wheel pressure or occupiers weight). This work presents a method to on-line evolve a fuzzy controller for commanding vehicles? pedals at low speeds; in this scenario, the slightest alteration in the vehicle or road conditions can vary controller?s behavior in a non predictable way. The proposal adapts singletons positions in real time, and trapezoids used to codify the input variables are modified according with historical data. Experimentation in both simulated and real vehicles are provided to show how fast and precise the method is, even compared with a human driver or using different vehicles.

Development of a human movement monitoring system based on wearable devices

It is essential to remotely and continuously monitor the movements of individuals in many social areas, for example, taking care of aging people, physical therapy, athletic training etc. Many methods have been used, such as video record, motion analysis or sensor-based methods. Due to the limitations in remote communication, power consumption, portability and so on, most of them are not able to fulfill the requirements. The development of wearable technology and cloud computing provides a new efficient way to achieve this goal. This paper presents an intelligent human movement monitoring system based on a smartwatch, an Android smartphone and a distributed data management engine. This system includes advantages of wide adaptability, remote and long-term monitoring capacity, high portability and flexibility. The structure of the system and its principle are introduced. Four experiments are designed to prove the feasibility of the system. The results of the experiments demonstrate the system is able to detect different actions of individuals with adequate accuracy.

Visualization of a student predictive model for 3D virtual environments driven to procedural training

This document presents theimplementation ofa Student Behavior Predictor Viewer(SBPV)for a student predictive model. The student predictive model is part of an intelligent tutoring system, and is built from logs of students’ behaviors in the “Virtual Laboratory of Agroforestry Biotechnology”implemented in a previous work.The SBPVis a tool for visualizing a 2D graphical representationof the extended automaton associated with any of the clusters ofthe student predictive model. Apart from visualizing the extended automaton, the SBPV supports the navigation across the automaton by means of desktop devices. More precisely, the SBPV allows user to move through the automaton, to zoom in/out the graphic or to locate a given state. In addition, the SBPV also allows user to modify the default layout of the automaton on the screen by changing the position of the states by means of the mouse. To developthe SBPV, a web applicationwas designedand implementedrelying on HTML5, JavaScript and C#.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.