Repercusión visual posterior al explante de lente intraocular opacificada

INTRODUCCIÓ: Valorar els resultats funcionals i quirúrgics posteriors a l’explant de la lent intraocular H60M opacificada. MATERIAL I MÈTODES: Estudi retrospectiu de vint-i-cinc ulls sotmesos a intercanvi de H60M opacificada. Anàlisi dels resultats visuals i complicacions, abans, durant i després de l’explante. RESULTATS: Trobarem millors resultats visuals i una menor taxa de complicacions quan no associem vitrectomia anterior a la cirurgia, així como amb la col•locació de la lent secundaria a sulcus. CONCLUSIÓ: L’intercanvi de la LIO Hidroview opacificada suposa un risc quirúrgic addicional, que podem minimitzar durant el període intraoperatori intentant preservar les estructures oculars el més anatòmicament possible.

Anàlisi del metabolisme ambiental de nuclis turístics litorals: el cas de Menorca

L’objectiu del present estudi és l’anàlisi del metabolisme energètic associat a nuclis turístics litorals de l’illa de Menorca (Mediterrani occidental) i el grau d’autosuficiència a partir d’energies renovables. La caracterització dels nuclis i la definició del perfil del turista s’ha realitzat mitjançant SIG i informació de qualitat a partir d’enquestes. Els principals resultats mostren que els nuclis turístics de Menorca tenen unes emissions associades entre 213 i 318 kg de CO2 per estada. De mitjana, el recorregut del turista fins a la illa (mobilitat externa) és de 1334 km (representant el 80% de les emissions), mentre que la mobilitat interna durant l’estada és de 22 km. A diari, cada turista consumeix entre 8 i 26 kWh d’electricitat, consum que es podria satisfer en un 100% amb la instal·lació de sistemes fotovoltaics a les cobertes del nucli.

Evaluación de la frecuencia cardíaca y sus variaciones en pilotos de automovilismo

OJETIVO: El objetivo de este estudio es demostrar el impacto a nivel cardiovascular que sufren los pilotos de automovilismo a través de la valoración de la frecuencia cardiaca y sus variaciones durante el momento de las carreras. MÉTODO: Para ello se utilizaron 3 aparatos de registro continuo Holter con los que se han obtenido las frecuencias cardiacas (FC) de pilotos amateurs y semiprofesionales durante el momento de la competición en el Circuito de Catalunya. RESULTADOS: Se analizaron los registros de 60 pilotos todos de sexo masculino con una edad media de 37 años (+/- 11,7 años), una altura de 175,9 cm (+/- 7 cm) y un peso de 75,6 Kg (+/- 10,9 Kg) que en promedio realizaron 27,1 vueltas al circuito, finalizando el 53% entre los primeros 10 puestos y el 19% en posiciones de podio. Destaca la presencia de una FC media de 119 lpm (+/- 15 lpm) y máxima de 177 lpm (+/- 16 lpm) que equivalen al 63% y al 96% de la FCMT. CONCLUSIONES: El aparato Holter es una herramienta eficaz para medir el impacto cardiovascular que sufren los pilotos a través del análisis de las FC. Los elevados valores de FC media y máxima alcanzados ponen de manifiesto el estrés cardiovascular sufrido por los pilotos. Este estrés cardiovascular aumenta en la medida en que los pilotos se acercan más a los puestos de meta. Un dato a destacar es que no se observan arritmias sostenidas durante las carreras

Estudi comparatiu de detecció de Virus del Papiloma Humà entre pell psoriàsica i pell normal.

Existeix un debat continu sobre la rellevància del Virus del papiloma humà (VPH) en l’etiopatogènia de la psoriasi. S’ha reportat un elevat percentatge de detecció de VPH en pacients psoriàsics, sobretot el VPH5 i 36, suggerint que podrien correspondre a l’antigen causal de la cascada inmunològica. El propòsit de l’estudi és utilitzar un mètode ampli per a la detecció d’ADN tant de beta com gamma-papilomavirus en 30 biòpsies de pell psoriàsica i 30 biòpsies de pell sana. Avaluarem si els VPH són detectats de forma més freqüent en pell psoriàsica en comparació amb pell sana, i quins són els VPH específics més sobreexpresats.

Preserving memories on-line : the exhibition 'Memories of our childhood' and the Catalan Immigration History Museum

Gràcies a la introducció d'Internet, les institucions de la memòria poden anar ara un pas endavant i proporcionar narratives i documentació en línia. La present comunicació intenta descobrir com poden ser utilitzades les memòries en línia basant-se en dues experiències: una exposició de nens de la guerra i un portal d'immigració.

Outcome after relapse of acute lymphoblastic leukemia in adult patients included in four consecutive risk-adapted trials by the PETHEMA Study Group

BACKGROUND About one half of adults with acute lymphoblastic leukemia are not cured of the disease and ultimately die. The objective of this study was to explore the factors influencing the outcome of adult patients with relapsed acute lymphoblastic leukemia. DESIGN AND METHODS. We analyzed the characteristics, the outcome and the prognostic factors for survival after first relapse in a series of 263 adult patients with acute lymphoblastic leukemia (excluding those with mature B-cell acute lymphoblastic leukemia) prospectively enrolled in four consecutive risk-adapted PETHEMA trials. RESULTS. The median overall survival after relapse was 4.5 months (95% CI, 4-5 months) with a 5-year overall survival of 10% (95% CI, 8%-12%); 45% of patients receiving intensive second-line treatment achieved a second complete remission and 22% (95% CI, 14%-30%) of them remained disease free at 5 years. Factors predicting a good outcome after rescue therapy were age less than 30 years (2-year overall survival of 21% versus 10% for those over 30 years old; P<0.022) and a first remission lasting more than 2 years (2-year overall survival of 36% versus 17% among those with a shorter first remission; P<0.001). Patients under 30 years old whose first complete remission lasted longer than 2 years had a 5-year overall survival of 38% (95% CI, 23%-53%) and a 5-year disease-free survival of 53% (95% CI, 34%-72%). CONCLUSIONS The prognosis of adult patients with acute lymphoblastic leukemia who relapse is poor. Those aged less than 30 years with a first complete remission lasting longer than 2 years have reasonable possibilities of becoming long-term survivors while patients over this age or those who relapse early cannot be successfully rescued using the therapies currently available.

An hybrid methodology for RL-based behavior coordination in a target following mission with an AUV

Proposes a behavior-based scheme for high-level control of autonomous underwater vehicles (AUVs). Two main characteristics can be highlighted in the control scheme. Behavior coordination is done through a hybrid methodology, which takes in advantages of the robustness and modularity in competitive approaches, as well as optimized trajectories

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

Efficient learning of reactive robot behaviors with a Neural-Q_learning approach

The purpose of this paper is to propose a Neural-Q_learning approach designed for online learning of simple and reactive robot behaviors. In this approach, the Q_function is generalized by a multi-layer neural network allowing the use of continuous states and actions. The algorithm uses a database of the most recent learning samples to accelerate and guarantee the convergence. Each Neural-Q_learning function represents an independent, reactive and adaptive behavior which maps sensorial states to robot control actions. A group of these behaviors constitutes a reactive control scheme designed to fulfill simple missions. The paper centers on the description of the Neural-Q_learning based behaviors showing their performance with an underwater robot in a target following task. Real experiments demonstrate the convergence and stability of the learning system, pointing out its suitability for online robot learning. Advantages and limitations are discussed

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Vision-based localization of an underwater robot in a structured environment

This paper presents a vision-based localization approach for an underwater robot in a structured environment. The system is based on a coded pattern placed on the bottom of a water tank and an onboard down looking camera. Main features are, absolute and map-based localization, landmark detection and tracking, and real-time computation (12.5 Hz). The proposed system provides three-dimensional position and orientation of the vehicle along with its velocity. Accuracy of the drift-free estimates is very high, allowing them to be used as feedback measures of a velocity-based low-level controller. The paper details the localization algorithm, by showing some graphical results, and the accuracy of the system

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task