Relevancia clínica y tratamiento de la insuficiencia pancreática exocrina en pancreatitis crónica

La pancreatitis crónica es un proceso fibroinflamatorio progresivo del páncreas que da como resultado la destrucción del parénquima pancreático y el desarrollo de fibrosis. Esto causa la pérdida irreversible de la función pancreática exocrina y endocrina. La pérdida de la función exocrina da lugar al desarrollo de déficits nutricionales que se asocian a un incremento de la mortalidad y del riesgo cardiovascular en pacientes con pancreatitis crónica. El tratamiento enzimático sustitutivo es efectivo, incrementando la absorción de grasa y de nitrógeno y mejorando el estado nutricional y calidad de vida de estos pacientes. Se debe optimizar el tratamiento enzimático sustitutivo para la normalización del estado nutricional

Embedding smart software agents in resource constrained Internet of Things devices

[ES]Los sistemas de sensorización en combinación con herramientas de tratamiento y gestión inteligente de información, constituyen la base sobre la que se construirán las ciudades y entornos urbanos del futuro. Avanzar en la investigación y desarrollo de estos nuevos escenarios inteligentes, es fundamental a la hora de alcanzar los objetivos de eficiencia, integración, sostenibilidad y calidad de vida de las personas que habitan nuestras ciudades. Para alcanzar estos objetivos, es fundamental indagar en el desarrollo de dispositivos hardware más baratos, precisos e inteligentes que serán la base de los entornos inteligentes del futuro. Debido a las previsiones realizadas para los próximos años, la cantidad de dispositivos conectados a Internet será de hasta 7 dispositivos por cada persona en el planeta. Esta avalancha de dispositivos llevará asociada una avalancha de datos que tendrán que ser manejados y almacenados por los centros de procesamiento de datos. Por todo ello, avanzar en el diseño de herramientas para el procesamiento de datos inteligente, así como en nuevos dispositivos de sensorización, es una tarea de vital importancia para la viabilidad futura de los entornos conectados. Por ello, en este trabajo de tesis doctoral se propone un sistema inteligente basado en agentes embebidos en dispositivos inalámbricos con capacidades reducidas (memoria y capacidad de cómputo limitada), para entornos del Internet de las cosas (IoT) donde sea posible un procesamiento inteligente de datos. En particular, se presenta una novedosa arquitectura multi-agente enfocada a la gestión de los datos generados por los dispositivos IoT, sobre la que construir una capa de servicios adaptada a las diferentes necesidades de los distintos entornos donde será posible desplegar el sistema de sensorización. Con el objetivo de validad el sistema propuesto, se ha diseñado un caso de estudio basado en redes de sensores en un entorno IoT de eficiencia energética a través de la optimización del consumo de batería de una bicicleta eléctrica

An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future

Promoting Social Media Dissemination of Digital Images Through CBR-Based Tag Recommendation

Multimedia content has become an essential tool to share knowledge, sell products or disseminate messages. Some social networks use multimedia content to promote information and create social communities. In order to increase the impact of the digital content, those images or videos are labeled with different words, denominated tags. In this paper, we propose a recommender system which analyzes multimedia content and suggests tags to maximize its influence in the social community. It implements a Case-Based Reasoning architecture (CBR), which allows to learn from previous tagged content. The system has been evaluated through cross fold validation with a training and validation sets carefully constructed and extracted from Instagram. The results demonstrate that the system can suggest good options to label our image and maximize the influence of the multimedia content

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

[EN]The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route

Combining Multi-Agent Systems and Wireless Sensor Networks for Monitoring Crop Irrigation

[EN]Monitoring mechanisms that ensure efficient crop growth are essential on many farms, especially in certain areas of the planet where water is scarce. Most farmers must assume the high cost of the required equipment in order to be able to streamline natural resources on their farms. Considering that many farmers cannot afford to install this equipment, it is necessary to look for more effective solutions that would be cheaper to implement. The objective of this study is to build virtual organizations of agents that can communicate between each other while monitoring crops. A low cost sensor architecture allows farmers to monitor and optimize the growth of their crops by streamlining the amount of resources the crops need at every moment. Since the hardware has limited processing and communication capabilities, our approach uses the PANGEA architecture to overcome this limitation. Specifically, we will design a system that is capable of collecting heterogeneous information from its environment, using sensors for temperature, solar radiation, humidity, pH, moisture and wind. A major outcome of our approach is that our solution is able to merge heterogeneous data from sensors and produce a response adapted to the context. In order to validate the proposed system, we present a case study in which farmers are provided with a tool that allows us to monitor the condition of crops on a TV screen using a low cost device.European Commision (EC). Funding H2020/MSCARISE. Project Code: 641794European Commision (EC). Funding FP7/SPE/SME. Project Code: 283638European Commision (EC). Funding FP7/SP1/ENV. Project Code: 28294

Endoscopic ultrasonography: Enhancing diagnostic accuracy

Endoscopic ultrasound (EUS) is an essential technique for the management of several diseases. Over the years, new technologies have been developed because to improve and overcome certain limitations related to EUS guided tissue acquisition. Among these new methods, EUS guided elastography and contrast enhanced EUS has arisen as the most widely recognized and available. We will review in this manuscript the different techniques of elastography and contrast enhancement. Nowadays, there are well establish indications for advance imaging, mainly for supporting the management of pancreatic diseases (diagnosis of chronic pancreatitis and differential diagnosis of solid and cystic pancreatic tumors) and characterization of lymph nodes. However, there are more data on new potential indications for the near futureS

ACCase 6 is the essential acetyl-CoA carboxylase involved in fatty acid and mycolic acid biosynthesis in mycobacteria

Mycolic acids are essential for the survival, virulence and antibiotic resistance of the human pathogen Mycobacterium tuberculosis. Inhibitors of mycolic acid biosynthesis, such as isoniazid and ethionamide, have been used as efficient drugs for the treatment of tuberculosis. However, the increase in cases of multidrug-resistant tuberculosis has prompted a search for new targets and agents that could also affect synthesis of mycolic acids. In mycobacteria, the acyl-CoA carboxylases (ACCases) provide the building blocks for de novo fatty acid biosynthesis by fatty acid synthase (FAS) I and for the elongation of FAS I products by the FAS II complex to produce meromycolic acids. By generating a conditional mutant in the accD6 gene of Mycobacterium smegmatis, we demonstrated that AccD6 is the essential carboxyltransferase component of the ACCase 6 enzyme complex implicated in the biosynthesis of malonyl-CoA, the substrate of the two FAS enzymes of Mycobacterium species. Based on the conserved structure of the AccD5 and AccD6 active sites we screened several inhibitors of AccD5 as potential inhibitors of AccD6 and found that the ligand NCI-172033 was capable of inhibiting AccD6 with an IC50 of 8 ìM. The compound showed bactericidal activity against several pathogenic Mycobacterium species by producing a strong inhibition of both fatty acid and mycolic acid biosynthesis at minimal inhibitory concentrations. Overexpression of accD6 in M. smegmatis conferred resistance to NCI-172033, confirming AccD6 as the main target of the inhibitor. These results define the biological role of a key ACCase in the biosynthesis of membrane and cell envelope fatty acids, and provide a new target, AccD6, for rational development of novel anti-mycobacterial drugsFil: Kurth, Daniel German. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Gago, Gabriela Marisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: de la Iglesia, Agustina Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Bazet Lyonnet, Bernardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; ArgentinaFil: Lin, Ting Wang. University of California; Estados UnidosFil: Morbidoni, Héctor Ricardo. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; ArgentinaFil: Tsai, Shiou Chuan. University of California; Estados UnidosFil: Gramajo, Hugo Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Biología Molecular y Celular de Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Biología Molecular y Celular de Rosario; Argentin

Monitoring and analysis of vital signs of a patient through a multi-agent application system

In the medical environment, the clinical study of the most basic vital signs of a patient represents the simplest and most effective way to detect and monitor health problems. There are many diseases that can be diagnosed and controlled through regular monitoring of these medical data. The purpose of this study is to develop a monitoring and tracking system for the various vital signs of a patient. In particular, this work focuses on the design of a multi-agent architecture composed of virtual organizations with capabilities to integrate different medical sensors on an open, low-cost hardware platform. This system integrates hardware and software elements needed for the routine measurement of vital signs, performed by the patient or caregiver without having to go to a medical center

Edge Face Recognition System Based on One-Shot Augmented Learning

There is growing concern among users of computer systems about how their data is handled. In this sense, IT (Information Technology) professionals are not unaware of this problem and are looking for solutions to meet the requirements and concerns of their users. During the last few years, various techniques and technologies have emerged that allow us to answer to the problem posed by users. Technologies such as edge computing and techniques such as one-shot learning and data augmentation enable progress in this regard. Thus, in this article, we propose the creation of a system that makes use of these techniques and technologies to solve the problem of face recognition and form a low-cost security system. The results obtained show that the combination of these techniques is effective in most of the face detection algorithms and allows an effective solution to the problem raised