The patterning and functioning of protrusive activity during convergence and extension of the Xenopus organiser

We discuss the cellular basis and tissue interactions regulating convergence and extension of the vertebrate body axis in early embryogenesls of Xenopus. Convergence and extension occur in the dorsal mesoderm (prospective notochord and somite) and in the posterior nervous system (prospective hindbrain and spinal cord) by sequential cell intercalations. Several layers of cells intercalate to form a thinner, longer array (radial intercalation) and then cells intercalate in the mediolateral orientation to form a longer, narrower array (mediolateral intercalation). Fluorescence microscopy of labeled mesodermal cells in explants shows that protrusive activity is rapid and randomly directed until the midgastrula stage, when it slows and is restricted to the medial and lateral ends of the cells. This bipolar protrusive activity results in elongation, alignment and mediolateral intercalation of the cells. Mediolateral intercalation behavior (MIB) is expressed in an anterior- posterior and lateral-medial progression in the mesoderm. MIB is first expressed laterally in both somitic and notochordal mesoderm. From its lateral origins in each tissue, MIB progresses medially. If convergence does not bring the lateral boundaries of the tissues closer to the medial cells in the notochordal and somitic territories, these cells do not express MIB. Expression of tissue-specific markers follows and parallels the expression of MIB. These facts argue that MIB and some aspects of tissue differentiation are induced by signals emanating from the lateral boundaries of the tissue territories and that convergence must bring medial cells and boundaries closer together for these signals to be effective. Grafts of dorsal marginal zone epithelium to the ventral sides of other embryos, to ventral explants and to UV-ventralized embryos show that it has a role in organising convergence and extension, and dorsal tissue differentiation among deep mesodermal cells. Grafts of involuting marginal zone to animal cap tissue of the early gastrula shows that convergence and extension of the hindbrain-spinal cord are induced by planar signals from the involuting marginal zone

Modificación de la planificación docente de la asignatura de Fundamentos de Telemática/Redes de Comunicaciones de cara a su adaptación al Espacio Europeo de Educación Superior: Planificación docente, perfiles de competencia, objetivos, contenidos y actividades

El Espacio Europeo de Educación Superior pronto se convertirá en una realidad y conviene preparar con antelación la adaptación de las asignaturas de los planes de estudios. En este artículo se abordan íntegramente toda una serie de aspectos relacionados con la planificación docente de una asignatura de cara a su adaptación al Espacio Europeo de Educación Superior. De acuerdo con los perfiles de competencia que deseen establecerse, se analizan los objetivos, contenidos y actividades que debe realizar el estudiante para adquirir una serie de habilidades. Con el fin de resolver las dudas que puedan surgir a muchos profesores durante este proceso, se presenta con un enfoque muy práctico como realizar la planificación docente de la asignatura de Redes de Comunicaciones/ Fundamentos de Telemática de cara a su adaptación al Espacio Europeo de Educación Superior con el fin de que el lector/docente pueda comprender mejor cuáles son las tareas a realizar y qué repercusiones tienen.Peer ReviewedPostprint (published version

Managing healthcare through social networks

Surveys show an increased reliance on physician and patient social networks, which promise to transform healthcare management. But challenges such as privacy and data accuracy remain.Postprint (published version

Power allocation and energy cooperation for UAV-enabled MmWave networks: A Multi-Agent Deep Reinforcement Learning approach

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.This work was supported by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under project PID2019-108713RB-C51 MCIN/AEI /10.13039/501100011033Postprint (published version

An overview of machine learning and 5G for people with disabilities

Currently, over a billion people, including children (or about 15% of the world’s population), are estimated to be living with disability, and this figure is going to increase to beyond two billion by 2050. People with disabilities generally experience poorer levels of health, fewer achievements in education, fewer economic opportunities, and higher rates of poverty. Artificial intelligence and 5G can make major contributions towards the assistance of people with disabilities, so they can achieve a good quality of life. In this paper, an overview of machine learning and 5G for people with disabilities is provided. For this purpose, the proposed 5G network slicing architecture for disabled people is introduced. Different application scenarios and their main benefits are considered to illustrate the interaction of machine learning and 5G. Critical challenges have been identified and addressed.This work has been supported by the Agencia Estatal de Investigación of Ministerio de Ciencia e Innovación of Spain under project PID2019-108713RB-C51 MCIN/ AEI /10.13039/501100011033.Postprint (published version

Deep learning and internet of things for beach monitoring: An experimental study of beach attendance prediction at Castelldefels beach

Smart seaside cities can fully exploit the capabilities brought by Internet of Things (IoT) and artificial intelligence to improve the efficiency of city services in traditional smart city applications: smart home, smart healthcare, smart transportation, smart surveillance, smart environment, cyber security, etc. However, smart coastal cities are characterized by their specific application domain, namely, beach monitoring. Beach attendance prediction is a beach monitoring application of particular importance for coastal managers to successfully plan beach services in terms of security, rescue, health and environmental assistance. In this paper, an experimental study that uses IoT data and deep learning to predict the number of beach visitors at Castelldefels beach (Barcelona, Spain) was developed. Images of Castelldefels beach were captured by a video monitoring system. An image recognition software was used to estimate beach attendance. A deep learning algorithm (deep neural network) to predict beach attendance was developed. The experimental results prove the feasibility of Deep Neural Networks (DNNs) for beach attendance prediction. For each beach, a classification of occupancy was estimated, depending on the number of beach visitors. The proposed model outperforms other machine learning models (decision tree, k-nearest neighbors, and random forest) and can successfully classify seven beach occupancy levels with the Mean Absolute Error (MAE), accuracy, precision, recall and F1-score of 0.03, 92.7%, 92.9%, 92.7%, and 92.7%, respectively.Postprint (published version

La conciencia lingüística a través de un viajero árabe del siglo XIV: Ibn Bat túta.

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