Frequency regulation in electric power systems using deferrable loads

Incluye bibliografía y anexosCon el advenimiento del paradigma de la red inteligente (Smart Grid) y las energías renovables, se hace necesario estudiar el almacenamiento de energía generada que no se consume al momento. En esta tesis, se indaga en el papel de un “load agregator” que administra un conjunto de cargas eléctricas y aprovecha la flexibilidad de las mismas para regular la frecuencia de una red. Se estudia el problema desde un punto de vista macroscópico, sin entrar en detalles de cargas individuales. Se propone un set de modelos ODE para predecir la evolución de la potencia consumida por el cluster de cargas y se diseñan controladores para estos modelos, con el fin de poder seguir las referencias de potencia externa. Finalmente, se sugieren algunos algoritmos posibles para implementar el control a cargas individuales. Las simulaciones muestran que este sistema podría proporcionar valiosos servicios a las redes eléctricas, si existiese suficiente infraestructura de comunicaciones.ANII - POS_NAC_2013_1_11675

Psicoanálisis aplicado a la literatura

Para el autor el psicoanálisis aplicado a la literatura difiere de la situación analítica ya que no se sitúa en el diálogo con el paciente sino que apunta al enfoque de diferentes aspectos de una obra dada en base a los descubrimientos del psicoanálisis. Centrándose en su alcance y limitaciones puntualiza que el psicoanálisis no nos da una visión unívoca y global de la obra sino que será siempre un análisis parcial. Considerados tipos de psicoanálisis aplicado: el que prescinde del autor y el que toma en cuenta los datos biográficos del mismo. Recorre ejemplos de la literatura: un episodio de la literatura medieval, Tristán e Isolda y el estudio de Freud sobre Moisés. Sostiene que entre poeta y lector hay un intercambio permanente aunque no diálogo estricto y que el analista recibe un constante mensaje del artista que provoca todo tipo de reacciones anímicas. Desde el punto de vista psicoanalítico no importan tanto los datos biográficos existentes como los que aquel puede aportar desde el estudio detenido de la obra. Rechaza un análisis aplicado que encasilla a los autores. Finalmente cuestiona las posturas estructuralistas que anteponen el texto al sujeto descartando el problema del creador

Methylation profiling RIN3 and MEF2C identifies epigenetic marks associated with sporadic early onset Alzheimer’s disease

A number of genetic loci associate with early onset Alzheimer’s disease (EOAD), however the drivers of this disease remains enigmatic. Genome wide association and in-vivo modelling have shown that loss-of-function e.g. ABCA7, reduced levels of SIRT1, MEFF2C or increases levels of PTK2β confer risk or link to the pathogenies. It is known that DNA methylation can profoundly affect gene expression and can impact on the composition of the proteome, therefore the aim of this study is to assess if genes associated with sporadic EOAD are differentially methylated. Epi-profiles of DNA extracted from blood and cortex were compared using a pyrosequencing platform. We identified significant group-wide hypomethylation in AD blood when compared to controls for 7 CpGs located within the 3’UTR of RIN3 (CpG1 P=0.019, CpG2 p=0.018, CpG3 p=0.012, CpG4 p=0.009, CpG5 p=0.002, CpG6 p=0.018 and CpG7 p=0.013 respectively; AD / Control n=22 / 26; Male / Female, 27 / 21). Observed effects were not gender specific. No group wide significant differences were found in the promoter methylation of PTK2β, ABCA7, SIRT1 or MEF2C, genes known to associate with LOAD. A rare and significant difference in methylation was observed for one CpG located upstream of the MEF2C promoter in one AD individual only (22% reduction in methylation, p=2.0E-10; Control n=26, AD n=25, Male / Female n=29 / 22). It is plausible aberrant methylation may mark sEOAD in blood and may manifest in some individuals as rare epi-variants for genes linked to sEOAD

Algoritmos de aprendizaje automático con aplicación a enjambres de robots

Los sistemas robóticos de enjambre o de múltiples agentes constituyen un área de investigación en creciente desarrollo. Para proveer infraestructura inalámbrica a demanda es necesario desplegar un equipo secundario de robots que garanticen la conectividad del enjambre. En este trabajo explicamos un algoritmo de posicionamiento óptimo para este equipo de robots, consistente en una etapa de optimización convexa sobre un modelo de canal probabilístico y una siguiente etapa de maximización de la conectividad de un grafo Laplaciano. Para mostrar la ventaja de esta formulación matemática, llevamos a cabo tanto simulaciones como experimentos que fueron realizados con una flota de 10 Vehículos Aéreos no Tripulados (UAV por sus siglas en inglés) -ensamblados y configurados por nuestro grupo de investigación- basados en el modelo DJI Flame-Wheel y equipados con mini-computadoras Intel NUC a bordo y conectividad Wi-Fi. Para los experimentos realizados, los UAVs establecieron una red ad-hoc a través de nodos ROS multi-master en sistema operativo Ubuntu 18. Existe a su vez otra familia de algoritmos autónomos de creciente interés conocida como aprendizaje por recompensas o Reinforcement Learning (RL), en los que el control a aplicar surge a partir de optimizar una señal de recompensa. En esta tesis estudiamos un problema de monitoreo, formulado a partir de restricciones de ocupación de regiones a monitorear por uno o múltiples agentes, que se lleva a un problema de RL en el que las variables duales actúan como señal de recompensa. Para resolver el problema en el caso de un único agente monitoreando varias regiones, diseñamos una parametrización por medio de una red neuronal que procesa en paralelo las variables primales y las duales. Con esta novedad estructural, la red aprende a elegir políticas de navegación en función del grado de satisfacción de las restricciones, que se observa en tiempo real a través de las variables duales. Para el caso de múltiples agentes, simulamos una versión simplificada del problema con un espacio de estados discreto y dos agentes, e imponiendo que los agentes tengan políticas distribuidas logramos un desempeño comparable al de una política centralizada.Swarm or multi-agent robotic systems are a growing area of research. To provide wireless infrastructure on demand, it is necessary to deploy a secondary team of robots that guarantee the connectivity of the swarm. In this paper we explain an optimal positioning algorithm for this team of robots, consisting of a convex optimization stage on a probabilistic channel model and a subsequent connectivity maximization stage of a Laplacian graph. To show the advantage of this mathematical formulation, we carried out both simulations and experiments that were carried out with a fleet of 10 Unmanned Aerial Vehicles (UAV) -assembled and con gured by our research group- based on the model DJI Flame-Wheel and equipped with onboard Intel NUC mini-computers and Wi-Fi connectivity. For the experiments carried out, the UAVs established an ad-hoc network through ROS multi-master nodes in the Ubuntu 18 operating system. There is also another family of autonomous algorithms of growing interest known as Reinforcement Learning (RL), in which the control to be applied arises from optimizing a reward signal. In this thesis we study a monitoring problem, formulated from the occupation restrictions of regions to be monitored by one or multiple agents, which leads to an RL problem in which the dual variables act as a reward signal. To solve the problem in the case of a single agent monitoring several regions, we designed a parameterization through a neural network that processes the primal and dual variables in parallel. With this structural novelty, the network learns to choose navigation policies based on the degree of satisfaction of the constraints, which is observed in real time through the dual variables. For the multi-agent case, we simulate a simpli ed version of the problem with a discrete state space and two agents, and by imposing that the agents have distributed policies, we achieve performance comparable to that of a centralized policy.Beca de Posgrado de la CAP, Udela

Multi-agent assignment via state augmented reinforcement learning

We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techniques for this purpose. Instead, we recur to a state augmentation approach in which the oscillation of dual variables is exploited by agents to alternate between tasks. In addition, we coordinate the actions of the multiple agents acting on their local states through these multipliers, which are gossiped through a communication network, eliminating the need to access other agent states. By these means, we propose a distributed multi-agent assignment protocol with theoretical feasibility guarantees that we corroborate in a monitoring numerical experiment.Comment: 12 pages, 3 figures, 6th Annual Conference on Learning for Dynamics and Contro

A Conserved PHD Finger Protein and Endogenous RNAi Modulate Insulin Signaling in Caenorhabditis elegans

Insulin signaling has a profound effect on longevity and the oxidative stress resistance of animals. Inhibition of insulin signaling results in the activation of DAF-16/FOXO and SKN-1/Nrf transcription factors and increased animal fitness. By studying the biological functions of the endogenous RNA interference factor RDE-4 and conserved PHD zinc finger protein ZFP-1 (AF10), which regulate overlapping sets of genes in Caenorhabditis elegans, we identified an important role for these factors in the negative modulation of transcription of the insulin/PI3 signaling-dependent kinase PDK-1. Consistently, increased expression of pdk-1 in zfp-1 and rde-4 mutants contributed to their reduced lifespan and sensitivity to oxidative stress and pathogens due to the reduction in the expression of DAF-16 and SKN-1 targets. We found that the function of ZFP-1 in modulating pdk-1 transcription was important for the extended lifespan of the age-1(hx546) reduction-of-function PI3 kinase mutant, since the lifespan of the age-1; zfp-1 double mutant strain was significantly shorter compared to age-1(hx546). We further demonstrate that overexpression of ZFP-1 caused an increased resistance to oxidative stress in a DAF-16–dependent manner. Our findings suggest that epigenetic regulation of key upstream signaling components in signal transduction pathways through chromatin and RNAi may have a large impact on the outcome of signaling and expression of numerous downstream genes.Leukemia & Lymphoma Society of America (3260-07 Special Fellow Award)Arnold and Mabel Beckman Foundation (Young Investigator Award)United States. National Institutes of Health (Director's New Innovator Award (1 DP2 OD006412-01))United States. National Institutes of Health (grant GM66269)modENCODE (grant U01 HG004270)United States. National Institutes of Health (training grant 5T32 GM07088-34

TLR7 agonist in combination with Salmonella as an effective antimelanoma immunotherapy

Aim: We evaluated a novel approach combining the use of attenuated Salmonella immunotherapy with a Toll-like receptor agonist, imiquimod, in B16F1 melanoma-bearing mice. Materials & methods: B16F1 melanoma-bearing mice were daily treated with topical imiquimod in combination with one intratumoral injection of attenuated Salmonella enterica serovar Typhimurium LVR01. Results: The combined therapy resulted in retarded tumor growth and prolonged survival. Combination treatment led to an enhancement in the expression of pro-inflammatory cytokines and chemokines in the tumor microenvironment, with a Th1-skewed profile, resulting in a broad antitumor response. The induced immunity was effective in controlling the occurrence of metastasis. Conclusion: Salmonella LVR01 immunotherapy in combination with imiquimod is a novel approach that could be considered as an effective antimelanoma therapy

Preclinical Evaluation of LVR01 Attenuated Salmonella as Neoadjuvant Intralesional Therapy in Combination with Chemotherapy for Melanoma Treatment

Treatment of malignant melanoma has improved in the last few years owing to early detection and new therapeutic options. Still, management of advanced disease remains a challenge because it requires systemic treatment. In such cases, dacarbazine-based chemotherapy has been widely used, despite low efficacy. Neoadjuvant therapies emerge as alternative options that could help chemotherapy to achieve increased benefit. In this work, we evaluate LVR01, an attenuated Salmonella enterica serovar typhimurium, as neoadjuvant intralesional therapy in combination with dacarbazine in a preclinical melanoma model. B16F1 melanoma‒bearing mice received intraperitoneal administration of dacarbazine for 3 consecutive days. LVR01 treatment, consisting of one single intratumoral injection, was applied 1 day before chemotherapy began. This therapeutic approach retarded tumor growth and prolonged overall survival, revealing a strong synergistic antitumor effect. Dacarbazine induced a drastic reduction of secondary lymphoid organ cellularity, which was partially restored by Salmonella, particularly potentiating activated cytotoxic cell compartments. Systemic immune reactivation could be a consequence of the intense inflammatory tumor microenvironment induced by LVR01. We propose that the use of LVR01 as neoadjuvant intralesional therapy could be considered as an interesting strategy with close clinical application to boost chemotherapy effect in patients with melanoma

A transcriptomic analysis of Echinococcus granulosus larval stages:implications for parasite biology and host adaptation

The cestode Echinococcus granulosus--the agent of cystic echinococcosis, a zoonosis affecting humans and domestic animals worldwide--is an excellent model for the study of host-parasite cross-talk that interfaces with two mammalian hosts. To develop the molecular analysis of these interactions, we carried out an EST survey of E. granulosus larval stages. We report the salient features of this study with a focus on genes reflecting physiological adaptations of different parasite stages.We generated ~10,000 ESTs from two sets of full-length enriched libraries (derived from oligo-capped and trans-spliced cDNAs) prepared with three parasite materials: hydatid cyst wall, larval worms (protoscoleces), and pepsin/H(+)-activated protoscoleces. The ESTs were clustered into 2700 distinct gene products. In the context of the biology of E. granulosus, our analyses reveal: (i) a diverse group of abundant long non-protein coding transcripts showing homology to a middle repetitive element (EgBRep) that could either be active molecular species or represent precursors of small RNAs (like piRNAs); (ii) an up-regulation of fermentative pathways in the tissue of the cyst wall; (iii) highly expressed thiol- and selenol-dependent antioxidant enzyme targets of thioredoxin glutathione reductase, the functional hub of redox metabolism in parasitic flatworms; (iv) candidate apomucins for the external layer of the tissue-dwelling hydatid cyst, a mucin-rich structure that is critical for survival in the intermediate host; (v) a set of tetraspanins, a protein family that appears to have expanded in the cestode lineage; and (vi) a set of platyhelminth-specific gene products that may offer targets for novel pan-platyhelminth drug development.This survey has greatly increased the quality and the quantity of the molecular information on E. granulosus and constitutes a valuable resource for gene prediction on the parasite genome and for further genomic and proteomic analyses focused on cestodes and platyhelminths