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Optimal siting of wind farms based on a pre-assessment of the spatiotemporal variability of wind resources is considered a suitable method for reducing fluctuations in the delivered output. In this study, we explore the potential for balancing wind energy generation in the Iberian Peninsula using Principal Component Analysis (PCA). This technique permits the discovery of possibly new promising locations for wind power harvesting and an evaluation of the existing wind farm network in terms of reliability in energy generation. Data input to the PCA consists of hourly wind capacity factor in a 5-km spatial resolution grid covering the entire peninsula. These data are derived from an equivalent wind farm power curve fed by modeled wind speed data from 80 m above ground level. PCA reveals three significant balancing patterns prevailing over the IP, where half of the currently operating wind farms in Spain are placed. Hence, among the many constituents of the existing wind farm network, these spots offer the best opportunity for stable power supply. The paper concludes by making proposals on an optimum wind capacity allocation based on the idea of equally distributing installed power between positive/negative dipoles emerging from balancing principal components.

Since pioneering work in the early 60s on the development of enzyme electrodes the field of sensors has evolved to different sophisticated technological platforms. Still, for biomedical applications, there are key requirements to meet in order to get fast, low-cost, real-time data acquisition, multiplexed and automatic biosensors. Nano-based sensors are one of the most promising healthcare applications of nanotechnology, and prone to be one of the first to become a reality. From all nanosensors strategies developed, Magnetic Relaxation Switches (MRSw) assays combine several features which are attractive for nanomedical applications such as safe biocompatibility of magnetic nanoparticles, increased sensitivity/specificity measurements, possibility to detect analytes in opaque samples (unresponsive to light-based interferences) and the use of homogeneous setting assay. This review aims at presenting the ongoing progress of MRSw technology and its most important applications in clinical medicine., Support was provided by the Spanish Ministry of Economy and Competitiveness with FEDER co-funding (FIS-PI14-1600 to DP), the Regional Ministry of Health (PI2013-375 to SL; PI2013-0559 to MPL), the Regional Ministry of Economy, Science and Innovation (P10-CTS-6928 and P11-CTS-8161 to DP), and the PAIDI Program from the Andalusian Government (CTS-677 to DP). DA holds a Marie Curie Fellowship (FP7-PEOPLE-2012-IEF, grant number 327151) from the European Commission.

A large number of studies have dealt with the Transmission Expansion Planning (TEP) problem. However, few investigations have focused on analyzing the impacts of network payment schemes on network configuration and the benefits/losses distribution among the participants in electricity markets. In this paper, we propose a multi-annual transmission expansion planning model considering four different network payment schemes to finance the construction of new transmission lines, seeking to reduce the total system costs. Wind and solar power generation are included in the model taking into account their variability. The proposed models are reformulated as Mixed Integer Linear Programming (MILP) problems. We use seven performance metrics related with congestion, nodal prices and generator benefits, among others, to evaluate the effect of each payment scheme. A realistic case study based on the main power system in Chile is analyzed to illustrate the proposed models. It is shown that integrating line cost-recovering equations into the TEP model may result into a more realistic and less congested power network. Also, total system cost is highly related with transmission tariff discrimination. In that way, tariffs with high location dependence perform better in the case studied, the Chilean power system.

Solar radiation plays a key role in the atmospheric system but its distribution throughout the atmosphere and at the surface is still very uncertain in atmospheric models, and further assessment is required. In this study, the shortwave downward total solar radiation flux (SWD) predicted by the Weather Research and Forecasting (WRF) Model at the surface is validated over Spain for a 10-yr period based on observations of a network of 52 radiometric stations. In addition to the traditional pointwise validation of modeled data, an original spatially continuous evaluation of the SWD bias is also conducted using a principal component analysis. Overall, WRF overestimates the mean observed SWD by 28.9 W m−2, while the bias of ERA-Interim, which provides initial and boundary conditions to WRF, is only 15.0 W m−2. An important part of the WRF SWD bias seems to be related to a very low cumulus cloud amount in the model and, possibly, a misrepresentation of the radiative impact of this type of cloud.

Equilibrium analysis is crucial in electricity market designs, with Nash equilibrium recognized as the most powerful one. Its most prominent hindrance, however, is an efficient methodology to compute an equilibrium point in large-scale systems. In this work, a Column-and-Constraint Generation (CCG) algorithm is proposed to tackle this challenge. More precisely, the master problem finds a candidate for Nash equilibrium and the oracle identifies whether this candidate point is indeed an equilibrium. A set of numerical experiments was conducted, comparing its computational performance with the solution of an Equilibrium Problem with Equilibrium Constraint (EPEC). We identify that the proposed algorithm overcomes the benchmark in the magnitude of 20 times on average and more than 30 times in the most demanding instances. Furthermore, the scalability of the EPEC formulation is challenged even for medium-scale instances, whilst the proposed algorithm was able to handle all tested instances in a reasonable computational time.

Although local protection methods can perform well at speed, in a real network where network uncertainties are unavoidable, these methods may result in inaccurate performance. Therefore, the use of other methods, such as wide area protection, can lead to more accurate results because of using comprehensive information via telecommunication links. In this paper, a wide area protection algorithm is presented to cover the mal-operation of relays due to the network uncertainties. In this method, an objective function is defined based on the performance status of various zones of distance relays, the values of which will be different for each line. Depending on the position of each relay on the transmission line, weighting factors for objective function are determined based on the optimization algorithm and taking into account the possible uncertainties of the power grid. The proposed method has been implemented on IEEE 9-bus system and has proved high detection precision and performance accuracy.

Currently, the characterization of electric energy storage units used for power system operation and planning models relies on two major assumptions: charge and discharge efficiencies, and power limits are constant and independent of the electric energy storage state of charge. This approach can misestimate the available storage flexibility. This work proposes a detailed model for the characterization of steady-state operation of Li-ion batteries in optimization problems. The model characterizes the battery performance, including non-linear charge and discharge power limits and efficiencies, as a function of the state of charge and requested power. We then derive a linear reformulation of the model without introducing binary variables, which achieves high computational efficiency, while providing high approximation accuracy. The proposed model characterizes more accurately the performance and technical operational limits associated with Li-ion batteries than those present in classical ideal models. The developed battery model has been compared with three modelling approaches: the complete non-convex formulation; an ideal model typically used in the power system community; and a mixed integer linear reformulation approach. The models have been tested on a network-constrained economic dispatch for a 24-bus system. Based on the simulations, we observed approximately 12% of energy mismatches between schedules that use an ideal model and those that use the model proposed in this study.

Over the last years, gas-fired power capacity investments in electricity networks have increased in many countries due to its low-rate of greenhouse gas emissions and the continuous reduction in the natural gas price. Besides, new power-to-gas technologies have attracted attention in the sector as an alternative for harvesting large shares of renewable generation. Thus, the coupling between power and gas networks is becoming more critical. New complementarities arise from the synergies of the co-optimization of both power and natural gas systems operation in terms of reliability, sustainability, and economic efficiency. In this regard, jointly planning of power and natural gas systems could leverage potential benefits for the optimal coordination of both grids. In this survey, we present a comprehensive investigation of recent literature in the contexts of coordinated planning of the gas and electricity systems. We first provide a thorough study of energy systems planning. Later, the planning issues of both power and gas grids are investigated, including modeling aspects, typical operational constraints, and objectives that drive investments, as well as uncertainties associated. Then, international experiences are presented to compare the mathematical approaches to the planning of electricity and natural grids. Finally, discussion about future research opportunities and conclusions are presented.

Progress in the field of biocompatible SERS nanoparticles has promising prospects for biomedical applications. In this work, we have developed a biocompatible Raman probe by combining anisotropic silver nanoparticles with the dye rhodamine 6G followed by subsequent coating with bovine serum albumin. This nanosystem presents strong SERS capabilities in the near infrared (NIR) with a very high (2.7 &times, 107) analytical enhancement factor. Theoretical calculations reveal the effects of the electromagnetic and chemical mechanisms in the observed SERS effect for this nanosystem. Finite element method (FEM) calculations showed a considerable near field enhancement in NIR. Using density functional quantum chemical calculations, the chemical enhancement mechanism of rhodamine 6G by interaction with the nanoparticles was probed, allowing us to calculate spectra that closely reproduce the experimental results. The nanosystem was tested in cell culture experiments, showing cell internalization and also proving to be completely biocompatible, as no cell death was observed. Using a NIR laser, SERS signals could be detected even from inside cells, proving the applicability of this nanosystem as a biocompatible SERS probe.

This paper presents an optimal fuzzy PD + I controller designed to control the trajectory of a mobile robot. To tune and obtain the best parameters of the controller, the PSO (Particle Swarm Optimization) algorithm is used. This algorithm minimizes an objective function, in this case, the Integral Square Error (ISE) index of the system, parameter that depends on the linear and angular velocity errors for the optimization process. With this algorithm, the parameters to be optimized are the gains and the membership functions of the fuzzy controller in order to obtain an optimal controller and improve its performance. Finally, in order to validate the proposal, a comparative analysis among the results of a classic PID controller, a conventional Fuzzy PD + I controller and the proposed optimal Fuzzy PD + I controller is performed, evaluating trajectories followed by the robot, the position errors and the control actions of each case.

The creation of the Renewable Energy Law (Law 1715 of 2014) promotes the introduction of large-scale renewable energy generation in the Colombian electricity market. The new legislation aims to diversify the country's generation matrix, mainly composed of hydro and fuel-based generation, with a share of 66% and 34% respectively. Currently, three wind generation projects, with an aggregated capacity of 500 MW, have been commissioned in the North of the country. This study analyses the economic impact of the large-scale introduction of wind generation on both, the market spot price and conventional generation plants operation. For this purpose, the study builds a unit commitment model to mimic the current market legislation and the system's generation data. We show that the introduction of wind energy into the Colombian electricity market would impact the generation share of large hydro and gas-fired power plants. The hydro generation has an important role in balancing the generation for fluctuations on the wind resource. Meanwhile, the gas-fired plants would decrease their participation in the market, proportionally to the introduction of wind generation in the system, by as low as 20% of its current operation., This paper is a preprint of a paper submitted to "IET Generation, Transmission & Distribution". If accepted, the copy of record will be available at the IET Digital Library

This paper presents a novel method for tuning a PI controller for a first-order plus time delay (FOPTD) system based on a Multi-gene Genetic Programming (MGGP) and a Particle Swarm Optimization (PSO) algorithm. In our approach, the PSO stablishes a set of optimal gains of the controller for a FOPTD system, based on the plant parameters. Then, the MGGP obtains the mathematical equations to estimate the optimal gains determined by PSO. Finally, to validate the methodology proposed, a group of random systems were selected and tested in MATLAB-SIMULINK, using the calculated equations, focused in its behavior with respect to the maximum overshoot (Mp) and the Integral Square Error (ISE).

[Background]] The immune response of visceral adipose tissue (VAT) in obesity, in particular the role of invariant natural killer T (iNKT) cells, has not yet been fully elucidated., [Objective] To characterize iNKT cells and its activation status in VAT and peripheral blood mononuclear cells (PBMC) in morbidly obese subjects (MO), and to analyze their association with metabolic parameters., [Subjects and Methods] Twenty non-obese and 20 MO subjects underwent Roux-en-Y gastric bypass (RYGB) and were studied before and 6 months after RYGB. VAT and PBMC were obtained., [Results] A decrease in VAT iNKT cells from MO was found, however, not in PBMC. Visceral adipocytes from MO presented increased CD1d expression (p = 0.032). MO presented an increase in early activated CD69+ iNKT cells in PBMC before RYGB (p, [Conclusions] There is a tissue-specific phenotype and activation of iNKT cells in VAT in morbid obesity, which could be involved in VAT immunometabolism dysregulation. Also, the increase in CD1d expression could be to offset the lack of VAT iNKT cells., This study has been supported by the Instituto de Salud Carlos III (ISCIII) (PI09/01016) (BA way to make Europe^), the Andalusian Ministry of Health (PI-2013-575), and the Andalusian Ministry of Economy, Science and Innovation (P10-CTS6928, P11-CTS8161 and P11-CTS8081) and co-financed by the European Regional Development Fund (FEDER) (BAndalusia moves with Europe^).

Poor conditions and indifferent society are some of the main causes that elderly people currently live alone and may suffer health problems or injuries. Thus, in those cases these people require an emergency service attention in their homes or care centers. However, in most cases, they cannot call for help due to their lack of access to technology, geographical location (rural areas) or by their physical limitations. Based on these conditions, this work presents the implementation of a Home Tele-assistance system, which can send personal information and geographical location through the network of elder or disabled people. This information is stored in a database and is sent to their relatives and the emergency services, in order to accelerate the time response in an emergency. The prototype was designed based on a social project supported by the University in collaboration with communities in Ecuador.

Two-stage robust unit commitment (RUC) models have been widely used for day-ahead energy and reserve scheduling under high renewable integration. The current state of the art relies on budget-constrained polyhedral uncertainty sets to control the conservativeness of the solutions. The associated lack of interpretability and parameter specification procedures, as well as the high computational burden exhibited by available exact solution techniques call for new approaches. In this paper, we use an alternative scenario-based framework whereby uncertain renewable generation is characterized by a polyhedral uncertainty set relying on the direct specification of its vertexes. Moreover, we present a simple, yet efficient, adaptive data-driven procedure to dynamically update the uncertainty set vertexes with observed daily renewable-output profiles. Within this setting, the proposed data-driven RUC ensures protection against the convex hull of realistic scenarios empirically capturing the complex and time-varying intra-day spatial and temporal interdependencies among renewable units. The resulting counterpart features advantageous properties from a computational perspective and can be effectively solved by the column-and-constraint generation algorithm until ε-global optimality. Out-of-sample experiments reveal that the proposed approach is capable of attaining efficient solutions in terms of cost and robustness while keeping the model tractable and scalable, Los modelos de compromiso de unidad robusta (RUC, por sus siglas en inglés) de dos etapas se han utilizado ampliamente para la programación de reservas y energía diaria bajo una alta integración renovable. El estado actual del arte se basa en conjuntos de incertidumbre poliédricos con restricciones presupuestarias para controlar el carácter conservador de las soluciones. La falta asociada de procedimientos de interpretación y especificación de parámetros, así como la alta carga computacional exhibida por las técnicas de solución exacta disponibles requieren nuevos enfoques. En este documento, utilizamos un marco alternativo basado en escenarios en el que la generación renovable incierta se caracteriza por un conjunto poliédrico de incertidumbre que se basa en la especificación directa de sus vértices. Además, presentamos un procedimiento basado en datos adaptativo simple, pero eficiente, para actualizar dinámicamente los vértices del conjunto de incertidumbre con perfiles de producción renovable diarios observados. Dentro de este entorno, el RUC basado en datos propuesto garantiza la protección contra el casco convexo de los escenarios realistas que capturan empíricamente las interdependencias espaciales y temporales intradiarias complejas y variables en el tiempo entre las unidades renovables. La contraparte resultante presenta propiedades ventajosas desde una perspectiva computacional y puede resolverse de manera efectiva mediante el algoritmo de generación de columnas y restricciones hasta la optimización global ε. Los experimentos fuera de la muestra revelan que el enfoque propuesto es capaz de lograr soluciones eficientes en términos de costo y solidez, manteniendo el modelo manejable y escalable. La contraparte resultante presenta propiedades ventajosas desde una perspectiva computacional y puede resolverse de manera efectiva mediante el algoritmo de generación de columnas y restricciones hasta la optimización global ε. Los experimentos fuera de la muestra revelan que el enfoque propuesto es capaz de lograr soluciones eficientes en términos de costo y solidez, manteniendo el modelo manejable y escalable. La contraparte resultante presenta propiedades ventajosas desde una perspectiva computacional y puede resolverse de manera efectiva mediante el algoritmo de generación de columnas y restricciones hasta la optimización global ε. Los experimentos fuera de la muestra revelan que el enfoque propuesto es capaz de lograr soluciones eficientes en términos de costo y solidez, manteniendo el modelo manejable y escalable

Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that primarily affects motor neurons and is accompanied by sustained unregulated immune responses, but without clear indications of the ultimate causative mechanisms. The identification of a diverse array of ALS phenotypes, a series of recently discovered mutations, and the links between ALS and frontotemporal degeneration have significantly increased our knowledge of the disease. In this review we discuss the main features involved in ALS pathophysiology in the context of recent advances in 'omics' approaches, including genomics, proteomics, and others. We emphasize the pressing need to combine clinical imaging with various different parameters taken from omics fields to facilitate early, accurate diagnosis and rational drug design in the treatment of ALS. ALS, or amyotrophic lateral sclerosis, is a progressive neurodegenerative disease that affects motor neurons. There is no cure for ALS. Although ALS is a brain disease closely related to Parkinson's, Alzheimer's, and Huntington's diseases, so far the complex descriptions of ALS-associated damage have not clarified the ultimate causative mechanisms.Current interventions are the result of unintentional discoveries or the non-specific application of cell-based therapies whose effects are not completely understood. However, research on ALS is currently thriving and the body of knowledge on the subject has increased remarkably in recent years.The emergence of functional immunomics for ALS from established omics technologies are opening new therapeutic avenues based on the smart manipulation of the immune system.Molecular imaging in the field of ALS is evolving. Thus, a combination of omics technologies and clinical imaging may very well be the key for breaking-down ALS., Financial support was provided by grants AI075285 and AI093903 from the US National Institutes of Health, RG4111A1 and JF2161-A-5 from the National Multiple Sclerosis Society, and PA0069 from the International Progressive MS Alliance (F.J.Q.), the Regional Ministry of Economy, Science and Innovation (P11-CTS-8161 to D.P.), the Regional Ministry of Health (PI13-575 to S.L.), the PAIDI Program from the Andalusian Government (CTS-677 to D.P.), and the Spanish Ministry of Economy and Competitiveness with FEDER co-funding (PI14-1600 to D.P.; SAF-2012-39720 and CP10-00527 to C.R.). D.C-H. holds a postdoctoral fellowship from CONACyT (Mexico). M.C-G. holds a PIF-University of Seville PhD fellowship.

A new evolutionary design of an ANN (artificial neural network) ensemble model is developed to generate hourly DNI (direct normal irradiance) estimates. The procedure combines a genetic algorithm for selecting the best inputs with an ANN ensemble method. The ensemble model was calibrated and evaluated using three years of Meteosat-9 images and data measured at 28 high-quality ground stations over an extensive area, mainly in Europe. The most valuable inputs for DNI estimation are shown to be the following: all Meteosat-9 channels except ch08 and ch11 ; relative air mass m , integral Rayleigh optical thickness δ r , extraterrestrial global irradiance G 0 , beam clear-sky index B cs , and the cosine of zenith angle θ . No additional atmospheric information such as turbidity, aerosol optical depth or water vapor content are required for the model. Ensemble estimates were nearly unbiased (MBE = 1.98%) and overall RMSE (root mean square error) was 24.29% across an independent spatial and temporal dataset. This represents an improvement of 35% over other common methods for estimating DNI. The estimates were reasonably reliable in all seasons, and were more accurate in clear-sky conditions.

Cancer disease is one of the leading causes of morbidity and mortality worldwide, with approximately 14 million new cases and around 8 million cancer-related deaths yearly. Estimates expect to increase these figures over the next few years. Therefore, it is very important to develop more effective and targeted therapies. Polysaccharides are widely used for biomedical and pharmaceutical applications due to their interesting properties, and can be utilised in the production of nanovehicles for drug delivery, since they frequently extend the half-life and improve the stability of chemotherapeutic agents in bloodstream allowing them to reach the tumour tissue. Moreover, polysaccharide-based nanovehicles are generally expected to increase the therapeutic benefit by reducing the undesired side effects and promoting a more efficient cellular uptake. Here, we highlight the application of various polysaccharides as nanovehicles in cancer therapy, focusing mainly on in vivo applications and describing the main advantages of each designed system in a critical way. The use of different polysaccharides interacting with metal nanoparticles to develop new nanovehicles for cancer therapy will also be discussed.

Electricity distribution networks are a critical infrastructure of the modern society. The continuity of the electricity distribution service is therefore a key topic. Network automation is aimed at improving service continuity. Among the several possible automation schemes, logic selectivity of protection relays is one of the most effective approach because it allows minimizing the extent of the area affected by the fault with a short clearing time. IEC 61850 provides a standard way to implement automation. This paper describes a laboratory setup where the logic selectivity—based on the IEC 61850 standard—was tested before moving protection relays to a real operating environment for field demonstration. An analysis of system configuration and performance is reported when phase-to-ground and phase-to-phase faults were tested. Failure of the communication infrastructure enabling the coordination among relays and a failure of the breaker were also simulated to determine how the system was fault tolerant. The results show that the logic implemented is fast enough to send the trip command to the breaker in less than 170 ms in the case of phase-to-ground fault and in less than 130 ms in the case of phase-to-phase fault, even considering the time needed for the coordination of relays.

In this work, a new method to derive hourly global horizontal irradiance (GHI) estimates from Meteosat Second Generation (MSG) imagery is presented. The method is based on an optimized Artificial Neural Network (ANN) ensemble model using a selection of the best ANN models identified from an initial ensemble that discerns between different sky conditions and an additional ensemble that considers all sky conditions together. For benchmarking purposes, hourly GHI estimates computed with the Heliosat-2 method, accounting for the diurnal variability of ground albedo, are used. Data collected during the 3-year period from 2009 to 2011 at 28 radiometric stations located in northern Africa, Middle East and Europe, are used in the procedure. From these stations, 7 are used to train the ANN models and the other 21 for independent validation. Results obtained with the proposed ANN ensemble model reduced the RMSE value of the Heliosat-2 model a 22% for all-sky conditions and a 42% for overcast conditions.

In this work, we evaluate the influence of land-use representation accuracy on the reliability of wind speed and direction estimates derived from the Weather Research and Forecasting (WRF) model. To this end, the 100-m spatial resolution Coordination of Information on the Environment (CORINE) land-use dataset was implemented as static geographic data in WRF. Next, a set of one-year long simulations at 1-km spatial resolution was conducted using both the CORINE and Global Land Cover Characterization (GLCC) land-use datasets, the latter the default in WRF. The simulations were conducted for three locations in southern Spain, and were characterized by variable land-use composition and topography. At these locations, wind speed and direction estimates were compared against observations at different measurement elevations. Results showed that the selection of land-use database has a major influence on wind estimate bias. The effect on the wind direction distribution is also significant, whereas that on the standard deviation is much weaker. CORINE provided a more reliable land-use representation than GLCC. Nevertheless, as a consequence of the interpolation procedure used for land use in the domain setup, this representation did not necessarily translate to a superior roughness length, thereby affecting wind speed and direction estimates. This was particularly so for areas of high spatial variability in land-use categories. In such areas, the misrepresentation of land use may result in large wind speed estimation errors.

We evaluate the extent to which a combination of wind power and concentrating solar power (CSP) may lead to stable and even baseload power by taking advantage of: 1) spatiotemporal balancing of solar and wind energy resources and 2) storage capabilities of CSP plants. A case study is conducted for the region of Andalusia in Spain. To this end, spatiotemporal variability of modeled CSP and wind capacity factors in a 3-km spatial resolution grid were analyzed based on principal component analysis (PCA) and canonical correlation analysis (CCA). Results reveal that renewable baseload power can be obtained in the study region by locating wind farms and CSP plants using balancing patterns derived from CCA and PCA. In addition, the power fluctuation reduction attained from these patterns was substantially higher than those obtained by interconnecting randomly-located wind farms and CSP plants across the study region. Results were particularly meaningful for the winter season. Upon considering storage capability of the CSP plants, results proved better. The main difference was a higher firm capacity value associated with spring and summer seasons. For the other seasons, the contribution of thermal storage capabilities of the CSP plants to stable power proved less relevant.

Cobo-Vuilleumier, Nadia et al., Type 1 diabetes mellitus (T1DM) is due to the selective destruction of islet beta cells by immune cells. Current therapies focused on repressing the immune attack or stimulating beta cell regeneration still have limited clinical efficacy. Therefore, it is timely to identify innovative targets to dampen the immune process, while promoting beta cell survival and function. Liver receptor homologue-1 (LRH-1) is a nuclear receptor that represses inflammation in digestive organs, and protects pancreatic islets against apoptosis. Here, we show that BL001, a small LRH-1 agonist, impedes hyperglycemia progression and the immune-dependent inflammation of pancreas in murine models of T1DM, and beta cell apoptosis in islets of type 2 diabetic patients, while increasing beta cell mass and insulin secretion. Thus, we suggest that LRH-1 agonism favors a dialogue between immune and islet cells, which could be druggable to protect against diabetes mellitus., This work was funded by grants from the Juvenile Diabetes Research Foundation (17-2013-372 to B.R.G.), the Consejeria de Salud, Fundacion Publica Andaluza Progreso y Salud, Junta de Andalucia (PI-0727-2010 to B.R.G. and P10CTS6505 to B.S.), Consejeria de Economia, Innovacion y Ciencia (P10.CTS.6359 to B.R.G.), the Ministerio de Economia y Competidividad co-funded by Fondos FEDER (PI10/00871, PI13/00593, and BFU2017-83588-P to B.R.G.; PI14/01015, RD12/0019/0028, and RD16/0011/0034 to B.S.; PI16/00259 to A.H.) and Deutsche Forschungsgemeinschaft (GRK-1789 ´CEMMA´ and DFG SCHI-505/6-1 to R.S.). Special thanks to the families of the DiabetesCero Foundation that graciously supported this work (to B.R.G.). A.M.M. is a recipient of a Miguel Servet grant (CP14/00105) from the Instituto de Salud Carlos III co-funded by Fondos FEDER whereas E.F.M. is a recipient of a Juan de la Cierva Fellowship. I.G.H.G. is supported by a fellowship from Amarna Therapeutics. In some instances, human islets were procured through the European Consortium for Islet Transplantation funded by Juvenile Diabetes Research Foundation (3-RSC-2016-162-I-X).

This chapter presents static and dynamic optimization-based models for planning the electric distribution network. Based on a branch flow model, two Mixed-Integer Conic Quadratic Programming (MICQP) convex formulations are proposed to solve the network expansion planning models including high modeling fidelity of the intrinsic interaction of the manifold elements of the networks. The objective of the presented models is to minimize investment and operation costs by optimally deciding on installing new feeders and/or changing existing ones for others with larger capacities, installing new substations or expanding existing ones and, finally, installing capacitor banks and voltage regulators, modifying the network topology. In addition, discrete tap settings of voltage regulators are modeled as a set of mixed-integer linear equations, which are embedded in an ac optimal power flow. The presented MICQP models are convex optimization problems. Therefore globality and convergence are guaranteed. Computational results to verify the efficiency of the proposed methodology are obtained for a 24-node test system. Finally, conclusions are duly drawn.

Aerosols play a primary role in the global climate system and the solar radiation budget at the Earth surface. Here we analyze the role of spaceborne aerosol observations and their uncertainties in the expected accuracy of the modeled cloudless surface shortwave downward radiation using a state-of-the-art numerical weather prediction model over the continental U.S. We compare five different modeling approaches for the aerosol optical effects with differing sophistication. Overall, we show that, counterintuitively, the direct and diffuse irradiances predicted by solar radiation models that use empirically adjusted fixed aerosol extinction may be more accurate than more sophisticated radiative transfer models that require inputs of aerosols. We conclude that, compared to ground observations, the mean absolute error in satellite-retrieved aerosol optical depth over the U.S., and possibly elsewhere, should be reduced to less than 0.025 aerosol optical depth unit to assure improvement over the predictions of a simpler, aerosol-insensitive radiation model.

This paper describes a detailed methodology that allows gesture recognition performed with the upper limbs of the human body, based on Dynamic Time Warping (DTW) technique using a Kinect in order to control a slide-show. Several involved parameters on the DTW algorithm will be analyzed allowing an accurate recognition, considering variations on the gesture velocity and even if subject's physical shape differ. A software was developed in Microsoft Visual C# to show and test how different parameters affects the performance of the algorithm. Finally an accuracy of 84.07% was obtained in this approach.

This paper presents the design and simulation of a passive chipless RFID Tag using fractal shapes. To design the fractal antenna the vicsek fractal was choosen on its different iterations. In order to obtain data regarding its detection, simulations were done covering the frequency range of 5GHz to 15GHz. The simulation will have two stages: In the first one, the tag will be illuminated by two horn antennas to obtain the return loss and forward transmission parameters, which shows the medium disturbance generated by the TAG induction. The second stage shows the monostatic Radar Cross Section (RCS) using a probe, located in a certain distance away from the chipless fractal TAG. Finally, the results shows that fractal antenna design after multiple iterations, improves their detection. The software used in this resarch is CST Microwave Studio.

Neuroinflammation mediated by chronically activated microglia, largely caused by abnormal accumulation of misfolded α-synuclein (αSyn) protein, is known to contribute to the pathophysiology of Parkinson's disease (PD). In this work, based on the immunomodulatory activities displayed by particular heat-shock proteins (HSPs), we tested a novel vaccination strategy that used a combination of αSyn and Grp94 (HSPC4 or Gp96) chaperone and a murine PD model. We used two different procedures, first, the adoptive transfer of splenocytes from αSyn/Grp94-immunized mice to recipient animals, and second, direct immunization with αSyn/Grp94, to study the effects in a chronic mouse MPTP-model of parkinsonism. We found that both approaches promoted a distinct profile in the peripheral system—supported by humoral and cellular immunity—consisting of a Th1-shifted αSyn-specific response accompanied by an immune-regulatory/Th2-skewed general phenotype. Remarkably, this mixed profile sustained by αSyn/Grp94 immunization led to strong suppression of microglial activation in the substantia nigra and striatum, pointing to a newly described positive effect of anti-αSyn Th1-responses in the context of PD. This strategy is the first to target αSyn and report the suppression of PD-associated microgliosis. Overall, we show that the αSyn/Grp94 combination supports a distinct and long-lasting immune profile in the peripheral system, which has an impact at the CNS level by suppressing chronic microglial activation in an MPTP model of PD. Furthermore, our study demonstrates that reshaping peripheral immunity by vaccination with appropriate misfolding protein/HSP combinations could be highly beneficial as a treatment for neurodegenerative misfolding diseases., Grant sponsor: Spanish Ministry of Economy, and cofunded by Fonds Europeen de Developpement Economique et Regional (FEDER): CP10/00527, SAF2012-39720 and RTC-2015-3309-1 (to CR); PI12/02574-ISCIII, Red TerCel ISCIII (RD12/0019/0033 and RD16/0011/0025) and RTC-2015-3309-1 (to JJTA); PI14/01600-ISCIII (to DP); P12-CTS-2739 (to JJTA) and P11-CTS8161 (to DP) from the Regional Ministry of Economy. The work of CMD and EJDG was supported by the Cambridge Center for Misfolding Diseases. CR holds a senior Servet II contract (CPII16/58) from the Spanish Ministry of Economy (Instituto de Salud Carlos III) cofounded by FEDER.

Despite being clinically described 150 years ago, the mechanisms underlying amyotrophic lateral sclerosis (ALS) pathogenesis have not yet been fully understood. Studies in both animal models of ALS and human patients reveal a plethora of alterations such as increased glutamate-mediated excitotoxicity, redox stress, increased apoptosis, defective axonal transport, protein-misfolding events, mitochondrial impairment and sustained unregulated immune responses. Regardless of being sporadic or familiar ALS, the final outcome at the cellular level is the death of upper and lower motor neurons, and once diagnosed, ALS is typically lethal within the next 5 years. There are neither clear biomarkers nor therapeutic or disease-modifying treatments for ALS.Accumulating evidence supports the concept that epigenetic-driven modifications, including altered chromatin remodelling events, RNA editing and non-coding RNA molecules, might shed light into the pathogenic mechanisms underlying sporadic/familiar ALS onset and/or severity to facilitate the identification of effective therapies, early diagnosis and potentially early-stage therapeutic interventions to increase the survival outcome of ALS patients.

Made available in DSpace on 2018-11-28T09:41:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-05-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Programa CSF-PAJT, Brazil This paper proposes a risk-based mixed integer quadratically-constrained programming model for the long-term VAr planning problem. Risk aversion is included in the proposed model by means of regret-based optimization to quantify the load shedding risk because of a reactive power deficit. The expected operation and expansion costs of new installed reactive power sources and load shedding risk are jointly minimized. Uncertainty in the active and reactive load demands has been included in the model. An epsilon-constraint approach is used to characterize the optimal efficient frontier. Also, discrete tap settings of tap-changing transformers are modeled as a set of mixed integer linear equations which are embedded into an ac optimal convex power flow. Computational results are obtained from a realistic South and South-East Brazilian power system to illustrate the proposed methodology. Finally, conclusions are duly drawn. Univ Cuenca, Sch Elect Engn, Fac Engn, Cuenca 010150, Ecuador Pontifical Catholic Univ Rio de Janeiro, Dept Elect Engn, BR-22430060 Rio De Janeiro, Brazil Univ Castilla La Mancha, Escuela Tecn Super Ingn Ind, E-13071 Ciudad Real, Spain Sao Paulo State Univ, Dept Elect Engn, Elect Power Syst Planning Lab, BR-18618000 Sao Paulo, Brazil Sao Paulo State Univ, Dept Elect Engn, Elect Power Syst Planning Lab, BR-18618000 Sao Paulo, Brazil FAPESP: 2014-24287-0 Programa CSF-PAJT, Brazil: 88887.064092-2014-00

Participants in competitive electricity markets make their dynamic decisions under uncertainty. Choosing a time-inconsistent formulation can lead to an incorrect procedure for risk and, consequently, to a sequence of inappropriate decisions. In a market context with uncertainty in energy prices, the net income of a company is the result of selling their energy in the spot market and through bilateral physical contracts. The purpose of this paper is to describe a dynamic multistage stochastic programming framework for sequential decision making under uncertainty that allows wind power producers to maximize their profit for a given risk level on profit variability. In this context, Conditional Value at Risk (CVaR) has been chosen as a time-consistent and dynamic risk measure. An example is provided to illustrate the methodology proposed.

We propose a method for analyzing the potential contribution of wind energy resources to stable (baseload) power within a region. The method uses principal component analysis (PCA) to analyze spatiotemporal balancing of wind energy resources and then assesses the optimal wind farm location to reduce wind power fluctuations. The ability of different reference wind turbines, alone or interconnected, to provide stable power is ultimately evaluated at selected locations. The method was tested in the southern Iberian Peninsula, including offshore areas. We used hourly wind energy estimates from the WRF mesoscale model at 3-km spatial resolution for the period 2008–2010. First, results show a valuable spatial balancing pattern between the wind energy resources in the northeast study region and Strait of Gibraltar area. The pattern was found to result from the interaction of mesoscale zonal flow with the complex topography of the region. Second, the results indicate that by taking advantage of the spatial balancing pattern, the optimal allocation and interconnection of wind farms across the region, can substantially reduce wind power fluctuations. This optimal allocation can in some cases generate stable power, thereby contributing to baseload power.

In this paper, we present a framework for analyzing cross-border power interconnection projects based on Cooperative Game Theory. Compared to existing studies, we not only quantify the benefits of interconnections and suggest cost-benefit allocation techniques, but also analyze the stability of the allocations, which is a crucial aspect in regions where coordination and mutual trust between countries have not been built yet. We apply our framework to the Northeast Asia where six countries (China, Russia, Mongolia, South Korea, North Korea, and Japan) are suggested for cross-border transmission expansion planning cooperation. Cost-benefits allocation of the interconnections is analyzed according to the marginal contribution of each country to the grand coalition and the minimal dissatisfaction of each coalition that ensures the stability of the solution. Accordingly, Game Theory concepts (the Shapley value and the Nucleolus) are used in our analysis. Moreover, we employ the Core concept to further analyze the stability of the allocation solution and present a visualization of the feasible space formed by all stable allocations. We found out that the grand coalition (i.e., the scenario where all countries agree on the cooperation) is the optimal and stable coalition, with $7.1 billion total savings per year. We also suggested a scheme of investment allocation and payments between the Northeast Asian countries in order to ensure that the proposed interconnections are plausible in practice.

En este trabajo se describe la política fiscal en Venezuela durante el período (1998-2010), a través de los datos disponibles, sobre gastos e ingresos fiscales, saldo presupuestario y niveles de deuda pública. En este sen- tido, se realizó un análisis sobre la estructura promedio de las variables fiscales, anteriormente mencionadas, y su comportamiento temporal durante el período de análisis. De las características más resaltantes de este análisis descriptivo se encuentran: que en promedio los niveles de gastos del sector público restringido son de alrededor de 32,4% del Producto Interno Bruto (PIB), mientras que sus ingresos totales representan un 31,3% del PIB con una relación similar al considerar dichos conceptos presupuestarios al nivel de gobierno central. De igual forma, según la información estadística disponible, la deuda pública en Venezuela presenta una tendencia descendente y en niveles por debajo del promedio de la mayoría de los países de Latinoamérica (BCV, 2011). Finalmente se realizó una aproximación, a través de un indicador de sostenibilidad fiscal derivado de la restricción presupues- taria del gobierno, del ingreso público permanente requerido que estabiliza la razón deuda/PIB el cual no pare- ce, atendiendo a las limitaciones de análisis, evidenciar problemas serios de sostenibilidad fiscal.Â

The daily Level-3 MODIS (dL3M) aerosol optical depth product is a global daily spatial aggregation of the Level-2 MODIS aerosol optical depth (10-km spatial resolution) into a regular grid with a resolution of 1° × 1°. Aerosol optical depth is a seminal parameter for surface solar radiation assessment, in particular, for those applications involving direct irradiance. However, the dL3M AOD is prone to data gaps originated mostly by the unfeasibility of retrieving reliable estimates under cloudy conditions. In addition, its usability is also constrained by regional biases owing to some other reasons. In this work we propose a methodology for bias reduction and data-gaps removal of the dL3M AOD dataset. The result is a database of daily regularly-gridded AOD suitable for use in surface solar radiation applications and large-scale and long-term studies involving AOD without requiring a previous costly data assimilation process involving numerical weather prediction models. The method consists of an empirical approach to bias reduction, data-gaps removal by kriging interpolation and, finally, where reliable ground observations are available, an optimal interpolation procedure. The method was tested in the North American region, where it was able to reduce the initial mean error from 0.067 to 0.001, the root mean square error from 0.130 to 0.057, and increase the squared correlation coefficient from 23% to 58%, as compared against ground measurements.

An optimized artificial neural network ensemble model is built to estimate daily global solar radiation over large areas. The model uses clear-sky estimates and satellite images as input variables. Unlike most studies using satellite imagery based on visible channels, our model also exploits all information within infrared channels of the Meteosat 9 satellite. A genetic algorithm is used to optimize selection of model inputs, for which twelve are selected e eleven 3-km Meteosat 9 channels and one clear-sky term. The model is validated in Andalusia (Spain) from January 2008 through December 2008. Measured data from 83 stations across the region are used, 65 for training and 18 independent ones for testing the model. At the latter stations, the ensemble model yields an overall root mean square error of 6.74% and correlation coefficient of 99%; the generated estimates are relatively accurate and errors spatially uniform. The model yields reliable results even on cloudy days, improving on current models based on satellite imagery.

[1] The ability of the Rapid Radiative Transfer Model for GCMs (RRTMG) and Goddard shortwave (SW) radiative schemes to reproduce global (GHI), direct (DNI), and diffuse (DIF) fluxes at the surface in clear-sky conditions is examined within the Weather Research and Forecasting (WRF) modeling framework. The models were forced using measurements of precipitable water and aerosol optical properties from five AERONET sites and were evaluated against high-quality observations of GHI, DNI, and DIF at four nearby or coincident sites of the SURFRAD network and one at the Atmospheric Radiation Measurement Central Facility in the contiguous US. In addition, the performance has been intercompared against the Dudhia broadband SW parameterization together with a regressive model to calculate DNI and DIF and the top-class SMARTS2 SW spectral model. The role of aerosols has been investigated by running the models with and without aerosols. All of them have shown outstanding skill at predicting GHI within the range of the expected observational error, regardless of whether aerosol information is provided as input. When aerosol information is not provided, DNI and DIF are affected by strong biases. In contrast, providing observed aerosols as input solves the bias issue for DNI and nearly does for DIF using the RRTMG scheme. The RRTMG has also proven to have high potential skill for long-term assessment of clear-sky GHI, DNI, and DIF irradiance, as long as reliable aerosol inputs are provided. Considering its simplicity, the Dudhia scheme has also shown a remarkable ability for solar resource assessment in clear-sky conditions.

This paper presents a new approach for the joint energy and reserves scheduling and unit commitment with n-K reliability constraints for the day-ahead market. The proposed method includes a novel n-K criterion where demand must be met with a specified probability under any simultaneous loss of K generating units. A chance-constrained method is proposed with an α-quantile measure to determine the confidence level to meet the demand under K simultaneous contingencies. The chance-constrained optimization problem is recast as a mixed integer linear programming optimization problem. Wind and demand uncertainty are included into the model. The methodology proposed is illustrated with several case studies where the effect of increasing wind power penetration is analyzed showing the performance of our model.

This article combines and discusses three independent validations of global horizontal irradiance (GHI) multi-day forecast models that were conducted in the US, Canada and Europe. All forecast models are based directly or indirectly on numerical weather prediction (NWP). Two models are common to the three validation efforts – the ECMWF global model and the GFS-driven WRF mesoscale model – and allow general observations: (1) the GFS-based WRF- model forecasts do not perform as well as global forecast-based approaches such as ECMWF and (2) the simple averaging of models’ output tends to perform better than individual models.

This paper reports on an evaluation of the relative roles of choice of parameterization scheme and terrain representation in the Weather Research and Forecasting (WRF) mesoscale model, in the context of a regional wind resource assessment. As a first step, 32 configurations using two different schemes for microphysics, cumulus, planetary boundary layer (PBL), or shortwave and longwave radiation were evaluated. In a second step, wind estimates that were obtained from various experiments with different spatial resolution (1, 3, and 9 km) were assessed. Estimates were tested against data from four stations, located in southern Spain, that provided hourly wind speed and direction data at 40 m above ground level. Results from the first analysis showed that wind speed standard deviation (STD) and bias values were mainly sensitive to the PBL parameterization selection, with STD differences up to 10% and bias differences between −15% and 10%. The second analysis showed a weak influence of spatial resolution on the STD values. On the other hand, the bias was found to be highly sensitive to model spatial resolution. The sign of the bias depended on terrain morphology and the spatial resolution, but absolute values tended to be much higher with coarser spatial resolution. Physical configuration was found to have little impact on wind direction distribution estimates. In addition, these estimates proved to be more sensitive to the ability of WRF to represent the terrain morphology around the station than to the model spatial resolution itself.

Like in the film Field of Dreams , the sentence “if you build it, he will come” also applies in power systems. In this sense, if a transmission planner suggests building some lines in anticipation of generation capacity investments, then it can induce generation companies to invest in a more socially efficient manner. In this paper, we solve for the optimal way of doing this anticipative power transmission planning. Inspired in the proactive transmission planning model proposed by Sauma and Oren (2006) we formulate a mixed integer linear programming optimization model that integrates transmission planning, generation investment, and market operation decisions and propose a methodology to solve for the optimal transmission expansion. Contrary to the proactive methodology proposed by Sauma and Oren (2006) , our model solves the optimal transmission expansion problem anticipating both generation investment and market clearing. We use the marginalist theory with production cost functions inversely related to the installed capacity in a perfectly competitive electricity market and we find all possible generation expansion pure Nash equilibria. We illustrate our results using 3-node and 4-node examples.

We present a three-level equilibrium model for the expansion of an electric network. The lower-level model represents the equilibrium of a pool-based market; the intermediate level represents the Nash equilibrium in generation capacity expansion, taking into account the outcomes on the spot market; and the upper-level model represents the anticipation of transmission expansion planning to the investment in generation capacity and the pool-based market equilibrium. The demand has been considered as exogenous and locational marginal prices are obtained as endogenous variables of the model. The three-level model is formulated as a mixed integer linear programming (MILP) problem. The model is applied to a realistic power system in Chile to illustrate the methodology and proper conclusions are reached.

Despite being clinically described 150 years ago, the mechanisms underlying amyotrophic lateral sclerosis (ALS) pathogenesis have not yet been fully understood. Studies in both animal models of ALS and human patients reveal a plethora of alterations such as increased glutamate-mediated excitotoxicity, redox stress, increased apoptosis, defective axonal transport, protein-misfolding events, mitochondrial impairment and sustained unregulated immune responses. Regardless of being sporadic or familiar ALS, the final outcome at the cellular level is the death of upper and lower motor neurons, and once diagnosed, ALS is typically lethal within the next 5 years. There are neither clear biomarkers nor therapeutic or disease-modifying treatments for ALS.Accumulating evidence supports the concept that epigenetic-driven modifications, including altered chromatin remodelling events, RNA editing and non-coding RNA molecules, might shed light into the pathogenic mechanisms underlying sporadic/familiar ALS onset and/or severity to facilitate the identification of effective therapies, early diagnosis and potentially early-stage therapeutic interventions to increase the survival outcome of ALS patients.

Traditionally, the accuracy of solar power forecasts has been measured in terms of classic metrics, such as root mean square error (RMSE) or mean absolute error (MAE), and it is widely accepted that the smaller the error, the greater the economic benefits. Nevertheless, this is not as straightforward as it may seem, because market conditions must be studied first. Relationships between magnitudes of deviations between forecast and actual production and market penalties that apply at each moment are crucial. In this study, we analyze various day-ahead production forecasts for a 1.86 MW photovoltaic plant considering different techniques and sets of inputs. A nRMSE of 22.54% was obtained for a Support Vector Regression model trained by numerical weather predictions (NWP). This model produced the most benefits. An annual forecasting value of 4788 with respect to a persistence model was obtained for trading in the Iberian (Spain and Portugal) day-ahead electricity market. Annual value added by the NWP service totaled 2801 and room for improvement regarding NWP variables rose to 3877. As a general trend, it was found that smaller errors (RMSE) generated higher incomes. For each 1 kW h improvement in RMSE, the annual value of forecasting increased 22.32. Nevertheless, some models that gave larger errors than others also brought greater benefits. Thus, market conditions must be considered to accurately evaluate model economic performance. © 2017 Elsevier Ltd

This study addresses the task of identifying optimum locations for solar and wind power plants so that the wind-plus- solar power generation meets certain conditions of efficiency and stability, thus allowing to overcome the downside that the natural variability of these renewable resources represents. The method was based on a simulated annealing algorithm and applied over the Iberian Peninsula, a region whose commitment to renewable energy is growing relatively fast. Obtained results are encouraging since a number of different sensitivity experiments support the spatio-temporal complementarity between solar and wind power, at least at the monthly time-scale, in this region.

20 The automatic and non-supervised detection of the planetary boundary layer height (zPBL) by means of lidar measurements was widely investigated during the last years. Despite the considerable advances achieved the experimental detection still present difficulties either because the PBL is stratified (typically, during night-time) either because advected aerosol layers are coupled to the PBL. The coupling uses to produce an overestimation of the zPBL. To improve the detection even in these complex atmospheric situations, we present a new algorithm, called POLARIS (PBL height estimatiOn based on Lidar 25 depolARISation). POLARIS applies the wavelet covariance transform (WCT) to the range corrected signal and to the perpendicular-to-parallel signal ratio (δ) profiles. Different candidates for zPBLare chosen and the attribution is done, based on the WCT applied to the RCS and the δ. We use two ChArMEx campaigns with lidar and microwave radiometer (MWR), conducted on 2012 and 2013, for the POLARIS’ adjustment and validation. POLARIS improves the zPBL detection thanks to the consideration of the relative changes in the depolarization capabilities of the aerosol particles in the lower part of the 30 atmospheric column. Taking the advantage of a proper determination of the zPBL determined by POLARIS and by MWR under Saharan dust events, we compare the POLARIS and MWR zPBL with the zPBL provided by the Weather Research and Forecasting (WRF) numerical weather prediction model. WRF underestimates the zPBL during daytime but agrees with the MWR during night-time. The zPBL provided by WRF showed a better temporal evolution during daytime than during nighttime. 35 Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2016-718, 2016 Manuscript under review for journal Atmos. Chem. Phys. Published: 2 November 2016 c © Author(s) 2016. CC-BY 3.0 License.

Dysregulated microglial responses are central in neurodegenerative proteinopathies, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar disease (FTLD). Pathologic TDP-43, which is typically found in intracellular inclusions, is a misfolding protein with emerging roles in ALS and FTLD. Recently, TDP-43 species have been found in extracellular fluids of patients; however, the overall implications of TDP-43-mediated signaling linked to neuroinflammation are poorly understood. Our work-the first, to our knowledge, to focus on innate immunity responses to TDP-43 aggregates-shows that such species are internalized by microglia and cause abnormal mobilization of endogenous TDP-43. Exposure to TDP-43 aggregates elicited not only IL-1β, but also NLRP3-dependent and noncanonical IL-18 processing. Moreover, we report a link between TDP-43 and neuronal loss via the apoptosis-independent emerging roles of caspase-3 in neurotoxic inflammation. Our results further support the view of noncell autonomous neurodegenerative mechanisms in ALS. Remarkably, we demonstrate that TDP-43 aggregates bind to and colocalize with MAPK/MAK/MRK overlapping kinase (MOK) and show that its phosphorylation status is disrupted. Finally, we show that this TDP-43-caused activation state can be altered by exogenous Hsp27 and Hsp70 chaperones. Our study provides new insight into the immune phenotype, mechanisms, and signaling pathways that operate in microglial neurotoxic activation in ALS.-Leal-Lasarte, M. M., Franco, J. M., Labrador-Garrido, A., Pozo, D., Roodveldt, C. Extracellular TDP-43 aggregates target MAPK/MAK/MRK overlapping kinase (MOK) and trigger caspase-3/IL-18 signaling in microglia.