Angiotensin Type 1 Receptor Antagonists Protect Against Alpha-Synuclein-Induced Neuroinflammation and Dopaminergic Neuron Death

Altres ajuts: This study received funding from the Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas' intramural program (2014/01 and 2017/02), Galician Government (Xunta de Galicia, Consellería de Educación; GRC2014/002), Navarra Government (Departamento de Salud; 046-2017), and Fondo Europeo de Desarrollo Regional (Regional European Development Fund).The loss of dopaminergic neurons and α-synuclein accumulation are major hallmarks of Parkinson's disease (PD), and it has been suggested that a major mechanism of α-synuclein toxicity is microglial activation. The lack of animal models that properly reproduce PD, and particularly the underlying synucleinopathy, has hampered the clarification of PD mechanisms and the development of effective therapies. Here, we used neurospecific adeno-associated viral vectors serotype 9 coding for either the wild-type or mutated forms of human alpha-synuclein (WT and SynA53T, respectively) under the control of a synapsin promoter to further induce a marked dopaminergic neuron loss together with an important microglial neuroinflammatory response. Overexpression of neuronal alpha-synuclein led to increased expression of angiotensin type 1 receptors and NADPH oxidase activity, together with a marked increase in the number of OX-6-positive microglial cells and expression of markers of phagocytic activity (CD68) and classical pro-inflammatory/M1 microglial phenotype markers such as inducible nitric oxide synthase, tumor necrosis factor alpha, interleukin-1β, and IL-6. Moreover, a significant decrease in the expression of markers of immunoregulatory/M2 microglial phenotype such as the enzyme arginase-1 was constantly observed. Interestingly, alpha-synuclein-induced changes in microglial phenotype markers and dopaminergic neuron death were inhibited by simultaneous treatment with the angiotensin type 1 blockers candesartan or telmisartan. Our results suggest the repurposing of candesartan and telmisartan as a neuroprotective strategy for PD

Unsupervised method to classify PM10 pollutant concentrations

In this paper a method based mainly on Data Fusion and Artificial Neural Networks to classify one of the most important pollutants such as Particulate Matter less than 10 micrometer in diameter (PM10) concentrations is proposed. The main objective is to classify in two pollution levels (Non-Contingency and Contingency) the pollutant concentration. Pollutant concentrations and meteorological variables have been considered in order to build a Representative Vector (RV) of pollution. RV is used to train an Artificial Neural Network in order to classify pollutant events determined by meteorological variables. In the experiments, real time series gathered from the Automatic Environmental Monitoring Network (AEMN) in Salamanca Guanajuato Mexico have been used. The method can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction

Design and characterization of immersion ultrasonic transducers for pulsed regime applications

Ultrasonic transducer design is focused to maximize performance in specific applications, usually leading to a complex design and expensive construction and assembly. With the aim to overcome this drawback, a general-purpose immersion ultrasonic transducer for pulsed regime applications have been developed. The design of each element of the transducer is described in this paper, wherein materials and geometries for each part have been recommended. A simple theoretical model have been proposed in order to predict the form of the received electric signal in the target transducer. The model is based in the assumption that the piezoelectric element acts as an underdamped oscillator, forced by the acoustic field coming from the propagation medium. Excellent agreement between the experimental measurements and the analytical model is achieved. Electrical impedance measurements reveal negligible differences between the resonance frequency of the active element and that of the assembled transducer. The designed devices have been characterized in water, using two identical transducers placed face to face with changeable orientation. Experimental results show a highly linear response and the generation of a collimated acoustic field. The effects of the thickness of the matching layer on the transmission coefficient have been also studied, resulting in a smooth decrease in the received amplitude, which may significantly lower large-scale production costs.Peer ReviewedPostprint (published version

Optimization of low-efficiency traffic in OpenFlow Software Defined Networks

Abstract — This paper proposes a method for optimizing bandwidth usage in Software Defined Networks (SDNs) based on OpenFlow. Flows of small packets presenting a high overhead, as the ones generated by emerging services, can be identified by the SDN controller, in order to remove header fields that are common to any packet in the flow, only during their way through the SDN. At the same time, several packets can be multiplexed together in the same frame, thus reducing the number of sent frames. Four kinds of small-packet traffic flows are considered (VoIP, UDP and TCP-based online games, and ACKs from TCP flows). Both IPv4 and IPv6 are tested, and significant bandwidth savings (up to 68 % for IPv4 and 78 % for IPv6) can be obtained for the considered kinds of traffic

Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America

The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.EEA SaltaFil: Graesser, Jordan. Boston University. Department of Earth and Environment; Estados UnidosFil: Stanimirova, Radost. Boston University. Department of Earth and Environment; Estados UnidosFil: Tarrio, Katelyn. Boston University. Department of Earth and Environment; Estados UnidosFil: Copati, Esteban J. Bolsa de Cereales (Buenos Aires); ArgentinaFil: Volante, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Verón, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Elena, Hernan Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Friedl, Mark A. Boston University. Department of Earth and Environment; Estados Unido