Experimental investigation of a thermal storage system using phase change materials

A home-made heat exchanger (HE), used in the evaluation of the performance of different phase change materials (PCMs), was designed, mounted and operated. The HE unit was used as a heat thermal storage system for recovering the residual energy coming from a hydrogen cycle, which could be in turn used in building air-conditioning facilities. Four PCMs (Rubitherm® RT28, Rubitherm® RT48, Rubitherm® RT55 and Mikrocaps PCM35; the latter supplied as a slurry of microcapsules) was selected for their suitable thermal properties. Water was used as the heat transfer fluid (HTF) while the PCM was tightly stored inside the shell. Among all the studied PCMs, Rubitherm® RT48 presented the best thermal performance since it accumulated the maximum amount of energy. The influence of the HTF flow rate on the thermal performance of the shell and tube HE was also evaluated. Low HTF flow rates led to high values of heat transferred. Finally, different operation modes (watertight and countercurrent PCM flow) were compared by using Mikrocaps PCM35. PCM countercurrent flow system showed to be the best experimental set-up configuration system for energy transfer, reaching values of heat accumulation about 71% higher than that shown by the watertight mode.Se diseñó, montó y operó un intercambiador de calor casero (HE), utilizado en la evaluación del desempeño de diferentes materiales de cambio de fase (PCM). La unidad HE se utilizó como sistema de almacenamiento térmico de calor para recuperar la energía residual procedente de un ciclo de hidrógeno, que a su vez podría utilizarse en la construcción de instalaciones de aire acondicionado. Se seleccionaron cuatro PCM (Rubitherm® RT28, Rubitherm® RT48, Rubitherm® RT55 y Mikrocaps PCM35; este último suministrado como una suspensión de microcápsulas) por sus propiedades térmicas adecuadas. Se usó agua como fluido de transferencia de calor (HTF) mientras que el PCM se almacenó herméticamente dentro de la carcasa. Entre todos los PCM estudiados, Rubitherm® RT48 presentó el mejor desempeño térmico ya que acumuló la máxima cantidad de energía. También se evaluó la influencia del caudal de HTF en el rendimiento térmico de la carcasa y el tubo HE. Las bajas tasas de flujo de HTF condujeron a altos valores de transferencia de calor. Finalmente, se compararon diferentes modos de operación (flujo PCM estanco y contracorriente) utilizando Mikrocaps PCM35. El sistema de flujo a contracorriente PCM demostró ser el mejor sistema de configuración experimental para la transferencia de energía, alcanzando valores de acumulación de calor un 71% superiores a los mostrados por el modo estanco

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.This work has been jointly supported by the Fundación Séneca (Agencia Regional de Ciencia y Tecnología de la Región de Murcia) under grant 15290/PI/2010, by the Spanish MINECO and the European Commission FEDER funds under grants TIN2009-14475-C04 and TIN2012-31345, and by the Catholic University of Murcia (UCAM) under grant PMAFI/26/12. This work was partially supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain

Matrix metalloproteinase-10 effectively reduces infarct size in experimental stroke by enhancing fibrinolysis via a thrombin-activatable fibrinolysis inhibitor-mediated mechanism

BACKGROUND: The fibrinolytic and matrix metalloproteinase (MMP) systems cooperate in thrombus dissolution and extracellular matrix proteolysis. The plasminogen/plasmin system activates MMPs, and some MMPs have been involved in the dissolution of fibrin by targeting fibrin(ogen) directly or by collaborating with plasmin. MMP-10 has been implicated in inflammatory/thrombotic processes and vascular integrity, but whether MMP-10 could have a profibrinolytic effect and represent a promising thrombolytic agent is unknown. METHODS AND RESULTS: The effect of MMP-10 on fibrinolysis was studied in vitro and in vivo, in MMP-10-null mice (Mmp10(-/-)), with the use of 2 different murine models of arterial thrombosis: laser-induced carotid injury and ischemic stroke. In vitro, we showed that MMP-10 was capable of enhancing tissue plasminogen activator-induced fibrinolysis via a thrombin-activatable fibrinolysis inhibitor inactivation-mediated mechanism. In vivo, delayed fibrinolysis observed after photochemical carotid injury in Mmp10(-/-) mice was reversed by active recombinant human MMP-10. In a thrombin-induced stroke model, the reperfusion and the infarct size in sham or tissue plasminogen activator-treated animals were severely impaired in Mmp10(-/-) mice. In this model, administration of active MMP-10 to wild-type animals significantly reduced blood reperfusion time and infarct size to the same extent as tissue plasminogen activator and was associated with shorter bleeding time and no intracranial hemorrhage. This effect was not observed in thrombin-activatable fibrinolysis inhibitor-deficient mice, suggesting thrombin-activatable fibrinolysis inhibitor inactivation as one of the mechanisms involved in the MMP-10 profibrinolytic effect. CONCLUSIONS: A novel profibrinolytic role for MMP-10 in experimental ischemic stroke is described, opening new pathways for innovative fibrinolytic strategies in arterial thrombosis

Training memory without aversion: Appetitive hole-board spatial learning increases adult hippocampal neurogenesis.

Learning experiences are potent modulators of adult hippocampal neurogenesis (AHN). However, the vast majority of findings on the learning-induced regulation of AHN derive from aversively-motivated tasks, mainly the water maze paradigm, in which stress is a confounding factor that affects the AHN outcome. Currently, little is known regarding the effect of appetitively-motivated training on AHN. Hence we studied how spatial learning to find food rewards in a hole-board maze modulates AHN (cell proliferation and immature neurons) and AHN-related hippocampal neuroplasticity markers (BDNF, IGF-II and CREB phosphorylation) in mice. The 'Trained' mice were tested for both spatial reference and working memory and compared to 'Pseudotrained' mice (exposed to different baited holes in each session, thus avoiding the reference memory component of the task) and 'Control' mice (exposed to the maze without rewards). In contrast to Pseudotrained and Control mice, Trained mice reduced the number of proliferating hippocampal cells but they notably increased their population of immature neurons assessed by immunohistochemistry. This evidence shows that hole-board spatial reference learning diminishes cell proliferation in favor of enhancing young neurons' survival. Interestingly, the enhanced AHN in the Trained mice (specifically in the suprapyramidal blade) positively correlated with their reference memory performance, but not with their working memory. Furthermore, the Trained animals increased the hippocampal protein expression of all the neuroplasticity markers analyzed by western blot. Results show that the appetitively-motivated hole-board task is an useful paradigm to potentiate and/or investigate AHN and hippocampal plasticity minimizing aversive variables such as fear or stress.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This study was funded by grants from the Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigación) co-funded by the European Research Development Fund -AEI/FEDER, UE- (PSI2015-73156-JIN ‘Jóvenes Investigadores grant’ to E.C.O. and PSI2013-44901-P to L.J.S. and C.P.), from ‘Junta de Andalucía’ SEJ1863 to C.P. and from University of Málaga (Plan Propio 2017 – ‘Ayudas para proyectos puente’) to M.G.F. Author P.S.P. holds a ‘Juan de la Cierva-formación‘grant from the Spanish Ministry of Economy, Industry and Competitiveness (code: FJCI-2015-23925) and a ‘D.3. Estancia de investigadores de reconocido prestigio en la UMA‘ grant from the University of Málaga. Authors R.D.M.F. and D.L.G.M. hold ‘FPU’ grants from the Spanish Ministry of Education, Culture and Sports (code: FPU14-01610 and FPU13/04819, respectively). Author F.J.P. holds a ‘Miguel Servet’ grant (code: CP14/00212) from the National System of Health-Instituto de Salud Carlos-III co-funded by FEDER, UE

Can Genetic Programming Do Manifold Learning Too?

Exploratory data analysis is a fundamental aspect of knowledge discovery that aims to find the main characteristics of a dataset. Dimensionality reduction, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, we propose a genetic programming approach to manifold learning called GP-MaL which evolves functional mappings from a high-dimensional space to a lower dimensional space through the use of interpretable trees. We show that GP-MaL is competitive with existing manifold learning algorithms, while producing models that can be interpreted and re-used on unseen data. A number of promising future directions of research are found in the process.Comment: 16 pages, accepted in EuroGP '1

Refractive index sensing setup based on a taper and an intrinsic micro Fabry-Perot interferometer

In this work, a refractive index sensor setup based on a biconically tapered fiber (BTF) concatenated to an intrinsic all-fiber micro Fabry-Perot interferometer (MFPI) is presented. Here, the power of the MFPI spectral fringes decreases as the refractive index interacts with theevanescent field of the BTF segment. Furthermore, it is demonstrated that the RI sensitivity can be enhanced by bending the BTF segment.Finally, it is shown that by using this sensing arrangement, at ~1.53 µm wavelength, it is possible to detect refractive index changeswithin the measurement range of 1.3 to 1.7 RIU, with a sensitivity of 39.92 dB/RIU and a RI resolution of 2.5 x 10^-3 RIU with a curvature of C = 18.02 m^-1