Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. 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 improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.We thank 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

Improving drug discovery using hybrid softcomputing methods

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 in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, 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 and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.We thank 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

Análisis, diseño y desarrollo de una aplicación para la realización automática de pentesting

El objetivo del trabajo final de máster es el análisis, diseño y desarrollo de una aplicación que permita la automatización de algunas de las etapas de las auditorías técnicas de seguridad o pentesting. El sistema recibirá como entrada información relativa al objetivo, por ejemplo, el dominio de la infraestructura, usuarios objetivos, etc. La aplicación empleará técnicas de auditoría activas y/o pasivas, con el fin de implementar de manera automática distintas fases del proceso de pentesting

Drug solubility prediction with support vector machines on graphic processor units

En este trabajo se emplean métodos de inteligencia computacional, tales como las máquinas de soporte vectorial (MSV) para optimizar la predicción de la solubilidad de compuestos. Estas se entrenan con una base de datos de compuestos solubles e insolubles conocidos, y dicha información es posteriormente empleada para mejorar la predicción obtenida mediante cribado virtual. Los grandes avances en el campo de la computación de alto rendimiento ofrecen nuevas oportunidades en la simulación de sistemas biológicos y aplicaciones en bioinformática, biología computacional y química computacional. El uso de bases de datos de mayor tamaño aumenta las posibilidades en la generación de candidatos potenciales, pero el tiempo de cálculo necesario no sólo aumenta con el tamaño de la base de datos, sino también con la exactitud de los métodos de cribado virtual (CV) y del modelo. Se discuten los beneficios del uso de arquitecturas masivamente paralelas, en particular las unidades de procesamientos gráfico, demostrando empíricamente que están bien adaptadas para la aceleración de las MSV, obteniendo una aceleración de hasta 45 veces, en comparación con su versión secuencial.In this work we discuss the benefits of using computational intelligence methods, like Support Vector Machines (SVM) for the optimization of the prediction of compounds solubility. SVMs are trained with a database of known soluble and insoluble compounds, and this information is being exploited afterwards to improve Virtual Screening (VS) prediction. The landscape in the high performance computing arena opens up great opportunities in the simulation of relevant biological systems and for applications in bioinformatics, computational biology and computational chemistry. Larger databases increase the chances of generating hits or leads, but the computational time needed for the calculations increases not only with the size of the database but also with the accuracy of the VS methods and the model. We discussed the benefits of using massively parallel architectures, in particular graphics processing units. We empirically demonstrate that GPUs are well-suited architecture for the acceleration of SVM, obtaining up to 15 times sustained speedup compared to its sequential counterpart version.Este trabajo ha sido parcialmente financiado por los proyectos: NILS Mobility Project 012-ABEL-CM-2014A y Fundación Séneca 18946/JLI/13

Analysis of urban parameters in the characterization of seismic vulnerability: application to the city of Lorca.

Estimación de la intensidad tras el terremoto de Lorca

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

CD45 expression discriminates waves of embryonic megakaryocytes in the mouse

Embryonic megakaryopoiesis starts in the yolk sac on gestational day 7.5 as part of the primitive wave of hematopoiesis, and it continues in the fetal liver when this organ is colonized by hematopoietic progenitors between day 9.5 and 10.5, as the definitive hematopoiesis wave. We characterized the precise phenotype of embryo megakaryocytes in the liver at gestational day 11.5, identifying them as CD41++CD45-CD9++CD61+MPL+CD42c+ tetraploid cells that express megakaryocyte-specific transcripts and display differential traits when compared to those present in the yolk sac at the same age. In contrast to megakaryocytes from adult bone marrow, embryo megakaryocytes are CD45- until day 13.5 of gestation, as are both the megakaryocyte progenitors and megakaryocyte/erythroid-committed progenitors. At gestational day 11.5, liver and yolk sac also contain CD41+CD45+ and CD41+CD45- cells. These populations, and that of CD41++CD45-CD42c+ cells, isolated from liver, differentiate in culture into CD41++CD45-CD42c+ proplatelet-bearing megakaryocytes. Also present at this time are CD41-CD45++CD11b+ cells, which produce low numbers of CD41++CD45-CD42c+ megakaryocytes in vitro, as do fetal liver cells expressing the macrophage-specific Csf receptor-1 (Csf1r/CD115) from MaFIA transgenic mice, which give rise poorly to CD41++CD45-CD42c+ embryo megakaryocytes both in vivo and in vitro In contrast, around 30% of adult megakaryocytes (CD41++CD45++CD9++CD42c+) from C57BL/6 and MaFIA mice express CD115. We propose that differential pathways operating in the mouse embryo liver at gestational day 11.5 beget CD41++CD45-CD42c+ embryo megakaryocytes that can be produced from CD41+CD45- or from CD41+CD45+ cells, at difference from those from bone marrow.This work was supported by grants from the Ministerio de Ciencia e Innovacion (MICINN SAF2009-12596) and from the Ministerio de Economia y Competitividad (MINECO SAF2012-33916 and SAF2015-70880-R MINECO/FEDER). NS was the recipient of a fellowship from the Centro de Biologia Molecular Severo Ochoa (CBMSO) and IC received a fellowship from the MICINN. The CBMSO receives institutional funding from Fundacion Ramon Areces. The CNIC is supported by the MEIC and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505).S

Carbon redistribution by erosion processes in an intensively disturbed catchment

29 Pags.- 6 Tabls.- 6 Figs. This article belongs to a special issue of Catena titled "Geoecology in Mediterranean mountain areas. Tribute to Professor José María García Ruiz". The definitive version is available at: http://www.sciencedirect.com/science/journal/03418162Understanding how organic carbon (OC) moves with sediments along the fluvial system is crucial to determining catchment scale carbon budgets and helps the proper management of fragile ecosystems. Especially challenging is the analysis of OC dynamics during fluvial transport in heterogeneous, fragile, and disturbed environments with ephemeral and intense hydrological pulses, typical of Mediterranean conditions. This paper explores the catchment scale OC redistribution by lateral flows in extreme Mediterranean environmental conditions, from a geomorphological perspective. The study area is a catchment (Cárcavo) in SE Spain with a semiarid climate, erodible lithologies, and shallow soils, which is highly disturbed by agricultural terraces, land levelling, reforestation, and construction of check-dams. To increase our understanding of catchment scale OC redistribution induced by erosion, we studied in detail the subcatchments of eight check-dams distributed along the catchments main channel. We determined 137Cs, physicochemical characteristics, and the OC pools of the catchment soils and sediments deposited behind each check-dam, performed spatial analysis of catchment properties and buffer areas around the check-dams, and carried out geomorphological analysis of the slope-channel connections. The soils showed very low total organic carbon (TOC) values, oscillating between 15.2 and 4.4 g kg− 1 for forest and agricultural soils, respectively. Sediments mobilized by erosion were poor in TOC (6.6 ± 0.7 g kg– 1) compared to the eroded (forest) soils, and the redistribution of OC through the catchment, especially of the mineral associated organic carbon (MAC) pool, showed the same pattern as clay particles and 137Cs. The TOC erosion rates estimated for the Cárcavo watershed are relatively low (0.031 ± 0.03 Mg ha− 1 y− 1) but similar to those reported for subhumid Mediterranean catchments that are less fragile and more conducive to plant growth. The TOC erosion/total erosion ratio was lower (0.06%) than other estimates, although the average OC concentration of the sediments was higher than that of the agricultural soils of the catchment, underlining the problem of maintaining sustainable soil OC contents. The OC in deposited sediments came not only from surface erosion processes, but also from deeper soil or sediment layers mobilized by concentrated erosion processes. Sediment richer in OC came from the surface soil of vegetated (reforested) areas close and well connected to the channels. Subcatchments dominated by laminar erosion processes showed a TOC erosion/total erosion ratio that was two times higher than that of subcatchments dominated by concentrated flow erosion processes. The lithology, soils, and geomorphology exert a more important control on OC redistribution than land use and vegetation cover in this geomorphologically very active catchment.This work was financially supported by the projects ADAPT (CGL2013-42009-R) and DISECO (CGL2014-55-405-R) from the Spanish Government, National Plan of Science; the project CAMBIO (18933/JLI/13) of the Seneca Foundation, Regional Government of Murcia (Spain); and the project SOGLO (P7/24 IAP BELSPO) from the Belgian Government. Joris de Vente was supported by a ‘Ramón y Cajal’ grant (RYC-2012-10375).Peer reviewe

CO2 Concentration in Day Care Centres is Related to Wheezing in Attending Children

Poor ventilation at day care centres (DCCs) was already reported, although its effects on attending children are not clear. This study aimed to evaluate the association between wheezing in children and indoor CO2 (a ventilation surrogate marker) in DCC and to identify behaviours and building characteristics potentially related to CO2. In phase I, 45 DCCs from Lisbon and Oporto (Portugal) were selected through a proportional stratified random sampling. In phase II, 3 months later, 19 DCCs were further reassessed after cluster analysis for the greatest difference comparison. In both phases, children’s respiratory health was assessed by ISAAC-derived questionnaires. Indoor CO2 concentrations and building characteristics of the DCC were evaluated in both phases, using complementary methods. Mixed effect models were used to analyze the data. In phase I, which included 3,186 children (mean age 3.1±1.5 years), indoor CO2 concentration in the DCC rooms was associated with reported wheezing in the past 12months (27.5 %) (adjusted odds ratio (OR) for each increase of 200 ppm 1.04, 95 % CI 1:01 to 1:07). In phase II, the association in the subsample of 1,196 children seen in 19 out of the initial 45 DCCs was not significant (adjusted OR 1.02, 95 % CI 0.96 to 1.08). Indoor CO2 concentration was inversely associated with the practices of opening Windows and internal doors and with higher wind velocity. A positive trend was observed between CO2 and prevalence of reported asthma (4.7 %). Conclusion: Improved ventilation is needed to achieve a healthier indoor environment in DCC