Estudio en laboratorio sobre licuefacción de arena parcialmente saturada

This experimental study was designed to assess the effects of soil water saturation on the liquefaction of Hostun RF sand. Cyclic undrained triaxial tests were conducted at different soil saturation levels, as given by Skempton’s coefficient, and liquefaction potential curves constructed for each value of this coefficient. Our findings indicate that a lower soil saturation level results in the increased resistance of the sand to liquefaction, in agreement with the tendency observed in other sands. In addition, the variation in sand resistance to liquefaction produced with Skempton’s coefficient was found to be consistent with the semi-empirical relation proposed by Yang et al. (2004).Este estudio experimental fue diseñado para comprobar los efectos de la saturación de agua en suelos bajo la licuefacción de arena RF Hostun. Tests cíclicos de tipo triaxial no drenado fueron elaborados a diferentes niveles de saturación del suelo, como se obtiene por el coeficiente de Skempton, y se obtuvieron curvas de potencial de licuefacción para cada uno de los valores de este coeficiente. Nuestros resultados indican que un nivel de saturación bajo de suelo durante el incremento de la resistencia de la arena a la licuefacción, estando de acuerdo con la tendencia observada en otras arenas. Por otro lado, se observó que la variación de la resistencia de las arenas a la licuefacción producida mediante el coeficiente de Skempton es consistente con la relación semiempírica propuesta por Yang et al. (2004)

Lagrangian and ALE Formulations For Soil Structure Coupling with Explosive Detonation

Simulation of Soil-Structure Interaction becomes more and more the focus of computational engineering in civil and mechanical engineering, where FEM (Finite element Methods) for soil and structural mechanics and Finite Volume for CFD (Computational Fluid Dynamics) are dominant. New advanced formulations have been developed for FSI (Fluid Structure Interaction) applications using ALE (Arbitrary Lagrangian Eulerian), mesh free and SPH (Smooth Particle Hydrodynamic) methods. In defence industry, engineers have been developing protection systems for many years to reduce the vulnerability of light armoured vehicles (LAV) against mine blast using classical Lagrangian FEM methods. To improve simulations and assist in the development of these protections, experimental tests and new numerical techniques are performed. Initial conditions such as the loading prescribed by a mine on a structure should be simulated adequately in order to conduct these numerical calculations. The effects of blast on structures often depend on how the initial conditions are estimated and applied. This article uses two methods to simulate a mine blast, namely the classical Lagrangian as well as the ALE formulations. The comparison was carried out for a simple and also a more complex target. Particle methods as SPH method can also be used for soil structure interaction

Influence of cracks on the soil-atmosphere interaction: numerical coupled model of thermo- atmosphereporous media

Soil shrinks as it desiccates, and the magnitude of shrinkage can be large for clayey soils. The drying of soil leads to cracks formation, causing high suctions to develop within. Cracks expose the deep soil and more evaporation can be expected in dry periods. To illustrate the effect of cracking, a numerical model of soil-atmosphere interaction has been developed taking into account the thermo-fluid coupling of an unsaturated clay soil. The model is used to simulate the evolution of evaporation during the drying process. The main results show a significant influence of the presence of cracks on the evaporation. This study also offers a simple method for taking into account the presence of cracks in the soil-atmosphere exchange

Multiple Linear Regression and Machine Learning for Predicting the Drinking Water Quality Index in Al-Seine Lake

This is the final version. Available on open access from MDPI via the DOI in this recordData Availability Statement: The data sets are available from the corresponding author on reasonable request.Ensuring safe and clean drinking water for communities is crucial, and necessitates effective tools to monitor and predict water quality due to challenges from population growth, industrial activities, and environmental pollution. This paper evaluates the performance of multiple linear regression (MLR) and nineteen machine learning (ML) models, including algorithms based on regression, decision tree, and boosting. Models include linear regression (LR), least angle regression (LAR), Bayesian ridge chain (BR), ridge regression (Ridge), k-nearest neighbor regression (K-NN), extra tree regression (ET), and extreme gradient boosting (XGBoost). The research’s objective is to estimate the surface water quality of Al-Seine Lake in Lattakia governorate using the MLR and ML models. We used water quality data from the drinking water lake of Lattakia City, Syria, during years 2021–2022 to determine the water quality index (WQI). The predictive performance of both the MLR and ML models was evaluated using statistical methods such as the coefficient of determination (R2) and the root mean square error (RMSE) to estimate their efficiency. The results indicated that the MLR model and three of the ML models, namely linear regression (LR), least angle regression (LAR), and Bayesian ridge chain (BR), performed well in predicting the WQI. The MLR model had an R2 of 0.999 and an RMSE of 0.149, while the three ML models had an R2 of 1.0 and an RMSE of approximately 0.0. These results support using both MLR and ML models for predicting the WQI with very high accuracy, which will contribute to improving water quality management

GEPAS, a web-based tool for microarray data analysis and interpretation

Gene Expression Profile Analysis Suite (GEPAS) is one of the most complete and extensively used web-based packages for microarray data analysis. During its more than 5 years of activity it has continuously been updated to keep pace with the state-of-the-art in the changing microarray data analysis arena. GEPAS offers diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and functional profiling of the experiment. New options for time-course (or dose-response) experiments, microarray-based class prediction, new clustering methods and new tests for differential expression have been included. The new pipeliner module allows automating the execution of sequential analysis steps by means of a simple but powerful graphic interface. An extensive re-engineering of GEPAS has been carried out which includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. GEPAS is nowadays the most quoted web tool in its field and it is extensively used by researchers of many countries and its records indicate an average usage rate of 500 experiments per day. GEPAS, is available at http://www.gepas.org

Antifungal activity of olive cake extracts

Powdered, dried olive (Olea europaea) cake was extracted with hexane, methanol and butanol. Six phenolic compounds, coumaric acid, ferulic acid, oleuropein, caffeic acid, protocatechuic acid and cinnamic acid, were isolated from these extracts after fractionation. The fractions were tested for their antifungal activity against Verticillium sp., Fusarium oxysporum, Rhizopus sp., Penicillium italicum, Rhizoctonia solani, Stemphylium solani, Cladosporium sp., Mucor sp., Colletotrichum sp. and Pythium sp. Strongest activity was reported against Fusarium oxysporum and Verticillium sp. No effect was observed against Alternaria sp

A large scale survey reveals that chromosomal copy-number alterations significantly affect gene modules involved in cancer initiation and progression

Background Recent observations point towards the existence of a large number of neighborhoods composed of functionally-related gene modules that lie together in the genome. This local component in the distribution of the functionality across chromosomes is probably affecting the own chromosomal architecture by limiting the possibilities in which genes can be arranged and distributed across the genome. As a direct consequence of this fact it is therefore presumable that diseases such as cancer, harboring DNA copy number alterations (CNAs), will have a symptomatology strongly dependent on modules of functionally-related genes rather than on a unique "important" gene. Methods We carried out a systematic analysis of more than 140,000 observations of CNAs in cancers and searched by enrichments in gene functional modules associated to high frequencies of loss or gains. Results The analysis of CNAs in cancers clearly demonstrates the existence of a significant pattern of loss of gene modules functionally related to cancer initiation and progression along with the amplification of modules of genes related to unspecific defense against xenobiotics (probably chemotherapeutical agents). With the extension of this analysis to an Array-CGH dataset (glioblastomas) from The Cancer Genome Atlas we demonstrate the validity of this approach to investigate the functional impact of CNAs. Conclusions The presented results indicate promising clinical and therapeutic implications. Our findings also directly point out to the necessity of adopting a function-centric, rather a gene-centric, view in the understanding of phenotypes or diseases harboring CNAs.Spanish Ministry of Science and Innovation (grant BIO2008-04212)Spanish Ministry of Science and Innovation (grant FIS PI 08/0440)GVA-FEDER (PROMETEO/2010/001)Red Temática de Investigación Cooperativa en Cáncer (RTICC) (grant RD06/0020/1019)Instituto de Salud Carlos III (ISCIII)Spanish Ministry of Science and InnovationSpanish Ministry of Health (FI06/00027

Gene set enrichment analysis of microarray data from Pimephales promelas (Rafinesque), a non-mammalian model organism

<p>Abstract</p> <p>Background</p> <p>Methods for gene-class testing, such as Gene Set Enrichment Analysis (GSEA), incorporate biological knowledge into the analysis and interpretation of microarray data by comparing gene expression patterns to pathways, systems and emergent phenotypes. However, to use GSEA to its full capability with non-mammalian model organisms, a microarray platform must be annotated with human gene symbols. Doing so enables the ability to relate a model organism's gene expression, in response to a given treatment, to potential human health consequences of that treatment. We enhanced the annotation of a microarray platform from a non-mammalian model organism, and then used the GSEA approach in a reanalysis of a study examining the biological significance of acute and chronic methylmercury exposure on liver tissue of fathead minnow (<it>Pimephales promelas</it>). Using GSEA, we tested the hypothesis that fathead livers, in response to methylmercury exposure, would exhibit gene expression patterns similar to diseased human livers.</p> <p>Results</p> <p>We describe an enhanced annotation of the fathead minnow microarray platform with human gene symbols. This resource is now compatible with the GSEA approach for gene-class testing. We confirmed that GSEA, using this enhanced microarray platform, is able to recover results consistent with a previous analysis of fathead minnow exposure to methylmercury using standard analytical approaches. Using GSEA to compare fathead gene expression profiles to human phenotypes, we also found that fathead methylmercury-treated livers exhibited expression profiles that are homologous to human systems & pathways and results in damage that is similar to those of human liver damage associated with hepatocellular carcinoma and hepatitis B.</p> <p>Conclusions</p> <p>This study describes a powerful resource for enabling the use of non-mammalian model organisms in the study of human health significance. Results of microarray gene expression studies involving fathead minnow, typically used for aquatic ecological toxicology studies, can now be used to generate hypotheses regarding consequences of contaminants and other stressors on humans. The same approach can be used with other model organisms with microarray platforms annotated in a similar manner.</p

S.cerevisiae Complex Function Prediction with Modular Multi-Relational Framework

Proceeding of: 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Córdoba, Spain, June 1-4, 2010Determining the functions of genes is essential for understanding how the metabolisms work, and for trying to solve their malfunctions. Genes usually work in groups rather than isolated, so functions should be assigned to gene groups and not to individual genes. Moreover, the genetic knowledge has many relations and is very frequently changeable. Thus, a propositional ad-hoc approach is not appropriate to deal with the gene group function prediction domain. We propose the Modular Multi-Relational Framework (MMRF), which faces the problem from a relational and flexible point of view. The MMRF consists of several modules covering all involved domain tasks (grouping, representing and learning using computational prediction techniques). A specific application is described, including a relational representation language, where each module of MMRF is individually instantiated and refined for obtaining a prediction under specific given conditions.This research work has been supported by CICYT, TRA 2007-67374-C02-02 project and by the expert biological knowledge of the Structural Computational Biology Group in Spanish National Cancer Research Centre (CNIO). The authors would like to thank members of Tilde tool developer group in K.U.Leuven for providing their help and many useful suggestions.Publicad

Selection upon Genome Architecture: Conservation of Functional Neighborhoods with Changing Genes

An increasing number of evidences show that genes are not distributed randomly across eukaryotic chromosomes, but rather in functional neighborhoods. Nevertheless, the driving force that originated and maintains such neighborhoods is still a matter of controversy. We present the first detailed multispecies cartography of genome regions enriched in genes with related functions and study the evolutionary implications of such clustering. Our results indicate that the chromosomes of higher eukaryotic genomes contain up to 12% of genes arranged in functional neighborhoods, with a high level of gene co-expression, which are consistently distributed in phylogenies. Unexpectedly, neighborhoods with homologous functions are formed by different (non-orthologous) genes in different species. Actually, instead of being conserved, functional neighborhoods present a higher degree of synteny breaks than the genome average. This scenario is compatible with the existence of selective pressures optimizing the coordinated transcription of blocks of functionally related genes. If these neighborhoods were broken by chromosomal rearrangements, selection would favor further rearrangements reconstructing other neighborhoods of similar function. The picture arising from this study is a dynamic genomic landscape with a high level of functional organization