Experimental Method to Determine Some Physical Properties in Physics Classes

ABSTRACT Particle density, gravimetric and volumetric water contents and porosity are important basic concepts to characterize porous systems such as soils. This paper presents a proposal of an experimental method to measure these physical properties, applicable in experimental physics classes, in porous media samples consisting of spheres with the same diameter (monodisperse medium) and with different diameters (polydisperse medium). Soil samples are not used given the difficulty of working with this porous medium in laboratories dedicated to teaching basic experimental physics. The paper describes the method to be followed and results of two case studies, one in monodisperse medium and the other in polydisperse medium. The particle density results were very close to theoretical values for lead spheres, whose relative deviation (RD) was -2.9 % and +0.1 % RD for the iron spheres. The RD of porosity was also low: -3.6 % for lead spheres and -1.2 % for iron spheres, in the comparison of procedures – using particle and porous medium densities and saturated volumetric water content – and monodisperse and polydisperse media.

An adaptive multiscale method for density-driven instabilities

Accurate modeling of flow instabilities requires computational tools able to deal with several interacting scales, from the scale at which fingers are triggered up to the scale at which their effects need to be described. The Multiscale Finite Volume (MsFV) method offers a framework to couple fine-and coarse-scale features by solving a set of localized problems which are used both to define a coarse-scale problem and to reconstruct the fine-scale details of the flow. The MsFV method can be seen as an upscaling-downscaling technique, which is computationally more efficient than standard discretization schemes and more accurate than traditional upscaling techniques. We show that, although the method has proven accurate in modeling density-driven flow under stable conditions, the accuracy of the MsFV method deteriorates in case of unstable flow and an iterative scheme is required to control the localization error. To avoid large computational overhead due to the iterative scheme, we suggest several adaptive strategies both for flow and transport. In particular, the concentration gradient is used to identify a front region where instabilities are triggered and an accurate (iteratively improved) solution is required. Outside the front region the problem is upscaled and both flow and transport are solved only at the coarse scale. This adaptive strategy leads to very accurate solutions at roughly the same computational cost as the non-iterative MsFV method. In many circumstances, however, an accurate description of flow instabilities requires a refinement of the computational grid rather than a coarsening. For these problems, we propose a modified iterative MsFV, which can be used as downscaling method (DMsFV). Compared to other grid refinement techniques the DMsFV clearly separates the computational domain into refined and non-refined regions, which can be treated separately and matched later. This gives great flexibility to employ different physical descriptions in different regions, where different equations could be solved, offering an excellent framework to construct hybrid methods.

Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes

We present a heuristic method for learning error correcting output codes matrices based on a hierarchical partition of the class space that maximizes a discriminative criterion. To achieve this goal, the optimal codeword separation is sacrificed in favor of a maximum class discrimination in the partitions. The creation of the hierarchical partition set is performed using a binary tree. As a result, a compact matrix with high discrimination power is obtained. Our method is validated using the UCI database and applied to a real problem, the classification of traffic sign images.

Extrapolation theory for the real interpolation method

We develop an abstract extrapolation theory for the real interpolation method that covers and improves the most recent versions of the celebrated theorems of Yano and Zygmund. As a consequence of our method, we give new endpoint estimates of the embedding Sobolev theorem for an arbitrary domain Omega

The mean-variance model from the inverse of the variance-covariance matrix

En este artículo, a partir de la inversa de la matriz de varianzas y covarianzas se obtiene el modelo Esperanza-Varianza de Markowitz siguiendo un camino más corto y matemáticamente riguroso. También se obtiene la ecuación de equilibrio del CAPM de Sharpe.

High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Carriers of the fragile X mental retardation 1 (FMR1) premutation allele present with increased levels of cytokine IL-10.

BACKGROUND: Fragile X-associated tremor/ataxia syndrome (FXTAS) is an inherited late-onset neurodegenerative disorder, characterized both by neurological and cognitive deficits. It is caused by the expansion of CGG repeats (55 to 200 repeats) in the noncoding region of the fragile X mental retardation 1 (FMR1) gene. Abnormal immunological patterns are often associated with neurodegenerative disorders and implicated in their etiology. We therefore investigated the immune status of FXTAS patients, which had not been assessed prior to this study. METHOD: Peripheral blood mononuclear cells (PBMCs) were collected from 15 asymptomatic FMR1 premutation carriers and 20 age-matched controls. Concentrations of three cytokines (IL-6, IL-8, IL-10) were measured in PBMC supernatants using ELISA assays. RESULTS: We found a significant increase in the concentration of the major anti-inflammatory cytokine IL-10 in supernatants of PBMCs derived from premutation carriers, when compared with controls (P = 0.019). This increase correlated significantly with the number of CGG repeats (P = 0.002). CONCLUSIONS: Elevated IL-10 levels were observed in all premutation carriers, before appearance of the classical neurological symptoms; therefore, IL-10 may be one of the early biomarkers of FXTAS.

A novel method of dynamic culture surface expansion improves mesenchymal stem cell proliferation and phenotype.

Repeated passaging in conventional cell culture reduces pluripotency and proliferation capacity of human mesenchymal stem cells (MSC). We introduce an innovative cell culture method whereby the culture surface is dynamically enlarged during cell proliferation. This approach maintains constantly high cell density while preventing contact inhibition of growth. A highly elastic culture surface was enlarged in steps of 5% over the course of a 20-day culture period to 800% of the initial surface area. Nine weeks of dynamic expansion culture produced 10-fold more MSC compared with conventional culture, with one-third the number of trypsin passages. After 9 weeks, MSC continued to proliferate under dynamic expansion but ceased to grow in conventional culture. Dynamic expansion culture fully retained the multipotent character of MSC, which could be induced to differentiate into adipogenic, chondrogenic, osteogenic, and myogenic lineages. Development of an undesired fibrogenic myofibroblast phenotype was suppressed. Hence, our novel method can rapidly provide the high number of autologous, multipotent, and nonfibrogenic MSC needed for successful regenerative medicine.

Novel Approach Identifies SNPs in SLC2A10 and KCNK9 with Evidence for Parent-of-Origin Effect on Body Mass Index.

The phenotypic effect of some single nucleotide polymorphisms (SNPs) depends on their parental origin. We present a novel approach to detect parent-of-origin effects (POEs) in genome-wide genotype data of unrelated individuals. The method exploits increased phenotypic variance in the heterozygous genotype group relative to the homozygous groups. We applied the method to >56,000 unrelated individuals to search for POEs influencing body mass index (BMI). Six lead SNPs were carried forward for replication in five family-based studies (of ∼4,000 trios). Two SNPs replicated: the paternal rs2471083-C allele (located near the imprinted KCNK9 gene) and the paternal rs3091869-T allele (located near the SLC2A10 gene) increased BMI equally (beta = 0.11 (SD), P<0.0027) compared to the respective maternal alleles. Real-time PCR experiments of lymphoblastoid cell lines from the CEPH families showed that expression of both genes was dependent on parental origin of the SNPs alleles (P<0.01). Our scheme opens new opportunities to exploit GWAS data of unrelated individuals to identify POEs and demonstrates that they play an important role in adult obesity.

A multilevel multiscale finite volume method

The Multiscale Finite Volume (MsFV) method has been developed to efficiently solve reservoir-scale problems while conserving fine-scale details. The method employs two grid levels: a fine grid and a coarse grid. The latter is used to calculate a coarse solution to the original problem, which is interpolated to the fine mesh. The coarse system is constructed from the fine-scale problem using restriction and prolongation operators that are obtained by introducing appropriate localization assumptions. Through a successive reconstruction step, the MsFV method is able to provide an approximate, but fully conservative fine-scale velocity field. For very large problems (e.g. one billion cell model), a two-level algorithm can remain computational expensive. Depending on the upscaling factor, the computational expense comes either from the costs associated with the solution of the coarse problem or from the construction of the local interpolators (basis functions). To ensure numerical efficiency in the former case, the MsFV concept can be reapplied to the coarse problem, leading to a new, coarser level of discretization. One challenge in the use of a multilevel MsFV technique is to find an efficient reconstruction step to obtain a conservative fine-scale velocity field. In this work, we introduce a three-level Multiscale Finite Volume method (MlMsFV) and give a detailed description of the reconstruction step. Complexity analyses of the original MsFV method and the new MlMsFV method are discussed, and their performances in terms of accuracy and efficiency are compared.

Delirium upon admission to Swiss nursing homes: a cross-sectional study.

QUESTIONS UNDER STUDY: We wished to investigate the prevalence of delirium in patients upon admission to nursing homes and whether or not the previous place of residence predicts delirium. METHODS: The Resident Assessment Instrument Minimum Data Set (RAI-MDS) and the Nursing Home Confusion Assessment Method (NHCAM) were used to determine whether the previous place of residence (community, nursing home, acute care, psychiatric, rehabilitation hospital) predicted the prevalence of sub-syndromal or full delirium in nursing home residents in three Swiss cantons (n = 11745). RESULTS: 39.7% had sub-syndromal and 6.5% had full delirium. Lower cognitive performance and increased depressive symptoms were significant predictors of higher NHCAM values independent of previous residence. Age, civil status, continence, newly introduced drugs, and basic activities of daily living were predictors in some resident groups. The variance of NHCAM scores explained varied between 25.1% and 32.3% depending on previous residence. CONCLUSIONS: Sub-syndromal and full delirium are common upon nursing home admission. Increased dependence and depression are consistently associated with higher NHCAM scores. Patients from psychiatric settings have an increased risk of delirium. Although factors associated with delirium depend on a patient's previous residence, all patients must be carefully screened for sub-syndromal and full delirium.

Development of a two-step screening ESI-TOF-MS method for rapid determination of significant stress-induced metabolome modifications in plant leaf extracts: the wound response in Arabidopsis thaliana as a case study.

To study the stress-induced effects caused by wounding under a new perspective, a metabolomic strategy based on HPLC-MS has been devised for the model plant Arabidopsis thaliana. To detect induced metabolites and precisely localise these compounds among the numerous constitutive metabolites, HPLC-MS analyses were performed in a two-step strategy. In a first step, rapid direct TOF-MS measurements of the crude leaf extract were performed with a ballistic gradient on a short LC-column. The HPLC-MS data were investigated by multivariate analysis as total mass spectra (TMS). Principal components analysis (PCA) and hierarchical cluster analysis (HCA) on principal coordinates were combined for data treatment. PCA and HCA demonstrated a clear clustering of plant specimens selecting the highest discriminating ions given by the complete data analysis, leading to the specific detection of discrete-induced ions (m/z values). Furthermore, pool constitution with plants of homogeneous behaviour was achieved for confirmatory analysis. In this second step, long high-resolution LC profilings on an UPLC-TOF-MS system were used on pooled samples. This allowed to precisely localise the putative biological marker induced by wounding and by specific extraction of accurate m/z values detected in the screening procedure with the TMS spectra.

Identification and validation of copy number variants using SNP genotyping arrays from a large clinical cohort.

BACKGROUND: Genotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets. RESULTS: Here we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs. CONCLUSION: Our new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.