BG Group and “Conditions” to Arbitral Jurisdiction

Although the Supreme Court has over the last decade generated a robust body of arbitration caselaw, its first decision in the area of investment arbitration under a Bilateral Investment Treaty was only handed down in 2014. BG Group v. Argentina was widely anticipated and has attracted much notice, and general approval, on the part of the arbitration community. In this paper we assess the Court’s decision from two different perspectives—the first attempts to situate it in the discourse of the American law of commercial arbitration; the second considers it in light of the expectations of the international community surrounding the proper construction of Conventions between states. Our initial goal had been to write jointly, with the hope that we could bridge our differences to find, if not common, at least neighboring, ground. On some points we did so, but ultimately our divergent appreciations of the proper way to interpret the condition precedent in the investment treaty in BG Group overcame the idealism with which we commenced the project. Nonetheless we have decided to present the two papers together to emphasize the dichotomous approaches to treaty interpretation that two moderately sensible people, who inhabit overlapping but non-congruent interpretive communities, can have.The Kay Bailey Hutchison Center for Energy, Law, and Busines

Differential meta-analysis of RNA-seq data from multiple studies

High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question. We demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. To conclude, the p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the R Forge

Title Empirical Bayes Estimation of Dynamic Bayesian Networks Version 1.2.3 Date 2013-07-26

Description This package is used to infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks. License GPL (> = 3

The Effects of Imitation, Modeling, and Prompting on Play Skills of Young Children with Disabilities

The purpose of this action research was to determine if direct instruction of play skills would improve the play of three-year-old children with disabilities in the inclusive setting. Participants included five children with disabilities ranging in age from 38 to 48 months old. Quantitative data was collected using event sampling to assess the number of different independent play actions performed by participants before and after intervention. Qualitative data was collected to determine commonalities in which intervention action steps resulted in successful imitation. The intervention implemented was a combined systematic approach of contingent imitation, modeling, and a system of least prompts within the inclusive setting during free play. Analysis of data revealed the intervention had a positive effect on the number of different independent play actions performed by three-year-old children with disabilities in the inclusive setting. Acquisition of new play skills promotes acquisition of other skills through play-based instruction. Performance of a larger variety of independent play skills also promotes inclusion and acceptance among typically developing peers. Limitations and need for further study are discussed

Inhibition of geranylgeranyl diphosphate synthesis in in vitro systems

The incorporation of [14C]mevalonate and [14C]isopentenyl diphosphate into geranylgeranyl diphosphate was investigated in in vitro systems from Cucurbita pepo (pumpkin) endosperm and from Avena sativa etioplasts. Mevalonate incorporation was effectively inhibited in the pumpkin system by geranylgeranyl diphosphate and geranylgeranyl monophosphate but less effectively by phytyl diphosphate or inorganic diphosphate. Membrane lipids, geranyllinalool, or lecithin enhanced mevalonate incorporation in the Cucurbita system. Incorporation of isopentenyl diphosphate was also enhanced by lecithin and inhibited by geranylgeranyl diphosphate in the Cucurbita system. No lipid enhancement was found in the Avena system; inhibition by GGPP required a much higher GGPP concentration than in the Cucurbita system

Reverse engineering gene regulatory networks using approximate Bayesian computation

Gene regulatory networks are collections of genes that interact with one other and with other substances in the cell. By measuring gene expression over time using high-throughput technologies, it may be possible to reverse engineer, or infer, the structure of the gene network involved in a particular cellular process. These gene expression data typically have a high dimensionality and a limited number of biological replicates and time points. Due to these issues and the complexity of biological systems, the problem of reverse engineering networks from gene expression data demands a specialized suite of statistical tools and methodologies. We propose a non-standard adaptation of a simulation-based approach known as Approximate Bayesian Computing based on Markov chain Monte Carlo sampling. This approach is particularly well suited for the inference of gene regulatory networks from longitudinal data. The performance of this approach is investigated via simulations and using longitudinal expression data from a genetic repair system in Escherichia coli.Comment: 16 pages, 11 figure

Clustering high-throughput sequencing data with Poisson mixture models

In recent years gene expression studies have increasingly made use of next generation sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression has flourished, primarily in the context of normalization and differential analysis. In this work, we focus on the question of clustering digital gene expression profiles as a means to discover groups of co-expressed genes. We propose two parameterizations of a Poisson mixture model to cluster expression profiles of high-throughput sequencing data. A set of simulation studies compares the performance of the proposed models with that of an approach developed for a similar type of data, namely serial analysis of gene expression. We also study the performance of these approaches on two real high-throughput sequencing data sets. The R package HTSCluster used to implement the proposed Poisson mixture models is available on CRAN.De plus en plus, les études d'expression de gènes utilisent les techniques de séquençage de nouvelle génération, entraînant une recherche grandissante sur les méthodes les plus appropriées pour l'exploitation des données digitales d'expression, à commencer pour leur normalisation et l'analyse différentielle. Ici, nous nous intéressons à la classification non supervisée des profils d'expression pour la découverte de groupes de gènes coexprimés. Nous proposons deux paramétrisations d'un modèle de mélange de Poisson pour classer des données de séquençage haut-débit. Par des simulations, nous comparons les performances de ces modèles avec des méthodes similaires conçus pour l'analyse en série de l'expression des gènes (SAGE). Nous étudions aussi les performances de ces modèles sur deux jeux de données réelles. Le package R HTSCluster associé à cette étude est disponible sur le CRAN

APPROXIMATE BAYESIAN APPROACHES FOR REVERSE ENGINEERING BIOLOGICAL NETWORKS

Genes are known to interact with one another through proteins by regulating the rate at which gene transcription takes place. As such, identifying these gene-to-gene interactions is essential to improving our knowledge of how complex biological systems work. In recent years, a growing body of work has focused on methods for reverse-engineering these so-called gene regulatory networks from time-course gene expression data. However, reconstruction of these networks is often complicated by the large number of genes potentially involved in a given network and the limited number of time points and biological replicates typically measured. Bayesian methods are particularly well-suited for dealing with problems of this nature, as they provide a systematic way to deal with different sources of variation and allow for a measure of uncertainty in parameter estimates through posterior distributions, rather than point estimates. Our current work examines the application of approximate Bayesian methodology for the purpose of reverse engineering regulatory networks from time-course gene expression data. We demonstrate the advantages of our proposed approximate Bayesian approaches by comparing their performance on a well-characterized pathway in Escherichia coli

Mutational biases and selective forces shaping the structure of <i>Arabidopsis</i> genes

Recently features of gene expression profiles have been associated with structural parameters of gene sequences in organisms representing a diverse set of taxa. The emerging picture indicates that natural selection, mediated by gene expression profiles, has a significant role in determining genic structures. However the current situation is less clear in plants as the available data indicates that the effect of natural selection mediated by gene expression is very weak. Moreover, the direction of the patterns in plants appears to contradict those observed in animal genomes. In the present work we analized expression data for &gt;18000 Arabidopsis genes retrieved from public datasets obtained with different technologies (MPSS and high density chip arrays) and compared them with gene parameters. Our results show that the impact of natural selection mediated by expression on genes sequences is significant and distinguishable from the effects of regional mutational biases. In addition, we provide evidence that the level and the breadth of gene expression are related in opposite ways to many structural parameters of gene sequences. Higher levels of expression abundance are associated with smaller transcripts, consistent with the need to reduce costs of both transcription and translation. Expression breadth, however, shows a contrasting pattern, i.e. longer genes have higher breadth of expression, possibly to ensure those structural features associated with gene plasticity. Based on these results, we propose that the specific balance between these two selective forces play a significant role in shaping the structure of Arabidopsis genes