Labor Unions and Coalitions in Buffalo

Labor unions have evolved tremendously since their inception in 1866 in the United States. Today, some unions in the Buffalo region are responding to free market fundamentalism with the development of multiple coalition partners. Coalitions are composed of unions and like-minded activist organizations. This creative response to a long-term economic crisis has created a high road social infrastructure. Unions have moved beyond their traditional roles of collective bargaining and representation to a more community-oriented mission of improving the quality of local jobs

Movement Patterns of Carabid Beetles Between Heterogenous Crop and Noncrop Habitats

Habitats adjacent to crop fields can increase natural enemy populations by providing additional food, shelter and overwintering sites. While many studies have focused on the role of non-crop borders for supporting natural enemies, here we investigate the influence of adjacent crop habitats as well. We monitored the movement of carabid beetles (Coleoptera: Carabidae) between wheat fields and adjacent crop and non-crop habitats using bi-directional pitfall traps. We found greater movement of carabids from corn into wheat fields than from forest and soybean, with intermediate levels of movement from roadside vegetation. Additionally, significantly more carabids were captured moving into corn from wheat than into any other habitat. We also found that carabid community assemblages at habitat borders were different from those in the interior of wheat fields. Our findings suggest that agricultural ecosystems composed of a variety of both non- crop and crop habitats are necessary to maintain carabid abundance and diversity

Gene expression in large pedigrees: analytic approaches.

BackgroundWe currently have the ability to quantify transcript abundance of messenger RNA (mRNA), genome-wide, using microarray technologies. Analyzing genotype, phenotype and expression data from 20 pedigrees, the members of our Genetic Analysis Workshop (GAW) 19 gene expression group published 9 papers, tackling some timely and important problems and questions. To study the complexity and interrelationships of genetics and gene expression, we used established statistical tools, developed newer statistical tools, and developed and applied extensions to these tools.MethodsTo study gene expression correlations in the pedigree members (without incorporating genotype or trait data into the analysis), 2 papers used principal components analysis, weighted gene coexpression network analysis, meta-analyses, gene enrichment analyses, and linear mixed models. To explore the relationship between genetics and gene expression, 2 papers studied expression quantitative trait locus allelic heterogeneity through conditional association analyses, and epistasis through interaction analyses. A third paper assessed the feasibility of applying allele-specific binding to filter potential regulatory single-nucleotide polymorphisms (SNPs). Analytic approaches included linear mixed models based on measured genotypes in pedigrees, permutation tests, and covariance kernels. To incorporate both genotype and phenotype data with gene expression, 4 groups employed linear mixed models, nonparametric weighted U statistics, structural equation modeling, Bayesian unified frameworks, and multiple regression.Results and discussionRegarding the analysis of pedigree data, we found that gene expression is familial, indicating that at least 1 factor for pedigree membership or multiple factors for the degree of relationship should be included in analyses, and we developed a method to adjust for familiality prior to conducting weighted co-expression gene network analysis. For SNP association and conditional analyses, we found FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) and SOLAR-MGA (Sequential Oligogenic Linkage Analysis Routines -Major Gene Analysis) have similar type 1 and type 2 errors and can be used almost interchangeably. To improve the power and precision of association tests, prior knowledge of DNase-I hypersensitivity sites or other relevant biological annotations can be incorporated into the analyses. On a biological level, eQTL (expression quantitative trait loci) are genetically complex, exhibiting both allelic heterogeneity and epistasis. Including both genotype and phenotype data together with measurements of gene expression was found to be generally advantageous in terms of generating improved levels of significance and in providing more interpretable biological models.ConclusionsPedigrees can be used to conduct analyses of and enhance gene expression studies

On the origin of the Trojan asteroids: Effects of Jupiter's mass accretion and radial migration

We present analytic and numerical results which illustrate the effects of Jupiter's accretion of nebular gas and the planet's radial migration on its Trojan companions. Initially, we approximate the system by the planar circular restricted three-body problem and assume small Trojan libration amplitudes. Employing an adiabatic invariant calculation, we show that Jupiter's thirty-fold growth from a $10 M_\oplus$ core to its present mass causes the libration amplitudes of Trojan asteroids to shrink by a factor of about 2.5 to $\sim 40$ of their original size. The calculation also shows that Jupiter's radial migration has comparatively little effect on the Trojans; inward migration from 6.2 to 5.2 AU causes an increase in Trojan libration amplitudes of $\sim4$. In each case, the area enclosed by small tadpole orbits, if made dimensionless by using Jupiter's semimajor axis, is approximately conserved. Similar adiabatic invariant calculations for inclined and eccentric Trojans show that Jupiter's mass growth leaves the asteroid's eccentricities and inclinations essentially unchanged, while one AU of inward migration causes an increase in both of these quantities by $\sim 4$. Numerical integrations confirm and extend these analytic results. We demonstrate that our predictions remain valid for Trojans with small libration amplitudes even when the asteroids have low, butComment: Submitted to Icarus - 13 Fig

Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships

Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the molecular representation becomes a critical ingredient in ML model predictive accuracy. We introduce a series of revised autocorrelation functions (RACs) that encode relationships between the heuristic atomic properties (e.g., size, connectivity, and electronegativity) on a molecular graph. We alter the starting point, scope, and nature of the quantities evaluated in standard ACs to make these RACs amenable to inorganic chemistry. On an organic molecule set, we first demonstrate superior standard AC performance to other presently-available topological descriptors for ML model training, with mean unsigned errors (MUEs) for atomization energies on set-aside test molecules as low as 6 kcal/mol. For inorganic chemistry, our RACs yield 1 kcal/mol ML MUEs on set-aside test molecules in spin-state splitting in comparison to 15-20x higher errors from feature sets that encode whole-molecule structural information. Systematic feature selection methods including univariate filtering, recursive feature elimination, and direct optimization (e.g., random forest and LASSO) are compared. Random-forest- or LASSO-selected subsets 4-5x smaller than RAC-155 produce sub- to 1-kcal/mol spin-splitting MUEs, with good transferability to metal-ligand bond length prediction (0.004-5 {\AA} MUE) and redox potential on a smaller data set (0.2-0.3 eV MUE). Evaluation of feature selection results across property sets reveals the relative importance of local, electronic descriptors (e.g., electronegativity, atomic number) in spin-splitting and distal, steric effects in redox potential and bond lengths.Comment: 43 double spaced pages, 11 figures, 4 table

Examining the cognitive costs of counterfactual language comprehension: Evidence from ERPs

Recent empirical research suggests that understanding a counterfactual event (e.g. ‘If Josie had revised, she would have passed her exams’) activates mental representations of both the factual and counterfactual versions of events. However, it remains unclear when readers switch between these models during comprehension, and whether representing multiple ‘worlds’ is cognitively effortful. This paper reports two ERP studies where participants read contexts that set up a factual or counterfactual scenario, followed by a second sentence describing a consequence of this event. Critically, this sentence included a noun that was either consistent or inconsistent with the preceding context, and either included a modal verb to indicate reference to the counterfactual-world or not (thus referring to the factual-world). Experiment 2 used adapted versions of the materials used in Experiment 1 to examine the degree to which representing multiple versions of a counterfactual situation makes heavy demands on cognitive resources by measuring individuals’ working memory capacity. Results showed that when reference to the counterfactual-world was maintained by the ongoing discourse, readers correctly interpreted events according to the counterfactual-world (i.e. showed larger N400 for inconsistent than consistent words). In contrast, when cues referred back to the factual-world, readers showed no difference between consistent and inconsistent critical words, suggesting that they simultaneously compared information against both possible worlds. These results support previous dual-representation accounts for counterfactuals, and provide new evidence that linguistic cues can guide the reader in selecting which world model to evaluate incoming information against. Crucially, we reveal evidence that maintaining and updating a hypothetical model over time relies upon the availability of cognitive resources

Interrelationships among international stock market indices: Europe, Asia and the Americas

In this paper, we investigate the price interdependence between seven international stock markets, namely Irish, UK, Portuguese, US, Brazilian, Japanese and Hong Kong, using a new testing method, based on the wavelet transform to reconstruct the data series, as suggested by Lee (2002). We find evidence of intra-European (Irish, UK and Portuguese) market co-movements with the US market also weakly influencing the Irish market. We also find co-movement between the US and Brazilian markets and similar intra-Asian co-movements (Japanese and Hong Kong). Finally, we conclude that the circle of impact is that of the European markets (Irish, UK and Portuguese) on both American markets (US and Brazilian), with these in turn impacting on the Asian markets (Japanese and Hong Kong) which in turn influence the European markets. In summary, we find evidence for intra-continental relationships and an increase in importance of international spillover effects since the mid 1990’s, while the importance of historical transmissions has decreased since the beginning of this century

Interdependence between emerging and major markets

In this paper, we investigate the price spillover effects among two developed markets, (the US and the UK ), and two developing markets, (Irish and Portuguese), using a new testing method suggested by Lee (2002). We find that there are interrelationships between any two of the Irish, the UK and Portuguese markets and that the co-movements between the emerging markets and the US are statistically significant but weak. We also found that the US market is slightly influenced by the UK but not vice versa

Modelling drug coatings: A parallel cellular automata model of ethylcellulose-coated microspheres

Pharmaceutical companies today face a growing demand for more complex drug designs. In the past few decades, a number of probabilistic models have been developed, with the aim of improving insight on microscopic features of these complex designs. Of particular interest are models of controlled release systems, which can provide tools to study targeted dose delivery. Controlled release is achieved by using polymers with different dissolution characteristics. We present here an approach for parallelising a large-scale model of a drug delivery system based on Monte Carlo methods, as a framework for Cellular Automata mobility. The model simulates drug release in the gastro-intestinal tract, from coated ethylcellulose microspheres. The objective is high performance simulation of coated drugs for targeted delivery. The overall aim is to understand the importance of various molecular effects with respect to system evolution over time. Important underlying mechanisms of the process, such as erosion and diffusion, are described

Nonfamily Abducted Children: National Estimates and Characteristics.

Presents national estimates of children abducted by nonfamily perpetrators, based on surveys of households and law enforcement agencies. The Bulletin, which is part of a series summarizing findings from the Second National Incidence Studies of Missing, Abducted, Runaway, and Thrownaway Children (NISMART–2), also analyzes characteristics of victims, perpetrators, and episodes. During the study period, an estimated 58,200 children were abducted by nonfamily perpetrators; 115 were victims of stereotypical kidnappings. Teenagers were the most frequent victims. Nearly half of all victims were sexually assaulted. In 40 percent of stereotypical kidnappings, the child was killed; in another 4 percent, the child was not recovered. The Bulletin also discusses policy implications of the findings