Formas Plurais De Governança: Uma Análise Das Transações De Suprimento Entre Frigoríficos E Pecuaristas

This work examined the existence of plural forms in the transaction between slaughterhouses and beef cattle farmers, analyzing the determinants of companies' choice of governance structures. Three slaughterhouses were studied, using the literature of plural forms of governance as theoretical framework. It was found that only one of the slaughterhouses adopts plural forms to coordinate their beef cattle purchase transactions, and the other two companies obtained all of their live cattle supply through spot market trades. The determinants of plural forms adoption were aligned with the theoretical model. Ambiguity, complexity and strategic behavior variables determine the choice of the governance structure. For the other two companies, we noted little or no ambiguity and complexity in the transactions, and the absence of strategic motivations for the use of plural forms. 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Feed-links for network extensions

Road network data is often incomplete, making it hard to perform network analysis. This paper discusses the problem of extending partial road networks with reasonable links, using the concept of dilation (also known as crow flight conversion coefficient). To this end, we study how to connect a point (relevant location) inside a polygon (face of the known part of the road network) to the boundary so that the dilation from that point to any point on the boundary is not too large. We provide algorithms and heuristics, and give a computational and experimental analysis

DCC gene network in the prefrontal cortex is associated with total brain volume in childhood

BACKGROUND: Genetic variation in the guidance cue DCC gene is linked to psychopathologies involving dysfunction in the prefrontal cortex. We created an expression-based polygenic risk score (ePRS) based on the DCC coexpression gene network in the prefrontal cortex, hypothesizing that it would be associated with individual differences in total brain volume. METHODS: We filtered single nucleotide polymorphisms (SNPs) from genes coexpressed with DCC in the prefrontal cortex obtained from an adult postmortem donors database (BrainEAC) for genes enriched in children 1.5 to 11 years old (BrainSpan). The SNPs were weighted by their effect size in predicting gene expression in the prefrontal cortex, multiplied by their allele number based on an individual's genotype data, and then summarized into an ePRS. We evaluated associations between the DCC ePRS and total brain volume in children in 2 community-based cohorts: the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and University of California, Irvine (UCI) projects. For comparison, we calculated a conventional PRS based on a genome-wide association study of total brain volume. RESULTS: Higher ePRS was associated with higher total brain volume in children 8 to 10 years old (β = 0.212, p = 0.043; n = 88). The conventional PRS at several different thresholds did not predict total brain volume in this cohort. A replication analysis in an independent cohort of newborns from the UCI study showed an association between the ePRS and newborn total brain volume (β = 0.101, p = 0.048; n = 80). The genes included in the ePRS demonstrated high levels of coexpression throughout the lifespan and are primarily involved in regulating cellular function. LIMITATIONS: The relatively small sample size and age differences between the main and replication cohorts were limitations. CONCLUSION: Our findings suggest that the DCC coexpression network in the prefrontal cortex is critically involved in whole brain development during the first decade of life. Genes comprising the ePRS are involved in gene translation control and cell adhesion, and their expression in the prefrontal cortex at different stages of life provides a snapshot of their dynamic recruitment

Compilation of parameterized seismogenic sources in Iberia for the SHARE European-scale seismic source model.

Abstract: SHARE (Seismic Hazard Harmonization in Europe) is an EC-funded project (FP7) that aims to evaluate European seismic hazards using an integrated, standardized approach. In the context of SHARE, we are compiling a fully-parameterized active fault database for Iberia and the nearby offshore region. The principal goal of this initiative is for fault sources in the Iberian region to be represented in SHARE and incorporated into the source model that will be used to produce seismic hazard maps at the European scale. The SHARE project relies heavily on input from many regional experts throughout the Euro-Mediterranean region. At the SHARE regional meeting for Iberia, the 2010 Working Group on Iberian Seismogenic Sources (WGISS) was established; these researchers are contributing to this large effort by providing their data to the Iberian regional integrators in a standardized format. The development of the SHARE Iberian active fault database is occurring in parallel with IBERFAULT, another ongoing effort to compile a database of active faults in the Iberian region. The SHARE Iberian active fault database synthesizes a wide range of geological and geophysical observations on active seismogenic sources, and incorporates existing compilations (e.g., Cabral, 1995; Silva et al., 2008), original data contributed directly from researchers, data compiled from the literature, parameters estimated using empirical and analytical relationships, and, where necessary, parameters derived using expert judgment. The Iberian seismogenic source model derived for SHARE will be the first regional-scale source model for Iberia that includes fault data and follows an internationally standardized approach (Basili et al., 2008; 2009). This model can be used in both seismic hazard and risk analyses and will be appropriate for use in Iberian- and European-scale assessments

Crises and collective socio-economic phenomena: simple models and challenges

Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the Random Field Ising model (RFIM) indeed provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilising self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of RFIM-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith's invisible hand can badly fail at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria to be reached. As a theoretical challenge, the study of so-called "detailed-balance" violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several minor improvements along reviewers' comment

CCNP innovations in neuropsychopharmacology award: DCC gene network in the prefrontal cortex is associated with total brain volume in childhood

Background: Genetic variation in the guidance cue DCC gene is linked to psychopathologies involving dysfunction in the prefrontal cortex. We created an expression-based polygenic risk score (ePRS) based on the DCC coexpression gene network in the prefrontal cortex, hypothesizing that it would be associated with individual differences in total brain volume. Methods: We filtered single nucleotide polymorphisms (SNPs) from genes coexpressed with DCC in the prefrontal cortex obtained from an adult postmortem donors database (BrainEAC) for genes enriched in children 1.5 to 11 years old (BrainSpan). The SNPs were weighted by their effect size in predicting gene expression in the prefrontal cortex, multiplied by their allele number based on an individual’s genotype data, and then summarized into an ePRS. We evaluated associations between the DCC ePRS and total brain volume in children in 2 community-based cohorts: The Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and University of California, Irvine (UCI) projects. For comparison, we calculated a conventional PRS based on a genome-wide association study of total brain volume. Results: Higher ePRS was associated with higher total brain volume in children 8 to 10 years old (β = 0.212, p = 0.043; n = 88). The conventional PRS at several different thresholds did not predict total brain volume in this cohort. A replication analysis in an independent cohort of newborns from the UCI study showed an association between the ePRS and newborn total brain volume (β = 0.101, p = 0.048; n = 80). The genes included in the ePRS demonstrated high levels of coexpression throughout the lifespan and are primarily involved in regulating cellular function. Limitations: The relatively small sample size and age differences between the main and replication cohorts were limitations. Conclusion: Our findings suggest that the DCC coexpression network in the prefrontal cortex is critically involved in whole brain development during the first decade of life. Genes comprising the ePRS are involved in gene translation control and cell adhesion, and their expression in the prefrontal cortex at different stages of life provides a snapshot of their dynamic recruitment

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types