Additive effects of two growth QTL on cattle chromosome 14

The document attached has been archived with permission from the World Congress on Genetics Applied to Livestock Production.DNA-marker technology has the potential to assist seed-stock beef producers with genetic improvement of traits that are difficult to measure, and to assist research workers in identifying chromosomal regions containing quantitative trait loci (QTL), and eventually genes, which control animal performance traits. A collaborative study was established in 1995 between AgResearch in New Zealand (NZ) and Adelaide University in Australia to search for DNA markers significantly linked to production, carcass and meat quality traits in beef cattle. The present paper reports on a sub-set of that data, namely evidence from microsatellite markers on chromosome (Chr) 14 of significant linkage to growth traits and hot carcass weight (HSCW) at a standard level of trim

Stochastic Feedback and the Regulation of Biological Rhythms

We propose a general approach to the question of how biological rhythms spontaneously self-regulate, based on the concept of ``stochastic feedback''. We illustrate this approach by considering the neuroautonomic regulation of the heart rate. The model generates complex dynamics and successfully accounts for key characteristics of cardiac variability, including the $1/f$ power spectrum, the functional form and scaling of the distribution of variations, and correlations in the Fourier phases. Our results suggest that in healthy systems the control mechanisms operate to drive the system away from extreme values while not allowing it to settle down to a constant output.Comment: 15 pages, latex2e using rotate and epsf, with 4 ps figures. Submitted to PR

Open TURNS: An industrial software for uncertainty quantification in simulation

The needs to assess robust performances for complex systems and to answer tighter regulatory processes (security, safety, environmental control, and health impacts, etc.) have led to the emergence of a new industrial simulation challenge: to take uncertainties into account when dealing with complex numerical simulation frameworks. Therefore, a generic methodology has emerged from the joint effort of several industrial companies and academic institutions. EDF R&D, Airbus Group and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial challenges attached to uncertainties, which are transparency, genericity, modularity and multi-accessibility. This paper focuses on OpenTURNS and presents its main features: openTURNS is an open source software under the LGPL license, that presents itself as a C++ library and a Python TUI, and which works under Linux and Windows environment. All the methodological tools are described in the different sections of this paper: uncertainty quantification, uncertainty propagation, sensitivity analysis and metamodeling. A section also explains the generic wrappers way to link openTURNS to any external code. The paper illustrates as much as possible the methodological tools on an educational example that simulates the height of a river and compares it to the height of a dyke that protects industrial facilities. At last, it gives an overview of the main developments planned for the next few years

State–Space Forecasting of Schistosoma haematobium Time-Series in Niono, Mali

Adequate forecasting and early warning systems are based upon observations of human behavior, population, disease time-series, climate, environment, and/or a combination thereof, whichever option best compromises among realism, feasibility, robustness, and parsimony. Fully automatic and user-friendly state–space forecasting frameworks, incorporating myriad options (e.g., expert opinion, univariate, multivariate, and spatial-temporal), could considerably enhance disease control and hazard mitigation efforts in regions where vulnerability to neglected tropical diseases is pervasive and statistical expertise is scarce. The operational simplicity, generality, and flexibility of state–space frameworks, encapsulating multiple methods, could conveniently allow for 1) unsupervised model selection without disease-specific methodological tailoring, 2) on-line adaptation to disease time-series fluctuations, and 3) automatic switches between distinct forecasting methods as new time-series perturbations dictate. In this investigation, a univariate state–space framework with the aforementioned properties was successfully applied to the Schistosoma haematobium time-series for the district of Niono, Mali, to automatically generate contemporaneous on-line forecasts and hence, providing a basis for local re-organization and strengthening public health programs in this and potentially other Sahelian districts

Gas hydrate on the northern Cascadia margin: regional geophysics and structural framework

Integrated Ocean Drilling Program Expedition 311 is based on extensive site survey data and historic research at the northern Cascadia margin since 1985. This research includes various regional geophysical surveys using a broad spectrum of seismic techniques, coring and logging by the Ocean Drilling Program Leg 146, heat flow measurements, shallow piston coring, and bottom video observations across a cold-vent field, as well as novel controlled-source electromagnetic and seafloor compliance surveying techniques. The wealth of data available allowed construction of structural cross-sections of the margin, development of models for the formation of gas hydrate in an accretionary prism, and estimation of gas hydrate and free gas concentrations. Expedition 311 established for the first time a transect of drill sites across the northern Cascadia margin to study the evolution of gas hydrate formation over the entire gas hydrate stability field of the accretionary complex. This paper reviews the tectonic framework at the northern Cascadia margin and summarizes the scientific studies that led to the drilling objectives of Expedition 311 Cascadia gas hydrate

Limits of the seismogenic zone in the epicentral region of the 26 December 2004 great Sumatra-Andaman earthquake: Results from seismic refraction and wide-angle reflection surveys and thermal modeling

The 26 December 2004 Sumatra earthquake (Mw = 9.1) initiated around 30 km depth and ruptured 1300 km of the Indo-Australian Sunda plate boundary. During the Sumatra OBS (ocean bottom seismometer) survey, a wide angle seismic profile was acquired across the epicentral region. A seismic velocity model was obtained from combined travel time tomography and forward modeling. Together with reflection seismic data from the SeaCause II cruise, the deep structure of the source region of the great earthquake is revealed. Four to five kilometers of sediments overlie the oceanic crust at the trench, and the subducting slab can be imaged down to a depth of 35 km. We find a crystalline backstop 120 km from the trench axis, below the fore arc basin. A high velocity zone at the lower landward limit of the raycovered domain, at 22 km depth, marks a shallow continental Moho, 170 km from the trench. The deep structure obtained from the seismic data was used to construct a thermal model of the fore arc in order to predict the limits of the seismogenic zone along the plate boundary fault. Assuming 100C-150C as its updip limit, the seismogenic zone is predicted to begin 530 km from the trench. The downdip limit of the 2004 rupture as inferred from aftershocks is within the 350C 450C temperature range, but this limit is 210-250 km from the trench axis and is much deeper than the fore arc Moho. The deeper part of the rupture occurred along the contact between the mantle wedge and the downgoing plate

Multiple shades of grey: Opening the black box of public sector executives' hybrid role identities

Public sector reforms of recent decades in Europe have promoted managerialism and aimed at introducing private sector thinking and practices. However, with regard to public sector executives' self-understanding, managerial role identities have not replaced bureaucratic ones; rather, components from both paradigms have combined. In this article, we introduce a bi-dimensional approach (attitudes and practices) that allows for different combinations and forms of hybridity. Empirically, we explore the role identities of public sector executives across Europe, building on survey data from over 7,000 top public officials in 19 countries (COCOPS survey). We identify country-level profiles, as well as patterns across countries, and find that administrative traditions can account for these profiles and patterns only to a limited extent. Rather, they have to be complemented by factors such as stability of the institutional environment (indicating lower shares of hybrid combinations) or extent of reform pressures (indicating higher shares of hybrid combinations)

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors