Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Semivariogram models for estimating fig fly population density throughout the year

The objective of this work was to select semivariogram models to estimate the population density of fig fly (Zaprionus indianus; Diptera: Drosophilidae) throughout the year, using ordinary kriging. Nineteen monitoring sites were demarcated in an area of 8,200 m2, cropped with six fruit tree species: persimmon, citrus, fig, guava, apple, and peach. During a 24 month period, 106 weekly evaluations were done in these sites. The average number of adult fig flies captured weekly per trap, during each month, was subjected to the circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, hole effect, K-Bessel, J-Bessel, and stable semivariogram models, using ordinary kriging interpolation. The models with the best fit were selected by cross-validation. Each data set (months) has a particular spatial dependence structure, which makes it necessary to define specific models of semivariograms in order to enhance the adjustment to the experimental semivariogram. Therefore, it was not possible to determine a standard semivariogram model; instead, six theoretical models were selected: circular, Gaussian, hole effect, K-Bessel, J-Bessel, and stable.

Predicting chick body mass by artificial intelligence-based models

The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.

Field data and prediction models of pesticide spray drift on coffee crop

The objective of this work was to generate drift curves from pesticide applications on coffee plants and to compare them with two European drift-prediction models. The used methodology is based on the ISO 22866 standard. The experimental design was a randomized complete block with ten replicates in a 2x20 split-plot arrangement. The evaluated factors were: two types of nozzles (hollow cone with and without air induction) and 20 parallel distances to the crop line outside of the target area, spaced at 2.5 m. Blotting papers were used as a target and placed in each of the evaluated distances. The spray solution was composed of water+rhodamine B fluorescent tracer at a concentration of 100 mg L-1, for detection by fluorimetry. A spray volume of 400 L ha-1 was applied using a hydropneumatic sprayer. The air-induction nozzle reduces the drift up to 20 m from the treated area. The application with the hollow cone nozzle results in 6.68% maximum drift in the nearest collector of the treated area. The German and Dutch models overestimate the drift at distances closest to the crop, although the Dutch model more closely approximates the drift curves generated by both spray nozzles.

Models de comportament de la demanda turística britànica i alemanya a les Balears, i efectes de l'ecotaxa per a aquests mercats

En aquest article s’estimen models de comportament de la demanda turística alemanya i britànica posant èmfasi a treballar amb la sèrie del deflactor dels preus de l’hostaleria balear, tot evitant fer estimacions amb preus declarats de “paquets” turístics, que tenen l’ inconvenient de no recollir els descomptes reals de darrera hora, especialment importants en el cas del mercat britànic

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Effects of particulate matters on inflammatory markers in the general adult population

Summary: Particulate air pollution is associated with increased cardiovascular risk. The induction of systemic inflammation following particle inhalation represents a plausible mechanistic pathway. The purpose of this study was to assess the associations of short-term exposure to ambient particulate matters of aerodynamic diameter less than 10 μm (PM10) with circulating inflammatory markers in 6183 adults in Lausanne, Switzerland. The results show that short-term exposure to PM10 was associated with higher levels of circulating IL-6 and TNF-α. The positive association of PM10 with markers of systemic inflammation materializes the link between air pollution and cardiovascular risk. Background: Variations in short-term exposure to particulate matters (PM) have been repeatedly associated with daily all-cause mortality. Particle-induced inflammation has been postulated to be one of the important mechanisms for increased cardiovascular risk. Experimental in-vitro, in-vivo and controlled human studies suggest that interleukin 6 (IL-6) and tumor-necrosis-factor alpha (TNF-α) could represent key mediators of the inflammatory response to PM. The associations of short-term exposure to ambient PM with circulating inflammatory markers have been inconsistent in studies including specific subgroups so far. The epidemiological evidence linking short-term exposure to ambient PM and systemic inflammation in the general population is scarce. So far, large-scale population-based studies have not explored important inflammatory markers such as IL-6, IL-1β or TNF-α. We therefore analyzed the associations between short-term exposure to ambient PM10 and circulating levels of high-sensitive CRP (hs-CRP), IL-6, IL-1β and TNF-α in the population-based CoLaus study. Objectives: To assess the associations of short-term exposure to ambient particulate matters of aerodynamic diameter less than 10 μm (PM10) with circulating inflammatory markers, including hs-CRP, IL-6, IL-1β and TNF-α, in adults aged 35 to 75 years from the general population. Methodology: All study subjects were participants to the CoLaus study (www.colaus.ch) and the baseline examination was carried out from 2003 to 2006. Overall, 6184 participants were included. For the present analysis, 6183 participants had data on at least one of the four measured circulating inflammatory markers. The monitoring data was obtained from the website of Swiss National Air Pollution Monitoring Network (NABEL). We analyzed data on PM10 as well as outside air temperature, pressure and humidity. Hourly concentrations of PM10 were collected from 1 January 2003 to 31 December 2006. Robust linear regression (PROC ROBUSTREG) was used to evaluate the relationship between cytokine inflammatory and PM10. We adjusted all analyses for age, sex, body mass index, smoking status, alcohol consumption, diabetes status, hypertension status, education levels, zip code, and statin intake. All data were adjusted for the effects of weather by including temperature, barometric pressure, and season as covariates in the adjusted models. We performed simple and multiple logistic regression analyses. Descriptive statistical analysis used the Wilcoxon rank sum test (for medians). All data analyses were performed using SAS software (version 9.2; SAS Institute Inc., Cary, NC, USA), and a two-sided significance level of 5% was used. Results: PM10 levels averaged over 24 hours were significantly and positively associated with continuous IL-6 and TNF-α levels, in the whole study population both in unadjusted and adjusted analyses. For each cytokine, there was a similar seasonal pattern, with wider confidence intervals in summer than during the other seasons, which might partly be due to the smaller number of participants examined in summer. The associations of PM10 with IL-6 and TNF-α were also found after having dichotomized these cytokines into high versus low levels, which suggests that the associations of PM10 with the continuous cytokine levels are very robust to any distributional assumption and to potential outlier values. In contrast with what we observed for continuous IL-1β levels, high PM10 levels were significantly associated with high IL-1β. PM10 was significantly associated with IL-6 and TNF-α in men, but with TNF-α only in women. However, there was no significant statistical interaction between PM10 and sex. For IL-6 and TNF-α, the associations tended to be stronger in younger people, with a significant interaction between PM10 and age groups for IL-6. PM10 was significantly associated with IL-6 and TNF-α in the healthy group and also in the "non-healthy" group, although the statistical interaction between healthy status and PM10 was not significant. Conclusion: In summary, we found significant independent positive associations of short-term exposure to PM10 with circulating levels of IL-6 and TNF-α in the adult population of Lausanne. Our findings strongly support the idea that short-term exposure to PM10 is sufficient to induce systemic inflammation on a broad scale in the general population. From a public health perspective, the reported association of elevated inflammatory cytokines with short-term exposure to PM10 in a city with relatively clean air such as Lausanne supports the importance of limiting urban air pollution levels.

Hepatitis C virus infection: in vivo and in vitro models.

Major advances in the understanding of the molecular biology of hepatitis C virus (HCV) have been made recently. While the chimpanzee is the only established animal model of HCV infection, several in vivo and in vitro models have been established that allow us to study various aspects of the viral life cycle. In particular, the replicon system and the production of recombinant infectious virions revolutionized the investigation of HCV-RNA replication and rendered all steps of the viral life cycle, including entry and release of viral particles, amenable to systematic analysis. In the following we will review the different in vivo and in vitro models of HCV infection.

Forecasting coal resources and reserves in heterogeneous coal zones using 3D facies models (As Pontes Basin, NW Spain)

Forecasting coal resources and reserves is critical for coal mine development. Thickness maps are commonly used for assessing coal resources and reserves; however they are limited for capturing coal splitting effects in thick and heterogeneous coal zones. As an alternative, three-dimensional geostatistical methods are used to populate facies distributionwithin a densely drilled heterogeneous coal zone in the As Pontes Basin (NWSpain). Coal distribution in this zone is mainly characterized by coal-dominated areas in the central parts of the basin interfingering with terrigenous-dominated alluvial fan zones at the margins. The three-dimensional models obtained are applied to forecast coal resources and reserves. Predictions using subsets of the entire dataset are also generated to understand the performance of methods under limited data constraints. Three-dimensional facies interpolation methods tend to overestimate coal resources and reserves due to interpolation smoothing. Facies simulation methods yield similar resource predictions than conventional thickness map approximations. Reserves predicted by facies simulation methods are mainly influenced by: a) the specific coal proportion threshold used to determine if a block can be recovered or not, and b) the capability of the modelling strategy to reproduce areal trends in coal proportions and splitting between coal-dominated and terrigenousdominated areas of the basin. Reserves predictions differ between the simulation methods, even with dense conditioning datasets. Simulation methods can be ranked according to the correlation of their outputs with predictions from the directly interpolated coal proportion maps: a) with low-density datasets sequential indicator simulation with trends yields the best correlation, b) with high-density datasets sequential indicator simulation with post-processing yields the best correlation, because the areal trends are provided implicitly by the dense conditioning data.

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity.

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we developed a predictive model of connectivity alterations in MS: a whole-brain connectivity matrix was built for each subject from the slow oscillations (<0.11Hz) of region-averaged time series, and a pattern recognition technique was used to learn a discriminant function indicating which particular functional connections are most affected by disease. Classification performance using strict cross-validation yielded a sensitivity of 82% (above chance at p<0.005) and specificity of 86% (p<0.01) to distinguish between MS patients and controls. The most discriminative connectivity changes were found in subcortical and temporal regions, and contralateral connections were more discriminative than ipsilateral connections. The pattern of decreased discriminative connections can be summarized post hoc in an index that correlates positively (ρ=0.61) with white matter lesion load, possibly indicating functional reorganisation to cope with increasing lesion load. These results are consistent with a subtle but widespread impact of lesions in white matter and in gray matter structures serving as high-level integrative hubs. These findings suggest that predictive models of resting state fMRI can reveal specific anomalies due to MS with high sensitivity and specificity, potentially leading to new non-invasive markers.

Predictive models for nanotoxicology: current challenges and future opportunities.

Characterizing the risks posed by nanomaterials is extraordinarily complex because these materials can have a wide range of sizes, shapes, chemical compositions and surface modifications, all of which may affect toxicity. There is an urgent need for a testing strategy that can rapidly and efficiently provide a screening approach for evaluating the potential hazard of nanomaterials and inform the prioritization of additional toxicological testing where necessary. Predictive toxicity models could form an integral component of such an approach by predicting which nanomaterials, as a result of their physico-chemical characteristics, have potentially hazardous properties. Strategies for directing research towards predictive models and the ancillary benefits of such research are presented here.

Investigation of discrete imaging models and iterative image reconstruction in differential X-ray phase-contrast tomography.

Differential X-ray phase-contrast tomography (DPCT) refers to a class of promising methods for reconstructing the X-ray refractive index distribution of materials that present weak X-ray absorption contrast. The tomographic projection data in DPCT, from which an estimate of the refractive index distribution is reconstructed, correspond to one-dimensional (1D) derivatives of the two-dimensional (2D) Radon transform of the refractive index distribution. There is an important need for the development of iterative image reconstruction methods for DPCT that can yield useful images from few-view projection data, thereby mitigating the long data-acquisition times and large radiation doses associated with use of analytic reconstruction methods. In this work, we analyze the numerical and statistical properties of two classes of discrete imaging models that form the basis for iterative image reconstruction in DPCT. We also investigate the use of one of the models with a modern image reconstruction algorithm for performing few-view image reconstruction of a tissue specimen.

Empirical Tests of Asset Pricing Models in Finnish Stock Market

Tämän tutkielman tavoitteena on selvittää mitkä riskitekijät vaikuttavat osakkeiden tuottoihin. Arvopapereina käytetään kuutta portfoliota, jotka ovat jaoteltu markkina-arvon mukaan. Aikaperiodi on vuoden 1987 alusta vuoden 2004 loppuun. Malleina käytetään pääomamarkkinoiden hinnoittelumallia, arbitraasihinnoitteluteoriaa sekä kulutuspohjaista pääomamarkkinoiden hinnoittelumallia. Riskifaktoreina kahteen ensimmäiseen malliin käytetään markkinariskiä sekä makrotaloudellisia riskitekijöitä. Kulutuspohjaiseen pääomamarkkinoiden hinnoinoittelumallissa keskitytään estimoimaan kuluttajien riskitottumuksia sekä diskonttaustekijää, jolla kuluttaja arvostavat tulevaisuuden kulutusta. Tämä työ esittelee momenttiteorian, jolla pystymme estimoimaan lineaarisia sekä epälineaarisia yhtälöitä. Käytämme tätä menetelmää testaamissamme malleissa. Yhteenvetona tuloksista voidaan sanoa, että markkinabeeta onedelleen tärkein riskitekijä, mutta löydämme myös tukea makrotaloudellisille riskitekijöille. Kulutuspohjainen mallimme toimii melko hyvin antaen teoreettisesti hyväksyttäviä arvoja.