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See detailPrediction of cow pregnancy status using conventional and novel mid-infrared predicted milk traits
Hammami, Hedi ULg; Bastin, Catherine ULg; Gillon, Alain et al

in Book of Abstracts of the 62nd Annual Meeting of the European Association for Animal Production (2011, August)

The objective of this study was to determine the ability of conventional milk cow characteristics and novel traits predicted by mid infrared (MIR) obtained from milk recording to predict the pregnancy ... [more ▼]

The objective of this study was to determine the ability of conventional milk cow characteristics and novel traits predicted by mid infrared (MIR) obtained from milk recording to predict the pregnancy status once the cow was inseminated. Conventional milk recording, spectral, and reproductive data collected in Luxembourg Hoslteins between 2008 and 2010 were used. Cows were defined as pregnant if they were positively checked and calved between 267 and 295 d later after the last AI or if they had calved between the later intervals when no checks were recorded. Pregnant or not within 3 intervals after last AI (<=35 d, 45-60 d, and 60-90 d) was modeled using logistic regression models firstly as a function of conventional cow milk characteristics and extended to fatty acids as novel traits predicted by MIR in a second step. The lactation curve characteristics for milk, fat, protein, and lactose yields were estimated using modified best prediction method. Test-day fatty acid contents were estimated from collected MIR spectra using an appropriate calibration equation. Two third proportion and one third of the whole data set were randomly selected for calibration and validation models respectively. The relation between the predicted and observed probabilities of cow pregnancy was approximately linear for calibration and validation models. The sensitivity-specificity combination for cow pregnancy increased when fatty acids were added to conventional milk characteristics as inputs to the different models (from 78 to 85% for sensitivity and from 40 to 52% for specificity). Results based on those models showed that it would be possible to help breeders to manage cow fertility using such tool implemented in the milk recording organizations. [less ▲]

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See detailPrediction of daily milk, fat, and protein production by a random regression test-day model
Mayeres, P.; Stoll, J.; Bormann, J. et al

in Journal of Dairy Science (2004), 87(6), 1925-1933

Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been ... [more ▼]

Test-day genetic evaluation models have many advantages compared with those based on 305-d lactations; however, the possible use of test-day model (TDM) results for herd management purposes has not been emphasized. The aim of this paper was to study the ability of a TDM to predict production for the next test day and for the entire lactation. Predictions of future production and detection of outliers are important factors for herd management (e. g., detection of health and management problems and compliance with quota). Because it is not possible to predict the herd-test-day (HTD) effect per se, the fixed HTD effect was split into 3 new effects: a fixed herd-test month-period effect, a fixed herd-year effect, and a random HTD effect. These new effects allow the prediction of future production for improvement of herd management. Predicted test-day yields were compared with observed yields, and the mean prediction error computed across herds was found to be close to zero. Predictions of performance records at the herd level were even more precise. Discarding herds enrolled in milk recording for <1 yr and animals with very few tests in the evaluation file improved correlations between predicted and observed yields at the next test day (correlation of 0.864 for milk in first-lactation cows as compared with a correlation of 0.821 with no records eliminated). Correlations with the observed 305-d production ranged from 0.575 to 1 for predictions based on 0 to 10 test-day records, respectively. Similar results were found for second and third lactation records for milk and milk components. These findings demonstrate the predictive ability of a TDM. [less ▲]

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See detailPrediction of deformation textures in zirconium based on a simplified modelling of grain interaction
Lelotte, Thomas; Delannay, L.; Habraken, Anne ULg

in Khan, A. S. (Ed.) Proceedings of the Plasticity Conference 2006 (2006)

The goal of this study is to apply a “multisite” crystal plasticity model to the prediction of texture evolution in zirconium deformed at high temperature. The model under consideration is an extension of ... [more ▼]

The goal of this study is to apply a “multisite” crystal plasticity model to the prediction of texture evolution in zirconium deformed at high temperature. The model under consideration is an extension of the elastic-viscoplastic “multisite model” proposed by Delannay et al. [2005] which has been adapted to account for a new type of grain interaction (Van Houtte et al. [2005]). Predictions of the model are compared to experimental data. Two forming processes are considered: sheet rolling (Lebensohn et al. [1994]) and the torsion of a cylindrical bar (Sanchez et al. [2001]). [less ▲]

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See detailPrediction of distortion during cooling of steel rolled rings using thermal-mechanical-metallurgical finite element model
Pascon, Frédéric ULg; Blès, Guilhem; Bouffioux, Chantal ULg et al

in Steel Grips 2 (2004), Supplement Metal Forming 1-3

This work takes place in the framework of a CRAFT European project gathering three universities, three companies who produce rings through the ring rolling process and a manufacturer of temperature and ... [more ▼]

This work takes place in the framework of a CRAFT European project gathering three universities, three companies who produce rings through the ring rolling process and a manufacturer of temperature and dimension measurement devices. The final goal of the project is to develop and set up a system, integrated in the industrial process, capable of predicting the geometrical characteristics of final pieces just after the ring rolling stage and to allow the rolling process to avoid dimensional defects through online adaption. In fact, ring rolling production does not imply only the rolling process, but also the cooling and quench stages of steel rings. During all these phases, the dimensions of the pieces change dramatically. In particular, due to the lack of symmetry in the cooling conditions, ring distortions include contraction and rotation of the ring section. The modeling of the cooling phase requires taking into account a large number of phenomena resulting from the coupling of thermal, mechanical and metallurgical effects. A numerical model has been implemented in the non-linear finite element code LAGAMINE, developed by the University of Liège. Such a model can help to better understand the evolution of the geometry during the cooling phase and also the effects of each physical and microstructural parameter implemented in the model on the ring final shape. Effectively, several parameters can affect the ring distortions and the model should take them into account; in particular, the mechanical and thermal behavior of each phase present in the material (metastable austenite, ferrite, pearlite, bainite and martensite). Phase transformation modeling implies the integration of a wide data base of material properties (thermo-physical and mechanical properties of the phases, TTT and CCT diagrams, enthalpy and strain of phase transformation, strain of transformation plasticity…) but only a few of these data are available in literature. Some of them have been found for the reference material (42CrMo4 steel), but additional laboratory experiments have been performed at the Universities of Padua and Liège in order to characterize thermal, mechanical and plastic behaviour of phases. Finally, this paper presents the model validation on an industrial case (measurements of temperature and dimensions of rings have been provided by the manufacturer). Then, some applications are presented, demonstrating the importance of some factors such as some material properties, the shape of the rings, the type of cooling (and the cooling rate) or the symmetry of the cooling scheme on final ring distortion. [less ▲]

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See detailPrediction of DP steel fracture by FEM simulations using an advanced Gurson model
Fansi Tchonko, Joseph ULg

Doctoral thesis (2013)

This numerical investigation of an advanced Gurson–Tvergaard–Needleman (GTN) model is an extension of the original work of Ben Bettaieb et al. (2011). The model has been implemented as a user-defined ... [more ▼]

This numerical investigation of an advanced Gurson–Tvergaard–Needleman (GTN) model is an extension of the original work of Ben Bettaieb et al. (2011). The model has been implemented as a user-defined material model subroutine (VUMAT) in the Abaqus/explicit FE code. The current damage model extends the previous version by integrating the three damage mechanisms: nucleation, growth and coalescence of voids. Physically based void nucleation and growth laws are considered, including an effect of the kinematic hardening. These new contributions are based and validated on experimental results provided by high-resolution X-ray absorption tomography measurements. Also, the numerical implementation of the kinematic hardening in this damage extension has obliged to readapt the classical triaxiality definition. Besides, a secondary fracture initiation criterion based on the ultimate average inter-cavities distance has been integrated to localize and quantify with good accuracy the strain distribution just before the material fails apart. The current damage model is applied in industrial conditions to predict the damage evolution, the stress state and the fracture initiation in various tensile thin flat sheet geometries and the cross-die drawing tests. [less ▲]

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See detailPrediction Of Epitopes And Production Of Monoclonal Antibodies Against Gastric H,K-Atpase
Irnaten, M.; Gallet, X.; Festy, F. et al

in Protein Engineering (1998), 11(10), 949-55

Monoclonal antibodies (mAbs) were produced against gastric H,K-ATPase using a theoretical and experimental strategy based on prediction of linear epitopes by molecular modelling followed by production of ... [more ▼]

Monoclonal antibodies (mAbs) were produced against gastric H,K-ATPase using a theoretical and experimental strategy based on prediction of linear epitopes by molecular modelling followed by production of anti-peptide antibodies. By analysing the alpha subunit sequence, we predicted several epitopes corresponding to amino acids K519-L533, E543-Y553 and S786-L798 and produced monoclonal antibodies HK519, HK543 and HK786. All three react against gastric H,K-ATPase in RaLISA, immunohistochemistry and Western blots demonstrating that they recognize the native and the SDS-denatured ionic pump and that the epitopes are located at the surface of the native ATPase. Antibody Kd are in the range 6-10x10(-8) M. Monoclonal antibody HK519 is a competitive inhibitor of ATP, in agreement with ATP binding to K519. Neither mAb 543, nor mAb 786 inhibit the ATPase activity. Monoclonal antibody 95111, whose epitope is mapped between residues C529 and E561, competes with mAb HK543 but not with the other two. We suggest that the 95111 epitope is overlapping or very close to the HK543-553 sequence. Induction of E1 conformer by binding FITC to K519 increases the number of mAb 95111 and mAb HK543 epitopes but not that of mAb 786, supporting the fact that the fragment E543-Y553 changes accessibility, maybe during the E1-E2 transconformation. [less ▲]

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See detailPrediction of exercise pulmonary hypertension in asymptomatic degenerative mitral regurgitation
Magne, Julien ULg; Lancellotti, Patrizio ULg; O'Connor, K. et al

in Journal of the American Society of Echocardiography (2011), 24(9), 1004-1012

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See detailPrediction of genetic interactions in yeast using machine learning
Schrynemackers, Marie ULg

Poster (2009, October)

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See detailPrediction of genetic interactions in yeast using machine learning
Schrynemackers, Marie ULg

Poster (2010, September)

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See detailPrediction of genetic interactions in yeast using machine learning
Schrynemackers, Marie ULg

Conference (2009, December 14)

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See detailPrediction of genetic risk of complex diseases by supervised learning
Botta, Vincent ULg; Geurts, Pierre ULg; Hansoul, Sarah et al

Scientific conference (2008, May)

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See detailPrediction of hypertension from adolescence to adulthood : a 10 years follow-up study
Saint-Remy, Annie ULg; Rorive, Georges ULg

in CVD Epidemiology Newsletter (American Heart Association) (1992), 47

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See detailPrediction of individual methane emission by dairy cattle from mid-infrared spectra
Vanlierde, Amélie ULg; Delfosse, Camille; Dehareng, Frédéric et al

Conference (2010, July 14)

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See detailPrediction of individual methane emission by dairy cattle from mid-infrared spectra
Vanlierde, Amélie ULg; Delfosse, Camille; Dehareng, Frédéric et al

in Journal of Dairy Science, 94(E-Suppl. 1) (2011)

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See detailPrediction of knee loads using a lower extremity model based on the Klein Horsman data set
Schwartz, Cédric ULg; Lund, Morten; de Zee, Mark et al

Conference (2010, June)

In this paper, the in-vivo loads of the knee joint provided by an instrumented prosthesis (Fregly et al., 2010, Lin et al., 2010, Kim et al., 2009) are compared to the results obtained from an ... [more ▼]

In this paper, the in-vivo loads of the knee joint provided by an instrumented prosthesis (Fregly et al., 2010, Lin et al., 2010, Kim et al., 2009) are compared to the results obtained from an implementation of the Klein Horsman data set (2007) in the AnyBody Modeling System. The lateral and medial knee contact forces are estimated directly from the knee modeled as a modified revolute joint. As such, this study presents what can be achieved by estimating the knee contact forces from a simplified knee model. [less ▲]

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See detailPrediction of landslide susceptibility in a seismically active high mountain region using data mining methods - a study from Maily-Say, Kyrgyzstan
Braun, Anika; Fernandez-Steeger, Tomas; Havenith, Hans-Balder ULg et al

in Reicherter, Klaus; Rudersdorf, Andreas; Grützner, Christoph (Eds.) Seismic Hazard, Critical Facilities and Slow Active Faults, Proceedings (2013, October)

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See detailPrediction of macroscopic mechanical properties of a polycrystalline microbeam subjected to material uncertainties
Lucas, Vincent ULg; Wu, Ling ULg; Arnst, Maarten ULg et al

in Cunha, Álvaro; Caetano, Elsa; Ribeiro, Pedro (Eds.) et al Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014 (2014, June)

The first resonance frequency is a key performance characteristic of MEMS vibrometers. In batch fabrication, this first resonance frequency can exhibit scatter owing to various sources of manufacturing ... [more ▼]

The first resonance frequency is a key performance characteristic of MEMS vibrometers. In batch fabrication, this first resonance frequency can exhibit scatter owing to various sources of manufacturing variability involved in the fabrication process. The aim of this work is to develop a stochastic multiscale model for predicting the first resonance frequency of MEMS microbeams constituted of polycrystals while accounting for the uncertainties in the microstructure due to the grain orientations. At the finest scale, we model the microstructure of polycrystaline materials using a random Voronoï tessellation, each grain being assigned a random orientation. Then, we apply a computational homogenization procedure on statistical volume elements to obtain a stochastic characterization of the elasticity tensor at the second scale of interest, the meso-scale. In the future, using a stochastic finite element method, we will propagate these meso-scale uncertainties to the first resonance frequency at the coarser scale. [less ▲]

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See detailPrediction of maternal predisposition to preeclampsia
Emonts, Patrick ULg; Seaksan, S.; Seidel, Laurence ULg et al

in Hypertension in Pregnancy : Official Journal of the International Society for the Study of Hypertension in Pregnancy (2008), 27(3), 237-45

Objective: To derive a prediction index based on the most salient patient history, laboratory, and clinical parameters for identifying women at high risk for developing preeclampsia (PE). Methods ... [more ▼]

Objective: To derive a prediction index based on the most salient patient history, laboratory, and clinical parameters for identifying women at high risk for developing preeclampsia (PE). Methods: Nonpregnant women with a history of PE (n = 101) were compared with nonpregnant parous women with a history of one or more successful normotensive pregnancies (n = 50) but with comparable age, gestation, and parity profiles. The parameters included a medical examination (demographics, patient history, family history, and clinical and obstetrical findings), laboratory investigations (hemostasis, coagulation, and vitamins), and morphological and functional tests (cardiovascular and renal functions). Stepwise logistic regression analysis was applied to develop a three-step PE prediction index based on the most discriminant parameters. Results: Patients with and without PE differed significantly (p < 0.05) with respect to 1) maternal history of chronic hypertension, body mass index, and blood pressure; 2) APTT, PT, activated factor VIII, homocystein, free protein S and vitamin B1; and 3) relative plasma volume. Based on these three sets of parameters, a three-step PE prediction index was developed. The likelihood ratio of a positive index score was equal to 3.4, 7.3, and 8.8, respectively. Thus, assuming a PE prevalence (or prior probability) of 5%, a patient's chances of developing PE when presenting with a positive score on the three-step prediction index were 15%, 28%, and 32%, respectively. Discussion: In the absence of welldefined pre-pregnancy screening guidelines for PE, the present study attempts to proceed in a stepwise fashion by looking at medical examination data first, requesting, if necessary, specific hemostasis and coagulation tests next, and finally measuring the relative plasma volume for confirmatory purposes. This approach offers a satisfactory positive predictive value and cost efficiency ratio. [less ▲]

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