Estimation Of Quantitative Parameters Research Articles

DeltaGen: A Comprehensive Decision Support Tool for Plant Breeders

In this paper, we introduce a unique new plant breeding decision support software tool DeltaGen, implemented in R and its package Shiny. DeltaGen provides plant breeders with a single integrated solution for experimental design generation, data quality control, statistical and quantitative genetic analyses, breeding strategy evaluation, simulation, and cost analysis, pattern analysis, index selection, and underlying basic theory on quantitative genetics. Key analysis procedures in DeltaGen were demonstrated using three datasets generated from forage breeding trials in Australia, New Zealand, and the United States. Analyses of the perennial ryegrass seasonal growth data in Case Study 1 was based on residual maximum likelihood analysis and pattern analysis. A graphical summary of the performance of entries across locations was generated, and entries with specific and broad adaptation were identified. The quantitative genetic analysis and breeding method simulation procedures applied to the perennial ryegrass half‐sib (HS) family data in Case Study 2 enabled estimation of quantitative genetic parameters, prediction of genetic gain, and calculation of costs per selection cycle. These results enabled comparison of three breeding methods, which also included genomic selection, and their simulation. Data from Case Study 3 were analyzed to investigate a multivariate approach to identify HS families of switchgrass with breeding values that would enable an increase in biomass dry matter yield (DMY) and cell wall ethanol (CWE) and a decrease in Klason lignin (KL). The Smith–Hazel index developed enabled identification of HS families with genetic worth for increasing DMY and CWE and reducing KL, in contrast with individual trait selection. Analysis of the datasets in all three case studies provides a snapshot of the key analyses available within DeltaGen. This software tool could also be used as a teaching resource in plant breeding courses. DeltaGen is available as freeware at http://agrubuntu.cloudapp.net/PlantBreedingTool/

Practicable methods for histological section thickness measurement in quantitative stereological analyses

The accuracy of quantitative stereological analysis tools such as the (physical) disector method substantially depends on the precise determination of the thickness of the analyzed histological sections. One conventional method for measurement of histological section thickness is to re-embed the section of interest vertically to its original section plane. The section thickness is then measured in a subsequently prepared histological section of this orthogonally re-embedded sample. However, the orthogonal re-embedding (ORE) technique is quite work- and time-intensive and may produce inaccurate section thickness measurement values due to unintentional slightly oblique (non-orthogonal) positioning of the re-embedded sample-section. Here, an improved ORE method is presented, allowing for determination of the factual section plane angle of the re-embedded section, and correction of measured section thickness values for oblique (non-orthogonal) sectioning. For this, the analyzed section is mounted flat on a foil of known thickness (calibration foil) and both the section and the calibration foil are then vertically (re-)embedded. The section angle of the re-embedded section is then calculated from the deviation of the measured section thickness of the calibration foil and its factual thickness, using basic geometry. To find a practicable, fast, and accurate alternative to ORE, the suitability of spectral reflectance (SR) measurement for determination of plastic section thicknesses was evaluated. Using a commercially available optical reflectometer (F20, Filmetrics®, USA), the thicknesses of 0.5 μm thick semi-thin Epon (glycid ether)-sections and of 1–3 μm thick plastic sections (glycolmethacrylate/ methylmethacrylate, GMA/MMA), as regularly used in physical disector analyses, could precisely be measured within few seconds. Compared to the measured section thicknesses determined by ORE, SR measures displayed less than 1% deviation. Our results prove the applicability of SR to efficiently provide accurate section thickness measurements as a prerequisite for reliable estimates of dependent quantitative stereological parameters.

Sex-dependent expression of behavioural genetic architectures and the evolution of sexual dimorphism.

Empirical studies imply that sex-specific genetic architectures can resolve evolutionary conflicts between males and females, and thereby facilitate the evolution of sexual dimorphism. Sex-specificity of behavioural genetic architectures has, however, rarely been considered. Moreover, as the expression of genetic (co)variances is often environment-dependent, general inferences on sex-specific genetic architectures require estimates of quantitative genetics parameters under multiple conditions. We measured exploration and aggression in pedigreed populations of southern field crickets (Gryllus bimaculatus) raised on either naturally balanced (free-choice) or imbalanced (protein-deprived) diets. For each dietary condition, we measured for each behavioural trait (i) level of sexual dimorphism, (ii) level of sex-specificity of survival selection gradients, (iii) level of sex-specificity of additive genetic variance, and (iv) strength of the cross-sex genetic correlation. We report here evidence for sexual dimorphism in behaviour as well as sex-specificity in the expression of genetic (co)variances as predicted by theory. The additive genetic variances of exploration and aggression were significantly greater in males compared with females. Cross-sex genetic correlations were highly positive for exploration but deviating (significantly) from one for aggression; findings were consistent across dietary treatments. This suggests that genetic architectures characterize the sexually dimorphic focal behaviours across various key environmental conditions in the wild. Our finding also highlights that sexual conflict can be resolved by evolving sexually independent genetic architectures.

Quantitative ultrasound molecular imaging by modeling the binding kinetics of targeted contrast agent

Ultrasound molecular imaging (USMI) is an emerging technique to monitor diseases at the molecular level by the use of novel targeted ultrasound contrast agents (tUCA). These consist of microbubbles functionalized with targeting ligands with high-affinity for molecular markers of specific disease processes, such as cancer-related angiogenesis. Among the molecular markers of angiogenesis, the vascular endothelial growth factor receptor 2 (VEGFR2) is recognized to play a major role. In response, the clinical-grade tUCA BR55 was recently developed, consisting of VEGFR2-targeting microbubbles which can flow through the entire circulation and accumulate where VEGFR2 is over-expressed, thus causing selective enhancement in areas of active angiogenesis. Discrimination between bound and free microbubbles is crucial to assess cancer angiogenesis. Currently, this is done non-quantitatively by looking at the late enhancement, about 10 min after injection, or by calculation of the differential targeted enhancement, requiring the application of a high-pressure ultrasound (US) burst to destroy all the microbubbles in the acoustic field and isolate the signal coming only from bound microbubbles. In this work, we propose a novel method based on mathematical modeling of the binding kinetics during the tUCA first pass, thus reducing the acquisition time and with no need for a destructive US burst. Fitting time-intensity curves measured with USMI by the proposed model enables the assessment of cancer angiogenesis at both the vascular and molecular levels. This is achieved by estimation of quantitative parameters related to the microvascular architecture and microbubble binding. The proposed method was tested in 11 prostate-tumor bearing rats by performing USMI after injection of BR55, and showed good agreement with current USMI methods. The novel information provided by the proposed method, possibly combined with the current non-quantitative methods, may bring deeper insight into cancer angiogenesis, and thus potentially improve cancer diagnosis and management.

Development of Data Processing Tools for the Analysis of Radargrams in Utility Detection Using Ground Penetrating Radar

The extraction of quantitative information from Ground Penetrating Radar (GPR) data sets (radargrams) to detect and map underground utility pipelines is a challenging task. This study proposes several algorithms included in the main stages of a data processing chain associated with radargrams. It comprises preprocessing, hyperbola enhancing, hyperbola detection and localization, and parameter extraction. Additional parameters related to the GPR system such as the frequency band and the polarization bring data sets additional information that need to be exploited. Presently, the algorithms have been applied step by step on synthetic and experimental data. The results help to guide future developments in signal processing for quantitative parameter estimation.

Hybrid Linear Moments and ANFIS-GA to Predict Groundwater Salinity

There is, unfortunately, a lack of exhaustive qualitative and quantitative information about Iran groundwater resources. That is why various models are used in estimation of qualitative and quantitative groundwater parameters. The present paper presents a comparison of the hybrid of Adaptive Neuro Fuzzy Inference System (ANFIS) with Genetic Algorithm (GA) model and L-moments regarding their power and efficiency in regional and at-site anticipation of salinity of groundwater at Kerman plain. In doing so, electrical conductivity is considered the dependent variable, while, through regression analysis, total cat ions, magnesium ion, sodium percentage, and level of groundwater are assumed to be independent parameters. The correlation coefficient between input values and anticipated ones is the criterion the study takes into account in comparisons as well as in the election of the optimum model. Wells of study area were classified into three homogenous regions. Hass-King Heterogeneity and Incongruity Criterion were calculated for each site. The best result for regional analysis is achieved in well No.17 with correlation coefficient (C.C) 0.9958 whereas the best result for at-site analysis is calculated in well No.2 with C.C 0.9787. Results showed that, in regions with lower heterogeneity criterion, ANFIS-GA regional anticipations were slightly more accurate than at-site anticipations.

Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models.

Summary Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. ‘Animal models’, which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations.However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non‐randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology.We first illustrate how the occurrence of non‐random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce ‘genetic group’ methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents.We summarize the conceptual foundations of genetic group animal models and provide extensive, step‐by‐step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods.Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow.We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.

Food Consumption and Handling Survey for Quantitative Microbiological Consumer Phase Risk Assessments

In the consumer phase of a typical quantitative microbiological risk assessment (QMRA), mathematical equations identify data gaps. To acquire useful data we designed a food consumption and food handling survey (2,226 respondents) for QMRA applications that is especially aimed at obtaining quantitative data. For a broad spectrum of food products, the survey covered the following topics: processing status at retail, consumer storage, preparation, and consumption. Questions were designed to facilitate distribution fitting. In the statistical analysis, special attention was given to the selection of the most adequate distribution to describe the data. Bootstrap procedures were used to describe uncertainty. The final result was a coherent quantitative consumer phase food survey and parameter estimates for food handling and consumption practices in The Netherlands, including variation over individuals and uncertainty estimates.

Analysis of Coherent and Diffuse Scattering Using a Reference Phantom.

The estimation of many spectral-based quantitative ultrasound parameters assumes that backscattered echo signals are from a stationary, incoherent scattering process. The accuracy of these assumptions in real tissue can limit the diagnostic value of these parameters and the physical insight about tissue microstructure they can convey. This work presents an empirical decision test to determine the presence of significant coherent contributions to echo signals and whether they are caused by low scatterer number densities or the presence of specular reflectors or scatterers with periodic spacing. This is achieved by computing parameters from echo signals that quantify stationary or nonstationary features related to coherent scattering, and then comparing their values to thresholds determined from a reference material providing diffuse scattering. The paper first presents a number of parameters with demonstrated sensitivity to coherent scattering and describes criteria to select those with the highest sensitivity using simulated and phantom-based echo data. Results showed that the echo amplitude signal-to-noise ratio and the multitaper-generalized spectrum were the parameters with the highest sensitivity to coherent scattering with stationary and nonstationary features, respectively. These parameters were incorporated into the reference-based decision test, which successfully identified regions in simulated and tissue-mimicking phantoms with different incoherent and coherent scattering conditions. When scatterers with periodic organization were detected, the combination of stationary and nonstationary analysis permitted the estimation of the mean spacing below and above the resolution limit imposed by the pulse size. Preliminary applications of this algorithm to human cervical tissue ex vivo showed correspondence between regions of B-mode images showing bright reflectors, tissue interfaces, and hypoechoic regions with regions classified as specular reflectors and low scatterer number density. These results encourage further application of the algorithm to more structurally complex phantoms and tissue.

Analysis of pairwise correlations in multi-parametric PET/MR data for biological tumor characterization and treatment individualization strategies.

Biological tumor characterization based on functional and molecular imaging might be highly valuable for radiotherapy (RT). On the one hand, it could allow for an improved target volume definition and an individualized dose prescription within the tumor according to local biological characteristics. Such dose painting strategies can be readily applied with the technical availability of intensity modulated RT (IMRT). Moreover, functional imaging might be of high value for early response assessment and potential treatment adaptation in the course of fractionated RT [1, 2]. Other fields of application are the assessment of chemotherapy and the application of targeted agents, such as hypoxia-sensitizing or antiangiogenic drugs [3, 4]. Both positron emission tomography (PET) and magnetic resonance imaging (MRI) may provide functional information beneficial for personalized treatment strategies. PET imaging using [ 18F]-fluorodeoxyglucose (FDG) can be used to monitor glucose metabolism, whereas the hypoxic status of the tumor can be assessed using dedicated tracers such as [ 18F]-fluoromisonidazole (FMISO). Diffusion weighted MRI (DW-MRI) provides the possibility to quantify the diffusion of water molecules, which is related to cellular density [5]. Dynamic contrast-enhanced MRI (DCE-MRI) yields a temporally varying signal due to the distribution of contrast agent in blood pool and tissue. By compartmental modeling estimates of quantitative physiological parameters can be derived [6]. With the advent of combined PET/MR imaging [7, 8] the acquisition of simultaneous, intrinsically registered PET and MR data has become possible. This facilitates the comparison and combined analysis of PET- and MR-derived functional imaging data. Simultaneous PET/MR may thus be of high potential for treatment individualization [9, 10]. Recent studies have associated different functional imaging information with RT outcome for head and neck (HN) cancer. This applies to FDG-PET [11, 12], static as well as dynamic FMISO-PET [13–16], apparent diffusion coefficients (ADCs) inferred by DW-MRI [17], as well as DCE-MRI [18, 19]. These studies provide a rationale to adapt RT treatment plans according to functional imaging information. It is not clear yet if datasets from different functional imaging modalities are completely complementary, or if information is to some extent redundant. Initial analyses of correlations between different functional datasets have already been performed in recent studies. The studies of Rajendran et al. [20] and Thorwarth et al. [21] revealed good voxel-by-voxel correlation of FDG and FMISO in some HN tumors, whereas others showed no clear correlation. The biological basis of the observed correlations may be the hypoxia-inducible factor 1 α (HIF 1 α) [20]. Similar results were obtained by Zegers et al. [22] comparing uptake of FDG and the hypoxia PET tracer [ 18F]-HX4 in patients with non–small cell lung cancer. Houweling et al. [23] quantified correlations between FDG and ADC maps of HN tumors on a voxel level, and found a negative correlation in most patients. Both Newbold et al. [24] and Donaldson et al. [25] found correlations between hypoxia derived from pimonidazole staining and DCE-derived parameter maps on a region-of-interest (ROI) level. A study by Jansen et al. [26] found that neck nodal metastases with positive FMISO uptake differed significantly in median Ktrans values from those with no FMISO uptake. Earlier studies have shown that a dynamic imaging protocol may be superior compared to a single time frame for hypoxia quantification using FMISO-PET [16]. However, in addition to a late static scan several hours post injection (p.i.), such a dynamic protocol requires a PET acquisition during tracer wash-in in the first minutes p.i. [27], which may hamper its usage in clinical routine. A positive correlation result between early FMISO and DCE information would potentially provide the possibility to infer early FMISO information from DCE, which would facilitate its clinical usage. To address the question if available functional information of PET/MR is complementary or to some extend redundant, this study extends beyond existing studies by considering a comprehensive set of functional data. Correlations of various functional datasets are quantified on a voxel as well as on a regional level within HN tumors by means of the Spearman correlation coefficient. For the analysis, FDG-PET, FMISO-PET acquired in the wash-in, as well as in the retention phase, ADC maps extracted from DW-MRI, and DCE-MRI derived maps are taken into account. The study is a first explorative, hypothesis generating approach to investigate the utilization of integrated PET/MR for personalized treatment strategies.

Detection of recurrent nerve paralysis: Development of a Computer Aided Diagnosis system

Recurrent nerve paralysis (RP) is one of the most frequent complications of thyroid surgery. It reduces vocal fold mobility. Nasal endoscopy, a mini-invasive procedure, is the reference procedure to detect RP, and is based on the examination of vocal fold mobility. A new approach based on laryngeal ultrasound acquisition and using a dedicated data analysis was designed to help with the automated detection of RP.One hundred and fifty subjects were enrolled for this feasibility study: 50 controls, 50 patients with RP and 50 patients without RP, according to nasal endoscopy. The ultrasound protocol was based on a ten-second B-mode acquisition in an axial plane during normal breathing. Image processing included three steps: 1) the detection of two consecutive closing and opening images corresponding to extreme positions of vocal folds in the sequence of B-mode images; 2) the positioning of three landmarks and the robust tracking of these points using multi-pyramidal refined optical flow approaches; 3) the estimation of quantitative parameters indicating left and right fractions of mobility and motion symmetry.Results provided by the first two image processing steps were compared to those obtained by an expert. Motion symmetry and fraction of mobility indices were systematically computed using the automated procedures. Associated sensitivity and specificity values for detecting RP were then calculated. To optimize the performances of the system, a mixed CAD system, which integrates the automatic steps of image processing and a possible correction of its results by a trained operator, was developed.Laryngeal ultrasound combined with appropriate image processing helped in the diagnosis of recurrent nerve paralysis and could be proposed as a first-line method.

Study of quantitative genetics of gum arabic production complicated by variability in ploidy level of Acacia senegal (L.) Willd

Gum arabic is an important international commodity produced by trees of Acacia senegal across Sahelian Africa, but documented results of breeding activities are limited. The objective of this study was to provide reliable estimates of quantitative genetic parameters in order to shed light on the breeding potential for improvement of gum yield and quality. For this purpose, we measured growth on 617 offspring from 60 open-pollinated trees after 18 years, and gum yield and quality based on two seasons, 18 and 19 years after establishment. Genotyping with eight microsatellite markers revealed that progenies consisted of both diploid and polyploid trees, and growth, gum yield, and gum quality varied substantially among ploidy level, populations, and progenies. Analysis of molecular variance and estimates of outcrossing rate supported that trees within open-pollinated families of diploids were half sibs, while the open-pollinated families of polyploids showed low variation within families. The difference in sibling relationship observed between ploidy levels complicated estimation of genetic parameters. However, based on the diploid trees, we conclude that heritability in gum arabic production is low to high with presence of high levels of additive genetic variation, although the genetic parameters could only be estimated with fairly high standard error. The findings suggest that improvement through breeding can increase the productivity of A. senegal substantially. However, the results also stress the importance of testing ploidy levels of selected material and use of genetic markers to qualify the assumptions in the quantitative genetic analysis.

TH-CD-207-03: A Constrained Linear Reference Region Model For DCE-MRI

Purpose: Dynamic contrast enhanced (DCE)-MRI characterizes perfusion in tissues using time-series data and a quantitative model. This study presents a modification to the linear reference region model (LRRM) that uses two fitting parameters instead of three, resulting in improved accuracy and precision of estimated parameters. Methods: The number of fitting parameters in the LRRM is reduced by noting that the 1st and 2nd parameters are related by the kep of the reference region. This relation is accounted by the proposed constrained LRRM (CLRRM). The performance of CLRRM was compared against LRRM through simulations of concentration-time curves produced using the Tofts model, with KTrans=0.1, v_ e=0.1 for reference tissue, and KTrans=0.25, v_e=0.4 for tumour. SNR in reference curve is generally high, therefore Gaussian noise was added only to the tumour curves for an SNR range of 5 to 50 in 10 steps with temporal resolutions of 1s, 10s, 30s and 60s. 10,000 realizations were simulated for each SNR and temporal resolution combination. The in-vivo performance of both methods was evaluated by applying them to data acquired in sarcoma. Results: The accuracy was evaluated through the mean percent error, and precision was judged by the standard deviation of the percent error. Both CLRRM and LRRM had similar accuracy at high SNR, but CLRRM was more accurate at lower SNR, especially for kep. The mean percent error for kep was-36% for LRRM and-6% for CLRRM at SNR 5 and 30s temporal resolution. The standard deviation for CLRRM was roughly 5 times lower. The in-vivo results showed that CLRRM produced kep maps with less noise and higher values in the solid tumour region. Conclusion: A modified version of the linear reference region model improves quantitative DCE-MRI parameter estimates at low SNR and low temporal resolution. Funding from the RI-MUHC (Montreal General Hospital Foundation), NSERC CREATE MPRTN (Grant no. 432290), and McGill University Faculty of Medicine Fellowships.

Quantitative genetics of disease traits.

John James authored two key papers on the theory of risk to relatives for binary disease traits and the relationship between parameters on the observed binary scale and an unobserved scale of liability (James Annals of Human Genetics, 1971; 35: 47; Reich, James and Morris Annals of Human Genetics, 1972; 36: 163). These two papers are John James' most cited papers (198 and 328 citations, November 2014). They have been influential in human genetics and have recently gained renewed popularity because of their relevance to the estimation of quantitative genetics parameters for disease traits using SNP data. In this review, we summarize the two early papers and put them into context. We show recent extensions of the theory for ascertained case-control data and review recent applications in human genetics.

Maternal Age-Related Depletion of Offspring Genetic Variance in Immune Response to Phytohaemagglutinin in the Blue Tit (Cyanistes caeruleus).

Studies examining age-specific patterns in genetic variance have focussed primarily on changes in the genetic variance within cohorts. It remains unclear whether parental age may affect the genetic variance among offspring. To date, such an effect has been reported only in a single study performed in a wild bird population. Here, we provide experimental evidence that the additive genetic variance (VA) observed among offspring may be related to parental age in a wild passerine—the blue tit (Cyanistes caeruleus). To separate genetic and environmental components of phenotypic variance in nestling body size and immune function we cross-fostered nestlings between pairs of broods born to young and old mothers and used an animal model to estimate VA. We show that the genetic variance in immune response to phytohaemagglutinin (PHA) and body weight among offspring depends on maternal age. VA in response to PHA appeared to be lower among nestlings of older mothers. Such a tendency was not observed for tarsus length. We argue that the lower VA may result either from depletion of additive genetic variation due to selection acting on parents across age classes or from environmental effects confounded with parental age. Thus, our study suggests that parental age may significantly affect estimates of quantitative genetic parameters in the offspring.

Insights on geologic and vegetative controls over hydrologic behavior of a large complex basin – Global Sensitivity Analysis of an integrated parallel hydrologic model

Summary This study demonstrated the first application of a GSA technique to a transient ISSHM–LSM application developed for a large-scale river basin. The Morris method was used to identify the spatially and temporally variable sensitivity amongst a large number of model parameters to provide insights on hydrologic processes dominating behavior in the basin and to identify a small subset of parameters that should be evaluated in subsequent, more computationally intensive quantitative GSA and parameter estimation techniques. Results showed that in the upper region of the basin, evapotranspiration (ET), total streamflow and peak streamflow were less sensitive to surficial aquifer system characteristics, but highly sensitive to the hydraulic conductivity of the confining unit separating the surficial aquifer and the regional aquifer system and leaf area index of near stream vegetation. In the lower region of the basin, hydraulic conductivity of the regional aquifer system was found to have a significant effect on ET, total stream flow, and groundwater contributions to streamflow while surface–groundwater dynamics during storm events was most sensitive to storage properties of the regional aquifer system. Peak streamflow in the lower basin was most sensitive to the hydraulic conductivity of the confining unit in the upper basin, and the Manning’s coefficient of upper basin streams, indicating that all peak storm flows originate in the upper basin. Throughout the basin ET was sensitive to soil/geologic properties and vegetation properties, with unsaturated zone processes and relevant parameters gaining importance in moisture limited conditions existing in the lower regions of the basin.

Estimation of quantitative genetic parameters for grain yield and quality in winter wheat under high and low nitrogen fertilization

The main question connected with developing wheat cultivars adapted to low nitrogen (N) is whether separate breeding programs for low and high input conditions are necessary. Nineteen wheat cultivars were grown over three years in Croatia in a total of eight environments at high N and low N in order to determine the effect of two N levels on means, variances and heritability of grain yield and bread-making quality and to assess the relative efficiency of indirect selection under high N in improving trait means under low N. Means of grain yield and grain protein content decreased under low N 10 and 13 %, respectively compared to the high N, whereas higher reductions of means due to lower fertilization were observed for grain N yield (21 %), wet gluten content (20 %), Zeleny sedimentation value (27 %) and for most rheological parameters, whose mean values were reduced from 20 % to as much as 57 %. Heritabilities for grain yield and grain N yield at high N were 0.82 and 0.76, respectively, and 0.77 and 0.43 at low N. Heritability for dough development time, stability and resistance also tended to be higher at high N than at low N due to a decrease in genetic variance and an increase in error variance at low N. The genetic correlation coefficients between high and low N were higher than 0.90 and the efficiency of indirect selection under high N for performance under low N was near 1.0 for grain yield and for most bread-making quality traits.

SU‐E‐QI‐05: Denoising Intravoxel Incoherent Motion Magnetic Resonance Images Using Non‐Local Mean Technique for Oropharyngeal Cancer Study

Purpose:Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) normally shows a low signal to noise ratio (SNR) due to the presence of noise which complicates and biases the estimation of quantitative diffusion parameters. In this study, a Non‐local Means (NLM) approach was applied to remove the noise in oropharyngeal cancer IVIMMRI images.Methods:Eight male patients with squamous cell carcinoma of the oropharynx were included in this study under an approved IRB protocol. IVIM‐MRI was carried out on a 3.0‐T GE MRI. NLM denoising technique was performed using the MIPAV (V7, NIH). IVIM parameters (D, pure diffusion coefficient; f, perfusion fraction; D*, pseudodiffusion coefficient) were calculated on a pixel‐by‐pixel basis using a bi‐exponential model implemented within ImageJ (V1.47, NIH) with ten b‐values ranging from 0 – 800 s/mm2. SNR with and without denoising processing was calculated and compared in tumor regions. The agreements of IVIM‐MRI parameters estimated between with and without NLM processing in tumor region were assessed by Bland‐Altman plots.Results:Examples of IVIM images (b=800 s/mm2) and parametric maps (D, D* and f) with and without NLM applied are illustrated in Fig. 1 for an oropharynx cancer patient. Results of SNR and IVIM diffusion parameters between two different processing, as mean and standard deviation on 8 patients, are reported in Table 1. Bland‐Altman plots (Fig. 2) between the two approaches show better concordance for D (0.22% ± 1.42%) and f (0.56% ± 3.49%), whereas larger discrepancies were found for D* (0.86% ± 6.25%).Conclusion:NLM approach gives better SNR and quantitative data quality, also shows a good agreement between the IVIM parameters processed with and without NLM approaches. Therefore, NLM denoising technique can be applied to improve performance in terms of denoising quality and estimation of IVIM parameter as a post‐processing step without increasing the scanning time.

English

Protein and fat percent as content of milk are high-priority criteria for financial aims and selection of programs in dairy cattle. In order to incorporate information on marker- quantitative trait locus (QTL) associations, QTL detection and parameter estimation should also be performed. Microsatellite markers are most valuable markers in association studies between different loci with the production traits. With the aim of analysis markers previously identified by other researchers in QTLs of milk fat and protein percent traits in Iranian Holstein dairy cattle sample population, we use ten microsatellite markers on chromosomes 1, 6, 7 and 20. All microsatellite markers were genotype in 156 dairy cattle which have at least one year data of milk fat and protein record using 6 to 8% polyacrylamide gel electrophoreses determined with silver nitrate staining. We find 7, 5 and 8 out of 10 markers showing significant effect on milk fat percent in 1 to 120, 120 to 240 and 240 to 360 milking days, respectively. Our results show 6, 8 and 9 out of 10 markers have significant effect on protein percent in 1 to 120, 120 to 240 and 240 to 360 milking days respectively.   Key words: Fat percent, Iranian Holstein cattle, microsatellites, milking days, protein percent, quantitative trait locus (QTL).

Cardiological Ultrasound Imaging

This review paper is intended for the interested outsider of the field of echocardiography and it presents a short introduction into the numerous ultrasound (US) methods and techniques for anatomical and functional diagnosis of the heart. The basic techniques are generally used for some times already, as there are one dimensional (1D) M(otion) mode, the real time 2D B(rightness) mode technique and the various Doppler measurement techniques and imaging modes. The M-mode technique shows the movements of the tissue in a 1D B-mode display vs. time. The 2D B-mode images are showing the heart contractions and dilations in real time, thus making this technique the basic tool for detecting anatomical disturbances and myocardial (localized) abnormal functioning. Improved image quality is achieved by Second Harmonic Imaging and myocardial perfusion can be quantified using Contrast Agent Imaging. Doppler techniques were introduced in the fifties of last century and used for blood flow velocity measurement. Continuous wave (CW) Doppler has the advantage of allowing measurement of high velocities, as may occur in vascular or valvular stenosis and insufficiency. The exact location of the major Doppler signal received cannot be estimated making this technique ambiguous in some clinical problems. Single gated Pulse Wave (PW) Doppler velocity measurement delivers exact location of the measurement position by using an interactively positioned time (=depth) gate in which the velocity is being measured. The disadvantage of this technique is the relatively low maximum velocity that can be measured. Multigate PW Doppler techniques can be used for the assessment of a velocity profile over the vessel cross section. A more sophisticated use of this technique is the combination with 2D B-mode imaging in the color Doppler mode, called "color flow mapping", in which the multigate Doppler signal is color coded and shown in 2D format overlayed in the conventional 2D B mode image. In the past two decades, technique to quantify myocardial function were developed: Tissue Doppler Imaging (TDI), Strain Rate and Strain Imaging. The temporal resolution of ultrasound imaging can be further improved by Plane Wave Imaging, and Synthetic Aperture Imaging. The recent introduction of 2D matrix transducers extended the real time imaging potential by allowing 3D imaging and sophisticated segmentation techniques for the estimation of quantitative functional parameters, as for instance cardiac output.