Lateralized EEG components with direction information for the preparation of saccades versus finger movements

During preparation of horizontal saccades in humans, several lateralized (relative to saccade direction), event-related EEG components occur that have been interpreted as reflecting activity of frontal and parietal eye fields. We investigated to what degree these components are specific to saccade preparation. EEG lateralization was examined within the interval (1 s) between a first (S1) and a second (S2) stimulus, after which a response had to be made (look left or right, or press a button with the left or right index finger). The visual S1 indicated either the direction (left vs right) and/or the effector (eye vs finger), and S2 (visual/auditory in different blocks) added the information not given by S1. An occipital component (220 ms after S1) was effector-independent, probably reflecting processing of the direction code. The following parietotemporal component (320 ms after S1) was specific for direction information. This component seems more relevant for finger movements than for saccades and may reflect a link between visual perception to action. A later frontal component (480 ms after S1) was specific for direction information and may be related to the planning of a lateral movement. One component was entirely specific for the preparation of a finger movement (the lateralized readiness potential before S2). Thus, several different lateralized processes in the S1-S2 interval could be delineated, reflecting hand-specific preparation, processing of the direction code, and the coordination of perception and action, but no components were observed as being specific for saccade preparation.

Prediction of additive genetic variances of descendants for complex families based on Mendelian sampling variances

The ability to predict the outcome of selection and mating decisions enables breeders to make strategically better selection decisions. To improve genetic progress, those individuals need to be selected whose offspring can be expected to show high genetic variance next to high breeding values. Previously published approaches enable to predict the variance of descendants of 2 future generations for up to 4 founding haplotypes, or 2 outbred individuals, based on phased genotypes, allele effects, and recombination frequencies. The purpose of this study was to develop a general approach for the analytical calculation of the genetic variance in any future generation. The core development is an equation for the prediction of the variance of double haploid lines, under the assumption of no selection and negligible drift, stemming from an arbitrary number of founder haplotypes. This double haploid variance can be decomposed into gametic Mendelian sampling variances (MSVs) of ancestors of the double haploid lines allowing usage for non-double haploid genotypes that enables application in animal breeding programs as well as in plant breeding programs. Together with the breeding values of the founders, the gametic MSV may be used in new selection criteria. We present our idea of such a criterion that describes the genetic level of selected individuals in 4 generations. Since breeding programs do select, the assumption made for predicting variances is clearly violated, which decreases the accuracy of predicted gametic MSV caused by changes in allele frequency and linkage disequilibrium. Despite violating the assumption, we found high predictive correlations of our criterion to the true genetic level that was obtained by means of simulation for the "corn" and "cattle" genome models tested in this study (0.90 and 0.97). In practice, the genotype phases, genetic map, and allele effects all need to be estimated meaning inaccuracies in their estimation will lead to inaccurate variance prediction. Investigation of variance prediction accuracy when input parameters are estimated was not part of this study.

The use of a genomic relationship matrix for breed assignment of cattle breeds: comparison and combination with a machine learning method

To develop a breed assignment model, three main steps are generally followed: 1) The selection of breed informative SNP; 2) The training of a model, based on a reference population, that allows to classify animals to their breed of origin; and 3) The validation of the developed model on external animals i.e., that were not used in previous steps. However, there is no consensus in the literature about which methodology to follow for the first step, nor about the number of SNP to be selected. This can raise many questions when developing the model and lead to the use of sophisticated methodologies for selecting SNP (e.g., with iterative algorithms, partitions of SNP or combination of several methods). Therefore, it may be of interest to avoid the first step by the use of all the available SNP. For this purpose, we propose the use of a genomic relationship matrix (GRM), combined or not with a machine learning method, for breed assignment. We compared it with a previously developed model based on selected informative SNP. Four methodologies were investigated: 1) The PLS_NSC methodology: selection of SNP based on a partial least square-discriminant analysis (PLS-DA) and breed assignment by classification based on the nearest shrunken centroids (NSC) method; 2) Breed assignment based on the highest mean relatedness of an animal to the reference populations of each breed (referred to mean_GRM); 3) Breed assignment based on the highest SD of the relatedness of an animal to the reference populations of each breed (referred to SD_GRM) and 4) The GRM_SVM methodology: the use of means and SD of the relatedness defined in mean_GRM and SD_GRM methodologies combined with the linear support vector machine (SVM), a machine learning method used for classification. Regarding mean global accuracies, results showed that the use of mean_GRM or GRM_SVM was not significantly different (Bonferroni corrected P > 0.0083) than the model based on a reduced SNP panel (PLS_NSC). Moreover, the mean_GRM and GRM_SVM methodology were more efficient than PLS_NSC as it was faster to compute. Therefore, it is possible to bypass the selection of SNP and, by the use of a GRM, to develop an efficient breed assignment model. In routine, we recommend the use of GRM_SVM over mean_GRM as it gave a slightly increased global accuracy, which can help endangered breeds to be maintained. The script to execute the different methodologies can be accessed on: https : //github.com/hwilmot675/Breed_assignment.

Imputation of low-density marker chip data in plant breeding: Evaluation of methods based on sugar beet

Low-density genotyping followed by imputation reduces genotyping costs while still providing high-density marker information. An increased marker density has the potential to improve the outcome of all applications that are based on genomic data. This study investigates techniques for 1k to 20k genomic marker imputation for plant breeding programs with sugar beet (Beta vulgaris L. ssp. vulgaris) as an example crop, where these are realistic marker numbers for modern breeding applications. The generally accepted 'gold standard' for imputation, Beagle 5.1, was compared with the recently developed software AlphaPlantImpute2 which is designed specifically for plant breeding. For Beagle 5.1 and AlphaPlantImpute2, the imputation strategy as well as the imputation parameters were optimized in this study. We found that the imputation accuracy of Beagle could be tremendously improved (0.22 to 0.67) by tuning parameters, mainly by lowering the values for the parameter for the effective population size and increasing the number of iterations performed. Separating the phasing and imputation steps also improved accuracies when optimized parameters were used (0.67 to 0.82). We also found that the imputation accuracy of Beagle decreased when more low-density lines were included for imputation. AlphaPlantImpute2 produced very high accuracies without optimization (0.89) and was generally less responsive to optimization. Overall, AlphaPlantImpute2 performed relatively better for imputation whereas Beagle was better for phasing. Combining both tools yielded the highest accuracies.

Improving selection decisions with mating information by accounting for Mendelian sampling variances looking two generations ahead

BACKGROUND

Breeding programs are judged by the genetic level of animals that are used to disseminate genetic progress. These animals are typically the best ones of the population. To maximise the genetic level of very good animals in the next generation, parents that are more likely to produce top performing offspring need to be selected. The ability of individuals to produce high-performing progeny differs because of differences in their breeding values and gametic variances. Differences in gametic variances among individuals are caused by differences in heterozygosity and linkage. The use of the gametic Mendelian sampling variance has been proposed before, for use in the usefulness criterion or Index5, and in this work, we extend existing approaches by not only considering the gametic Mendelian sampling variance of individuals, but also of their potential offspring. Thus, the criteria developed in this study plan one additional generation ahead. For simplicity, we assumed that the true quantitative trait loci (QTL) effects, genetic map and the haplotypes of all animals are known.

RESULTS

In this study, we propose a new selection criterion, ExpBVSelGrOff, which describes the genetic level of selected grand-offspring that are produced by selected offspring of a particular mating. We compare our criterion with other published criteria in a stochastic simulation of an ongoing breeding program for 21 generations for proof of concept. ExpBVSelGrOff performed better than all other tested criteria, like the usefulness criterion or Index5 which have been proposed in the literature, without compromising short-term gains. After only five generations, when selection is strong (1%), selection based on ExpBVSelGrOff achieved 5.8% more commercial genetic gain and retained 25% more genetic variance without compromising inbreeding rate compared to selection based only on breeding values.

CONCLUSIONS

Our proposed selection criterion offers a new tool to accelerate genetic progress for contemporary genomic breeding programs. It retains more genetic variance than previously published criteria that plan less far ahead. Considering future gametic Mendelian sampling variances in the selection process also seems promising for maintaining more genetic variance.

Structure distribution relationship of iodine-123-iodobenzamides as tracers for the detection of melanotic melanoma

The influence of systematic structure variations on the biodistribution of positional isomers of N-(N'-dialkylaminoethylene)-[123I]iodobenzamide (ABA) derivatives in melanoma-bearing animals was investigated.

METHODS

Radioiodination of six bromo benzamide precursors was achieved by Cu(1)-assisted nonisotopic halogen exchange. Organ distribution, scintigraphic and metabolic studies were performed in nude mice bearing a human melanotic MM (SK-MEL 25). A patient suffering from melanotic melanoma underwent scintigraphy with a [123I]iodobenzamide.

RESULTS

High radiochemical yields of 80%-95% and specific activities of > 5 TBq/ mumole were obtained. Animal studies revealed specific tumor uptake of all compounds with longest retention of the most lipophilic derivative p-[123I]ABA 2-2. At shorter times, however, o-[123I]ABA 2-2 exhibited the highest tumor uptake (8.9% ID/g, 1 hr p.i., 10.9% ID/g, 4 hr p.i.). Metabolization of o-[123I]ABA 2-2, mainly to o-[123I]iodohippuric acid (OIH), followed by fast renal excretion of the metabolites lead to tumor/nontumor ratios (T/NT) of > 400 for tumor/blood, and > 70 for tumor/liver at 48 h p.i. Unknown metastases could be localized in a patient using o-[123I]ABA 2-2.

CONCLUSION

The effects of the structure variation of iodobenzamides on their lipophilicity, metabolism and thus pharmacokinetics lead to the suggestion of o-[123I]ABA 2-2 as a favorable melanoma imaging agent.