The development of efficient methods for amplifying random DNA sequences by the polymerase chain reaction has created the basis for mapping virtually unlimited numbers of mixed-phase dominant DNA markers in one population. Although dominant markers can be efficiently mapped using many different kinds of matings, recombination frequencies and locus orders are often mis-estimated from repulsion F2 matings. The major problem with these matings, apart from excessive sampling errors of recombination frequency (θ) estimates, is the bias of the maximum-likelihood estimator (MLE) of θ (θ ML). [Formula: see text] when the observed frequency of double-recessive phenotypes is 0 and the observed frequency of double-dominant phenotypes is less than 2/3 - the bias for those samples is - θ. We used simulation to estimate the mean bias of θ ML. Mean bias is a function of n and θ and decreases as n increases. Valid maps of dominant markers can be built by using sub-sets of markers linked in coupling, thereby creating male and feamle coupling maps, as long as the maps are fairly dense (about 5 cM) - the sampling errors of θ increase as θ increases for coupling linkages and are equal to those for backcross matings when θ=0. The use of F2 matings for mapping dominant markers is not necessarily proscribed because they yield twice as many useful markers as a backcross population, albeit in two maps, for the same number of DNA extractions and PCR assays; however, dominant markers can be more effeciently exploited by using doubled-haploid, recombinant-inbred, or other inbred populations.

A soybean restriction fragment length polymorphism (RFLP) map (ISU/USDA‐ARS‐FCR, Ames, IA) was derived from an interspecific cross of Glycine max (L.) Merr. × G. soja Siebold & Zucc. using early maturity group genotypes. We characterized the feasibility of the application of this map to physiologically distant soybean genotypes mainly maturity group (MG) V to IX. A total of 108 genotypes of G. max were surveyed. Germplasm represented ancestral genotypes, breeding lines and elite cultivars. The RFLP markers (83 probes) used in this research spanned fifteen major linkage groups at an average distance of 26 centimorgans (cM). Fifty‐four percent of the probes were non informative. Thirty‐five percent had a probability of detecting polymorphism between any two random genotypes with a frequency above 0.3. The RFLP probes detecting polymorphism with high frequency were identified. Restriction fragment length polymorphism was associated with cultivar pedigree and relation to ancestral genotypes. The majority of genotypes showed molecular similarities to ‘Ralsoy’, ‘Dorman’, ‘Dunfield’, and ‘Ogden’ germplasms; a smaller group of genotypes showed molecular similarities to S‐100. Genotypic similarities were observed among most genotypes. However, genotypes of MG VI and VII retained potentially valuable levels of genetic diversity. Soybean cyst nematode and bacterial pustule resistance were present in many different genetic backgrounds and no association with donor genotypes was observed in principal components analysis. The set of identified RFLP probes with high frequency of polymorphism detection should serve as a core of molecular markers for initiating mapping of agronomic traits and detection of gene linkages across a wide range of maturity groups of cultivated soybean. Genomic diversity described by the principal components analysis may be useful in germplasm selection to develop populations for genome mapping in soybean.

The aim of the present research was 1) to extract carnosine from different low economic value poultry tissues and 2) to measure their antioxidant activities using different analytical methods. Low economic value poultry tissues such as the head, liver, lungs, tail, gizzard, brain, and heart were used in this study. Results have indicated that carnosine was present in all the tissue samples investigated. The liver had the highest (102.29 mg/g) and brain the lowest carnosine content (0.95 mg/g; P ≤ 0.05). Except for the brain, all tissue ultrafiltrates and reconstituted dry powders showed TBA reactive species inhibition ranging from 20.87 to 39.57% and 5.66 -14.47%, respectively. Free radical scavenging activity of ultrafiltrate from all tissues samples ranged from 25.11 to 79.38%, whereas this activity was higher (29.76 to 84.05%) in the reconstituted dry powder of all tissue samples. Conclusions include that extraction of bioactive dipeptide carnosine can be exploited from low economic value poultry tissues to increase the economy of the poultry industry.

Expected selection response (R) is widely used in plant breeding to compare populations and selection schemes, but methods have not been described for estimating variances or confidence intervals of R, or, if they have, their validity is uncertain. In this paper, we describe an exact and approximate standard error of R and normal‐approximation intervals of R. We used simulation to test the validity of these intervals by comparing realized coverage probabilities to stated coverage probabilities for different experiment sizes, stated coverage probability values, and values of family‐mean heritability (H). Coverages of the normal‐approximation interval estimated using the exact standard error (interval R1) ranged from 0.874 to 0.922 and 0.932 to 0.966 for stated coverages of 0.90 and 0.95, respectively. Coverages of the normal‐approximation interval estimated using the approximate standard error (interval R2) ranged from 0.880 to 0.940 and 0.934 to 0.980 for stated coverages of 0.90 and 0.95, respectively. Both R1 and R2 are valid interval estimators for R if the usual assumptions of the analysis of variance are met.

Weights and body condition scores (BCS) were measured and assessed on Angus females (n = 367) over 14 yr (1981 to 1994) to allow calculation of weight adjustments for different BCS. Data were collected at five time periods: prepartum, postpartum, prebreeding, postbreeding, and midgestation. Individual cows with multiple records were included in the analysis as repeated measures to yield 3,912 total observations. Body condition score was assigned on a scale of 1 = emaciated to 9 = obese. Only BCS 2 through 8 were analyzed, as there were zero recorded observations of BCS 1 or 9. The final model included age and the time period by BCS interaction as fixed effects. Year by animal within age interaction and a residual error term were treated as random effects. Animal was included to correct for repeated measures across time periods and years for individual animals. All these effects were significant (P < 0.0001). Weight adjustments for BCS were calculated for each time period. Cow weight and weight adjustments for BCS were not consistent for each time period. Overall weight adjustments to adjust cows to BCS of 5 were (kg +/- SEM) BCS = 2 (68 +/- 12), BCS = 3 (50 +/- 4), BCS = 4 (21 +/- 1), BCS = 5 (0), BCS = 6(-24 +/- 2), BCS = 7(-51 +/- 3), and BCS = 8 (-73 +/- 7).