Research Notes: U.S. Regional Soybean Laboratory, USDA-ARS, University of Illinois, Urbana-Champaign, and University of Georgia

Segregation for male sterility was observed in an F3 row from the cross of L67-533 (Clark-S, short internode) x SRF300 at Urbana, Illinois in 1971. The observed segregation was 63 fertile: 21 sterile (expected 63:21, 50 assuming sterility controlled by a single recessive gene). This hypothesis was confirmed in 1972, when, totaled over 49 segregating rows, the observed segregation was 1,551 fertile : 528 sterile plants (expected 1,559:520)

Research Notes: Evidence of a Second Gene Controlling a Short Internode (Zigzag Stem) Character

Kilen and Hartwig (1975) have described a short internode character found in PI 227.224 which causes a zigzag stem appearance. They indicated that the short internode character was probably determined by a single recessive gene pair based on classification of the presence or absence of the zigzag stem appearance. In the F2 generations of the crosses, PI 227.224 x \u27Coker 338\u27 and PI 227.224 x \u27Davis,\u27 we observed two different ratios

QTL for seed protein and amino acids in the Benning × Danbaekkong soybean population

Soybean, rather than nitrogen-containing forages, is the primary source of quality protein in feed formulations for domestic swine, poultry, and dairy industries. As a sole dietary source of protein, soybean is deficient in the amino acids lysine (Lys), threonine (Thr), methionine (Met), and cysteine (Cys). Increasing these amino acids would benefit the feed industry. The objective of the present study was to identify quantitative trait loci (QTL) associated with crude protein (cp) and amino acids in the ‘Benning’ × ‘Danbaekkong’ population. The population was grown in five southern USA environments. Amino acid concentrations as a fraction of cp (Lys/cp, Thr/cp, Met/cp, Cys/cp, and Met + Cys/cp) were determined by near-infrared reflectance spectroscopy. Four QTL associated with the variation in crude protein were detected on chromosomes (Chr) 14, 15, 17, and 20, of which, a QTL on Chr 20 explained 55 % of the phenotypic variation. In the same chromosomal region, QTL for Lys/cp, Thr/cp, Met/cp, Cys/cp and Met + Cys/cp were detected. At these QTL, the Danbaekkong allele resulted in reduced levels of these amino acids and increased protein concentration. Two additional QTL for Lys/cp were detected on Chr 08 and 20, and three QTL for Thr/cp on Chr 01, 09, and 17. Three QTL were identified on Chr 06, 09 and 10 for Met/cp, and one QTL was found for Cys/cp on Chr 10. The study provides information concerning the relationship between crude protein and levels of essential amino acids and may allow for the improvement of these traits in soybean using marker-assisted selection

Rapid automatic assessment of microvascular density in sidestream dark field images

The purpose of this study was to develop a rapid and fully automatic method for the assessment of microvascular density and perfusion in sidestream dark field (SDF) images. We modified algorithms previously developed by our group for microvascular density assessment and introduced a new method for microvascular perfusion assessment. To validate the new algorithm for microvascular density assessment, we reanalyzed a selection of SDF video clips (n = 325) from a study in intensive care patients and compared the results to (semi-)manually found microvascular densities. The method for microvascular perfusion assessment (temporal SDF image contrast analysis, tSICA) was tested in several video simulations and in one high quality SDF video clip where the microcirculation was imaged before and during circulatory arrest in a cardiac surgery patient. We found that the new method for microvascular density assessment was very rapid (<30 s/clip) and correlated excellently with (semi-)manually measured microvascular density. The new method for microvascular perfusion assessment (tSICA) was shown to be limited by high cell densities and velocities, which severely impedes the applicability of this method in real SDF images. Hence, here we present a validated method for rapid and fully automatic assessment of microvascular density in SDF images. The new method was shown to be much faster than the conventional (semi-)manual method. Due to current SDF imaging hardware limitations, we were not able to automatically detect microvascular perfusion