Key messageA novel TaGW2-A1 allele was identified from a stable, robust QTL region, which is pleiotropic for thousand grain weight, grain number per spike, and grain morphometric parameters in wheat.AbstractThousand grain weight (TGW) and grain number per spike (GNS) are two crucial determinants of wheat spike yield, and genetic dissection of their relationships can help to fine-tune these two components and maximize grain yield. By evaluating 191 recombinant inbred lines in 11 field trials, we identified five genomic regions on chromosomes 1B, 3A, 3B, 5B, or 7A that solely influenced either TGW or GNS, and a further region on chromosome 6A that concurrently affected TGW and GNS. The QTL of interest on chromosome 6A, which was flanked by wsnp_BE490604A_Ta_2_1 and wsnp_RFL_Contig1340_448996 and designated as QTgw/Gns.cau-6A, was finely mapped to a genetic interval shorter than 0.538 cM using near isogenic lines (NILs). The elite NILs of QTgw/Gns.cau-6A increased TGW by 8.33%, but decreased GNS by 3.05% in six field trials. Grain Weight 2 (TaGW2-A1), a well-characterized gene that negatively regulates TGW and grain width in wheat, was located within the finely mapped interval of QTgw/Gns.cau-6A. A novel and rare TaGW2-A1 allele with a 114-bp deletion in the 5′ flanking region was identified in the parent with higher TGW, and it reduced TaGW2-A1 promoter activity and expression. In conclusion, these results expand our knowledge of the genetic and molecular basis of TGW-GNS trade-offs in wheat. The QTLs and the novel TaGW2-A1 allele are likely useful for the development of cultivars with higher TGW and/or higher GNS.Electronic supplementary materialThe online version of this article (10.1007/s00122-017-3017-y) contains supplementary material, which is available to authorized users.

Carcass and meat quality traits are economically important in pigs. In this study, 17 carcass composition traits and 23 meat quality traits were recorded in 1028 F(2) animals from a White Duroc x Erhualian resource population. All pigs in this experimental population were genotyped for 194 informative markers covering the entire porcine genome. Seventy-seven genome-wide significant quantitative trait loci (QTL) for carcass traits and 68 for meat quality were mapped to 34 genomic regions. These results not only confirmed many previously reported QTL but also revealed novel regions associated with the measured traits. For carcass traits, the most prominent QTL was identified for carcass length and head weight at 57 cM on SSC7, which explained up to 50% of the phenotypic variance and had a 95% confidence interval of only 3 cM. Moreover, QTL for kidney and spleen weight and lengths of cervical vertebrae were reported for the first time in pigs. For meat quality traits, two significant QTL on SSC5 and X were identified for both intramuscular fat content and marbling score in the longissimus muscle, while three significant QTL on SSC1 and SSC9 were found exclusively for IMF. Both LM and the semimembranous muscle showed common QTL for colour score on SSC4, 5, 7, 8, 13 and X and discordant QTL on other chromosomes. White Duroc alleles at a majority of QTL detected were favourable for carcass composition, while favourable QTL alleles for meat quality originated from both White Duroc and Erhualian.

Purpose To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. Methods 102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm3, pre‐inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite‐nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a “clean” MM spectrum using the Osprey software. Pearson’s correlation coefficient was calculated between integrals and age for the 14 MM signals. One‐way ANOVA was performed to determine differences between age groups. An independent t‐test was carried out to determine differences between sexes. Results MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One‐way ANOVA also suggested no age‐group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM‐predominant CSO (R = –0.29) and GM‐predominant PCC regions (R = –0.57) respectively while CSF increased significantly with age in both regions. Conclusion Our findings suggest that a pre‐defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.

BackgroundJ-difference-edited 1H-MR spectra require modeling to quantify signals of low-concentration metabolites. Two main approaches are used for this spectral modeling: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data.PurposeThe aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach would improve quantification of Hadamard-edited data compared with simple peak fitting.MethodsA test–retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE = 80 ms for HERMES and TE = 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variation (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate.ResultsThe reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0–9.9%. For MEGA-PRESS GSH data, reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3 and 8.8%. GABA + CVs from HERMES were 16.7 and 15.2%, respectively for the two models.ConclusionLCM with simulated basis functions substantially improved the reproducibility of GSH quantification for HERMES data.

Expert consensus recommends linear-combination modeling (LCM) of 1 H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions are usually derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue-and relaxation-corrected metabolite levels were compared between MM strategies.Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values

The gray mold caused by Botrytis cinerea has a significant impact on tomato production throughout the world. Although the synthetic fungicide fludioxonil can effectively control B. cinerea, there have been several reports of resistance to this fungicide. This study indicated that all of the fludioxonil-resistant strains tested, including one field-resistant isolate and four laboratory strains, had reduced fitness relative to sensitive isolates. In addition to having reduced growth, sporulation, and pathogenicity, the resistant strains were more sensitive to osmotic stress and had significantly (P < 0.05) higher peroxidase activity. BOs1, a kinase in the high-osmolarity glycerol stress response signal transduction pathway, is believed to harbor mutations related to fludioxonil resistance. Sequence analysis of their BOs1 sequences indicated that the fludioxonil-resistant field isolate, XXtom1806, had four point mutations resulting in four amino acid changes (I365S, S531G, T565N, and T1267A) and three amino acids (I365S, S531G, and T565N) in the histidine kinases, adenylyl cyclases, methyl-accepting chemotaxis receptors, and phosphatases domain, which associated with fludioxonil binding. Similarly, two of the laboratory strains, XXtom-Lab1 and XXtom-Lab4, had three (Q846S, I1126S, and G415D) and two (P1051S and V1241M) point mutations, respectively. A third strain, XXtom-lab3, had a 52-bp insertion that included a stop codon at amino acid 256. Interestingly, the BOs1 sequence of the fourth laboratory strain, XXtom-lab5, was identical to those of the sensitive isolates, indicating that an alternative resistance mechanism exists. The study also found evidence of positive cross-resistance between fludioxonil and the dicarboximide fungicides procymidone and iprodione, but no cross-resistance was detected with any other fungicides tested, including boscalid, carbendazim, tebuconazole, and fluazinam.

Purpose: The neurometabolic timecourse of healthy aging is not well-established, in part due to diversity of quantification methodology. In this study, a large structured cross-sectional cohort of male and female subjects throughout adulthood was recruited to investigate neurometabolic changes as a function of age, using consensus-recommended magnetic resonance spectroscopy quantification methods. Methods: 102 healthy volunteers, with approximately equal numbers of male and female participants in each decade of age from the 20s, 30s, 40s, 50s, and 60s, were recruited with IRB approval. MR spectroscopic data were acquired on a 3T MRI scanner. Metabolite spectra were acquired using PRESS localization (TE = 30 ms; 96 transients) in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Water-suppressed spectra were modeled using the Osprey algorithm, employing a basis set of 18 simulated metabolite basis functions and a cohort-mean measured macromolecular spectrum. Pearson correlations were conducted to assess relationships between metabolite concentrations and age for each voxel; paired t-tests were run to determine whether metabolite concentrations differed between the PCC and CSO. Results: Two datasets were excluded (1 ethanol; 1 unacceptably large lipid signal). Statistically significant age-by-metabolite correlations were seen for tCr (R2=0.36; p<0.001), tCho (R2=0.11; p<0.001), sI (R2=0.11; p=0.004), and mI (R2=0.10; p<0.001) in the CSO, and tCr (R2=0.15; p<0.001), tCho (R2=0.11; p<0.001), and GABA (R2=0.11; p=0.003) in the PCC. No significant correlations were seen between tNAA, NAA, GSH, Glx or Glu and age in either region (all p>0.25). Levels of sI were significantly higher in the PCC in female subjects (p<0.001) than in male subjects. There was a significant positive correlation between linewidth and age. Conclusion: The results indicated age correlations for tCho, tCr, sI, and mI in CSO and for tCr, tCho and GABA in PCC, while no age-related changes were found for NAA, tNAA, GSH, Glu or Glx. Our results provide a normative foundation for future work investigating the neurometabolic time course of healthy aging using MRS.

Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B 0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher ...