Pinb-D1p is an elite allele for improving end-use quality in wheat (Triticum aestivum L.).

KEY MESSAGE: We identified ten QTLs controlling SDS-SV trait in a RIL population derived from ND3331 and Zang1817. Pinb-D1p is an elite allele from Tibetan semi­wild wheat for good end-use quality. Gluten strength is an important factor for wheat processing and end-product quality and is commonly characterized using the sodium dodecyl sulfate-sedimentation volume (SDS-SV) test. The objective of this study was to identify quantitative trait loci (QTLs) associated with wheat SDS-SV traits using a recombinant inbred line (RIL) population derived from common wheat line NongDa3331 (ND3331) and Tibetan semi-wild wheat accession Zang1817. We detected 10 QTLs controlling SDS-SV on chromosomes 1A, 1B, 3A, 4A, 4B, 5A, 5D, 6B and 7A, with individual QTLs explaining 2.02% to 15.53% of the phenotypic variation. They included four major QTLs, Qsdss-1A, Qsdss-1B.1, Qsdss-1B.2, and Qsdss-5D, whose effects on SDS-SV were due to the Glu-A1 locus encoding the high-molecular-weight glutenin subunit 1Ax1, the 1B/1R translocation, 1Bx7 + 1By8 at the Glu-B1 locus, and the hardness-controlling loci Pina-D1 and Pinb-D1, respectively. We developed KASP markers for the Glu-A1, Glu-B1, and Pinb-D1 loci. Importantly, we showed for the first time that the hardness allele Pinb-D1p positively affects SDS-SV, making it a good candidate for wheat quality improvement. These results broaden our understanding of the genetic characterization of SDS-SV, and the QTLs identified are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

A k-mer-based pangenome approach for cataloging seed-storage-protein genes in wheat to facilitate genotype-to-phenotype prediction and improvement of end-use quality.

Wheat is a staple food for more than 35% of the world's population, with wheat flour used to make hundreds of baked goods. Superior end-use quality is a major breeding target; however, improving it is especially time-consuming and expensive. Furthermore, genes encoding seed-storage proteins (SSPs) form multi-gene families and are repetitive, with gaps commonplace in several genome assemblies. To overcome these barriers and efficiently identify superior wheat SSP alleles, we developed "PanSK" (Pan-SSP k-mer) for genotype-to-phenotype prediction based on an SSP-based pangenome resource. PanSK uses 29-mer sequences that represent each SSP gene at the pangenomic level to reveal untapped diversity across landraces and modern cultivars. Genome-wide association studies with k-mers identified 23 SSP genes associated with end-use quality that represent novel targets for improvement. We evaluated the effect of rye secalin genes on end-use quality and found that removal of ω-secalins from 1BL/1RS wheat translocation lines is associated with enhanced end-use quality. Finally, using machine-learning-based prediction inspired by PanSK, we predicted the quality phenotypes with high accuracy from genotypes alone. This study provides an effective approach for genome design based on SSP genes, enabling the breeding of wheat varieties with superior processing capabilities and improved end-use quality.

Efficacy and safety of glibenclamide therapy after intracerebral haemorrhage (GATE-ICH): A multicentre, prospective, randomised, controlled, open-label, blinded-endpoint, phase 2 clinical trial.

Background: Glibenclamide is a promising agent for treating brain oedema, but whether it improves clinical outcomes in patients with intracerebral haemorrhage (ICH) remains unclear. In this study, we aimed to explore the efficacy and safety of glibenclamide treatment in patients with acute ICH. Methods: The Glibenclamide Advantage in Treating Oedema after Intracerebral Haemorrhage (GATE-ICH) study was a randomised controlled phase 2 clinical trial conducted in 26 hospitals in the northwest of China, recruiting patients with acute ganglia ICH no more than 72 h after onset from Dec 12, 2018 to Sept 23, 2020. During the first 7 days after enrolment, patients randomly assigned to the glibenclamide group were given glibenclamide orally (1.25 mg, 3/day) and standard care, while patients randomly assigned to the control group were given standard care alone. The computer-generated randomisation sequence was prepared by a statistician not involved in the rest of the study. Randomisation was computer-generated with a block size of four. The allocation results were unblinded to participants and investigators. The primary outcome was the percentage of patients with poor outcome (defined as modified Rankin Scale [mRS] score of ≥3) at day 90. The trial was registered at ClinicalTrials.gov (NCT03741530). Findings: 220 participants were randomised and 200 participants (mean [standard deviation] age, 56 [11] years; sex, 128 [64.0%] male and 72 [36.0%] female) were included in the final analysis, with 101 participants randomly assigned to the control group and 99 to the glibenclamide group. The incidence of poor outcome at day 90 was 20/99 (20.2%) in glibenclamide group and 30/101 (29.7%) in control group (absolute difference, 9.5%; 95% confidence interval [CI], -3.2%-21.8%; P = 0.121) with adjusted odds ratios of 0.54 (95% CI, 0.24-1.20; P = 0.129). No significant difference was found in the overall rates of adverse events or serious adverse events between groups. However, the incidence of asymptomatic hypoglycaemia was significantly higher in glibenclamide group than control group (15/99 [15.2%] vs 0/101 [0.0%]; absolute difference, 15.2%; 95% CI, 7.5%-24.1%; P < 0.001). Interpretation: Our study provides no evidence that glibenclamide (1.25 mg, 3/day) significantly reduces the proportion of poor outcome at day 90 after ICH. In addition, glibenclamide could result in higher incidence of hypoglycaemia. Larger trials of glibenclamide with optimised medication regimen are warranted. Funding: Shaanxi Province Key Research and Development Project (2017DCXL-SF-02-02) and Shaanxi Province Special Support Program for Leading Talents in Scientific and Technological Innovation (tzjhjw).

[The new method monitoring agricultural drought based on SWIR-Red spectrum feature space].

Drought was a chronic, natural disaster, and Remote sensing drought monitoring had become a potential research field. In the present, short-wave infrared and red bands which sensitive to moisture variation were selected to monitor farmland drought conditions by analyzing the spectral characteristics of vegetation and soil. The goal of this paper was to provide a new method of drought monitoring--normalized drought monitoring index (NPDI), based on new constructed spectrum feature space by the difference of SWIR and Red and the sum of SWIR and Red. Field surveyed soil moisture verified NPDI model, and the result showed that NDPI and MPDI model could effectively monitor agricultural drought, and that had high correlation with soil moisture. The R2 was 0.583 and 0.438 with soil water of 10 cm. The monitoring effect of NPDI model was better than the MPDL. This model was further improvement to PDI and MPDI, and it could monitor the drought condition of different vegetation coverage and whole growing season. It has high application potential and popularization value.

[Vegetation landscape health assessment in Changshan Archipelago, North Yellow Sea].

Island vegetation is an important component of island ecosystem. Multi-targets of island ecosystem health integrated with landscape ecology theory were employed to construct the index system for island vegetation health assessment in terms of landscape vigor, landscape stressing intensity and landscape stability. The Changshan Archipelago in the North Yellow Sea was chosen as a case to apply the island vegetation health assessment index system. The results showed that the overall vegetation health status in Changshan Archipelago was good and had a big island variation. The vegetation health index for Haiyang Island and Zhangzi Island was above 0.80, belonging to first eco-health level area, whereas that for Dachangshan Island, Xiaochangshan Island and Dawangjia Island ranged from 0.70 to 0.80, which could be categorized as the second eco-health level area. Guanglu Island and Shichen Island could be termed as the third eco-health level area with the vegetation health index below 0.70. The distance of island to mainland, area of island together with industrial structure were the main driving forces for the variation of vegetation landscape heath between different islands.