Meta-Quantitative Trait Loci Analysis and Candidate Gene Mining for Drought Tolerance-Associated Traits in Maize (Zea mays L.).

Drought is one of the major abiotic stresses with a severe negative impact on maize production globally. Understanding the genetic architecture of drought tolerance in maize is a crucial step towards the breeding of drought-tolerant varieties and a targeted exploitation of genetic resources. In this study, 511 quantitative trait loci (QTL) related to grain yield components, flowering time, and plant morphology under drought conditions, as well as drought tolerance index were collected from 27 published studies and then projected on the IBM2 2008 Neighbors reference map for meta-analysis. In total, 83 meta-QTL (MQTL) associated with drought tolerance in maize were identified, of which 20 were determined as core MQTL. The average confidence interval of MQTL was strongly reduced compared to that of the previously published QTL. Nearly half of the MQTL were confirmed by co-localized marker-trait associations from genome-wide association studies. Based on the alignment of rice proteins related to drought tolerance, 63 orthologous genes were identified near the maize MQTL. Furthermore, 583 candidate genes were identified within the 20 core MQTL regions and maize-rice homologous genes. Based on KEGG analysis of candidate genes, plant hormone signaling pathways were found to be significantly enriched. The signaling pathways can have direct or indirect effects on drought tolerance and also interact with other pathways. In conclusion, this study provides novel insights into the genetic and molecular mechanisms of drought tolerance in maize towards a more targeted improvement of this important trait in breeding.

Safety and efficacy of thalidomide in patients with transfusion-dependent ß-thalassemia: a randomized clinical trial.

Thalidomide induces γ-globin expression in erythroid progenitor cells, but its efficacy on patients with transfusion-dependent ß-thalassemia (TDT) remains unclear. In this phase 2, multi-center, randomized, double-blind clinical trial, we aimed to determine the safety and efficacy of thalidomide in TDT patients. A hundred patients of 14 years or older were randomly assigned to receive placebo or thalidomide for 12 weeks, followed by an extension phase of at least 36 weeks. The primary endpoint was the change of hemoglobin (Hb) level in the patients. The secondary endpoints included the red blood cell (RBC) units transfused and adverse effects. In the placebo-controlled period, Hb concentrations in patients treated with thalidomide achieved a median elevation of 14.0 (range, 2.5 to 37.5) g/L, whereas Hb in patients treated with placebo did not significantly change. Within the 12 weeks, the mean RBC transfusion volume for patients treated with thalidomide and placebo was 5.4 ± 5.0 U and 10.3 ± 6.4 U, respectively (P < 0.001). Adverse events of drowsiness, dizziness, fatigue, pyrexia, sore throat, and rash were more common with thalidomide than placebo. In the extension phase, treatment with thalidomide for 24 weeks resulted in a sustainable increase in Hb concentrations which reached 104.9 ± 19.0 g/L, without blood transfusion. Significant increase in Hb concentration and reduction in RBC transfusions were associated with non ß0/ß0 and HBS1L-MYB (rs9399137 C/T, C/C; rs4895441 A/G, G/G) genotypes. These results demonstrated that thalidomide is effective in patients with TDT.

Evaluation of Cloud 3D Printing Order Task Execution Based on the AHP-TOPSIS Optimal Set Algorithm and the Baldwin Effect.

Focusing on service control factors, rapid changes in manufacturing environments, the difficulty of resource allocation evaluation, resource optimization for 3D printing services (3DPSs) in cloud manufacturing environments, and so on, an indicator evaluation framework is proposed for the cloud 3D printing (C3DP) order task execution process based on a Pareto optimal set algorithm that is optimized and evaluated for remotely distributed 3D printing equipment resources. Combined with the multi-objective method of data normalization, an optimization model for C3DP order execution based on the Pareto optimal set algorithm is constructed with these agents' dynamic autonomy and distributed processing. This model can perform functions such as automatic matching and optimization of candidate services, and it is dynamic and reliable in the C3DP order task execution process based on the Pareto optimal set algorithm. Finally, a case study is designed to test the applicability and effectiveness of the C3DP order task execution process based on the analytic hierarchy process and technique for order of preference by similarity to ideal solution (AHP-TOPSIS) optimal set algorithm and the Baldwin effect.