Identification of a stable major-effect quantitative trait locus for pre-harvest sprouting in common wheat (Triticum aestivum L.) via high-density SNP-based genotyping.

KEY MESSAGE: A major and stable QTL cQSGR.sau.3D, which can explain 33.25% of the phenotypic variation in SGR, was mapped and validated, and cQSGR.sau.3D was found to be independent of GI. In this study, a recombinant inbred line (RIL) population containing 304 lines derived from the cross of Chuan-nong17 (CN17) and Chuan-nong11 (CN11) was genotyped using the Wheat55K single-nucleotide polymorphism array. A high-density genetic map consisting of 8329 markers spanning 4131.54 cM and distributed across 21 wheat chromosomes was constructed. QTLs for whole spike germination rate (SGR) were identified in multiple years. Six and fourteen QTLs were identified using the Inclusive Composite Interval Mapping-Biparental Populations and Multi-Environment Trial methods, respectively. A total of 106 digenic epistatic QTLs were also detected in this study. One of the additive QTLs, cQSGR.sau.3D, which was mapped in the region from 3.5 to 4.5 cM from linkage group 3D-2 on chromosome 3D, can explain 33.25% of the phenotypic variation in SGR and be considered a major and stable QTL for SGR. This QTL was independent of the seeds' germination traits, such as germination index. One Kompetitive Allele-Specific PCR (KASP) marker, KASP-AX-110772653, which is tightly linked to cQSGR.sau.3D, was developed. The genetic effect of cQSGR.sau.3D on SGR in the RIL and natural populations was successfully confirmed. Furthermore, within the interval in which cQSGR.sau.3D is located in Chinese Spring reference genomes, thirty-seven genes were found. cQSGR.sau.3D may provide new resources for pre-harvest sprouting resistance breeding of wheat in the future.

Safe medicine recommendation via star interactive enhanced-based transformer model.

With the rapid development of electronic medical records (EMRs), most existing medicine recommendation systems based on EMRs explore knowledge from the diagnosis history to help doctors prescribe medication correctly. However, due to the limitations of the EMRs' content, recommendation systems cannot explicitly reflect relevant medical data, such as drug interactions. In recent years, medicine recommendation approaches based on medical knowledge graphs and graph neural networks have been proposed, and the methods based on the Transformer model have been widely used in medicine recommendation systems. Transformer-based medicine recommendation approaches are readily applicable to inductive problems. Unfortunately, traditional Transformer-based medicine recommendation approaches require complex computing power and suffer information loss among the multi-heads in Transformer model, which causes poor performance. At the same time, these approaches have rarely considered the side effects of drug interaction in traditional medical recommendation approaches. To overcome the drawbacks of the current medicine recommendation approaches, we propose a Star Interactive Enhanced-based Transformer (SIET) model. It first constructs a high-quality heterogeneous graph by bridging EMR (MIMIC-III) and a medical knowledge graph (ICD-9 ontology and DrugBank). Then, based on the constructed heterogeneous graph, it extracts a disease homogeneous graph, a medicine homogeneous graph, and a negative factors homogeneous graph to get auxiliary information of disease or drug (named enhanced neighbors). These are fed into the SIET model in conjunction with the relevant information in the EMRs to obtain representations of diseases and drugs. It finally generates the recommended drug list by calculating the cosine similarity between disease combination representations and drug combination representations. Extensive experiments on the MIMIC-III, DrugBank, and ICD-9 ontology datasets demonstrate the outstanding performance of our proposed model. Meanwhile, we show that our SIET model outperforms strong baselines on an inductive medicine recommendation task.

A Glass-Ultra-Thin PDMS Film-Glass Microfluidic Device for Digital PCR Application Based on Flexible Mold Peel-Off Process.

The microfluidic device (MFD) with a glass−PDMS−glass (G-P-G) structure is of interest for a wide range of applications. However, G-P-G MFD fabrication with an ultra-thin PDMS film (especially thickness less than 200 µm) is still a big challenge because the ultra-thin PDMS film is easily deformed, curled, and damaged during demolding and transferring. This study aimed to report a thickness-controllable and low-cost fabrication process of the G-P-G MFD with an ultra-thin PDMS film based on a flexible mold peel-off process. A patterned photoresist layer was deposited on a polyethylene terephthalate (PET) film to fabricate a flexible mold that could be demolded softly to achieve a rigid structure of the glass−PDMS film. The thickness of ultra-thin patterned PDMS could reach less than 50 µm without damage to the PDMS film. The MFD showcased the excellent property of water evaporation inhibition (water loss < 10%) during PCR thermal cycling because of the ultra-thin PDMS film. Its low-cost fabrication process and excellent water evaporation inhibition present extremely high prospects for digital PCR application.