Power Optimization in Multi-IRS Aided Delay-Constrained IoVT Systems

With the advancement of video sensors in the Internet of Things, Internet of Video Things (IoVT) systems, capable of delivering abundant and diverse information, have been increasingly deployed for various applications. However, the extensive transmission of video data in IoVT poses challenges in terms of delay and power consumption. Intelligent reconfigurable surface (IRS), as an emerging technology, can enhance communication quality and consequently improve system performance by reconfiguring wireless propagation environments. Inspired by this, we propose a multi-IRS aided IoVT system that leverages IRS to enhance communication quality, thereby reducing power consumption while satisfying delay requirements. To fully leverage the benefits of IRS, we jointly optimize power control for IoVT devices and passive beamforming for IRS to minimize long-term total power consumption under delay constraints. To solve this problem, we first utilize Lyapunov optimization to decouple the long-term optimization problem into each time slot. Subsequently, an alternating optimization algorithm employing optimal solution-seeking and fractional programming is proposed to effectively solve the optimization problems at each time slot. Simulation results demonstrate that the proposed algorithm significantly outperforms benchmark algorithms in terms of long-term total power consumption. Moreover, a trade-off between the number of IRS elements and system performance is also proved

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection (GS) is a powerful tool for improving genetic gain in maize breeding. However, its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms. Although sequencing-based and array-based genotyping platforms have been used for GS, few studies have compared prediction performance among platforms. In this study, we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing (GBS), a 40K SNP array, and target sequence capture (TSC) using eight GS models. The GBS marker dataset yielded the highest predictabilities for all traits, followed by TSC and SNP array datasets. We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs, and BayesB, GBLUP, and RKHS performed well, while XGBoost performed poorly in most cases. We also selected significant SNP subsets using genome-wide association study (GWAS) analyses in three panels to predict hybrid performance. GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost, but depended heavily on the GWAS panel. We conclude that there is still room for optimization of the existing SNP array, and using genotyping by target sequencing (GBTS) techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays

Data from: The maternal cytoplasmic environment may be involved in the viability selection of gametes and zygotes

Segregation distortion is the phenomenon whereby the observed genotypic frequencies of a locus fall outside the expected Mendelian segregation ratio, and it is increasingly recognised as a potentially powerful evolutionary force. The main reason for segregation distortion is a difference in the viability of gametes and zygotes caused by viability loci in the segregating progeny. However, the maternal cytoplasm may also be involved in the viability selection of gametes and zygotes. The objectives of this study were to map the segregation distortion loci (SDL) in maize and to test the hypothesis that the viability of gametes and zygotes may also be associated with the maternal cytoplasmic environment. In the present study, a reciprocal mating design was conducted to generate an F2-segregating population. A linkage map was constructed with 126 microsatellite markers. A whole-genome scan was performed to detect the SDL in segregating populations with different maternal cytoplasm environments. Altogether, 14 SDL with strong LOD (logarithm (base 10) of odds) supports were identified in the specifically designed F2 populations. Interestingly, we found dramatic changes in the genotypic frequencies of the SDL in the two maternal cytoplasmic backgrounds, which indicated a change in the viability of gametes and zygotes in different cytoplasmic environments. Furthermore, in the JB cytoplasmic background, most of the detected SDL and complete distortion markers exhibited similar bias patterns favouring the Y53 alleles. These results suggested that selfish cytoplasmic elements may have an important role in shaping the patterns of segregation distortion in F2 populations through selective viability of gametes and zygotes

Nucleotide polymorphisms and haplotype diversity of RTCS gene in China elite maize inbred lines.

The maize RTCS gene, encoding a LOB domain transcription factor, plays important roles in the initiation of embryonic seminal and postembryonic shoot-borne root. In this study, the genomic sequences of this gene in 73 China elite inbred lines, including 63 lines from 5 temperate heteroric groups and 10 tropic germplasms, were obtained, and the nucleotide polymorphisms and haplotype diversity were detected. A total of 63 sequence variants, including 44 SNPs and 19 indels, were identified at this locus, and most of them were found to be located in the regions of UTR and intron. The coding region of this gene in all tested inbred lines carried 14 haplotypes, which encoding 7 deferring RTCS proteins. Analysis of the polymorphism sites revealed that at least 6 recombination events have occurred. Among all 6 groups tested, only the P heterotic group had a much lower nucleotide diversity than the whole set, and selection analysis also revealed that only this group was under strong negative selection. However, the set of Huangzaosi and its derived lines possessed a higher nucleotide diversity than the whole set, and no selection signal were identified

Genome-wide association study identifies novel candidate loci or genes affecting stalk strength in maize

Stalk strength increases resistance to stalk lodging, which causes maize (Zea mays L.) production losses worldwide. The genetic mechanisms regulating stalk strength remain unclear. In this study, three stalk strength-related traits (rind penetrometer resistance, stalk crushing strength, and stalk bending strength) and four plant architecture traits (plant height, ear height, stem diameter, stem length) were measured in three field trials. Substantial phenotypic variation was detected for these traits. A genome-wide association study (GWAS) was conducted using general and mixed linear models and 372,331 single-nucleotide polymorphisms (SNPs). A total of 94 quantitative trait loci including 241 SNPs were detected. By combining the GWAS data with public gene expression data, 56 candidate genes within 50 kb of the significant SNPs were identified, including genes encoding flavonol synthase (GRMZM2G069298, ZmFLS2), nitrate reductase (GRMZM5G878558, ZmNR2), glucose-1-phosphate adenylyltransferase (GRMZM2G027955), and laccase (GRMZM2G447271). Resequencing GRMZM2G069298 and GRMZM5G878558 in all tested lines revealed respectively 47 and 2 variants associated with RPR. Comparison of the RPR of the zmnr2 EMS mutant and the wild-type plant under high- and low-nitrogen conditions verified the GRMZM5G878558 function. These findings may be useful for clarifying the genetic basis of stalk strength. The identified candidate genes and variants may be useful for the genetic improvement of maize lodging resistance