Performance Testing and Analysis of Automotive-Grade CMOS Sensors in Different Environments

Automotive-grade Complementary Metal-Oxide-Semiconductor(CMOS)sensors play a crucial role in automotive electronic systems,especially in the context of the rapid development of Advanced Driver Assistance Systems(ADAS)and autonomous driving technologies.Their performance is directly related to the safety and reliability of vehicles.However,automobiles will face a variety of complex environmental conditions during the actual operation,such as high temperature,low temperature,vibration,humidity changes,and light changes,which may have an impact on the performance of CMOS sensors.Therefore,it is of great significance to study the performance of automotive-grade CMOS sensors in different environments.

Analysis of genetic effects and correlations for nutrient quality traits of indica rice in different environments

Understanding the variation for the expressionof genes in different environments is one of themajor goals in qualitative genetics. In this pa-per, the genetic models for quantitative traitsof endosperm in cereal crops were used to eval-uate the seed, cytoplasm and maternal geneticeffects as well as the genotype × environment(GE) interaction effects, and to predict thebreeding value of parents and genotypic corre-lation for nutrient quality traits of indica rice (Oryza sativa L.).

Analysis of genetic effects and heritabilities for amylose content of indica rice in different environments

An understanding of the genetic effects of em-bryo genes for rice quality traits is of impor-tance for rice quality improvement. Analysisof embryo, endosperm, cytoplasmic, and ma-ternal genetic effects and genotype×environ-

QTL Analysis of Some Agronomic Traits in Rice Under Different Growing Environments

The culm length, panicle length, spikelets per panicle and panicle exsertion were evaluated by using F2：3 population including 200 lines derived from the cross of indica and japonica Milyang 23/Jileng 1 under five different environments of Beijing （natural normal growing environment）, Kunming （natural cold environment）, Gongzhuling of China （cold water irrigation） and Chuncheon of Korea （natural normal growing environment and cold water irrigation）, and QTLs of these traits were analyzed by using SSR markers. The results showed that 44 QTLs related to these agronomic traits were detected under five different growing environments, and these QTLs have been located on 11 chromosomes except chromosome 9. The QTLs for qCLla, qCL1b, qCL5a, qCL6b, qPLla, qPL3a, qPL6b, qPL6c, qPL7b, qSP8b, qSPlc, qSP11a, qSP12, and qPE1 have been detected under more than two growing environments, and those that were little affected by environments, were stable QTLs. Among them, qCLla, qCLlb, qPLla, qSPlc, and qPE1 explained 24.2-55.2%, 22.7-39.9%, 12.5-27.7%, 14.4-33.5%, and 26.6-28.7% of observed phynotypic variation, respectively, which were major genes mainly appearing as overdominance. These QTLs cause the increase in action to culm length, panicle length, spikelets per panicle, and panicle exsertion under cold environment, showing that these QTLs were correlated with cold tolerance.

Responses of fractal dimensions of Picea koraiensis seedlings to different light environments

The changes of fractal dimension ofPicea koraiensis seedlings under different light intensities in natural secondary forests was studied. The results showed that with the change of light environment, crown characters ofPicea koraiensis seedlings exhibited a greater plastic in lateral number, lateral increment, lateral dry weight, and specific leaf area. The range of calculated fractal dimensions of seedling crowns was confined between 2.5728 and 2.1036, but maximum of fractal dimension achieved in term moderate shading and in extreme low light conditions fractal dimension was least.

Mapping theArabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions

Metabolic genome-wide association studies （mGWAS）, whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309Arabidopsis accessions were grown under two independent environmental conditions （control and stress） and subjected to untargeted LC-MS- based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two- condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci （p ≤ 1.0E-08）, 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was fol- lowed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between struc- tural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different ablotic environments for identifying metabollte-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis.