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SUMMARY In this study, we characterized a panel of 1264 maize near‐isogenic lines (NILs), developed from crosses between 18 diverse inbred lines and the recurrent parent B73, referred to as nested NILs (nNILs). In this study, 888 of the nNILs were genotyped using genotyping‐by‐sequencing (GBS). Subsequently, 24 of these nNILs, and all the parental lines, were re‐genotyped using a high‐density single nucleotide polymorphism (SNP) chip. A novel pipeline for calling introgressions, which does not rely on knowing the donor parent of each nNIL, was developed based on a hidden Markov model (HMM) algorithm. By comparing the introgressions detected using GBS data with those identified using chip data, we optimized the HMM parameters for analyzing the entire nNIL population. A total of 2969 introgressions were identified across the 888 nNILs. Individual introgression blocks ranged from 21 bp to 204 Mbp, with an average size of 17 Mbp. By comparing SNP genotypes within introgressed segments to the known genotypes of the donor lines, we determined that in about one third of the lines, the identity of the donors did not match expectation based on their pedigrees. We characterized the entire nNIL population for three foliar diseases. Using these data, we mapped a number of quantitative trait loci (QTL) for disease resistance in the nNIL population and observed extensive variation in effects among the alleles from different donor parents at most QTL identified. This population will be of significant utility for dissecting complex agronomic traits and allelic series in maize. 

The overarching goal herein is to identify the factors dominating the performance of a‐IGZO‐based memristors. Despite the highest on/off ratio, greater than 10⁴with a preferred minimal set/reset bias achieved from a‐IGZO‐based memristors, it is observed that the switching performance and stability/reliability of the devices is significantly dominated by theV_O^··density and metallization material, depending on their reactivity with IGZO. As the first governing factor, ensuring optimalV_O^··concentration in the switching layer IGZO (V_O^··/O_O^xratio 24.3% in this study) is crucial to obtain the tractable formation and rupture of conduction filament. Neither higher nor lowerV_O^··density than the optimized results in detrimental reliability issues, which may be ascribed to an uncontrollable filament in an abundant vacancy environment or a weak conducting path, respectively. As the second governing mechanism determining the memristor performance and reliability, it is suggested that metallization materials need to be carefully selected based on the thermodynamic redox potential and interfacial stability of the metallization material with IGZO. Metallization materials with larger reduction potential and interfacial stability are found to yield higher switching on/off ratio and greater device performance reliability.