The maize kernel row number (KRN) is a key component that contributes to grain yield and has high broad-sense heritability (H 2 ). Quantitative trait locus/loci (QTL) mapping using a high-density genetic map is a powerful approach to detecting loci that are responsible for traits of interest. Bulked segregant ribonucleic acid (RNA) sequencing (BSR-seq) is another rapid and cost-effective strategy to identify QTL. Combining QTL mapping using a high-density genetic map and BSR-seq may dissect comprehensively the genetic architecture underlying the maize KRN. A panel of 300 F2 individuals derived from inbred lines abe2 and B73 were genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) method. A total of 4,579 high-quality polymorphic SLAF markers were obtained and used to construct a high-density genetic map with a total length of 2,123 centimorgan (cM) and an average distance between adjacent markers of 0.46 cM. Combining the genetic map and KRN of F2 individuals, four QTL (qKRN1, qKRN2, qKRN5, and qKRN8-1) were identified on chromosomes 1, 2, 5, and 8, respectively. The physical intervals of these four QTL ranged from 4.36 Mb for qKRN8-1 to 7.11 Mb for qKRN1 with an average value of 6.08 Mb. Based on high-throughput sequencing of two RNA pools bulked from leaves of plants with extremely high and low KRNs, two QTL were detected on chromosome 8 in the 10–25 Mb (BSR_QTL1) and 60–150 Mb (BSR_QTL2) intervals. According to the physical positions of these QTL, qKRN8-1 was included by BSR_QTL2. In addition, qKRN8-1 was validated using QTL mapping with a recombinant inbred lines population that was derived from inbred lines abe2 and B73. In this study, we proved that combining QTL mapping using a high-density genetic map and BSR-seq is a powerful and cost-effective approach to comprehensively revealing genetic architecture underlying traits of interest. The QTL for the KRN detected in this study, especially qKRN8-1, can be used for performing fine mapping experiments and marker-assisted selection in maize breeding.

Genome editing by clustered regularly interspaced short palindromic sequences (CRISPR)/CRISPR‐associated protein 9 (Cas9) has revolutionized functional gene analysis and genetic improvement. While reporter‐assisted CRISPR/Cas systems can greatly facilitate the selection of genome‐edited plants produced via stable transformation, this approach has not been well established in seed crops. Here, we established the seed fluorescence reporter (SFR)‐assisted CRISPR/Cas9 systems in maize (Zea mays L.), using the red fluorescent DsRED protein expressed in the endosperm (En‐SFR/Cas9), embryos (Em‐SFR/Cas9), or both tissues (Em/En‐SFR/Cas9). All three SFRs showed distinct fluorescent patterns in the seed endosperm and embryo that allowed the selection of seeds carrying the transgene of having segregated the transgene out. We describe several case studies of the implementation of En‐SFR/Cas9, Em‐SFR/Cas9, and Em/En‐ SFR/Cas9 to identify plants not harboring the genome‐editing cassette but carrying the desired mutations at target genes in single genes or in small‐scale mutant libraries, and report on the successful generation of single‐target mutants and/or mutant libraries with En‐SFR/Cas9, Em‐SFR/Cas9, and Em/En‐SFR/Cas9. SFR‐assisted genome editing may have particular value for application scenarios with a low transformation frequency and may be extended to other important monocot seed crops.

Summary: The integration of haploid induction and genome editing, termed HI‐Edit/IMGE, is a promising tool for generating targeted mutations for crop breeding. However, the technical components and stacking suitable for the maize seed industry have yet to be fully characterised and tested. Here, we developed and assessed three HI‐Edit/IMGE maize lines: Edit^Wx, Edit^Sh, and Edit^Wx&Sh, using the haploid inducer CHOI3 and lines engineered using the CRISPR‐Cas9 system targeting the Waxy1 (Wx1) and Shrunken2 (Sh2) genes. We meticulously characterised the HI‐Edit/IMGE systems, focusing on copy numbers and the mutant alleles mtl and dmp, which facilitate haploid induction. Using B73 and six other parental lines of major commercial varieties as recipients, HI‐Edit/IMGE demonstrated maternal haploid induction efficiencies ranging from 8.55% to 20.89% and targeted mutation rates between 0.38% and 1.46%. Comprehensive assessment verified the haploid identification, target gene editing accuracy, genome background integrity, and related agronomic traits. Notably, Edit^Wx&Sh successfully combined distinct CRISPR‐Cas9 systems to induce multiple desired mutations, highlighting the potential of HI‐Edit/IMGE in accelerating the integration of edited traits into commercial maize varieties. Our findings underscore the importance of meticulous Cas9 copy number characterisation and highlight potential challenges related to somatic chimerism. We also validated the performance of single‐cross haploids derived using the HI‐Edit/IMGE process. Our results confirm the industrial applicability of generating targeted mutations through pollination and provide critical insights for further optimising this technology.

Base editing technologies can improve crops, but their efficiency in maize remains suboptimal. This study attempts to overcome these limitations by examining optimized cytosine and adenine base editors (CBEs and ABEs), namely evoAPOBEC1, evoFERNY, evoCDA1, TadA8.20, and TadA8e, for precise genome editing in transient and stable expression maize cells. Employing a seed fluorescence reporter (SFR) system for rapid screening of BE transformants and transgene‐free progenies, we enhanced editing efficiencies and heritability. Notably, TadA8.20 and evoCDA1 attained multiplexed editing efficiencies of up to 100.0% and 79.0% at the tested loci, respectively, with some homozygous and bi‐allelic mutants exceeding 72.4% and 73.7%. Precise editing of ZmACC1/2 (acetyl‐CoA carboxylase) improved herbicide resistance, with ZmACC2 mutants displaying improved performance. This study advances crop genetic engineering by facilitating robust, multi‐locus modifications without altered agronomic performance, enhancing herbicide tolerance in maize. The successful utilization of these BE is a significant step forward in agricultural biotechnology and precision breeding.

Mobile apps have been an integral part in our daily life. As these apps become more complex, it is critical to provide automated analysis techniques to ensure the correctness, security, and performance of these apps. A key component for these automated analysis techniques is to create a graphical user interface (GUI) model of an app, i.e., a window transition graph (WTG), that models windows and transitions among the windows. While existing work has provided both static and dynamic analysis to build the WTG for an app, the constructed WTG misses many transitions or contains many infeasible transitions due to the coverage issues of dynamic analysis and over-approximation of the static analysis. We propose ProMal, a "tribrid" analysis that synergistically combines static analysis, dynamic analysis, and machine learning to construct a precise WTG. Specifically, ProMal first applies static analysis to build a static WTG, and then applies dynamic analysis to verify the transitions in the static WTG. For the unverified transitions, ProMal further provides machine learning techniques that leverage runtime information (i.e., screenshots, UI layouts, and text information) to predict whether they are feasible transitions. Our evaluations on 40 real-world apps demonstrate the superiority of ProMal in building WTGs over static analysis, dynamic analysis, and machine learning techniques when they are applied separately.