Genome editing is a relevant, versatile, and preferred tool for crop improvement, as well as for functional genomics. In this review, we summarize the advances in gene-editing techniques, such as zinc-finger nucleases (ZFNs), transcription activator-like (TAL) effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR) associated with the Cas9 and Cpf1 proteins. These tools support great opportunities for the future development of plant science and rapid remodeling of crops. Furthermore, we discuss the brief history of each tool and provide their comparison and different applications. Among the various genome-editing tools, CRISPR has become the most popular; hence, it is discussed in the greatest detail. CRISPR has helped clarify the genomic structure and its role in plants: For example, the transcriptional control of Cas9 and Cpf1, genetic locus monitoring, the mechanism and control of promoter activity, and the alteration and detection of epigenetic behavior between single-nucleotide polymorphisms (SNPs) investigated based on genetic traits and related genome-wide studies. The present review describes how CRISPR/Cas9 systems can play a valuable role in the characterization of the genomic rearrangement and plant gene functions, as well as the improvement of the important traits of field crops with the greatest precision. In addition, the speed editing strategy of gene-family members was introduced to accelerate the applications of gene-editing systems to crop improvement. For this, the CRISPR technology has a valuable advantage that particularly holds the scientist’s mind, as it allows genome editing in multiple biological systems.
Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.
The luminous efficiency of inorganic white light‐emitting diodes, to be used by the next generation as light initiators, is continuously progressing and is an emerging interest for researchers. However, low color‐rendering index (Ra), high correlated color temperature (CCT), and poor stability limit its wider application. Herein, it is reported that Sm3+‐ and Eu3+‐doped calcium scandate (CaSc2O4 (CSO)) are an emerging deep‐red‐emitting material with promising light absorption, enhanced emission properties, and excellent thermal stability that make it a promising candidate with potential applications in emission display, solid‐state white lighting, and the device performance of perovskite solar cells (PSCs). The average crystal structures of Sm3+‐doped CSO are studied by synchrotron X‐ray data that correspond to an extremely rigid host structure. Samarium ion is incorporated as a sensitizer that enhances the emission intensity up to 30%, with a high color purity of 88.9% with a 6% increment. The impacts of hosting the sensitizer are studied by quantifying the lifetime curves. The CaSc2O4:0.15Eu3+,0.03Sm3+ phosphor offers significant resistance to thermal quenching. The incorporation of lanthanide ion‐doped phosphors CSOE into PSCs is investigated along with their potential applications. The CSOE‐coated PSCs devices exhibit a high current density and a high power conversion efficiency (15.96%) when compared to the uncoated control devices.
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.
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