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<jats:sec><jats:title>Background</jats:title><jats:p>The cochineal cactus (<jats:italic>Opuntia cochenillifera</jats:italic>), notable for its substantial agricultural and industrial applications, predominantly undergoes clonal reproduction, which presents significant challenges in breeding and germplasm innovation. Recent developments in mitochondrial genome engineering offer promising avenues for introducing heritable mutations, potentially facilitating selective sexual reproduction through the creation of cytoplasmic male sterile genotypes. However, the lack of comprehensive mitochondrial genome information for <jats:italic>Opuntia</jats:italic> species hinders these efforts. Here, we intended to sequence and characterize its mitochondrial genome to maximize the potential of its genomes for evolutionary studies, molecular breeding, and molecular marker developments.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We sequenced the total DNA of the <jats:italic>O. cochenillifera</jats:italic> using DNBSEQ and Nanopore platforms. The mitochondrial genome was then assembled using a hybrid assembly strategy using Unicycler software. We found that the mitochondrial genome of <jats:italic>O. cochenillifera</jats:italic> has a length of 1,156,235 bp, a GC content of 43.06%, and contains 54 unique protein-coding genes and 346 simple repeats. Comparative genomic analysis revealed 48 homologous fragments shared between mitochondrial and chloroplast genomes, with a total length of 47,935 bp. Additionally, the comparison of mitochondrial genomes from four Cactaceae species highlighted their dynamic nature and frequent mitogenomic reorganizations.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Our study provides a new perspective on the evolution of the organelle genome and its potential application in genetic breeding. These findings offer valuable insights into the mitochondrial genetics of Cactaceae, potentially facilitating future research and breeding programs aimed at enhancing the genetic diversity and adaptability of <jats:italic>O. cochenillifera</jats:italic> by leveraging its unique mitochondrial genome characteristics.</jats:p></jats:sec>

<jats:sec><jats:title>Introduction</jats:title><jats:p>Phosphorus (P), which plays a vital role in plant growth, is continually added to soil to maximize biomass production, leading to excessive P accumulation and water eutrophication.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In this study, a pot experiment using a subtropical tobacco-growing soil fertilized with four P levels—no P, low P, medium P, and high P—was conducted and rhizosphere and bulk soils were analyzed.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>P addition significantly increased tobacco biomass production (except under low P input) and total soil P and available P content (<jats:italic>P</jats:italic>&amp;lt;0.05), whereas total nitrogen content decreased in the rhizosphere soils, although this was only significant with medium P application. P fertilization also significantly altered the bacterial communities of rhizosphere soils (<jats:italic>P</jats:italic>&amp;lt;0.05), but those of bulk soils were unchanged (<jats:italic>P</jats:italic>&amp;gt;0.05). Moreover, a significant difference was found between rhizosphere soils with low (LR) and high (HR) P inputs (<jats:italic>P</jats:italic>&amp;lt;0.05). Additionally, compared with rhizosphere soils with no P (CKR), Shannon diversity showed a declining trend, which was significant with LR and HR (<jats:italic>P</jats:italic>&amp;lt;0.05), whereas an increasing tendency was observed for Chao1 diversity except in LR (<jats:italic>P</jats:italic>&amp;gt;0.05). Functional prediction revealed that P application significantly decreased the total P and N metabolism of microorganisms in rhizosphere soils (<jats:italic>P</jats:italic>&amp;lt;0.05).</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>Collectively, our results indicate that maintaining sustainable agricultural ecosystems under surplus P conditions requires more attention to be directed toward motivating the potential of soil functional microbes in P cycling, rather than just through continual P input.</jats:p></jats:sec>

<jats:p>Wheat stem rust caused by <jats:italic>Puccinia graminis</jats:italic> f. sp. <jats:italic>tritici (Pgt)</jats:italic> threatens wheat production worldwide. The objective of this study was to characterize wheat stem rust resistance in ‘Linkert’, a variety with adult plant resistance effective to emerging wheat stem rust pathogen strain Ug99. Two doubled haploid (DH) populations and one recombinant inbred line (RIL) population were developed with ‘Linkert’ as a stem rust resistant parent. Hard red spring wheat variety ‘Forefront’ and genetic stock ‘LMPG’ were used as stem rust susceptible parents of the DH populations. Breeding line ‘MN07098-6’ was used as a susceptible parent of the RIL population. Both DH and RIL populations with their parents were evaluated both at the seedling stage and in the field against <jats:italic>Pgt</jats:italic> races. Genotyping data of the DH populations were generated using the wheat iSelect 90k SNP assay. The RIL population was genotyped by genotyping-by-sequencing. We found QTL consistently associated with wheat stem rust resistance on chromosome 2BS for the Linkert/Forefront DH population and the Linkert/MN07098-6 RIL population both in Ethiopia and Kenya. Additional reliable QTL were detected on chromosomes 5BL (125.91 cM) and 4AL (<jats:italic>Sr7a</jats:italic>) for the Linkert/LMPG population in Ethiopia and Kenya. Different QTL identified in the populations reflect the importance of examining the genetics of resistance in populations derived from adapted germplasm (Forefront and MN07098-6) in addition to a genetic stock (LMPG). The associated markers in this study could be used to track and select for the identified QTL in wheat breeding programs.</jats:p>

<jats:p>Rodents are essential to the balance of the grassland ecosystem, but their population outbreak can cause major economic and ecological damage. Rodent monitoring is crucial for its scientific management, but traditional methods heavily depend on manual labor and are difficult to be carried out on a large scale. In this study, we used UAS to collect high–resolution RGB images of steppes in Inner Mongolia, China in the spring, and used various object detection algorithms to identify the holes of Brandt’s vole (<jats:italic>Lasiopodomys brandtii</jats:italic>). Optimizing the model by adjusting evaluation metrics, specifically, replacing classification strategy metrics such as precision, recall, and F1 score with regression strategy-related metrics FPPI, MR, and MAPE to determine the optimal threshold parameters for IOU and confidence. Then, we mapped the distribution of vole holes in the study area using position data derived from the optimized model. Results showed that the best resolution of UAS acquisition was 0.4 cm pixel<jats:sup>–1</jats:sup>, and the improved labeling method improved the detection accuracy of the model. The FCOS model had the highest comprehensive evaluation, and an R<jats:sup>2</jats:sup> of 0.9106, RMSE of 5.5909, and MAPE of 8.27%. The final accuracy of vole hole counting in the stitched orthophoto was 90.20%. Our work has demonstrated that UAS was able to accurately estimate the population of grassland rodents at an appropriate resolution. Given that the population distribution we focus on is important for a wide variety of species, our work illustrates a general remote sensing approach for mapping and monitoring rodent damage across broad landscapes for studies of grassland ecological balance, vegetation conservation, and land management.</jats:p>

<jats:sec><jats:title>Introduction</jats:title><jats:p>Resprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The resprouting prediction on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree’s resprouting after pruning purely based on their empirical knowledge. In this study, we explore how far such predictions can also be made by machine learning.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Table-topped annually pruned Platanus × hispanica trees at a nursery were LiDAR-scanned for two consecutive years. Topological structures for these trees were abstracted by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with the GaussianNB model is 82.1%, but its balanced accuracy is reduced to 42.9%.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>The results were validated with a separate dataset, proving the feasibility of resprouting prediction after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research.</jats:p></jats:sec>

<jats:p><jats:italic>Panax notoginseng</jats:italic> is a highly valued perennial medicinal herb plant in Yunnan Province, China, and the taproots are the main medicinal parts that are rich in active substances of <jats:italic>P. notoginseng</jats:italic> saponins. The main purpose of this study is to uncover the physiological and molecular mechanism of <jats:italic>Panax notoginseng</jats:italic> saponin accumulation triggered by methyl jasmonate (MeJA) under arbuscular mycorrhizal fungi (AMF) by determining physiological indices, high-throughput sequencing and correlation analysis. Physiological results showed that the biomass and saponin contents of <jats:italic>P. notoginseng</jats:italic>, the concentrations of jasmonic acids (JAs) and the key enzyme activities involved in notoginsenoside biosynthesis significantly increased under AMF or MeJA, but the interactive treatment of AMF and MeJA weakened the effect of AMF, suggesting that a high concentration of endogenous JA have inhibitory effect. Transcriptome sequencing results indicated that differential expressed genes (DEGs) involved in notoginsenoside and JA biosynthesis were significantly enriched in response to AMF induction, e.g., upregulated genes of diphosphocytidyl-2-C-methyl-d-erythritol kinases (<jats:italic>ISPEs</jats:italic>), cytochrome P450 monooxygenases (<jats:italic>CYP450s</jats:italic>)<jats:italic>_</jats:italic>and glycosyltransferases (<jats:italic>GTs</jats:italic>), while treatments AMF-MeJA and salicylhydroxamic acid (SHAM) decreased the abundance of these DEGs. Interestingly, a high correlation presented between any two of saponin contents, key enzyme activities and expression levels of DEGs. Taken together, the inoculation of AMF can improve the growth and saponin accumulation of <jats:italic>P. notoginseng</jats:italic> by strengthening the activities of key enzymes and the expression levels of encoding genes, in which the JA regulatory pathway is a key link. This study provides references for implementing ecological planting of <jats:italic>P. notoginseng</jats:italic>, improving saponin accumulation and illustrating the biosynthesis mechanism.</jats:p>

<jats:p>Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in crop production, potentially leading to economic losses. Viruses, fungi, bacteria, and other infectious organisms can affect numerous plant parts, including roots, stems, and leaves. Traditional techniques for plant disease detection are time-consuming, require expertise, and are resource-intensive. Therefore, automated leaf disease diagnosis using artificial intelligence (AI) with Internet of Things (IoT) sensors methodologies are considered for the analysis and detection. This research examines four crop diseases: tomato, chilli, potato, and cucumber. It also highlights the most prevalent diseases and infections in these four types of vegetables, along with their symptoms. This review provides detailed predetermined steps to predict plant diseases using AI. Predetermined steps include image acquisition, preprocessing, segmentation, feature selection, and classification. Machine learning (ML) and deep understanding (DL) detection models are discussed. A comprehensive examination of various existing ML and DL-based studies to detect the disease of the following four crops is discussed, including the datasets used to evaluate these studies. We also provided the list of plant disease detection datasets. Finally, different ML and DL application problems are identified and discussed, along with future research prospects, by combining AI with IoT platforms like smart drones for field-based disease detection and monitoring. This work will help other practitioners in surveying different plant disease detection strategies and the limits of present systems.</jats:p>

<jats:p>Gene targeting (GT) is a promising tool for precise manipulation of genome sequences, however, GT in seed plants remains a challenging task. The simple and direct way to improve the efficiency of GT via homology-directed repair (HDR) is to increase the frequency of double-strand breaks (DSBs) at target sites in plants. Here we report an all-in-one approach of GT in Arabidopsis by combining a transcriptional and a translational enhancer for the Cas expression. We find that facilitating the expression of Cas9 and Cas12a variant by using enhancers can improve DSB and subsequent knock-in efficiency in the Arabidopsis genome. These results indicate that simply increasing Cas protein expression at specific timings - egg cells and early embryos - can improve the establishment of heritable GTs. This simple approach allows for routine genome engineering in plants.</jats:p>