A Novel Method for Wheat Spike Phenotyping Based on Instance Segmentation and Classification

The phenotypic analysis of wheat spikes plays an important role in wheat growth management, plant breeding, and yield estimation. However, the dense and tight arrangement of spikelets and grains on the spikes makes the phenotyping more challenging. This study proposed a rapid and accurate image-based method for in-field wheat spike phenotyping consisting of three steps: wheat spikelet segmentation, grain number classification, and total grain number counting. Wheat samples ranging from the early filling period to the mature period were involved in the study, including three varieties: Zhengmai 618, Yannong 19, and Sumai 8. In the first step, the in-field collected images of wheat spikes were optimized by perspective transformation, augmentation, and size reduction. The YOLOv8-seg instance segmentation model was used to segment spikelets from wheat spike images. In the second step, the number of grains in each spikelet was classified by a machine learning model like the Support Vector Machine (SVM) model, utilizing 52 image features extracted for each spikelet, involving shape, color, and texture features as the input. Finally, the total number of grains on each wheat spike was counted by adding the number of grains in the corresponding spikelets. The results showed that the YOLOv8-seg model achieved excellent segmentation performance, with an average precision (AP) @[0.50:0.95] and accuracy (A) of 0.858 and 100%. Meanwhile, the SVM model had good classification performance for the number of grains in spikelets, and the accuracy, precision, recall, and F1 score reached 0.855, 0.860, 0.865, and 0.863, respectively. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were as low as 1.04 and 5% when counting the total number of grains in the frontal view wheat spike images. The proposed method meets the practical application requirements of obtaining trait parameters of wheat spikes and contributes to intelligent and non-destructive spike phenotyping

Efficacy of Adjunctive Bioactive Materials in the Treatment of Periodontal Intrabony Defects: A Systematic Review and Meta-Analysis

Objectives. Lots of bioactive materials have been additionally applied for the treatment of periodontal intrabony defect. However, there is dearth of studies to systematically evaluate the supplementary role of them in periodontal regeneration. The goal of this meta-analysis is to evaluate the adjunctive effects of bioactive materials such as platelet-rich plasma (PRP), platelet-rich fibrin (PRF), enamel matrix derivative (EMD), and amnion membrane (AM) on the outcomes of bone grafting treatment for periodontal intrabony defects. Methods. Articles published before December 2017 were searched electronically in three databases (PubMed, Embase, and Cochrane Central), with no date or language limits. Randomized controlled trials (RCTs) on the assessment of effectiveness of the four biomaterials in conjunction with demineralized freeze-dried bone allografts (DFDBA) in the treatment of periodontal intrabony defects were enrolled in this meta-analysis. Data were analyzed with STATA 12. Results. Nine studies were included. PRF and PRP significantly improved pocket depth (PD) reduction and clinical attachment loss (CAL) gain. Only PRF exhibited a positive result in recession reduction (RecRed). Only PRP showed a statistically significant increase in bone fill. AM merely gained more CAL. EMD did not improve any clinical outcome. Conclusion. Our data suggest that PRF/PRP could be taken as a preferred adjunct to facilitate periodontal regeneration of intrabony defects

Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population

The fungal disease of maize known as Gray Leaf Spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, southern Africa, and USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTL) [Here and throughout manuscript: the plural or QTL is QTL (loci), not QTLs – please correct QTLs to QTL] associated with resistance to gray leaf spot (GLS) in multiple locations including Dehong (2013), Enshi (2013 and 2014), Sayinpan (2019), Xindu (2019 and 2020), and Pixian (2020). The analysis of seven distinct environments revealed a total of 58 QTLs, with 49 QTLs forming 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. Through comparison with published research findings, we identified co-located QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we have identified a total of 110 genes that exhibit robust disparities in both gene expression and structural alterations (including modifications within coding regions, insertions/deletions, premature terminations, and large segmental changes). Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains such as: putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a useful resource and linked loci for GLS marker resistance selection (MRS) breeding in maize.This is a manuscript of an article published as Cui, L., Sun, M., Zhang, L. et al. Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population. Theor Appl Genet 137, 183 (2024). https://doi.org/10.1007/s00122-024-04694-x. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Posted with permission