Peach (Prunus persica) is an economically important fruit crop and a well-characterized model for studying Prunus species. Here we explore the evolutionary history of peach using a large-scale SNP data set generated from 58 high-coverage genomes of cultivated peach and closely related relatives, including 44 newly re-sequenced accessions and 14 accessions from a previous study. Our analyses suggest that peach originated about 2.47 Mya in southwest China in glacial refugia generated by the uplift of the Tibetan plateau. Our exploration of genomic selection signatures and demographic history supports the hypothesis that frugivore-mediated selection occurred several million years before the eventual human-mediated domestication of peach. We also identify a large set of SNPs and/or CNVs, and candidate genes associated with fruit texture, taste, size, and skin color, with implications for genomic-selection breeding in peach. Collectively, this study provides valuable information for understanding the evolution and domestication of perennial fruit tree crops.
We report the electronic and magnetic phase diagram of Ca3(Ru1−xTix)2O7. With Ti doping, the system evolves from a quasi-2D metal with ferromagnetic (FM) bilayers coupled antiferromagnetically along the c-axis (AFM-b) for x = 0, to a weakly localized state for 0 < x < 0.05 and finally to a Mott insulator with G-type antiferromagnetic (G-AFM) order for x ≥ 0.05. The magnetic state switching from the AFM-b to the G-AFM occurs in the weakly localized state near x = 0.03. We show that such a magnetic transition is controlled by the charge carrier itinerancy and can be understood in light of competing interactions between FM double-exchange and AFM superexchange. An incommensurate component is also observed due to competing magnetic interactions.
Background
Breast cancer (BC) is the most common cancer in women and the second leading cause of cancer‐related deaths among women worldwide. Single nucleotide polymorphisms (SNPs) in cytokine genes have been shown to alter their expressions or functions in patients with BC. In recent years, the molecular structure and function of IL‐1 have been studied. Its genetic polymorphism could affect the transcription and expression of the IL‐1 gene. Moreover, it is closely related to several diseases. This fact and plethora of gene polymorphism data prompted us to investigate the relationship between IL‐1 polymorphisms and IL‐1 protein expression in Chinese Han BC patients.
Method
In total, 298 patients with BC and 287 healthy control women were studied. The genetic polymorphisms for IL‐1 were analyzed by the MassARRAY sequencing method. Tumor markers and IL‐1β levels were measured by electrochemiluminescence and ELISA, respectively. All gene selection GRCh38 version.
Results
The rs1143623 (NC_000002.12:g.112838252C>G) (GC), rs16944 (NC_000002.12:g.112837290A>G)(AG), and rs10490571 (NC_000002.12:g.102100877C>T) (CC) SNPs were found to be significantly lower in the BC group than in the controls. The variant G/C genotype of rs1143623 was associated with a significantly increased risk for BC (OR = 2.34, p < 0.05). The alleles for rs16944 (A/G; OR = 3.15, p < 0.05) and rs10490571 (T/C; OR = 2.48, p < 0.05) were also significantly associated with BC. Moreover, the genotypes of rs1143623, rs16944, and rs10490571 were significantly correlated with serum IL‐1β levels and other tumor markers.
Conclusion
Our data reveal the association between genetic polymorphisms of IL‐1 and BC susceptibility in the Chinese Han population and indicates that IL‐1 polymorphisms are closely associated with tumor markers and IL‐1β protein expression in BC patients.
Visual inspection and assessment of the condition of metal structures are essential for safety. Pulse thermography produces visible infrared images, which have been widely applied to detect and characterize defects in structures and materials. When active thermography, a non-destructive testing tool, is applied, the necessity of considerable manual checking can be avoided. However, detecting an internal crack with active thermography remains difficult, since it is usually invisible in the collected sequence of infrared images, which makes the automatic detection of internal cracks even harder. In addition, the detection of an internal crack can be hindered by a complicated inspection environment. With the purpose of putting forward a robust and automatic visual inspection method, a computer vision-based thresholding method is proposed. In this paper, the image signals are a sequence of infrared images collected from the experimental setup with a thermal camera and two flash lamps as stimulus. The contrast of pixels in each frame is enhanced by the Canny operator and then reconstructed by a triple-threshold system. Two features, mean value in the time domain and maximal amplitude in the frequency domain, are extracted from the reconstructed signal to help distinguish the crack pixels from others. Finally, a binary image indicating the location of the internal crack is generated by a K-means clustering method. The proposed procedure has been applied to an iron pipe, which contains two internal cracks and surface abrasion. Some improvements have been made for the computer vision-based automatic crack detection methods. In the future, the proposed method can be applied to realize the automatic detection of internal cracks from many infrared images for the industry.
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