Background: Inbreeding in seed orchards is expected to increase with the advancement of breeding cycles, which results in the delivery of crops with suboptimal genetic gain, reduced genetic diversity, and lower seed set. Here, a genetic distance-dependent method for clonal spatial deployment in seed orchards was developed and demonstrated, which reduced the inbreeding levels. The method's main evaluation parameter of inbreeding is the genetic distance among individuals and the deployment method used an improved adaptive parallel genetic algorithm (IAPGA) based on Python language. Using inbreeding-prone Chinese Mongolian pine breeding population material originating from a single natural population, the proposed method was compared to a traditional orchard design and a distance-based design; namely, complete randomized block (RCB) and optimum neighborhood (ONA) designs, respectively. Results: With the advancement of selective breeding cycles, group separation among orchard related individuals is expected to increase. Based on the genetic distance among individuals, the IAPGA design was superior in significantly reducing the inbreeding level as compared to the two existing designs, confirming its suitability to advanced-generation orchards where relatedness among parents is common. In the 1st, 2nd, and mixed generations clonal deployment schemes, the IAPGA design produced lower inbreeding with 87.22%, 81.49%, and 87.23% of RCB, and 92.78%, 91.30%, and 91.67% of ONA designs, respectively. Conclusions: The IAPGA clonal deployment proposed in this study has the obvious advantage of controlling inbreeding, and it is expected to be used in clonal deployment in seed orchards on a large-scale. Further studies are needed to focus on the actual states of pollen dispersal and mating in seed orchards, and more assumptions should be taken into account for the optimized deployment method.

Abstract. Extraction of ROI (Region-Of-Interest) in dermatosis images can be used in content-based image retrieval (CBIR). Image segmentation takes an important part in it. And the performance of the segmentation algorithm directly influences the efficiency of the ROI extraction results. In this paper, a method of Chan-Vese segmentation based on saliency detection to extract the ROI of the dermatosis images is proposed. Firstly the spectral residual approach (SR)[11] is used to get the saliency map of the dermatosis images. Secondly threshold segmentation is used to get the initial ROI images. Finally the Chan-Vese model is used to segment the initial ROI images to get the final ROI images, which can ensure the active contours evolve close to the object and remove the redundant information from the complex background. The experiment results show that the proposed method has the better performance than only using Chan-Vese method.

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While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement, E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show that E-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile, E-index vector is proposed as well to capture more subtle differences of developmental patterns.