基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

Modelling of chromatic contrast for retrieval of wallpaper images

Colour remains one of the key factors in presenting an object and, consequently, has been widely applied in retrieval of images based on their visual contents. However, a colour appearance changes with the change of viewing surroundings, the phenomenon that has not been paid attention yet while performing colour‐based image retrieval. To comprehend this effect, in this article, a chromatic contrast model, CAMcc, is developed for the application of retrieval of colour intensive images, cementing the gap that most of existing colour models lack to fill by taking simultaneous colour contrast into account. Subsequently, the model is applied to the retrieval task on a collection of museum wallpapers of colour‐rich images. In comparison with current popular colour models including CIECAM02, HSI and RGB, with respect to both foreground and background colours, CAMcc appears to outperform the others with retrieved results being closer to query images. In addition, CAMcc focuses more on foreground colours, especially by maintaining the balance between both foreground and background colours, while the rest of existing models take on dominant colours that are perceived the most, usually background tones. Significantly, the contribution of the investigation lies in not only the improvement of the accuracy of colour‐based image retrieval but also the development of colour contrast model that warrants an important place in colour and computer vision theory, leading to deciphering the insight of this age‐old topic of chromatic contrast in colour science. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 361–373, 2015     

道在山林，周弥六合——浙东天台山“洞天福地”的山岳景观流变及文化意象研究

道教洞天福地作为中国名山风景的经典类型之一，在宗教山岳景观中占据独特地位。浙东天台山水神秀，历代高道以入山隐修为主要目的，形成了道教在区域山林的景观文化基础。从天台山“神仙之乡”的文化背景出发，从“想象与实践”的视角切入，梳理了天台山洞天福地的景观流变：分析其“不死之福庭”地域性景观的形成经历了“赤城→桐柏”的信仰转移过程；以天台山为坐标，洞天福地格局打破了区域“层级”分布特征，而呈现大范围“州郡”空间格局。作为“联结点”的天台山，洞天世界沟通了宇宙、山、人3个基本场域，由此衍生出“洞宫”式和“周回”式山岳空间营建典范。旨在挖掘洞天福地中典型案例的价值，为中国洞天福地体系的构建提供理论依据。     

A Student's t-based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

Image color clustering is a basic technique in image processing and computer vision, which is often applied in image segmentation, color transfer, contrast enhancement, object detection, skin color capture, and so forth. Various clustering algorithms have been employed for image color clustering in recent years. However, most of the algorithms require a large amount of memory or a predetermined number of clusters. In addition, some of the existing algorithms are sensitive to the parameter configurations. In order to tackle the above problems, we propose an image color clustering method named Student's t-based density peaks clustering with superpixel segmentation (tDPCSS), which can automatically obtain clustering results, without requiring a large amount of memory, and is not dependent on the parameters of the algorithm or the number of clusters. In tDPCSS, superpixels are obtained based on automatic and constrained simple non-iterative clustering, to automatically decrease the image data volume. A Student's t kernel function and a cluster center selection method are adopted to eliminate the dependence of the density peak clustering on parameters and the number of clusters, respectively. The experiments undertaken in this study confirmed that the proposed approach outperforms k-means, fuzzy c-means, mean-shift clustering, and density peak clustering with superpixel segmentation in the accuracy of the cluster centers and the validity of the clustering results.     

Dietary advanced glycation end‐products: Perspectives linking food processing with health implications

Dietary advanced glycation end products (dAGEs) are complex and heterogeneous compounds derived from nonenzymatic glycation reactions during industrial processing and home cooking. There is mounting evidence showing that dAGEs are closely associated with various chronic diseases, where the absorbed dAGEs fuel the biological AGEs pool to exhibit noxious effects on human health. Currently, due to the uncertain bioavailability and rapid renal clearance of dAGEs, the relationship between dAGEs and biological AGEs remains debatable. In this review, we provide the most updated information on dAGEs including their generation in processed foods, analytical and characterization techniques, metabolic fates, interaction with AGE receptors, implications on human health and reducing strategies. Available evidence demonstrating a relevance between dAGEs and food allergy is also included. AGEs are ubiquitous in foods and their contents largely depend on the reactivity of carbonyl and amino groups, along with surrounding condition mainly pH and heating procedures. Once being digested and absorbed into the circulation, two separate pathways can be involved in the deleterious effects of dAGEs: an AGE receptor‐dependent way to stimulate cell signals, and an AGE receptor‐independent way to dysregulate proteins via forming complexes. Inhibition of AGEs formation during food processing and reduction in the diet are two potent approaches to restrict health‐hazardous dAGEs. To elucidate the biological role of dAGEs toward human health, the following significant perspectives are raised: molecular size and complexity of dAGEs; interactions between unabsorbed dAGEs and gut microbiota; and roles played by concomitant compounds in the heat‐processed foods.     

Hetero-assembly of colloidal particles in concentrated non-aqueous suspensions by polymer dispersant design

Although many colloidal assembling systems have been reported, most systems suffer from severe aggregation under high solid concentrations, which can often be observed in typical hetero-aggregation system. In this study, we created a hetero-assembly system using concentrated (~50 vol%) suspensions by mixing large SiO₂ particles modified with polyacrylic acid partially complexed with oleylamine (PAA-OAm) and small SiO₂ particles modified with polyethyleneimine partially complexed with oleic acid (PEI-OA) in a non-aqueous solvent. We demonstrated that hetero-assembly is driven by the interactions between the uncomplexed carboxyl/amine groups of the PAA/PEI present on the particles, while severe aggregation is simultaneously prevented by the steric repulsions of the aliphatic oleyl chains. Comparison of the cross sections of the in-situ solidified hetero-assembled suspensions with those of ideally assembled structures which were reproduced by a simulation considering the statistical distribution of particles strongly supported successful particle assembling via the proposed approach. The results revealed that the OA content in the PEI-OA complex was the dominant factor that controlled the dispersion and assembling state of the binary particles. The significance of this study is that our findings will provide a class of colloidal dispersion state which binary particles were assembled in a high solid content suspension without forming strong aggregates.     

改进HSR-FCN的服装图像识别分类算法研究

目前网络上的服装图像数量增长迅猛，对于大量服装图像实现智能分类的需求日益增加。将基于区域的全卷积网络（Region-Based Fully Convolutional Networks，R-FCN）引入到服装图像识别中，针对服装图像分类中网络训练时间长、形变服装图像识别率低的问题，提出一种新颖的改进框架HSR-FCN。新框架将R-FCN中的区域建议网络和HyperNet网络相融合，改变图片特征学习方式，使得HSR-FCN可以在更短的训练时间内达到更高的准确率。在模型中引入了空间转换网络，对输入服装图像和特征图进行了空间变换及对齐，加强了对多角度服装和形变服装的特征学习。实验结果表明，改进后的HSR-FCN模型有效地加强了对形变服装图像的学习，且在训练时间更短的情况下，比原来的网络模型R-FCN平均准确率提高了大约3个百分点，达到96.69%。     

Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.     

Teff (Eragrostis tef) processing,utilization and future opportunities: a review

The demand for food production has been constantly increasing due to rising population. In developed countries, for example, the emergence of regional production of old grains that are rarely utilized, along with the production of commonly consumed grains, has gained importance in recent years. These grains, known collectively as ancient or heirloom grains, have offered both farmers and consumers novel ways of cultivation and products with interesting taste, characteristics and nutritional value. Among the 30 000 plant species known, only five cereals currently provide more than 50% of the world's energy intake – bread wheat (Triticum aestivum), rice (Oryza sativa), sorghum (Sorghum bicolor), millets (Panicum sp.) and maize (Zea mays). The excessive utilization of these selected species has a great potential to cause genetic losses and difficulty in bridging future agricultural demands. Teff (Eragrostis tef), an ancient grain extensively cultivated in countries like Eritrea and Ethiopia, provides promising alternatives for new food uses since its nutritional value is significantly higher than most others cereal grains. The absence of gluten allows flexibility in food utilization since it can be directly substituted to gluten-containing products. The grain also offers an excellent balance of essential amino acids and minerals, which can fulfil the recommended daily intake and eliminates the need for fortification and enrichment. This review provides a general overview of the physical properties and nutritional composition of teff grains related to processing and applications in the food and feed industries. The current status of teff utilization, as well as the challenges in production and commercialization, and future opportunities is presented and discussed.