Fox Hunting in Wild Apples: Searching for Novel Genes in Malus Sieversii

Malus sieversii is considered the progenitor of modern apple (Malus pumila) cultivars and to represent a valuable source of genetic diversity. Despite the importance of M. sieversii as a source of disease resistance, stress tolerance, and novel fruit traits, little is known about gene function and diversity in M. sieversii. Notably, a publicly annotated genome sequence for this species is not available. In the current study, the FOX (Full-length cDNA OvereXpressing) gene hunting system was used to construct a library of transgenic lines of Arabidopsis in which each transgenic line overexpresses a full-length gene obtained from a cDNA library of the PI619283 accession of M. sieversii. The cDNA library was constructed from mRNA obtained from bark tissues collected in late fall–early winter, a time at which many abiotic stress-adaptative genes are expressed. Over 4000 apple FOX Arabidopsis lines have been established from the pool of transgenic seeds and cDNA inserts corresponding to various Gene Ontology (GO) categories have been identified. A total of 160 inserts appear to be novel, with no or limited homology to M. pumila, Arabidopsis, or poplar. Over 1300 lines have also been screened for freezing resistance. The constructed library of transgenic lines provides a valuable genetic resource for exploring gene function and diversity in Malus sieversii. Notably, no such library of t-DNA lines currently exists for any Malus species.     

基于深度语义分割的多源遥感图像海面溢油监测

针对遥感图像海面溢油区域通常受到斑噪声以及强度不均等因素的影响,从而导致溢油区域监测效果较差的问题,本文引入了深度语义分割的方法,将深度卷积神经网络与全连接条件随机场相结合,形成端对端连接。以Resnet结构为基础,首先通过深度卷积神经网络对多源遥感图像粗分割并作为输入,然后经过改进的全连接条件随机场,利用高斯成对势和平均场近似定理,建立条件随机场形成递归神经网络作为输出。通过多源遥感图像对海面溢油区域进行监测,并利用可见光图像估计溢油区域面积。实验在所建立的多源遥感图像数据集上与其它先进模型进行对比,结果表明本文方法提高了溢油区域的分割精度以及精细细节程度,平均交并比为82.1%,监测效果具有明显地改善。     

Identification of Proteins Modulated in the Date Palm Stem Infested with Red Palm Weevil (Rhynchophorus ferrugineus Oliv.) Using Two Dimensional Differential Gel Electrophoresis and Mass Spectrometry

A state of the art proteomic methodology using Matrix Assisted Laser Desorption/Ionization-Time of Flight (MALDI TOF) has been employed to characterize peptides modulated in the date palm stem subsequent to infestation with red palm weevil (RPW). Our analyses revealed 32 differentially expressed peptides associated with RPW infestation in date palm stem. To identify RPW infestation associated peptides (I), artificially wounded plants (W) were used as additional control beside uninfested plants, a conventional control (C). A constant unique pattern of differential expression in infested (I), wounded (W) stem samples compared to control (C) was observed. The upregulated proteins showed relative fold intensity in order of I > W and downregulated spots trend as W > I, a quite interesting pattern. This study also reveals that artificially wounding of date palm stem affects almost the same proteins as infestation; however, relative intensity is quite lower than in infested samples both in up and downregulated spots. All 32 differentially expressed spots were subjected to MALDI-TOF analysis for their identification and we were able to match 21 proteins in the already existing databases. Relatively significant modulated expression pattern of a number of peptides in infested plants predicts the possibility of developing a quick and reliable molecular methodology for detecting plants infested with date palm.     

Younger and older users׳ recognition of virtual agent facial expressions

As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent׳s social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell et al., 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck and Reichenbach, 2005, Courgeon et al., 2009, Courgeon et al., 2011, Breazeal, 2003); however, little research has compared in-depth younger and older adults’ ability to label a virtual agent׳s facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a possible explanation for age-related differences in emotion recognition. First, our findings show age-related differences in the recognition of emotions expressed by a virtual agent, with older adults showing lower recognition for the emotions of anger, disgust, fear, happiness, sadness, and neutral. These age-related difference might be explained by older adults having difficulty discriminating similarity in configural arrangement of facial features for certain emotions; for example, older adults often mislabeled the similar emotions of fear as surprise. Second, our results did not provide evidence for the dynamic formation improving emotion recognition; but, in general, the intensity of the emotion improved recognition. Lastly, we learned that emotion recognition, for older and younger adults, differed by character type, from best to worst: human, synthetic human, and then iCat. Our findings provide guidance for design, as well as the development of a framework of age-related differences in emotion recognition.     

BE-ACGAN: Photo-realistic residual bit-depth enhancement by advanced conditional GAN

Since more demands for high quality visualization have been raised in various fields, monitors with higher bit-depth (HBD) become popular in recent years. However, most digital images are at low bit-depth (LBD) and usually of low visual quality with annoying false contours when displayed on HBD monitors directly. To reconstruct visually pleasant HBD images, many bit-depth enhancement (BE) algorithms have been proposed from various aspects, but the recovered HBD images are usually unsatisfactory with conspicuous false contours or over-blurred textures. Inspired by discriminative learning, we propose a residual BE algorithm based on advanced conditional generative adversarial network (BE-ACGAN), in which the discriminator adversarially helps assess image quality and train the generator to achieve more photo-realistic recovery performance. Besides, since it is hard to distinguish between the reconstructed and real HBD images with similar structures, the discriminator takes residual images as input and further takes LBD images as conditions to achieve more reliable performance. In addition, we present a novel loss function to deal with the difficulty of unstable adversarial training. The proposed algorithm outperforms the state-of-the-art methods on large-scale benchmark datasets. Source codes are available at https://github.com/TJUMMG/BE- ACGAN/.     

MicroRNA expression profile and lipid metabolism characteristics in liver of rat undergoing high-fat diet

This study aimed to investigate the microRNA expression profile and the characteristics of lipid metabolism in the livers of rats undergoing a high-fat diet. Fifty male Sprague-Dawley (SD) rats were divided into a standard chow group (C group, N = 10) and a high-fat diet group (H group, N = 40). After 12 weeks, the rat body weight, body length, fat mass, and serum lipid concentration were measured. The expression profile of microRNAs and the gene and protein expression levels involved in lipid metabolism in rat liver were detected. Body fat and serum lipid concentrations were all significantly higher in the H group than those in the C group (p < 0.05 or p < 0.01). The expression of 10 microRNAs showed significant differences in the liver (p < 0.05). In particular, the let-7 family expression levels significantly increased (p < 0.05) in the H group compared with those in the C group. Compared with the C group, the high-fat diet resulted in low FAS, CPT1A, and ApoAI mRNA expression levels (p < 0.05 or p < 0.01) and high PPARα and FAT/CD36 mRNA expression levels in the H group rat liver (p < 0.01). Meanwhile, the protein PPARα, FAS, CPT1A, FAT/CD36, and ApoAI expression levels were all significantly lower in the H group than those in the C group (p < 0.05 or p < 0.01). In conclusion, the high-fat diet increased the body fat and serum lipid levels and altered the 10 microRNA expression levels in the liver. The high-fat diet may affect hepatic carbohydrate metabolism and increase ectopic fat accumulation through let-7 family overexpression. The high-fat diet for 12 weeks decreased lipid metabolism level in the liver, thereby decreasing fatty acid synthesis, oxidation, and transport by down-regulating the PPARα, FAS, CPT1A, FAT/CD36, and ApoAI protein levels.     

基于卷积神经网络的表情识别

在传统静态表情识别研究基础上,提出一种简单的人脸裁剪方法,再用浅层卷积神经网络进一步提取特征并进行表情识别。以CK+和JAFFE为实验数据集,进行预处理效果对比实验、数据增强实验、单种表情识别实验和跨数据集六分类实验。结果表明,针对数据量较少的情况,提出的表情识别方法效果明显且鲁棒性更优。     

Clustering biomedical and gene expression datasets with kernel density and unique neighborhood set based vein detection

It is a crucial need for a clustering technique to produce high-quality clusters from biomedical and gene expression datasets without requiring any user inputs. Therefore, in this paper we present a clustering technique called KUVClust that produces high-quality clusters when applied on biomedical and gene expression datasets without requiring any user inputs. The KUVClust algorithm uses three concepts namely multivariate kernel density estimation, unique closest neighborhood set and vein-based clustering. Although these concepts are known in the literature, KUVClust combines the concepts in a novel manner to achieve high-quality clustering results. The performance of KUVClust is compared with established clustering techniques on real-world biomedical and gene expression datasets. The comparisons were evaluated in terms of three criteria (purity, entropy, and sum of squared error (SSE)). Experimental results demonstrated the superiority of the proposed technique over the existing techniques for clustering both the low dimensional biomedical and high dimensional gene expressions datasets used in the experiments.