Frog skins keep redox homeostasis by antioxidant peptides with rapid radical scavenging ability

The question of how amphibians can protect themselves from reactive oxygen species when exposed to the sun in an oxygen-rich atmosphere is important and interesting, not only from an evolutionary viewpoint, but also as a primer for researchers interested in mammalian skin biology, in which such peptide systems for antioxidant defense are not well studied. The identification of an antioxidant peptide named antioxidin-RL from frog (Odorrana livida) skin in this report supports the idea that a peptide antioxidant system may be a widespread antioxidant strategy among amphibian skins. Its ability to eliminate most of the 2,2'-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) radical tested within 2 s, which is much faster than the commercial antioxidant factor butylated hydroxytoluene, suggests that it has a potentially large impact on redox homeostasis in amphibian skins. Cys10 is proven to be responsible for its rapid radical scavenging function and tyrosines take part in the binding of antioxidin-RL to radicals according to our nuclear magnetic resonance assay. (C) 2010 Elsevier Inc. All rights reserved.

Relaxation of selective constraints on avian mitochondrial DNA following the degeneration of flight ability

The evolution of flight is the most important feature of birds, and this ability has helped them become one of the most successful groups of vertebrates. However, some species have independently lost their ability to fly. The degeneration of flight abilit

Avoidant Learning Ability in Free Flying Housefly (Aldrichina grahami)by Electric Shock

下载PDF阅读器研究证实:蜜蜂和果蝇具有良好的学习记忆能力.利用自主改良的研究装置对另一种具有强大生存本能的双翅目昆虫--巨尾阿丽蝇(Aldrichina grahami)在自由状态下电击同避学习能力进行研究.结果表明,巨尾阿丽蝇具有良好的学习记忆能力,因为当刺激电压范围为5V到45V时,观察到巨尾阿丽蝇有显著的回避电刺激行为,而当电压达到60V时会受到明显伤害.由此推测,巨尾阿丽蝇适合作为神经系统研究的动物模型.该实验所采用的实验范例较以往有所改进,适合作为自由状态下研究昆虫的工具.

Comparative Analysis of Five Different Homologous Feeder Cell Lines in the Ability to Support Rhesus Embryonic Stem Cells

我们以前的研究建立了五株猕猴饲养层细胞系来支持猕猴胚胎干细胞(rESCs)的生长:一岁猴耳皮肤成纤维细胞(MESFs)、两岁猴输卵管成纤维细胞(MOFs)、成年猴卵泡颗粒成纤维样细胞(MFGs)、成年猴卵泡颗粒上皮样细胞(MFGEs),以及MESFs的克隆成纤维细胞(CMESFs).我们发现MESFs、CMESFs、MOFs和MFGs,而不足MFGEs支持猕猴胚胎干细胞(rESCs,rhesus embryonic stem cells)的生长.通过半定量PCR的方法,我们在支持性的饲养层细胞中检测到了一些基因的高表达.在本研究中,我们运用Affymetrix公司的GeneChip Rhesus Macaque Genome Array芯片来研究这五株同源饲养层的表达谱,希望发现哪些细胞因子和信号通路在维持rESCs中起到重要作用.结果表明,除MFGE外,包括GREM2、bFGF,、KITLG,、DKK3、GREM1、AREG、SERPINF1和LTBF1等八个基因的mRNA在支持性的饲养层细胞中高表达.本研究结果提示,很多信号通路在支持rESCs的未分化生长和多潜能性方面可能起到了冗余的作用.

Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.

MCBoost: Multiple classifier boosting for perceptual co-clustering of images and visual features

We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-clustered. Co-clustering is performed in a way that maximises discrimination of object images from non-object images, thus emphasizing discriminative features. This provides a way of obtaining perceptual joint-clusters of object images and features. We tackle the problem by simultaneously boosting multiple strong classifiers which compete for images by their expertise. Each boosting classifier is an aggregation of weak-learners, i.e. simple visual features. The obtained classifiers are useful for object detection tasks which exhibit multimodalities, e.g. multi-category and multi-view object detection tasks. Experiments on a set of pedestrian images and a face data set demonstrate that the method yields intuitive image clusters with associated features and is much superior to conventional boosting classifiers in object detection tasks.

ID ENGINE: Image Classifier

C++ Prototype implementation of multi-modal image classification and retrieval method for construction site images

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. © 2012 Kadiallah et al.

Multiple classifier boosting and tree-structured classifiers

Visual recognition problems often involve classification of myriads of pixels, across scales, to locate objects of interest in an image or to segment images according to object classes. The requirement for high speed and accuracy makes the problems very challenging and has motivated studies on efficient classification algorithms. A novel multi-classifier boosting algorithm is proposed to tackle the multimodal problems by simultaneously clustering samples and boosting classifiers in Section 2. The method is extended into an online version for object tracking in Section 3. Section 4 presents a tree-structured classifier, called Super tree, to further speed up the classification time of a standard boosting classifier. The proposed methods are demonstrated for object detection, tracking and segmentation tasks. © 2013 Springer-Verlag Berlin Heidelberg.