Using cellular automata to generate image representation for biological sequences

Summary. A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419–424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their fingerprint. It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246–255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.     

The ‘Frank J. Myers Rotifera Collection’ at the Academy of Natural Sciences of Philadelphia

The Academy of Natural Sciences of Philadelphia (ANSP) houses the world’s most comprehensive collection of Rotifera on microscope slides. A particular strength of this major reference collection and type repository lies in the excellent preparation of specimens in life-like extended state and its balanced coverage of the phylum. The collection is almost worldwide in scope, and currently comprises 774 valid taxa, equalling a taxon coverage of 88.2% (families), 75.4% (genera), and 39.1% (species) compared to taxon numbers currently known to occur worldwide. A searchable database plus illustrated catalog has now been published on CD-ROM and on the Academy’s website. The poor representation of Rotifera in natural history collections and their generally poor curation and conservation is commented upon.     

Electric imaging through active electrolocation: implication for the analysis of complex scenes

The electric sense of mormyrids is often regarded as an adaptation to conditions unfavourable for vision and in these fish it has become the dominant sense for active orientation and communication tasks. With this sense, fish can detect and distinguish the electrical properties of the close environment, measure distance, perceive the 3-D shape of objects and discriminate objects according to distance or size and shape, irrespective of conductivity, thus showing a degree of abstraction regarding the interpretation of sensory stimuli. The physical properties of images projected on the sensory surface by the fish's own discharge reveal a "Mexican hat" opposing centre-surround profile. It is likely that computation of the image amplitude to slope ratio is used to measure distance, while peak width and slope give measures of shape and contrast. Modelling has been used to explore how the images of multiple objects superimpose in a complex manner. While electric images are by nature distributed, or 'blurred', behavioural strategies orienting sensory surfaces and the neural architecture of sensory processing networks both contribute to resolving potential ambiguities. Rostral amplification is produced by current funnelling in the head and chin appendage regions, where high density electroreceptor distributions constitute foveal regions. Central magnification of electroreceptive pathways from these regions particularly favours the detection of capacitive properties intrinsic to potential living prey. Swimming movements alter the amplitude and contrast of pre-receptor object-images but image modulation is normalised by central gain-control mechanisms that maintain excitatory and inhibitory balance, removing the contrast-ambiguity introduced by self-motion in much the same way that contrast gain-control is achieved in vision.     

一种新的小样本建模方法──非线性映象降维建模方法

本文应用多元统计分析中的距离概念,降低变量空间的维数,以解决建立预测模型时遇到的小样本多变量问题。     

Mapping tropical forest fractional cover from coarse spatial resolution remote sensing imagery

At regional to global scales the only feasible approach to mapping and monitoring forests is through the use of coarse spatial resolution remotely sensed imagery. Significant errors in mapping may arise as such imagery may be dominated by pixels of mixed land cover composition which cannot be accommodated by conventional mapping approaches. This may lead to incorrect assessments of forest extent and thereby processes such as deforestation which may propagate into studies of environmental change. A method to unmix the class composition of image pixels is presented and used to map tropical forest cover in part of the Mato Grosso, Brazil. This method is based on an artificial neural network and has advantages over other techniques used in remote sensing. Fraction images depicting the proportional class coverage in each pixel were produced and shown to correspond closely to the actual land cover. The predicted and actual forest cover were, for instance, strongly correlated (up to r = 0.85, significant at the 99% level of confidence) and the predicted extent of forest over the test site much closer to the actual extent than that derived from a conventional approach to mapping from remotely sensed imagery.     

Recent developments in molecular simulation approaches to study spherical virus capsids

Viruses are particularly challenging systems to study via molecular simulation methods. Virus capsids typically consist of over 100 subunit proteins and reach dimensions of over 100 nm; solvated viruses capsid systems can be over 1 million atoms in size. In this review, I will present recent developments which have attempted to overcome the significant computational expense to perform simulations which can inform experimental studies, make useful predictions about biological phenomena and calculate material properties relevant to nanotechnology design efforts.     

基于多尺度分割和决策树算法的山区遥感影像变化检测方法——以四川攀西地区为例

山区遥感影像变化检测面临地形效应明显、空间异质性高等不利因素的影响,构建适用于复杂地形条件的变化检测方法一直是遥感应用研究的难点。在对影像进行多尺度分割的基础上,构建对象的多种光谱、形状及地形特征,将地形阴影、物候等造成的虚假变化当作"未变化"训练样本,利用决策树算法自动提取检测规则,建立复杂地形条件下面向对象的遥感影像变化检测方法,并将该方法用于四川攀西山区1989年和2009年TM影像的检测试验,最后对方法的不足和改进措施进行了讨论。主要结论包括:(1)文中构建的方法能够有效减弱山区复杂地形条件对遥感影像变化检测的不利影响,采用地面调查数据和分层随机采样的总体验证精度为93.57%,Kappa系数为0.8706。(2)C5.0决策树算法对于只有"变化"和"未变化"两种类别且同类间训练样本高度异质化的影像分类仍能取得较好的效果,具有较好的鲁棒性和适应能力。通过将地形、物候等引起的虚假变化当作"未变化"训练样本可以有效提高检测精度。(3)光谱特征仍是TM影像遥感分析的主要信息源,影像间NDVI的差值对于植被覆盖区域土地覆盖格局变化的检测具有良好的表征作用。(4)攀西地区1989—2009年间土地覆盖格局变化明显且与人类活动关系密切,典型的驱动方式包括退耕还林(草)工程、水利工程建设和矿山开采等。共检测出变化面积740.2km2,占影像总面积的2.49%。