The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Data Mining Practical Machine Learning Tools and Techniques

The PASCAL Visual Object Classes Challenge

IEEE Transactions on Pattern Analysis and Machine Intelligence

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

This paper presents a two-stage method for multi-oriented video character segmentation. Words segmented from video text lines are considered for character segmentation in the present work. Words can contain isolated or non-touching characters, as well as touching characters. Therefore, the character segmentation problem can be viewed as a two stage problem. In the first stage, text cluster is identified and isolated (non-touching) characters are segmented. The orientation of each word is computed and the segmentation paths are found in the direction perpendicular to the orientation. Candidate segmentation points computed using the top distance profile are used to find the segmentation path between the characters considering the background cluster. In the second stage, the segmentation results are verified and a check is performed to ascertain whether the word component contains touching characters or not. The average width of the components is used to find the touching character components. For segmentation of the touching characters, segmentation points are then found using average stroke width information, along with the top and bottom distance profiles. The proposed method was tested on a large dataset and was evaluated in terms of precision, recall and f-measure. A comparative study with existing methods reveals the superiority of the proposed method.

/pdf/a-new-method-for-character-segmentation-from-multi-oriented-26bkck5bbn.pdf

A New Method for Character Segmentation from Multi-oriented Video Words

Accurate morphological characterization of the multiple neuronal classes of the brain would facilitate the elucidation of brain function and the functional changes that underlie neurological disorders such as Parkinson's diseases or Schizophrenia. Manual morphological analysis is very time-consuming and suffers from a lack of accuracy because some cell characteristics are not readily quantified. This paper presents an investigation in automating the classification of dopaminergic neurons located in the brainstem of the rodent, a region critical to the regulation of motor behaviour and is implicated in multiple neurological disorders including Parkinson's disease. Using a Carl Zeiss Axioimager Z1 microscope with Apotome, salient information was obtained from images of dopaminergic neurons using a structural feature extraction technique. A data set of 100 images of neurons was generated and a set of 17 features was used to describe their morphology. In order to identify differences between neurons, 2-dimensional and 3-dimensional image representations were analyzed. This paper compares the performance of three popular classification methods in bioimage classification (Support Vector Machines (SVMs), Back Propagation Neural Networks (BPNNs) and Multinomial Logistic Regression (MLR)), and the results show a significant difference between machine classification (with 97% accuracy) and human expert based classification (72% accuracy).

/pdf/automated-classification-of-dopaminergic-neurons-in-the-48dxc9pbi0.pdf

Automated classification of dopaminergic neurons in the rodent brain

A reliable deterioration model is essential in bridge asset management. Most deterioration modeling requires a large amount of well-distributed condition rating data along with all bridge ages to calculate the probability of condition rating deterioration. This means that the model can only function properly when a full set of data is available. To overcome this shortcoming, an improved artificial intelligence (AI)-based model is presented in this study to effectively predict long-term deterioration of bridge elements. The model has four major components: (1) categorizing bridge element condition ratings; (2) using the neural network-based backward prediction model (BPM) to generate unavailable historical condition ratings for applicable bridge elements; (3) training by an Elman neural network (ENN) for identifying historical deterioration patterns; and (4) using the ENN to predict long-term performance. The model has been tested using bridge inspection records that demonstrate satisfactory results. This study primarily focuses on the establishment of a new methodology to address the research problems identified. A series of case studies, hence, need to follow to ensure the method is appropriately developed and validated.

Michael Blumenstein

Papers

A New Method for Character Segmentation from Multi-oriented Video Words

Automated classification of dopaminergic neurons in the rodent brain

Development of a Long-Term Bridge Element Performance Model Using Elman Neural Networks

Signature Veriﬁcation Competition for Online and Ofﬂine Skilled Forgeries (SigComp2011)

Piece-wise linearity based method for text frame classification in video