13160483
MEGA2: molecular evolutionary genetics analysis software
UNLABELLED
We have developed a new software package, Molecular Evolutionary Genetics Analysis version 2 (MEGA2), for exploring and analyzing aligned DNA or protein sequences from an evolutionary perspective. MEGA2 vastly extends the capabilities of MEGA version 1 by: (1) facilitating analyses of large datasets; (2) enabling creation and analyses of groups of sequences; (3) enabling specification of domains and genes; (4) expanding the repertoire of statistical methods for molecular evolutionary studies; and (5) adding new modules for visual representation of input data and output results on the Microsoft Windows platform.


AVAILABILITY
http://www.megasoftware.net.


CONTACT
s.kumar@asu.edu
25064321
TimeTree: a public knowledge-base of divergence times among organisms
UNLABELLED
Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature.


AVAILABILITY
TimeTree is available at http://www.timetree.net
1061170
Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations
BackgroundModern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns.ResultsThe effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches.ConclusionsWe conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns.
14954871
Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis
Abstract: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data.
6663815
Mesh segmentation schemes for error resilient coding of 3-D graphic models
Most existing coding techniques for three-dimensional (3-D) graphic models focus on coding efficiency. Due to irregular structure of the 3-D mesh and the use of variable length entropy codes, channel errors often propagate in the coded bitstream and severely distort the decoded model. By segmenting a 3-D graphic model (or its connected components) into small pieces, the impact of channel errors is confined to directly corrupted pieces rather than the whole mesh. The mesh segmentation schemes should be compatible with the underlying encoding techniques to achieve the low computational and coding overhead. In this research, we examine four mesh segmentation schemes, i.e., multiseed traversal, threshold traversal, morphing-based volume splitting, and content-based segmentation, and apply them to the context of error resilient mesh coding based on the constructive traversal coding technique. The advantages and shortcomings of each segmentation method are discussed. These schemes segment a mesh into pieces according to the target piece size that is determined according to the channel error rate.
1631355
Threads Yield Continuations
Just as a traditional continuation represents the rest of acomputation from a given point in the computation, a subcontinuationrepresents the rest of a subcomputation from agiven point in the subcomputation. Subcontinuationsare more expressive than traditional continuations and have been shown to beuseful for controlling tree-structured concurrency, yet they havepreviously been implemented only on uniprocessors. This article describes aconcurrent implementation of one-shot subcontinuations. Like one-shotcontinuations, one-shot subcontinuations are first-class but may be invokedat most once, a restriction obeyed by nearly all programs that usecontinuations. The techniques used to implement one-shot subcontinuationsmay be applied directly to other one-shot continuation mechanisms and may begeneralized to support multi-shot continuations as well. A novel feature ofthe implementation is that continuations are implemented in terms ofthreads. Because the implementation model does not rely upon any speciallanguage features or compilation techniques, the model is applicable toany language or language implementation that supports a small set of threadprimitives.
15130627
Using model checking to debug device firmware
Device firmware is a piece of concurrent software that achieves high performance at the cost of software complexity. They contain subtle race conditions that make them difficult to debug using traditional debugging techniques. The problem is further compounded by the lack of debugging support on the devices. This is a serious problem because the device firmware is trusted by the operating system.Model checkers are designed to systematically verify properties of concurrent systems. Therefore, model checking is a promising approach to debugging device firmware. However, model checking involves an exponential search. Consequently, the models have to be small to allow effective model checking.This paper describes the abstraction techniques used by the ESP compiler to extract abstract models from device firmware written in ESP. The abstract models are small because they discard some of the details in the firmware that is irrelevant to the particular property being verified. The programmer is required to specify the abstractions to be performed. The ESP compiler uses the abstraction specification to extract models conservatively. Therefore, every bug in the original program will be present in the extracted model.This paper also presents our experience with using Spin model checker to develop and debug VMMC firmware for the Myrinet network interfaces. An earlier version of the ESP compiler yielded models that were too large to check for system-wide properties like absence of deadlocks. The new version of the compiler generated abstract models that were used to identify several subtle bugs in the firmware. So far, we have not encountered any bugs that were not caught by Spin.
1282113
Discriminative Fields for Modeling Spatial Dependencies in Natural Images
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial dependencies in the labels as well as the observed data. The proposed model exploits local discriminative models and allows to relax the assumption of conditional independence of the observed data given the labels, commonly used in the Markov Random Field (MRF) framework. The parameters of the DRF model are learned using penalized maximum pseudo-likelihood method. Furthermore, the form of the DRF model allows the MAP inference for binary classification problems using the graph min-cut algorithms. The performance of the model was verified on the synthetic as well as the real-world images. The DRF model outperforms the MRF model in the experiments.