6685885
Comprehensive statistical method for protein fold recognition
We present a protein fold recognition method that uses a comprehensive statistical interpretation of structural Hidden Markov Models (HMMs). The structure/fold recognition is done by summing the probabilities of all sequence-to-structure alignments Conventionally, Boltzmann statistics dictate that the optimal alignment can give an estimate of the lowest free energy of the sequence conformation imposed by the structural model. The alignment is optimized for a scoring function that is interpreted as a free energy of an amino acid in a structural environment. Near-optimal alignments are ignored, regardless of how likely they might be compared to the optimal alignment. Here we investigate an alternative view. A structure model can be seen as a statistical representation of an ensemble of similar structures. The optimal alignment is always the most probable, but sub-optimal alignments may have comparable probabilities. These sub-optimal alignments can be interpreted as optimal alignments to the “other” structures from the ensemble or optimal alignments under minor fluctuations in the scoring function. Summing probabilities for all alignments gives an estimate of sequence-model compatibility. We have built a set of structural HMMs for 188 protein structures, and have compared two methods for identifying the structure compatible with a sequence: by the optimal alignment probability and by the total probability. Fold recognition by total probability was 40% more accurate than fold recognition by the optimal alignment probability.
9064289
Automatic Pattern Embedding in Protein Structure Models
This article describes an implementation of a set of rules for automatically embedding a minimal functional sequence pattern in a structural hidden Markov model. The final product is a library of wide ranging fold models with encoded information about the functional families.
10411630
The ADEPT Concept-Based Digital Learning Environment
We describe the design and application of a Digital Learning Environment (DLE) that is integrated with the collections and services of the Alexandria Digital Library (ADL). This DLE is in operational use in undergraduate teaching environments. Its design and development incorporate the assumption that deep understanding of both scientific phenomena and scientific methods is facilitated when learning materials are explicitly organized, accessed and presented at the level of granularity of appropriate sets of scientific concepts and their interrelationships. The DLE supports services for the creating, searching, and displaying: (1) knowledge bases (KBs) of strongly structured models of scientific concepts; (2) DL collections of information objects organized and accessible by the concepts of the KBs; and (3) collections of presentation materials, such as lectures and laboratory materials, that are organized as trajectories through the KB of concepts.
12375394
The Alexandria digital earth prototype
This note summarizes the system development activities of the Alexandr ia Digital Earth Prototype (ADEPT) Project.5 ADEPT and the Alexandria Digital Library (ADL) are, respectively, the research and operational components of the Alexandria Digital Library Project. The goal of ADEPT is to build a distributed digital library (DL) of personalized collections of geospatially referenced information. This DL is characterized by: (1) services for building, searching, and using personalized collections; (2) collections of georeferenced multimedia information, including dynamic simulation models of spatially distributed processes; and (3) user interfaces employing the concept of a “Digital Earth”. Important near-term objectives for ADEPT are to build prototype collections that support undergraduate learning in physical, human, and cultural geography and related disciplines, and then to evaluate whether using such resources helps students learn to reason scientifically. Collections and services developed by ADEPT researchers will migrate to ADL as they mature.
16631800
Fast convergence with a greedy tag-phrase dictionary
Lexical categories have been shown to assist in giving good compression results when incorporated into context models. This paper describes a greedy dictionary-based model that maintains a dictionary of tag-phrases, along with separate lexicons for each unique tag. The text is tagged with part-of-speech (POS) labels and then given to the encoder, which uses the tags to construct the phrase dictionary in a manner similar to LZ78. The output is a sequence of arithmetically encoded phrase number coupled with the information needed to match the correct word with each tag in the phrase. Each unique word (defined as each novel word/tag pair) is transmitted once when it is first encountered, then retained in the appropriate dictionary and thereafter arithmetically encoded according to the empirical distribution for that dictionary whenever the word is encountered. We present results from some empirical tests showing that this "tag-phrase dictionary" technique achieves nearly identical compression as that obtainable using PPM, an explicit-context model. This goes against the widely held view that greedy dictionary schemes require much larger samples of text before they can compete with statistical context methods. Some interesting theoretical issues pertaining to text compression in general are implied, and these are also discussed.
7247766
Semantic and Generative Models for Lossy Text Compression
The complementary paradigms of text compression and image compression suggest that there may be potential for applying methods developed for one domain to the other. In image coding, lossy techniques yield compression factors that are vastly superior to those of the best lossless schemes, and we show that this is also the case for text. This paper investigates the resulting tradeoff between subjective quality of the transmission and its compression factor. Two different methods are described, which can be combined into an extremely effective technique that provides far better compression than the present state of the art and yet preserves a reasonable degree of perceived match between the original and received text. The major challenge for lossy text compression is the quantitative evaluation of the quality of this match.
2795329
User interfaces when and where they are needed: an infrastructure for recombinant computing
Users in ubiquitous computing environments need to be able to make serendipitous use of resources that they did not anticipate and of which they have no prior knowledge. The Speakeasy recombinant computing framework is designed to support such ad hoc use of resources on a network. In addition to other facilities, the framework provides an infrastructure through which device and service user interfaces can be made available to users on multiple platforms. The framework enables UIs to be provided for connections involving multiple entities, allows these UIs to be delivered asynchronously, and allows them to be injected by any party participating in a connection.