Mapping the polysaccharide degradation potential of Aspergillus niger

<p>Abstract</p> <p>Background</p> <p>The degradation of plant materials by enzymes is an industry of increasing importance. For sustainable production of second generation biofuels and other products of industrial biotechnology, efficient degradation of non-edible plant polysaccharides such as hemicellulose is required. For each type of hemicellulose, a complex mixture of enzymes is required for complete conversion to fermentable monosaccharides. In plant-biomass degrading fungi, these enzymes are regulated and released by complex regulatory structures. In this study, we present a methodology for evaluating the potential of a given fungus for polysaccharide degradation.</p> <p>Results</p> <p>Through the compilation of information from 203 articles, we have systematized knowledge on the structure and degradation of 16 major types of plant polysaccharides to form a graphical overview. As a case example, we have combined this with a list of 188 genes coding for carbohydrate-active enzymes from <it>Aspergillus niger</it>, thus forming an analysis framework, which can be queried. Combination of this information network with gene expression analysis on mono- and polysaccharide substrates has allowed elucidation of concerted gene expression from this organism. One such example is the identification of a full set of extracellular polysaccharide-acting genes for the degradation of oat spelt xylan.</p> <p>Conclusions</p> <p>The mapping of plant polysaccharide structures along with the corresponding enzymatic activities is a powerful framework for expression analysis of carbohydrate-active enzymes. Applying this network-based approach, we provide the first genome-scale characterization of all genes coding for carbohydrate-active enzymes identified in <it>A. niger</it>.</p

Synthesizing Program Input Grammars

We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program. Our algorithm addresses shortcomings of existing grammar inference algorithms, which both severely overgeneralize and are prohibitively slow. Our implementation, GLADE, leverages the grammar synthesized by our algorithm to fuzz test programs with structured inputs. We show that GLADE substantially increases the incremental coverage on valid inputs compared to two baseline fuzzers

Measurements, Models, Systems and Design

531 s.

Treebank-Based Deep Grammar Acquisition for French Probabilistic Parsing Resources

Motivated by the expense in time and other resources to produce hand-crafted grammars, there has been increased interest in wide-coverage grammars automatically obtained from treebanks. In particular, recent years have seen a move towards acquiring deep (LFG, HPSG and CCG) resources that can represent information absent from simple CFG-type structured treebanks and which are considered to produce more language-neutral linguistic representations, such as syntactic dependency trees. As is often the case in early pioneering work in natural language processing, English has been the focus of attention in the first efforts towards acquiring treebank-based deep-grammar resources, followed by treatments of, for example, German, Japanese, Chinese and Spanish. However, to date no comparable large-scale automatically acquired deep-grammar resources have been obtained for French. The goal of the research presented in this thesis is to develop, implement, and evaluate treebank-based deep-grammar acquisition techniques for French. Along the way towards achieving this goal, this thesis presents the derivation of a new treebank for French from the Paris 7 Treebank, the Modified French Treebank, a cleaner, more coherent treebank with several transformed structures and new linguistic analyses. Statistical parsers trained on this data outperform those trained on the original Paris 7 Treebank, which has five times the amount of data. The Modified French Treebank is the data source used for the development of treebank-based automatic deep-grammar acquisition for LFG parsing resources for French, based on an f-structure annotation algorithm for this treebank. LFG CFG-based parsing architectures are then extended and tested, achieving a competitive best f-score of 86.73% for all features. The CFG-based parsing architectures are then complemented with an alternative dependency-based statistical parsing approach, obviating the CFG-based parsing step, and instead directly parsing strings into f-structures

Dynamic Protocol Reverse Engineering a Grammatical Inference Approach

Round trip engineering of software from source code and reverse engineering of software from binary files have both been extensively studied and the state-of-practice have documented tools and techniques. Forward engineering of protocols has also been extensively studied and there are firmly established techniques for generating correct protocols. While observation of protocol behavior for performance testing has been studied and techniques established, reverse engineering of protocol control flow from observations of protocol behavior has not received the same level of attention. State-of-practice in reverse engineering the control flow of computer network protocols is comprised of mostly ad hoc approaches. We examine state-of-practice tools and techniques used in three open source projects: Pidgin, Samba, and rdesktop . We examine techniques proposed by computational learning researchers for grammatical inference. We propose to extend the state-of-art by inferring protocol control flow using grammatical inference inspired techniques to reverse engineer automata representations from captured data flows. We present evidence that grammatical inference is applicable to the problem domain under consideration