On modeling pollution-generating technologies

Distinguishing between intended ("good") production and unintended or residual ("bad") generation, we introduce the concept of by-production. In by-production technologies, pollution is an output that satises a "costly disposability" assumption and violates standard free disposability with respect to pollution-causing inputs. Our approach therefore differs substantially from standard approaches to modeling pollution-generating technologies. We show how by-production can be modeled using data envelopment analysis (DEA) methods. With an electric power plant database, we illustrate shortcomings under by-production of two popular eciency indexes: the hyperbolic index and the directional distance function. We propose and implement an alternative eciency index with superior properties.pollution-generating technologies, free disposability, weak disposability, data envelopment analysis, environmental and technical eciency measurement

DILIMOT: discovery of linear motifs in proteins

Discovery of protein functional motifs is critical in modern biology. Small segments of 3–10 residues play critical roles in protein interactions, post-translational modifications and trafficking. DILIMOT (DIscovery of LInear MOTifs) is a server for the prediction of these short linear motifs within a set of proteins. Given a set of sequences sharing a common functional feature (e.g. interaction partner or localization) the method finds statistically over-represented motifs likely to be responsible for it. The input sequences are first passed through a set of filters to remove regions unlikely to contain instances of linear motifs. Motifs are then found in the remaining sequence and ranked according to a statistic that measure over-representation and conservation across homologues in related species. The results are displayed via a visual interface for easy perusal. The server is available a

3did: interacting protein domains of known three-dimensional structure

The database of 3D Interacting Domains (3did) is a collection of domain–domain interactions in proteins for which high-resolution three-dimensional structures are known. 3did exploits structural information to provide critical molecular details necessary for understanding how interactions occur. It also offers an overview of how similar in structure are interactions between different members of the same protein family. The database also contains Gene Ontology-based functional annotations and interactions between yeast proteins from large-scale interaction discovery studies. A web-based tool to query 3did is available at http://3did.embl.de

CATHEDRAL: A Fast and Effective Algorithm to Predict Folds and Domain Boundaries from Multidomain Protein Structures

We present CATHEDRAL, an iterative protocol for determining the location of previously observed protein folds in novel multidomain protein structures. CATHEDRAL builds on the features of a fast secondary-structure–based method (using graph theory) to locate known folds within a multidomain context and a residue-based, double-dynamic programming algorithm, which is used to align members of the target fold groups against the query protein structure to identify the closest relative and assign domain boundaries. To increase the fidelity of the assignments, a support vector machine is used to provide an optimal scoring scheme. Once a domain is verified, it is excised, and the search protocol is repeated in an iterative fashion until all recognisable domains have been identified. We have performed an initial benchmark of CATHEDRAL against other publicly available structure comparison methods using a consensus dataset of domains derived from the CATH and SCOP domain classifications. CATHEDRAL shows superior performance in fold recognition and alignment accuracy when compared with many equivalent methods. If a novel multidomain structure contains a known fold, CATHEDRAL will locate it in 90% of cases, with <1% false positives. For nearly 80% of assigned domains in a manually validated test set, the boundaries were correctly delineated within a tolerance of ten residues. For the remaining cases, previously classified domains were very remotely related to the query chain so that embellishments to the core of the fold caused significant differences in domain sizes and manual refinement of the boundaries was necessary. To put this performance in context, a well-established sequence method based on hidden Markov models was only able to detect 65% of domains, with 33% of the subsequent boundaries assigned within ten residues. Since, on average, 50% of newly determined protein structures contain more than one domain unit, and typically 90% or more of these domains are already classified in CATH, CATHEDRAL will considerably facilitate the automation of protein structure classification

On the Relative K(2) of Non Commutative Local Rings.

This dissertation examines topics in Algebraic K-Theory, concerning the computation of absolute and relative Milnor groups, K\sb2(R) and K\sb2(R,I), for both commutative and non-commutative classes of rings, including the relative K\sb2 of non-commutative (not necessarily commutative) rings, and the absolute K\sb2 of commutative semilocal rings. Our main theorem is a natural extension of a result by Maazen and Stienstra (H. Maazen and J. Steinstra, A presentation of K\sb2 of split radical pairs, J. Pure Appl. Algebra 10(1977), 271-294) which determines the relative K\sb2 of rings in a commutative setting. We prove the non-commutative analog of this result for local rings. Other results proved in this dissertation include the redundancy of two relations given in Dennis and Stein\u27s presentation for K\sb2 of a discrete valuation ring (R. K. Dennis and M. R. Stein, K\sb2 of discrete valuation rings, Adv. Math. 18(1975), 182-238) and a proof that a normal form used by Kolster (M. Kolster, K\sb2 of Non-Commutative Local Rings, J. Algebra 95(1985), 173-200) does not apply more generally to semilocal rings