Wilkes University

About: Wilkes University is a education organization based out in Wilkes-Barre, Pennsylvania, United States. It is known for research contribution in the topics: Population & Pharmacy. The organization has 616 authors who have published 1032 publications receiving 21050 citations. The organization is also known as: Wilkes & Wilkes College.

Structural and biochemical characterization of the biuret hydrolase (BiuH) from the cyanuric acid catabolism pathway of Rhizobium leguminasorum bv. viciae 3841.

Abstract: Biuret deamination is an essential step in cyanuric acid mineralization. In the well-studied atrazine degrading bacterium Pseudomonas sp. strain ADP, the amidase AtzE catalyzes this step. However, Rhizobium leguminosarum bv. viciae 3841 uses an unrelated cysteine hydrolase, BiuH, instead. Herein, structures of BiuH, BiuH with bound inhibitor and variants of BiuH are reported. The substrate is bound in the active site by a hydrogen bonding network that imparts high substrate specificity. The structure of the inactive Cys175Ser BiuH variant with substrate bound in the active site revealed that an active site cysteine (Cys175), aspartic acid (Asp36) and lysine (Lys142) form a catalytic triad, which is consistent with biochemical studies of BiuH variants. Finally, molecular dynamics simulations highlighted the presence of three channels from the active site to the enzyme surface: a persistent tunnel gated by residues Val218 and Gln215 forming a potential substrate channel and two smaller channels formed by Val28 and a mobile loop (including residues Phe41, Tyr47 and Met51) that may serve as channels for co-product (ammonia) or co-substrate (water).

Social Class, Family Formation, and Delinquency in Early Adulthood

Abstract: Recent research suggests increasing heterogeneity in the transition from adolescence to early adulthood. This study considers how this heterogeneity may influence delinquency between these two deve...

Isomorphisms of NP complete problems on random instances

Abstract: Polynomial isomorphisms are defined for NP-complete sets on random instances. Not only are polynomial-time computable and invertible bijections among complete sets considered, but also it is required that these bijections preserve distributions on random instances of these sets. Sufficient conditions for randomized decision problems to be polynomially isomorphic are shown. It is then proved that all the known average-case NP-complete problems under many-one reductions are polynomially isomorphic. These problems include the randomized tiling problem, the randomized halting problem, the randomized Post correspondence problem, and the randomized word problem for Thue systems. >

Sparse Grassmannian Embeddings for Hyperspectral Data Representation and Classification

Abstract: We propose an approach for the representation and classification of hyperspectral data that exploits the geometric framework, the Grassmann manifold, i.e., a parameterization of $k$ -dimensional subspaces of $\mathbb {R}^{n}$ . Multiple pixels from a data class are used to capture the variability of the class information using a subspace representation. We use two metrics defined on the Grassmannian, chordal and geodesic, and several pseudometrics to measure the pairwise distances between the points, i.e., subspaces. Once a distance matrix is generated, classical multidimensional scaling is applied to find a configuration of points with preserved or approximated original distances, thus realizing an embedding of the Grassmannian in Euclidean space. A sparse support vector machine trained in the embedding space simultaneously classifies embedded subspaces and selects a subset of optimal dimensions (features) using a weight ratio criterion. The resulting embedding affords substantial model order reduction for classification and data visualization. In many cases, this framework provides linearly separable representations even when raw data are not linearly separable. We analyze frameworks and compare binary classification results for several distances. Finally, we illustrate the embedding of multiple data classes.