Improved estimation of duality gap in binary quadratic programming using a weighted distance measure

We present in this paper an improved estimation of duality gap between binary quadratic program and its Lagrangian dual. More specifically, we obtain this improved estimation using a weighted distance measure between the binary set and certain affine subspace. We show that the optimal weights can be computed by solving a semidefinite programming problem. We further establish a necessary and sufficient condition under which the weighted distance measure gives a strictly tighter estimation of the duality gap than the existing estimations.     

DNA Degradation of Genetically Modified Cottonseed Meal During Feed Processing

The effects of dry heating, wet heating, and extrusion on the degradation of DNA in cottonseed meal (CSM) were studied using polymerase chain reaction (PCR) and real-time PCR approach. Both the sad1 DNA, ranging between 128 and 883 bp in size, and the cry1Ab/c gene, ranging between 183 and 652 bp in size, were detectable in all dry-heated CSM and cottonseed. During wet heating, the sad1 gene (≥883 bp) and the cry1Ab/c (≥952 bp) gene were thoroughly degraded at 105 and 120 °C, respectively. Sizes from 128 to 530 bp for the sad1 gene and sizes from 183 to 652 bp for the cry1Ab/c gene were detected during extrusion at temperatures ranging from 75 to 135 °C. Fragments ≤883 bp for the sad1 gene and ≤952 bp for the cry1Ab/c gene were detected in all of the extruded samples with water content varying between 26 and 34 %. The copy number ratio of cry1Ab/c to sad1 in samples of Bt cottonseed meal decreased rapidly when the temperature increased during the heating process. In conclusion, feed processing markedly degrades the larger DNA fragments of sad1 and cry1Ab/c, with high temperature and water content being the main factors for that degradation.     

Efficient Oxidation of Sulfides Catalyzed by Transition Metal Salts with Molecular Oxygen in the Presence of Aldehydes

Oxidation of sulfides to sulfoxides and sulfones was achieved in moderate to high yields with a good selectivity, by using Fe₂O₃, MnO₂, Cu(OH)₂ and Cu(OAc)₂ as catalyst, with molecular oxygen in the presence of isovaleraldehyde under mild conditions.     

Intramolecular Nitrene Insertion into Saturated C−H-Bond-Mediated C−N Bond Cleavage of a Coordinated NHC Ligand

Ruthenium(II) complexes bearing a tridentate bis(N-heterocyclic carbene) ligand reacted with iminoiodanes (PhI=NR) resulting in the formation of isolable ruthenium(III)–amido intermediates, which underwent cleavage of a C−N bond of the tridentate ligand and formation of an N-substituted imine group. The Ru^III–amido intermediates have been characterized by ¹H NMR, UV/Vis, ESI-MS, and X-ray crystallography. DFT calculations were performed to provide insight into the reaction mechanism.     

High‐Level Incorporation of Silver in Gold Nanoclusters: Fluorescence Redshift upon Interaction with Hydrogen Peroxide and Fluorescence Enhancement with Herbicide

High‐level incorporation of Ag in Au nanoclusters (NCs) is conveniently achieved by controlling the concentration of Ag⁺ in the synthesis of bovine serum albumin (BSA)‐protected Au NCs, and the resulting structure is determined to be bimetallic Ag₂₈Au₁₀‐BSA NCs through a series of characterizations including energy‐dispersive X‐ray spectroscopy, mass spectroscopy, and X‐ray photoelectron spectroscopy, together with density functional theory simulations. Interestingly, the Ag₂₈Au₁₀ NCs exhibit a significant fluorescence redshift rather than quenching upon interaction with hydrogen peroxide, providing a new approach to the detection of hydrogen peroxide through direct comparison of their fluorescence peaks. Furthermore, the Ag₂₈Au₁₀ NCs are also used for the sensitive and selective detection of herbicide through fluorescence enhancement. The detection limit for herbicide (0.1 nm ) is far below the health value established by the U.S. Environmental Protection Agency; such sensitive detection was not achieved by using AuAg NCs with low‐level incorporation of Ag or by using the individual metal NCs.     

Urinary metabonomic study using a CUMS rat model of depression

Chronic unpredictable mild stress (CUMS) is a well‐validated model of depression. In this study, a urinary metabonomics method based on the NMR spectrometry was used to study the metabolic perturbation in CUMS‐induced rat depression model. With pattern recognition analysis, a clear separation of CUMS rats and healthy controls was achieved, and nine endogenous metabolites contributing to the separation were identified. CUMS‐treated rats were characterized by the increase of glycine, pyruvate, glutamine, and asparagines, as well as the decrease of 2‐oxoglutarate, dimethylglycine, citrate, succinate, and acetate. The urinary biochemical changes related to the metabolic disturbance in CUMS induced depression, and the possible correlations with live qi stagnation in traditional Chinese medicine are discussed. The work shows that CUMS is a reliable model for studying depression, and the noninvasive urinary metabolomic method is a valuable tool to investigate the biochemical pertubations in depression as an early diagnostic means. Copyright © 2012 John Wiley & Sons, Ltd.     

Network-based naive Bayes model for social network

Naive Bayes (NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes (NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational effciency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance. A real data example is also analyzed for illustration purpose.