Methyl syringate: An efficient phenolic mediator for bacterial and fungal laccases

The aim of the present work is to provide insight into the mechanism of laccase reactions using syringyl-type mediators. We studied the pH dependence and the kinetics of oxidation of syringyl-type phenolics using the low CotA and the high redox potential TvL laccases. Additionally, the efficiency of these compounds as redox mediators for the oxidation of non-phenolic lignin units was tested at different pH values and increasing mediator/non-phenolic ratios. Finally, the intermediates and products of reactions were identified by LC-MS and H-1 NMR. These approaches allow concluding on the (1) mechanism involved in the oxidation of phenolics by bacterial laccases, (2) importance of the chemical nature and properties of phenolic mediators, (3) apparent independence of the enzyme's properties on the yields of non-phenolics conversion, (4) competitive routes involved in the catalytic cycle of the laccase-mediator system with several new C-O coupling type structures being proposed.

Detect and pointing algorithm's performance for a planar smart antenna array: a review

An adaptive antenna array combines the signal of each element, using some constraints to produce the radiation pattern of the antenna, while maximizing the performance of the system. Direction of arrival (DOA) algorithms are applied to determine the directions of impinging signals, whereas beamforming techniques are employed to determine the appropriate weights for the array elements, to create the desired pattern. In this paper, a detailed analysis of both categories of algorithms is made, when a planar antenna array is used. Several simulation results show that it is possible to point an antenna array in a desired direction based on the DOA estimation and on the beamforming algorithms. A comparison of the performance in terms of runtime and accuracy of the used algorithms is made. These characteristics are dependent on the SNR of the incoming signal.

Explicit series solution for a glucose-induced electrical activity model of pancreatic beta-cells

In this work, we present the explicit series solution of a specific mathematical model from the literature, the Deng bursting model, that mimics the glucose-induced electrical activity of pancreatic beta-cells (Deng, 1993). To serve to this purpose, we use a technique developed to find analytic approximate solutions for strongly nonlinear problems. This analytical algorithm involves an auxiliary parameter which provides us with an efficient way to ensure the rapid and accurate convergence to the exact solution of the bursting model. By using the homotopy solution, we investigate the dynamical effect of a biologically meaningful bifurcation parameter rho, which increases with the glucose concentration. Our analytical results are found to be in excellent agreement with the numerical ones. This work provides an illustration of how our understanding of biophysically motivated models can be directly enhanced by the application of a newly analytic method.

GPU implementation of a hyperspectral coded aperture algorithm for compressive sensing

This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA) algorithm for compressive sensing on graphics processing units (GPUs). HYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. The proposed algorithm is evaluated not only in terms of reconstruction error but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experimental results using real data reveals signficant speedups up with regards to serial implementation.

Parallel hyperspectral coded aperture for compressive sensing on GPUs

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.

Sulfonated Schiff base copper(ii) complexes as efficient and selective catalysts in alcohol oxidation: syntheses and crystal structures

The reaction between 2-aminobenzenesulfonic acid and 2-hydroxy-3-methoxybenzaldehyde produces the acyclic Schiff base 2-[(2-hydroxy-3-methoxyphenyl) methylideneamino] benzenesulfonic acid (H2L center dot 3H(2)O) (1). In situ reactions of this compound with Cu(II) salts and, eventually, in the presence of pyridine (py) or 2,2'-bipyridine (2,2'-bipy) lead to the formation of the mononuclear complexes [CuL(H2O)(2)] (2) and [CuL(2,2'-bipy)]center dot DMF center dot H2O (3) and the diphenoxo-bridged dicopper compounds [CuL(py)](2) (4) and [CuL(EtOH)](2)center dot 2H(2)O (5). In 2-5 the L-2-ligand acts as a tridentate chelating species by means of one of the O-sulfonate atoms, the O-phenoxo and the N-atoms. The remaining coordination sites are then occupied by H2O (in 2), 2,2'-bipyridine (in 3), pyridine (in 4) or EtOH (in 5). Hydrogen bond interactions resulted in R-2(2) (14) and in R-4(4)(12) graph sets leading to dimeric species (in 2 and 3, respectively), 1D chain associations (in 2 and 5) or a 2D network (1). Complexes 2-5 are applied as selective catalysts for the homogeneous peroxidative (with tert-butylhydroperoxide, TBHP) oxidation of primary and secondary alcohols, under solvent-and additive-free conditions and under low power microwave (MW) irradiation. A quantitative yield of acetophenone was obtained by oxidation of 1-phenylethanol with compound 4 [TOFs up to 7.6 x 10(3) h(-1)] after 20 min of MW irradiation, whereas the oxidation of benzyl alcohol to benzaldehyde is less effective (TOF 992 h(-1)). The selectivity of 4 to oxidize the alcohol relative to the ene function is demonstrated when using cinnamyl alcohol as substrate.

Text classification using compression-based dissimilarity measures

Arguably, the most difficult task in text classification is to choose an appropriate set of features that allows machine learning algorithms to provide accurate classification. Most state-of-the-art techniques for this task involve careful feature engineering and a pre-processing stage, which may be too expensive in the emerging context of massive collections of electronic texts. In this paper, we propose efficient methods for text classification based on information-theoretic dissimilarity measures, which are used to define dissimilarity-based representations. These methods dispense with any feature design or engineering, by mapping texts into a feature space using universal dissimilarity measures; in this space, classical classifiers (e.g. nearest neighbor or support vector machines) can then be used. The reported experimental evaluation of the proposed methods, on sentiment polarity analysis and authorship attribution problems, reveals that it approximates, sometimes even outperforms previous state-of-the-art techniques, despite being much simpler, in the sense that they do not require any text pre-processing or feature engineering.

Hyperspectral unmixing algorithm via dependent component analysis

This paper introduces a new method to blindly unmix hyperspectral data, termed dependent component analysis (DECA). This method decomposes a hyperspectral images into a collection of reflectance (or radiance) spectra of the materials present in the scene (endmember signatures) and the corresponding abundance fractions at each pixel. DECA assumes that each pixel is a linear mixture of the endmembers signatures weighted by the correspondent abundance fractions. These abudances are modeled as mixtures of Dirichlet densities, thus enforcing the constraints on abundance fractions imposed by the acquisition process, namely non-negativity and constant sum. The mixing matrix is inferred by a generalized expectation-maximization (GEM) type algorithm. This method overcomes the limitations of unmixing methods based on Independent Component Analysis (ICA) and on geometrical based approaches. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.