Antithyroid drugs and their analogues: synthesis, structure, and mechanism of action

Thyroid hormones are essential for the development and differentiation of all cells of the human body. They regulate protein, fat, and carbohydrate metabolism. In this Account, we discuss the synthesis, structure, and mechanism of action of thyroid hormones and their analogues. The prohormone thyroxine (14) is synthesized on thyroglobulin by thyroid peroxidase (TPO), a heme enzyme that uses iodide and hydrogen peroxide to perform iodination and phenolic coupling reactions. The monodeiodination of T4 to 3,3',5-triiodothyronine (13) by selenium-containing deiodinases (ID-1, ID-2) is a key step in the activation of thyroid hormones. The type 3 deiodinase (ID-3) catalyzes the deactivation of thyroid hormone in a process that removes iodine selectively from the tyrosyl ring of T4 to produce 3,3',5'-triiodothyronine (rT3). Several physiological and pathological stimuli influence thyroid hormone synthesis. The overproduction of thyroid hormones leads to hyperthyroidism, which is treated by antithyroid drugs that either inhibit the thyroid hormone biosynthesis and/or decrease the conversion of T4 to T3. Antithyroid drugs are thiourea-based compounds, which indude propylthiouracil (PTU), methimazole (MM I), and carbimazole (CBZ). The thyroid gland actively concentrates these heterocyclic compounds against a concentration gradient Recently, the selenium analogues of PTU, MMI, and CBZ attracted significant attention because the selenium moiety in these compounds has a higher nucleophilicity than that of the sulfur moiety. Researchers have developed new methods for the synthesis of the selenium compounds. Several experimental and theoretical investigations revealed that the selone (C=Se) in the selenium analogues is more polarized than the thione (C=S) in the sulfur compounds, and the selones exist predominantly in their zwitterionic forms. Although the thionamide-based antithyroid drugs have been used for almost 70 years, the mechanism of their action is not completely understood. Most investigations have revealed that MMI and PTU irreversibly inhibit TPO. PTU, MTU, and their selenium analogues also inhibit ID-1, most likely by reacting with the selenenyl iodide intermediate. The good ID-1 inhibitory activity of Pill and its analogues can be ascribed to the presence of the -N(H)-C(=O)- functionality that can form hydrogen bonds with nearby amino add residues in the selenenyl sulfide state. In addition to the TPO and ID-1 inhibition, the selenium analogues are very good antioxidants. In the presence of cellular reducing agents such as GSH, these compounds catalytically reduce hydrogen peroxide. They can also efficiently scavenge peroxynitrite, a potent biological oxidant and nitrating agent.

Exploring novel methods to achieve sensitivity limits for high operating temperature infrared detectors

A review of high operating temperature (HOT) infrared (IR) photon detector technology vis-a-vis material requirements, device design and state of the art achieved is presented in this article. The HOT photon detector concept offers the promise of operation at temperatures above 120 K to near room temperature. Advantages are reduction in system size, weight, cost and increase in system reliability. A theoretical study of the thermal generation-recombination (g-r) processes such as Auger and defect related Shockley Read Hall (SRH) recombination responsible for increasing dark current in HgCdTe detectors is presented. Results of theoretical analysis are used to evaluate performance of long wavelength (LW) and mid wavelength (MW) IR detectors at high operating temperatures. (C) 2013 Elsevier B.V. All rights reserved.

A POSTERIORI ERROR CONTROL OF DISCONTINUOUS GALERKIN METHODS FOR ELLIPTIC OBSTACLE PROBLEMS

In this article, we derive an a posteriori error estimator for various discontinuous Galerkin (DG) methods that are proposed in (Wang, Han and Cheng, SIAM J. Numer. Anal., 48: 708-733, 2010) for an elliptic obstacle problem. Using a key property of DG methods, we perform the analysis in a general framework. The error estimator we have obtained for DG methods is comparable with the estimator for the conforming Galerkin (CG) finite element method. In the analysis, we construct a non-linear smoothing function mapping DG finite element space to CG finite element space and use it as a key tool. The error estimator consists of a discrete Lagrange multiplier associated with the obstacle constraint. It is shown for non-over-penalized DG methods that the discrete Lagrange multiplier is uniformly stable on non-uniform meshes. Finally, numerical results demonstrating the performance of the error estimator are presented.

Between photocatalysis and photosynthesis: Synchrotron spectroscopy methods on molecules and materials for solar hydrogen generation

Energy research is to a large extent materials research, encompassing the physics and chemistry of materials, including their synthesis, processing toward components and design toward architectures, allowing for their functionality as energy devices, extending toward their operation parameters and environment, including also their degradation, limited life, ultimate failure and potential recycling. In all these stages, X-ray and electron spectroscopy are helpful methods for analysis, characterization and diagnostics for the engineer and for the researcher working in basic science.This paper gives a short overview of experiments with X-ray and electron spectroscopy for solar energy and water splitting materials and addresses also the issue of solar fuel, a relatively new topic in energy research. The featured systems are iron oxide and tungsten oxide as photoanodes, and hydrogenases as molecular systems. We present surface and subsurface studies with ambient pressure XPS and hard X-ray XPS, resonant photoemission, light induced effects in resonant photoemission experiments and a photo-electrochemical in situ/operando NEXAFS experiment in a liquid cell, and nuclear resonant vibrational spectroscopy (NRVS). (C) 2012 Elsevier B.V. All rights reserved.

Optical microscopy methods for understanding learning and memory

Structural dynamics of dendritic spines is one of the key correlative measures of synaptic plasticity for encoding short-term and long-term memory. Optical studies of structural changes in brain tissue using confocal microscopy face difficulties of scattering. This results in low signal-to-noise ratio and thus limiting the imaging depth to few tens of microns. Multiphoton microscopy (MpM) overcomes this limitation by using low-energy photons to cause localized excitation and achieve high resolution in all three dimensions. Multiple low-energy photons with longer wavelengths minimize scattering and allow access to deeper brain regions at several hundred microns. In this article, we provide a basic understanding of the physical phenomena that give MpM an edge over conventional microscopy. Further, we highlight a few of the key studies in the field of learning and memory which would not have been possible without the advent of MpM.

Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction

The sparse recovery methods utilize the l(p)-normbased regularization in the estimation problem with 0 <= p <= 1. These methods have a better utility when the number of independent measurements are limited in nature, which is a typical case for diffuse optical tomographic image reconstruction problem. These sparse recovery methods, along with an approximation to utilize the l(0)-norm, have been deployed for the reconstruction of diffuse optical images. Their performancewas compared systematically using both numerical and gelatin phantom cases to show that these methods hold promise in improving the reconstructed image quality.

Scalable sequential alternating proximal methods for sparse structural SVMs and CRFs

Structural Support Vector Machines (SSVMs) and Conditional Random Fields (CRFs) are popular discriminative methods used for classifying structured and complex objects like parse trees, image segments and part-of-speech tags. The datasets involved are very large dimensional, and the models designed using typical training algorithms for SSVMs and CRFs are non-sparse. This non-sparse nature of models results in slow inference. Thus, there is a need to devise new algorithms for sparse SSVM and CRF classifier design. Use of elastic net and L1-regularizer has already been explored for solving primal CRF and SSVM problems, respectively, to design sparse classifiers. In this work, we focus on dual elastic net regularized SSVM and CRF. By exploiting the weakly coupled structure of these convex programming problems, we propose a new sequential alternating proximal (SAP) algorithm to solve these dual problems. This algorithm works by sequentially visiting each training set example and solving a simple subproblem restricted to a small subset of variables associated with that example. Numerical experiments on various benchmark sequence labeling datasets demonstrate that the proposed algorithm scales well. Further, the classifiers designed are sparser than those designed by solving the respective primal problems and demonstrate comparable generalization performance. Thus, the proposed SAP algorithm is a useful alternative for sparse SSVM and CRF classifier design.

Electronic structure of Nd1-xYxMnO3 from Mn K edge absorption spectroscopy and DFT methods

The electronic structure of Nd1-xYxMnO3 (x-0-0.5) is studied using x-ray absorption near-edge structure (XANES) spectroscopy at the Mn K-edge along with the DFT-based LSDA+U and real space cluster calculations. The main edge of the spectra does not show any variation with doping. The pre-edge shows two distinct features which appear well-separated with doping. The intensity of the pre-edge decreases with doping. The theoretical XANES were calculated using real space multiple scattering methods which reproduces the entire experimental spectra at the main edge as well as the pre-edge. Density functional theory calculations are used to obtain the Mn 4p, Mn 3d and O 2p density of states. For x=0, the site-projected density of states at 1.7 eV above Fermi energy shows a singular peak of unoccupied e(g) (spin-up) states which is hybridized Mn 4p and O 2p states. For x=0.5, this feature develops at a higher energy and is highly delocalized and overlaps with the 3d spin-down states which changes the pre-edge intensity. The Mn 4p DOS for both compositions, show considerable difference between the individual p(x), p(y) and p(z)), states. For x=0.5, there is a considerable change in the 4p orbital polarization suggesting changes in the Jahn-Teller effect with doping. (C) 2013 Elsevier Ltd. All rights reserved.

Quantifying Vocal Mimicry in the Greater Racket-Tailed Drongo: A Comparison of Automated Methods and Human Assessment

Objective identification and description of mimicked calls is a primary component of any study on avian vocal mimicry but few studies have adopted a quantitative approach. We used spectral feature representations commonly used in human speech analysis in combination with various distance metrics to distinguish between mimicked and non-mimicked calls of the greater racket-tailed drongo, Dicrurus paradiseus and cross-validated the results with human assessment of spectral similarity. We found that the automated method and human subjects performed similarly in terms of the overall number of correct matches of mimicked calls to putative model calls. However, the two methods also misclassified different subsets of calls and we achieved a maximum accuracy of ninety five per cent only when we combined the results of both the methods. This study is the first to use Mel-frequency Cepstral Coefficients and Relative Spectral Amplitude - filtered Linear Predictive Coding coefficients to quantify vocal mimicry. Our findings also suggest that in spite of several advances in automated methods of song analysis, corresponding cross-validation by humans remains essential.

Going beyond Red with a Tri- and Tetracoordinate Boron Conjugate: Intriguing Near-IR Optical Properties and Applications in Anion Sensing

The design and synthesis of a new tri- and tetracoordinate boron conjugate is reported. The conjugate shows broad near-IR emission (similar to 625-850 nm) and is found to be a selective colorimetric and ratiometric sensor for fluoride ions.

SPATIO-TEMPORAL DYNAMICS ALONG THE TERRAIN GRADIENT OF DIVERSE LANDSCAPE

Land use (LU) land cover (LC) information at a temporal scale illustrates the physical coverage of the Earth's terrestrial surface according to its use and provides the intricate information for effective planning and management activities. LULC changes are stated as local and location specific, collectively they act as drivers of global environmental changes. Understanding and predicting the impact of LULC change processes requires long term historical restorations and projecting into the future of land cover changes at regional to global scales. The present study aims at quantifying spatio temporal landscape dynamics along the gradient of varying terrains presented in the landscape by multi-data approach (MDA). MDA incorporates multi temporal satellite imagery with demographic data and other additional relevant data sets. The gradient covers three different types of topographic features, planes; hilly terrain and coastal region to account the significant role of elevation in land cover change. The seasonality is another aspect to be considered in the vegetation dominated landscapes; variations are accounted using multi seasonal data. Spatial patterns of the various patches are identified and analysed using landscape metrics to understand the forest fragmentation. The prediction of likely changes in 2020 through scenario analysis has been done to account for the changes, considering the present growth rates and due to the proposed developmental projects. This work summarizes recent estimates on changes in cropland, agricultural intensification, deforestation, pasture expansion, and urbanization as the causal factors for LULC change.

Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

A Communication-Theoretic Framework for 2-DMR Channel Modeling: Performance Evaluation of Coding and Signal Processing Methods

We develop a communication theoretic framework for modeling 2-D magnetic recording channels. Using the model, we define the signal-to-noise ratio (SNR) for the channel considering several physical parameters, such as the channel bit density, code rate, bit aspect ratio, and noise parameters. We analyze the problem of optimizing the bit aspect ratio for maximizing SNR. The read channel architecture comprises a novel 2-D joint self-iterating equalizer and detection system with noise prediction capability. We evaluate the system performance based on our channel model through simulations. The coded performance with the 2-D equalizer detector indicates similar to 5.5 dB of SNR gain over uncoded data.

Error analysis of discontinuous Galerkin methods for the Stokes problem under minimal regularity

In this article, we analyse several discontinuous Galerkin (DG) methods for the Stokes problem under minimal regularity on the solution. We assume that the velocity u belongs to H-0(1)(Omega)](d) and the pressure p is an element of L-0(2)(Omega). First, we analyse standard DG methods assuming that the right-hand side f belongs to H-1(Omega) boolean AND L-1(Omega)](d). A DG method that is well defined for f belonging to H-1(Omega)](d) is then investigated. The methods under study include stabilized DG methods using equal-order spaces and inf-sup stable ones where the pressure space is one polynomial degree less than the velocity space.

CONVERGENCE ANALYSIS OF THE LOWEST ORDER WEAKLY PENALIZED ADAPTIVE DISCONTINUOUS GALERKIN METHODS

In this article, we prove convergence of the weakly penalized adaptive discontinuous Galerkin methods. Unlike other works, we derive the contraction property for various discontinuous Galerkin methods only assuming the stabilizing parameters are large enough to stabilize the method. A central idea in the analysis is to construct an auxiliary solution from the discontinuous Galerkin solution by a simple post processing. Based on the auxiliary solution, we define the adaptive algorithm which guides to the convergence of adaptive discontinuous Galerkin methods.