Singlet Oxygen‐Induced Furan Oxidation for Site‐Specific and Chemoselective Peptide Ligation

A novel chemoselective ligation methodology has been developed for the facile construction of peptide‐based fluorescent probes. Furan‐containing peptides were activated by singlet oxygen and covalently engaged by nitrogen nucleophiles to yield stable conjugates. Singlet oxygen was compatible with sensitive amino acid residues within the peptides and a range of fluorophores, bearing different functionalities, were successfully incorporated, illustrating the broad scope of the developed strategy.     

Combined Effect of Temperature and Ternary Mobile Phase Composition on the Retention in Ternary Isocratic and Gradient Elution RP-LC under Isothermal Conditions. Application to the Retention Prediction of Four Macrolide Antibiotics

Retention models considering simultaneously ternary mobile phase organic contents and column temperature (T) were developed by a direct combination of equations expressing separately the dependence of the retention upon each of these factors. Thus, a combination of a linear dependence of the logarithm of the solute retention factor, ln k (T), against 1/T, i.e. ln k (T) = a + b/T, and of either a linear or a quadratic dependence of ln k upon the compositions of a binary mobile phase, gave two different expressions for the logarithm of the solute retention factor in terms of both temperature and organic contents in the ternary mobile phase. The effectiveness of the above models was tested in the prediction of isothermal retention of a mixture of four macrolide antibiotics under ternary isocratic and gradient elution conditions using mobile phases modified by methanol and acetonitrile. The limiting case of using ternary eluents with constant ratio of the concentrations of the two organic modifiers was also tested.     

Physical understanding of complex multiscale biochemical models via algorithmic simplification: Glycolysis in Saccharomyces cerevisiae

Large-scale models of cellular reaction networks are usually highly complex and characterized by a wide spectrum of time scales, making a direct interpretation and understanding of the relevant mechanisms almost impossible. We address this issue by demonstrating the benefits provided by model reduction techniques. We employ the Computational Singular Perturbation (CSP) algorithm to analyze the glycolytic pathway of intact yeast cells in the oscillatory regime. As a primary object of research for many decades, glycolytic oscillations represent a paradigmatic candidate for studying biochemical function and mechanisms. Using a previously published full-scale model of glycolysis, we show that, due to fast dissipative time scales, the solution is asymptotically attracted on a low dimensional manifold. Without any further input from the investigator, CSP clarifies several long-standing questions in the analysis of glycolytic oscillations, such as the origin of the oscillations in the upper part of glycolysis, the importance of energy and redox status, as well as the fact that neither the oscillations nor cell-cell synchronization can be understood in terms of glycolysis as a simple linear chain of sequentially coupled reactions.     

K-sample subsampling in general spaces: The case of independent time series

The problem of subsampling in two-sample and K-sample settings is addressed where both the data and the statistics of interest take values in general spaces. We focus on the case where each sample is a stationary time series, and construct subsampling confidence intervals and hypothesis tests with asymptotic validity. Some examples are also given, and the problem of optimal block size choice is discussed.     

Harmonic generalized functions in generalized function algebras

We investigate basic properties of harmonic generalized functions within the framework of J. F. Colombeau??s theory of generalized functions. In particular, we present various theorems concerning the Maximum principle, Liouville??s theorem, singularities and Poisson formula.     

Robust sample average approximation

Mathematical Programming - Sample average approximation (SAA) is a widely popular approach to data-driven decision-making under uncertainty. Under mild assumptions, SAA is both tractable and enjoys...     

Tractable Approximations to Robust Conic Optimization Problems

In earlier proposals, the robust counterpart of conic optimization problems exhibits a lateral increase in complexity, i.e., robust linear programming problems (LPs) become second order cone problems (SOCPs), robust SOCPs become semidefinite programming problems (SDPs), and robust SDPs become NP-hard. We propose a relaxed robust counterpart for general conic optimization problems that (a) preserves the computational tractability of the nominal problem; specifically the robust conic optimization problem retains its original structure, i.e., robust LPs remain LPs, robust SOCPs remain SOCPs and robust SDPs remain SDPs, and (b) allows us to provide a guarantee on the probability that the robust solution is feasible when the uncertain coefficients obey independent and identically distributed normal distributions. The research of the author was partially supported by the Singapore-MIT alliance. The research of the author is supported by NUS academic research grant R-314-000-066-122 and the Singapore-MIT alliance.     

Gallium-nitride-based plasmonic multilayer operating at 1.55 μm

In this Letter, we have designed and fabricated a III-V semiconductor multilayer based on surface plasmon resonance (SPR) operating at the telecom wavelength. Optimization of the optogeometrical parameters and the metal/semiconductor layers required for this novel structure was conducted accurately by theoretical tools using the Maxwell equations. Technological fabrication of the device and its experimental characterizations using an evanescent coupling configuration was performed: the results have confirmed the existence of SPR associated to a sharp width response. This study could be a first step in the design of new plasmonic-semiconductor-based optical devices such as modulators and switches.     

Lyapunov exponents estimation for hysteretic systems

This article discusses the Lyapunov exponent estimation of non-linear hysteretic systems by adapting the classical algorithm by Wolf and co-workers [Wolf, A., Swift, J.B., Swinney, H.L., Vastano, J.A., 1985. Determining Lyapunov exponents from a times series. Physica D 16, 285–317.]. This algorithm evaluates the divergence of nearby orbits by monitoring a reference trajectory, evaluated from the equations of motion of the original hysteretic system, and a perturbed trajectory resulting from the integration of the linearized equations of motion. The main issue of using this algorithm for non-linear, rate-independent, hysteretic systems is related to the procedure of linearization of the equations of motion. The present work establishes a procedure of linearization performing a state space split and assuming an equivalent viscous damping in order to represent hysteretic dissipation in the linearized system. The dynamical response of a single-degree of freedom pseudoelastic shape memory alloy (SMA) oscillator is discussed as an application of the proposed algorithm. The restitution force of the oscillator is provided by an SMA element described by a rate-independent, hysteretic, thermomechanical constitutive model. Two different modeling cases are considered for isothermal and non-isothermal heat transfer conditions, and numerical simulations are performed for both cases. The evaluation of the Lyapunov exponents shows that the proposed procedure is capable of quantifying chaos capturing the non-linear dissipation of hysteretic systems.     

A statistical model for evaluation and prediction of the noise exposure in a construction equipment area

A statistical model for evaluating and predicting the equivalent noise level and the noise exposure at a working area was made. The method is based on the statistical theory of regression analysis. The results are expressed as a functional expansion and the stochastic models that give the solution to the problem are confidence zones and prediction zones of noise, which are valid in every similar case.