Simultaneous refolding and purification of recombinant proteins by macro-(affinity ligand) facilitated three-phase partitioning

A strategy called macro-(affinity ligand) facilitated three-phase partitioning (MLFTPP) is described for refolding of a diverse set of recombinant proteins starting from the solubilized inclusion bodies. It essentially consists of: (i) binding of the protein with a suitable smart polymer and (ii) precipitating the polymer-protein complex as an interfacial layer by mixing in a suitable amount of ammonium sulfate and t-butanol. Smart polymers are stimuli-responsive polymers that become insoluble on the application of a suitable stimulus (e.g., a change in the temperature, pH, or concentration of a chemical species such as Ca 2+ or K +). The MLFTPP process required approximately 10min, and the refolded proteins were found to be homogeneous on sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The folded proteins were characterized by fluorescence emission spectroscopy, circular dichroism spectroscopy, biological activity, melting temperature, and surface hydrophobicity measurements by 8-anilino-1-naphthalenesulfonate fluorescence. Two refolded antibody fragments were also characterized by measuring K D by Biacore by using immobilized HIV-1 gp120. The data demonstrate that MLFTPP is a rapid and convenient procedure for refolding a variety of proteins from inclusion bodies at high concentration. Although establishing the generic nature of the approach would require wider trials by different groups, its success with the diverse kinds of proteins tried so far appears to be promising.

Simultaneous Detection of Two Triplets: A Time-Resolved Resonance Raman Study

Solvents are known to affect the triplet state structure and reactivity. In this paper, we have employed time-resolved resonance Raman (TR3) spectroscopy to understand solvent-induced subtle structural changes in the lowest excited triplet state of thioxanthone. Density functional theory (DFT) combined with the self-consistent reaction field (SCRF) implicit solvation model has been used to calculate the vibrational frequencies in the solvents. Here, we report a unique observation of the coexistence of two triplets, which has been substantiated by the probe wavelength-dependent Raman experiments. The coexistence of two triplets has been further supported by photoreduction experiments carried out at various temperatures.

Phonon anomalies and structural transition in spin ice Dy2Ti2O7: a simultaneous pressure-dependent and temperature-dependent Raman study

We revisit the assignment of Raman phonons of rare-earth titanates by performing Raman measurements on single crystals of O18 isotope-rich spin ice Dy2Ti2O718 and nonmagnetic Lu2Ti2O718 pyrochlores and compare the results with their O16 counterparts. We show that the low-wavenumber Raman modes below 250 cm-1 are not due to oxygen vibrations. A mode near 200 cm-1, commonly assigned as F2g phonon, which shows highly anomalous temperature dependence, is now assigned to a disorder-induced Raman active mode involving Ti4+ vibrations. Moreover, we address here the origin of the new Raman mode, observed below TC similar to 110 K in Dy2Ti2O7, through a simultaneous pressure-dependent and temperature-dependent Raman study. Our study confirms the new mode to be a phonon mode. We find that dTC/dP = + 5.9 K/GPa. Temperature dependence of other phonons has also been studied at various pressures up to similar to 8 GPa. We find that pressure suppresses the anomalous temperature dependence. The role of the inherent vacant sites present in the pyrochlore structure in the anomalous temperature dependence is also discussed. Copyright (c) 2012 John Wiley & Sons, Ltd.

Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments

Facet-based sentiment analysis involves discovering the latent facets, sentiments and their associations. Traditional facet-based sentiment analysis algorithms typically perform the various tasks in sequence, and fail to take advantage of the mutual reinforcement of the tasks. Additionally,inferring sentiment levels typically requires domain knowledge or human intervention. In this paper, we propose aseries of probabilistic models that jointly discover latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner. This is achieved by simultaneously capturing both short-range syntactic structure and long range semantic dependencies between the sentiment and facet words. The models further incorporate coherence in reviews, where reviewers dwell on one facet or sentiment level before moving on, for more accurate facet and sentiment discovery. For reviews which are supplemented with ratings, our models automatically order the latent sentiment topics, without requiring seed-words or domain-knowledge. To the best of our knowledge, our work is the first attempt to combine the notions of syntactic and semantic dependencies in the domain of review mining. Further, the concept of facet and sentiment coherence has not been explored earlier either. Extensive experimental results on real world review data show that the proposed models outperform various state of the art baselines for facet-based sentiment analysis.

Simultaneous measurement of mechanical and electrical contact resistances during nanoindentation of NiTi shape memory alloys

The nanoindentation technique can be employed in shape memory alloys (SMAs) to discern the transformation temperatures as well as to characterize their mechanical behavior. In this paper, we use it with simultaneous measurements of the mechanical and the electrical contact resistances (ECR) at room temperature to probe two SMAs: austenite (RTA) and martensite (RTM). Two different types of indenter tips - Berkovich and spherical - are employed to examine the SMAs' indentation responses as a function of the representative strain, epsilon(R). In Berkovich indentation, because of the sharp nature of the tip, and in consequence the high levels of strain imposed, discerning the two SMAs on the basis of the indentation response alone is difficult. In the case of the spherical tip, epsilon(R) is systematically varied and its effect on the depth recovery ratio, eta(d), is examined. Results indicate that RTA has higher eta(d) than RTM, but the difference decreases with increasing epsilon(R) such that eta(d) values for both the alloys would be similar in the fully plastic regime. The experimental trends in eta(d) vs. epsilon(R) for both the alloys could be described well with a eta(d) proportional to (epsilon(R))(-1) type equation, which is developed on the basis of a phenomenological model. This fit, in turn, directs us to the maximum epsilon(R), below which plasticity underneath the indenter would not mask the differences in the two SMAs. It was demonstrated that the ECR measurements complement the mechanical measurements in demarcating the reverse transformation from martensite to austenite during unloading of RTA, wherein a marked increase in the voltage was noted. A correlation between recovery due to reverse transformation during unloading and increase in voltage (and hence the electrical resistance) was found. (C) 2013 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Simultaneous feature selection and clustering using particle swarm optimization

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Simultaneous attitude control and trajectory tracking of a micro quadrotor: a SNAC aided nonlinear dynamic inversion approach

This paper presents an advanced single network adaptive critic (SNAC) aided nonlinear dynamic inversion (NDI) approach for simultaneous attitude control and trajectory tracking of a micro-quadrotor. Control of micro-quadrotors is a challenging problem due to its small size, strong coupling in pitch-yaw-roll and aerodynamic effects that often need to be ignored in the control design process to avoid mathematical complexities. In the proposed SNAC aided NDI approach, the gains of the dynamic inversion design are selected in such a way that the resulting controller behaves closely to a pre-synthesized SNAC controller for the output regulation problem. However, since SNAC is based on optimal control theory, it makes the dynamic inversion controller to operate near optimal and enhances its robustness property as well. More important, it retains two major benefits of dynamic inversion: (i) closed form expression of the controller and (ii) easy scalability to command tracking application even without any apriori knowledge of the reference command. Effectiveness of the proposed controller is demonstrated from six degree-of-freedom simulation studies of a micro-quadrotor. It has also been observed that the proposed SNAC aided NDI approach is more robust to modeling inaccuracies, as compared to the NDI controller designed independently from time domain specifications.

Simultaneous source localization and boundary mapping for contaminants

This paper addresses the problem of localizing the sources of contaminants spread in the environment, and mapping the boundary of the affected region using an innovative swarm intelligence based technique. Unlike most work in this area the algorithm is capable of localizing multiple sources simultaneously while also mapping the boundary of the contaminant spread. At the same time the algorithm is suitable for implementation using a mobile robotic sensor network. Two types of agents, called the source localization agents (or S-agents) and boundary mapping agents (or B-agents) are used for this purpose. The paper uses the basic glowworm swarm optimization (GSO) algorithm, which has been used only for multiple signal source localization, and modifies it considerably to make it suitable for both these tasks. This requires the definition of new behaviour patterns for the agents based on their terminal performance as well as interactions between them that helps the swarm to split into subgroups easily and identify contaminant sources as well as spread along the boundary to map its full length. Simulations results are given to demonstrate the efficacy of the algorithm.

Simultaneous co-ordination of three cytosine ligands displaying different binding sites around the copper centres

We report the synthesis and structural characterization of a polymeric ternary copper-cytosine-phenanthroline complex, Cu-4(phen)(3)-(mu(3)-cyt)(2)(mu-OH)(cyt)(OH)Cl-3](n)center dot 16H(2)O, where three cytosine ligands with different binding sites have simultaneously complexed to the four copper metal centres. Interestingly, the complex exhibits two different coordination geometries around the metal centres.

Hexamethylenetetramine mediated simultaneous nitrogen doping and reduction of graphene oxide for a metal-free SERS substrate

We report a one-pot hydrothermal synthesis of nitrogen doped reduced graphene oxide (N-rGO) and Ag nanoparticle decorated N-rGO hybrid nanostructures from graphene oxide (GO), metal ions and hexamethylenetetramine (HMT). HMT not only reduces GO and metal ions simultaneously but also acts as the source for the nitrogen (N) dopant. We show that the N-rGO can be used as a metal-free surface enhanced Raman spectroscopy (SERS) substrate, while the Ag nano-particles decorated N-rGO can be used as an effective SERS substrate as well as a template for decorating various other nanostructures on N-rGO.

Simultaneous Perturbation Newton Algorithms for Simulation Optimization

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman-Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.

Simultaneous measurements of thickness and temperature profile of the lubricant film at chip-tool interface during machining process using luminescent sensors

Simultaneous measurements of thickness and temperature profile of the lubricant film at chip-tool interface during machining have been studied in this experimental programme. Conventional techniques such as thermography can only provide temperature measurement under controlled environment in a laboratory and without the addition of lubricant. The present study builds on the capabilities of luminescent sensors in addition to direct image based observations of the chip-tool interface. A suite of experiments conducted using different types of sensors are reported in this paper, especially noteworthy are concomitant measures of thickness and temperature of the lubricant. (C) 2014 Elsevier Ltd.

Simultaneous perturbation methods for adaptive labor staffing in service systems

We consider the problem of optimizing the workforce of a service system. Adapting the staffing levels in such systems is non-trivial due to large variations in workload and the large number of system parameters do not allow for a brute force search. Further, because these parameters change on a weekly basis, the optimization should not take longer than a few hours. Our aim is to find the optimum staffing levels from a discrete high-dimensional parameter set, that minimizes the long run average of the single-stage cost function, while adhering to the constraints relating to queue stability and service-level agreement (SLA) compliance. The single-stage cost function balances the conflicting objectives of utilizing workers better and attaining the target SLAs. We formulate this problem as a constrained parameterized Markov cost process parameterized by the (discrete) staffing levels. We propose novel simultaneous perturbation stochastic approximation (SPSA)-based algorithms for solving the above problem. The algorithms include both first-order as well as second-order methods and incorporate SPSA-based gradient/Hessian estimates for primal descent, while performing dual ascent for the Lagrange multipliers. Both algorithms are online and update the staffing levels in an incremental fashion. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter tuned by our algorithms onto the discrete set. The smoothness is necessary to ensure that the underlying transition dynamics of the constrained Markov cost process is itself smooth (as a function of the continuous-valued parameter): a critical requirement to prove the convergence of both algorithms. We validate our algorithms via performance simulations based on data from five real-life service systems. For the sake of comparison, we also implement a scatter search based algorithm using state-of-the-art optimization tool-kit OptQuest. From the experiments, we observe that both our algorithms converge empirically and consistently outperform OptQuest in most of the settings considered. This finding coupled with the computational advantage of our algorithms make them amenable for adaptive labor staffing in real-life service systems.

Simultaneous Determination of Catecholamines in Presence of Uric Acid and Ascorbic Acid at a Highly Sensitive Electrochemically Activated Carbon Paste Electrode

A simple yet remarkable, electrochemically activated carbon paste electrode (EACPE) was prepared by successive potential cycling of carbon paste in a 0.1 M NaOH solution and was effectively used for the simultaneous determination of catecholamines such as dopamine (DA), epinephrine (E) and Norepinephrine (NE) in presence of uric acid (UA) and ascorbic acid (AA). Taking DA as the ideal catecholamine, the electrochemical behaviors of DA, UA and AA such as scan rate and pH variation was studied by cyclic voltammetry (CV) in phosphate buffer solution (PBS, pH 7.1). This electrochemical sensor exhibited strong electrocatalytic activity towards the oxidation of a mixture of catecholamines, UA and AA with apparent reduction of overpotentials. Crider optimum conditions, limit of detection (S/N = 3) of DA, E, NE, UA and AA was found to be 0.08, 0.08, 0.07, 0.1 and 6.0 mu M, respectively by differential pulse voltammetry (DPV). The analytical performance of this modified electrode as a biosensor was also demonstrated for the determination of DA, UA and AA in dopamine injection, human urine and vitamin C tablets, respectively, in presence of other interfering substances. (C) 2015 The Electrochemical Society. All-rights reserved.

Simultaneous enhancement in mechanical strength, electrical conductivity, and electromagnetic shielding properties in PVDF-ABS blends containing PMMA wrapped multiwall carbon nanotubes

A unique approach was adopted to drive the multiwall carbon nanotubes (MWNTs) to the interface of immiscible PVDF-ABS blends by wrapping the nanotubes with a mutually miscible homopolymer (PMMA). A tailor made interface with an improved stress transfer was achieved in the blends with PMMA wrapped MWNTs. This manifested in an impressive 108% increment in the tensile strength and 48% increment in the Young's modulus with 3 wt% PMMA wrapped MWNTs in striking contrast to the neat blends. As the PMMA wrapped MWNTs localized at the interface of PVDF-ABS blends, the electrical conductivity could be tuned with respect to only MWNTs, which were selectively localized in the PVDF phase, driven by thermodynamics. The electromagnetic shielding properties were assessed using a vector network analyser in a broad range of frequency, X-band (8-12 GHz) and Ku-band (12-18 GHz). Interestingly, enhanced EM shielding was achieved by this unique approach. The blends with only MWNTs shielded the EM waves mostly by reflection however, the blends with PMMA wrapped MWNTs (3 wt%) shielded mostly by absorption (62%). This study opens new avenues in designing materials, which show simultaneous improvement in mechanical, electrical conductivity and EM shielding properties.