Microwave-assisted Cu-catalyzed Ullmann ether synthesis in a continuous-flow milli-plant

The combination of milli-scale processing and microwave heating has been investigated for the Cu-catalyzed Ullmann etherification in fine-chemical synthesis, providing improved catalytic activity and selective catalyst heating. Wall-coated and fixed-bed milli-reactors were designed and applied in the Cu-catalyzed Ullmann-type CO coupling of phenol and 4-chloropyridine. In a batch reactor the results show clearly increased yields for the microwave heated process at low microwave powers, whereas high powers and catalyst loadings reduced the benefits of microwave heating. Slightly higher yields were found in the Cu/ZnO wall-coated as compared to the Cu/TiO fixed-bed flow-reactor. The benefit here is that the reaction occurs at the surface of the metal nanoparticles confined within a support film making the nano-copper equally accessible. Catalyst deactivation was mainly caused by Cu oxidation and coke formation; however, at longer process times leaching played a significant role. Catalyst activity could partially be recovered by removal of deposited by-product by means of calcination. After 6h on-stream the reactor productivities were 28.3 and 55.1kgprod/(mR3h) for the fresh Cu/ZnO wall-coated and Cu/TiO fixed-bed reactor, respectively. Comparison of single- and multimode microwaves showed a threefold yield increase for single-mode microwaves. Control of nanoparticles size and loading allows to avoid high temperatures in a single-mode microwave field and provides a novel solution to a major problem for combining metal catalysis and microwave heating. Catalyst stability appeared to be more important and provided twofold yield increase for the CuZn/TiO catalyst as compared to the Cu/TiO catalyst due to stabilized copper by preferential oxidation of the zinc. For this catalyst a threefold yield increase was observed in single-mode microwaves which, to the best of our knowledge, led to a not yet reported productivity of 172kgprod/(mR3h) for the microwave and flow Ullmann CO coupling. © 2012 Elsevier B.V.

Assessing reproducibility of data obtained with instruments based on continuous measurements

Data obtained with any research tool must be reproducible, a concept referred to as reliability. Three techniques are often used to evaluate reliability of tools using continuous data in aging research: intraclass correlation coefficients (ICC), Pearson correlations, and paired t tests. These are often construed as equivalent when applied to reliability. This is not correct, and may lead researchers to select instruments based on statistics that may not reflect actual reliability. The purpose of this paper is to compare the reliability estimates produced by these three techniques and determine the preferable technique. A hypothetical dataset was produced to evaluate the reliability estimates obtained with ICC, Pearson correlations, and paired t tests in three different situations. For each situation two sets of 20 observations were created to simulate an intrarater or inter-rater paradigm, based on 20 participants with two observations per participant. Situations were designed to demonstrate good agreement, systematic bias, or substantial random measurement error. In the situation demonstrating good agreement, all three techniques supported the conclusion that the data were reliable. In the situation demonstrating systematic bias, the ICC and t test suggested the data were not reliable, whereas the Pearson correlation suggested high reliability despite the systematic discrepancy. In the situation representing substantial random measurement error where low reliability was expected, the ICC and Pearson coefficient accurately illustrated this. The t test suggested the data were reliable. The ICC is the preferred technique to measure reliability. Although there are some limitations associated with the use of this technique, they can be overcome.

Gapped Two-Body Hamiltonian for Continuous-Variable Quantum Computation

We introduce a family of Hamiltonian systems for measurement-based quantum computation with continuous variables. The Hamiltonians (i) are quadratic, and therefore two body, (ii) are of short range, (iii) are frustration-free, and (iv) possess a constant energy gap proportional to the squared inverse of the squeezing. Their ground states are the celebrated Gaussian graph states, which are universal resources for quantum computation in the limit of infinite squeezing. These Hamiltonians constitute the basic ingredient for the adiabatic preparation of graph states and thus open new venues for the physical realization of continuous-variable quantum computing beyond the standard optical approaches. We characterize the correlations in these systems at thermal equilibrium. In particular, we prove that the correlations across any multipartition are contained exactly in its boundary, automatically yielding a correlation area law. © 2011 American Physical Society.

The structure of compact disjointness preserving operators on continuous functions

Let T be a compact disjointness preserving linear operator from C0(X) into C0(Y), where X and Y are locally compact Hausdorff spaces. We show that T can be represented as a norm convergent countable sum of disjoint rank one operators. More precisely, T = Snd ?hn for a (possibly finite) sequence {xn }n of distinct points in X and a norm null sequence {hn }n of mutually disjoint functions in C0(Y). Moreover, we develop a graph theoretic method to describe the spectrum of such an operator

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

Metamaterials Nanosensor for Label-free Analysis of Conformation and Molecular Interaction of Biomolecules

Analysis of molecular interaction and conformational dynamics of biomolecules is of paramount importance in understanding of their vital functions in complex biological systems, disease detection, and new drug development. Plasmonic biosensors based upon surface plasmon resonance and localized surface plasmon resonance have become the predominant workhorse for detecting accumulated biomass caused by molecular binding events. However, unlike surface-enhanced Raman spectroscopy (SERS), the plasmonic biosensors indeed are not suitable tools to interrogate vibrational signatures of conformational transitions required for biomolecules to interact. Here, we show that plasmonic metamaterials can offer two transducing channels for parallel acquisition of optical transmission and sensitive SERS spectra at the biointerface, simultaneously probing the conformational states and binding affinity of biomolecules, e.g. G-quadruplexes, in different environments (Fig. 1). We further demonstrate the use of the metamaterials for fingerprinting and detection of arginine-glycine-glycine domain of nucleolin, a cancer biomarker which specifically binds to a G-quadruplex, with the picomolar sensitivity. The dual-mode nanosensor will significantly contribute to unraveling the complexes of the conformational dynamics of biomolecules as well as to improving specificity of biodetection assays.

Label-free Analysis of Conformational Dynamics and Molecular Interaction of Biomolecules by Vis-NIR Metasensor

Analysis of binding recognition and conformation of biomolecules is of paramount important in understanding of their vital functions in complex biological systems. By enabling sub-wavelength light localization and strong local field enhancement, plasmonic biosensors have become dominant tools used for such analysis owing to their label-free and real-time attributes1,2. However, the plasmonic biosensors are not well-suited to provide information regarding conformation or chemical fingerprint of biomolecules. Here, we show that plasmonic metamaterials, consisting of periodic arrays of artificial split-ring resonators (SRRs)3, can enable capabilities of both sensing and fingerprinting of biomolecules. We demonstrate that by engineering geometry of individual SRRs, localized surface plasmon resonance (LSPR) frequency of the metamaterials could be tuned to visible-near infrared regimes (Vis-NIR) such that they possess high local field enhancement for surface-enhanced Raman scattering spectroscopy (SERS). This will provide the basis for the development of a dual mode label-free conformational-resolving and quantitative detection platform. We present here the ability of each sensing mode to independently detect binding adsorption and to identify different conformational states of Guanine (G)-rich DNA monolayers in different environment milieu. Also shown is the use of the nanosensor for fingerprinting and detection of Arginine-Glycine-Glycine (RGG) peptide binding to the G-quadruplex aptamer. The dual-mode nanosensor will significantly contribute to unraveling the complexes of the conformational dynamics of biomolecules as well as to improving specificity of biodetection assays that the conventional, population-averaged plasmonic biosensors cannot achieve.

Metamaterials-Based Label-Free Nanosensor for Conformation and Affinity Biosensing

Analysis of molecular interaction and conformational dynamics of biomolecules is of paramount importance in understanding of their vital functions in complex biological systems, disease detection, and new drug development. Plasmonic biosensors based upon surface plasmon resonance and localized surface plasmon resonance have become the predominant workhorse for detecting accumulated biomass caused by molecular binding events. However, unlike surface-enhanced Raman spectroscopy (SERS), the plasmonic biosensors indeed are not suitable tools to interrogate vibrational signatures of conformational transitions required for biomolecules to interact. Here, we show that highly tunable plasmonic metamaterials can offer two transducing channels for parallel acquisition of optical transmission and sensitive SERS spectra at the biointerface, simultaneously probing the conformational states and binding affinity of biomolecules, e.g. G-quadruplexes, in different environments. We further demonstrate the use of the metamaterials for fingerprinting and detection of arginine-glycine-glycine domain of nucleolin, a cancer biomarker which specifically binds to a G-quadruplex, with the picomolar sensitivity.

Evaluation of an algorithm to guide patients with type 1 diabetes treated with continuous subcutaneous insulin infusion on how to respond to real-time continuous glucose levels:A randomized controlled trial

OBJECTIVE - To evaluate an algorithm guiding responses of continuous subcutaneous insulin infusion (CSII)-treated type 1 diabetic patients using real-time continuous glucose monitoring (RT-CGM). RESEARCH DESIGN AND METHODS - Sixty CSII-treated type 1 diabetic participants (aged 13-70 years, including adult and adolescent subgroups, with A1C =9.5%) were randomized in age-, sex-, and A1C-matched pairs. Phase 1 was an open 16-week multicenter randomized controlled trial. Group A was treated with CSII/RT-CGM with the algorithm, and group B was treated with CSII/RT-CGM without the algorithm. The primary outcome was the difference in time in target (4-10 mmol/l) glucose range on 6-day masked CGM. Secondary outcomes were differences in A1C, low (=3.9 mmol/l) glucose CGM time, and glycemic variability. Phase 2 was the week 16-32 follow-up. Group A was returned to usual care, and group B was provided with the algorithm. Glycemia parameters were as above. Comparisons were made between baseline and 16 weeks and 32 weeks. RESULTS - In phase 1, after withdrawals 29 of 30 subjects were left in group A and 28 of 30 subjects were left in group B. The change in target glucose time did not differ between groups. A1C fell (mean 7.9% [95% CI 7.7-8.2to 7.6% [7.2-8.0]; P <0.03) in group A but not in group B (7.8% [7.5-8.1] to 7.7 [7.3-8.0]; NS) with no difference between groups. More subjects in group A achieved A1C =7% than those in group B (2 of 29 to 14 of 29 vs. 4 of 28 to 7 of 28; P = 0.015). In phase 2, one participant was lost from each group. In group A, A1C returned to baseline with RT-CGM discontinuation but did not change in group B, who continued RT-CGM with addition of the algorithm. CONCLUSIONS - Early but not late algorithm provision to type 1 diabetic patients using CSII/RT-CGM did not increase the target glucose time but increased achievement of A1C =7%. Upon RT-CGM cessation, A1C returned to baseline. © 2010 by the American Diabetes Association.