Application of Dissolution Dynamic Nuclear Polarization to the Characterization of Reactions Involving Large Molecules

Nuclear magnetic resonance (NMR) spectroscopy is one of the most important analytical tools for organic and biological chemistry. It provides not only detailed information on the structure of small molecules and macromolecules, but also on molecular interactions. Because of the inherent low sensitivity of NMR, a long signal averaging time or a high spin concentration is often required. A variety of methods have been explored to improve the sensitivity of NMR. Especially, large signal gains can be obtained by hyperpolarization of the nuclear spins. NMR signals of hyperpolarized samples are enhanced by several orders of magnitude. Dissolution Dynamic Nuclear Polarization (D-DNP) is a versatile technique capable of polarizing many different nuclei in the solid state, and subsequently providing a hyperpolarized liquid sample following a dissolution step. The resulting signal enhancement has made it possible to obtain detailed information in research fields as varied as metabolic imaging or enzyme catalysis. This dissertation aims to extend the applicability of D-DNP into new areas of chemistry, which involve the characterization of interactions and reactions involving large molecules. In a first project, fluorine hyperpolarization is exploited to investigate protein-ligand interactions. The enhancement of 19F signal allows for the detection of submicromolar concentrations of fluorinated ligands in the strong-, intermediate-, and weak-binding regimes. Several NMR parameters are utilized to observe ligand binding to the macromolecule, and to determine dissociation constants. In a second project, competitive binding of ligands to the same binding pocket on a protein is investigated. Here, polarization flows from a first ligand hyperpolarized on protons to the protein, and then to the second ligand. The buildup in function of time of the signals due to this relayed nuclear Overhauser effect contains structural information on the binding epitope. In a third project, the aim is to directly detect a larger molecule, a polymer, which has been synthesized starting from hyperpolarized monomers. Using DNP, single scan observation of 13C, a common nucleus with large chemical shift dispersion, is possible. Time resolved 13C NMR spectroscopy in combination with kinetic models permits the description of polymerization reaction of the living anionic polymerization of styrene. In summary, several approaches have been investigated for utilizing a large hyperpolarization initially produced on small molecules, for the benefit of characterizing properties of macromolecules. These developments extend the capabilities of D-DNP and demonstrate the potential for leading to new applications in fields as diverse as drug discovery and polymer science

SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis

<p>Abstract</p> <p>Background</p> <p>Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly.</p> <p>Results</p> <p>We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', r²and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient (r²). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis.</p> <p>Conclusion</p> <p>SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use.</p

An Inexpensive Cardiovascular Flow Simulator for Cardiac Catheterization Procedure Using a Pulmonary Artery Catheter

Cardiac catheterization associated with central vein cannulation can involve potential thrombotic and infectious complications due to multiple cannulation trials or improper placement. To minimize the risks, medical simulators are used for training. Simulators are also employed to test medical devices such as catheters before performing animal tests because they are more cost-effective and still reveal necessary improvements. However, commercial simulators are expensive, simplified for their purpose, and provide limited access sites. Inexpensive and anatomical cardiovascular simulators with central venous access for cannulation are sparse. Here, we developed an anatomically and physiologically accurate cardiovascular flow simulator to help train medical professionals and test medical devices. Our simulator includes an anatomical right atrium/ventricle, femoral and radial access sites, and considers the variability of arm position. It simulates physiological pulsatile blood flow with a setting for constant flow from 3 to 6 L/min and mimics physiological temperature (37◦C). We demonstrated simulation by inserting a catheter into the system at radial/femoral access sites, passing it through the vasculature, and advancing it into the heart. We expect that our simulator can be used as an educational tool for cardiac catheterization as well as a testing tool that will allow for design iteration before moving to animal trials

Development of a Real-Time Virtual Nitric Oxide Sensor for Light-Duty Diesel Engines

This study describes the development of a semi-physical, real-time nitric oxide (NO) prediction model that is capable of cycle-by-cycle prediction in a light-duty diesel engine. The model utilizes the measured in-cylinder pressure and information obtained from the engine control unit (ECU). From the inputs, the model takes into account the pilot injection burning and mixing, which affects the in-cylinder mixture formation. The representative in-cylinder temperature for NO formation was determined from the mixture composition calculation. The selected temperature and mixture composition was substituted using a simplified form of the NO formation rate equation for the cycle-by-cycle estimation. The reactive area and the duration of NO formation were assumed to be limited by the fuel quantity. The model predictability was verified not only using various steady-state conditions, including the variation of the EGR rate, the boost pressure, the rail pressure, and the injection timing, but also using transient conditions, which represent the worldwide harmonized light vehicles test procedure (WLTC). The WLTC NO prediction results produced less than 3% error with the measured value. In addition, the proposed model maintained its reliability in terms of hardware aging, the changing and artificial perturbations during steady-state and transient engine operations. The model has been shown to require low computational effort because of the cycle-by-cycle, engine-out NO emission prediction and control were performed simultaneously in an embedded system for the automotive application. We expect that the developed NO prediction model can be helpful in emission calibration during the engine design stage or in the real-time controlling of the exhaust NO emission for improving fuel consumption while satisfying NO emission legislation

Explorative Supercooling Technology for Prevention of Freeze Damages in Vaccines

Most freeze-sensitive vaccines are stored between 2 &deg;C and 8 &deg;C upon manufacturing and until they are eventually administered in intermediate vaccine stores and health facilities. This so-called &ldquo;cold chain&rdquo; of vaccine distribution is strictly regulated at these specific temperatures to avoid freeze damage. Liquid formulations of particular vaccines (e.g., aluminum-adsorbed tetanus toxoid (TT)) will irreversibly lose their immunogenicity once frozen. Using an oscillating magnetic field (OMF), supercooling can inhibit ice crystal nucleation effectively; water is susceptible to influence by a strong magnetic field, allowing normal water dynamics even in subzero freezing conditions. This recently developed technology&mdash;composed of a custom-designed electromagnet unit producing an optimal field strength (50 mT) at a specific frequency (1 Hz)&mdash;was successfully used to inhibit the formation of ice crystals in aluminum adjuvant TT vaccines, therefore preventing any visible damage in the vaccines&rsquo; microscopic structure. Despite being subject to temperatures far below their freezing point (up to &minus;14 &deg;C) for up to seven days, the TT vaccines showed no freeze damage on physical appearances. Results were further validated using shake tests and light microscopy. As storage and freeze-protection become more critical during times of increased vaccination efforts&mdash;particularly against COVID-19&mdash;this supercooling technology can be a promising solution to distribution problems by removing concern for temperature abuse or shock-induced freezing

Development of semi-empirical soot emission model for a CI engine

Soot is one of the main harmful emissions of diesel engines that is mainly generated in the reacting fuel jet of diesel injection. Over 99% of the engine-out soot can be filtered by a diesel particulate filter (DPF). However, when the soot load of the DPF is high, a regeneration process that oxidizes the accumulated soot reduces fuel economy. A real-time soot estimation model can contribute to real-time feedback soot control under transient conditions to minimize the engine-out soot emission and frequency of DPF regeneration. A zero-dimensional engine-out soot estimation model for a diesel engine is developed in this study. The semi-empirical soot model considers both the formation and oxidation of soot. In the model, soot formation was correlated with the cross-sectional average equivalence ratio at the lift-off length of the fuel spray. The equivalence ratio at the lift-off length is an indicator of how much air and vaporized fuel are mixed as the fuel reaches the reaction zone. The mass of the injected fuel and combustion duration were also correlated with soot formation. The Nagle and Strickland Constable mechanism, which calculates the soot oxidation rate was correlated with the soot oxidation in this study. The results of the soot estimation showed an R-2 of 0.901 and root mean square error of 10.8 mg/m(3) for steady-state experimental cases. The engine-out soot model was also combined with the in-cylinder pressure model proposed by the authors, and validated through the transient Worldwide Harmonized Light Vehicles Test Cycle (WLTC) mode. The estimates agreed with the measured soot, with an accumulated soot error of approximately 6% during the WLTC, even without using an in-cylinder pressure sensor. The soot model developed in this study can help minimize tailpipe-out soot emissions and improve fuel economy by influencing the real-time feedback control during transient and frequent DPF regeneration.N

Framework of an IoT-based Industrial Data Management for Smart Manufacturing

The Internet of Things (IoT) is the global network of interrelated physical devices such as sensors, actuators, smart applications, objects, computing devices, mechanical machines, and people that are becoming an essential part of the internet. In an industrial environment, these devices are the source of data which provide abundant information in manufacturing processes. Nevertheless, the massive, heterogeneous, and time-sensitive nature of the data brings substantial challenges to the real-time collection, processing, and decision making. Therefore, this paper presents a framework of an IoT-based Industrial Data Management System (IDMS) which can manage the huge industrial data, support online monitoring, and control smart manufacturing. The framework contains five basic layers such as physical, network, middleware, database, and application layers to provide a service-oriented architecture for the end users. Experimental results from a smart factory case study demonstrate that the framework can manage the regular data and urgent events generated from various factory devices in the distributed industrial environment through state-of-the-art communication protocols. The collected data is converted into useful information which improves productivity and the prognosis of production lines

Copper(I)-Catalyzed Synthesis of 1,4-Disubstituted 1,2,3-Triazoles from Azidoformates and Aryl Terminal Alkynes

The copper­(I)-catalyzed azide–alkyne cycloaddition reaction has been extensively studied and widely applied in organic synthesis. However, the formation of 1,2,3-triazoles with electron-deficient azide has been a challenging problem. In this report, we have demonstrated the formation of regioselective 1,4-disubstituted 1,2,3-triazoles from various types of aryl terminal alkynes and azidoformates, which are electron-deficient azides, using a commercialized [Cu­(CH₃CN)₄]­PF₆ copper­(I) catalyst under mild conditions

Copper(I)-Catalyzed Synthesis of 1,4-Disubstituted 1,2,3-Triazoles from Azidoformates and Aryl Terminal Alkynes

The copper­(I)-catalyzed azide–alkyne cycloaddition reaction has been extensively studied and widely applied in organic synthesis. However, the formation of 1,2,3-triazoles with electron-deficient azide has been a challenging problem. In this report, we have demonstrated the formation of regioselective 1,4-disubstituted 1,2,3-triazoles from various types of aryl terminal alkynes and azidoformates, which are electron-deficient azides, using a commercialized [Cu­(CH₃CN)₄]­PF₆ copper­(I) catalyst under mild conditions