Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Control theory & Artificial neural network. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Role of the carbohydrate moieties in chiral recognition on teicoplanin-based LC stationary phases.

Abstract: For this study, we used the macrocyclic antibiotic teicoplanin, a molecule consisting of an aglycone peptide "basket" with three attached carbohydrate (sugar) moieties. The sugar units were removed and the aglycone was purified. Two chiral stationary phases (CSPs) were prepared in a similar way, one with the native teicoplanin molecule and the other with the aglycone. Twenty-six compounds were evaluated on the two CSPs with seven RPLC mobile phases and two polar organic mobile phases. The compounds were 13 amino acids or structurally related compounds (including DOPA, folinic acid, etc.) and 13 other compounds (such as carnitine, bromacil, etc.). The chromatographic results are given as the retention, selectivity, and resolution factors along with the peak efficiency and the enantioselective free energy difference corresponding to the separation of the two enantiomers. The polarities of the two CSPs are similar. It is clearly established that the aglycone is responsible for the enantioseparation of amino acids. The difference in enantioselective free energy between the aglycone CSP and the teicoplanin CSP was between 0.3 and 1 kcal/mol for amino acid enantioseparations. This produced resolution factors 2-5 times higher with the aglycone CSP. Four non amino acid compounds were separated only on the teicoplanin CSP. Six and five compounds were better separated on the teicoplanin and aglycone CSPs, respectively. Although the sugar units decrease the resolution of alpha-amino acid enantiomers, they can contribute significantly to the resolution of a number of non amino acid enantiomeric pairs.

CNN-based image analysis for malaria diagnosis

Abstract: Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and the diagnosis depends on the experience and the knowledge of the person doing the examination. Automatic image recognition technologies based on machine learning have been applied to malaria blood smears for diagnosis before. However, the practical performance has not been sufficient so far. This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected. In a ten-fold cross-validation based on 27,578 single cell images, the average accuracy of our new 16-layer CNN model is 97.37%. A transfer learning model only achieves 91.99% on the same images. The CNN model shows superiority over the transfer learning model in all performance indicators such as sensitivity (96.99% vs 89.00%), specificity (97.75% vs 94.98%), precision (97.73% vs 95.12%), F1 score (97.36% vs 90.24%), and Matthews correlation coefficient (94.75% vs 85.25%).

Pt−Ru Nanoparticles Supported on Carbon Nanotubes as Methanol Fuel Cell Catalysts

Abstract: Bimetallic Pt−Ru alloy catalysts have been demonstrated to be more active than pure Pt catalysts in the electrooxidation of methanol. We report here a study on Pt−Ru nanoparticle catalysts supported on sonochemically functionalized carbon nanotubes. The catalysts were prepared by directly reducing the corresponding salts, K2PtCl4 and K2RuCl5, in an ethylene glycol aqueous solution containing dispersed carbon nanotubes. Three catalysts of different Pt to Ru atomic ratios, namely, Pt53Ru47, Pt69Ru31, and Pt77Ru23, were prepared for investigation of the compositional effects. It was shown that highly dispersed bimetallic Pt−Ru alloy nanoparticles with no agglomeration can be synthesized on the carbon nanotubes with average particle sizes of less than 3.0 nm in diameter. The Pt−Ru nanoparticles are uniform and cover only the outside of the carbon nanotubes. It was found that the polyol process produced alloy compositions that are not consistent with the metal ratios in the precursors. It was also found that t...

How Generic Scale Invariance Influences Quantum and Classical Phase Transitions

Abstract: This review discusses a paradigm that has become of increasing importance in the theory of quantum phase transitions, namely, the coupling of the order-parameter fluctuations to other soft modes and the resulting impossibility of constructing a simple Landau-Ginzburg-Wilson theory in terms of the order parameter only. The soft modes in question are manifestations of generic scale invariance, i.e., the appearance of long-range order in whole regions in the phase diagram. The concept of generic scale invariance and its influence on critical behavior is explained using various examples, both classical and quantum mechanical. The peculiarities of quantum phase transitions are discussed, with emphasis on the fact that they are more susceptible to the effects of generic scale invariance than their classical counterparts. Explicit examples include the quantum ferromagnetic transition in metals, with or without quenched disorder; the metal-superconductor transition at zero temperature; and the quantum antiferromagnetic transition. Analogies with classical phase transitions in liquid crystals and classical fluids are pointed out, and a unifying conceptual framework is developed for all transitions that are influenced by generic scale invariance.