Investigation of a Complex Reaction Network: II. Kinetics, Mechanism and Parameter Estimation

Conventional Strategies for Discrimination of Intrinsic and Apparent Kinetics from Crushed- and Whole-Catalyst-Pellet Experimental Data, Respectively, Do Not Yield Satisfactory Results for the Reaction Network in the Manufacture of Α-Aminomethyl-2-Furanmethanol (Aminoalcohol) from Α-Nitromethyl-2-Furanmethanol (Nitroalcohol). Laboratory Trickle-Bed Reactor Tests in the Range of Concentration and Product Yield of Commercial Interest Are Utilized to Yield a Reasonable Set of Kinetic Parameters, Which Are Otherwise Unobtainable. This is Accomplished by Proposing a Reaction Network, a Plausible Mechanism, and Optimizing the Kinetic Parameters based on the Reactor-Model-Generated Performance Data to Fit Experimental Output Concentrations of All Species for the Entire Set of Experiments. a Complex Reaction Network for the Key Reactions in the System is Developed based on the Reaction Scheme in Part I. Fitting of Trickle-Bed Reactor Data to This Model is Attempted to Yield an Insight into the Actual Kinetics. the Results Show Promise of Obtaining an overall Network Kinetic Model, Even with the Limited Data Available

Investigation of a Complex Reaction Network: I. Experiments in a High-Pressure Trickle-Bed Reactor

A High-Pressure Trickle-Bed Reactor Was Used to Achieve High Productivity and Selectivity for the Manufacture of a Key Herbicide Intermediate (Α-Aminomethyl-2-Furanmethanol (Amino Alcohol, AA) from Α-Nitromethyl-2-Furanmethanol (Nitro Alcohol, NA). Raney Nickel Catalysts of Varying Activity Were Prescreened for Suitability in Trickle-Bed Operation. the Effect of Operating Parameters Such as Reactant Feed Concentration, Liquid Mass Velocity, and Temperature on the Yield of Amino Alcohol (AA) for RNi-A Are Discussed. the Superiority of Trickle-Bed Reactors over Others Such as Semibatch and Batch Slurry Systems is Demonstrated. the AA Yield Increases with Decrease in Feed Reactant Concentration and Liquid Mass Velocity, as Well as with Lowering of the Operating Temperature. a Maximum Product Yield of 90.1% Was Obtained at 8.3 Wt. % Feed Concentration of Nitroalcohol (NA), While at the Highest Feed Concentration of 40 Wt. % NA, the Maximum Product Yield Was 58%. the Volumetric Productivity of AA Was Significantly Higher at Higher Reactant Feed Concentrations, Even Though the Yield Was Lower under These Conditions. the Operating Temperature Was Also an Important Parameter, with a Lower Temperature Being Preferable for Trickle-Bed Experiments. Bed Dilution with Inert Fines Improved Catalyst Utilization and Increased the AA Yield and Productivity in the Laboratory-Scale Trickle-Bed Reactor

Optimization and Evaluation of Antiparasitic Benzamidobenzoic Acids as Inhibitors of Kinetoplastid Hexokinase 1

Kinetoplastid-based infections are neglected diseases that represent a significant human health issue. Chemotherapeutic options are limited due to toxicity, parasite susceptibility, and poor patient compliance. In response, we studied a molecular-target-directed approach involving intervention of hexokinase activity—a pivotal enzyme in parasite metabolism. A benzamidobenzoic acid hit with modest biochemical inhibition of Trypanosoma brucei hexokinase 1 (TbHK1, IC50=9.1 μm), low mammalian cytotoxicity (IMR90 cells, EC50>25 μm), and no appreciable activity on whole bloodstream-form (BSF) parasites was optimized to afford a probe with improved TbHK1 potency and, significantly, efficacy against whole BSF parasites (TbHK1, IC50=0.28 μm; BSF, ED50=1.9 μm). Compounds in this series also inhibited the hexokinase enzyme from Leishmania major (LmHK1), albeit with less potency than toward TbHK1, suggesting that inhibition of the glycolytic pathway may be a promising opportunity to target multiple disease-causing trypanosomatid protozoa

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as Mitchell’s, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present two sets of experiments testing human comprehensibility of logic programs. In the first experiment we test human comprehensibility with and without predicate invention. Results indicate comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. In the second experiment we directly test whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in humans trials. Results show participants were not able to learn the relational concept on their own from a set of examples but they were able to apply the relational definition provided by the ILP system correctly. This implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. We believe improved understanding of this class could have potential relevance to contexts involving human learning, teaching and verbal interaction