Supramolecular inclusion complex formation and application of - cyclodextrin with heteroanthracene ring cationic dyes, Anal

Abstract -Cyclodextrin forms 1:1 inclusion complexes with methylene blue, azure A, toluidine blue, resorcinol blue, neutral red, safranine T, indigo carmine and acridine orange in aqueous media. The formation constants are determined by differential pulse polarography and spectrophotometry. The supramolecular interaction in the inclusion complexes can be employed to immobilize dyes on an electrode. This gives high sensitivity and stable electrochemical behavior for H 2 O 2 detection at the mmol l À1 level by means of the supramolecular interaction between -CD and dye molecules.

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.Comment: 27 pages, 17 figure

Determination of Active Components in Rhubarb by Cyclodextrin-modified Capillary Zone Electrophoresis

A cyclodextrin-modified capillary zone electrophoresis was developed for the determination of four bioactive components in Rhubarb. Effects of pH, b-CD concentration and organic modifier content on the migration and separation of the components were studied. The four components were separated in the running buffer of 50 mmol/L NaOHH3BO3 (pH 10.7) containing 10 mmol/L b-CD and 12.5% v/v ethanol. The analytical performance of the method was tested with respect to linearity response, precision and recovery. The determination of two commercial Rhubarb samples under the optimized conditions showed a satisfactory result

Direct Electrochemistry of Glucose Oxidase at a Gold Electrode Modified with Single-Wall Carbon Nanotubes

The direct electrochemistry of glucose oxidase (GOD) was accomplished at a gold electrode modified with single-wall carbon nanotubes (SWNTs). A pair of welldefined redox peaks was obtained for GOD with the reduction peak potential at –0.465 V and a peak potential separation of 23 mV at pH 7.0. Both FT-IR spectra and the dependence of the reduction peak current on the scan rate revealed that GOD adsorbed onto the SWNT surfaces. The redox wave corresponds to the redox center of the flavin adenine dinucleotide(FAD) of the GOD adsorbate. The electron transfer rate of GOD redox reaction was greatly enhanced at the SWNT-modified electrode. The peak potential was shown to be pH dependent. Verified by spectral methods, the specific enzyme activity of GOD adsorbates at the SWNTs appears to be retained

Determination of Active Components in Rhubarb by Cyclodextrin-modified Capillary Zone Electrophoresis

Abstract: A cyclodextrin-modified capillary zone electrophoresis was developed for the determination of four bioactive components in Rhubarb. Effects of pH, β-CD concentration and organic modifier content on the migration and separation of the components were studied. The four components were separated in the running buffer of 50 mmol/L NaOH-H3BO3 (pH 10.7) containing 10 mmol/L β-CD and 12.5 % v/v ethanol. The analytical performance of the method was tested with respect to linearity response, precision and recovery. The determination of two commercial Rhubarb samples under the optimized conditions showed a satisfactory result

Direct electrochemistry of glucose oxidase at a gold electrode modified with single-wall carbon nanotubes

Abstract: The direct electrochemistry of glucose oxidase (GOD) was accomplished at a gold electrode modified with single-wall carbon nanotubes (SWNTs). A pair of welldefined redox peaks was obtained for GOD with the reduction peak potential at –0.465 V and a peak potential separation of 23 mV at pH 7.0. Both FT-IR spectra and the dependence of the reduction peak current on the scan rate revealed that GOD adsorbed onto the SWNT surfaces. The redox wave corresponds to the redox center of the flavin adenine dinucleotide(FAD) of the GOD adsorbate. The electron transfer rate of GOD redox reaction was greatly enhanced at the SWNT-modified electrode. The peak potential was shown to be pH dependent. Verified by spectral methods, the specific enzyme activity of GOD adsorbates at the SWNTs appears to be retained

Self-Attention ConvLSTM for Spatiotemporal Prediction

Spatiotemporal prediction is challenging due to the complex dynamic motion and appearance changes. Existing work concentrates on embedding additional cells into the standard ConvLSTM to memorize spatial appearances during the prediction. These models always rely on the convolution layers to capture the spatial dependence, which are local and inefficient. However, long-range spatial dependencies are significant for spatial applications. To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM. Specifically, a novel self-attention memory (SAM) is proposed to memorize features with long-range dependencies in terms of spatial and temporal domains. Based on the self-attention, SAM can produce features by aggregating features across all positions of both the input itself and memory features with pair-wise similarity scores. Moreover, the additional memory is updated by a gating mechanism on aggregated features and an established highway with the memory of the previous time step. Therefore, through SAM, we can extract features with long-range spatiotemporal dependencies. Furthermore, we embed the SAM into a standard ConvLSTM to construct a self-attention ConvLSTM (SA-ConvLSTM) for the spatiotemporal prediction. In experiments, we apply the SA-ConvLSTM to perform frame prediction on the MovingMNIST and KTH datasets and traffic flow prediction on the TexiBJ dataset. Our SA-ConvLSTM achieves state-of-the-art results on both datasets with fewer parameters and higher time efficiency than previous state-of-the-art method