I. ³¹P and ¹³C Nuclear Magnetic Resonance Studies of Nicotinamide Adenine Denucleotide and Related Compounds. II. ¹⁹F Nuclear Magnetic Resonance Studies of Rabbit Muscle Glyceraldehyde-3-Phosphate Dehydrogenase Covalently Labeled with a Trifluoromethyl Group

The 31P spectra of the reduced and oxidized forms of nicotinamide adenine dinucleotides as well as several related mono- and dinucleotides have been obtained and analyzed. From the spectral differences between the reduced and oxidized nucleotides, as well as from the determination of the pKa values of the phosphate group in the mononucleotides, it is postulated that in the oxidized nucleotides there is an electrostatic interaction between the positively charged nitrogen of the pyridine ring and a negatively charged oxygen of the diphosphate backbone. The natural abundance 13C spectra of nicotinamide adenine dinucleotide and related compounds have been recorded and assigned. These spectra yielded further evidence for the nicotinamide-phosphate interaction in oxidized nucleotides. Small effects on the 13C spectra were observed upon changes in pH or nucleotide concentration. Rabbit muscle glyceraldehyde-3-phosphate dehydrogenase has been reacted with bromotrifluoroacetone, leading to a modified protein containing a trifluoromethyl group at each of its four active site cysteines. Addition of NAD+ or NADH caused changes in the 19F spectrum arising from the bound trifluoromethyl group. Also changes in pH affected the 19F spectrum. At high pH, two resonances were observed, leading to the conclusion that the enzyme, which is composed of four subunits having identical amino acid sequences, exists as an α2α2' protein. The chemical shift changes observed in this study (up to 19 parts per million) are considerably larger than those previously observed in 19F nuclear magnetic resonance studies of proteins. Measurements of the spin-spin and spin-lattice relaxation times of the enzyme bound trifluoromethyl group have been performed. It has been shown that the bound label has very little mobility, and interacts with solvent protons, as well as protons on other residues of the protein.</p

Extraction of Dynamic Trajectory on Multi-Stroke Static Handwriting Images Using Loop Analysis and Skeletal Graph Model

The recovery of handwriting’s dynamic stroke is an effective method to help improve the accuracy of any handwriting’s authentication or verification system. The recovered trajectory can be considered as a dynamic feature of any static handwritten images. Capitalising on this temporal information can significantly increase the accuracy of the verification phase. Extraction of dynamic features from static handwritings remains a challenge due to the lack of temporal information as compared to the online methods. Previously, there are two typical approaches to recover the handwriting’s stroke. The first approach is based on the script’s skeleton. The skeletonisation method has highly computational efficiency whereas it often produces noisy artifacts and mismatches on the resulted skeleton. The second approach deals with the handwriting’s contour, crossing areas and overlaps using parametric representations of lines and thickness of strokes. This method can avoid the artifacts, but it requires complicated mathematical models and may lead to computational explosion. Our paper is based on the script’s extracted skeleton and provides an approach to processing static handwriting’s objects, including edges, vertices and loops, as the important aspects of any handwritten image. Our paper is also to provide analysing and classifying loops types and human’s natural writing behavior to improve the global construction of stroke order. Then, a detailed tracing algorithm on global stroke reconstruction is presented. The experimental results reveal the superiority of our method as compared with the existing ones

Terjunan emas Jun Hoong - Pandelela: gangsa turut menjadi milik kontinjen negara acara 10m seirama

Abstract-Similar to many other pattern recognition problems, feature extraction contributes significantly to the overall performance of an off-line signature verification system. To be successful, a feature extraction technique must be tolerant to different types of variation whilst preserving essential information of input patterns. In this paper, we describe a grid-based feature extraction technique that utilises directional information extracted from the signature contour, i.e. the chain code histogram. Our experimental results for signature verification indicated that, by applying a suitable 2D Gaussian filter on the matrices containing the chain code histograms, an average error rate (AER) of 13.90% can be obtained whilst maintaining the false acceptance rate (FAR) for random forgeries as low as 0.02%. These figures are comparable or better than those reported by other state of the art feature extraction techniques such as the Modified Direction Feature (MDF) and the Gradient feature.Griffith Sciences, School of Information and Communication TechnologyNo Full Tex

Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline

For time series classification task using 1D-CNN, the selection of kernel size is critically important to ensure the model can capture the right scale salient signal from a long time-series. Most of the existing work on 1D-CNN treats the kernel size as a hyper-parameter and tries to find the proper kernel size through a grid search which is time-consuming and is inefficient. This paper theoretically analyses how kernel size impacts the performance of 1D-CNN. Considering the importance of kernel size, we propose a novel Omni-Scale 1D-CNN (OS-CNN) architecture to capture the proper kernel size during the model learning period. A specific design for kernel size configuration is developed which enables us to assemble very few kernel-size options to represent more receptive fields. The proposed OS-CNN method is evaluated using the UCR archive with 85 datasets. The experiment results demonstrate that our method is a stronger baseline in multiple performance indicators, including the critical difference diagram, counts of wins, and average accuracy. We also published the experimental source codes at GitHub (https://github.com/Wensi-Tang/OS-CNN/)

Investigating the Feasibility of a 3D Virtual World Technology for People with Dementia

Three Dimensional Virtual Worlds (3DVWs) are computer-generated, simulated, graphical and multimedia environments, designed so that users can ‘live in’ and engage via their own digital and graphical self-representations known as ‘avatars’. The purpose of this study was to evaluate the feasibility of using 3DVWs to enhance engagement and quality of life in people with dementia. A mixed-methods research design, guided by a feasibility framework, was used, with data collected from semi-structured interviews, observations, and surveys. Eleven residents expressed interest in the 3DVWs intervention after reading an advertisement and attended an introductory session. After this, eight people expressed a desire to participate in the six-session intervention. Participants generally enjoyed the experience of using 3DVWs. Of those who completed all six sessions, two-thirds showed a positive change in their quality of life score. Moreover, those who participated in almost all sessions showed higher satisfaction with the use of the 3DVW than those who dropped out. Both residents and care staff perceived the 3DVW as engaging, fun and memory stimulating. The findings support the feasibility of using 3DVWs with people with dementia, and this justifies the need for further research

Designing a Blockchain-Based Digital Twin for Cyber-Physical Production Systems

Trust in all processes on the shopfloor is crucial for the success of a production process, especially in cross company scenarios such as shared manufacturing, in which independent parties interact with each other. A cyber-physical production system (CPPS) contributes to the vision of a decentralized, self-configuring and flexible production. Digital twins (DTs) can visualize the material, information and financial flows in real time and improve the process transparency of such production systems. The efficiency of digital twins depends on the integrity of the provided data, especially if data is shared across company borders. Due to its characteristics such as immutability and transparency, blockchain technology (BCT) provides a basis for establishing the desired trust in the systems on the shopfloor. This paper proposes the design of a BCT-based DT in CPPS. The design is demonstrated by a prototype including smart contracts attached to a CPPS simulation model visualizing the information and material flow. Tasks are decentrally allocated, deployed and safely documented via blockchain. The demonstrator is revealing supplementary benefits in terms of transparency provided by the BCT. This paper further examines whether BCT can enrich existing solutions and provide a reliable information basis for profound data and process analysis