Comparative in vitro studies of the metabolism of six 4-substituted methamphetamines and their inhibition of cytochrome P450 2D6 by GC-MS with trifluoroacetyl derivatization.

Use of new amphetamine-type stimulants (ATS) as designer drugs is a serious problem worldwide. ATS are used in tablet, capsule, and powder forms, and can be mixed with other drugs. There is little information available on how these new drugs are metabolized or their ability to inhibit the metabolism of co-administered drugs. This study aimed to investigate the metabolism of six 4-substituted analogs of methamphetamine (MA), and their potential inhibition of MA metabolism. The metabolism of MA and the 4-substituted MAs was examined in vitro using human metabolic enzymes. Metabolite analyses were performed using trifluoroacetyl derivatization and GC-MS. The experiments showed that cytochrome P450 2D6 (CYP2D6) was involved in the major metabolic pathway of MA, where it catalyzed N-demethylation of 4-fluoromethamphetamine (4-FMA), 4-chloromethamphetamine (4-CMA), 4-bromomethamphetamine (4-BMA), 4-iodomethamphetamine (4-IMA) and 4-nitromethamphetamine (4-NMA), and O-demethylation of 4-methoxymethamphetamine (4-MMA). The half maximal inhibitory concentration (IC50) values for CYP2D6 using MA as substrate were different for each of the 4-substituted MAs. The strongest inhibitors of amphetamine production from MA were, in order, 4-IMA, 4-BMA, 4-CMA, 4-MMA, 4-FMA, and 4-NMA. The same order was observed for the IC50 values for inhibition of p-hydroxymethamphetamine production from MA, except for the IC50 of 4-MMA. The IC50 values of 4-IMA were lower than the IC50 values of fluoxetine and higher than that of quinidine. The results of this study imply that the risk of illicit drug interactions fluctuates so widely that unintentional fatal drug poisonings could occur.学位論文題目 : Study on forensic drug testing of methamphetamines and related compounds by GC-MS with trifluoroacetyl derivatization.滋賀医科大学平成25年

Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning

Self-supervised learning (SSL) using mixed images has been studied to learn various image representations. Existing methods using mixed images learn a representation by maximizing the similarity between the representation of the mixed image and the synthesized representation of the original images. However, few methods consider the synthesis of representations from the perspective of mathematical logic. In this study, we focused on a synthesis method of representations. We proposed a new SSL with mixed images and a new representation format based on many-valued logic. This format can indicate the feature-possession degree, that is, how much of each image feature is possessed by a representation. This representation format and representation synthesis by logic operation realize that the synthesized representation preserves the remarkable characteristics of the original representations. Our method performed competitively with previous representation synthesis methods for image classification tasks. We also examined the relationship between the feature-possession degree and the number of classes of images in the multilabel image classification dataset to verify that the intended learning was achieved. In addition, we discussed image retrieval, which is an application of our proposed representation format using many-valued logic.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Visual Exploration System for Analyzing Trends in Annual Recruitment Using Time-varying Graphs

Annual recruitment data of new graduates are manually analyzed by human resources specialists (HR) in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Every year, different applicants send in job applications to companies. The relationships between applicants' attributes (e.g., English skill or academic credential) can be used to analyze the changes in recruitment trends across multiple years' data. However, most attributes are unnormalized and thus require thorough preprocessing. Such unnormalized data hinder the effective comparison of the relationship between applicants in the early stage of data analysis. Thus, a visual exploration system is highly needed to gain insight from the overview of the relationship between applicants across multiple years. In this study, we propose the Polarizing Attributes for Network Analysis of Correlation on Entities Association (Panacea) visualization system. The proposed system integrates a time-varying graph model and dynamic graph visualization for heterogeneous tabular data. Using this system, human resource specialists can interactively inspect the relationships between two attributes of prospective employees across multiple years. Further, we demonstrate the usability of Panacea with representative examples for finding hidden trends in real-world datasets and then describe HR specialists' feedback obtained throughout Panacea's development. The proposed Panacea system enables HR specialists to visually explore the annual recruitment of new graduates

Learning Compliant Stiffness by Impedance Control-Aware Task Segmentation and Multi-objective Bayesian Optimization with Priors

Rather than traditional position control, impedance control is preferred to ensure the safe operation of industrial robots programmed from demonstrations. However, variable stiffness learning studies have focused on task performance rather than safety (or compliance). Thus, this paper proposes a novel stiffness learning method to satisfy both task performance and compliance requirements. The proposed method optimizes the task and compliance objectives (T/C objectives) simultaneously via multi-objective Bayesian optimization. We define the stiffness search space by segmenting a demonstration into task phases, each with constant responsible stiffness. The segmentation is performed by identifying impedance control-aware switching linear dynamics (IC-SLD) from the demonstration. We also utilize the stiffness obtained by proposed IC-SLD as priors for efficient optimization. Experiments on simulated tasks and a real robot demonstrate that IC-SLD-based segmentation and the use of priors improve the optimization efficiency compared to existing baseline methods.Comment: Accepted to IROS202