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P450 BM3 (CYP102A1) from Bacillus megaterium catalyzes the subterminal hydroxylation of fatty acids with 12-22 carbons at the ω-1, ω-2 and ω-3 positions. Several amino acids located at the substrate channel and active sites are known to be important for the catalytic activity of CYP102A1. The L188 residue at the C-terminus of α-helix F undergoes a large shift upon substrate binding and has frequently been found in different combinations of multiple mutations showing enhanced and altered activities. In this study, we examined the role of the L188 residue by comparing the catalytic activities of wild-type CYP102A1 and 19 mutants of L188. The mutants were made with site-directed mutagenesis and functionally expressed in Escherichia coli. The enzymatic properties of the mutants for a set of fatty acids (C10-C16) were compared to the properties of the wild-type. L188Q and L188 P mutants showed especially strong increases in hydroxylase activity toward C10-C13 fatty acids, although they did not have activity changes for C14-C16 fatty acids. Although most mutants showed very similar overall hydroxylation rates for myristic acid, 14 mutants showed apparent changes in the regioselectivity of hydroxylation with a preference for the ω-3 position over the ω-1 position. A possible role for the L188 residue has been discussed in the context of the structure and function of CYP102A1.

To plan successful activity-based workplaces (ABW), architects need to clearly understand user-specific activity patterns through the accurate recognition of user activity. Because user activity is closely associated with space, equipment, and users, such diverse activity-related information should be essentially considered for accurate activity recognition. However, previous activity recognition methods have limitations for accurately recognizing user activity for ABW planning, because they only relied on sensor-estimated data and are, therefore, unable to comprehensively consider diverse activity-related information. The study thus integrates site investigation and sensor estimation using a Bluetooth Low Energy beacon and accelerometer for accurately recognizing user activity based on diverse activity-related information. We defined five important items of activity-related information (user actions, number of nearby users, function of space, equipment located in space, and space use policy) and developed a user-specific activity pattern generation (UAPG) framework consisting of three stages: (1) the preparation stage, (2) sensor-estimation stage, and (3) activity pattern generation stage. The demonstration was conducted through scenario-based experiments in an academic office building. In the demonstration, the UAPG framework achieved 91.8% of activity recognition accuracy and successfully generated user-specific activity patterns. In addition, information regarding space usage, proportion of activities, and spatial preference of the user was generated based on a user-specific activity pattern. Such objective information provided by the UAPG framework enables evidence-based ABW planning that efficiently accommodates users with minimal office space, while simultaneously increasing their satisfaction and productivity.

Accurate face-to-face interaction estimation is required for a successful data-driven design in workplaces. In previous studies, various sensor-based interaction estimation methods which use proximity and speaking data have been developed. However, these data alone cannot confirm the presence of interactions because non-interacting users also engage in speaking activities. This study aims to develop a novel turn-taking pattern-based interaction estimation (i.e., TIE) framework that integrates turn-taking with location data. The framework estimates interactions in three steps: 1) co-location estimation using a Bluetooth Low Energy beacon; 2) speaking-turn ascertainment through volume-based speaker identification; and 3) interaction group recognition based on turn-taking pattern analysis. Using three different experimental scenarios, the interaction estimation accuracy of the framework was demonstrated to be 77.7%. In the absence of co-location estimation errors, the interaction estimation accuracy increases to 95.5%. The demonstration results indicate that the TIE framework has potential for accurate interaction estimation in workplaces.

Two NADPH-cytochrome P450 reductase (CPR) genes (CaCPR1 and CaCPR2) were isolated from hot pepper (Capsicum annuum L. cv. Bukang). At the red ripe stage, the expression level of CaCPR1 was more than 6-fold greater than that in leaves or flowers. It gradually increased during fruit ripening. The CaCPR2 gene seemed to be expressed constitutively in all of the tested tissues. To investigate the enzymatic properties of CaCPR1, the cDNA of CaCPR1 was heterologously expressed in Escherichia coli without any modification of amino acid sequences, and CaCPR1 was purified. The enzymatic properties of CaCPR1 were confirmed using cytochrome c and cytochrome b5 as protein substrates. The CaCPR1 could support human CYP1A2-catalyzed reaction. It also reduced tetrazolium salts and ferricyanide. These results show that CaCPR1 is the major CPR in most hot pepper tissues. It is suggested that the CaCPR1 can be used a prototype for studying biological functions and biotechnological applications of plant CPRs.