Revealing substrate-induced structural changes in active site of human CYP51 in the presence of its physiological substrates.

The human sterol 14α-demethylases (CYP51, CYP is an abbreviation for cytochrome P450) catalyze three-step oxidative removal of 14α-methyl group of lanosterol by first forming an alcohol, then an aldehyde, and finally conducting a CC bond cleavage reaction. This present study utilizes a combination of Resonance Raman spectroscopy and Nanodisc technology to probe the active site structure of CYP51 in the presence of its hydroxylase and lyase substrates. Ligand-binding induced partial low-to-high-spin conversion is observed by applying electronic absorption spectroscopy and Resonance Raman (RR) spectroscopy. This low degree of spin conversion of CYP51 is contributed by the retention of the water ligand coordinated to the heme iron as well as direct interaction between the hydroxyl group of lyase substrate and the iron center. No significant changes in active site structure are found between detergent-stabilized CYP51 and nanodisc-incorporated CYP51, nevertheless, it is demonstrated that nanodisc-incorporated assemblies provide much more well-defined active site RR spectroscopic responses, which induces a larger conversion from low-to-high-spin state in presence of the substrates. Moreover, a positive polar environment around the exogenous diatomic ligand is detected, providing insight into the mechanism of this essential CC bond cleavage reaction.

Identifying spatiotemporal information of the point pollutant source indoors based on the adjoint-regularization method.

Fast and accurate identification of the pollutant source location and release rate is important for improving indoor air quality. From the perspective of public health, identification of the airborne pathogen source in public buildings is particularly important for ensuring people's safety and health. The existing adjoint probability method has difficulty in distinguishing the temporal source, and the optimization algorithm can only analyze a few potential sources in space. This study proposed an algorithm combining the adjoint-pulse and regularization methods to identify the spatiotemporal information of the point pollutant source in an entire room space. We first obtained a series of source-receptor response matrices using the adjoint-pulse method in the room based on the validated CFD model, and then used the regularization method and composite Bayesian inference to identify the release rate and location of the dynamic pollutant source. The results showed that the MAPEs (mean absolute percentage errors) of estimated source intensities were almost less than 15%, and the source localization success rates were above 25/30 in this study. This method has the potential to be used to identify the airborne pathogen source in public buildings combined with sensors for disease-specific biomarkers.

Propagation and Diffusion of Fluorescent Substances with Footprints in Indoor Environments.

Some studies have shown that contaminants can be transferred between floors and the soles, and there are few studies on pollutant propagation caused by human walking in real-life situations. This study explored the propagation and diffusion law of ground pollutants from rubber soles to poly vinyl chloride (PVC) floor during indoor walking through employing a fluorescent solution as a simulant. The footprint decay (D) and transfer efficiency (τ) of the fluorescent solution transferred from the sole to the indoor floor during walking were analyzed based on the fluorescent footprint imaging. The effects of namely body weight (50-75 kg), walking frequency (80-120 steps/min), and solution viscosity (oil and water) were also investigated. It was found that the total fluorescence gray value on the ground decreased exponentially as the number of walking steps (i) increased. The relationship between the normalized gray value of the fluorescent solution (D) on each floor panel i was Di=aebi,2.1≤a≤3.8,-1.4≤b≤-0.7, and τ was distributed in the range of 0.51-0.72. All influencing factors had a significant effect on a, and a greater body weight resulted in a smaller a value, while only the body weight had a significant effect on b and τ, and a greater body weight led to larger b and lower τ values.

Estimation of pollutant sources in multi-zone buildings through different deconvolution algorithms.

Effective identification of pollution sources is particularly important for indoor air quality. Accurate estimation of source strength is the basis for source effective identification. This paper proposes an optimization method for the deconvolution process in the source strength inverse calculation. In the scheme, the concept of time resolution was defined, and combined with different filtering positions and filtering algorithms. The measures to reduce effects of measurement noise were quantitatively analyzed. Additionally, the performances of nine deconvolution inverse algorithms under experimental and simulated conditions were evaluated and scored. The hybrid algorithms were proposed and compared with single algorithms including Tikhonov regularization and iterative methods. Results showed that for the filtering position and algorithm, Butterworth filtering performed better, and different filtering positions had little effect on the inverse calculation. For the calculation time step, the optimal Tr (time resolution) was 0.667% and 1.33% in the simulation and experiment, respectively. The hybrid algorithms were found to not perform better than the single algorithms, and the SART (simultaneous algebraic reconstruction technique) algorithm from CAT (computer assisted tomography) yielded better performances in the accuracy and stability of source strength identification. The relative errors of the inverse calculation for source strength were typically below 25% using the optimization scheme.