M13 bacteriophage-based colorimetric sensors, especially multi-array sensors, have been successfully demonstrated to be a powerful platform for detecting extremely small amounts of target molecules. Colorimetric sensors can be fabricated easily using self-assembly of genetically engineered M13 bacteriophage which incorporates peptide libraries on its surface. However, the ability to discriminate many types of target molecules is still required. In this work, we introduce a statistical method to efficiently analyze a huge amount of numerical results in order to classify various types of target molecules. To enhance the selectivity of M13 bacteriophage-based colorimetric sensors, a multi-array sensor system can be an appropriate platform. On this basis, a pattern-recognizing multi-array biosensor platform was fabricated by integrating three types of sensors in which genetically engineered M13 bacteriophages (wild-, RGD-, and EEEE-type) were utilized as a primary building block. This sensor system was used to analyze a pattern of color change caused by a reaction between the sensor array and external substances, followed by separating the specific target substances by means of hierarchical cluster analysis. The biosensor platform could detect drug contaminants such as hormone drugs (estrogen) and antibiotics. We expect that the proposed biosensor system could be used for the development of a first-analysis kit, which would be inexpensive and easy to supply and could be applied in monitoring the environment and health care.

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Particulate matter (PM) in buildings are mostly sourced from the transport of outdoor particles through a heating, ventilation, and air conditioning (HVAC) system and generation of particle within the building itself. We investigated the concentrations and characteristic of indoor and outdoor particles and airborne bacteria concentrations across four floors of a building located in a high-traffic area. In all the floors we studied (first, second, fifth, and eighth), the average concentrations of particles less than 10 μm (PM10) in winter for were higher than those in summer. On average, a seasonal variation in the PM10 level was found for the first, fifth, and eighth floors, such that higher values occurred in the winter season, compared to the summer season. In addition, in winter, the indoor concentrations of PM10 on the first, fifth, and eighth floors were higher than those of the outdoor PM10. The maximum level of airborne bacteria concentration was found in a fifth floor office, which held a private academy school consisting of many students. Results indicated that the airborne bacteria remained at their highest concentration throughout the weekday period and varied by students' activity. The correlation coefficient (R (2)) and slope of linear approximation for the concentrations of particulate matter were used to evaluate the relationship between the indoor and outdoor particulate matter. These results can be used to predict both the indoor particle levels and the risk of personal exposure to airborne bacteria.

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