Abstract: The present invention relates generally to materials and methods for detection of bacteria, and for testing and determination of antibiotic susceptibility of bacteria in specimens of bodily fluid and other samples. The invention also relates to materials and methods for monitoring the physiological response of bacteria to antimicrobial agents, and for reducing background and increasing sensitivity of assays that involve the detection and/or measurement of RNA, such as rRNA. The invention provides kits comprising an RNase packaged for use in the methods described herein.

Abstract: A method for qualitative and/or quantitative characterization of a coating surface is provided, comprising: providing a program recognizing coating surface defect types; determining, by the program, whether a camera(s) coupled to the program is within a predefined distance range and/or within a predefined image acquisition angle range relative to a currently presented coating surface; depending on the determination: generating a feedback signal indicative of whether adjustment of the position of the camera(s) is within predefined distance range and/or within the predefined image acquisition angle range, and/or automatically adjusting the relative distance of the camera and and/or automatically adjusting the angle of the camera; enabling the camera to acquire an image of the coating surface only when the camera(s) is/are within the predefined distance range and/or image acquisition angle range; processing the digital image for recognizing coating surface defects; and outputting a characterization of the

Abstract: The invention relates to a method for providing a coating composition-related prediction program, the method comprising: providing a database (204, 904) comprising associations of qualitative and/or quantitative characterizations of coating surfaces and one or more parameters; training a machine learning model for providing a predictive model (M2, M3) having learned to correlate qualitative and/or quantitative characterizations of one or more coating surfaces with one or more of the parameters; and providing a composition-quality-prediction program configured for using the predictive model (M2) for predicting the properties of a coating surface to be produced from one or more input parameters; and/or providing a composition-specification-prediction program configured for using the predictive model (M3) for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to a coating composition predicted to generate a coating surface having