SERS spectra of 12 bacterial strains of urinary tract infection (UTI) clinical isolates grown and enriched from urine are reported. A partial least squares-discriminant analysis (PLS-DA) classification treatment of these SERS spectra results in strain level identification with >95% sensitivity and >99% specificity. The classification model successfully identified the SERS spectra of a urine-cultured strain not used to build this statistical model. Enrichment was accomplished by a filtration and centrifugation protocol. The predetermined drug susceptibility profiles of these clinical isolates thus allowed the SERS methodology to provide appropriate UTI antibiotic information in less than 1 h. Most of this time was used for sample preparation procedures (enrichment and washing) for this proof of principle study. SERS spectra of the enriched bacterial samples are dominated by nucleotide degradation metabolites: adenine, hypoxanthine, xanthine, guanine, uric acid, AMP, and guanosine. Strain-specific specificity is due to the different relative amounts of these purines contributing to the corresponding SERS spectra of these clinical isolates. All measurements were made at the minimal bacterial concentration in urine for UTI diagnosis (10 cfu/mL). Graphical abstract The relative contribution of each of the seven purines found to contribute to the bacterial SERS spectra are summarized in this bar graph. Although strain specific differences are evident, it can be see how the pattern of contributing purines is more different between the four species than between strains of a given species.

We continuously interact with computerized systems to achieve goals and perform tasks in our personal and professional lives. Therefore, the ability to program such systems is a skill needed by everyone. Consequently, computational thinking skills are essential for everyone, which creates a challenge for the educational system to teach these skills at scale and allow students to practice these skills. To address this challenge, we present a novel approach to providing formative feedback to students on programming assignments. Our approach uses dynamic evaluation to trace intermediate results generated by student's code and compares them to the reference implementation provided by their teachers. We have implemented this method as a Python library and demonstrate its use to give students relevant feedback on their work while allowing teachers to challenge their students' computational thinking skills.

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