Gonzaga University is a small, private, liberal arts institution that values engaging, humanistic learning environments. During the coronavirus pandemic, the University moved its in-person learning to a distance-learning format, and an extra week of spring break was given to faculty to oversee this transition. This communication focuses on how our introductory biochemistry lab course was modified for students to complete the final 6 weeks of the semester in the distance-learning format. The instructors worked collaboratively on this transition and focused on reassessing the learning objectives, finding creative solutions for students to experience laboratory techniques, and supporting and engaging our students. A survey administered to students at the end of the semester highlighted the strengths and weaknesses of both our in-person and distance-learning lab formats. This sudden change to course delivery in spring 2020 highlighted that we need to be prepared to teach under many unexpected scenarios. The student responses, along with faculty reflections, are being used to plan how our lab can be improved upon and delivered in a variety of modalities, including in-person, distancing-learning, and hybrid models.

Multiple-probe fluorescence imaging applications demand an ever-increasing number of resolvable probes, and the use of fluorophores with resolvable fluorescence lifetimes can help meet this demand. Green fluorescent protein (GFP) and its variants have been widely used in spectrally resolved multiprobe imaging, but as yet, there has not been a systematic set of mutants generated with resolvable lifetimes. Therefore, to generate such mutants, we have utilized error-prone PCR and fluorescence lifetime imaging to screen for mutants of UV-excited green fluorescent protein (GFPuv) that exhibit altered fluorescence decay lifetimes. This has resulted in the isolation of GFPuv mutants displaying at least three distinctly different lifetimes in the range of 1.9-2.8 ns. Mutation of Y145 to either histidine or cysteine was found to shift the fluorescence lifetime of GFPuv from 3.03 +/- 0.03 to 2.78 +/- 0.05 ns for the Y145H mutant and to 2.74 +/- 0.05 ns for Y145C. Some of the shorter-lifetime mutants exhibited excitation peaks that were red-shifted relative to their maximal absorption, indicating that the mutations allowed the adoption of additional conformations relative to wtGFPuv. The utility of these mutants for applications in simultaneous imaging and quantification is shown by the ability to quantify the composition of binary mixtures in time-resolved images using a single detector channel. The application of the screening method for generating lifetime mutants of other fluorescent proteins is also discussed.

The effectiveness of neural networks and the optimization of parameters for implementing neural networks were evaluated for use in the identification of single molecules according to their fluorescence lifetime. The best network architecture and training parameters were determined for both ideal and nonideal single-molecule fluorescence data. The effectiveness of the neural network is compared to that of the maximum likelihood estimator on the basis of its ability to correctly identify single molecules. For ideal single-molecule data, it was found that the neural networks and the maximum likelihood estimator perform approximately equally well. For nonideal single-molecule fluorescence data, neural networks were able to correctly identify a larger percentage of single-molecule events than the MLE method.