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Project-based learning (PjBL) has been increasingly adopted in computer science courses to improve students’ engagement and learning outcomes. Although a computer networks course is in great need of a PjBL course module, no such module is available due to the huge gap between PjBL’s design requirements and the current structure and content of the course. This paper introduces a novel PjBL module for a computer networks course, which challenges the students with a real world problem of developing the communication system for a smart lock. Following the PjBL design principles, we devise several scaffolding activities and assignments, which can be integrated into a semester-long computer networks course. We test ran the PjBL module in both undergraduate- and graduate-level computer networks courses. Our preliminary evaluation results show that the proposed PjBL module is well received by the students and helps improve their learning outcomes. 

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download atwww.gitlab.com/treangenlab/seqscreen.