Search for: All records

Emotions are an integral part of human nature. Emotion awareness is critical to any form of interpersonal communication and collaboration, including these in the software development process. Recently, the SE community starts having growing interests in emotion awareness in software development. While researchers have accomplished many valuable results, most extant research ignores the dynamic nature of emotion. To investigate the emotion dynamics, SE community needs an effective approach to capture and model emotion dynamics rather than focuses on extracting isolated emotion states. In this paper, we proposed such an approach–EmoD. EmoD is able to automatically collect project teams' communication records, identify the emotions and their intensities in them, model the emotion dynamics into time series, and provide efficient data management. We developed a prototype tool that instantiates the EmoD approach by assembling state-of-the-art NLP, SE, and time series techniques. We demonstrate the utility of the tool using the IPython's project data on GitHub and a visualization solution built on EmoD. Thus, we demonstrate that EmoD can provide end-to-end support for various emotion awareness research and practices through automated data collection, modeling, storage, analysis, and presentation. 

About 17 years after the severe acute respiratory syndrome coronavirus (SARS-CoV) epidemic, the world is currently facing the COVID-19 pandemic caused by SARS coronavirus 2 (SARS-CoV-2). According to the most optimistic projections, it will take more than a year to develop a vaccine, so the best short-term strategy may lie in identifying virus-specific targets for small molecule–based interventions. All coronaviruses utilize a molecular mechanism called programmed −1 ribosomal frameshift (−1 PRF) to control the relative expression of their proteins. Previous analyses of SARS-CoV have revealed that it employs a structurally unique three-stemmed mRNA pseudoknot that stimulates high −1 PRF rates and that it also harbors a −1 PRF attenuation element. Altering −1 PRF activity impairs virus replication, suggesting that this activity may be therapeutically targeted. Here, we comparatively analyzed the SARS-CoV and SARS-CoV-2 frameshift signals. Structural and functional analyses revealed that both elements promote similar −1 PRF rates and that silent coding mutations in the slippery sites and in all three stems of the pseudoknot strongly ablate −1 PRF activity. We noted that the upstream attenuator hairpin activity is also functionally retained in both viruses, despite differences in the primary sequence in this region. Small-angle X-ray scattering analyses indicated that the pseudoknots in SARS-CoV and SARS-CoV-2 have the same conformation. Finally, a small molecule previously shown to bind the SARS-CoV pseudoknot and inhibit −1 PRF was similarly effective against −1 PRF in SARS-CoV-2, suggesting that such frameshift inhibitors may be promising lead compounds to combat the current COVID-19 pandemic.