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Objective: To investigate the effectiveness of a virtual hackathon in fostering interdisciplinary working amongst undergraduate students in global surgery. Methodology: In this study, we developed a 3 day event consisting of guest lectures, a documentary screening and a hackathon supported by academics and experts in the field, to provide students with the opportunity to learn more about and work in interdisciplinary teams within global surgery. Students had the option to attend just the lectures or both the lectures and hackathon. Quantitative and qualitative results were collected through a pre and post session survey. Results: A total of 21 responses were received for the hackathon and 26 responses for the general event (response rate for event = 26 %, response rate for hackathon = 24.7 %). There was a significant improvement in understanding of interdisciplinary working in global surgery between the pre and post-session survey, with an increase in median from 3 (IQR = 2-3.5, n = 21) to 4 (IQR = 4-5, n = 21) (p < 0.05). Respondents noted that the benefits of a hackathon were that it was very engaging, and brought in diversity of thought and expertise. The drawbacks to the hackathon were that it was fast-paced, required prior knowledge and the virtual platform it was hosted on. Conclusion: Our study demonstrates that hackathons are an effective, inclusive and equitable way for students to engage in and learn about interdisciplinary working. It is important that as institutions recognise and develop global surgery courses, these courses reflect the interdisciplinary nature of the field.
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MOTIVATION: Accurate prediction of genes in genomes has always been a challenging task for bioinformaticians and computational biologists. The discovery of existence of distinct scaling relations in coding and non-coding sequences has led to new perspectives in the understanding of the DNA sequences. This has motivated us to exploit the differences in the local singularity distributions for characterization and classification of coding and non-coding sequences. RESULTS: The local singularity density distribution in the coding and non-coding sequences of four genomes was first estimated using the wavelet transform modulus maxima methodology. Support vector machines classifier was then trained with the extracted features. The trained classifier is able to provide an average test accuracy of 97.7%. The local singularity features in a DNA sequence can be exploited for successful identification of coding and non-coding sequences. CONTACT: Available on request from bd.kulkarni@ncl.res.in.
