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The diversity and flexibility of life offers a wide variety of molecules and systems useful for biosensing. A biosensor device should be robust, specific and reliable. Inorganic arsenic is a highly toxic water contaminant with worldwide distribution that poses a threat to public health. With the goal of developing an arsenic biosensor, we designed an incoherent feed-forward loop (I-FFL) genetic circuit to correlate its output pulse with the input signal in a relatively time-independent manner. The system was conceived exclusively based on the available BioBricks in the iGEM Registry of Standard Biological Parts. The expected behavior in silico was achieved; upon arsenic addition, the system generates a short-delayed reporter protein pulse that is dose dependent to the contaminant levels. This work is an example of the power and variety of the iGEM Registry of Standard Biological Parts, which can be reused in different sophisticated system designs like I-FFLs. Besides the scientific results, one of the main impacts of this synthetic biology project is the influence it had on team’s members training and career choices which are summarized at the end of this article.

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as the data augmentation, better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly on a personal laptop.

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