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Biology subjects

Jacucci, G.

Publications and source records attributed to Jacucci, G..

2 recordsLinked to original sources

Bionic 3D printed corals

Symbiotic corals have evolved as a highly optimised photon augmentation system leading to space-efficient microalgal growth and photosynthetic quantum efficiencies that approach theoretical limits1-3. Corals are characterized by an elastic animal tissue hosting microalgae and a light scattering calcium carbonate skeleton that maximizes light delivery towards otherwise shaded algal-containing tissues4,5. Rapid light attenuation due to algal self-shading is a key limiting factor for the upscaling of microalgal cultivation6,7. Coral-inspired light management systems could overcome this limitation and facilitate scalable bioenergy and bioproduct generation8,9. Here, we developed 3D printed bionic corals capable of growing various types of microalgae with cell densities approaching 109 cells mL-1, up to 100 times greater than in liquid culture. The hybrid photosynthetic biomaterials are produced with a new 3D bioprinting platform which mimics morphological features of living coral tissue and the underlying skeleton with micron resolution, including their optical and mechanical properties. The programmable synthetic microenvironment thus allows for replicating both structural and functional traits of the coral-algal symbiosis. Our work defines a new class of bionic materials capable of interacting with living organisms, that can be exploited for the design of next generation photobioreactors7 and disruptive approaches for coral reef conservation10.

bioengineering↗

MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection

BackgroundDispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. Devising a visualization that synthesizes multiple sources in such a way that links between data sources are transparent, and uncertainties, such as data conflicts, are salient is challenging.\n\nResultsTo investigate the requirements and challenges of uncertainty-aware visualizations of linked data, we developed MediSyn, a system that synthesizes medical datasets to support drug treatment selection. It uses a matrix-based layout to visually link drugs, targets (e.g., mutations), and tumor types. Data uncertainties are salient in MediSyn; for example, (i) missing data are exposed in the matrix view of drug-target relations; (ii) inconsistencies between datasets are shown via overlaid layers; and (iii) data credibility is conveyed through links to data provenance.\n\nConclusionsThrough the synthesis of two manually curated datasets, cancer treatment biomarkers and drug-target bioactivities, a use case shows how MediSyn effectively supports the discovery of drug-repurposing opportunities. A study with six domain experts indicated that MediSyn benefited the drug selection and data inconsistency discovery. Though linked publication sources supported user exploration for further information, the causes of inconsistencies were not easy to find. Additionally, MediSyn could embrace more patient data to increase its informativeness. We derive design implications from the findings.

bioinformatics↗