Giovanni Volpe has been awarded with the ERC Proof of Concept Grant for the research project LUCERO: Smart Optofluidic micromanipulation of Biological Samples.
The grant, consisting of 150k EUR, is meant to commercialize the research project LUCERO, providing an innovative method that combines artificial intelligence and optical tweezers to analyze cells easily and inexpensively.
The current technologies for cell analysis have many limitations: they require access to a large number of cells and considerable expertise. The available methods are also labor-intensive and in some cases the cells are destroyed.
The new method developed in LUCERO simplifies the work and lowers the costs of biomedical research by allowing ordinary standard microscopes, which are already in use in biomedical laboratories, to be used to perform the cell analysis.
The method of LUCERO can be used in several areas, from artificial insemination to forensic medicine. It has potentially a large commercial market.
Giovanni Volpe expects that LUCERO will provide around 20 jobs for university-trained experts and researchers within the next five years.
The project LUCERO has already received initial funding and support from two different organizations (Venture Cup and SPIE). Two doctoral students, Falko Schmidt and Martin B. Mojica, are part of LUCERO’s contributors team.
Researchers from ICFO, UVic, Gothenborg University, Politecnica de Valencia and Potsdam University organize the AnDi challenge, a physics challenge to address Brownian motion and Anomalous diffusion.
Brownian motion was first observed in 1827 by Robert Brown: pollen grains suspended in water show a characteristic erratic motion. Almost 80 years after, Albert Einstein provided a theoretical foundation for the Brownian motion. Though the Brownian motion is observed in many different systems, significant deviations from it have also been observed, starting from biological systems to economics.
The deviation from Brownian motion is indicated with the term Anomalous diffusion. It is connected to non-equilibrium phenomena, complex environments, flows of energy and information, and transport in living systems. To understand the nature of such systems one must correctly identify the physical origin of the anomalous diffusion, and correctly characterize it, through the calculation of its properties. A simple data analysis of trajectories, though, often provides limited information, in particular when the trajectories are either short, or noisy, or irregularly sampled, or featuring mixed behaviors. Several methods going beyond the calculation of classical estimators have been proposed, in the last years, to quantify anomalous diffusion.
The AnDi challenge has been thought as a competition to test these methods as well as other alternative approaches, by bringing together the scientific community currently working on the quantification of the anomalous diffusion.
The use of the same reference datasets will allow an unbiased assessment of the performance of published and unpublished methods for characterizing anomalous diffusion from single trajectories. Participants can submit the results of their analysis on the internet until November 1st, 2020. These results will be then automatically scored and ranked among all competitors.
In addition to the main objective of the AnDi Challenge, the top-ranked participants will be invited to present their results in a workshop held at ICFO, in Barcelona, on February 17-20, 2021.
Machine learning has proven to be very useful for the study of active matter, a collective term referring to things like cells and microorganisms. The field is quite new and growing fast. In an attempt to inspire more researchers to try the methods a group of scientists have published a paper in prestigious publication Nature Machine Intelligence reviewing what has been accomplished so far – and what lies ahead. Continue reading (English)
Researchers at the University of Gothenburg, together with researchers from Portugal, have now found a way to estimate the probability that a patient will suffer from a common genetic disease by training an algorithm using patient data. Continue reading (in English)