The article Extracting quantitative biological information from brightfield cell images using deep learning
has been featured in: “Staining Cells Virtually Offers Alterative Approach to Chemical Dyes”, AIP SciLight (July 23, 2021).
Scilight showcases the most interesting research across the physical sciences published in AIP Publishing Journals.
Scilight is published weekly (52 issues per year) by AIP Publishing.
The article Improving epidemic testing and containment strategies using machine learning has been featured in the News of the Faculty of Science of Gothenburg University.
Here the links to the press releases:
Swedish: Maskininlärning kan bidra till att bromsa framtida pandemier
English: Machine learning can help slow down future pandemics
The articles was also featured in:
AI ska bromsa framtidens pandemier Metal Supply (23/04/2021)
El papel de la inteligencia artificial para frenar futuras pandemias El Nacional.cat. (16/04/2021)
AI could be critical to preventing future pandemics – study Health Tech World. (16/04/2021)
Machine Learning Slows Down Future Pandemics MedIndia. (15/04/2021)
Machine Learning May Be Key to Avoiding the Next Possible Pandemic News18.com (15/04/2021)
Så kan AI bromsa nästa pandemi – svensk forskningförfinar testningen Computer Sweden (15/04/2021)
AI could prevent future pandemics Electronics360 (14/04/2021)
L’IA peut contribuer à limiter la propagation des infections lors des futures épidémies (étude) Ecofin Telecom. (14/04/2021)
Machine Learning can help slow down future pandemics:Study SocialNews.xyz (14/04/2021)
Machine learning can help slow down future pandemics —ScienceDaily Sortiwa Trending Viral News Portal (14/04/2021)
AI mot smittspridning Sveriges Radio Vetenskapsradion. (14/04/2021)
The article Intercellular Communication Induces Glycolytic Synchronisation Waves published in PNAS has been featured in the News of the Faculty of Science of Gothenburg University.
Here the links to the press releases:
Swedish: Forskare har knäckt koden för cellkommunikation
English: Researchers have broken the code for cell communication
Giovanni Volpe has been interviewed by GU Journalen.
Here you can find the link to the article (text, Swedish)
and to the interview (movie, English).
The feature article explains the project and its main aim of creating smart particles that react to their environment to a general audience.
The article is available both in English and in Swedish.
Links:
Skapar smarta partiklar med naturen som förebild (Swedish)
Creating smart particles modeled on nature (English)
Photos of Johan Wingborg, taken from Creating smart particles modeled on nature
The article Enhanced force-field calibration via machine learning
has been featured in: “Machine Learning Outperforms Standard Force-Field Calibration Techniques”, AIP SciLight (November 6, 2020).
Scilight showcases the most interesting research across the physical sciences published in AIP Publishing Journals.
Scilight is published weekly (52 issues per year) by AIP Publishing.
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.
Links:
Press release of the Swedish Research Council: in English, in Swedish.
News on Gothenburg University website: in Swedish.
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.
Organizers:
Website: www.andi-challenge.org
Codalab: https://competitions.codalab.org/competitions/23601
e-mail: andi.challenge@gmail.com
twitter: @AndiChallenge
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)
Press release:
Shaping the future of machine learning for active matter (In English)
Formar framtiden för AI-forskning på aktiv materia (In Swedish)
Article:
Machine learning for active matter
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)
Press release:
Algoritm lär sig diagnostisera genetisk sjukdom (in Swedish)
An algorithm that learns to diagnose genetic disease (in English)
Article: Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning
