The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random forests to analyse time series. Yet machine learning can also help to disentangle the complexity of biological active matter, helping, for example, to establish a relation between genetic code and emergent bacterial behaviour, to find navigation strategies in complex environments, and to map physical cues to animal behaviours. In this Review, we highlight the current state of the art in the application of machine learning to active matter and discuss opportunities and challenges that are emerging. We also emphasize how active matter and machine learning can work together for mutual benefit.
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)
Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning
Anna Pina, Saga Helgadottir, Rosellina Margherita Mancina, Chiara Pavanello, Carlo Pirazzi, Tiziana Montalcini, Roberto Henriques, Laura Calabresi, Olov Wiklund, M Paula Macedo, Luca Valenti, Giovanni Volpe, Stefano Romeo
European Journal of Preventive Cardiology (2020)
Familial hypercholesterolemia (FH) is the most common genetic disorder of lipid metabolism. The gold standard for FH diagnosis is genetic testing, available, however, only in selected university hospitals. Clinical scores – for example, the Dutch Lipid Score – are often employed as alternative, more accessible, albeit less accurate FH diagnostic tools. The aim of this study is to obtain a more reliable approach to FH diagnosis by a “virtual” genetic test using machine-learning approaches.
Methods and results
We used three machine-learning algorithms (a classification tree (CT), a gradient boosting machine (GBM), a neural network (NN)) to predict the presence of FH-causative genetic mutations in two independent FH cohorts: the FH Gothenburg cohort (split into training data (N = 174) and internal test (N = 74)) and the FH-CEGP Milan cohort (external test, N = 364). By evaluating their area under the receiver operating characteristic (AUROC) curves, we found that the three machine-learning algorithms performed better (AUROC 0.79 (CT), 0.83 (GBM), and 0.83 (NN) on the Gothenburg cohort, and 0.70 (CT), 0.78 (GBM), and 0.76 (NN) on the Milan cohort) than the clinical Dutch Lipid Score (AUROC 0.68 and 0.64 on the Gothenburg and Milan cohorts, respectively) in predicting carriers of FH-causative mutations.
In the diagnosis of FH-causative genetic mutations, all three machine-learning approaches we have tested outperform the Dutch Lipid Score, which is the clinical standard. We expect these machine-learning algorithms to provide the tools to implement a virtual genetic test of FH. These tools might prove particularly important for lipid clinics without access to genetic testing.
In addition, Lucero was awarded one of the three Early Stage Entrepreneurship Travel Grants to attend the semi-final.
The start-up is aiming to make cutting-edge laser technology easy to use and available to anyone by combining it with commercial microscope. The product and software combo utilizes optical tweezers in a brand-new way – and bridges the gap between physics and other scientific fields that would greatly benefit from easier access to this tool.
Optics & Photonics News has picked Saga Helgadóttir and Aykut Argun’s work on deep learning for particle tracking (DeepTrack) as a top break-through of the year.
“This has been a really good year for me, research-wise. My publication, presenting a new AI method, garnered a lot of attention,” says Saga Helgadóttir, PhD at the Department of Physics.
The research article in question, which is now included in Optics & Photonics News’ best-of-2019 list, identifies a new way of implementing neural networks and machine learning in order to track particle motion and study surrounding microenvironments.
Currently, Saga Helgadottir is collaborating with a group of scientists at Sahlgrenska’s Wallenberg Laboratory. They are working on new ways of using deep learning in the medical field.
“I started my PhD research studying bio-hybrid microswimmers, but ended up more within the area of artificial intelligence and optics. I like this work a lot, and the positive response to my publication earlier this year has allowed me to establish myself in the AI-field.”
Controlled generation of high power optical vortex arrays, and their frequency-doubling characteristics
Seminar by Harshith Bachimanchi from the Indian Institute of Science Education and Research, Pune (IISER Pune).
Optical vortices, beams carrying orbital angular momentum (OAM) per photon are of supreme interest in recent times for their wide variety of applications in quantum information, micro-manipulation, and material lithography [1, 2, 3]. Due to a helical phase variation in propagation, and an undefined phase at the centre, these beams have a phase singularity in their wavefront, resulting in the doughnut-shaped intensity distribution. Though the vortex beams have been widely explored in the past, the recent advancements on multiple particle trapping, single-shot material lithography, and multiplexing in quantum information  demand an array of optical vortices in a simple experimental scheme.
While the majority of the existing mode converters transform the Gaussian beam into a single vortex beam, the intrinsic advantage of the dynamic phase modulation through holographic technique allow the spatial light modulators (SLMs) to generate vortex arrays directly from a Gaussian beam. However, the low damage threshold of SLMs restricts their usage for high power vortex array applications.
Here, we elaborate a simple experimental scheme to generate high power, ultrafast, higher order optical vortex arrays. Simply by using a dielectric Microlens array (MLA) and a plano-convex lens we generate an array of beams carrying the spatial property of the input beam. Though we’ve verified the technique for the case of optical vortices, it holds true for a useful subset of structured optical beams. Considering the MLA as a 2D sinusoidal phase grating, we have numerically calculated the intensity pattern of the array beams in close agreement with the experimental results. We have also theoretically derived the parameters controlling the intensity pattern, size and the pitch of array and verified experimentally. The single-pass frequency doubling of the vortex array at 1064 nm in a 1.2 mm BiBO crystal produced green vortex arrays of orders as high as lsh = 12, twice the order of the pump array beam, with a conversion efficiency as high as ∼3.65% .
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