Anqi Lyu starts her PhD at the Physics Department of the University of Gothenburg on 8 January 2025.
Anqi has a Master degree in Medical Bioinformatics from University of Verona, Italy.
In her PhD, she will focus on delineating how plasma factors globally influence endothelial cells, with emphasis on their roles in health, ageing and disease, by utilizing computational tools in combination with interdisciplinary approaches.
Viktor Martvall, Henrik Klein Moberg, Athanasios Theodoridis, David Tomeček, Pernilla Ekborg-Tanner, Sara Nilsson, Giovanni Volpe, Paul Erhart, Christoph Langhammer
ACS Sensors 10, 376–386 (2025)
arXiv: 2312.15372
doi: 10.1021/acssensors.4c02616
Rapidly detecting hydrogen leaks is critical for the safe large-scale implementation of hydrogen technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets under technically relevant conditions. Here, we demonstrate how a tailored long short-term transformer ensemble model for accelerated sensing (LEMAS) speeds up the response of an optical plasmonic hydrogen sensor by up to a factor of 40 and eliminates its intrinsic pressure dependence in an environment emulating the inert gas encapsulation of large-scale hydrogen installations by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Moreover, LEMAS provides a measure for the uncertainty of the predictions that are pivotal for safety-critical sensor applications. Our results advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection.
Gerhard Jung, Rinske M. Alkemade, Victor Bapst, Daniele Coslovich, Laura Filion, François P. Landes, Andrea J. Liu, Francesco Saverio Pezzicoli, Hayato Shiba, Giovanni Volpe, Francesco Zamponi, Ludovic Berthier & Giulio Biroli
Nature Review Physics (2025)
doi: 10.1038/s42254-024-00791-4
arxiv: 2311.14752
Unravelling the connections between microscopic structure, emergent physical properties and slow dynamics has long been a challenge when studying the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key physical mechanisms underpinning slow dynamics. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. We explore the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present both successful ML applications and open problems for the future, such as transferability and interpretability of ML approaches. To foster a collaborative community effort, we also highlight the ‘GlassBench’ dataset, which provides simulation data and benchmarks for both 2D and 3D glass formers. We compare the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. Our goal is to provide guidelines for the development of ML techniques in systems displaying slow dynamics and inspire new directions to improve our theoretical understanding of glassy liquids.
Shuyang Yao, Arvid Harder, Fahimeh Darki, Yu-Wei Chang , Ang Li, Kasra Nikouei, Giovanni Volpe, Johan N Lundström, Jian Zeng , Naomi Wray, Yi Lu, Patrick F Sullivan, Jens Hjerling-Leffler
Nature Communications 16, 395 (2025)
doi: 10.1038/s41467-024-55611-1
medRxiv: 10.1101/2024.01.18.24301478
Identifying cell types and brain regions critical for psychiatric disorders and brain traits is essential for targeted neurobiological research. By integrating genomic insights from genome-wide association studies with a comprehensive single-cell transcriptomic atlas of the adult human brain, we prioritized specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals the whole-brain impact of schizophrenia genetic risk, with subregions in the hippocampus and amygdala exhibiting the most significant enrichment of SNP-heritability. Using functional MRI connectivity, we further confirmed the significance of the central and lateral amygdala, hippocampal body, and prefrontal cortex in distinguishing schizophrenia cases from controls. Our findings underscore the value of single-cell transcriptomics in understanding the polygenicity of psychiatric disorders and suggest a promising alignment of genomic, transcriptomic, and brain imaging modalities for identifying common biological targets.
He received his Ph.D. in Theoretical Physics in 2020 from Stockholm University under the supervision of Supriya Krishnamurthy. His thesis, titled “Nonequilibrium Thermodynamics at the Microscopic Scales,” focused on finite and short-time fluctuations in non-equilibrium systems, as opposed to the large-time asymptotic properties studied within the framework of large deviation theory. One of the key outcomes of his Ph.D. research was the development of a method to infer entropy production rates directly from experimentally accessible trajectories in a model-independent manner.
Following his PhD, Sreekanth received the NORDITA postdoctoral fellowship for independent research. During this time, he expanded on his earlier work by developing generalizations of the inference scheme for entropy production and integrating it with machine-learning tools for practical inference of dissipative forces and entropy production from experimental data. Later, in 2022, he was awarded the Wallenberg Scholarship for postdoctoral research at Stanford, where he developed machine-learning-based non-equilibrium control techniques for targeted self-assembly and transport of biomolecular systems.
Currently he is interested in combining methods from Non-equilibrium Physics and Machine Learning to quantitatively characterize and control nanoscale biophysical processes.
Jesús Pineda, Sergi Masó-Orriols, Joan Bertran, Mattias Goksör, Giovanni Volpe and Carlo Manzo
arXiv: 2412.00173
Single-molecule localization microscopy (SMLM) generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multimodal Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO’s transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO’s robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.
Dana Hassan, Jesús Domínguez, Benjamin Midtvedt, Henrik Klein Moberg, Jesús Pineda, Christoph Langhammer, Giovanni Volpe, Antoni Homs Corbera, Caroline B. Adiels
Advanced Photonics 6, 064001 (2024)
doi: 10.1117/1.AP.6.6.064001
Recent advancements in deep learning (DL) have propelled the virtual transformation of microscopy images across optical modalities, enabling unprecedented multimodal imaging analysis hitherto impossible. Despite these strides, the integration of such algorithms into scientists’ daily routines and clinical trials remains limited, largely due to a lack of recognition within their respective fields and the plethora of available transformation methods. To address this, we present a structured overview of cross-modality transformations, encompassing applications, data sets, and implementations, aimed at unifying this evolving field. Our review focuses on DL solutions for two key applications: contrast enhancement of targeted features within images and resolution enhancements. We recognize cross-modality transformations as a valuable resource for biologists seeking a deeper understanding of the field, as well as for technology developers aiming to better grasp sample limitations and potential applications. Notably, they enable high-contrast, high-specificity imaging akin to fluorescence microscopy without the need for laborious, costly, and disruptive physical-staining procedures. In addition, they facilitate the realization of imaging with properties that would typically require costly or complex physical modifications, such as achieving superresolution capabilities. By consolidating the current state of research in this review, we aim to catalyze further investigation and development, ultimately bringing the potential of cross-modality transformations into the hands of researchers and clinicians alike.
Giovanni Volpe
FEMTO-ST’s Internal Seminar 2024
Date: 26 November 2024
Time: 15:00
Place: Besançon, Paris
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions.
To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.1 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
Giovanni Volpe
SPAOM 2024
Date: 22 November 2024
Time: 10:15-10:45
Place: Toledo, Spain
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.
The article Playing with active matter, published in the American Journal of Physics, has been featured on Scilight with a news with title “Using Hexbugs to model active matter”.
The news highlights that the approach used in the featured paper will make possible for students in the primary and secondary school system to demonstrate complex active motion principles in the classroom, at an affordable budget.
In fact, experiments at the microscale often require very expensive equipment. The commercially available toys called Hexbugs used in the publication provide a macroscopic analogue of active matter at the microscale and have the advantage of being affordable for experimentation in the classroom.
About Scilight:
Scilight showcase the most interesting research across the physical sciences published in AIP Publishing Journals.
Reference:
Hannah Daniel, Using Hexbugs to model active matter, Scilight 2024, 431101 (2024)
doi: 10.1063/10.0032401
