Samuel Jarman

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

Graphic abstract. Credit: The European Physical Journal E (2024). DOI: 10.1140/epje/s10189-024-00435-6

Small-angle scattering (SAS) is a powerful technique for studying nanoscale samples. So far, however, its use in research has been held back by its inability to operate without some prior knowledge of a sample's chemical composition. Through new research published in The European Physical Journal E, Eugen Anitas at the Bogoliubov Laboratory of Theoretical Physics in Dubna, Russia, presents a more advanced approach, which integrates SAS with machine learning algorithms.

Named α-SAS, the technique can analyze molecular samples without any need for extensive preparation or computing resources, and could enable researchers to gain more detailed insights into the properties of complex biomolecules: such as proteins, lipids, and carbohydrates.

SAS measures the deflection of radiation—typically X-rays or neutrons—after interacting with molecular structures suspended in a solvent. By adjusting the solvent's composition, researchers can enhance or diminish the visibility of certain components of the system: a technique named "contrast variation." For this to work, however, researchers still need some knowledge of the sample's chemical composition before the experiment begins.

α-SAS for Janus particles. Credit: E M Anitas.

Through his study, Anitas overcame this limitation by integrating SAS with machine learning algorithms, creating a technique named α-SAS. This approach estimated the results of small-angle neutron scattering (SANS) by running many random simulations of the suspended sample, and analyzing the distribution of their results.

Anitas demonstrated the capabilities of α-SAS through two different case studies. The first of these investigated "Janus particles": artificial, self-propelling structures with a well-known contrast variation and neutron scattering intensity. Secondly, he tested the technique on a complex, protein-based molecular system.

In each case, Anitas' measurements of the molecular structures were far more efficient than they would have been without any integration with machine learning. Based on these promising results, Anitas is now hopeful that, through his approach, SAS could soon become an even more powerful tool for analyzing molecular structures.

More information: Eugen Mircea Anitas, Integrating machine learning with α-SAS for enhanced structural analysis in small-angle scattering: applications in biological and artificial macromolecular complexes, The European Physical Journal E (2024). DOI: 10.1140/epje/s10189-024-00435-6 Journal information: European Physical Journal E

Provided by SciencePOD

Small-angle scattering (SAS) is a powerful technique for studying nanoscale samples. So far, however, its use in research has been held back by its inability to operate without some prior knowledge of a sample's chemical composition. Through new research published in The European Physical Journal E, Eugen Anitas at the Bogoliubov Laboratory of Theoretical Physics in Dubna, Russia, presents a more advanced approach, which integrates SAS with machine learning algorithms.

Integrating small-angle neutron scattering with machine learning enhances measurements of complex molecular structures

3421693177718d54d443ceef0e9df41d1c0c1340

A research team has combined two emerging imaging technologies to better view a wide range of biomolecules, including proteins, lipids and DNA, at the nanoscale. Their technique brings together expansion microscopy and stimulated Raman scattering microscopy.

Researchers magnify hidden biological structures with MAGNIFIERS: Combination of expansion microscopy and stimulated Raman scattering microscopy allow for nanoscale vibrational imaging of biomolecules

A team of scientists at Los Alamos National Laboratory is applying machine-learning algorithms to subsurface imaging that will impact a variety of applications, including energy exploration, carbon capture and sequestration and estimating pathways of subsurface contaminant transport, according to new research published in IEEE Signal Processing Magazine.

Physics-guided machine-learning models will improve subsurface imaging

Ever since their discovery at CERN in the 1980s, the W± and Z bosons have been the subject of extensive research. As the carriers of the weak force, measurements of their properties are crucial for understanding elementary interactions. For instance, measurements of the W-boson mass are a critical test of the validity of the Standard Model of particle physics. The weak mixing angle, which gives the relationship between the W± and Z bosons masses, is another such essential input to consistency tests. At the 2023 LHCP conference, physicists from the ATLAS Experiment presented precise new measurements of the W± and Z boson transverse momentum (pT) distributions at two centre-of-mass energies: 13 TeV and, for the first time, 5.02 TeV. These results give unprecedented information on these pT shape spectra, providing crucial input for other studies of these bosons. For example, when determining the W-boson mass, incorporating a direct measurement of the W-boson transverse momentum will reduce the uncertainty on underlying assumptions about how the W boson is produced. These new results also allowed researchers to study Quantum Chromodynamics (QCD, the theory that describes the interactions that bind quarks together) from the small transverse-momentum range up to large boosts of the bosons in the transverse plane. Precise measurements of the W and Z boson transverse momentum are crucial input for several key studies of these bosons. New approach for new precision ATLAS event display: A W-boson candidate decays to one muon (orange line) and one neutrino (missing transverse momentum, shown with red arrow). The W-boson is reconstructed in a beam crossing with two additionally reconstructed primary vertices from the minimum bias interactions, as shown in the bottom panel. (Image: ATLAS Collaboration/CERN)While Z bosons can be well measured by the ATLAS detector, W± bosons are much more challenging. Unlike Z bosons, which decay into detectable lepton pairs, for these measurements researchers study W± bosons which decay into one charged lepton (either an electron or a muon) and a neutrino, a particle which cannot be measured directly by ATLAS. Thus, ATLAS physicists needed a unique approach to achieve a precise measurement of the W-boson transverse momentum. This approach involved special experimental conditions during the data-taking step, where ATLAS operated with far fewer proton-proton collisions occurring simultaneously (low-pileup conditions). As a result, physicists were able to accurately measure the final states of W± bosons in a 'clean' experimental environment. To reconstruct the W± boson's momentum, physicists studied the detectable system of particles recoiling against it. This system is mostly composed of hadrons (the 'hadronic recoil'). They did so using a novel technique involving Particle Flow Objects – algorithms that reconstruct the nature and kinematic properties of the particles – which reached their best resolution thanks to the low-pileup conditions. The hadronic recoil was calibrated with sub-percent-level precision, enabling researchers to study effects stemming from the collision activity with a very high degree of complexity. This, in turn, facilitated the correction of the mean, width and tails of the hadronic recoil distributions in simulations. Since the Z-boson transverse momentum can be measured either through its decay to a dilepton pair or through the hadronic recoil, it provided an important cross-check to thoroughly validate the method and calibrations used for the W± boson. The W+ boson transverse momentum spectrum at 5.02 TeV, compared to several predictions (left), together with its uncertainties (right). (Image: ATLAS Collaboration/CERN)Results across the spectra Six individual spectra, one for each particle at both centre-of-mass energies, were obtained. As an example, the figures above show the measured spectrum for the W+ at 5.02 TeV compared with theory predictions. These measurements reach a precision of 1-2% in the peak of the distribution – a marked improvement over previous ATLAS results. Researchers also compared the 13 TeV and 5.02 TeV spectra; these will also be a useful input to theoretical QCD predictions. In addition, physicists were able to determine the total cross sections for the W± and Z bosons. This measurement was substantially improved by ATLAS' recent precise luminosity measurement, resulting in an increased precision of the cross-sections by a factor of two or more. This ensemble of results is important input for the theory effort within the LHC Physics Centre at CERN, helping theorists and experimentalists to better understand QCD predictions and setting the path for future W-boson-mass measurements by the ATLAS Collaboration. Explore the interactive event display Dynamic view of a W-boson candidate decaying to one muon (red line) and one neutrino (missing transverse momentum, dashed line). (Image: ATLAS Collaboration/CERN)Learn more Precise measurements of W and Z transverse momentum spectra with the ATLAS detector at 5.02 TeV and 13 TeV (ATLAS-CONF-2023-028) LHCP 2023 presentation by Fabrice Balli: W/Z precision and differential measurements LHCP 2023 presentation by Sarah Demers: ATLAS status and overview Measurement of the transverse momentum distribution of W bosons in proton–proton collisions at 7 TeV with the ATLAS detector (Phys. Rev. D 85, 012005, arXiv: 1108.6308, see figures) Measurement of W± and Z– boson production cross sections in proton–proton collisions at 13 TeV with the ATLAS detector (Phys. Lett. B 759 (2016) 601, arXiv: 1603.09222, see figures) New ATLAS result weighs in on the W boson, ATLAS Physics Briefing, March 2023

New high-precision measurements of W and Z boson properties

The brain is made up of billions of neurons that communicate with each other by forming trillions of synapses. These connection points between neurons are complex molecular machines that are constantly changing as a result of external stimuli and internal dynamics, a process commonly referred to as 'synaptic plasticity'. In the neocortex, a key area associated with learning of high-level cognitive functions in mammals, Pyramidal Cells (PCs) account for 80%-90% of the neurons and are known to play a major role in learning. Despite their importance, the long-term dynamics of their synaptic changes have been experimentally characterized between only a few types of PCs, providing little coverage of the complex neural circuits they form, especially across the stereotypical cortical layers, which dictate how the diverse cortical regions interact. How then can we formulate a comprehensive view of the synaptic plasticity dynamics governing learning in neocortical circuits?

In a paper recently published in Nature Communications, a group of scientists from the EPFL Blue Brain Project, together with collaborators from Université de Montréal, Université de Paris, Hebrew University of Jerusalem, Instituto Cajal, and Harvard Medical School, introduce to the neuroscience community a model of synaptic plasticity for PCs in the neocortex based on data-constrained postsynaptic calcium dynamics. By comparing their results to the available experimental data, they show that their synaptic plasticity model can capture the varied plasticity dynamics of the diverse PCs making up the neocortical microcircuit. They achieve this with only one unified model parameter set, indicating the plasticity rules of the neocortex could be shared across pyramidal cell-types.

A complete picture – a full comprehensive model and in vivo prediction
What is unique about this study is that the group focused on finding a solution to the problem of parameterizing a model for the large set of heterogeneous synapses across the different PC connection types in the neocortex, given the sparse experimental constraints available. Moreover, the majority of these plasticity experiments were performed on brain slices in vitro, where the calcium dynamics driving synaptic transmission and plasticity are significantly altered compared to learning in the intact brain in vivo. This study had the particular aim of generalizing the plasticity predictions of the model to physiological calcium dynamics in vivo. Importantly, the study predicts qualitatively different plasticity dynamics from the reference experiments performed in vitro, which, if confirmed, could have profound implications for our understanding of plasticity and learning in the brain.

Lead Author, Giuseppe Chindemi explains, "In this study we designed a model of synaptic plasticity for one specific type of PCs, namely the thick tufted PCs of neocortical layer 5. We found that this model could be transplanted as-is to other PC types to reproduce the results of Long-Term Potentiation (LTP) and Long-Term Depression (LTD) experiments in the same cortical region. Our proposed model and fitting procedure offer a systematic method to predict plasticity outcomes for connections that have yet to be experimentally explored, and a flexible framework for studying cortical learning algorithms in
silico ", he concludes.

This has resulted in the first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open source integrated modeling framework opening the door to exploring the input/output conditions relevant for plasticity in vivo, and ultimately the biological algorithms of learning in the neocortex.

Team-science and open-science accelerate progress
"What is exciting about this study", comments Prof. Henry Markram, Founder and Director of the Blue Brain Project, "is that this is further confirmation for scientists that we can overcome gaps in experimental knowledge using a modelling approach when studying the brain. In addition, the model is open source, and here we have shared hundreds of plastic pyramidal cell connections of different types. Not only is it the most extensively validated plasticity model to date, but it also represents the most comprehensive prediction thus far of the differences between plasticity observed in a petri dish, and in an intact brain. This leap is made possible because of our collaborative team-science approach. Moreover, the community can take it further and develop their own versions by modifying or adding to it – this is open science, and it will accelerate progress".

"It opens up a world of new directions for scientific inquiry into how we learn," confirms co-senior author, Prof. Eilif Muller, who co-led the study at the Blue Brain Project prior to his appointment as Assistant Professor at the Université de Montréal, the CHU Ste-Justine Research Center, and the Québec Artificial Intelligence Institute (Mila). "Neurons are shaped like trees, and synapses are the leaves on their branches. Prior approaches to model plasticity have ignored this tree structure, but now we have the computational tools to test the idea that synaptic interactions on branches play a fundamental role in guiding learning in vivo. This has important implications for understanding the mechanisms of neurodevelopmental disorders such as autism and schizophrenia, but also for developing powerful new AI approaches inspired by neuroscience".

_____________________

Citation - Chindemi, G., Abdellah, M., Amsalem, O., Benavides-Piccione, R., Delattre, V., Doron, M., Ecker, A., Jaquier, A. T., King, J., Kumbhar, P., Monney, C., Perin, R., Rössert, C., Tuncel, A. M., Van Geit, W., DeFelipe, J., Graupner, M., Segev, I., Markram, H., & Muller, E. B. (2022). A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex. Nature Communications, 13(1), 3038. https://doi.org/10.1038/s41467-022-30214-w

For more information, contact – Blue Brain Communications Manager Kate Mullins – [email protected]

Data and Code Availability
The synapse model, simulations and analysis code used in this work are publicly available on Zenodo at https://doi.org/10.5281/zenodo.5654788

Simulations can be reproduced using EModelRunner https://github.com/BlueBrain/EModelRunner - an open source Python package developed to run cell models provided by the Blue Brain Project portals.

More about the neocortical microcircuit model
In 2015, the Reconstruction and Simulation of Neocortical Microcircuitry was published in Cell, representing the most complete description of any neural microcircuit to date. The model comprises 163,271 compartmental neurons, based on morphological reconstructions of 17 pyramidal cell types across six layers in the rat somatosensory cortex. Extensively validated, it was used here to test the hypothesis that a calcium-based modelling approach can capture the synaptic plasticity behavior and diversity of Pyramidal Cells in the neocortex.

About the EPFL Blue Brain Project
The aim of the EPFL Blue Brain Project, a Swiss brain research initiative founded and directed by Professor Henry Markram, is to establish simulation neuroscience as a complementary approach alongside experimental, theoretical and clinical neuroscience to understanding the brain, by building the world's first biologically detailed digital reconstructions and simulations of the mouse brain.

About the Authors
Led by the EPFL Blue Brain Project, the scientists involved in this study are afffiliated with the following institutions:
Department of Neurobiology and the Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Israel
Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
Instituto Cajal, Consejo Superior de Investigaciones Científicas, Madrid, Spain
Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Spain
Laboratory of Neural Microcircuitry, École Polytechnique Fédérale de Lausanne, Switzerland
Université de Paris, SPPIN - Saints-Pères Paris Institute for the Neurosciences, CNRS, France
Department of Neurosciences, Faculty of Medicine, Université de Montréal, Canada
CHU Sainte-Justine Research Center, Montréal, Canada
Quebec Artificial Intelligence Institute (Mila), Canada