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Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called "SYNAPSE"(Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium.

Mapping the large neural networks of animal and human brains is a fundamental but so far elusive task, because of the massive amount of data and the consequent prohibitively long image taking and processing times. We developed an effective strategy called “AXON” (Accelerated X-ray Observation of Neurons) to solve this problem. AXON can achieve comprehensive whole-brain mapping within a reasonable time by combining fast image taking and processing, plus two other critical performances: three-dimensional (3D) imaging with high and isotropic spatial resolution, and multi-scale resolution. We successfully tested this strategy with coordinated experiments at four synchrotron facilities in Japan, Taiwan, Singapore and Korea on two animal models, Drosophila and mouse. Its performances notably allowed full 3D mapping of the Drosophila brain in a few days. With reasonable improvements, AXON can deliver full mapping of large animal and human brains on a realistic time scale of a few years.

Rechargeable batteries, such as LiFePO4/graphite cells, age differently by variability in manufacturing, charging (energy inflow) policy, temperature, discharging conditions, etc. Great economic and environmental value can be extracted if we can predict how a battery ages and ascertain its current state of health and residual useful life, based on just a few cycles of testing. Here, by developing novel-architecture deep neural networks with a special convolutional training strategy and taking advantage of recently published battery cycling data, we show that one can predict the residual life of a battery to a mean absolute percentage error of 6.46%, using only one cycle of testing. The cycle-by-cycle profiles, such as discharge voltage, capacity, and power curves of any given cycle, of used batteries with unknown age can also be accurately predicted for the first time. Moreover, our models can extract data-driven features from the data which were much more influential on the predicted properties than human-picked features. This work has shown that single cycle data contains a sufficient amount of information to predict essential battery properties with high accuracy. It is expected to provide tremendous economic and environmental benefits since reuse and recycling of batteries can be better planned and less lithium-ion batteries end up in landfills.

article i nfo Complete profiling would substantially facilitate the fundamental understanding of tumor angiogenesis and of possible anti-angiogenesis cancer treatments. We developed an integrated synchrotron-based methodology withexcellent performances: detection of very small vessels by high spatialresolution (~1 μm)and nanoparticle contrast enhancement, in vivo dynamics investigations with high temporal resolution (~1 ms), and three- dimensional quantitative morphology parametrization by computer tracing. The smallest (3-10 μm) microves- sels were found to constitute >80% of the tumor vasculature and exhibit many structural anomalies. Practical applications are presented, including vessel microanalysis in xenografted tumors, monitoring the effects of anti-angiogenetic agents and in vivo detection of tumor vascular rheological properties.

Dynamics of the formation of self-assembled sub-monolayer networks of colloidal nanoparticles is studied in the two-dimensional lattice gas model by Monte Carlo simulation. The site-percolation threshold ( p c ) is studied for various temperatures and chemical potentials. Our numerical results show that p c is greatly reduced due to the evaporation-driving self-organization. The results are qualitatively consistent with our experimental observation.