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A workflow for 3D characterization of the mouse small intestine with optical projection tomography allows the identification of sparsely-distributed regions of interest in large volumes while retaining compatibility with high-resolution microscopy modalities.

Traumatic brain injury (TBI) is caused by a wide range of physical events and can induce an even larger spectrum of short- to long-term pathophysiologies. Neuroscientists have relied on animal models to understand the relationship between mechanical damages and functional alterations of neural cells. These in vivo and animal-based in vitro models represent important approaches to mimic traumas on whole brains or organized brain structures but are not fully representative of pathologies occurring after traumas on human brain parenchyma. To overcome these limitations and to establish a more accurate and comprehensive model of human TBI, we engineered an in vitro platform to induce injuries via the controlled projection of a small drop of liquid onto a 3D neural tissue engineered from human iPS cells. With this platform, biological mechanisms involved in neural cellular injury are recorded through electrophysiology measurements, quantification of biomarkers released, and two imaging methods [confocal laser scanning microscope (CLSM) and optical projection tomography (OPT)]. The results showed drastic changes in tissue electrophysiological activities and significant releases of glial and neuronal biomarkers. Tissue imaging allowed us to reconstruct the injured area spatially in 3D after staining it with specific nuclear dyes and to determine TBI resulting in cell death. In future experiments, we seek to monitor the effects of TBI-induced injuries over a prolonged time and at a higher temporal resolution to better understand the subtleties of the biomarker release kinetics and the cell recovery phases.

Abstract The growth of data throughput in optical microscopy has triggered the extensive use of supervised learning (SL) models on compressed datasets for automated analysis. Investigating the effects of image compression on SL predictions is therefore pivotal to assess their reliability, especially for clinical use. We quantify the statistical distortions induced by compression through the comparison of predictions on compressed data to the raw predictive uncertainty, numerically estimated from the raw noise statistics measured via sensor calibration. Predictions on cell segmentation parameters are altered by up to 15% and more than 10 standard deviations after 16-to-8 bits pixel depth reduction and 10:1 JPEG compression. JPEG formats with higher compression ratios show significantly larger distortions. Interestingly, a recent metrologically accurate algorithm, offering up to 10:1 compression ratio, provides a prediction spread equivalent to that stemming from raw noise. The method described here allows to set a lower bound to the predictive uncertainty of a SL task and can be generalized to determine the statistical distortions originated from a variety of processing pipelines in AI-assisted fields.

This review provides a comprehensive overview of the structural dynamics in topical gas- and condensed-phase systems on multiple length and time scales. Starting from vibrationally induced dissociation of small molecules in the gas phase, the question of vibrational and internal energy redistribution through conformational dynamics is further developed by considering coupled electron/proton transfer in a model peptide over many orders of magnitude. The influence of the surrounding solvent is probed for electron transfer to the solvent in hydrated I−. Next, the dynamics of a modified PDZ domain over many time scales is analyzed following activation of a photoswitch. The hydration dynamics around halogenated amino acid side chains and their structural dynamics in proteins are relevant for iodinated TyrB26 insulin. Binding of nitric oxide to myoglobin is a process for which experimental and computational analyses have converged to a common view which connects rebinding time scales and the underlying dynamics. Finally, rhodopsin is a paradigmatic system for multiple length- and time-scale processes for which experimental and computational methods provide valuable insights into the functional dynamics. The systems discussed here highlight that for a comprehensive understanding of how structure, flexibility, energetics, and dynamics contribute to functional dynamics, experimental studies in multiple wavelength regions and computational studies including quantum, classical, and more coarse grained levels are required.