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Hydrogen energy is a possible solution for storage in the future. The resistance of packaging materials such as stainless steels has to be guaranteed for a possible use of these materials as containers for highly pressurized hydrogen. The effect of hydrogen charging on the nucleation and growth of microdamage in two different austenitic stainless steels AISI316 and AISI316L was studied using in situ tensile tests in synchrotron X-ray tomography. Information about damage nucleation, void growth and void shape were obtained. AISI316 was found to be more sensitive to hydrogen compared to AISI316L in terms of ductility loss. It was measured that void nucleation and growth are not affected by hydrogen charging. The effect of hydrogen was however found to change the morphology of nucleated voids from spherical cavities to micro-cracks being oriented perpendicular to the tensile axis.

This paper presents a method of acquisition and analysis of three-dimensional images in the task of automatic location and evaluation of defects on the surface of ceramic tiles. It presents a brief description of selected defects appearing on the surface of tiles, along with the analysis of their formation. The paper includes the presentation of the method of constructing a 3D image of the tile surface using the Laser Triangulation Method (LTM), along with the surface imaging parameters employed in the research. The algorithms of three-dimensional surface image analysis of ceramic tiles used in the process of image filtering and defect identification are presented. For selected defects, the method of measuring defect parameters and the method of visualization of defects on the surface are also presented. The developed method was tested on defective products to confirm its effectiveness in the field of quick defect detection in automated control systems installed on production lines.

Ultra‐high‐field magnetic resonance imaging (MRI) enables sub‐millimetre resolution imaging of the human brain, allowing the study of functional circuits of cortical layers at the meso‐scale. An essential step in many functional and structural neuroimaging studies is segmentation, the operation of partitioning the MR images in anatomical structures. Despite recent efforts in brain imaging analysis, the literature lacks in accurate and fast methods for segmenting 7‐tesla (7T) brain MRI. We here present CEREBRUM‐7T, an optimised end‐to‐end convolutional neural network, which allows fully automatic segmentation of a whole 7T T1w MRI brain volume at once, without partitioning the volume, pre‐processing, nor aligning it to an atlas. The trained model is able to produce accurate multi‐structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on and is suitable for neuroimaging studies, such as layer functional MRI studies. Taking advantage of a fine‐tuning operation on a reduced set of volumes, we also show how it is possible to effectively apply CEREBRUM‐7T to different sites data. Furthermore, we release the code, 7T data, and other materials, including the training labels and the Turing test. We here present CEREBRUM‐7T, an optimised end‐to‐end convolutional neural network, that allows fully automatic segmentation of a whole 7T T1w magnetic resonance imaging (MRI) brain volume at once, neither partitioning the volume, pre‐processing, nor aligning it to an atlas. The trained model is able to produce accurate multi‐structure segmentation masks on six different classes plus background in only a few seconds. The experimental part, a combination of objective numerical evaluations and subjective analysis, confirms that the proposed solution outperforms the training labels it was trained on and is suitable for neuroimaging studies, such as layer functional MRI studies.

AbstractThe study of pores in historic bricks is important for characterizing and comparing brick materials, evaluating the degree of deterioration, predicting behavior in future weathering conditions, studying the effectiveness of protective measures, and analyzing the potential effects of cleaning treatments. High-resolution micro-CT coupled with 3D image analysis is a promising new approach for studying porosity and pore systems in bricks. In this technique, hundreds or even thousands of X-ray projection images are acquired at 360 degrees around a sample. The X-radiation passing through the sample is absorbed, with radiation attenuated to varying degrees depending on the varying densities of phases within the object. The 3D volume is reconstructed by a computer algorithm, producing images where each voxel has a grayscale intensity value associated with the component it represents. Recent new instrument designs allow fast scanning with good spatial resolution. In this research, we present a set of protocols for creating optimal images of brick pores in micro-CT scans and for conducting 3D image analysis to extract both qualitative and quantitative data from those scans. Small samples give better spatial resolution for imaging of pores, so given the typical heterogeneity of bricks, scanning multiple samples from each brick ensures that the results are more likely to be representative. Machine learning and deep learning with convolutional neural networks were found to be important tools for better distinguishing pores from the surrounding matrix in the segmentation process, especially at the very limits of spatial resolution. Statistical analyses revealed which of the many parameters that can be measured are potentially most significant for characterizing the pore systems of bricks. These significant pore variables came from a multi-staged image analysis approach and include the total volume percent occupied by pores, the percentage of those pores accessible to the surface versus isolated interior ones, a variety of statistical properties of individual pores related to their size and shape, the average number of connections that pores have to other pores, and the length, diameter, and directness of those connections.

Background Synapses, as specialised cell–cell contacts, allow for a faithful and controlled signal transmission between a neuron and a target cell. Presynapses, the sites of neurotransmitter release, form de novo throughout the development of an organism. Although this process is fundamental to the development and function of synaptic circuits, how developing neurons control number and distribution of individual synapses remains poorly understood. In-vivo imaging analysis of synapse formation at the neuromuscular junction of anaesthetised Drosophila third instar larvae allows for spatial and temporal resolution of the underlying molecular processes. However, high-throughput, comprehensive analysis are hampered by the manual and time-consuming imaging analysis methods applied hitherto. Here, we focus on the early presynaptic formation steps, that is, the presynaptic seeding, initiated by the formation of transient Liprin- α /SYD1 seeding sites, either stabilised or disintegrated over a time span of 30–90 min. Results To investigate the dynamics of the Liprin- α /SYD1 seeding sites, we developed an automated analysis pipeline for 3D confocal images from in-vivo imaging at distinct time points to analyse fluorescently labelled presynaptic protein dynamics during early synapse formation. The workflow is realised in the data analysis software Amira , utilising the hierarchical watershed algorithm, and was designed for automatic processing with an option for manual proofreading. Compared to the previous 2D manual quantification, this automated approach provides a higher sensitivity in single Liprin- α seeding site detection in low-intensity areas and in regions of dense seeding sites. In addition, it substantially reduces the work time. To account for possible errors occurring in the automated processing, we implemented an additional proofreading step allowing for a manual correction of Liprin- α seeding site segmentation and assignment, thus greatly improving the analysis while only marginally increasing work time by 10% to a total work time reduction of 70% compared to the 2D manual analysis paradigm. Conclusion The process of synaptogenesis underlies the general principles of locomotion, learning and memory formation. The developed fast and accurate semi-automated 3D workflow will provide a substantial progress in the analysis of this molecular process.

Background The three-dimensional nuclear arrangement of chromatin impacts many cellular processes operating at the DNA level in animal and plant systems. Chromatin organization is a dynamic process that can be affected by biotic and abiotic stresses. Three-dimensional imaging technology allows to follow these dynamic changes, but only a few semi-automated processing methods currently exist for quantitative analysis of the 3D chromatin organization. Results We present an automated method, Nuclear Object DetectionJ (NODeJ), developed as an imageJ plugin. This program segments and analyzes high intensity domains in nuclei from 3D images. NODeJ performs a Laplacian convolution on the mask of a nucleus to enhance the contrast of intra-nuclear objects and allow their detection. We reanalyzed public datasets and determined that NODeJ is able to accurately identify heterochromatin domains from a diverse set of Arabidopsis thaliana nuclei stained with DAPI or Hoechst. NODeJ is also able to detect signals in nuclei from DNA FISH experiments, allowing for the analysis of specific targets of interest. Conclusion and availability NODeJ allows for efficient automated analysis of subnuclear structures by avoiding the semi-automated steps, resulting in reduced processing time and analytical bias. NODeJ is written in Java and provided as an ImageJ plugin with a command line option to perform more high-throughput analyses. NODeJ can be downloaded from https://gitlab.com/axpoulet/image2danalysis/-/releases with source code, documentation and further information avaliable at https://gitlab.com/axpoulet/image2danalysis . The images used in this study are publicly available at https://www.brookes.ac.uk/indepth/images/ and https://doi.org/10.15454/1HSOIE .

3D cell culture has emerged as a promising approach to replicate the complex behaviors of cells within living organisms. This study aims to analyze spatiotemporal behavior of the morphological characteristics of cell structure at multiscale in 3D scaffold-free spheroids using chondrogenic progenitor ATDC5 cells. Over a 14-day culture period, it exhibited cell hypertrophy in the spheroids regarding cellular and nuclear size as well as changes in morphology. Moreover, biological analysis indicated a signification up-regulation of normal chondrocyte as well as hypertrophic chondrocyte markers, suggesting early hypertrophic chondrocyte differentiation. Cell nuclei underwent changes in volume, sphericity, and distribution in spheroid over time, indicating alterations in chromatin organization. The ratio of chromatin condensation volume to cell nuclear volume decreased as the cell nuclei enlarged, potentially signifying changes in chromatin state during hypertrophic chondrocyte differentiation. Our image analysis techniques in this present study enabled detailed morphological measurement of cell structure at multi-scale, which can be applied to various 3D culture models for in-depth investigation.

Measurement of auricle parameters for planning and post-operative evaluation presents substantial challenges due to the complex 3D structure of the human auricle. Traditional measurement methods rely on manual techniques, resulting in limited precision. This study introduces a novel automated surface-based three-dimensional measurement method for quantifying human auricle parameters. The method was applied to virtual auricles reconstructed from Computed Tomography (CT) scans of a cadaver head and subsequent measurement of important clinically relevant aesthetical auricular parameters (length, width, protrusion, position, auriculocephalic angle, and inclination angle). Reference measurements were done manually (using a caliper and using a 3D landmarking method) and measurement precision was compared to the automated method. The CT scans were performed using both a contemporary high-end and a low-end CT scanner. Scans were conducted at a standard scanning dose, and at half the dose. The automatic method demonstrated significantly higher precision in measuring auricle parameters compared to manual methods. Compared to traditional manual measurements, precision improved for auricle length (9×), width (5×), protrusion (5×), Auriculocephalic Angle (5–54×) and posteroanterior position (23×). Concerning parameters without comparison with a manual method, the precision level of supero-inferior position was 0.489 mm; and the precisions of the inclination angle measurements were 1.365 mm and 0.237 mm for the two automated methods investigated. Improved precision of measuring auricle parameters was associated with using the high-end scanner. A higher dose was only associated with a higher precision for the left auricle length. The findings of this study emphasize the advantage of automated surface-based auricle measurements, showcasing improved precision compared to traditional methods. This novel algorithm has the potential to enhance auricle reconstruction and other applications in plastic surgery, offering a promising avenue for future research and clinical application.

Recombinant adeno-associated viral vectors (rAAVs) are the leading gene delivery vehicles in clinical development, yet efficient nuclear delivery remains a major barrier to effective transduction. This limitation is partly due to the incomplete understanding of rAAV’s complex subcellular trafficking dynamics. Here, we establish a longitudinal confocal live-cell imaging workflow that tracks rAAV2 from 4 to 12 h post-transduction, paired with an automated 3D analysis pipeline that quantifies spatiotemporal vector distribution, cytoplasmic trafficking, nuclear accumulation, and transgene expression at single-cell resolution. We use this platform to evaluate the effects of vector dose, cell cycle progression, and the behavior of empty particles. We identify previously undescribed trafficking features associated with high transgene expression. Higher rAAV2 doses enhanced cytoplasmic trafficking and nuclear delivery, while cell cycle progression facilitated both trafficking efficiency and transgene expression. We also characterize empty rAAV2 particles, revealing distinct trafficking patterns and markedly reduced nuclear accumulation compared to genome-containing vectors. By uncovering new bottlenecks in rAAV transduction, this platform provides mechanistic insights and potential strategies to improve AAV-based gene therapy. Its generalizable design further supports broad applicability to other non-enveloped viruses.

Background The technical development of imaging techniques in life sciences has enabled the three-dimensional recording of living samples at increasing temporal resolutions. Dynamic 3D data sets of developing organisms allow for time-resolved quantitative analyses of morphogenetic changes in three dimensions, but require efficient and automatable analysis pipelines to tackle the resulting Terabytes of image data. Particle image velocimetry (PIV) is a robust and segmentation-free technique that is suitable for quantifying collective cellular migration on data sets with different labeling schemes. This paper presents the implementation of an efficient 3D PIV package using the Julia programming language—quickPIV. Our software is focused on optimizing CPU performance and ensuring the robustness of the PIV analyses on biological data. Results QuickPIV is three times faster than the Python implementation hosted in openPIV, both in 2D and 3D. Our software is also faster than the fastest 2D PIV package in openPIV, written in C++. The accuracy evaluation of our software on synthetic data agrees with the expected accuracies described in the literature. Additionally, by applying quickPIV to three data sets of the embryogenesis of Tribolium castaneum , we obtained vector fields that recapitulate the migration movements of gastrulation, both in nuclear and actin-labeled embryos. We show normalized squared error cross-correlation to be especially accurate in detecting translations in non-segmentable biological image data. Conclusions The presented software addresses the need for a fast and open-source 3D PIV package in biological research. Currently, quickPIV offers efficient 2D and 3D PIV analyses featuring zero-normalized and normalized squared error cross-correlations, sub-pixel/voxel approximation, and multi-pass. Post-processing options include filtering and averaging of the resulting vector fields, extraction of velocity, divergence and collectiveness maps, simulation of pseudo-trajectories, and unit conversion. In addition, our software includes functions to visualize the 3D vector fields in Paraview.