High-throughput micro and nano-tomography

Rau, Christoph; Marathe, Shashidhara; Bodey, Andrew J.; Storm, Malte; Batey, Darren; Cipiccia, Silvia; Ziesche, R.

doi:10.1117/12.2598470

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Three-dimensional data, such as micro-CT data, can also be collected with new robotic technologies like autoloaders (Rau et al, 2021). Autoloaders allow users to set up multiple specimens for micro-CT and synchrotron scanning, set distinct parameters for each scan, and subsequently run the autoloader without supervision.…”

Section: ____________________________________________________________...mentioning

confidence: 99%

“…Autoloaders allow users to set up multiple specimens for micro-CT and synchrotron scanning, set distinct parameters for each scan, and subsequently run the autoloader without supervision. The autoloader processes specimens in a queue, pulling each from the stand using a robotic arm, and setting up distinct parameters for each (Rau et al, 2021). This fully automated process results in greater efficiency of acquisition, as the number of specimens digitised via this method increases when digitisation can occur without technician supervision.…”

Section: ____________________________________________________________...mentioning

confidence: 99%

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Challenges and opportunities in applying AI to evolutionary morphology

He,

Mulqueeney,

Watt

et al. 2024

Preprint

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Artificial intelligence (AI) is poised to transform many aspects of society, and the study of evolutionary morphology is no exception. Machine learning-grade methods of AI such as Principal Component Analysis (PCA) and Cluster Analysis have been commonplace in evolutionary morphology for decades, but the last decade has seen increasing application of Deep Learning to ecology and evolutionary biology, opening up the potential to circumvent longstanding barriers to rapid, big data analysis of phenotype. Here we review the current state of AI methods available for the study of evolutionary morphology and discuss the prospectus for near-term advances in specific subfields of this research area, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. We introduce the main available AI techniques, categorising them into three stages based on their order of appearance: (i) Machine Learning, (ii) Deep Learning with neural networks and (iii) the most recent advancements in large-scale models and multimodal learning. Next, we present existing AI approaches and case studies using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, phylogenetics, and biomechanics. Finally, we discuss areas where there is potential, but no current application of AI to key areas in evolutionary morphology. Combined, these advancements and potential developments have the capacity to transform the evolutionary analysis of organismal phenotype into evolutionary phenomics, launch it fully in the “Big Data'' sphere, and align it with genomics and other areas of bioinformatics.

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Section: ____________________________________________________________...mentioning

confidence: 99%

Section: ____________________________________________________________...mentioning

confidence: 99%

Challenges and opportunities in applying AI to evolutionary morphology

He,

Mulqueeney,

Watt

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The sample was cut down with a scalpel in a quasi-cylindrical shape of 100µm diameter and imaged at the I13-1 beamline at Diamond Light Source [31] using a fly-scanning method based on the approach described in detail in ref [19]. In this method, the maximum acquisition rate is set by the maximum acquisition rate of the detector.…”

Section: Large Volume Imagingmentioning

confidence: 99%

Fast X-ray ptychography: towards nanoscale imaging of large volume of brain

Cipiccia,

Fratini,

Erin

et al. 2024

Eur. Phys. J. Plus

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X-ray ptychography, a scanning coherent diffraction imaging technique, is one of the most used techniques at synchrotron facilities for high resolution imaging, with applications spanning from life science to nano-electronics. In the recent years there has been a great effort to make the technique faster to enable high throughput nanoscale imaging. Here we apply a fast ptychography scanning method to image in 3D $$10^6\upmu \mathrm{{m}}^3$$ 10 6 μ m 3 of brain-like phantom at 3 kHz, in a 7 h acquisition with a resolution of 270 nm. We then present the latest advances in fast ptychography by showing 2D images acquired at 110 kHz by combining the fast-acquisition scheme with a high-acquisition rate prototype detector from DECTRIS Ltd. We finally review the experimental outcome and discuss the prospective use of fast ptychography schemes for the investigation of mm$$^{3}$$ 3 size samples of brain-like phantom, by extrapolating the current results to the high coherent flux scenario of diffraction limited storage rings.

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Opportunities and Challenges in Applying AI to Evolutionary Morphology

He,

Mulqueeney,

Watt

et al. 2024

Integrative Organismal Biology

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Artificial intelligence (AI) is poised to revolutionise many aspects of science, including the study of evolutionary morphology. While classical AI methods such as principal component analysis and cluster analysis have been commonplace in the study of evolutionary morphology for decades, recent years have seen increasing application of deep learning to ecology and evolutionary biology. As digitised specimen databases become increasingly prevalent and openly available, AI is offering vast new potential to circumvent long standing barriers to rapid, big data analysis of phenotypes. Here, we review the current state of AI methods available for the study of evolutionary morphology, which are most developed in the area of data acquisition and processing. We introduce the main available AI techniques, categorising them into three stages based on their order of appearance: (i) machine learning, (ii) deep learning, and (iii) the most recent advancements in large-scale models and multimodal learning. Next, we present case studies of existing approaches using AI for evolutionary morphology, including image capture and segmentation, feature recognition, morphometrics, and phylogenetics. We then discuss the prospectus for near-term advances in specific areas of inquiry within this field, including the potential of new AI methods that have not yet been applied to the study of morphological evolution. In particular, we note key areas where AI remains underutilised and could be used to enhance studies of evolutionary morphology. This combination of current methods and potential developments have the capacity to transform the evolutionary analysis of the organismal phenotype into evolutionary phenomics, leading to an era of “Big Data'' that aligns the study of phenotypes with genomics and other areas of bioinformatics.

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High-throughput micro and nano-tomography

Cited by 4 publications

References 32 publications

Challenges and opportunities in applying AI to evolutionary morphology

Challenges and opportunities in applying AI to evolutionary morphology

Fast X-ray ptychography: towards nanoscale imaging of large volume of brain

Opportunities and Challenges in Applying AI to Evolutionary Morphology

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