Biology subjects

Shaw, M. J.

Publications and source records attributed to Shaw, M. J..

7 recordsLinked to original sources

bioRxiv2026

A method for correcting background non-uniformity while preserving specimen-level information in Fourier ptychographic microscopy images

Fourier ptychographic microscopy (FPM) enables high resolution imaging across large fields of view, but reconstructed images often exhibit background non-uniformity that interferes with quantitative image analysis. Here, a post-reconstruction background correction approach is presented that identifies specimen-containing regions and corrects only the surrounding background, leaving specimen pixel intensities unchanged. The method was effective across amplitude and phase reconstructions of multiple biological specimen types with no loss in image resolution. The method also improved object detection compared with analysis of the uncorrected image data. FPM background correction reduced residual background non-uniformity to 0.61% with only 0.04% structural error, compared with 6.13% structural error for pseudo flat field correction. These results demonstrate that FPM background correction can support more accurate quantitative analysis of FPM image data while preserving specimen information and image resolution, enabling more reliable segmentation and measurement across large fields of view.

biophysics↗

bioRxiv2026

Cross-scale Imaging of Live Botanical Specimens using Fourier Ptychographic Microscopy

Light microscopic imaging of botanical specimens is typically performed using diffraction-limited methods which can image either a small field of view (FOV) with a shallow depth of field (DOF) at high resolution, or a large FOV and large DOF at low resolution. As such, many of the important small features, such as cells in the root cap of Arabidopsis, chloroplasts in algae and leaf guard cells, cannot be resolved in high numbers for meaningful statistical interpretation of results. Fourier ptychographic microscopy (FPM) is an accessible low-cost solution for large format bioimaging which makes use of multiangle illumination and iterative phase retrieval to recover high-resolution estimates of sample phase and amplitude. To date much FPM research has focused on optimising hardware and image reconstruction techniques with biological and biomedical applications limited primarily to mammalian cell cultures and histological tissue sections. We have, for the first time, applied FPM for label-free imaging of these large, living botanical specimens to investigate cellular and sub-cellular structures over multiple length scales simultaneously, with the aim of resolving structures that cannot be seen with diffraction-limited methods over a large field of view. With our FPM system we demonstrate imaging of living samples mounted in water with a resolution of 615 nm over a 3.6 mm diagonal FOV, producing a 9-fold improvement in resolution compared to conventional brightfield imaging. This allows for individual root cap cells of Arabidopsis thaliana to be resolved within the context or larger root structures, individual chloroplasts identified in large section of Spirogyra varians, and high-resolution colour imaging of Tradescantia zebrina stomata and chloroplasts across large leaf sections.

plant biology↗

bioRxiv2026

Dimensionally traceable 3D microstructures for multimodal microscope calibration

High-resolution microscopy techniques are used across research and industry to analyse biological systems, from biomolecules to subcellular organelles, multicellular models and tissues. As multimodal imaging workflows and quantitative analysis of bioimaging data become increasingly widespread, there is a growing need for materials and methods to calibrate imaging systems and evaluate the fidelity of generated image data. Here, we present three-dimensional microscopy phantoms fabricated using two-photon photolithography from transparent resins that exhibit both broadband visible autofluorescence and Raman scattering across the fingerprint and C-H stretching regions. Suitable for analysis using optical profilometry, the phantoms were dimensionally calibrated with SI traceability using a metrological confocal microscope. Immersible in air and common aqueous imaging media, the phantoms are compatible with a wide variety of optical microscopy techniques, including one and two-photon excited fluorescence and coherent Raman scattering microscopy. We employed a forked wedge design to validate image deconvolution results and a stacked lattice phantom to recover image distortion matrices under realistic biological imaging conditions. We demonstrate the impact of correcting chromatic offsets and axial scaling errors for a representative application: analysis of a cell seeded scaffold using confocal laser scanning fluorescence microscopy. These phantoms provide a versatile platform for calibration, quality control and validation of multimodal imaging pipelines and improved quantitative optical microscopy.

bioengineering↗

bioRxiv2026

Open Fourier Ptychographic Microscopy (OpenFPM)

Fourier ptychographic microscopy (FPM) uses sequential multi-angle illumination and iterative phase retrieval to recover a high-resolution complex image from a series of low-resolution brightfield and darkfield images. We present OpenFPM, an open-source FPM platform in which conventional and optomechanical hardware is replaced with compact, low-cost 3D printed components. Illumination, sample and objective positioning, and camera triggering are controlled using a Python-based interface on a Raspberry Pi microcomputer. With a 10 x /0.25 NA objective lens and 636 nm illumination, OpenFPM experimentally achieves amplitude and phase reconstructions with an effective synthetic NA of 0.90 over a 1 mm field-of-view. This platform gives researchers accessible and affordable hardware for developing and testing LED-array microscopy techniques for a range of biomedical imaging applications.

biophysics↗

bioRxiv2022

MesoTIRF: a Total Internal Reflection Fluorescence illuminator for axial super-resolution membrane imaging at the mesoscale

Total Internal Reflection Fluorescence (TIRF) illumination bypasses the axial diffraction limit of light by using an evanescent field to excite fluorophores close to a sample substrate. TIRF illumination significantly improves image contrast, allowing researchers to study membrane structure and dynamics with localized reductions in photobleaching. However, a significant limitation of most TIRF microscopes is the relatively small field of view (FOV). TIRF objectives require a high numerical aperture (NA) to generate the evanescent wave. Such lenses invariably have a high magnification and result in a [~] 50 {micro}m diameter imaging field, requiring many subsequent images for accurate statistical analysis. Waveguide and prism-based TIRF systems are, in principle, compatible with lower magnification lenses to widen the FOV but these have a correspondingly low NA and lateral resolution. To overcome these limitations, we present a prism-based TIRF illuminator for the Mesolens - a specialist objective lens with the unusual combination of low magnification and high NA. This new imaging mode - MesoTIRF - enables TIRF imaging across a 4.4 mm x 3.0 mm FOV. We demonstrate evanescent wave illumination of cell specimens, and show the multi-wavelength capability of the modality across more than 700 cells in a single image. MesoTIRF images have up to a 6-fold improvement in signal-to-background ratio compared to widefield epi-fluorescence illumination, and we illustrate the benefit of this improved contrast for the detection and quantification of focal adhesions in fixed cells. Fluorescence intensities and resolvable structural detail do not vary considerably in homogeneity across the MesoTIRF FOV.

biophysics↗

bioRxiv2022

Super-resolved imaging deciphers ligand dependent membrane behaviour of the onco-immunogenic CCR5 receptor.

The ability of tumors to establish a pro-tumorigenic microenvironment is becoming an important point of investigation in the search for new therapeutics. Tumors form microenvironments in part by the education of immune cells attracted via chemotactic axes such as that of CCR5-CCL5. Further, CCR5 upregulation by cancer cells, coupled with its association with pro-tumorigenic features such as drug-resistance and metastasis, has suggested CCR5 as a target for therapeutic inhibition. However, with several conformational "pools" being reported, phenotypic investigations must be capable of unveiling heterogeneity. Addressing this challenge, we performed structured illumination (SIM) and Partially TIRF coupled HILO (PaTCH) microscopy for super-resolution imaging and single-molecule imaging of CCR5 in fixed cells. Determining the positions of super-resolved CCR5 assemblies revealed a non-random spatial orientation. Further, intensity-tracking of assemblies revealed a distribution of molecular stoichiometries indicative of dimeric sub-units independent of CCL5 perturbation. These biophysical methods can provide important insights into the structure and function of onco-immunogenic receptors and a plethora of other biomolecules. HighlightsO_LIWe use SIM and novel PaTCH microscopy for precise bioimaging and single-molecule tracking of receptor protein CCR5 in model cell line C_LIO_LIBy tracking the position of CCR5 assemblies we conclude that they are clustered in the plasma membrane beyond a level expected from a random distribution C_LIO_LIWe use these high-precision data to determine molecular stoichiometries of CCR5 assemblies C_LI

biophysics↗

bioRxiv2018

Analysis and Prediction of Unplanned Intensive Care Unit Readmission using Recurrent Neural Networks with Long Short-Term Memory

BackgroundUnplanned readmission of a hospitalized patient is an extremely undesirable outcome as the patient may have been exposed to additional risks. The rates of unplanned readmission are, therefore, regarded as an important performance indicator for the medical quality of a hospital and healthcare system. Identifying high-risk patients likely to suffer from readmission before release benefits both the patients and the medical providers. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to develop efficient discharge decision-making support system for physicians.\n\nMethods and FindingsWe used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate) that are significant in time series with temporal dependencies, which cannot be properly captured by traditional static models, but can be captured by our proposed deep neural network based model. We incorporate multiple types of features including chart events, demographic, and ICD9 embeddings. Our machine learning models identifies ICU readmissions at a higher sensitivity rate (0.742) and an improved Area Under the Curve (0.791) compared with traditional methods. We also illustrate the importance of each portion of the features and different combinations of the models to verify the effectiveness of the proposed model.\n\nConclusionOur manuscript highlights the ability of machine learning models to improve our ICU decision making accuracy, and is a real-world example of precision medicine in hospitals. These data-driven results enable clinicians to make assisted decisions within their patient cohorts. This knowledge could have immediate implications for hospitals by improving the detection of possible readmission. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.

bioinformatics↗