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Fourier ptychographic microscopy (FPM) reconstructs high-resolution images through multiple iterations on a large number of sub-images at different angles, a process that is time-consuming. For a long time, various methods for optimizing the efficiency of FPM based on the acquisition process and algorithms have been proposed. However, there has been no specific analysis of the impact that the sub-images involved in the reconstruction have on the final result. In this Letter, we conduct the first, to our knowledge, analysis of the impact of a single sub-image on the reconstruction result of a high-resolution image in different numbers of iterations and obtain a curve depicting the change in image quality after the sub-images are involved in the reconstruction in different cycles. By analyzing this curve, the sub-images that exert a negative impact on the resulting image are exported along with their corresponding LED positions. On this basis, we propose the concept of bright-field spectral overlap ratio to distinguish whether the sub-images have a positive impact on the reconstruction results under different acquisition conditions and remove the sub-images that have a negative impact on the results during the iterative process. Both simulation and real experimental results demonstrate that our algorithm can reduce the reconstruction time while maintaining image quality. Moreover, it can be combined with other methods to improve reconstruction efficiency.
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http://dx.doi.org/10.1364/OL.533856 | DOI Listing |
PLoS One
September 2025
NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology University College London, London, United Kingdom.
Objectives: To describe the research principles and cohort characteristics of the multi-disciplinary Project HERCULES, an innovative model of safe high-volume outpatient eye-care service for patients with stable chronic eye diseases. Results and analyses of the workstreams within Project HERCULES will be reported elsewhere. The rationale was to improve eye-care capacity in the National Health Service (NHS) in England through the creation of technician-delivered monitoring in a large retail-unit in a London shopping-centre, with remote asynchronous review of results by clinicians (named Eye-Testing and Review through Asynchronous Clinic (Eye-TRAC)).
View Article and Find Full Text PDFCereb Cortex
August 2025
Section on Functional Imaging Methods & Functional MRI Core Facility, National Institute of Mental Health, 10 Center Drive, Rm 1D80, Bethesda, MD 20892, United States.
Statistical Parametric Mapping (SPM) has been profoundly influential to neuroimaging as it has fostered rigorous, statistically grounded structure for model-based inferences that have led to mechanistic insights about the human brain over the past 30 years. The statistical constructs shared with the world through SPM have been instrumental for deriving meaning from neuroimaging data; however, they require simplifying assumptions which can provide results that, while statistically sound, may not accurately reflect the mechanisms of brain function. A platform that fosters the exploration of the rich and varying neuronal and physiologic underpinnings of the measured signals and their associations to behavior and physiologic measures needs a different set of tools.
View Article and Find Full Text PDFBackground: Transforming Clinical Practice Guideline (CPG) recommendations into computer readable language is a complex and ongoing process that requires significant resources, including time, expertise, and funds. The objective is to provide an extension of the widely used GIN-McMaster Guideline Development Checklist (GDC) and Tool for the development of computable guidelines (CGs).
Methods: Based on an outcome from the Human Centered Design (HCD) workshop hosted by the Guidelines International Network North America (GIN-NA), a team was formed to develop the checklist extension.
Nat Microbiol
September 2025
Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.
View Article and Find Full Text PDFBackground: The study aimed to adapt a stress and well-being intervention delivered via a mobile health (mHealth) app for Latinx Millennial caregivers. This demographic, born between 1981 and 1996, represents a significant portion of caregivers in the United States, with unique challenges due to higher mental distress and poorer physical health compared to non-caregivers. Latinx Millennial caregivers face additional barriers, including higher uninsured rates and increased caregiving burdens.
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