Biology subjects

Schwarz, C. G.

Publications and source records attributed to Schwarz, C. G..

3 recordsLinked to original sources

bioRxiv2026

Multi-Contrast MRI Inputs Enable Self-Consistent Tissue Segmentation & Robust Perivascular Space Identification

Different MRI image contrasts are designed to highlight various tissue properties and combining them allows extension of probabilistic segmentation beyond the commonly used "gray-white-CSF" models. This work describes a fully automated method that combines T1-weighted, T2-FLAIR, and conventional T2-weighted images to provide internal consistency across prediction of tissue segmentations including segmentation of superficial and deep gray matter, white matter hyperintensities, and MR-visible perivascular spaces. Results from 773 imaging datasets from 403 participants in the Mayo Clinic Study of Aging and Mayo Clinic Alzheimers Disease Research Center (ADRC) are presented.

neuroscience↗

bioRxiv2025

Characterization of the maize (Zea mays)-Ustilago maydis interaction in a warming climate

Maize is among the most grown crops worldwide. Concurrently, infections with Ustilago maydis, causing corn smut disease, occur in all major growing regions. This is accompanied by loss of biomass, yields, and silage quality. As global warming increases, modeling analysis predicts an increase in pathogen infestation. Nevertheless, there are no maize lines resistant to U. maydis and no effective fungicides available. Based on the evaluation of climate data for the State of Bavaria (Germany), maize-U. maydis infection trials and RNAseq analysis were conducted on a variety of maize cultivars under different temperature conditions generating a large phenotypic and transcriptomic dataset to determine the influence of temperature changes and differences in plant susceptibility. Even a minimal increase in temperature resulted in increased symptoms and significant variances in expression. Infection-gene expression association analysis followed by in vivo tests identified GIBBERELLIC ACID STIMULATED TRANSCRIPT-LIKE4 (GSL4) and{gamma} -aminobutyric acid as important factors for the infection.

plant biology↗

bioRxiv2022

Synthesizing Images of Tau Pathology from Cross-modal Neuroimaging using Deep Learning

Given the prevalence of dementia and the development of pathology-specific disease modifying therapies, high-value biomarker strategies to inform medical decision making are critical. In-vivo tau positron emission tomography (PET) is an ideal target as a biomarker for Alzheimers disease diagnosis and treatment outcome measure. However, tau PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that impute tau PET images from more widely-available cross-modality imaging inputs. Participants (n=1,192) with brain MRI, fluorodeoxyglucose (FDG) PET, amyloid PET, and tau PET were included. We found that a CNN model can impute tau PET images with high accuracy, the highest being for the FDG-based model followed by amyloid PET and MRI. In testing implications of AI-imputed tau PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and MRI-based models utilized the non-local input from physically remote ROIs to estimate the tau PET, but this was not the case for the PiB-based model. This implies that the model can learn the distinct biological relationship between FDG PET, MRI, and tau PET from the relationship between amyloid PET and tau PET. Our study suggests that extending neuroimagings use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

bioengineering↗