Biology subjects

Glass, B.

Publications and source records attributed to Glass, B..

6 recordsLinked to original sources

bioRxiv2025

A comparison shopper's guide to forest datasets

Recent advances in remote sensing, data availability, and cloud-based computing have led to a rapid expansion of publicly accessible datasets characterizing forest cover and land use. These datasets are widely used in ecological research, natural resource management, and policymaking. However, the sheer number of available products--and the often-subtle differences among them--pose significant challenges for users seeking the most appropriate dataset for their specific objectives. Here, we evaluate 27 derived products from 12 publicly available sources that quantify tree or forest cover and use across the conterminous United States (CONUS), with temporal coverage ranging from 5 to 30 years. The products include both satellite-based remote sensing data and ground-based national forest inventory data. We ask: How, why, and where do these datasets differ in their estimates of forest extent and change over time? Our analysis reveals that estimates of total forest area at the CONUS scale differ by over 2,000,000 km{superscript 2}, and correlations among forest change estimates vary widely in both direction and statistical significance. To support dataset selection and interpretation, we developed an open-access map comparison tool using Google Earth Engine. Our findings highlight the substantial implications of dataset choice for understanding forest dynamics and underscore the need for careful selection and transparent reporting in forest-related analyses.

ecology↗

bioRxiv2025

Quantification of collagen and associated features from H&E-stained whole slide pathology images across cancer types using a physics-based deep learning model

BackgroundCollagen is the major component of the extracellular matrix (ECM). Collagen structural organization undergoes significant transformation during tumorigenesis. The visualization of collagen in histological tissue sections would aid in the study of tumor growth, encapsulation, and invasion. However, such visualization requires the use of special stains such as Picrosirius Red (PSR) or Massons Trichrome (MT), or more recently, second-harmonic generation imaging (SHG) in unstained tissue sections. However, PSR and MT both suffer from significant inter- (and intra-) lab stain variabilities, and SHG, while considered a ground truth by many, suffers from issues of system complexity/reliability, cost, and speed/throughput. These technical hurdles limit more widespread assessment of collagen in tissue samples. MethodsUsing high-contrast, high-throughput polarization imaging on PSR-stained slides to generate ground truth training polarization images, we developed a deep learning model (iQMAI) to infer the presence of collagen directly from hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with high specificity. After iQMAI inference across WSIs, individual collagen fibers were extracted, and features describing overall collagen intensity and fiber morphology were computed. iQMAI pixel-and feature-wise outputs were compared to ground truth polarization imaging to assess model performance. The trained iQMAI model was deployed on H&E-stained WSI from the TCGA LUAD, LUSC, LIHC, and PAAD datasets for evaluation. iQMAI-derived collagen features were compared to tissue composition, gene expression, and overall survival. ResultsThe iQMAI model shows significant generalization across multiple indications. iQMAI collagen predictions were similar to polarization imaging measurements of the same sample, with a mean structural similarity index (SSIM) of 0.84 (95% CI 0.69-0.93), a mean patch-wise RMSE of 0.04 (95% CI 0.02-0.08), and a linear correlation (R2=0.93). Comparing features of the collagen fibers extracted from iQMAI vs. polarization images yielded similar linear correlations between computed fiber tortuosity, length, width, and relative angle. The relationship between collagen fiber density and fibroblast density was distinct in non-small cell lung cancer (LUAD and LUSC), hepatocellular carcinoma (LIHC), and pancreatic ductal adenocarcinoma (PAAD). In PAAD, fiber density and fiber width were both negatively associated with the LRRC-15 gene expression signature, and increased fiber width was associated with longer overall survival. ConclusionsiQMAI is a deep learning model that accurately predicts collagen from an H&E-stained WSI, allowing for spatially resolved quantification of collagen morphology and enabling investigation of the interplay between collagen and other TME components. We demonstrate an example of the utility of iQMAI-based collagen assessment in PAAD, where collagen features are associated with immunosuppressive cancer-associated fibroblasts and overall survival. Understanding the relationship between collagen, the tumor microenvironment composition, and disease progression may aid the development of effective immunotherapies in PAAD and other cancer types.

pathology↗

bioRxiv2022

Characterization of a sperm motility signaling pathway in a gonochoric coral suggests conservation across sexual systems

Many marine invertebrates liberate their gametes into the water column broadcast spawning, where fertilization hinges upon the successful activation of sperm motility. Here, we investigated the molecular mechanisms underpinning sperm motility in the broadcast spawning coral Astrangia poculata. We found that cytosolic alkalinization activates the pH-sensing enzyme soluble adenylyl cyclase (sAC), followed by motility, in A. poculata sperm. In addition, we show for the first time in any cnidarian that sAC activity is required to activate protein kinase A (PKA) in sperm, and that PKA activity is required for the initiation of sperm motility. Ultrastructures of A. poculata sperm displayed morphological homology to other gonochoric cnidarians, and investigation of cnidarian proteomes revealed that sAC, the central signaling node in the sperm motility pathway, demonstrates broad structural and functional conservation across a diversity of cnidarian species. Ultimately, these results suggest that the role of sAC signaling in sperm motility is conserved between sperm from gonochoric and hermaphroditic corals, which is surprising given their morphological dissimilarities. This study also offers insight into the evolution of the mechanisms controlling metazoan sperm motility. Summary statementFor broadcast spawning marine invertebrates, the initiation of sperm motility is essential for fertilization. Here, we provide evidence for conservation of a sperm motility pathway across sexual systems in corals.

physiology↗

bioRxiv2020

Maturation of the matrix and viral membrane of HIV-1

Gag - the main structural protein of HIV-1 - is recruited to the plasma membrane for virus assembly by its matrix (MA) domain. Gag is subsequently cleaved into its component domains, causing structural maturation to repurpose the virion for cell entry. We determined the structure and arrangement of MA within immature and mature HIV-1, providing a basis to understand MAs role in virus assembly. Unexpectedly, we found that MA rearranges during maturation, to form a new, hexameric lattice in which the acyl chain of a phospholipid extends out of the membrane to bind a pocket in MA. Our data suggest that proteolytic maturation of HIV-1 not only achieves assembly of the viral capsid surrounding the genome, but extends to repurpose the membrane-bound MA lattice for an entry or post-entry function, and causes partial removal of 2,500 acyl chains from the viral membrane.

microbiology↗

bioRxiv2020

Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer

While computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction, lack of interpretability remains a significant barrier to clinical integration. In this study, we present a novel approach for predicting clinically-relevant molecular phenotypes from histopathology whole-slide images (WSIs) using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5,700 WSIs to train deep learning models for high-resolution tissue classification and cell detection across entire WSIs in five cancer types. Combining cell- and tissue-type models enables computation of 607 HIFs that comprehensively capture specific and biologically-relevant characteristics of multiple tumors. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment (TME) and can predict diverse molecular signatures, including immune checkpoint protein expression and homologous recombination deficiency (HRD). Our HIF-based approach provides a novel, quantitative, and interpretable window into the composition and spatial architecture of the TME.

bioinformatics↗

bioRxiv2018

Response of extremophile microbiome to a rare rainfall reveals a two-step adaptation mechanism

Understanding the mechanisms underlying microbial resistance and resilience to perturbations is essential to predict the impact of climate change on Earths ecosystems. However, the resilience and adaptation mechanisms of microbial communities to natural perturbations remain relatively unexplored, particularly in extreme environments. The response of an extremophile community inhabiting halite (salt rocks) in the Atacama Desert to a catastrophic rainfall provided the opportunity to characterize and de-convolute the temporal response of a highly specialized community to a major disturbance. With shotgun metagenomic sequencing, we investigated the halite microbiome taxonomic composition and functional potential over a 4-year longitudinal study, uncovering the dynamics of the initial response and of the recovery of the community after a rainfall event. The observed changes can be recapitulated by two general modes of community shifts - a rapid Type 1 shift and a more gradual Type 2 adjustment. In the initial response, the community entered an unstable intermediate state after stochastic niche re-colonization, resulting in broad predicted protein adaptations to increased water availability. In contrast, during recovery, the community returned to its former functional potential by a gradual shift in abundances of the newly acquired taxa. The general characterization and proposed quantitation of these two modes of community response could potentially be applied to other ecosystems, providing a theoretical framework for prediction of taxonomic and functional flux following environmental changes.

microbiology↗