Aligning pathology images with proteins and language improves generalization of biomarker prediction.
Discrete diffusion models on protein sequences can generate novel proteins.
Predicting evolutionary rate is an effective pretraining task for genome language models.
Challenging benchmarks for protein engineering applications.
A systematic analysis of intrinstically disordered regions in human proteins.
Sign language recognition models do not learn phonology.
A comprehensive benchmark for morphological profiling.
Autoregressive image generation synthesizes microscopy images of proteins in cells.
Children are underrepresented in public medical imaging datasets.
A matched dataset of cell painting and single cell transcriptomics.
Using evolution to condition generative models of enhancers designs sequences without prior knowledge of function.
Expanding sign language dictionaries without extensive ML model retraining.
Strategies for looking up signs in dictionaries.
Exploring how ML and sign language experts can collaborate.
Integrating microscopy images and chemical compounds improves image representations.
Deep learning-based feature extractors for single cell microscopy images are confounded by backgroudn cells.
Proposed single-cell foundation models fail to outperform basic baselines zero-shot.
A corpus of 254 STEM Wikipedia articles interpreted into over 300 hours of American Sign Language.
A critical meta-analysis of recent AI paper in sign languages reveals systematic biases.
Convolutional models are competitive with transformers for protein sequences.
A generative model of protein structure using diffusion models, inspired by how proteins fold.
A systematic benchmark of protein language models reveal that they do not scale for any tasks except structure.
Optimal transport can correct for batch effects in digital pathology.
A large-scale crowdsourced dataset of isolated signs in ASL advances sign language recognition.
Simply rearranging data leads to robust deep learning for microscopy.
Self-supervised learning exploiting principles of comparative genomics can help us understand the intrinsically disordered dark proteome.
A large scale collection of publicly curated microscopy images.
Flow-based generative models can synthesize microscopy images from molecules.
We argue that evolution effectively acts as phylogenetic augmentation for contrastive learning.
We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells.
We created a public dataset of 132,209 images of mouse cells, COOS-7, to test how robust classifiers are to covariate shifts.
By training models with a self-supervised learning task, we learn highly effective representations of protein biology with no labels.
Integrating 400,000 images from 24 experiments conducted on yeast to reveal novel aspects of cell biology.
Matrix metalloproteinase2 has been implicated in tendon pathology caused by repetitive movements. However, its activity in the early stages of the tendon’s response to overuse, and its presence in the circulation as a possible indicator of tendon degradation, remain unknown. Human tendon cells were repetitively stretched for 5 days, and the rabbit Achilles tendon complex underwent repetitive motion 3× per week for 2 weeks. Quantitative polymer chain reaction analysis was performed to detect matrix metalloproteinase2/14 and tissue inhibitor of matrix metalloproteinase2 messenger ribonucleic acid of cells and rabbit tissue, and matrix metalloproteinase2 protein levels were determined with an enzyme linked immunoassay. Matrix metalloproteinase2 activity was examined using zymography of the conditioned media, tendon and serum. Immunohistochemistry was used to localize matrix metalloproteinase2 in tendon tissue, and the density of fibrillar collagen in tendons was examined using second harmonic generation microscopy. Tendon cells stretched with high strain or high frequency demonstrated increased matrix metalloproteinase2 messenger ribonucleic acid and protein levels. Matrix metalloproteinase2 activity was increased in the rabbit Achilles tendon tissue at weeks 1 and 2; however, serum activity was only increased at week 1. After 2 weeks of exercise, the collagen density was lower in specific regions of the exercised rabbit Achilles tendon complex. Matrix metalloproteinase2 expression in exercised rabbit Achilles tendons was detected surrounding tendon fibroblasts. Repetitive mechanical stimulation of tendon cells results in a small increase in matrix metalloproteinase2 levels, but it appears unlikely that serum matrix metalloproteinase2 will be a useful indicator of tendon overuse injury.
A simple k-nearest neighbor algorithm can locally correct for covariate shifts when comparing image screens.
The mechanisms that regulate angiogenic activity in injured or mechanically loaded tendons are poorly understood. The present study examined the potential role of angiopoietin-like 4 (ANGPTL4) in the angiogenic response of tendons subjected to repetitive mechanical loading or injury. Cyclic stretching of human tendon fibroblasts stimulated the expression and release of ANGPTL4 protein via transforming growth factor-β (TGF-β) and hypoxia-inducible factor 1α (HIF-1α) signalling, and the released ANGPTL4 was pro-angiogenic. Angiogenic activity was increased following ANGPTL4 injection into mouse patellar tendons, whereas the patellar tendons of ANGPTL4 knockout mice displayed reduced angiogenesis following injury. In human rotator cuff tendons, the expression of ANGPTL4 was correlated with the density of tendon endothelial cells. To our knowledge, this is the first study characterizing a role of ANGPTL4 in the tendon. ANGPTL4 may assist in the regulation of vascularity in the injured or mechanically loaded tendon. TGF-β and HIF-1α comprise two signalling pathways that modulate the expression of ANGPTL4 by mechanically stimulated tendon fibroblasts and, in the future, these could be manipulated to influence tendon healing or adaptation.
Clinical studies have suggested an association between dyslipidemia and tendon injuries or chronic tendon pain; the mechanisms underlying this association are not yet known. The objectives of this study were (1) to evaluate the impact of a high fat diet on the function of load-bearing tendons and on the distribution in tendons of oxidized low density lipoprotein (oxLDL), and (2) to examine the effect of oxLDL on tendon fibroblast proliferation and gene expression.
Mechanical stimulation (e.g. slow heavy loading) has proven beneficial in the rehabilitation of chronic tendinopathy, however the optimal parameters of stimulation have not been experimentally determined. In this study of mechanically stimulated human tenocytes, the influence of rest insertion and cycle number on (1) the protein and mRNA levels of type I and III collagen; (2) the mRNA levels of transforming growth factor beta (TGFB1) and scleraxis (SCXA); and (3) tenocyte morphology, were assessed.
Despite the widespread use of CD34-family sialomucins (CD34, podocalyxin and endoglycan) as vascular endothelial cell markers, there is remarkably little known of their vascular function. Podocalyxin (gene name Podxl), in particular, has been difficult to study in adult vasculature as germ-line deletion of podocalyxin in mice leads to kidney malformations and perinatal death. We generated mice that conditionally delete podocalyxin in vascular endothelial cells (PodxlΔEC mice) to study the homeostatic role of podocalyxin in adult mouse vessels. Although PodxlΔEC adult mice are viable, their lungs display increased lung volume and changes to the matrix composition. Intriguingly, this was associated with increased basal and inflammation-induced pulmonary vascular permeability. To further investigate the etiology of these defects, we isolated mouse pulmonary endothelial cells. PodxlΔEC endothelial cells display mildly enhanced static adhesion to fibronectin but spread normally when plated on fibronectin-coated transwells. In contrast, PodxlΔEC endothelial cells exhibit a severely impaired ability to spread on laminin and, to a lesser extent, collagen I coated transwells. The data suggest that, in endothelial cells, podocalyxin plays a previously unrecognized role in maintaining vascular integrity, likely through orchestrating interactions with extracellular matrix components and basement membranes, and that this influences downstream epithelial architecture.
We have previously found an increased mast cell density in tendon biopsies from patients with patellar tendinopathy compared to controls. This study examined the influence of mast cells on basic tenocyte functions, including production of the inflammatory mediator prostaglandin E2 (PGE2), extracellular matrix remodeling and matrix metalloproteinase (MMP) gene transcription, and collagen synthesis.
Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented accuracy out to two weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence (AI) models. However, model skill drops precipitously at subseasonal timescales (2 - 6 weeks ahead), due to compounding errors, systemic model biases, and the chaotic nature of the atmosphere. To counter this degradation, we introduce probabilistic bias correction (PBC), a machine learning framework that substantially reduces systematic error by learning to correct historical probabilistic forecasts. When applied to the leading dynamical and AI models from the European Centre for Medium-Range Weather Forecasts (ECMWF), PBC doubles the modest subseasonal skill of the AI Forecasting System and improves the skill of the operationally-debiased dynamical model for 91% of pressure, 92% of temperature, and 98% of precipitation targets. We designed PBC for operational deployment, and, in ECMWF’s 2025 real-time forecasting competition, its global forecasts placed first for all weather variables and lead times, outperforming the dynamical models from six operational forecasting centers, an international dynamical multi-model ensemble, ECMWF’s AI Forecasting System, and the forecasting systems of 34 teams worldwide. These probabilistic skill gains translate into more accurate prediction of extreme events and have the potential to improve agricultural planning, energy management, and disaster preparedness in vulnerable communities.