Search

TU Tau (= HD 38218 = HIP 27135) is a binary system consisting of a C-N carbon star primary and an A-type secondary. We report on new photometry and spectroscopy which tracked the recent disappearance of the A-star secondary. The dimming of the A-star was gradual and irregular, with one or more brief brightenings, implying the presence of nonhomogeneities in the carbon star outflow. We also present evidence that the A-star is actively accreting s-process enriched material from the carbon star and suggest that it will therefore eventually evolve into a Barium giant. This is an important system as well because the A-type star can serve as a probe of the outer atmosphere of the carbon star., Comment: 9 pages, 9 figures, 4 tables, a number of amateur observatories made significant contributions to this research. Paper accepted for publication in The Astronomical Journal

Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license., Comment: Updated Figure 2 and Table A.5