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Abstract Members of the familyTrichomeriaceae,belonging to theChaetothyrialesorder and theAscomycotaphylum, are known for their capability to inhabit hostile environments characterized by extreme temperatures, oligotrophic conditions, drought, or presence of toxic compounds. The genusKnufiaencompasses many polyextremophilic species. In this report, the genomic and morphological features of the strain FJI-L2-BK-P2 presented, which was isolated from the Mars 2020 mission spacecraft assembly facility located at the Jet Propulsion Laboratory in Pasadena, California. The identification is based on sequence alignment for marker genes, multi-locus sequence analysis, and whole genome sequence phylogeny. The morphological features were studied using a diverse range of microscopic techniques (bright field, phase contrast, differential interference contrast and scanning electron microscopy). The phylogenetic marker genes of the strain FJI-L2-BK-P2 exhibited highest similarities with type strain ofKnufia obscura(CBS 148926^T) that was isolated from the gas tank of a car in Italy. To validate the species identity, whole genomes of both strains (FJI-L2-BK-P2 and CBS 148926^T) were sequenced, annotated, and strain FJI-L2-BK-P2 was confirmed asK. obscura.The morphological analysis and description of the genomic characteristics ofK. obscuraFJI-L2-BK-P2 may contribute to refining the taxonomy ofKnufiaspecies. Key morphological features are reported in thisK. obscurastrain, resembling microsclerotia and chlamydospore-like propagules. These features known to be characteristic features in black fungi which could potentially facilitate their adaptation to harsh environments. 

Abstract Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation–assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning–based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. Significance:An end-to-end pipeline for deep learning–assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.