Ovarian Carcinoma‐Associated Mesenchymal Stem Cells Arise from Tissue‐Specific Normal Stroma

Carcinoma‐associated mesenchymal stem cells (CA‐MSCs) are critical stromal progenitor cells within the tumor microenvironment (TME). We previously demonstrated that CA‐MSCs differentially express bone morphogenetic protein family members, promote tumor cell growth, increase cancer “stemness,” and chemotherapy resistance. Here, we use RNA sequencing of normal omental MSCs and ovarian CA‐MSCs to demonstrate global changes in CA‐MSC gene expression. Using these expression profiles, we create a unique predictive algorithm to classify CA‐MSCs. Our classifier accurately distinguishes normal omental, ovary, and bone marrow MSCs from ovarian cancer CA‐MSCs. Suggesting broad applicability, the model correctly classifies pancreatic and endometrial cancer CA‐MSCs and distinguishes cancer associated fibroblasts from CA‐MSCs. Using this classifier, we definitively demonstrate ovarian CA‐MSCs arise from tumor mediated reprograming of local tissue MSCs. Although cancer cells alone cannot induce a CA‐MSC phenotype, the in vivo ovarian TME can reprogram omental or ovary MSCs to protumorigenic CA‐MSCs (classifier score of >0.96). In vitro studies suggest that both tumor secreted factors and hypoxia are critical to induce the CA‐MSC phenotype. Interestingly, although the breast cancer TME can reprogram bone marrow MSCs into CA‐MSCs, the ovarian TME cannot, demonstrating for the first time that tumor mediated CA‐MSC conversion is tissue and cancer type dependent. Together these findings (a) provide a critical tool to define CA‐MSCs and (b) highlight cancer cell influence on distinct normal tissues providing powerful insights into the mechanisms underlying cancer specific metastatic niche formation. Stem Cells 2019;37:257–269Ovarian cancer reprograms normal tissue derived mesenchymal stem cells (MSCs) into ovarian cancer promoting carcinoma‐associated mesenchymal stem cells (CA‐MSCs) in a tissue specific manner. Ovarian cancer cells convert ovary and omental MSCs into CA‐MSCs but fail to reprogram bone marrow (BM)‐MSCs whereas breast cancer cells convert BM‐MSCs into breast cancer supporting CA‐MSCs.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147827/1/stem2932_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147827/2/stem2932.pd

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40X magnification in 2.5 seconds per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi

Beyond scaling and locality in turbulence

An analytic perturbation theory is suggested in order to find finite-size corrections to the scaling power laws. In the frame of this theory it is shown that the first order finite-size correction to the scaling power laws has following form $S(r) \cong cr^{\alpha_0}[\ln(r/\eta)]^{\alpha_1}$, where $\eta$ is a finite-size scale (in particular for turbulence, it can be the Kolmogorov dissipation scale). Using data of laboratory experiments and numerical simulations it is shown shown that a degenerate case with $\alpha_0 =0$ can describe turbulence statistics in the near-dissipation range $r > \eta$, where the ordinary (power-law) scaling does not apply. For moderate Reynolds numbers the degenerate scaling range covers almost the entire range of scales of velocity structure functions (the log-corrections apply to finite Reynolds number). Interplay between local and non-local regimes has been considered as a possible hydrodynamic mechanism providing the basis for the degenerate scaling of structure functions and extended self-similarity. These results have been also expanded on passive scalar mixing in turbulence. Overlapping phenomenon between local and non-local regimes and a relation between position of maximum of the generalized energy input rate and the actual crossover scale between these regimes are briefly discussed.Comment: extended versio

Lifetimes of states in 19Ne above the 15 O + alpha breakup threshold

The 15O(alpha,gamma)19Ne reaction plays a role in the ignition of Type I x-ray bursts on accreting neutron stars. The lifetimes of states in 19Ne above the 15O + alpha threshold of 3.53 MeV are important inputs to calculations of the astrophysical reaction rate. These levels in 19Ne were populated in the 3He(20Ne,alpha)19Ne reaction at a 20Ne beam energy of 34 MeV. The lifetimes of six states above the threshold were measured with the Doppler shift attenuation method (DSAM). The present measurements agree with previous determinations of the lifetimes of these states and in some cases are considerably more precise

Lifetime of 19Ne*(4.03 MeV)

The Doppler-shift attenuation method was applied to measure the lifetime of the 4.03 MeV state in 19Ne. Utilizing a 3He-implanted Au foil as a target, the state was populated using the 20Ne(3He,alpha)19Ne reaction in inverse kinematics at a 20Ne beam energy of 34 MeV. De-excitation gamma rays were detected in coincidence with alpha particles. At the 1 sigma level, the lifetime was determined to be 11 +4, -3 fs and at the 95.45% confidence level the lifetime is 11 +8, -7 fs.Comment: 6 pages, submitted to Phys. Rev.

The Comoving Infrared Luminosity Density: Domination of Cold Galaxies across 0<z<1

In this paper we examine the contribution of galaxies with different infrared (IR) spectral energy distributions (SEDs) to the comoving infrared luminosity density, a proxy for the comoving star formation rate (SFR) density. We characterise galaxies as having either a cold or hot IR SED depending upon whether the rest-frame wavelength of their peak IR energy output is above or below 90um. Our work is based on a far-IR selected sample both in the local Universe and at high redshift, the former consisting of IRAS 60um-selected galaxies at z<0.07 and the latter of Spitzer 70um selected galaxies across 0.1<z<1. We find that the total IR luminosity densities for each redshift/luminosity bin agree well with results derived from other deep mid/far-IR surveys. At z<0.07 we observe the previously known results: that moderate luminosity galaxies (L_IR<10^11 Lsun) dominate the total luminosity density and that the fraction of cold galaxies decreases with increasing luminosity, becoming negligible at the highest luminosities. Conversely, above z=0.1 we find that luminous IR galaxies (L_IR>10^11 Lsun), the majority of which are cold, dominate the IR luminosity density. We therefore infer that cold galaxies dominate the IR luminosity density across the whole 0<z<1 range, hence appear to be the main driver behind the increase in SFR density up to z~1 whereas local luminous galaxies are not, on the whole, representative of the high redshift population.Comment: 5 pages, 3 figures, accepted for publication in MNRA

A Defective mRNA Cleavage and Polyadenylation Complex Facilitates Expansions of Transcribed (GAA) n Repeats Associated with Friedreich’s Ataxia

Expansions of microsatellite repeats are responsible for numerous hereditary diseases in humans, including myotonic dystrophy and Friedreich's ataxia. Whereas the length of an expandable repeat is the main factor determining disease inheritance, recent data point to genomic trans modifiers that can impact the likelihood of expansions and disease progression. Detection of these modifiers may lead to understanding and treating repeat expansion diseases. Here, we describe a method for the rapid, genome-wide identification of trans modifiers for repeat expansion in a yeast experimental system. Using this method, we found that missense mutations in the endoribonuclease subunit (Ysh1) of the mRNA cleavage and polyadenylation complex dramatically increase the rate of (GAA) n repeat expansions but only when they are actively transcribed. These expansions correlate with slower transcription elongation caused by the ysh1 mutation. These results reveal an interplay between RNA processing and repeat-mediated genome instability, confirming the validity of our approach. Keywords: genome instability; repeat expansion; RNA polyadenylation; RNA processing; transcription-replication conflicts; Friedreich’s ataxia; DNA double-strand breaks; trans-modifiers of repeat expansions; genetic screen; whole-genome sequencin

Claims of Potential Expansion throughout the U.S. by Invasive Python Species Are Contradicted by Ecological Niche Models

BACKGROUND: Recent reports from the United States Geological Survey (USGS) suggested that invasive Burmese pythons in the Everglades may quickly spread into many parts of the U.S. due to putative climatic suitability. Additionally, projected trends of global warming were predicted to significantly increase suitable habitat and promote range expansion by these snakes. However, the ecological limitations of the Burmese python are not known and the possible effects of global warming on the potential expansion of the species are also unclear. METHODOLOGY/PRINCIPAL FINDINGS: Here we show that a predicted continental expansion is unlikely based on the ecology of the organism and the climate of the U.S. Our ecological niche models, which include variables representing climatic extremes as well as averages, indicate that the only suitable habitat in the U.S. for Burmese pythons presently occurs in southern Florida and in extreme southern Texas. Models based on the current distribution of the snake predict suitable habitat in essentially the only region in which the snakes are found in the U.S. Future climate models based on global warming forecasts actually indicate a significant contraction in suitable habitat for Burmese pythons in the U.S. as well as in their native range. CONCLUSIONS/SIGNIFICANCE: The Burmese python is strongly limited to the small area of suitable environmental conditions in the United States it currently inhabits due to the ecological niche preferences of the snake. The ability of the Burmese python to expand further into the U.S. is severely limited by ecological constraints. Global warming is predicted to significantly reduce the area of suitable habitat worldwide, underscoring the potential negative effects of climate change for many species

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a novel, clinically-oriented approach to uncertainty quantification (UQ) for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without UQ, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that UQ thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts