A breast cancer stem cell niche supported by juxtacrine signalling from monocytes and macrophages

The cell-biological program termed the epithelial-mesenchymal transition (EMT) confers on cancer cells mesenchymal traits and an ability to enter the cancer stem cell (CSC) state. However, the interactions between CSCs and their surrounding microenvironment are poorly understood. Here we show that tumour-associated monocytes and macrophages (TAMs) create a CSC niche through juxtacrine signalling with CSCs. We performed quantitative proteomic profiling and found that the EMT program upregulates the expression of CD90, also known as Thy1, and EphA4, which mediate the physical interactions of CSCs with TAMs by directly binding with their respective counter-receptors on these cells. In response, the EphA4 receptor on the carcinoma cells activates Src and NF-κ B. In turn, NF-κ B in the CSCs induces the secretion of a variety of cytokines that serve to sustain the stem cell state. Indeed, admixed macrophages enhance the CSC activities of carcinoma cells. These findings underscore the significance of TAMs as important components of the CSC niche.National Institutes of Health (U.S.) (Grant R01-CA078461)National Institutes of Health (U.S.) (Grant P01-CA080111)National Institutes of Health (U.S.) (Grant U54-CA163109

Observations of the Crab Nebula and Pulsar with the Large-Sized Telescope Prototype of the Cherenkov Telescope Array

CTA (Cherenkov Telescope Array) is the next generation ground-based observatory for gamma-ray astronomy at very-high energies. The Large-Sized Telescope prototype (\LST{}) is located at the Northern site of CTA, on the Canary Island of La Palma. LSTs are designed to provide optimal performance in the lowest part of the energy range covered by CTA, down to $\simeq 20$ GeV. \LST{} started performing astronomical observations in November 2019, during its commissioning phase, and it has been taking data since then. We present the first \LST{} observations of the Crab Nebula, the standard candle of very-high energy gamma-ray astronomy, and use them, together with simulations, to assess the basic performance parameters of the telescope. The data sample consists of around 36 hours of observations at low zenith angles collected between November 2020 and March 2022. \LST{} has reached the expected performance during its commissioning period - only a minor adjustment of the preexisting simulations was needed to match the telescope behavior. The energy threshold at trigger level is estimated to be around 20 GeV, rising to $\simeq 30$ GeV after data analysis. Performance parameters depend strongly on energy, and on the strength of the gamma-ray selection cuts in the analysis: angular resolution ranges from 0.12 to 0.40 degrees, and energy resolution from 15 to 50\%. Flux sensitivity is around 1.1\% of the Crab Nebula flux above 250 GeV for a 50-h observation (12\% for 30 minutes). The spectral energy distribution (in the 0.03 - 30 TeV range) and the light curve obtained for the Crab Nebula agree with previous measurements, considering statistical and systematic uncertainties. A clear periodic signal is also detected from the pulsar at the center of the Nebula.Comment: Submitted to Ap

Deep learning-based imaging in radio interferometry

Context. The sparse layouts of radio interferometers result in an incomplete sampling of the sky in Fourier space which leads to artifacts in the reconstructed images. Cleaning these systematic effects is essential for the scientific use of radiointerferometric images. Aims. Established reconstruction methods are often time-consuming, require expert knowledge, and suffer from a lack of reproducibility. We have developed a prototype deep learning-based method that generates reproducible images in an expedient fashion. Methods. To this end, we take advantage of the efficiency of convolutional neural networks to reconstruct image data from incomplete information in Fourier space. The neural network architecture is inspired by super-resolution models that utilize residual blocks. Using simulated data of radio galaxies that are composed of Gaussian components, we trained deep learning models whose reconstruction capability is quantified using various measures. Results. The reconstruction performance is evaluated on clean and noisy input data by comparing the resulting predictions with the true source images. We find that source angles and sizes are well reproduced, while the recovered fluxes show substantial scatter, albeit not worse than existing methods without fine-tuning. Finally, we propose more advanced approaches using deep learning that include uncertainty estimates and a concept to analyze larger images

Anaerobic Alcohol Conversion to Carbonyl Compounds over Nanoscaled Rh-Doped SrTiO3 under Visible Light

Photocatalytic oxidation of organic compounds on semiconductors provides a mild approach for organic synthesis and solar energy utilization. Herein, we identify the key points for the photocatalytic oxidation over Pt-loaded Rh-doped strontium titanate allowing the conversion of alcohols efficiently and selectively to aldehydes and ketones under anaerobic conditions and visible light with an apparent quantum efficiency of pure benzyl alcohol oxidation at 420 nm of <= 49.5%. Mechanistic investigations suggest that thermodynamically the controlled valence band edge position via Rh doping provides a suitable oxidation ability of photogenerated holes, avoiding the powerful hydroxyl radical intermediates prone to overoxidation resulting in high selectivity. Kinetically, oxygen vacancies induced by Rh3+ substitution in the SrTiO3 lattice not only favor the dissociative adsorption of alcohols yielding alkoxy species but also induce the weakening of the alpha-C-H bond facilitating its cleavage by the photogenerated holes. Pt nanoparticles deposited as a cocatalyst contribute to the final hydrogen evolution

An Algorithm that Predicts the Viability and the Yield of Human Hepatocytes Isolated from Remnant Liver Pieces Obtained from Liver Resections

<div><p>Isolated human primary hepatocytes are an essential <i>in vitro</i> model for basic and clinical research. For successful application as a model, isolated hepatocytes need to have a good viability and be available in sufficient yield. Therefore, this study aims to identify donor characteristics, intra-operative factors, tissue processing and cell isolation parameters that affect the viability and yield of human hepatocytes. Remnant liver pieces from tissue designated as surgical waste were collected from 1034 donors with informed consent. Human hepatocytes were isolated by a two-step collagenase perfusion technique with modifications and hepatocyte yield and viability were subsequently determined. The accompanying patient data was collected and entered into a database. Univariate analyses found that the viability and the yield of hepatocytes were affected by many of the variables examined. Multivariate analyses were then carried out to confirm the factors that have a significant relationship with the viability and the yield. It was found that the viability of hepatocytes was significantly decreased by the presence of fibrosis, liver fat and with increasing gamma-glutamyltranspeptidase activity and bilirubin content. Yield was significantly decreased by the presence of liver fat, septal fibrosis, with increasing aspartate aminotransferase activity, cold ischemia times and weight of perfused liver. However, yield was significantly increased by chemotherapy treatment. In conclusion, this study determined the variables that have a significant effect on the viability and the yield of isolated human hepatocytes. These variables have been used to generate an algorithm that can calculate projected viability and yield of isolated human hepatocytes. In this way, projected viability can be determined even before isolation of hepatocytes, so that donors that result in high viability and yield can be identified. Further, if the viability and yield of the isolated hepatocytes is lower than expected, this will highlight a methodological problem that can be addressed.</p></div

Liver variables that have significant relationships with the viability (%) of hepatocytes after linear regression analyses.

<p>Figures show relationships between viability and (<b>A</b>) fibrosis, (<b>B</b>) liver fat or (<b>C</b>) Ludwig score. Values were deemed significant when P<0.05. For the variable of Ludwig score, variables not sharing the same alphabet are significantly different, <i>P</i><0.05.</p

Variables considered for statistical analyses.

<p>Variables considered for statistical analyses.</p

Variables measured in the blood or serum that have significant relationships with the yield (million hepatocytes/gram liver) after linear regression analyses.

<p>Figures show relationships between yield and (<b>A</b>) alkaline phosphatase activity (AP; U/L), (<b>B</b>) aspartate aminotransferase activity (GOT; U/L), (<b>C</b>) gamma-glutamyltranspeptidase activity (GGT; U/L), (<b>D</b>) alanine aminotransferase activity (GPT; U/L), (<b>E</b>) bilirubin (mg/dL), (<b>F</b>) partial thromboplastin time (PTT; s) or (<b>G</b>) quick value (%). Values were deemed significant when P<0.05.</p

The model for calculating projected viability is appropriate.

<p>(<b>A</b>) Residuals versus fitted plot. (<b>B</b>) Normal quantile plot. (<b>C</b>) Square root of the standardised residuals versus fitted plot. (<b>D</b>) Standardised residuals versus leverage plot.</p