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Background Theracyte is a polytetrafluoroethylene membrane macroencapsulation system designed to induce neovascularization at the tissue interface, protecting the cells from host's immune rejection, thereby circumventing the problem of limited half-life and variation in circulating levels. Endostatin is a potent inhibitor of angiogenesis and tumor growth. Continuous delivery of endostatin improves the efficacy and potency of the antitumoral therapy. The purpose of this study was to determine whether recombinant fibroblasts expressing endostatin encapsulated in Theracyte immunoisolation devices can be used for delivery of this therapeutic protein for treatment of mice bearing B16F10 melanoma and Ehrlich tumors. Results Mice were inoculated subcutaneously with melanoma (B16F10 cells) or Ehrlich tumor cells at the foot pads. Treatment began when tumor thickness had reached 0.5 mm, by subcutaneous implantation of 10 7 recombinant encapsulated or non-encapsulated endostatin producer cells. Similar melanoma growth inhibition was obtained for mice treated with encapsulated or non-encapsulated endostatin-expressing cells. The treatment of mice bearing melanoma tumor with encapsulated endostatin-expressing cells was decreased by 50.0%, whereas a decrease of 56.7% in tumor thickness was obtained for mice treated with non-encapsulated cells. Treatment of Ehrlich tumor-bearing mice with non-encapsulated endostatin-expressing cells reduced tumor thickness by 52.4%, whereas lower tumor growth inhibition was obtained for mice treated with encapsulated endostatin-expressing cells: 24.2%. Encapsulated endostatin-secreting fibroblasts failed to survive until the end of the treatment. However, endostatin release from the devices to the surrounding tissues was confirmed by immunostaining. Decrease in vascular structures, functional vessels and extension of the vascular area were observed in melanoma microenvironments. Conclusions This study indicates that immunoisolation devices containing endostatin-expressing cells are effective for the inhibition of the growth of melanoma and Ehrlich tumors. Macroencapsulation of engineered cells is therefore a reliable platform for the refinement of innovative therapeutic strategies against tumors.

We characterize the ability of the Dark Energy Camera (DECam) to perform relative astrometry across its 500 Mpix, 3-deg² science field of view and across four years of operation. This is done using internal comparisons of ∼4 × 10⁷ measurements of high signal-to-noise ratio stellar images obtained in repeat visits to fields of moderate stellar density, with the telescope dithered to move the sources around the array. An empirical astrometric model includes terms for optical distortions; stray electric fields in the CCD detectors; chromatic terms in the instrumental and atmospheric optics; shifts in CCD relative positions of up to ≈10 μm when the DECam temperature cycles; and low-order distortions to each exposure from changes in atmospheric refraction and telescope alignment. Errors in this astrometric model are dominated by stochastic variations with typical amplitudes of 10–30 mas (in a 30 s exposure) and 5′–10′ coherence length, plausibly attributed to Kolmogorov-spectrum atmospheric turbulence. The size of these atmospheric distortions is not closely related to the seeing. Given an astrometric reference catalog at density ≈0.7 arcmin−2, e.g., from Gaia, the typical atmospheric distortions can be interpolated to ≈7 mas rms accuracy (for 30 s exposures) with 1′ coherence length in residual errors. Remaining detectable error contributors are 2–4 mas rms from unmodelled stray electric fields in the devices, and another 2–4 mas rms from focal plane shifts between camera thermal cycles. Thus the astrometric solution for a single DECam exposure is accurate to 3–6 mas (≈0.02 pixels, or ≈300 nm) on the focal plane, plus the stochastic atmospheric distortion.

We characterize the ability of the Dark Energy Camera (DECam) to perform relative astrometry across its 500 Mpix, 3-deg2 science field of view and across four years of operation. This is done using internal comparisons of ∼4 × 107 measurements of high signal-to-noise ratio stellar images obtained in repeat visits to fields of moderate stellar density, with the telescope dithered to move the sources around the array. An empirical astrometric model includes terms for optical distortions; stray electric fields in the CCD detectors; chromatic terms in the instrumental and atmospheric optics; shifts in CCD relative positions of up to 10 m when the DECam temperature cycles; and low-order distortions to each exposure from changes in atmospheric refraction and telescope alignment. Errors in this astrometric model are dominated by stochastic variations with typical amplitudes of 10-30 mas (in a 30 s exposure) and 5′-10′ coherence length, plausibly attributed to Kolmogorov-spectrum atmospheric turbulence. The size of these atmospheric distortions is not closely related to the seeing. Given an astrometric reference catalog at density 0.7 arcmin − 2 , e.g., from Gaia, the typical atmospheric distortions can be interpolated to 7 mas rms accuracy (for 30 s exposures) with 1 ′ coherence length in residual errors. Remaining detectable error contributors are 2-4 mas rms from unmodelled stray electric fields in the devices, and another 2-4 mas rms from focal plane shifts between camera thermal cycles. Thus the astrometric solution for a single DECam exposure is accurate to 3-6 mas ( 0.02 pixels, or 300 nm) on the focal plane, plus the stochastic atmospheric distortion.

The stellar occultation technique is a powerful tool to study distant small solar system bodies. Currently, around 2 500 trans-neptunian objects (TNOs) and Centaurs are known. With the astrometry from Gaia and large surveys like the Large Synoptic Survey Telescope (LSST), accurate predictions of occultation events will be available to tens of thousands of TNOs and Centaurs and boost the knowledge of the outer solar system.

We present a catalog of 689 galaxy cluster candidates detected at significance \\(\\xi>4\\) via their thermal Sunyaev-Zel'dovich (SZ) effect signature in 95 and 150 GHz data from the 500-square-degree SPTpol survey. We use optical and infrared data from the Dark Energy Camera and the Wide-field Infrared Survey Explorer (WISE) and \\spitzer \\ satellites, to confirm 544 of these candidates as clusters with \\(\\sim94\\%\\) purity. The sample has an approximately redshift-independent mass threshold at redshift \\(z>0.25\\) and spans \\(1.5 \\times 10^{14} < M_{500c} < 9.1 \\times 10^{14}\\) \\(M_\\odot/h_{70}\\) \\ and \\(0.031\\). We use external radio data from the Sydney University Molonglo Sky Survey (SUMSS) to estimate contamination to the SZ signal from synchrotron sources. The contamination reduces the recovered \\(\\xi\\) by a median value of 0.032, or \\(\\sim0.8\\%\\) of the \\(\\xi=4\\) threshold value, and \\(\\sim7\\%\\) of candidates have a predicted contamination greater than \\(\\Delta \\xi = 1\\). With the exception of a small number of systems \\((<1\\%)\\), an analysis of clusters detected in single-frequency 95 and 150 GHz data shows no significant contamination of the SZ signal by emission from dusty or synchrotron sources. This cluster sample will be a key component in upcoming astrophysical and cosmological analyses of clusters. The SPTpol millimeter-wave maps and associated data products used to produce this sample are available at https://pole.uchicago.edu/public/data/sptpol_500d_clusters/index.html, and the NASA LAMBDA website. An interactive sky server with the SPTpol maps and Dark Energy Survey data release 2 images is also available at NCSA https://skyviewer.ncsa.illinois.edu.

Number counts of galaxy clusters across redshift are a powerful cosmological probe, if a precise and accurate reconstruction of the underlying mass distribution is performed -- a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing by clusters has become the method of choice to perform this measurement. We measure and validate the weak gravitational lensing (WL) signature in the shape of galaxies observed in the first 3 years of the DES Y3 caused by galaxy clusters selected in the first all-sky survey performed by SRG/eROSITA. These data are then used to determine the scaling between X-ray photon count rate of the clusters and their halo mass and redshift. We empirically determine the degree of cluster member contamination in our background source sample. The individual cluster shear profiles are then analysed with a Bayesian population model that self-consistently accounts for the lens sample selection and contamination, and includes marginalization over a host of instrumental and astrophysical systematics. To quantify the accuracy of the mass extraction of that model, we perform mass measurements on mock cluster catalogs with realistic synthetic shear profiles. This allows us to establish that hydro-dynamical modelling uncertainties at low lens redshifts (\\(z<0.6\\)) are the dominant systematic limitation. At high lens redshift the uncertainties of the sources' photometric redshift calibration dominate. With regard to the X-ray count rate to halo mass relation, we constrain all its parameters. This work sets the stage for a joint analysis with the number counts of eRASS1 clusters to constrain a host of cosmological parameters. We demonstrate that WL mass calibration of galaxy clusters can be performed successfully with source galaxies whose calibration was performed primarily for cosmic shear experiments.

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered \"Planet 9\", may be present in the outer solar system. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a data set consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimized, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) = 0.996 0.001. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.

We present an alternative calibration of the MagLim lens sample redshift distributions from the Dark Energy Survey (DES) first three years of data (Y3). The new calibration is based on a combination of a Self-Organising Maps based scheme and clustering redshifts to estimate redshift distributions and inherent uncertainties, which is expected to be more accurate than the original DES Y3 redshift calibration of the lens sample. We describe in detail the methodology, we validate it on simulations and discuss the main effects dominating our error budget. The new calibration is in fair agreement with the fiducial DES Y3 redshift distributions calibration, with only mild differences (\\(<3\\sigma\\)) in the means and widths of the distributions. We study the impact of this new calibration on cosmological constraints, analysing DES Y3 galaxy clustering and galaxy-galaxy lensing measurements, assuming a \\(\\Lambda\\)CDM cosmology. We obtain \\(\\Omega_{\\rm m} = 0.30\\pm 0.04\\), \\(\\sigma_8 = 0.81\\pm 0.07 \\) and \\(S_8 = 0.81\\pm 0.04\\), which implies a \\(\\sim 0.4\\sigma\\) shift in the \\(\\Omega_{\\rm}-S_8\\) plane compared to the fiducial DES Y3 results, highlighting the importance of the redshift calibration of the lens sample in multi-probe cosmological analyses.

Low Surface Brightness Galaxies (LSBGs) are excellent probes of quenching and other environmental processes near massive galaxies. We study an extensive sample of LSBGs near massive hosts in the local universe that are distributed across a diverse range of environments. The LSBGs with surface-brightness \\(\\mu_{\\rm eff,g}> \\)24.2 mag arcsec\\(^{-2}\\) are drawn from the Dark Energy Survey Year 3 catalog while the hosts with masses \\(9.0< log(M_{\\star}/M_{\\odot})< 11.0\\) comparable to the Milky Way and the Large Magellanic Cloud are selected from the z0MGS sample. We study the projected radial density profiles of LSBGs as a function of their color and surface brightness around hosts in both the rich Fornax-Eridanus cluster environment and the low-density field. We detect an overdensity with respect to the background density, out to 2.5 times the virial radius for both hosts in the cluster environment and the isolated field galaxies. When the LSBG sample is split by \\(g-i\\) color or surface brightness \\(\\mu_{\\rm eff,g}\\), we find the LSBGs closer to their hosts are significantly redder and brighter, like their high surface-brightness counterparts. The LSBGs form a clear 'red sequence' in both the cluster and isolated environments that is visible beyond the virial radius of the hosts. This suggests a pre-processing of infalling LSBGs and a quenched backsplash population around both host samples. However, the relative prominence of the 'blue cloud' feature implies that pre-processing is ongoing near the isolated hosts compared to the cluster hosts.