Tumor-specific granulocyte/macrophage colony-stimulating factor and interferon γ secretion is associated with in vivo therapeutic efficacy of activated tumor-draining lymph node cells

In this study, cytokine release by tumor-draining lymph node cells sensitized in vitro (IVS-TDLN) was examined and correlated with therapeutic efficacy in adoptive immunotherapy. Mice bearing immunologically distinct MCA 207 and MCA 205 sarcoma tumors were utilized in criss-cross experiments. IVS-TDLN obtained from mice bearing 10-day subcutaneous (s. c.) tumors mediated immunologically specific regression of established 3-day pulmonary metastases, but demonstrated non-specific cytolytic reactivity against both tumors in a 4-h 51 Cr-release assay. By contrast, these IVS-TDLN cells were found specifically to secrete granulocyte/macrophage colony-stimulating factor (GM-CSF) and interferon γ (IFNγ) when restimulated in vitro with irradiated tumor cells. To determine the predictive value of tumor-specific cytokine release with in vivo therapeutic efficacy, a kinetic analysis of antitumor activities of TDLN obtained from animals bearing MCA 207 tumors for increasing lengths of time was performed. IVS-TDLN cells from mice bearing day-7, -10 and-14 s. c. tumors manifested tumor-specific release of GM-CSF and IFNγ, and mediated significant antitumor reactivity in vivo. In contrast IVS-LN cells from day-0 and day-21 tumor-bearing animals did not release significant amounts of GM-CSF and IFNγ, and were not therapeutically efficacious in vivo. Day-4 IVS-TDLN released high levels of GM-CSF and IFNγ non-specifically, and were not therapeutic in adoptive immunotherapy at doses effective for day-7 and day-14 IVS-TDLN cells. In other experiments, IVS cells generated from different lymph node groups in animals bearing 10-day established s. c. tumors were examined and found to have unique profiles of cytokine release. In these studies, the ability of IVS cells to release specifically both cytokines as opposed to one was associated with greater therapeutic efficacy on a per cell basis. Our findings suggest that the tumor-specific releases of GM-CSF and IFNγ are useful parameters to assess the in vivo therapeutic efficacy of immune lymphocytes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46862/1/262_2005_Article_BF01517220.pd

HOLISMOKES -- X. Comparison between neural network and semi-automated traditional modeling of strong lenses

Modeling of strongly gravitationally lensed galaxies is often required in order to use them as astrophysical or cosmological probes. With current and upcoming wide-field imaging surveys, the number of detected lenses is increasing significantly such that automated and fast modeling procedures for ground-based data are urgently needed. This is especially pertinent to short-lived lensed transients in order to plan follow-up observations. Therefore, we present in a companion paper (submitted) a neural network predicting the parameter values with corresponding uncertainties of a Singular Isothermal Ellipsoid (SIE) mass profile with external shear. In this work, we present a newly-developed pipeline glee_auto.py to model consistently any galaxy-scale lensing system. In contrast to previous automated modeling pipelines that require high-resolution images, glee_auto.py is optimized for ground-based images such as those from the Hyper-Suprime-Cam (HSC) or the upcoming Rubin Observatory Legacy Survey of Space and Time. We further present glee_tools.py, a flexible automation code for individual modeling that has no direct decisions and assumptions implemented. Both pipelines, in addition to our modeling network, minimize the user input time drastically and thus are important for future modeling efforts. We apply the network to 31 real galaxy-scale lenses of HSC and compare the results to the traditional models. In the direct comparison, we find a very good match for the Einstein radius especially for systems with $\theta_E \gtrsim 2$". The lens mass center and ellipticity show reasonable agreement. The main discrepancies are on the external shear as expected from our tests on mock systems. In general, our study demonstrates that neural networks are a viable and ultra fast approach for measuring the lens-galaxy masses from ground-based data in the upcoming era with $\sim10^5$ lenses expected.Comment: 17+28 pages, 7+31 figures, 2+5 tables, submitted to A&

Activation by anti-CD3 of tumor-draining lymph node cells for specific adoptive immunotherapy

Lymph nodes draining progressive tumors contain tumor-sensitized but not functional preeffector T lymphocytes. These cells can acquire antitumor reactivity after stimulation with tumor cells and interleukin-2 (IL-2). We demonstrated here that, in the absence of tumor cells, preeffector cells could be stimulated and expanded by sequential culture with anti-CD3 monoclonal antibody and IL-2. The adoptive transfer of such activated cells mediated immunologically specific reductions of established pulmonary metastases. The therapeutic effects could be enhanced by the administration of IL-2. This activation represents a secondary immune response because effector cells could be generated only from tumor-draining but not from normal or adjuvant-stimulated lymph nodes. Furthermore, treatment of advanced metastases with these cells resulted in prolongation of survival and cure of the disease. Thus, anti-CD3 may serve as a universal reagent for activating tumor-sensitized T lymphocytes for cancer therapy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29366/1/0000436.pd

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. Especially with the large number of detections in current and upcoming surveys such as the Rubin Legacy Survey of Space and Time (LSST), it is timely to investigate in automated and fast analysis techniques beyond the traditional and time consuming Markov chain Monte Carlo sampling methods. Building upon our convolutional neural network (CNN) presented in Schuldt et al. (2021b), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center $x$ and $y$, ellipticity $e_x$ and $e_y$, Einstein radius $\theta_E$) and the external shear ($\gamma_{ext,1}$, $\gamma_{ext,2}$) from ground-based imaging data. In contrast to our CNN, this ResNet further predicts a 1$\sigma$ uncertainty for each parameter. To train our network, we use our improved pipeline from Schuldt et al. (2021b) to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find overall very good recoveries for the SIE parameters, while differences remain in predicting the external shear. From our tests, most likely the low image resolution is the limiting factor for predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to predict the next appearing image and time delays of lensed transients in time. Therefore, we also present the performance of the network on these quantities in comparison to our simulations. Our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU such that we are able to process efficiently the huge amount of expected galaxy-scale lenses in the near future.Comment: 16 pages, including 11 figures, accepted for publication by A&

Adoptive immunotherapy of cancer with polyclonal, 10(8)-fold hyperexpanded, CD4(+ )and CD8(+ )T cells

T cell-mediated cancer immunotherapy is dose dependent and optimally requires participation of antigen-specific CD4(+ )and CD8(+ )T cells. Here, we isolated tumor-sensitized T cells and activated them in vitro using conditions that led to greater than 10(8)-fold numerical hyperexpansion of either the CD4(+ )or CD8(+ )subset while retaining their capacity for in vivo therapeutic efficacy. Murine tumor-draining lymph node (TDLN) cells were segregated to purify the CD62L(low )subset, or the CD4(+ )subset thereof. Cells were then propagated through multiple cycles of anti-CD3 activation with IL-2 + IL-7 for the CD8(+ )subset, or IL-7 + IL-23 for the CD4(+ )subset. A broad repertoire of TCR Vβ families was maintained throughout hyperexpansion, which was similar to the starting population. Adoptive transfer of hyper-expanded CD8(+ )T cells eliminated established pulmonary metastases, in an immunologically specific fashion without the requirement for adjunct IL-2. Hyper-expanded CD4(+ )T cells cured established tumors in intracranial or subcutaneous sites that were not susceptible to CD8(+ )T cells alone. Because accessibility and antigen presentation within metastases varies according to anatomic site, maintenance of a broad repertoire of both CD4(+ )and CD8(+ )T effector cells will augment the overall systemic efficacy of adoptive immunotherapy

Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks

Quasars experiencing strong lensing offer unique viewpoints on subjects related to the cosmic expansion rate, the dark matter profile within the foreground deflectors, and the quasar host galaxies. Unfortunately, identifying them in astronomical images is challenging since they are overwhelmed by the abundance of non-lenses. To address this, we have developed a novel approach by ensembling cutting-edge convolutional networks (CNNs) -- for instance, ResNet, Inception, NASNet, MobileNet, EfficientNet, and RegNet -- along with vision transformers (ViTs) trained on realistic galaxy-quasar lens simulations based on the Hyper Suprime-Cam (HSC) multiband images. While the individual model exhibits remarkable performance when evaluated against the test dataset, achieving an area under the receiver operating characteristic curve of $>$97.3% and a median false positive rate of 3.6%, it struggles to generalize in real data, indicated by numerous spurious sources picked by each classifier. A significant improvement is achieved by averaging these CNNs and ViTs, resulting in the impurities being downsized by factors up to 50. Subsequently, combining the HSC images with the UKIRT, VISTA, and unWISE data, we retrieve approximately 60 million sources as parent samples and reduce this to 892,609 after employing a photometry preselection to discover $z>1.5$ lensed quasars with Einstein radii of $\theta_\mathrm{E}<5$ arcsec. Afterward, the ensemble classifier indicates 3080 sources with a high probability of being lenses, for which we visually inspect, yielding 210 prevailing candidates awaiting spectroscopic confirmation. These outcomes suggest that automated deep learning pipelines hold great potential in effectively detecting strong lenses in vast datasets with minimal manual visual inspection involved.Comment: Accepted for publication in the Astronomy & Astrophysics journal. 28 pages, 11 figures, and 3 tables. We welcome comments from the reade

Toward an internally consistent astronomical distance scale

Accurate astronomical distance determination is crucial for all fields in astrophysics, from Galactic to cosmological scales. Despite, or perhaps because of, significant efforts to determine accurate distances, using a wide range of methods, tracers, and techniques, an internally consistent astronomical distance framework has not yet been established. We review current efforts to homogenize the Local Group's distance framework, with particular emphasis on the potential of RR Lyrae stars as distance indicators, and attempt to extend this in an internally consistent manner to cosmological distances. Calibration based on Type Ia supernovae and distance determinations based on gravitational lensing represent particularly promising approaches. We provide a positive outlook to improvements to the status quo expected from future surveys, missions, and facilities. Astronomical distance determination has clearly reached maturity and near-consistency.Comment: Review article, 59 pages (4 figures); Space Science Reviews, in press (chapter 8 of a special collection resulting from the May 2016 ISSI-BJ workshop on Astronomical Distance Determination in the Space Age

Gravitational Lensing

Gravitational lensing has developed into one of the most powerful tools for the analysis of the dark universe. This review summarises the theory of gravitational lensing, its main current applications and representative results achieved so far. It has two parts. In the first, starting from the equation of geodesic deviation, the equations of thin and extended gravitational lensing are derived. In the second, gravitational lensing by stars and planets, galaxies, galaxy clusters and large-scale structures is discussed and summarised.Comment: Invited review article to appear in Classical and Quantum Gravity, 85 pages, 15 figure

LensWatch: I. Resolved HST Observations and Constraints on the Strongly-Lensed Type Ia Supernova 2022qmx ("SN Zwicky")

Supernovae (SNe) that have been multiply-imaged by gravitational lensing are rare and powerful probes for cosmology. Each detection is an opportunity to develop the critical tools and methodologies needed as the sample of lensed SNe increases by orders of magnitude with the upcoming Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope. The latest such discovery is of the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky"; Goobar et al. 2022) at z = 0.3544. SN Zwicky was discovered by the Zwicky Transient Facility (ZTF) in spatially unresolved data. Here we present follow-up Hubble Space Telescope observations of SN Zwicky, the first from the multi-cycle "LensWatch" program (www.lenswatch.org). We measure photometry for each of the four images of SN Zwicky, which are resolved in three WFC3/UVIS filters (F475W, F625W, F814W) but unresolved with WFC3/IR F160W, and produce an analysis of the lensing system using a variety of independent lens modeling methods. We find consistency between time delays estimated with the single epoch of HST photometry and the lens model predictions constrained through the multiple image positions, with both inferring time delays of <1 day. Our lens models converge to an Einstein radius of (0.168+0.009-0.005)", the smallest yet seen in a lensed SN. The "standard candle" nature of SN Zwicky provides magnification estimates independent of the lens modeling that are brighter by ~1.5 mag and ~0.8 mag for two of the four images, suggesting significant microlensing and/or additional substructure beyond the flexibility of our image-position mass models