Radiation-induced lymphopenia does not impact treatment efficacy in a mouse tumor model

Radiation-induced lymphopenia is a common occurrence in radiation oncology and an established negative prognostic factor, however the mechanisms underlying the relationship between lymphopenia and inferior survival remain elusive. The relevance of lymphocyte co-irradiation as critical normal tissue component at risk is an emerging topic of high clinical relevance, even more so in the context of potentially synergistic radiotherapy-immunotherapy combinations. The impact of the radiotherapy treatment volume on the lymphocytes of healthy and tumor-bearing mice was investigated in a novel mouse model of radiation-induced lymphopenia. Using an image-guided small-animal radiotherapy treatment platform, translationally relevant tumor-oriented volumes of irradiation with an anatomically defined increasing amount of normal tissue were irradiated, with a focus on the circulating blood and lymph nodes. In healthy mice, the influence of irradiation with increasing radiotherapy treatment volumes was quantified on the level of circulating blood cells and in the spleen. A significant decrease in the lymphocytes was observed in response to irradiation, including the minimally irradiated putative tumor area. The extent of lymphopenia correlated with the increasing volumes of irradiation. In tumor-bearing mice, differential radiotherapy treatment volumes did not influence the overall therapeutic response to radiotherapy alone. Intriguingly, an improved treatment efficacy in mice treated with draining-lymph node co-irradiation was observed in combination with an immune checkpoint inhibitor. Taken together, our study reveals compelling data on the importance of radiotherapy treatment volume in the context of lymphocytes as critical components of normal tissue co-irradiation and highlights emerging challenges at the interface of radiotherapy and immunotherapy. Keywords: Image-guided small animal radiotherapy platform; Lymphopenia; Normal tissue injury; Radioimmunotherapy; Radiotherap

Mikrokurse

In den Lehrplänen einiger Bundesländer gibt es noch keinen eigenständigen Themenbereich Teilchenphysik. Für diesen Fall sind die hier vorgestellten Mikrokurse zusammengestellt worden. Alle Kurse schlagen auf originelle Weise eine Brücke von klassischen Lehrplanthemen zu aktuellen Forschungsgegenständen. Denn viele der im Physikunterricht behandelten Themen lassen sich leicht um einen Bezug zur modernen Physik und insbesondere der Teilchenphysik ergänzen. Der zeitliche Bedarf für die Behandlung eines Kurses beträgt ca. ein bis zwei Unterrichtsstunden. Vorkenntnisse zur Teilchenphysik sind kaum notwendig. Die Mikrokurse können und sollen deshalb auch gerade dort eingesetzt werden, wo nur wenig Zeit zur Verfügung steht oder das Thema Teilchenphysik nicht im Lehrplan verankert ist. Zu jedem Kurs werden Einsatzmöglichkeiten und wünschenswerte Vorkenntnisse der Schüler:innen angegeben. Auf mögliche Erweiterungen und Vertiefungen wird hingewiesen

Is there a Pronounced Giant Dipole Resonance in ^4He?

A four-nucleon calculation of the total ^4He photodisintegration cross section is performed. The full final-state interaction is taken into account for the first time. This is achieved via the method of the Lorentz integral transform. Semi-realistic NN interactions are employed. Different from the known partial two-body ^4He(\gamma,n)^3He and ^4He(\gamma,p)^3H cross sections our total cross section exhibits a pronounced giant resonance. Thus, in contrast to older $(\gamma,np)$ data, we predict quite a strong contribution of the $(\gamma,np)$ channel at the giant resonance peak energy.Comment: 10 pages, Latex (REVTEX), 4 Postscript figures, to appear in Phys. Rev. Let

Comment on ``Large-space shell-model calculations for light nuclei''

In a recent publication Zheng, Vary, and Barrett reproduced the negative quadrupole moment of Li-6 and the low-lying positive-parity states of He-5 by using a no-core shell model. In this Comment we question the meaning of these results by pointing out that the model used is inadequate for the reproduction of these properties.Comment: Latex with Revtex, 1 postscript figure in separate fil

Neural parameters estimation for brain tumor growth modeling

Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a system of partial differential equations, wherein the physiological processes that govern the growth of the tumor are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model is informed from empirical data, e.g., medical images obtained from diagnostic modalities, such as magnetic-resonance imaging. Existing model-observation coupling schemes require a large number of forward integrations of the biophysical model and rely on simplifying assumption on the functional form, linking the output of the model with the image information. In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. The technique allows for explicit evaluation of the posterior distribution of the parameters by sequentially training a mixture-density network, relaxing the constraint on the functional form and reducing the number of samples necessary to propagate through the forward model for the estimation. We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression

Metacognition as Evidence for Evidentialism

Metacognition is the monitoring and controlling of cognitive processes. I examine the role of metacognition in ‘ordinary retrieval cases’, cases in which it is intuitive that via recollection the subject has a justified belief. Drawing on psychological research on metacognition, I argue that evidentialism has a unique, accurate prediction in each ordinary retrieval case: the subject has evidence for the proposition she justifiedly believes. But, I argue, process reliabilism has no unique, accurate predictions in these cases. I conclude that ordinary retrieval cases better support evidentialism than process reliabilism. This conclusion challenges several common assumptions. One is that non-evidentialism alone allows for a naturalized epistemology, i.e., an epistemology that is fully in accordance with scientific research and methodology. Another is that process reliabilism fares much better than evidentialism in the epistemology of memory

Time--delay autosynchronization of the spatio-temporal dynamics in resonant tunneling diodes

The double barrier resonant tunneling diode exhibits complex spatio-temporal patterns including low-dimensional chaos when operated in an active external circuit. We demonstrate how autosynchronization by time--delayed feedback control can be used to select and stabilize specific current density patterns in a noninvasive way. We compare the efficiency of different control schemes involving feedback in either local spatial or global degrees of freedom. The numerically obtained Floquet exponents are explained by analytical results from linear stability analysis.Comment: 10 pages, 16 figure

Long Term Validation of Land Surface Temperature Retrieved from MSG/SEVIRI with Continuous in-Situ Measurements in Africa

Since 2005, the Land Surface Analysis Satellite Application Facility (LSA SAF) operationally retrieves Land Surface Temperature (LST) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG). The high temporal resolution of the Meteosat satellites and their long term availability since 1977 make their data highly valuable for climate studies. In order to ensure that the LSA SAF LST product continuously meets its target accuracy of 2 °C, it is validated with in-situ measurements from four dedicated LST validation stations. Three stations are located in highly homogenous areas in Africa (semiarid bush, desert, and Kalahari semi-desert) and typically provide thousands of monthly match-ups with LSA SAF LST, which are used to perform seasonally resolved validations. An uncertainty analysis performed for desert station Gobabeb yielded an estimate of total in-situ LST uncertainty of 0.8 ± 0.12 °C. Ignoring rainy seasons, the results for the period 2009–2014 show that LSA SAF LST consistently meets its target accuracy: the highest mean root-mean-square error (RMSE) for LSA SAF LST over the African stations was 1.6 °C while mean absolute bias was 0.1 °C. Nighttime and daytime biases were up to 0.7 °C but had opposite signs: when evaluated together, these partially compensated each other

Optimal margin and edge-enhanced intensity maps in the presence of motion and uncertainty

In radiation therapy, intensity maps involving margins have long been used to counteract the effects of dose blurring arising from motion. More recently, intensity maps with increased intensity near the edge of the tumour (edge enhancements) have been studied to evaluate their ability to offset similar effects that affect tumour coverage. In this paper, we present a mathematical methodology to derive margin and edge-enhanced intensity maps that aim to provide tumour coverage while delivering minimum total dose. We show that if the tumour is at most about twice as large as the standard deviation of the blurring distribution, the optimal intensity map is a pure scaling increase of the static intensity map without any margins or edge enhancements. Otherwise, if the tumour size is roughly twice (or more) the standard deviation of motion, then margins and edge enhancements are preferred, and we present formulae to calculate the exact dimensions of these intensity maps. Furthermore, we extend our analysis to include scenarios where the parameters of the motion distribution are not known with certainty, but rather can take any value in some range. In these cases, we derive a similar threshold to determine the structure of an optimal margin intensity map.National Cancer Institute (U.S.) (grant R01-CA103904)National Cancer Institute (U.S.) (grant R01-CA118200)Natural Sciences and Engineering Research Council of Canada (NSERC)Siemens AktiengesellschaftMassachusetts Institute of Technology. Hugh Hampton Young Memorial Fund fellowshi