Indirect signals from light neutralinos in supersymmetric models without gaugino mass unification

We examine indirect signals produced by neutralino self-annihilations, in the galactic halo or inside celestial bodies, in the frame of an effective MSSM model without gaugino-mass unification at a grand unification scale. We compare our theoretical predictions with current experimental data of gamma-rays and antiprotons in space and of upgoing muons at neutrino telescopes. Results are presented for a wide range of the neutralino mass, though our discussions are focused on light neutralinos. We find that only the antiproton signal is potentially able to set constraints on very low-mass neutralinos, below 20 GeV. The gamma-ray signal, both from the galactic center and from high galactic latitudes, requires significantly steep profiles or substantial clumpiness in order to reach detectable levels. The up-going muon signal is largely below experimental sensitivities for the neutrino flux coming from the Sun; for the flux from the Earth an improvement of about one order of magnitude in experimental sensitivities (with a low energy threshold) can make accessible neutralino masses close to O, Si and Mg nuclei masses, for which resonant capture is operative.Comment: 17 pages, 1 tables and 5 figures, typeset with ReVTeX4. The paper may also be found at http://www.to.infn.it/~fornengo/papers/indirect04.ps.gz or through http://www.astroparticle.to.infn.it/. Limit from BR(Bs--> mu+ mu-) adde

Early Integration of Palliative Care into Standard Oncology Care: Evidence and Overcoming Barriers to Implementation

In Canada, widespread discussion of medical aid in dying (http://www.parl.gc.ca/HousePublications/Publication.[...

Principles of clinical trials

Chapter in textboo

Improving retinanet for CT lesion detection with dense masks from weak recist labels.

Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Recent work on CT lesion detection employs two-stage region proposal based methods trained with centroid or bounding-box annotations. We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging. Specifically, we optimize the anchor configurations using a differential evolution search algorithm. For training, we leverage the response evaluation criteria in solid tumors (RECIST) annotation which are measured in clinical routine. We incorporate dense masks from weak RECIST labels, obtained automatically using GrabCut, into the training objective, which in combination with other advancements yields new state-of-the-art performance. We evaluate our method on the public DeepLesion benchmark, consisting of 32,735 lesions across the body. Our one-stage detector achieves a sensitivity of 90.77% at 4 false positives per image, significantly outperforming the best reported methods by over 5%