Carrasco-Ribelles, L. A., Pardo-Mas, J. R., Tortajada, S., Saez, C., Valdivieso, B., & Garcia-Gomez, J. M. (2021). Predicting morbidity by local similarities in multi-scale patient trajectories. J. Biomed. Inform., 120, 103837–9pp.
Abstract: Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.
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NA64 Collaboration(Cazzaniga, C. et al), & Molina Bueno, L. (2021). Probing the explanation of the muon (g-2) anomaly and thermal light dark matter with the semi-visible dark photon channel. Eur. Phys. J. C, 81(10), 959–6pp.
Abstract: We report the results of a search for a new vector boson (A') decaying into two dark matter particles chi 1 chi 2 of different mass. The heavier chi(2) particle subsequently decays to chi 1 and an off-shell Dark Photon A'* -> e(+)e(-). For a sufficiently largemass splitting, this model can explain in terms of new physics the recently confirmed discrepancy observed in themuon anomalous magnetic moment at Fermilab. Remark- ably, it also predicts the observed yield of thermal dark matter relic abundance. A detailed Monte-Carlo simulation was used to determine the signal yield and detection efficiency for this channel in the NA64 setup. The results were obtained reanalyzing the previous NA64 searches for an invisible decay A' -> chi(chi) over bar and axion-like or pseudo-scalar particles -> gamma gamma. With this method, we exclude a significant portion of the parameter space justifying the muon g-2 anomaly and being compatible with the observed dark matter relic density for A' masses from 2m(e) up to 390 MeV and mixing parameter e between 3 x 10(-5) and 2 x 10(-2).
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Centelles Chulia, S., Srivastava, R., & Vicente, A. (2021). The inverse seesaw family: Dirac and Majorana. J. High Energy Phys., 03(3), 248–29pp.
Abstract: After developing a general criterion for deciding which neutrino mass models belong to the category of inverse seesaw models, we apply it to obtain the Dirac analogue of the canonical Majorana inverse seesaw model. We then generalize the inverse seesaw model and obtain a class of inverse seesaw mechanisms both for Majorana and Dirac neutrinos. We further show that many of the models have double or multiple suppressions coming from tiny symmetry breaking “mu -parameters”. These models can be tested both in colliders and with the observation of lepton flavour violating processes.
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Chala, M., & Titov, A. (2021). Neutrino masses in the Standard Model effective field theory. Phys. Rev. D, 104(3), 035002–8pp.
Abstract: We compute the leading-logarithmic correction to the neutrino mass matrix in the Standard Model effective field theory (SMEFT) to dimension seven. In the limit of negligible lepton and down-type quark Yukawa couplings, it receives contributions from the Weinberg dimension-five operator as well as from 11 dimension-six and five dimension-seven independent interactions. Two of the main implications we derive from this result are the following. First, we find dimension-seven operators which, despite violating lepton number, do not renormalize neutrino masses at one loop. And second, we demonstrate that the presence of dimension-six operators around the TeV scale can modify the Standard Model prediction by up to O(50%). Our result comprises also one step forward towards the renormalization of the SMEFT to order v(3)/Lambda(3).
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Chianese, M., Fiorillo, D. F. G., Hajjar, R., Miele, G., & Saviano, N. (2021). Constraints on heavy decaying dark matter with current gamma-ray measurements. J. Cosmol. Astropart. Phys., 11(11), 035–13pp.
Abstract: Among the several strategies for indirect searches of dark matter, a very promising one is to look for the gamma-rays from decaying dark matter. Here we use the most up-to-date upper bounds on the gamma-ray flux from 10(5) to 10(11) GeV, obtained from CASA-MIA, KASCADE, KASCADE-Grande, Pierre Auger Observatory, Telescope Array and EAS-MSU. We obtain global limits on dark matter lifetime in the range of masses in m(DM) = [10(7)-10(15)] GeV. We provide the bounds for a set of decay channels chosen as representatives. The constraints derived here are new and cover a region of the parameter space not yet explored. We compare our results with the projected constraints from future neutrino telescopes, in order to quantify the improvement that will be obtained by the complementary high-energy neutrino searches.
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