Cervical Artery Dissection in Young Adults in the Stroke in Young Fabry Patients (sifap1) Study

Background: Patients with carotid artery dissection (CAD) have been reported to have different vascular risk factor profiles and clinical outcomes to those with vertebral artery dissection (VAD). However, there are limited data from recent, large international studies comparing risk factors and clinical features in patients with cervical artery dissection (CeAD) with other TIA or ischemic stroke (IS) patients of similar age and sex. Methods: We analysed demographic, clinical and risk factor profiles in TIA and IS patients ≤55 years of age with and without CeAD in the large European, multi-centre, Stroke In young FAbry Patients 1 (sifap1) study. Patients were further categorised according to age (younger: 18-44 years; middle-aged: 45-55 years), sex, and site of dissection. Results: Data on the presence of dissection were available in 4,208 TIA and IS patients of whom 439 (10.4%) had CeAD: 196 (50.1%) had CAD, 195 (49.9%) had VAD, and 48 had multiple artery dissections or no information regarding the dissected artery. The prevalence of CAD was higher in women than in men (5.9 vs. 3.8%, p < 0.01), whereas the prevalence of VAD was similar in women and men (4.6 vs. 4.7%, n.s.). Patients with VAD were younger than patients with CAD (median = 41 years (IQR = 35-47 years) versus median = 45 years (IQR = 39-49 years); p < 0.01). At stroke onset, about twice as many patients with either CAD (54.0 vs. 23.1%, p < 0.001) or VAD (63.4 vs. 36.6%, p < 0.001) had headache than patients without CeAD and stroke in the anterior or posterior circulation, respectively. Compared to patients without CeAD, hypertension, concomitant cardiovascular diseases and a patent foramen ovale were significantly less prevalent in both CAD and VAD patients, whereas tobacco smoking, physical inactivity, obesity and a family history of cerebrovascular diseases were found less frequently in CAD patients, but not in VAD patients. A history of migraine was observed at a similar frequency in patients with CAD (31%), VAD (27.8%) and in those without CeAD (25.8%). Conclusions: We identified clinical features and risk factor profiles that are specific to young patients with CeAD, and to subgroups with either CAD or VAD compared to patients without CeAD. Therefore, our data support the concept that certain vascular risk factors differentially affect the risk of CAD and VAD

Physics of Seismo-electromagnetic Phenomena

The seismo-electromagnetic phenomena (SEMG) are integrated in a relatively recent research field that studies diverse phenomena such as: unusual seismo-electrical signals [1], abnormal ultra-low-frequency (ULF) seismo-electromagnetic emissions [2], very-low-frequency (VLF) and low-frequency (LF) radiobroadcast anomalies associated with ionosphere perturbations [3], variation of total electron content of the ionosphere [4], and atypical infrared emissions [5], all related with the preparatory stage of impending earthquakes. In the past, like many other branches of science like Quantum Mechanics, SEMG have been responsible for intense debates about its credibility, in this case concerning its applicability to short-term earthquake prediction [6]. In fact, the development of a truly pre-quake forecasting system is still an elusive plan, but SEM emissions are now a very well established effect extensively reported in literature. Nevertheless, much of the Physics implicated is still not fully understood. Thus, our main effort is directed towards a systematic field observation of SEMG effects and the development of both constructive theoretical models and laboratorial experiments to promote a better understanding of the Physics engaged in these phenomena. In this presentation we will present a sum up of our recent achievements [7,8,9], focusing future work and improvements. [1] A. Konstantaras, et al., On the electric field transient anomaly observed at the time of the Kythira M=6.9 earthquake on January 2006, Nat. Hazards Earth Syst. Sci. 7, 677 (2007). [2] T. Bleier, et al., Investigation of ULF magnetic pulsations, air conductivity changes, and infra red signatures associated with the 30 October Alum Rock M5.4 earthquake, Nat. Hazards Earth Syst. Sci. 9, 585 (2009). [3] P. Biagi, et al., An overview on preseismic anomalies in LF radio signals revealed in Italy by wavelet analysis, Annals of Geophysics 51, 237 (2008). [4] V. Chauhan, et al., Ultra-low-frequency (ULF) and total electron content (TEC) anomalies observed at Agra and their association with regional earthquakes, Journal of Geodynamics 48, 68 (2009). [5] D. Ouzounov, et al., Outgoing long wave radiation variability from IR satellite data prior to major earthquakes, Tectonophysics 431, 211 (2007). [6] S. Uyeda, et al., Short-term earthquake prediction: Current status of seismo-electromagnetics, Tectonophysics 470, 205 (2009). [7] H.G. Silva, et al., Atmospheric electrical field anomalies associated with seismic activity, Nat. Hazards Earth Syst. Sci. 11, 987 (2011). [8] H. G. Silva, et al., Electric transport in different granitic rocks, EGU General Assembly 2011 (EGU 2011), 3-8 April 2011, Vienna (Austria). [9] H.G. Silva, et al., Piezoelectric effect during solid fracture causing electromagnetic emissions, International Conference on Computational Modelling of Fracture and Failure (CFRAC 2011), 6-8 June 2011, Barcelona (Spain)

Nodal precession of WASP-33b for 11 yr by Doppler tomographic and transit photometric observations

Stars and planetary system

Background rejection in NEXT using deep neural networks

[EN] We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the Ministerio de Economia y Competitividad of Spain and FEDER under grants CONSOLIDER-Ingenio 2010 CSD2008-0037 (CUP), FIS2014-53371-C04 and the Severo Ochoa Program SEV-2014-0398; GVA under grant PROMETEO/2016/120. Fermilab is operated by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the United States Department of Energy. JR acknowledges support from a Fulbright Junior Research Award.Renner, J.; Farbin, A.; Muñoz Vidal, J.; Benlloch-Rodríguez, J.; Botas, A.; Ferrario, P.; Gómez-Cadenas, J.... (2017). Background rejection in NEXT using deep neural networks. Journal of Instrumentation. 12. https://doi.org/10.1088/1748-0221/12/01/T01004S1