Double pulses and cascades above 2 PeV in IceCube

IceCube collaboration has seen an unexpected population of high energy neutrinos compatible with an astrophysical origin. We consider two categories of events that can help to diagnose cosmic neutrinos: double pulse, that may allow us to clearly discriminate the cosmic component of tau neutrinos; cascades with deposited energy above 2 PeV, including events produced by electron antineutrinos at the Glashow resonance, that can be used to investigate the neutrino production mechanisms. We show that one half of the double pulse signal is due to the neutrinos spectral region already probed by IceCube. By normalizing to HESE data, we find that 10 more years are required to obtain 90% probability to observe a double pulse. The cascades above 2 PeV provide us a sensitive probe of the high energy tail of the neutrino spectrum and are potentially observable, but even in this case, the dependence on type of the source is mild. In fact we find that pp or p{\gamma} mechanisms give a difference in the number of cascades above 2 PeV of about 25 % that can be discriminated at 2{\sigma} in about 50 years of data taking.Comment: 20 pages, 7 figures, accepted for publication in EPJ

ADAGSS: Automatic Dataset Generation for Semantic Segmentation

A common issue in medical deep learning research is the creation of dataset for training the neural networks. Medical data collection is also tied-up by privacy laws and even if a lot of medical data are available, often their elaboration can be time demanding. This problem can be avoided using neural networks architectures that can achieve a good predicting precision with few images (e.g. U-Net). In the case of semantic segmentation, the dataset generation is even more cumbersome since it requires the creation of segmentation masks manually. Some automatic ground-truth creation techniques may be employed like filtering, thresholding and Self Organized Maps1 (SOM). These automatic methods can be very powerful and useful, but they always have a bottle-neck phase: data validation. Due to algorithm reliability (that sometimes can fail), data needs to be validated manually before they can be included in a dataset for training. In this work, we propose a method to automatize this phase by moving manual intervention to an easier task: instead of creating masks and then validate them manually, we train a convolutional neural network to classify segmentation quality. Therefore, the validation is performed automatically. An initial manual phase is still required, but the classification task requires a smaller number of elements in the dataset that will feed a network employed for classification. After this phase, similar dataset creations will require less effort. This procedure is based on the fact that to obtain a high classification precision, fewer data are required than the data that are needed to obtain high precision in semantic segmentation. High classification score, can automatize validation procedure in dataset creation, being able to discard failure case in dataset creation. Being able to produce bigger dataset in less time can led to higher precision in semantic segmentation

Telematic solutions in plastic surgery during COVID-19 pandemic: Liability issues and risk management

During the COVID-19 pandemic, surgical elective procedures were stopped in our plastic surgery unit. Limitations for consultations and for follow-up of previous surgical procedures were imposed in order to minimize the risk of contagion in waiting rooms and outpatient clinics. We have identified telemedicine as an alternative way to follow patients during the lockdown. Nevertheless, we have experienced different difficulties. We have not had the possibility to use a secure teleconferencing software. In our unit we had not technological devices. Surgeons in our department were not able to use remote video technology for patient management. Guidelines for an appropriate selection of patients which could be served via telemedicine had to be created.Telemedicine must be regulated by healthcare organizations for legal, ethical, medico-legal and risk management aspects.Even if we have experienced an important need to use telematic solutions during the COVID-19 lockdown, liability and risk management issues has greatly limited this possibility in our unit. The need of telemedicine in the time of COVID-19 pandemic has encouraged us to implement future virtual encounters in order to reduce unnecessary in-person visits by taking into consideration all legal, ethical and medico-legal aspects

Impactos das mudanças climáticas sobre a distribuição espacial da podridão cinzenta em videira no Brasil.

Resumo: A podridão cinzenta, causada pelo fungo Botryotinia fuckeliana (Botrytis cinerea), existente em praticamente todos os vinhedos do mundo, causa sérias reduções na qualidade e na quantidade de uva produzida. O estudo teve como objetivo avaliar os impactos das mudanças climáticas na favorabilidade de ocorrência da podridão cinzenta da videira no Brasil. Foram utilizadas informações de temperatura média e período de molhamento foliar do período de referência (1961 ? 1990) e do futuro (2011 ? 2040, 2041 ? 2070 e 2071 ? 2100) para o Brasil, dos cenários A2 e B1, organizadas no banco de dados em Sistema de Informações Geográficas (SIG) Idrisi 32. A favorabilidade foi obtida aplicando-se equações lógicas, de acordo com as condições e restrições de período de molhamento foliar e temperatura média, resultando em mapas de distribuição espacial da podridão cinzenta no Brasil. De modo geral, é esperado um quadro positivo para o futuro, pois as áreas onde a condição para o desenvolvimento da doença era muito favorável diminuem, e as áreas de condição desfavorável apresentam um aumento principalmente durante o inverno. Abstract; The gray mold, caused by the fungus Botryotinia fuckeliana (Botrytis cinerea), which exists in almost all vineyards in the world, causes serious reductions in quality and quantity of grapes produced. The study aimed to evaluate the impacts of climate change on the favorability of occurrence of gray rot of grapevine in Brazil. Information was collected from average temperature and leaf wetness of the reference period (1961 - 1990) and future (2011 - 2040, 2041 - 2070 and 2071 - 2100) for Brazil, organized in the database in a Geographic Information System (GIS) Idrisi 32. The favorability was obtained by applying logic equations, according to the conditions and restrictions of leaf wetness and average temperature, resulting in maps of spatial distribution of gray mold in Brazil. In general, it can be expected a positive picture for the future, because the areas where the condition for the development of the disease was very favorable decreased, and the areas of unfavorable condition showed an increase mainly during the winter

Absolute calibration and beam reconstruction of MITO (a ground-based instrument in the millimetric region)

An efficient sky data reconstruction derives from a precise characterization of the observing instrument. Here we describe the reconstruction of performances of a single-pixel 4-band photometer installed at MITO (Millimeter and Infrared Testagrigia Observatory) focal plane. The strategy of differential sky observations at millimeter wavelengths, by scanning the field of view at constant elevation wobbling the subreflector, induces a good knowledge of beam profile and beam-throw amplitude, allowing efficient data recovery. The problems that arise estimating the detectors throughput by drift scanning on planets are shown. Atmospheric transmission, monitored by skydip technique, is considered for deriving final responsivities for the 4 channels using planets as primary calibrators.Comment: 14 pages, 6 fiugres, accepted for pubblication by New Astronomy (25 March

Mars Regolith Simulant Ameliorated by Compost as In Situ Cultivation Substrate Improves Lettuce Growth and Nutritional Aspects

Heavy payloads in future shuttle journeys to Mars present limiting factors, making self-sustenance essential for future colonies. Therefore, in situ resources utilization (ISRU) is the path to successful and feasible space voyages. This research frames the concept of planting leafy vegetables on Mars regolith simulant, ameliorating this substrate’s fertility by the addition of organic residues produced in situ. For this purpose, two butterhead lettuce (Lactuca sativa L. var. capitata) cultivars (green and red Salanova®) were chosen to be cultivated in four dierent mixtures of MMS-1 Mojave Mars simulant:compost (0:100, 30:70, 70:30 and 100:0; v:v) in a phytotron open gas exchange growth chamber. The impact of compost rate on both crop performance and the nutritive value of green- and red-pigmented cultivars was assessed. The 30:70 mixture proved to be optimal in terms of crop performance, photosynthetic activity, intrinsic water use eciency and quality traits of lettuce. In particular, red Salanova® showed the best performance in terms of these quality traits, registering 32% more phenolic content in comparison to 100% simulant. Nonetheless, the 70:30 mixture represents a more realistic scenario when taking into consideration the sustainable use of compost as a limited resource in space farming, while still accepting a slight significant decline in yield and quality in comparison to the 30:70 mixture