Satellites and the climate crisis: what are we orbiting towards?

On World Humanitarian Day, Alice Pellegrino, Ria Sen, and Federica Angeletti look at the humanitarian development potential of satellite technology, especially its ability to improve disaster and climate risk management. They discuss specific ways in which satellites can be used to manage disaster and climate risk, together with the current and future evolution of the satellite industry

Drilling down hotspots of infraspecific diversity to bring them into on-ground conservation of threatened species

Unprecedented rates of biodiversity loss raise the urgency for preserving species ability to cope with ongoing global changes. An approach in this direction is to target intra-specific hotspots of genetic diversity as conservation priorities. However, these hotspots are often identified by sampling at a spatial resolution too coarse to be useful in practical management of threatened species, hindering the long-appealed dialog between conservation stakeholders and conservation genetic researchers. Here, we investigated the spatial and temporal variation in species presence, genetic diversity, as well as potential risk factors, within a previously identified hotspot of genetic diversity for the endangered Apennine yellow-bellied toad Bombina pachypus. Our results show that this hotspot is neither a geographically homogeneous nor a temporally stable unit. Over a time-window spanning 10–40 years since previous assessments, B. pachypus populations declined in large portions of their hotspot, and their genetic diversity levels decreased. Considering the demographic trend, genetic and epidemiological data, and models of current and future climatic suitability, populations at the extreme south of the hotspot area still qualify for urgent in-situ conservation actions, whereas northern populations would be better managed through a mix of in-situ and ex-situ actions. Our results emphasize that identifying hotspots of genetic diversity, albeit an essential step, does not suffice to warrant on-ground conservation of threatened species. Hotspots should be analyzed at finer geographic and temporal scales, to provide conservation stakeholders with key knowledge to best define conservation priorities, and to optimize resource allocation to alternative management practices

Levantamento com scaner à laser na modelagem da igrejinha: um estudo do nível de desenvolvimento - LOD: Study of the level of development in the modeling of the igrejinha: based on laser scanner

O presente artigo é uma pesquisa sobre o uso de HBIM para o levantamento e documentação do Patrimônio Cultural, em que o objeto de estudo foi a Igreja Nossa Senhora de Fátima, localizada em Brasília, Distrito Federal. O levantamento arquitetônico foi feito com Scanner à Laser. A partir da nuvem de pontos gerada, foi feita a modelagem 3D utilizando o software Revit. O objetivo deste trabalho é analisar o Nível de Desenvolvimento (LOD) empregado na modelagem, com base no padrão desenvolvido pelo American Institute of Architects (AIA).  Foi possível notar que a ausência de documentação pode influenciar na modelagem 3D, por não ter informações suficientes para gerar um modelo HBIM. Portanto, reunir informações no modelo pode contribuir para a preservação patrimonial de forma mais efetiva ao longo do seu ciclo de vida. O uso de ferramentas como o LOD pode tornar o processo de documentação mais acurado e transparente, melhorando a qualidade de comunicação entre os usuários do modelo e pesquisadores

New perspectives in cardiovascular risk reduction: focus on HDL

Cardiovascular diseases represent the leading cause of morbidity and mortality worldwide, mostly contributing to hospitalizations and health care costs. Dyslipidemias represent one of the major cardiovascular risk factor and its management, throughout life-style modifications and pharmacological interventions, has shown to reduce cardiac events. The risk of adverse cardiovascular events is related not only to elevated LDL blood levels, but also to decreased HDL concentrations, that exhibit protective effects in the development of atherosclerotic process. Aim of this review is to summarize current evidences about defensing effects of such lipoproteins and to show the most recent pharmacological strategies to reduce cardiovascular risk through the increase of their circulating levels

Development and Metrological Characterization of a Multi-sensor Device for Indoor Environmental Quality (IEQ) monitoring

Indoor Environmental Quality (IEQ), which affects people's health, comfort, well-being and productivity, combines thermal, visual, acoustic and air quality conditions. This work deals with design, development and metrological characterization of a low-cost multi-sensor device that is able to detect the quality conditions of indoor environments for IEQ purposes. The device, hereafter referred as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort) embeds a set of low-cost sensors that measure air temperature and relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, particulate matter, formaldehyde, and nitrogen dioxide. The basic architecture of the device is described and the design criteria that are related to the measurement requirements are highlighted. Particular attention has been paid towards the traceability assurance of the measurements provided by PROMET&O by means of specifically conceived calibration procedures, which have been tailored to the requirements of each measurement quantity. The calibration is based on the comparison to reference standards following commonly employed or ad-hoc developed technical procedures. The defined calibration procedures can be applied both for the single sensors and for the set of sensors integrated in the multi-sensor case. For the latter, the effects of the percentage of permeable case surface and the sensors allocation are also investigated. A preliminary uncertainty evaluation of the proposed multi-sensor device is reported for the carbon dioxide and the illuminance sensors taking the defined calibration procedures into account

Characteristics and patterns of care of endometrial cancer before and during COVID-19 pandemic

Objective: Coronavirus disease 2019 (COVID-19) outbreak has correlated with the disruption of screening activities and diagnostic assessments. Endometrial cancer (EC) is one of the most common gynecological malignancies and it is often detected at an early stage, because it frequently produces symptoms. Here, we aim to investigate the impact of COVID-19 outbreak on patterns of presentation and treatment of EC patients. Methods: This is a retrospective study involving 54 centers in Italy. We evaluated patterns of presentation and treatment of EC patients before (period 1: March 1, 2019 to February 29, 2020) and during (period 2: April 1, 2020 to March 31, 2021) the COVID-19 outbreak. Results: Medical records of 5,164 EC patients have been retrieved: 2,718 and 2,446 women treated in period 1 and period 2, respectively. Surgery was the mainstay of treatment in both periods (p=0.356). Nodal assessment was omitted in 689 (27.3%) and 484 (21.2%) patients treated in period 1 and 2, respectively (p<0.001). While, the prevalence of patients undergoing sentinel node mapping (with or without backup lymphadenectomy) has increased during the COVID-19 pandemic (46.7% in period 1 vs. 52.8% in period 2; p<0.001). Overall, 1,280 (50.4%) and 1,021 (44.7%) patients had no adjuvant therapy in period 1 and 2, respectively (p<0.001). Adjuvant therapy use has increased during COVID-19 pandemic (p<0.001). Conclusion: Our data suggest that the COVID-19 pandemic had a significant impact on the characteristics and patterns of care of EC patients. These findings highlight the need to implement healthcare services during the pandemic

Practice patterns and 90-day treatment-related morbidity in early-stage cervical cancer

To evaluate the impact of the Laparoscopic Approach to Cervical Cancer (LACC) Trial on patterns of care and surgery-related morbidity in early-stage cervical cancer

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches