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Introduction: We investigated Quality of Life (QOL) in a sample of individuals (n = 341) who experienced different consequences in terms of displacement from their house after earthquake. Methods: Three groups were studied: those who had been displaced but are no longer so, those still displaced, and those who were never displaced. QOL for four time points was assessed: pre-earthquake, during displacement, at the time of the survey, and in ten years. Results: Different trajectories of QOL were observed in the three groups: not displaced individuals showed no significant variation, those who were displaced had a significant decline in QOL after the earthquake but a significant recovery after the displacement experience, and those who were still displaced at the time of the survey reported lowest QOL both after the earthquake and in the future, with no recovery. Predictors of perceived QOL decline were quality and type of temporary accommodation, place attachment, and perceived health impairment. Subsequent QOL perceived improvement was predicted by quality and type of temporary accommodation, risk awareness, and emotional well-being. Discussion: Our findings highlight the importance of minimizing the duration of temporary displacement and providing high-quality temporary accommodations, considering individual needs in the local contexts and communities.

Abstract Background Randomized clinical trials in non-critically ill COVID-19 patients showed that therapeutic-dose heparin increased survival with reduced organ support as compared with usual-care thromboprophylaxis, albeit with increased bleeding risk. The purpose of the study is to assess the safety of intermediate dose enoxaparin in hospitalized patients with moderate to severe COVID-19. Methods A phase II single-arm interventional prospective study including patients receiving intermediate dose enoxaparin once daily according to body weight: 60 mg for 45–60 kg, 80 mg for 61–100 kg or 100 mg for > 100 kg for 14 days, with dose adjustment according to anti-factor Xa activity (target range: 0.4–0.6 UI/ml); an observational cohort (OC) included patients receiving enoxaparin 40 mg day for comparison. Follow-up was 90 days. Primary outcome was major bleeding within 30 and 90 days after treatment onset. Secondary outcome was the composite of all-cause 30 and 90-day mortality rates, disease severity at the end of treatment, intensive care unit (ICU) admission and length of ICU stay, length of hospitalization. All outcomes were adjudicated by an independent committee and analyzed before and after propensity score matching (PSm). Results Major bleeding was similar in IC (1/98 1.02%) and in the OC (none), with only one event observed in a patient receiving concomitantly anti-platelet therapy. The composite outcome was observed in 53/98 patients (54%) in the IC and 132/203 (65%) patients in the OC (p = 0.07) before PSm, while it was observed in 50/90 patients (55.6%) in the IC and in 56/90 patients (62.2%) in the OC after PSm (p = 0.45). Length of hospitalization was lower in the IC than in OC [median 13 (IQR 8–16) vs 14 (11–21) days, p = 0.001], however it lost statistical significance after PSm (p = 0.08). At 30 days, two patients had venous thrombosis and two pulmonary embolism in the OC. Time to first negative RT-PCR were similar in the two groups. Conclusions Weight adjusted intermediate dose heparin with anti-FXa monitoring is safe with potential positive impact on clinical course in COVID-19 non-critically ill patients. Trial registration The study INHIXACOVID19 was registred on ClinicalTrials.gov with the trial registration number (TRN) NCT04427098 on 11/06/2020.

The EU Biodiversity strategy aims to plant 3 billion trees by 2030, in order to improve ecosystem restoration and biodiversity. Here, we compute the land area that would be required to support this number of newly planted trees by taking account of different tree species and planting regimes across the EU member states. We find that 3 billion trees would require a total land area of between 0.81 and 1.37 Mha (avg. 1.02 Mha). The historic forest expansion in the EU since 2010 was 2.44 Mha, meaning that despite 3 billion trees sounding like a large number this target is considerably lower than historic afforestation rates within the EU, i.e. only 40% of the past trend. Abandoned agricultural land is often proposed as providing capacity for afforestation. We estimate agricultural abandoned land areas from the HIstoric Land Dynamics Assessment+ database using two time thresholds (abandonment since 2009 or 2014) to identify potential areas for tree planting. The area of agricultural abandoned land was 2.6 Mha (potentially accommodating 7.2 billion trees) since 2009 and 0.2 Mha (potentially accommodating 741 million trees) since 2014. Our study highlights that sufficient space could be available to meet the 3 billion tree planting target from abandoned land. However, large-scale afforestation beyond abandoned land could have displacement effects elsewhere in the world because of the embodied deforestation in the import of agricultural crops and livestock. This would negate the expected benefits of EU afforestation. Hence, the EU’s relatively low ambition on tree planting may actually be better in terms of avoiding such displacement effects. We suggest that tree planting targets should be set at a level that considers physical ecosystem dynamics as well as socio-economic conditions.

The curse of dimensionality problem refers to a set of troubles arising when dealing with huge amount of data as happens, e.g., applying standard numerical methods to solve partial differential equations related to financial modeling. To overcome the latter issue, we propose a Deep Learning approach to efficiently approximate nonlinear functions characterizing financial models in a high dimension. In particular, we consider solving the Black–Scholes–Barenblatt non-linear stochastic differential equation via a forward-backward neural network, also calibrating the related stochastic volatility model when dealing with European options. The obtained results exhibit accurate approximations of the implied volatility surface. Specifically, our method seems to significantly reduce the neural network’s training time and the approximation error on the test set.

Summary: With the availability of social networks, specialized forums, and online news, sentiment analysis has become a common and useful technique for the analysis of economic and financial scenarios. Several data‐providers have also started computing proprietary sentiment indexes on financial assets to be delivered together with market price and trading volume. We develop a modified version of the mean‐reverting 4/2 stochastic volatility model introduced in Escobar‐Anel & Gong (2020) to describe the dynamics of commodities. In our specification, jumps are allowed in the asset price dynamics, and the drift coefficient may also switch between regimes related to a sentiment indicator. In this framework, we discuss the distributional characteristics of asset returns, provide a numerical procedure for model estimation, and give some preliminary results on the pricing of European‐style derivatives. Finally, the model is fitted to the market data for Gold and Crude Oil. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this observational retrospective study was to evaluate, in patients with a severe acute respiratory syndrome coronavirus 2 infection, the association between the severity of coronavirus disease 2019 (COVID-19) respiratory illness and the risk of infected patients to develop obstructive sleep apnea (OSA). Methods: Ninety-six patients with confirmed COVID-19 infection were enrolled in the study. The STOP-BANG questionnaire to investigate the risk of the OSA syndrome was filled in by the patients at admission. The enrolled patients were divided into 2 groups according to the respiratory disease: group 1 (72 patients), hospitalized patients undergoing conventional oxygen therapy; group 2 (24 patients), patients requiring enhanced respiratory support. STOP-BANG results of these 2 groups were compared to observe whether patients with high OSA risk more frequently presented a severe form of COVID-19. Results: 41.6% of the patients in group 2 had a STOP-BANG score between 5 and 8 (high risk of having apnea); in contrast, 20.8% of the patients in group 1 had a STOP-BANG score between 5 and 8, with a statistically significant difference between the 2 groups (P =.05). A complementary trend was observed regarding the proportion of patients in the range 0 to 2, which classifies patients at a low risk of OSA (48.6% vs 20.8% for groups 1 and 2, P =.01). Conclusions: According to our data, the chances of having a severe case of COVID-19 should be considered in patients at high risk of OSA. Current Knowledge/Study Rationale: Emerging research suggests that OSA could represent a potentially important risk factor for the severe forms of COVID-19. The purpose of this observational retrospective study was to evaluate the potential association between OSA and the severity of COVID-19 disease. Study Impact: According to our data, the likelihood of contracting a severe form of COVID-19 disease should be considered in patients at high risk of OSA. [ABSTRACT FROM AUTHOR]