The in-plane paraconductivity in La_{2-x}Sr_xCuO_4 thin film superconductors at high reduced-temperatures: Independence of the normal-state pseudogap

The in-plane resistivity has been measured in $La_{2-x}Sr_xCuO_4$ (LSxCO) superconducting thin films of underdoped ($x=0.10,0.12$), optimally-doped ($x=0.15$) and overdoped ($x=0.20,0.25$) compositions. These films were grown on (100)SrTiO$_3$ substrates, and have about 150 nm thickness. The in-plane conductivity induced by superconducting fluctuations above the superconducting transition (the so-called in-plane paraconductivity, $\Delta\sigma_{ab}$) was extracted from these data in the reduced-temperature range 10^{-2}\lsim\epsilon\equiv\ln(T/\Tc)\lsim1. Such a $\Delta\sigma_{ab}(\epsilon)$ was then analyzed in terms of the mean-field--like Gaussian-Ginzburg-Landau (GGL) approach extended to the high-$\epsilon$ region by means of the introduction of a total-energy cutoff, which takes into account both the kinetic energy and the quantum localization energy of each fluctuating mode. Our results strongly suggest that at all temperatures above Tc, including the high reduced-temperature region, the doping mainly affects in LSxCO thin films the normal-state properties and that its influence on the superconducting fluctuations is relatively moderate: Even in the high-$\epsilon$ region, the in-plane paraconductivity is found to be independent of the opening of a pseudogap in the normal state of the underdoped films.Comment: 35 pages including 10 figures and 1 tabl

Aging-related predictive factors for oxygenation improvement and mortality in COVID-19 and acute respiratory distress syndrome (ARDS) patients exposed to prone position: A multicenter cohort study

Background: Elderly patients are more susceptible to Coronavirus Disease-2019 (COVID-19) and are more likely to develop it in severe forms, (e.g., Acute Respiratory Distress Syndrome [ARDS]). Prone positioning is a treatment strategy for severe ARDS; however, its response in the elderly population remains poorly understood. The main objective was to evaluate the predictive response and mortality of elderly patients exposed to prone positioning due to ARDS-COVID-19. Methods: This retrospective multicenter cohort study involved 223 patients aged ≥ 65 years, who received prone position sessions for severe ARDS due to COVID-19, using invasive mechanical ventilation. The PaO2/FiO2 ratio was used to assess the oxygenation response. The 20-point improvement in PaO2/FiO2 after the first prone session was considered for good response. Data were collected from electronic medical records, including demographic data, laboratory/image exams, complications, comorbidities, SAPS III and SOFA scores, use of anticoagulants and vasopressors, ventilator settings, and respiratory system mechanics. Mortality was defined as deaths that occurred until hospital discharge. Results: Most patients were male, with arterial hypertension and diabetes mellitus as the most prevalent comorbidities. The non-responders group had higher SAPS III and SOFA scores, and a higher incidence of complications. There was no difference in mortality rate. A lower SAPS III score was a predictor of oxygenation response, and the male sex was a risk predictor of mortality. Conclusion: The present study suggests the oxygenation response to prone positioning in elderly patients with severe COVID-19-ARDS correlates with the SAPS III score. Furthermore, the male sex is a risk predictor of mortality

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio