Nonlocal Nonlinear Optical Response of Ionic Liquids under Violet Excitation

The nonlocal nonlinearity under violet excitation of ionic liquids was investigated using the Z-scan technique. The bis(trifluoromethylsulfonyl)imide (Tf2N−) was employed as anionic part, while the cationic part consisted of four different imidazolium (, with , 6, 8, and 10) derivatives. The thermooptical coefficients, nonlinear refractive indexes of thermal origin, and degree of nonlocality were obtained for laser excitation tuned at 410 nm. Our results indicate that ionic liquids can be exploited as very efficient nonlinear media with large nonlocal character under violet excitation

Chronic inflammatory diseases, subclinical atherosclerosis, and cardiovascular diseases: Design, objectives, and baseline characteristics of a prospective case-cohort study ‒ ELSA-Brasil

Objectives: This analysis describes the protocol of a study with a case-cohort to design to prospectively evaluate the incidence of subclinical atherosclerosis and Cardiovascular Disease (CVD) in Chronic Inflammatory Disease (CID) participants compared to non-diseased ones. Methods: A high-risk group for CID was defined based on data collected in all visits on self-reported medical diagnosis, use of medicines, and levels of high-sensitivity C-Reactive Protein >10 mg/L. The comparison group is the Aleatory Cohort Sample (ACS): a group with 10% of participants selected at baseline who represent the entire cohort. In both groups, specific biomarkers for DIC, markers of subclinical atherosclerosis, and CVD morbimortality will be tested using weighted Cox. Results: The high-risk group (n = 2,949; aged 53.6 ± 9.2; 65.5% women) and the ACS (n=1543; 52.2±8.8; 54.1% women) were identified. Beyond being older and mostly women, participants in the high-risk group present low average income (29.1% vs. 24.8%, p < 0.0001), higher BMI (Kg/m2) (28.1 vs. 26.9, p < 0.0001), higher waist circumference (cm) (93.3 vs. 91, p < 0.0001), higher frequencies of hypertension (40.2% vs. 34.5%, p < 0.0001), diabetes (20.7% vs. 17%, p = 0.003) depression (5.8% vs. 3.9%, p = 0.007) and higher levels of GlycA a new inflammatory marker (p < 0.0001) compared to the ACS. Conclusions: The high-risk group selected mostly women, older, lower-income/education, higher BMI, waist circumference, and of hypertension, diabetes, depression, and higher levels of GlycA when compared to the ACS. The strategy chosen to define the high-risk group seems adequate given that multiple sociodemographic and clinical characteristics are compatible with CID

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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

Er<SUP>3+</SUP>-doped BaTiO<SUB>3</SUB> nanocrystals for thermometry: Influence of nanoenvironment on the sensitivity of a fluorescence based temperature sensor

Frequency upconverted emissions centered at 526 and 547 nm from two thermodynamically coupled excited states of Er3+ doped in BaTiO3 nanocrystals were recorded in the temperature range from 322 to 466 K using a diode laser emitting at 980 nm as the excitation source. The ensemble measurements of the fluorescence intensity ratio (FIR) of the signals at 526 and 547 nm as a function of the temperature showed that the sensitivity (the rate in which the FIR changes with the temperature) of such sensor depends on the size of the nanocrystal. This is explained taking into consideration modifications of nonraditive relaxation mechanisms with the size of the nanocrystals

Third-order nonlinear optical response of colloidal gold nanoparticles prepared by sputtering deposition

The nonlinear optical responses of gold nanoparticles dispersed in castor oil produced by sputtering deposition were investigated, using the thermally managed Z-scan technique. Particles with spherical shape and 2.6 nm of average diameter were obtained and characterized by transmission electron microscopy and small angle X-ray scattering. This colloid was highly stable, without the presence of chemical impurities, neither stabilizers. It was observed that this system presents a large refractive third-order nonlinear response and a negligible nonlinear absorption. Moreover, the evaluation of the all-optical switching figures of merit demonstrated that the colloidal nanoparticles prepared by sputtering deposition have a good potential for the development of ultrafast photonic devices