Interactions of vanadates with carbohydrates in aqueous solutions

The interaction between the vanadate ion (VO3-, i.e. vanadium (V)) and the carbohydrates sucrose, glucose and fructose has been studied in aqueous solutions (pH [approximate]6, 298.15 K) using measurements of diffusion coefficients, electrical conductivity, Raman and multinuclear NMR spectroscopy. With sucrose and glucose, indications of hydrolysis of the anion in the absence of the sugars came from a decrease in the diffusion coefficient with increasing concentration. Significant effects on the diffusion coefficients were observed in the presence of sucrose and glucose, suggesting interactions between the carbohydrates and vanadate ion. Support for this came from electrical conductivity measurements, where there were indications of formation of oligomeric species. These were found to depend on the carbohydrate used: confirmation of oligomer formation came from Raman spectroscopy, where it was possible to identify these species, and see their dependence on the particular carbohydrate used. Information on the interactions between the carbohydrates glucose or sucrose and vanadate came from 51V and 1H NMR spectroscopy, where the dominant species appeared to be a 2:2 complex with glucose, possessing trigonal bipyramidal centres, whereas with sucrose it is suggested that octahedral species are formed. Studies with fructose were complicated by competing oxidation of this carbohydrate and reduction of vanadium (V).http://www.sciencedirect.com/science/article/B6TGS-4CTN53R-3/1/7da9099617b4eef5113ec0db3254629

Dependence of viscosity and diffusion on β-cyclodextrin and chloroquine diphosphate interactions

Mutual diffusion coefficients of chloroquine diphosphate (CDP) in aqueous solutions both without and with β-cyclodextrin (β-CD) were measured at concentrations from (0.0000 to 0.0100) mol dm−3 and 298.15 K, using the Taylor dispersion technique. Ternary mutual diffusion coefficients (Dik) measured by the same technique are reported for aqueous CDP + β-CD solutions at 298.15 K. The presence of β CD led to relevant changes in the diffusion process, as showed by nonzero values of the cross-diffusion coefficients, D12 and D21 . β-CD concentration gradients produced significant co-current coupled flows of CDP. In addition, the effects of β-CD on the transport of CDP are assessed by comparing the binary diffusion coefficient of aqueous CDP solutions with the main diffusion coefficient (D11 ) measured for ternary {CDP(1) + β-CD(2)} solutions. These observations are supported by viscosity analysis. All data allow to have a better interpretation on the effect of cyclodextrin on the transport behavior of CDP. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Coimbra Chemistry Centre - FundacAo para a Ciencia e a Tecnologia (FCT), Portuguese Agency for Scientific Research [UID/QUI/UI0313/2019]; COMPETE; Ministry of Education, Youth and Sports of the Czech Republic DKRVO [RP/CPS/2020/003]; University of Alcala (Spain)RP/CPS/2020/003; UID/QUI/UI0313/2019; Fundação para a Ciência e a Tecnologia, FCT; Universidad de Alcalá, UAH; Programa Operacional Temático Factores de Competitividade, POF

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

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

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

Diffusion Coefficients in Aqueous Solutions of Ammonium Monovanadate at 25°C

Differential diffusion coefficients of ammonium monovanadate in water at 25°C and at concentrations from 0.001 to 0.05 mol-dm-3, have been measured using a conductimetric cell and an automatic apparatus to follow diffusion. The cell uses an open-ended capillary method and a conductimetric technique is used to follow the diffusion process by measuring the resistance of solutions inside the capillaries at recorded times

The mouths of mental deficients

The Taylor dispersion technique has been used for measuring mutual diffusion coefficients of sodium hyaluronate in aqueous solutions at T = 298.15 K, and concentrations ranging from (0.00 to 0.50) g · dm−3. The results are interpreted on the basis of Nernst, and Onsager and Fuoss theoretical equations. From the diffusion coefficient at infinitesimal concentration, the limiting ionic conductivity and the tracer diffusion coefficient of hyaluronate ion were estimated. These studies have been complemented by molecular mechanics calculations