Environmental Controls In The Water Use Patterns Of A Tropical Cloud Forest Tree Species, Drimys Brasiliensis (winteraceae).

Trees from tropical montane cloud forest (TMCF) display very dynamic patterns of water use. They are capable of downwards water transport towards the soil during leaf-wetting events, likely a consequence of foliar water uptake (FWU), as well as high rates of night-time transpiration (Enight) during drier nights. These two processes might represent important sources of water losses and gains to the plant, but little is known about the environmental factors controlling these water fluxes. We evaluated how contrasting atmospheric and soil water conditions control diurnal, nocturnal and seasonal dynamics of sap flow in Drimys brasiliensis (Miers), a common Neotropical cloud forest species. We monitored the seasonal variation of soil water content, micrometeorological conditions and sap flow of D. brasiliensis trees in the field during wet and dry seasons. We also conducted a greenhouse experiment exposing D. brasiliensis saplings under contrasting soil water conditions to deuterium-labelled fog water. We found that during the night D. brasiliensis possesses heightened stomatal sensitivity to soil drought and vapour pressure deficit, which reduces night-time water loss. Leaf-wetting events had a strong suppressive effect on tree transpiration (E). Foliar water uptake increased in magnitude with drier soil and during longer leaf-wetting events. The difference between diurnal and nocturnal stomatal behaviour in D. brasiliensis could be attributed to an optimization of carbon gain when leaves are dry, as well as minimization of nocturnal water loss. The leaf-wetting events on the other hand seem important to D. brasiliensis water balance, especially during soil droughts, both by suppressing tree transpiration (E) and as a small additional water supply through FWU. Our results suggest that decreases in leaf-wetting events in TMCF might increase D. brasiliensis water loss and decrease its water gains, which could compromise its ecophysiological performance and survival during dry periods.

Magnesium-deficient High-fat Diet: Effects On Adiposity, Lipid Profile And Insulin Sensitivity In Growing Rats.

To determine if magnesium deficiency aggravates the effects of a high-fat diet in growing rats in terms of obesity, lipid profile and insulin resistance. The study population comprised 48 newly weaned male Wistar Hannover rats distributed into four groups according to diet, namely, control group (CT; n = 8), control diet provided ad libitum; pair-feeding control group (PF; n = 16), control diet but in the same controlled amount as animals that received high-fat diets; high-fat diet group (HF; n = 12), and magnesium-deficient high-fat diet group (HFMg(-); n = 12). The parameters investigated were adiposity index, lipid profile, magnesium status, insulin sensitivity and the phosphorylation of proteins involved in the insulin-signaling pathway, i.e. insulin receptor β-subunit, insulin receptor substrate 1 and protein kinase B. The HF and HFMg(-) groups were similar regarding gain in body mass, adiposity index and lipid profile, but were significantly different from the PF group. The HFMg(-) group exhibited alterations in magnesium homeostasis as revealed by the reduction in urinary and bone concentrations of the mineral. No inter-group differences were observed regarding glucose homeostasis. Protein phosphorylation in the insulin-signaling pathway was significantly reduced in the high-fat groups compared with the control groups, demonstrating that the intake of fat-rich diets increased insulin resistance, a syndrome that was aggravated by magnesium deficiency. Under the experimental conditions tested, the intake of a magnesium-deficient high-fat diet led to alterations in the insulin-signaling pathway and, consequently, increased insulin resistance.

Long-term Prospects For The Environmental Profile Of Advanced Sugar Cane Ethanol.

This work assessed the environmental impacts of the production and use of 1 MJ of hydrous ethanol (E100) in Brazil in prospective scenarios (2020-2030), considering the deployment of technologies currently under development and better agricultural practices. The life cycle assessment technique was employed using the CML method for the life cycle impact assessment and the Monte Carlo method for the uncertainty analysis. Abiotic depletion, global warming, human toxicity, ecotoxicity, photochemical oxidation, acidification, and eutrophication were the environmental impacts categories analyzed. Results indicate that the proposed improvements (especially no-til farming-scenarios s2 and s4) would lead to environmental benefits in prospective scenarios compared to the current ethanol production (scenario s0). Combined first and second generation ethanol production (scenarios s3 and s4) would require less agricultural land but would not perform better than the projected first generation ethanol, although the uncertainties are relatively high. The best use of 1 ha of sugar cane was also assessed, considering the displacement of the conventional products by ethanol and electricity. No-til practices combined with the production of first generation ethanol and electricity (scenario s2) would lead to the largest mitigation effects for global warming and abiotic depletion. For the remaining categories, emissions would not be mitigated with the utilization of the sugar cane products. However, this conclusion is sensitive to the displaced electricity sources.

Genetic Diversity Of Giardia Duodenalis: Multilocus Genotyping Reveals Zoonotic Potential Between Clinical And Environmental Sources In A Metropolitan Region Of Brazil.

Giardia duodenalis is a flagellate protozoan that parasitizes humans and several other mammals. Protozoan contamination has been regularly documented at important environmental sites, although most of these studies were performed at the species level. There is a lack of studies that correlate environmental contamination and clinical infections in the same region. The aim of this study is to evaluate the genetic diversity of a set of clinical and environmental samples and to use the obtained data to characterize the genetic profile of the distribution of G. duodenalis and the potential for zoonotic transmission in a metropolitan region of Brazil. The genetic assemblages and subtypes of G. duodenalis isolates obtained from hospitals, a veterinary clinic, a day-care center and important environmental sites were determined via multilocus sequence-based genotyping using three unlinked gene loci. Cysts of Giardia were detected at all of the environmental sites. Mixed assemblages were detected in 25% of the total samples, and an elevated number of haplotypes was identified. The main haplotypes were shared among the groups, and new subtypes were identified at all loci. Ten multilocus genotypes were identified: 7 for assemblage A and 3 for assemblage B. There is persistent G. duodenalis contamination at important environmental sites in the city. The identified mixed assemblages likely represent mixed infections, suggesting high endemicity of Giardia in these hosts. Most Giardia isolates obtained in this study displayed zoonotic potential. The high degree of genetic diversity in the isolates obtained from both clinical and environmental samples suggests that multiple sources of infection are likely responsible for the detected contamination events. The finding that many multilocus genotypes (MLGs) and haplotypes are shared by different groups suggests that these sources of infection may be related and indicates that there is a notable risk of human infection caused by Giardia in this region.

Trends and strategies of ozone application in environmental problems

Efforts presented by the scientific community in recent years towards the development of numerous green chemical processes and wastewater treatment technologies are presented and discussed. In the light of these approaches, environmentally friendly technologies, as well as the key role played by the well-known advanced oxidation processes, are discussed, giving special attention to the ones comprising ozone applications. Fundamentals and applied aspects dealing with ozone technology and its application are also presented.

Comparison between the 1% and 2% ibopamine provocative test in primary open-angle glaucoma patients: sensitivity, specificity and tolerability

PURPOSE: To compare intraocular pressure (IOP) rise in normal individuals and primary open-angle glaucoma patients and the safety and efficacy of ibopamine eye drops in different concentrations as a provocative test for glaucoma. METHODS: Glaucoma patients underwent (same eye) the ibopamine provocative test with two concentrations, 1% and 2%, in a random sequence at least 3 weeks apart, but not more than 3 months. The normal individuals were randomly submitted to one of the concentrations of ibopamine (1% and 2%). The test was considered positive if there was an IOP rise greater than 3 or 4 mmHg at 30 or 45 minutes to test which subset of the test has the best sensitivity (Se)/specificity (Sp). RESULTS: There was no statistically significant difference in any of the IOP measurements, comparing 1% with 2% ibopamine. The IOP was significantly higher at 30 and 45 minutes with both concentrations (p<0.001). The best sensitivity/specificity ratio was achieved with the cutoff point set as greater than 3 mmHg at 45 minutes with 2% ibopamine (area under the ROC curve: 0.864, Se: 84.6%; Sp:73.3%). All patients described a slight burning after ibopamine's instillation. CONCLUSION: 2% ibopamine is recommended as a provocative test for glaucoma. Because both concentrations have similar ability to rise IOP, 1% ibopamine may be used to treat ocular hypotony.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.