Molecularly imprinted polymer as sorbent phase for disposable pipette extraction: A potential approach for creatinine analysis in human urine samples.

Traditionally, creatinine determination is made by a spectrophotometric method; however, some compounds present in biological samples can interfere with creatinine determination, decreasing the sensitivity of the method in urine samples. Consequently, we report the development of a new molecularly imprinted polymer as a sorbent phase for disposable pipette extraction to determine creatinine in urine samples by high-performance liquid chromatography with UV detection. The synthesized polymer showed a high superficial area and presented a first-order kinetic reaction and a high selectivity for creatinine extraction compared to the non-molecularly imprinted polymer. The main disposable pipette extraction variables evaluated included the number of draw/eject cycles, the pH of the solution and desorption solvent type. The developed method showed an inter and intra-day precision from 1.3% to 2.0% and 0.8-1.6% respectively, accuracy values ranging from 82.3% to 102.1% respectively and recovery values ranging between 96.5% and 101.3%, with a limit of quantification of 0.302 g L-1. The application of the developed method in real urine samples showed creatinine concentrations ranging from 0.55 to 6.61 g L-1. Thus, the developed method was revealed to be an efficient strategy for creatinine determination, reducing analysis time (3 min) and solvent use, and increasing selectivity compared with DPX commercial sorbents.

Niobium Oxides as Heterogeneous Catalysts for Biginelli Multicomponent Reaction.

This study reports a simple, reusable, and recoverable niobium-based heterogeneous catalysts for Biginelli multicomponent reactions. Different methods of catalysts preparation were investigated. For this purpose, HY-340 (Nb2O5·nH2O) and Nb2O5 were chemically and/or thermally treated and investigated as catalysts for dihydropyrimidinones (DHPMs) production. The catalysts were characterized by scanning electron microscopy, high-resolution transmission electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction, temperature-programmed desorption of NH3, adsorption/desorption of N2 at -196 °C, and thermogravimetric and differential thermal analysis. The characterization results showed that niobium oxides have the potential to be used as catalysts because of high crystallinity and large surface area. Among the tested catalysts, Nb2O5 chemically treated (Nb2O5/T) showed the best catalytic performance. In the absence of solvents, 94% yield of DHPMs was achieved. Also, Nb2O5/T can be reused three times without a significant yield decrease. Additionally, a feasible reaction pathway was suggested based on the Knoevenagel mechanism for DHPM synthesis using niobium-based catalysts.

Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases.

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SOMG) and from the region of the Cerrado Mineiro; these crops did not receive phytosanitary treatments. The algorithms calibrated and tested for prediction were (a) Multiple linear regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d) Artificial Neural Networks (MLP). As dependent variables, we considered the monthly rates of coffee rust, cercospora, coffee miner, and coffee tree borer, and the weather elements were considered as independent (predictor) variables. Pearson correlation analyses were performed considering three different time periods, 1-10 d (from 1 to 10 days before the incidence evaluation), 11-20 d, and 21-30 d, and used to evaluate the unit correlations between the weather variables and infection rates of coffee diseases and pests. The models were calibrated in years of high and low yields, because the biannual variation of harvest yield of coffee beans influences the severity of the diseases. The models were compared by the Willmott's 'd', RMSE (root mean square error), and coefficient of determination (R2) indices. The result of the more accurate algorithm was specialized for the SOMG and Cerrado Mineiro regions using the kriging method. The weather variables that showed significant correlations with coffee rust disease were maximum air temperature, number of days with relative humidity above 80%, and relative humidity. RFT was more accurate in the prediction of coffee rust, cercospora, coffee miner, and coffee borer using weather conditions. In the SOMG, RFT showed a greater accuracy in the predictions for the Cerrado Mineiro in years of high and low yields and for all diseases. In SOMG, the RMSE values ranged from 0.227 to 0.853 for high-yield and 0.147 and 0.827 for low-yield coffee in the coffee borer forecasting.

Climatic potential for summer and winter wine production.

BACKGROUND: The geoviticultural multicriteria climatic classification (MCC) system provides an efficient guide for assessing the influence of climate on wine varieties. Paraná is one of the three states in southern Brazil that has great potential for the expansion of wine production, mainly due to the conditions that favour two harvests a year. The objective was to apply the geoviticultural MCC system in two production seasons. We used maximum, mean and minimum air temperature and precipitation for 1990-2015 for the state of Paraná. Air temperature and Precipitation were used to calculate the evapotranspiration and water balance. We applied the MCC system to identify potential areas for grapevine production for harvests in both summer and winter and then determined the climatic zones for each geoviticultural climate. RESULTS: Paraná has viticultural climates with conditions favourable for grapevine cultivation for the production of fine wines from summer and winter harvests. The conditions for the winter harvest provided wines with good coloration and high aromatic potential relative to the summer harvest. CONCLUSION: Chardonnay, Merlot, Pinot Blanc and Müller-Thurgau were suitable for regions with lower air temperatures and water deficits. Pinot Blanc and Müller-Thurgau were typical for the southern region of Paraná. © 2017 Society of Chemical Industry.

Effects of untreated and treated oilfield-produced water on seed germination, seedling development, and biomass production of sunflower (Helianthus annuus L.).

This study aims to evaluate possible toxic effects of oil and other contaminants from oilfield-produced water from oil exploration and production, on seed germination, and seedling development of sunflower (Helianthus annuus L.). In comparison, as treated by electroflocculation, oilfield-produced water, with lower oil and organic matter content, was also used. Electroflocculation treatment of oilfield-produced water achieved significant removals of chemical oxygen demand (COD) (94 %), oil and grease (O&G) (96 %), color (97 %), and turbidity (99 %). Different O&G, COD, and salt levels of untreated and treated oilfield-produced water did not influence germination process and seedling biomass production. Normal seedlings percentage and vigor tended to decrease more intensely in O&G and COD levels, higher than 337.5 mg L(-1) and 1321 mg O2 L(-1), respectively, using untreated oilfield-produced water. These results indicate that this industrial effluent must be treated, in order to not affect adversely seedling development. This way, electroflocculation treatment appears as an interesting alternative to removing oil and soluble organic matter in excess from oilfield-produced water improving sunflower's seedling development and providing a friendly environmental destination for this wastewater, reducing its potential to harm water resources, soil, and biota.

Performance of molecular methods for hepatitis C virus diagnosis: usefulness among chronic cases and during the course of infection.

BACKGROUND: Molecular methods are essential to define hepatitis C virus (HCV) infection. This study was conducted to evaluate the performance of molecular qualitative and quantitative methods for HCV RNA among chronic patients and individuals during the course of HCV infection. METHODS: Single serum samples were obtained from 82 HCV infected individuals where six of them donated serial serum samples (n = 52) during the course of HCV infection. Qualitative (in-house RT-nested PCR and COBAS AMPLICOR HCV Test v2.0 and TMA) and quantitative (COBAS AMPLICOR HCV Monitor Test v2.0 and bDNA) techniques were employed. RESULTS: TMA presented the highest rate (87.8%) of HCV detection among qualitative tests and it was the most sensitive for HCV RNA detection during the early and late phases of HCV infection. HCV RNA was quantified among 56 samples and significant correlation was observed between the two assays (r 0.92; p < 0.0001). CONCLUSIONS: It is concluded that both quantitative methods can be used among chronic and acute HCV cases, but TMA was the most efficient for HCV qualitative detection among chronic cases and in the early and late phases of HCV infection.