Optimizing evapotranspiration and crop irrigation requirements of tropical forages cropping systems in Southern Brazil.

Crop irrigation requirements are usually estimated based on crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETo) and crop coefficient (Kc). There is a lack of knowledge on the irrigation requirements of tropical forage crops in Brazil, contrasting with the increasing use of irrigation in pastures. The effort of this study was to investigate what would be the water needs of tropical forages in Southern Brazil, based on a robust experimental database. The study was carried out in São Paulo State-Brazil using different forages species and their combinations [Guinea grass (GG); Guinea grass + black oat + ryegrass (GOR); Bermuda grass (BG), and Bermuda + black oat + ryegrass (BOR)]. The experimental fields were fully irrigated, and the Kc values were derived from ETc measurements on lysimeters; ETo was estimated using daily data from a nearby weather station and the standard FAO56 parameterization. Mean daily ETc values for GG, GOR, BG and BOR were 4.1, 2.9, 3.6, and 3.4 mm, respectively, and respective mean Kc values were 0.99, 0.90, 1.0, and 0.94. Average Kc values for all plots decreased as ETo increased, producing a negative Kc-ETo relationship, mainly when ETo reached values greater than 5 mm d-1. This was most likely due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high ETo. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETo to improve the required irrigation depth.

Evapotranspiration adjustment for irrigated maize-soybean rotation systems in Nebraska, USA.

Irrigation water requirements are commonly estimated based on the estimated crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETr) and crop coefficient (Kc). Recent studies show that, at high evaporative demand (high ETr), Kc tends to decrease, creating an inverse ETr-Kc relationship. The focus of this long-term study is to, if at high atmosphere demand, there is the same inverse ETr-Kc relationship in Nebraska, USA, one of the most intensely irrigated regions in the world, and as a result, propose an adjustment to the Kc-ETr approach. The study was carried out in eastern Nebraska for maize-soybean rotation fields for the period 2002-2012. The Kc was estimated based on energy balance data from eddy covariance flux towers installed in the field and a nearby automated weather station throughout the growing seasons. We found that average Kc values varied depending on the year under high ETr; measured ETc agreed reasonably well with the FAO-56 manual predicted values, but in years with high ETr such as 2012 and 2002 affecting ETc values over the growing season. It was observed that Kc decreased as ETr increased, mainly when ETr reaches values greater than 6 mm d-1 (P values < 0.001). This most likely was due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high atmospheric demands. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETr to improve the required irrigation depth and irrigation management.

Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution.

Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide polymorphisms) on the expression levels of genes. Although different methods for evaluating the impact of these variations are proposed in the literature, the results obtained are mostly in disagreement, entailing a considerable number of false-positive predictions. For this reason, we propose an approach based on Logistic Model Trees that integrates the predictions of different eQTL mapping tools to produce more reliable results. More precisely, we employ a machine learning-based method using logistic functions to perform a linear regression able to classify the predictions of three eQTL analysis tools (namely, R/qtl, MatrixEQTL, and mRMR). Given the lack of a reference dataset and that computational predictions are not so easy to test experimentally, the performance of our approach is assessed using data from the DREAM5 challenge. The results show the quality of the aggregated prediction is better than that obtained by each single tool in terms of both precision and recall. We also performed a test on real data, employing genotypes and microRNA expression profiles from Caenorhabditis elegans, which proved that we were able to correctly classify all the experimentally validated eQTLs. These good results come both from the integration of the different predictions, and from the ability of this machine learning algorithm to find the best cutoff thresholds for each tool. This combination makes our integration approach suitable for improving eQTL predictions for testing in a laboratory, reducing the number of false-positive results.

STFT or CWT for the detection of Doppler ultrasound embolic signals.

Aiming reliable detection and localization of cerebral blood flow and emboli, embolic signals were added to simulated middle cerebral artery Doppler signals and analysed. Short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were used in the evaluation. The following parameters were used in this study: the powers of the embolic signals added were 5, 6, 6.5, 7, 7.5, 8 and 9 dB; the mother wavelets for CWT analysis were Morlet, Mexican hat, Meyer, Gaussian (order 4) and Daubechies (orders 4 and 8); and the thresholds for detection (equated in terms of false positive, false negative and sensitivity) were 2 and 3.5 dB for the CWT and STFT, respectively. The results indicate that although the STFT allows accurately detecting emboli, better time localization can be achieved with the CWT. Among the CWT, the current best overall results were obtained with Mexican Hat mother wavelet, with optimal results for sensitivity (100% detection rate) for nearly all emboli power values studied.