A Weed Species Spectral Detector Based on Neural Networks

A new neural network architecture for classification purposes is proposed. The Self-Organizing Map (SOM) neural network is used in a supervised way for a classification task. The neurons of the SOM become associated with local linear mappings (LLM). Error information obtained during training is used in a novel learning algorithm to train the classifier. The proposed method achieves fast convergence and good generalization. The classification method is then applied in a precision farming application, the classification of crops and different kinds of weeds by using spectral reflectance measurements. The classification performance of the proposed method is proven superior compared to other neural classifiers. Also, the proposed method compares favorably with the results obtained by using an optimal Bayesian classifier.     

Endophytic fungi associated with Mediterranean plants as a source of mycelium-bound lipases

A screening of endophytic fungi isolated from Mediterranean plants rendered a mycelium-bound lipase from a strain of Rhizopus oryzae that catalyzed the esterification of fatty acids in isooctane. The influence of various factors (water content, temperature, and pH) on ester synthesis was investigated. Catalytic activity was inversely correlated with water content. This enzyme was active over the entire pH range studied, from pH 3 to pH 8, and activity was maximal at pH 4 and pH 7. The enzyme was thermostable, with maximal activity at 60 degrees C.     

The role of population and quantitative genetics and modern sequencing technologies to understand evolved herbicide resistance and weed fitness

Evolution of resistance to multiple herbicides with different sites of action and of nontarget site resistance (NTSR) often involves multiple genes. Thus, single‐gene analyses, typical in studies of target site resistance, are not sufficient for understanding the genetic architecture and dynamics of NTSR and multiple resistance. The genetics of weed adaptation to varied agricultural environments is also generally expected to be polygenic. Recent advances in whole‐genome sequencing as well as bioinformatic and statistical tools have made it possible to use population and quantitative genetics methods to expand our understanding of how resistance and other traits important for weed adaptation are genetically controlled at the individual and population levels, and to predict responses to selection pressure by herbicides and other environmental factors. The use of tools such as quantitative trait loci mapping, genome‐wide association studies, and genomic prediction will allow pest management scientists to better explain how pests adapt to control tools and how specific genotypes thrive and spread across agroecosystems and other human‐disturbed systems. The challenge will be to use this knowledge in developing integrated weed management systems that inhibit broad resistance to current and future weed‐control methods. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Predicting fertility from seminal traits: Performance of several parametric and non-parametric procedures

This research aimed at assessing the efficacy of non-parametric procedures to improve the classification of the ejaculates in the artificial insemination (AI) centers according to their fertility rank predicted from characteristics of the AI doses. A total of 753 ejaculates from 193 bucks were evaluated at three different times from 5 to 9 months of age for 21 seminal variables (related to ejaculate pH and volume, sperm concentration, viability, morphology and acrosome reaction traits, and dose characteristic) and their corresponding fertility score after AI over crossbred females. Fertility rate was categorized into five classes of equal length. Linear Regression (LR), Ordinal Logistic Regression (OLR), Support Vector Regression (SVR), Support Vector Ordinal Regression (SVOR), and Non-deterministic Ordinal Regression (NDOR) were compared in terms of their predictive ability with two base line algorithms: MEAN and MODE which always predict the mean and mode value of the classes observed in the data set, respectively. Predicting ability was measured in terms of rate of erroneous classifications, linear loss (average of the distance between the predicted and the observed classes), the number of predicted classes and the F₁ statistic (which allows comparing procedures taking into account that they can predict different number of classes). The seminal traits with a bigger influence on fertility were established using stepwise regression and a nondeterministic classifier. MEAN, LR and SVR produced a higher percentage of wrong classified cases than MODE (taken as reference for this statistic), whereas it was 6%, 13% and 39% smaller for SVOR, OLR and NDOR, respectively. However, NDOR predicted an average of 2.04 classes instead of one class predicted by the other procedures. All the procedures except MODE showed a similar smaller linear loss than the reference one (MEAN) SVOR being the one with the best performance. The NDOR showed the highest value of the F₁ statistic. Values of linear loss and F₁ statistics were far from their best value indicating that possibly, the variation in fertility explained by this group of semen characteristics is very low. From the total amount of traits included in the full model, 11, 16, 15, 18 and 3 features were kept after performing variable selection with the LR, OLR, SVR, SVOR and NDOR methods, respectively. For all methods, the reduced models showed almost an irrelevant decrease in their predictive abilities compared to the corresponding values obtained with the full models.     

UHPLC-MS analysis of juvenile hormone II in Mediterranean corn borer (Sesamia nonagrioides) hemolymph using various ionization techniques

The juvenile hormones (JHs) have been considered the most versatile hormones in the animal kingdom. JH-II is the most abundant JH in Sesamia nonagrioides, important maize pests in the Mediterranean basin. This study compared the sensitivities and matrix effects of four ionization modes on analyzing JH-II in S. nonagrioides hemolymph using ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) in single ion monitoring (SIM) mode. The ionization techniques tested were electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), atmospheric pressure photoionization (APPI), and APPI with the lamp turned off, which corresponds to atmospheric pressure thermospray ionization (APTSI). ESI was discarded because of the high matrix effect. APPI was discarded because the correlation responses between solvent and matrix on the instrumental quality parameters were worse than those for APTSI and APCI. In our analytical conditions, APCI has shown the best validation parameter values. APCI ionization is widely available in instrumental laboratories.     

Modelling the effects of land-use change on runoff and sediment yield for a meso-scale catchment in the Southern Pyrenees

The Southern Pre-Pyrenees experienced a substantial land-use change over the second half of the 20th century owing to the reduction of agricultural activities towards the formation of a more natural forest landscape. The land-use change over the last 50 years with subsequent effects on water and sediment export was modelled with the process-based, spatially semi-distributed WASA-SED model for the meso-scale Canalda catchment in Catalonia, Spain. It was forwarded that the model yielded plausible results for runoff and sediment yield dynamics without the need of calibration, although the model failed to reproduce the shape of the hydrograph and the total discharge of several individual rainstorm events, hence the simulation capabilities are not yet considered sufficient for decision-making purposes for land management. As there are only a very limited amount of measured data available on sediment budgets with altered land-use and climate change settings, the WASA-SED model was used to obtain qualitative estimates on the effects of past and future change scenarios to derive a baseline for hypothesis building and future discussion on the evolution of sediment budgets in such a dryland setting. Simulating the effects of the past land-use change, the model scenarios resulted in a decrease of up to 75% of the annual sediment yield, whereas modelled runoff remained almost constant over the last 50 years. The relative importance of environmental change was evaluated by comparing the impact on sediment export of land-use change, that are driven by socio-economic factors, with climate change projections for changes in the rainfall regime. The modelling results suggest that a 20% decrease in annual rainfall results in a decrease in runoff and sediment yield, thus an ecosystem stabilisation in regard to sediment export, which can only be achieved by a substantial land-use change equivalent to a complete afforestation. At the same time, a 20% increase in rainfall causes a large export of water and sediment resources out of the catchment, equivalent to an intensive agricultural use of 100% of the catchment area. For wet years, the effects of agricultural intensification are more pronounced, so that in this case the intensive land-use change has a significantly larger impact on sediment generation than climate change. The WASA-SED model proved capable in quantifying the impacts of actual and potential environmental change, but the reliability of the simulation results is still circumscribed by considerable parameterisation and model uncertainties.     

Genetics of litter size in three maternal lines of rabbits: repeatability versus multiple-trait models

Variance components were estimated in 3 lines of rabbits selected for litter size at weaning (A, Prat, and V) to test one of the assumptions of the models used for selection: that litter size data at different parities are repeated measurements of the same trait. Multiple-trait analyses were performed for the total number of kits born (TB), the number of kits born alive (BA), and the number of kits weaned (NW) per litter. Estimates were obtained by REML in multivariate analyses, including all of the information of the selection criteria, under a repeatability model or a multiple-trait model, considering litter size at the first, second, and subsequent parities as different traits. Models included the fixed effects of the physiological status of the female and the year-season of mating day, buck and doe random permanent environmental effects, and doe additive genetic effects. Results indicated that prolificacy was determined mainly by doe components and that the service sire had a very small effect. Heritabilities for the first and second parities were greater than the estimates obtained under the repeatability model (0.04 to 0.14 for the repeatability model). In the A and V lines, similar values of heritability were found at the first and second parities, but in the Prat line heritability at the second parity was always greater than at the first and greater parities (values of 0.21, 0.17, and 0.15 for TB, BA, and NW, respectively, in second parities of the Prat line). Genetic correlations between the same traits at different parities were approximately 0.8 for all traits in line A, but much lower in the other 2 lines. On average, the values were 0.64 for TB, 0.48 for BA, and 0.39 for NW between the first and second parities, and 0.65 for TB, 0.56 for BA, and 0.45 for NW between the first and third and greater parities. Genetic correlations between the second and greater parities showed the greatest values (approximately 0.8) in lines A and Prat for all traits, but they were lower in line V (0.63 for BA and 0.37 for NW). The heterogeneity of heritabilities and genetic correlations between parities lower than 0.9 suggests that litter size at different parities could be considered as different traits when genetic evaluations are performed. However, when the accuracies of predicted breeding values under a multiple-trait and a repeatability model were calculated, assuming the first to be the true model, the values obtained were nearly the same for all traits in all lines.     

Micellization of bovine beta-casein studied by isothermal titration microcalorimetry and cryogenic transmission electron microscopy

The association behavior, critical micellization concentration (CMC), and enthalpy of demicellization (DeltaHdemic) of bovine beta-casein were studied, for the first time by isothermal titration calorimetry, in a pH 7.0 phosphate buffer with 0.1 ionic strength and in pure water. In the buffer solutions, the CMC decreased asymptotically from 0.15 to 0.006 mM as the temperature was raised from 16 to 45 degrees C. DeltaHdemic decreased with increasing temperature between 16 and 28 degrees C but increased from 28 to 45 degrees C. Thermodynamic analysis below 30 degrees C is consistent with the Kegeles shell model, which suggests a stepwise association process. At higher temperatures, this model exhibits limitations, and the micellization becomes much more cooperative. The CMC values in water, measured between 17 and 28 degrees C, decreased with increasing temperature and, expectedly, were higher than those found in the buffer solutions. beta-Casein micelles were visualized and characterized, for the first time in their hydrated state, using advanced digital-imaging cryogenic transmission electron microscopy. The images revealed small, oblate micelles, about approximately 13 nm in diameter. The micelles shape and dimensions remained nearly constant in the temperature range of 24-35 degrees C.