The Genome sequence of the SARS-associated coronavirus

We sequenced the 29,751-base genome of the severe acute respiratory syndrome (SARS)-associated coronavirus known as the Tor2 isolate. The genome sequence reveals that this coronavirus is only moderately related to other known coronaviruses, including two human coronaviruses, HCoV-OC43 and HCoV-229E. Phylogenetic analysis of the predicted viral proteins indicates that the virus does not closely resemble any of the three previously known groups of coronaviruses. The genome sequence will aid in the diagnosis of SARS virus infection in humans and potential animal hosts (using polymerase chain reaction and immunological tests), in the development of antivirals (including neutralizing antibodies), and in the identification of putative epitopes for vaccine development.     

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.     

Iron deficiency in citrus limon: effects on photo‐chlorophyllase synthetic pigments and chlorophyllase activity

The photosynthetic pigment composition of chlorotic leaves of Citrus limon L. cv. Verna, grown in the field under iron deficiency conditions was determined. A Fe‐polyflavonoid was used as fertilizer to control iron chloro‐ sis. The photosynthetic pigment content and the chlorophyllase activity were determined at 20 day intervals during the deficiency recovery period and compared to untreated similar material. The corresponding differences among treated and untreated control material were analyzed. Iron application increased the levels of all pigments, but the extent of the increase depended on the pigment affected. The chlorophylls/carotenoids and ß‐carotene/xantophylls ratios were increased as chlorosis diminished. A multivariance analysis was performed with the data obtained which revealed that chlorophyll a and ß‐carotene had the highest correlation coefficient. The chlorophyllase activity did not show significant changes, but it was lower in the treated leaves than in the untreated control leaves during all the sampling cycle.     

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.     

Model for Simulation of Particle Flow on a Centrifugal Fertiliser Spreader

An analysis of the motion of a particle on a rotating flat disc is conducted and a basic equation of motion derived for a single particle. The analysis is directly applied to the flow of particles on a centrifugal fertiliser spreader. Equations of motion are derived for flat disc/radial vanes type of spreaders and further generalised for the more common conical disc/pitched vanes type. The derived equations of motion form part of the basis of a simulation model for centrifugal (or spinning disc) fertiliser spreaders with the equations of motion through the air completing the model. In the simulation, the collective flow of particles is treated as a superposition of single-particle trajectories with different initial conditions. The spreader model is applicable for the development of spreader control systems for site-specific fertiliser application as well as for the design and optimisation of spreaders.     

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.     

Arsenosugars in raw and cooked edible seaweed: characterization and bioaccessibility

The aim of this study was to examine arsenic species contents in raw and cooked edible seaweed and the bioaccessibility (maximum soluble concentration in gastrointestinal medium) of arsenosugars (glycerol ribose, phosphate ribose, sulfonate ribose, and sulfate ribose). For the analysis, a new chromatographic separation was developed in anion exchange, coupled with thermooxidation-hydride generation-atomic fluorescence spectrometry. An in vitro digestion (pepsin, pH 2; pancreatin-bile extract, pH 7) was applied to estimate arsenosugar bioaccessibility. Cooking of Undaria pinnatifida and Porphyra sp. did not alter the arsenic species present in the methanol-water extract, but it produced a substantial increase (2 and 5 times) in the As(V) extracted from Hizikia fusiforme. In all of the seaweeds analyzed, arsenosugar bioaccessibility was high (>80%) and did not vary as a result of cooking. Arsenosugar degradation as a result of in vitro digestion was not observed.