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Lobesia botrana (Denis & Schiffermüller) (Lepidoptera: Tortricidae), is an important vineyard-pest in the European and Mediterranean areas and it was recently described in Argentina and Chile. Since knowledge on the L. botrana phenology on Argentina is still limited, the objective of this study was to develop a phenological model to predict voltinism of L. botrana in Argentina through a regional approach.Voltinism of L. botrana males was simulated based on occurrence of four non-overlapping flights. Nonlinear regression models were constructed using the weekly average trap catches from the agricultural seasons 2011-2012 to 2013-2014 and amount of degree-days accumulation. Weibull equation showed, on average for the four annual flights, the best estimate of the observed variability in the percentage of adult catches in relation to degree-day accumulation. It can be expected that 50% of male adult emergence for the first flight occurs at 443.9 DD; in the second flight at 1211.7 DD; while in the third and the fourth flights, the accumulation of degree days reaches values of 2077.8 DD and 2905 DD, respectively. The regional approach adopted in this work could explainthe variation found in field data and has a reasonable predictive and explicative capability as a component in the ongoing prospective analysis of the activity of L. botrana in Argentina.  

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Stock market timing is a very difficult task because of the complexity of the market. Since there are various factors affecting the market and therefore it is not a simple task to predict future stock price and its trend. This paper aims to apply advanced tools and algorithms such as the artificial neural networks (ANN) to model nonlinear processes and predict future stock price and its trend. More specifically, this study explores the abilities of the ANN to enhance the effectiveness of the technical analysis indicators to predict stock trend signals. Using a sample of 50 companies in the Tehran Stock Exchange (TSE), the results indicate that the ANN is capable to predict the direction of the short term movement in the future stock price. After considering the transaction costs, the results confirm that there is not significant difference among the returns gained from the ANN method, buy and hold strategy, and the most profitable technical indicators in the market when the trend is increasing. While, the ANN model yields higher returns compared to buy and hold strategy in the market when the trend is decreasing. Nevertheless, in the case of decreasing trend, the finding confirms the trend indicators (moving averages) achieve the highest returns.

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A new and optimized protocol, here called 6hDNA (i.e. a genomic DNA obtained by a six-hour extraction method), has been developed based on the traditional Cetyl-TrimethylAmmonium Bromide (CTAB) method. It allows a fast and easy isolation of genomic DNA from plant species, especially from those with high polyphenol and polysaccharide contents. Co-precipitation of polysaccharides was avoided by adding higher concentrations of selective precipitants of nucleic acid, CTAB 3% (w/v) and sodium chloride (NaCl) (1.42M). PolyVinylPyrrolidone (PVP) 1% (w/v) was applied to remove polyphenols as PCR inhibitors. Proteins were degraded by treatments of chloroform:isoamyl alchol (24:1) and phenol:chloroform:isoamyl alchol (25:24:1) and removed by centrifugation from plant extracts. The yield of total DNA from leaves of Vitis vinifera, Citrus sinensis and Olea europaea ranged from 42 to 980 ng µL^-1 with A260/A280 ratio values between 1.6 and 2.06. The purity and integrity of the obtained DNA guarantees successful downstream applications including PCR and microsatellite markers. The use of lyophilized plant material and the reduced time of the total procedure make this new 6hDNA protocol more convenient when compared to the most common DNA isolation protocols, such as: “Doyle and Doyle”, “Lodhi”, “Li”, or those using the DNAzol reagent and the Nucleospin Plant Minikit.