Characterization,feasibility and optimization of Agaricus subrufescens growth based on chemical elements on casing layer

The aim of this study was to analyze yields, biological efficiency, earliness (expressed as days to first harvest), and precociousness and establish models for the mushroom growing according to these parameters. The experiment followed a double factorial design with four sources of calcium (calcitic limestone, calcitic limestone + gypsum, dolomitic limestone and dolomitic limestone + gypsum) and 2 application times (25 days before casing and at the moment of casing), with 4 replicates for each treatment. Different calcium sources influenced differently Agaricus subrufescens production, especially as regards earliness, which showed significantly higher values when dolomitic limestone with gypsum was applied. Yield and biological efficiency were negatively correlated with H + AL, organic matter and Mg amount. Furthermore, earliness was positively correlated with H + Al, organic matter, and the amount of Mg and Fe. Finally, negative correlations were observed between precociousness and the amount of Ca, SB (sum of base), CEC (cation exchange capacity) and V% (percentage of base saturation). The models presented in this work are extremely important for predicting the agronomic performance of Agaricus subrufescens on the basis of chemical analysis provided by the casing soil.     

Modelling the effect of the physical and chemical characteristics of the materials used as casing layers on the production parameters of Agaricus bisporus

The aim of this research was to show the mathematical data obtained through the correlations found between the physical and chemical characteristics of casing layers and the final mushrooms’ properties. For this purpose, 8 casing layers were used: soil, soil + peat moss, soil + black peat, soil + composted pine bark, soil + coconut fibre pith, soil + wood fibre, soil + composted vine shoots and, finally, the casing of La Rioja subjected to the ruffling practice. The conclusion that interplays in the fructification process with only the physical and chemical characteristics of casing are complicated was drawn. The mathematical data obtained in earliness could be explained in non-ruffled cultivation. The variability observed for the mushroom weight and the mushroom diameter variables could be explained in both ruffled and non-ruffled cultivations. Finally, the properties of the final quality of mushrooms were established by regression analysis.     

Automated detection and classification of high frequency oscillations (HFOs) in human intracereberal EEG

Discrete high-frequency oscillations (HFOs) in the range of 80–500 Hz have previously been recorded from human epileptic brains using intracereberal EEG and seem to be a reliable biomarker of seizure onset zone in patients with intractable epilepsy. Visual marking of HFOs bursts is tedious, highly time-consuming particularly for analyzing long-term multichannel EEG recordings, inevitably subjective and can be error prone. Thus, the development of automatic, fast and robust detectors is necessary and crucial for HFOs investigation and for propelling their eventual clinical applications. This paper presents a proposed algorithm for detection and classification of HFOs, which is a combination of both smoothed Hilbert Huang Transform (HHT) and root mean square (RMS) feature. Performance evaluation in terms of sensitivity and false discovery rate (FDR) were respectively 90.72% and 8.23% related to process validation. Indeed, the proposed method was efficient in terms of high sensitivity in which the majority of HFOs visually identified by experienced reviewers was correctly detected, and had a lower FDR. This would mean that only a low rate of detected events was misclassified as candidate HFOs events. The presented software is extremely fast, suitable and can be considered a valuable clinical tool for HFOs investigation.