Experimental design and Bayesian networks for enhancement of delta-endotoxin production by Bacillus thuringiensis

Bacillus thuringiensis (Bt) is a Gram-positive bacterium. The entomopathogenic activity of Bt is related to the existence of the crystal consisting of protoxins, also called delta-endotoxins. In order to optimize and explain the production of delta-endotoxins of Bacillus thuringiensis kurstaki, we studied seven medium components: soybean meal, starch, KH2PO4, K2HPO4, FeSO4, MnSO4, and MgSO4 and their relationships with the concentration of delta-endotoxins using an experimental design (Plackett—Burman design) and Bayesian networks modelling. The effects of the ingredients of the culture medium on delta-endotoxins production were estimated. The developed model showed that different medium components are important for the Bacillus thuringiensis fermentation. The most important factors influenced the production of delta-endotoxins are FeSO4, K2HPO4, starch and soybean meal. Indeed, it was found that soybean meal, K2HPO4, KH2PO4 and starch also showed positive effect on the delta-endotoxins production. However, FeSO4 and MnSO4 expressed opposite effect. The developed model, based on Bayesian techniques, can automatically learn emerging models in data to serve in the prediction of delta-endotoxins concentrations. The constructed model in the present study implies that experimental design (Plackett—Burman design) joined with Bayesian networks method could be used for identification of effect variables on delta-endotoxins variation

Metagenomics and microscope revealed T. trichiura and other intestinal parasites in a cesspit of an Italian nineteenth century aristocratic palace

This study evidenced the presence of parasites in a cesspit of an aristocratic palace of nineteenth century in Sardinia (Italy) by the use of classical paleoparasitological techniques coupled with next-generation sequencing. Parasite eggs identified by microscopy included helminth genera pathogenic for humans and animals: the whipworm Trichuris sp., the roundworm Ascaris sp., the flatworm Dicrocoelium sp. and the fish tapeworm Diphyllobothrium sp. In addition, 18S rRNA metabarcoding and metagenomic sequencing analysis allowed the first description in Sardinia of aDNA of the human specific T. trichiura species and Ascaris genus. Their presence is important for understanding the health conditions, hygiene habits, agricultural practices and the diet of the local inhabitants in the period under study

A traffic management system for real-time traffic optimisation in railways

The increase in traffic intensity and complexity of the railway system demands new methods for real-time traffic control. This paper introduces the architecture, the approach and the current implementation of an advanced Traffic Management System (TMS) able to optimise traffic fluency in large railway networks equipped with either fixed or moving block signalling systems. The TMS takes into account both the actual position and speed of each train in the area and the actual status of the infrastructure, and the dynamic characteristics of the train and the characteristics of the infrastructure such gradients, admissible speeds, signal positions and signal patterns. Potential conflicts can be predicted in advance and solved in real time, by managing the order of trains, or using alternative routes if possible, and by issuing proper speed recommendations to train drivers. In this way, the TMS prevents or limits the number of unplanned stops and the accompanying journey time loss.

Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis

The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R2) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort. , Springer-Verlag GmbH Germany.Scopu