Studies on pesticides mixture degradation by white rot fungi

The capacity of five white rot fungi species to degrade linuron, metribuzin and chlorpyrifos when applied both as single pesticides and mixed together in different concentrations on nutritionally poor media. Our results suggested that Pleurotus ostreatus, Pycnoporus coccineus, Phlebiopsis gigatea and Τrametes versicolor showed remarkable tolerance to the pesticides, in all media tested. EC50 values presented a noticeable difference in the mixtures as compared with the individual ones. The minimum growth rate in the mixture was obtained by P. ostreatus whereas P. coccineus appeared to be more efficient than the rest fungal isolates, when cultivated in soil extract medium. P. coccineus, P. gigantea and T. versicolor produce high levels of polyphenol oxidase but only T. versicolor was capable to decompose linuron when combined with metribuzin and chlorpyrifos

Accurate microRNA target prediction correlates with protein repression levels

MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and diseas

Reduced Dimensionality Space for Post Placement Quality Inspection of Components based on Neural Networks

Abstract – The emergence of surface mount technology devices has resulted in several important advantages including increased component density and size reduction on the printed circuit board, on the expense of quality inspection. Classical visual inspection techniques require time-consuming image processing to improve the accuracy of the inspected results. In this paper we reduce the computational complexity of classical machine vision approaches by proposing two neural network based techniques. In the first we maintain image information only in the form of edges, whereas the second we preserve the entire content of info but compressed in a single dimension through image projections. Both algorithms are tested on real industrial data. The quality of inspection is preserved while reducing the computational time. 1

Evaluation of Loop Grouping Methods Based on Orthogonal Projection Spaces

This paper compares three similar loop-grouping methods. All methods are based on projecting the n-dimensional iteration space J onto a k-dimensional one, called the projected space, using (n-k) linear independent vectors. The dimension k is selected differently in each method giving various results. The projected space is divided into discrete groups of related iterations, which are assigned to different processors. Two of the methods preserve optimal time completion, by scheduling loop iterations according to the hyperplane method. The theoretical analysis of the experimental results indicates the appropriate method, for specific iteration spaces and target architectures

Energy audit in Athens metro stations for identifying energy consumption profiles of stationary loads

Metro transportation systems are significant energy consumers. Apart from the traction system, considerable amounts of electricity are consumed in metro stations. In the present study, energy audits of two Athens metro stations were conducted to ensure a detailed overview of energy consumption per stationary load. Data from on-site surveys as well as real-time measurements were elaborated. The energy profile over time was developed for the measured electrical loads. Results have shown that the total energy consumption of Sepolia and Peristeri stations was 117.09 and 99.17 kWh/m2/year respectively, as well as 0.12 and 0.52 kWh/passenger/year. Lighting and small power were significant consumers (23.3% of total energy consumed in Sepolia station, 51.5% in Peristeri station). The natural, piston effect induced ventilation system in Peristeri station showed significantly lower energy consumption (7.4%) than that of Sepolia station (25.9%) using forced ventilation for stations and tunnels

Hybrid Autofluorescence and Optoacoustic Microscopy for the Label-Free, Early and Rapid Detection of Pathogenic Infections in Vegetative Tissues

Agriculture plays a pivotal role in food security and food security is challenged by pests and pathogens. Due to these challenges, the yields and quality of agricultural production are reduced and, in response, restrictions in the trade of plant products are applied. Governments have collaborated to establish robust phytosanitary measures, promote disease surveillance, and invest in research and development to mitigate the impact on food security. Classic as well as modernized tools for disease diagnosis and pathogen surveillance do exist, but most of these are time-consuming, laborious, or are less sensitive. To that end, we propose the innovative application of a hybrid imaging approach through the combination of confocal fluorescence and optoacoustic imaging microscopy. This has allowed us to non-destructively detect the physiological changes that occur in plant tissues as a result of a pathogen-induced interaction well before visual symptoms occur. When broccoli leaves were artificially infected with Xanthomonas campestris pv. campestris (Xcc), eventually causing an economically important bacterial disease, the induced optical absorption alterations could be detected at very early stages of infection. Therefore, this innovative microscopy approach was positively utilized to detect the disease caused by a plant pathogen, showing that it can also be employed to detect quarantine pathogens such as Xylella fastidiosa

Reduced dimensionality space for post placement quality inspection of components based on neural networks.

Summarization: The emergence of surface mount technology devices has resulted in several important advantages including increased component density and size reduction on the printed circuit board, on the expense of quality inspection. Classical visual inspection techniques require time-consuming image processing to improve the accuracy of the inspected results. In this paper we reduce the computational complexity of classical machine vision approaches by proposing two neural network based techniques. In the first we maintain image information only in the form of edges, whereas the second we preserve the entire content of info but compressed in a single dimension through image projections. Both algorithms are tested on real industrial data. The quality of inspection is preserved while reducing the computational time.Παρουσιάστηκε στο: European Symposium on Artificial Neural Network