Molecular approaches for low-cost point-of-care pathogen detection in agriculture and forestry

Early detection of plant diseases is a crucial factor to prevent or limit the spread of a rising infection that could cause significant economic loss. Detection test on plant diseases in laboratory can be laborious, time consuming, expensive and normally requires specific technical expertise. Moreover in the developing Countries it is often difficult to find laboratories equipped for this kind of analysis. Therefore, in the last years a high effort has been made for the development of fast, specific, sensitive and cost-effective tests that can be successfully used in plant pathology directly in the field, by low-specialized personnel using minimal equipment. Nucleic acid-based methods have proven to be a good choice for the development of detection tools in several fields, such as human/animal health, food safety and water analysis and their application in plant pathogen detection is becoming more and more common. In the present review, the more recent nucleic acid-based protocols for point-of care plant pathogen detection and identification are described and analyzed. All these methods have a high potential for early detection of destructive diseases in agriculture and forestry; they should help making molecular detection for plant pathogens accessible to anyone, anywhere and at anytime. We do not suggest that on site methods should replace completely lab-testing, which remains crucial for more complex researches, such as identification and classification of new pathogens or the study of plant defence mechanisms. Instead, POC analysis can provide a useful, fast and efficient preliminary in field screening that is crucial in the struggle against plant pathogens

Fast network configuration in Software Defined Networking

Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%

Experimental Lagrangian Acceleration Probability Density Function Measurement

We report experimental results on the acceleration component probability distribution function at $R_\lambda = 690$ to probabilities of less than $10^{-7}$. This is an improvement of more than an order of magnitude over past measurements and allows us to conclude that the fourth moment converges and the flatness is approximately 55. We compare our probability distribution to those predicted by several models inspired by non-extensive statistical mechanics. We also look at acceleration component probability distributions conditioned on a velocity component for conditioning velocities as high as 3 times the standard deviation and find them to be highly non-Gaussian.Comment: submitted for the special issue of Physica D: "Anomalous Distributions" 11 pages, 6 figures revised version: light modifications of the figures and the tex

A truthful online mechanism for resource allocation in fog computing

Fog computing is a promising Internet of Things (IoT) paradigm in which data is processed near its source. Here, efficient resource allocation mechanisms are needed to assign limited fog resources to competing IoT tasks. To this end, we consider two challenges: (1) near-optimal resource allocation in a fog computing system; (2) incentivising self-interested fog users to report their tasks truthfully. To address these challenges, we develop a truthful online resource allocation mechanism called flexible online greedy. The key idea is that the mechanism only commits a certain amount of computational resources to a task when it arrives. However, when and where to allocate resources stays flexible until the completion of the task. We compare our mechanism to four benchmarks and show that it outperforms all of them in terms of social welfare by up to 10% and achieves a social welfare of about 90% of the offline optimal upper bound

Object-based image analysis for historic maps classification

Heritage maps represent fundamental information for the study of the evolution of a region, especially in terms of landscape and ecologic features. Historical maps present two kinds of hurdle before they can be used in a modern GIS: they must be geometrically corrected to correspond to the datum in use and they must be classified to exploit the information they contain. This study deals the latter problem: the Historical Cadaster Map, created between 1851 and 1861, for the Trentino region in the North of Italy is available as a collection of maps in the ETRS89/UTM 32N datum. The map is a high resolution scan (230 DPI, 24 bit) of the original map and has been used in several ecological studies, since it provides detailed information not only about land property but also about land use. In the past the cadaster map has been manually digitized and for each area a set of attributes has been recorded. Since this approach is time consuming and prone to errors, automatic and semi-automatic procedures have been tested. Traditional image classification techniques, such as maximum likelihood classification, supervised or un-supervised, pixelwise and contextual, do not provide satisfactory results for many reasons: map colors are very variable within the same area, symbols and characters are used to identify cadaster parcels and locations, lines, drawn by hand on the original map, have variable thickness and colors. The availability of FOSS tools for the Object-based Image Analysis (OBIA) has made possible the application of this technique to the cadaster map. This paper describes the use of GRASS GIS and R for the implementation of the OBIA approach for the supervised classification of the historic cadaster map. It describes the determination of the optimal segments, the choice of their attributes and relevant statistics, and their classification. The result has been evaluated with respect to a manually digitized map using Cohens Kappa and the analysis of the confusion matrix. The result of the OBIA classification has also been compared to the classification of the same map using maximum likelihood classification, un-supervised and supervised, both pixelwise and contextual. The OBIA approach has provided very satisfactory results with the ability to automatically remove the background and symbols and characters, creating a ready to be used classified map. This study highlights the effectiveness of the OBIA processing chain available in the FOSS4G ecosystem, and in particular the added value of the interoperability between GRASS GIS and R

Penta-hepta defect chaos in a model for rotating hexagonal convection

In a model for rotating non-Boussinesq convection with mean flow we identify a regime of spatio-temporal chaos that is based on a hexagonal planform and is sustained by the {\it induced nucleation} of dislocations by penta-hepta defects. The probability distribution function for the number of defects deviates substantially from the usually observed Poisson-type distribution. It implies strong correlations between the defects inthe form of density-dependent creation and annihilation rates of defects. We extract these rates from the distribution function and also directly from the defect dynamics.Comment: 4 pages, 5 figures, submitted to PR

Management of non-native tree species in forests of the Alpine space

This guide was prepared within the framework of the project ALPTREES (code ASP791), which is co-funded by the European Commission through the INTERREG Alpine Space financial mechanism. The INTERREG Alpine Space programme is a European transnational cooperation programme for the Alpine region. It provides a framework for facilitating cooperation between key economic, social, and environmental players in seven Alpine countries, as well as between various institutional levels. The programme is financed through the European Regional Development Fund (ERDF) as well as through national public and private co-funding in the Partner States

Early identification of root rot disease by using hyperspectral reflectance: the case of pathosystem grapevine/Armillaria

Armillaria genus represents one of the most common causes of chronic root rot disease in woody plants. Prompt recognition of diseased plants is crucial to control the pathogen. However, the current disease detection methods are limited at a field scale. Therefore, an alternative approach is needed. In this study, we investigated the potential of hyperspectral techniques to identify fungi-infected vs. healthy plants of Vitis vinifera. We used the hyperspectral imaging sensor Specim-IQ to acquire leaves’ reflectance data of the Teroldego Rotaliano grapevine cultivar. We analyzed three different groups of plants: healthy, asymptomatic, and diseased. Highly significant differences were found in the near-infrared (NIR) spectral region with a decreasing pattern from healthy to diseased plants attributable to the leaf mesophyll changes. Asymptomatic plants emerged from the other groups due to a lower reflectance in the red edge spectrum (around 705 nm), ascribable to an accumulation of secondary metabolites involved in plant defense strategies. Further significant differences were observed in the wavelengths close to 550 nm in diseased vs. asymptomatic plants. We evaluated several machine learning paradigms to differentiate the plant groups. The Naïve Bayes (NB) algorithm, combined with the most discriminant variables among vegetation indices and spectral narrow bands, provided the best results with an overall accuracy of 90% and 75% in healthy vs. diseased and healthy vs. asymptomatic plants, respectively. To our knowledge, this study represents the first report on the possibility of using hyperspectral data for root rot disease diagnosis in woody plants. Although further validation studies are required, it appears that the spectral reflectance technique, possibly implemented on unmanned aerial vehicles (UAVs), could be a promising tool for a cost-effective, non-invasive method of Armillaria disease diagnosis and mapping in-field, contributing to a significant step forward in precision viticultur

Modeling of forest landscape evolution at regional level: a FOSS4G approach

In the last decades the Alpine landscape has dramatically changed due to social and economic factors. The most visible impact has been the reduction of the population for mid and high altitude villages and the shrinking of the part of the land used for agriculture and grazing, with a progressive reduction of pastures and meadows and the expansion of the forested areas. For these reasons, a dataset describing the forest, meadows and pasture coverage for the Trentino region, in the eastern Italian Alps, has been created. A set of heterogeneous sources has been selected so that maps and images cover the longest possible time span on the whole Trentino region with comparable quality, creating a Land Use/Land Cover (LULC) map based on historical maps from 1859 to 1936 and aerial images from 1954 to 2015. The achieved accuracy ranges from 98% for historical maps to 94% for aereal imagery. The analysis of selected landscape metrics provided preliminary results about the forest distribution and patterns of recolonization during the last 155 years. It has been possible to create future scenarios for the forest evolution for the next 85 years. Given the large number of maps involved, the great flexibility provided by FOSS for spatial analysis, such as GRASS, R, QGIS and GAMA and the possibility of scripting all the operations have played a pivotal role in the success both in the creation of the dataset and in the extraction and modeling of land use change

Identification of Low Temperature Stress Regulated Transcript Sequences and Gene Families in Italian Cypress

© 2014, Springer Science+Business Media New York. Cold acclimation is a complex transcriptionally controlled process regulated by many different genes and genic-interactions in plants. The northward spreading of woody species is mainly limited by winter harshness. To increase our knowledge about the biological processes underlying cold acclimation, plants evolved in warmer climates can serve as models. In this work, a Suppression Subtractive Hybridization approach using PCR-select was used to isolate Italian cypress (Cupressus sempervirens L.) transcript sequences putatively expressed under low temperature stress. After assessing the reliability of the subtractive step, a total of 388 clones were selected and sequenced. Following sequence assembly and removal of the redundant cDNAs, 156 unique transcripts were identified and annotated in order to assign them a putative functional class. Most of the identified transcripts were functionally classified pertaining to stress in cellular and chloroplast membranes, which are previously known to be severely damaged by cold treatment. Among the identified functional gene families, the extensively represented ones were dehydrins, early light-inducible proteins, senescence-associated genes and oleosins. The last three gene families were further selected for phylogenetic analysis, with the corresponding protein sequences across the complete genomes of the model plants Populus trichocarpa, Vitis vinifera, Physcomitrella patens, and Arabidopsis thaliana. The relationship with the ortholog sequences coming from these species and their further implications are discussed