A new heuristic method for solving spatially constrained forest planning problems based on mitigation of infeasibilities radiating outward from a forced choice

[Abstract]

Method for evaluation of root hairs of common bean genotypes

The objective of this work was to test a simple method for root hair evaluation of 21 common bean (Phaseolus vulgaris) genotypes, most of them used in breeding programs in Brazil. Hairs of basal and primary roots of 5-day old seedlings, produced on germination paper with no phosphorus addition, were visually evaluated by a rating scale after staining with 0.05% trypan blue. The method reveals variability among the genotypes, and the standard error of the mean is relatively low.

Alternative genotyping method for the single nucleotide polymorphism A2959G (AF159246) of the bovine CAST gene

The objective of this work was to genotype the single nucleotide polymorphism (SNP) A2959G (AF159246) of bovine CAST gene by PCR-RFLP technique, and to report its use for the first time. For this, 147 Bos indicus and Bos taurus x Bos indicus animals were genotyped. The accuracy of the method was confirmed through the direct sequencing of PCR products of nine individuals. The lowest frequency of the meat tenderness favorable allele (A) in Bos indicus was confirmed. The use of PCR-RFLP for the genotyping of the bovine CAST gene SNP was shown to be robust and inexpensive, which will greatly facilitate its analysis by laboratories with basic structure.

A treatment planning method for sequentially combining radiopharmaceutical therapy and external radiation therapy.

PURPOSE: Effective cancer treatment generally requires combination therapy. The combination of external beam therapy (XRT) with radiopharmaceutical therapy (RPT) requires accurate three-dimensional dose calculations to avoid toxicity and evaluate efficacy. We have developed and tested a treatment planning method, using the patient-specific three-dimensional dosimetry package 3D-RD, for sequentially combined RPT/XRT therapy designed to limit toxicity to organs at risk. METHODS AND MATERIALS: The biologic effective dose (BED) was used to translate voxelized RPT absorbed dose (D(RPT)) values into a normalized total dose (or equivalent 2-Gy-fraction XRT absorbed dose), NTD(RPT) map. The BED was calculated numerically using an algorithmic approach, which enabled a more accurate calculation of BED and NTD(RPT). A treatment plan from the combined Samarium-153 and external beam was designed that would deliver a tumoricidal dose while delivering no more than 50 Gy of NTD(sum) to the spinal cord of a patient with a paraspinal tumor. RESULTS: The average voxel NTD(RPT) to tumor from RPT was 22.6 Gy (range, 1-85 Gy); the maximum spinal cord voxel NTD(RPT) from RPT was 6.8 Gy. The combined therapy NTD(sum) to tumor was 71.5 Gy (range, 40-135 Gy) for a maximum voxel spinal cord NTD(sum) equal to the maximum tolerated dose of 50 Gy. CONCLUSIONS: A method that enables real-time treatment planning of combined RPT-XRT has been developed. By implementing a more generalized conversion between the dose values from the two modalities and an activity-based treatment of partial volume effects, the reliability of combination therapy treatment planning has been expanded.

The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods.

The M-Coffee server is a web server that makes it possible to compute multiple sequence alignments (MSAs) by running several MSA methods and combining their output into one single model. This allows the user to simultaneously run all his methods of choice without having to arbitrarily choose one of them. The MSA is delivered along with a local estimation of its consistency with the individual MSAs it was derived from. The computation of the consensus multiple alignment is carried out using a special mode of the T-Coffee package [Notredame, Higgins and Heringa (T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 2000; 302: 205-217); Wallace, O'Sullivan, Higgins and Notredame (M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006; 34: 1692-1699)] Given a set of sequences (DNA or proteins) in FASTA format, M-Coffee delivers a multiple alignment in the most common formats. M-Coffee is a freeware open source package distributed under a GPL license and it is available either as a standalone package or as a web service from www.tcoffee.org.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

The diagnosis of chronic inflammatory demyelinating polyneuropathy: a Delphi-method approach.

The diagnosis of chronic inflammatory demyelinating polyneuropathy (CIDP) is based on a set of clinical and neurophysiological parameters. However, in clinical practice, CIDP remains difficult to diagnose in atypical cases. In the present study, 32 experts from 22 centers (the French CIDP study group) were asked individually to score four typical, and seven atypical, CIDP observations (TOs and AOs, respectively) reported by other physicians, according to the Delphi method. The diagnoses of CIDP were confirmed by the group in 96.9 % of the TO and 60.1 % of the AO (p < 0.0001). There was a positive correlation between the consensus of CIDP diagnosis and the demyelinating features (r = 0.82, p < 0.004). The European CIDP classification was used in 28.3 % of the TOs and 18.2 % of the AOs (p < 0.002). The French CIDP study group diagnostic strategy was used in 90 % of the TOs and 61 % of the AOs (p < 0.0001). In 3 % of the TOs and 21.6 % of the AOs, the experts had difficulty determining a final diagnosis due to a lack of information. This study shows that a set of criteria and a diagnostic strategy are not sufficient to reach a consensus for the diagnosis of atypical CIDP in clinical practice.

A simple halide-to-anion exchange method for heteroaromatic salts and ionic liquids

A broad and simple method permitted halide ions in quaternary heteroaromatic and ammonium salts to be exchanged for a variety of anions using an anion exchange resin (A− form) in non-aqueous media. The anion loading of the AER (OH− form) was examined using two different anion sources, acids or ammonium salts, and changing the polarity of the solvents. The AER (A− form) method in organic solvents was then applied to several quaternary heteroaromatic salts and ILs, and the anion exchange proceeded in excellent to quantitative yields, concomitantly removing halide impurities. Relying on the hydrophobicity of the targeted ion pair for the counteranion swap, organic solvents with variable polarity were used, such as CH3OH, CH3CN and the dipolar nonhydroxylic solvent mixture CH3CN:CH2Cl2 (3:7) and the anion exchange was equally successful with both lipophilic cations and anions.

Analysis of Markers for Recent Alcohol Consumption by Capillary Zone Electrophoresis and an Immunochemical Method

Le but de ce travail doctoral était le développement de méthodes analytiques pour la détermination dethyl glucuronide et dethyl sulfate. Ces deux substances sont des métabolites directs de lethanol qui peuvent être détectées pendant des heures jusqu'à des jours dans des fluides corporels, après que léthanol ait été complètement éliminé du corps humain. Ce sont donc des marqueurs de consommation récente d'alcool.La majorité des expériences ont été effectuées en utilisant l'électrophorèse capillaire. Il était envisagé de fournir des méthodes utilisables dans des laboratoires de routine. Des méthodes électrophorétiques ont été développées et optimisées pour la détermination dethyl sulfate dans le sérum et l'urine ainsi que pour lethyl glucuronide dans le sérum. Lethyl glucuronide urinaire a pu être déterminé par un immunoassay commerciale qui a en plus été adapté avec succès pour des échantillons de sérum. Avec toutes ces méthodes d'analyse il était possible d'observer les deux marqueurs de consommation d'alcool récente, même une consommation aussi basse qu'un verre de boissons alcooliques.Finalement, une étude englobant plus de 100 échantillons aété effectuée avec l'ambition de déterminer les valeurs de référence pour lethyl glucuronide dans le sérum et l'urine. De plus, la nécessité de normaliser les échantillons d'urine par rapport à la dilution a été investiguée. Grâce à cette étude des valeurs de cut-off et une base statistique pour l'interprétation probabiliste ont pu être proposées.

A new method to simulate the hydrological state of soil under natural conditions

Micro, macro and mesofauna in the soil often respond to fluctuating environmental conditions, resulting in changes of abundance and community structure. Effects of changing soil parameters are normally determined with samples taken in the field and brought to the laboratory, i.e. where natural environmental conditions may not apply. We devised a method (STAFD - soil tubes for artificial flood and drought), which simulates the hydrological state of soil in situ using implanted cores. Control tubes were compared with treatment tubes in which floods of 15, 30, 60 and 90 days, and droughts of 60, 90 and 120 days were simulated in the field. Flooding and drought were found to reduce number of individuals in all soil faunal groups, but the response to drought was slower and not in proportion to the expected decrease of the water content. The results of the simulated floods in particular show the value of the STAFD method for the investigation of such extreme events in natural habitats.

Software for interpolation of vegetative growth of yerba mate plants in 3D

The objective of this work was to build mock-ups of complete yerba mate plants in several stages of development, using the InterpolMate software, and to compute photosynthesis on the interpolated structure. The mock-ups of yerba-mate were first built in the VPlants software for three growth stages. Male and female plants grown in two contrasting environments (monoculture and forest understory) were considered. To model the dynamic 3D architecture of yerba-mate plants during the biennial growth interval between two subsequent prunings, data sets of branch development collected in 38 dates were used. The estimated values obtained from the mock-ups, including leaf photosynthesis and sexual dimorphism, are very close to those observed in the field. However, this similarity was limited to reconstructions that included growth units from original data sets. The modeling of growth dynamics enables the estimation of photosynthesis for the entire yerba mate plant, which is not easily measurable in the field. The InterpolMate software is efficient for building yerba mate mock-ups.

Suprachoroidal electrotransfer: a nonviral gene delivery method to transfect the choroid and the retina without detaching the retina.

Photoreceptors and retinal pigment epithelial cells (RPE) targeting remains challenging in ocular gene therapy. Viral gene transfer, the only method having reached clinical evaluation, still raises safety concerns when administered via subretinal injections. We have developed a novel transfection method in the adult rat, called suprachoroidal electrotransfer (ET), combining the administration of nonviral plasmid DNA into the suprachoroidal space with the application of an electrical field. Optimization of injection, electrical parameters and external electrodes geometry using a reporter plasmid, resulted in a large area of transfected tissues. Not only choroidal cells but also RPE, and potentially photoreceptors, were efficiently transduced for at least a month when using a cytomegalovirus (CMV) promoter. No ocular complications were recorded by angiographic, electroretinographic, and histological analyses, demonstrating that under selected conditions the procedure is devoid of side effects on the retina or the vasculature integrity. Moreover, a significant inhibition of laser induced-choroidal neovascularization (CNV) was achieved 15 days after transfection of a soluble vascular endothelial growth factor receptor-1 (sFlt-1)-encoding plasmid. This is the first nonviral gene transfer technique that is efficient for RPE targeting without inducing retinal detachment. This novel minimally invasive nonviral gene therapy method may open new prospects for human retinal therapies.

Iowa Fen Rapid Assessment Method for Wetlands v. 1 Quantitative Rating, 2016

Rapid assessment methods are valuable tools for collecting information about the quality and status of natural systems. However, they are not a substitute for detailed surveys of those systems. Users of this method should consider that the method may under-score or over-score the site that’s assessed, especially when the site is not a typical fen (i.e. a sedge meadow could score lower than fens, but in-fact be a relatively high quality sedge meadow). This assessment can be used throughout most of the spring, summer and fall period, however the ideal “index period” would be from late May through early October when native plant communities, as well as invasive species, are most apparent.

A fast automated screening method for the detection of blood transfusion in sports.

Currently, there is no simple direct screening method for the misuse of blood transfusions in sports. In this study, we investigated whether the measurement of iron in EDTA-plasma can serve as biomarker for such purpose. Our results revealed an increase of the plasma iron level up to 25-fold 6 h after blood re-infusion. The variable remained elevated 10-fold one day after the procedure. A specificity of 100% and a sensitivity of 93% were obtained with a proposed threshold at 45 µg/dL of plasma iron. Therefore, our test could be used as a simple, cost effective biomarker for the screening for blood transfusion misuse in sports. Copyright © 2014 John Wiley & Sons, Ltd.