Co-Cultivation of Fusarium, Alternaria, and Pseudomonas on Wheat-Ears Affects Microbial Growth and Mycotoxin Production

Mycotoxigenic fungal pathogens Fusarium and Alternaria are a leading cause of loss in cereal production. On wheat-ears, they are confronted by bacterial antagonists such as pseudomonads. Studies on these groups’ interactions often neglect the infection process’s temporal aspects and the associated priority effects. In the present study, the focus was on how the first colonizer affects the subsequent ones. In a climate chamber experiment, wheat-ears were successively inoculated with two different strains (Alternaria tenuissima At625, Fusarium graminearum Fg23, or Pseudomonas simiae Ps9). Over three weeks, microbial abundances and mycotoxin concentrations were analyzed and visualized via Self Organizing Maps with Sammon Mapping (SOM-SM). All three strains revealed different characteristics and strategies to deal with co-inoculation: Fg23, as the first colonizer, suppressed the establishment of At625 and Ps9. Nevertheless, primary inoculation of At625 reduced all of the Fusarium toxins and stopped Ps9 from establishing. Ps9 showed priority effects in delaying and blocking the production of the fungal mycotoxins. The SOM-SM analysis visualized the competitive strengths: Fg23 ranked first, At625 second, Ps9 third. Our findings of species-specific priority effects in a natural environment and the role of the mycotoxins involved are relevant for developing biocontrol strategies.

On the use of circulation classifications by self-organizing maps toward studying extreme weather

<p>The self-organizing maps (SOMs) have become a widespread tool for studying atmospheric circulation and its links to weather elements. The SOMs do not only produce a classification, but also a topology-preserving representation of the input data—a 2D array of circulation types (CTs). Consequently, one can analyse not only CT frequencies, persistence, and their conditioning of weather elements, but also visualise these parameters in a “continuum” of representative patterns. This latter characteristic makes it in theory plausible to define a (considerably) larger number of CTs compared to other classification approaches, and thus better represent extremes of circulation variability, without necessarily compromising the utility of the output by making it unintelligible.</p><p>Here, we investigate whether increasing the number of CTs (enlarging the SOM) leads to a classification better suitable to study synoptic forcing of extreme weather, and, in particular, what the effect is of various SOM parameters, which have to be chosen a priori more or less subjectively—such as array shape and size, radius and function of neighbourhood, learning rate, and initialization—on the utility of the resulting classification. Furthermore, we present the Sammon mapping, typically used to evaluate the topological structure of SOMs, as a standalone classification tool that shares some of the advantages with SOMs while potentially circumventing some of their weaknesses.</p>

A visual approach for analysis and inference of molecular activity spaces

Background Molecular space visualization can help to explore the diversity of large heterogeneous chemical data, which ultimately may increase the understanding of structure-activity relationships (SAR) in drug discovery projects. Visual SAR analysis can therefore be useful for library design, chemical classification for their biological evaluation and virtual screening for the selection of compounds for synthesis or in vitro testing. As such, computational approaches for molecular space visualization have become an important issue in cheminformatics research. The proposed approach uses molecular similarity as the sole input for computing a probabilistic surface of molecular activity (PSMA). This similarity matrix is transformed in 2D using different dimension reduction algorithms (Principal Coordinates Analysis ( PCooA), Kruskal multidimensional scaling, Sammon mapping and t-SNE). From this projection, a kernel density function is applied to compute the probability of activity for each coordinate in the new projected space. Results This methodology was tested over four different quantitative structure-activity relationship (QSAR) binary classification data sets and the PSMAs were computed for each. The generated maps showed internal consistency with active molecules grouped together for all data sets and all dimensionality reduction algorithms. To validate the quality of the generated maps, the 2D coordinates of test molecules were computed into the new reference space using a data transformation matrix. In total sixteen PSMAs were built, and their performance was assessed using the Area Under Curve (AUC) and the Matthews Coefficient Correlation (MCC). For the best projections for each data set, AUC testing results ranged from 0.87 to 0.98 and the MCC scores ranged from 0.33 to 0.77, suggesting this methodology can validly capture the complexities of the molecular activity space. All four mapping functions provided generally good results yet the overall performance of PCooA and t-SNE was slightly better than Sammon mapping and Kruskal multidimensional scaling. Conclusions Our result showed that by using an appropriate combination of metric space representation and dimensionality reduction applied over metric spaces it is possible to produce a visual PSMA for which its consistency has been validated by using this map as a classification model. The produced maps can be used as prediction tools as it is simple to project any molecule into this new reference space as long as the similarities to the molecules used to compute the initial similarity matrix can be computed.

MRI Brain Image Classification Based on S-Transform, Sammon Mapping and Naïve Bayes Classifier

In this paper, an efficient method for Magnetic Resonance Imaging (MRI) brain image classification is presented using Stockwell (S)-Transform, Sammon Mapping (SM) and Naïve Bayes (NB) classifier. Initially, the MRI brain images are represented in frequency domain by S-Transform. As the representation in frequency domain provides more detailed information than spatial domain, S-Transform is used for feature extraction. The high dimensional S-Transform feature space increases the complexity. Hence, SM technique is used to reduce it and then classification is made by NB classifier. The performance measures such as sensitivity, accuracy and specificity are computed to evaluate the system. Result shows the better classification accuracy of 94% is obtained by S-Transform based SM technique with NB classifier with 94% of sensitivity and specificity.

Interpretación del habitar utilizando dimensiones semánticas. Escalamiento multidimensional del espacio semántico de la Machiya japonesa como base de un método de diseño

Este estudio, consiste en extender con herramientas de análisis de datos nuestros estudios anteriores de las casas Machiya de Kioto, Japón, con el objetivo de crear la base de un método de diseño aplicable en arquitectura en general.Empezaremos repasando algunos conceptos básicos utilizados en nuestro estudio, seguido por un breve análisis de la Machiya japonesa y su espacio semántico. Luego de presentar la casa Machiya, veremos algunos de los problemas a los que se enfrenta en la actualidad y como éstos se relacionan con un problema de identidad que la arquitectura enfrenta hoy en día, para luego a explicar los objetivos de esta investigación.A continuación, presentaremos nuevas herramientas de análisis exploratorio de datos para analizar el espacio semántico, incluyendo agrupamientos y escalamiento multidimensional. Compararemos Sammon Mapping con Análisis de Componentes Principales, explorando sus usos potenciales para obtener información utilizable en el diseño de nuevos espacios arquitectónicos. Veremos las posibilidades de aplicar las herramientas de análisis no solo para proyectos basados en arquitectura tradicional, sino que, para todo tipo de proyectos arquitectónicos y la creación de nuevos espacios.

Interpretación del habitar utilizando dimensiones semánticas. Escalamiento multidimensional del espacio semántico de la Machiya japonesa como base de un método de diseño

<p class="p1">Este estudio, consiste en extender con herramientas de análisis de datos nuestros estudios anteriores de las casas Machiya de Kioto, Japón, con el objetivo de crear la base de un método de diseño aplicable en arquitectura en general.</p><p class="p2">Empezaremos repasando algunos conceptos básicos utilizados en nuestro estudio, seguido por un breve análisis de la Machiya japonesa y su espacio semántico. Luego de presentar la casa Machiya, veremos algunos de los problemas a los que se enfrenta en la actualidad y como éstos se relacionan con un problema de identidad que la arquitectura enfrenta hoy en día, para luego a explicar los objetivos de esta investigación.</p><p class="p2">A continuación, presentaremos nuevas herramientas de análisis exploratorio de datos para analizar el espacio semántico, incluyendo agrupamientos y escalamiento multidimensional. Compararemos Sammon Mapping con Análisis de Componentes Principales, explorando sus usos potenciales para obtener información utilizable en el diseño de nuevos espacios arquitectónicos.<span class="Apple-converted-space"> </span></p><p class="p2">Veremos las posibilidades de aplicar las herramientas de análisis no solo para proyectos basados en arquitectura tradicional, sino que, para todo tipo de proyectos arquitectónicos y la creación de nuevos espacios.</p>

Map- or list-based recommender agents? Does the map metaphor fulfill its promise?

We present a spatialization of digital library content based on item similarity and an experiment which compares the performance of this spatialization relative to a simple list-based display. Items in the library are elementary school, middle school, and high school science and engineering learning resources. Spatialization and visualization are accomplished through two-dimensional interactive Sammon mapping of pairwise item similarities computed from the joint occurrence of word bigrams. The 65 science teachers participating in the experiment were asked to search the library for curricular items they would consider using as part of one or more teaching assignments. The results indicate that whereas the spatializations adequately capture the salient features of the library’s content and teachers actively use them, item retrieval rates, task-completion time, and perceived utility do not significantly differ from the semantically poorer but easier to comprehend and navigate list-based representations. These results put into question the usefulness of the rapidly increasing supply of information spatializations.