Transition of Vibrio cholerae through a natural host induces resistance to environmental changes

The pandemic related strains of Vibrio cholerae are known to cause diarrheal disease in animal hosts. These bacteria must overcome rapid changes in their environment, such as the transition from fresh water to the gastrointestinal system of their host. To study the morphological adjustments during environmental transitions, we used zebrafish as a natural host. Using a combination of fluorescent light microscopy, cryogenic electron tomography and serial block face scanning electron microscopy, we studied the structural changes that occur during the infection cycle. We show that the transition from an artificial nutrient rich environment to a nutrient poor environment has a dramatic impact on the cell shape, most notably membrane dehiscence. In contrast, excreted bacteria from the host retain a uniform distance between the membranes as well as their vibrioid shape. Inside the intestine, V. cholerae cells predominantly colonized the anterior to midgut, forming microcolonies associated with the microvilli as well as within the lumen. The cells retained their vibrioid shape but changed their cell length depending on their localization. Our results demonstrate dynamic changes in morphological characteristics of V. cholerae during the transition between the different environments, and we propose that these structural changes are critical for the pathogens ability to colonize host tissues.

Evaluation of taxonomic and neural embedding methods for calculating semantic similarity

Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity.

Transportation Inequalities for Coupled Fractional Stochastic Evolution Equations Driven by Fractional Brownian Motion

In this paper, we consider the existence and uniqueness of the mild solution for a class of coupled fractional stochastic evolution equations driven by the fractional Brownian motion with the Hurst parameter H∈1/4,1/2. Our approach is based on Perov’s fixed-point theorem. Furthermore, we establish the transportation inequalities, with respect to the uniform distance, for the law of the mild solution.

Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques

The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learning task. The study involved 36 participants (students versus university teachers with and without previous knowledge). The scan-paths were registered during the viewing of video based on SRL. Subsequently, the participants were asked to solve a crossword puzzle, and relevant vs. non-relevant Areas of Interest (AOI) were defined. Conventional statistical techniques (ANCOVA) and data mining techniques (string-edit methods and k-means clustering) were applied. The former only detected differences for the crossword puzzle. However, the latter, with the Uniform Distance model, detected the participants with the most effective scan-path. The use of this technique successfully predicted 64.9% of the variance in learning results. The contribution of this study is to analyze the teaching–learning process with resources that allow a personalized response to each learner, understanding education as a right throughout life from a sustainable perspective.

Universal consistency of the k-NN rule in metric spaces and Nagata dimension

The k nearest neighbour learning rule (under the uniform distance tie breaking) is universally consistent in every metric space X that is sigma-finite dimensional in the sense of Nagata. This was pointed out by Cérou and Guyader (2006) as a consequence of the main result by those authors, combined with a theorem in real analysis sketched by D. Preiss (1971) (and elaborated in detail by Assouad and Quentin de Gromard (2006)). We show that it is possible to give a direct proof along the same lines as the original theorem of Charles J. Stone (1977) about the universal consistency of the k-NN classifier in the finite dimensional Euclidean space. The generalization is non-trivial because of the distance ties being more prevalent in the non-Euclidean setting, and on the way we investigate the relevant geometric properties of the metrics and the limitations of the Stone argument, by constructing various examples.

Hierarchical Hexagonal Clustering and Indexing

Space-filling curves (SFCs) represent an efficient and straightforward method for sparse-space indexing to transform an n-dimensional space into a one-dimensional representation. This is often applied for multidimensional point indexing which brings a better perspective for data analysis, visualization and queries. SFCs are involved in many areas such as big data analysis and visualization, image decomposition, computer graphics and geographic information systems (GISs). The indexing methods subdivide the space into logic clusters of close points and they differ in various parameters including the cluster order, the distance metrics, and the pattern shape. Beside the simple and highly preferred triangular and square uniform grids, the hexagonal uniform grids have gained high interest especially in areas such as GISs, image processing and data visualization for the uniform distance between cells and high effectiveness of circle coverage. While the linearization of hexagons is an obvious approach for memory representation, it seems there is no hexagonal SFC indexing method generally used in practice. The main limitation of hexagons lies in lacking infinite decomposition into sub-hexagons and similarity of tiles on different levels of hierarchy. Our research aims at defining a fast and robust hexagonal SFC method. The Gosper fractal is utilized to preserve the benefits of hexagonal grids and to efficiently and hierarchically linearize points in a hexagonal grid while solving the non-convex shape and recursive transformation issues of the fractal. A comparison to other SFCs and grids is conducted to verify the robustness and effectiveness of our hexagonal method.

Design Analysis of a Mobile-Based Gait Analyzer

Considering a system capable of identifying abnormalities in people's walking conditions in real-time, simply by studying his/her walking profile over a short period of time is a phenomenal breakthrough in the field of biotechnology. Such abnormalities could be as a result of injury, old age, or disease termed gait which could be analyzed using the pressure mapping technology. Pressure points in the feet of an injured person as he/she walks is analyzed by sets of sensors (capacitive sensors) carefully design with a rectangular 5.1cm by 2cm parallel aluminium plate and placed on developed footwear with a uniform distance of 1cm across the dielectric material. The output of the pre-processing stage gives varying values which are calibrated and sent to the microcontroller. All placed on a portable sized Printed Circuit Board (PCB) making it moveable from one place to another (that is, mobile), is the pre-processing circuit that converts measured or evaluated result to the transmittable signal through a Mobile Communication System which can be received on a Personal Computer (PC) in form of a periodic chat and/ or report. The result of the analysis is shown both in simulation and hardware implementation of the system