Domen Mongus

Gut Microbiota Patterns Associated with Colonization of Different Clostridium difficile Ribotypes

C. difficile infection is associated with disturbed gut microbiota and changes in relative frequencies and abundance of individual bacterial taxons have been described. In this study we have analysed bacterial, fungal and archaeal microbiota by denaturing high pressure liquid chromatography (DHPLC) and with machine learning methods in 208 faecal samples from healthy volunteers and in routine samples with requested C. difficile testing. The latter were further divided according to stool consistency, C. difficile presence or absence and C. difficile ribotype (027 or non-027). Lower microbiota diversity was a common trait of all routine samples and not necessarily connected only to C. difficile colonisation. Differences between the healthy donors and C. difficile positive routine samples were detected in bacterial, fungal and archaeal components. Bifidobacterium longum was the single most important species associated with C. difficile negative samples. However, by machine learning approaches we have identified patterns of microbiota composition predictive for C. difficile colonization. Those patterns also differed between samples with C. difficile ribotype 027 and other C. difficile ribotypes. The results indicate that not only the presence of a single species/group is important but that certain combinations of gut microbes are associated with C. difficile carriage and that some ribotypes (027) might be associated with more disturbed microbiota than the others.

Efficient method for lossless LIDAR data compression

Light Detection and Ranging (LIDAR) has become one of the prime technologies for rapid collection of vast spatial data, usually stored in a LAS file format (LIDAR data exchange format standard). In this article, a new method for lossless LIDAR LAS file compression is presented. The method applies three consequent steps: a predictive coding, a variable-length coding and an arithmetic coding. The key to the method is the prediction schema, where four different predictors are used: three predictors for x, y and z coordinates and a predictor for scalar values, associated with each LIDAR point. The method has been compared with the popular general-purpose methods and with a method developed specially for compressing LAS files. The proposed method turns out to be the most efficient in all test cases. On average, the LAS file is losslessly compressed to 12% of its original size.

A hybrid evolutionary algorithm for tuning a cloth-simulation model

Abstract: Textile simulation models are notorious for being difficult to tune. The physically based derivations of energy functions, as mostly used for mapping the characteristics of real-world textiles on to simulation models, are labour-intensive and not guarantee satisfactory results. The extremely complex behaviour of textiles requires additional adjustment over a wide-range of parameters in order to achieve realistic real-life behaviour of the model. Furthermore, such derivations might not even be possible when dealing with mass-spring particle system-based models. Since there is no explicit correlation between the physical characteristics of textiles and the stiffnesses of springs that control a models behaviour, this remains an unresolved issue. This paper proposes a hybrid evolutionary algorithm (EA), in order to solve this problem. The initial parameters of the model are written in individuals genes, where the number of genes is predefined for different textile types in order to limit the search-space. By mimicking the evolution processes, the EA is used to search the stability domain of the model to find a set of parameters that persuasively imitate the behaviour of a given real-world textile (e.g. silk, cotton or wool). This evaluation is based on the drape measurement, a characteristic often used when evaluating fabrics within the textile industry. The proposed EA is multi-objective, as textile drape is analysed using different quantifications. Local search is used to heuristically improve convergence towards a solution, while the efficiency of the method is demonstrated in comparison to a simple EA. To the best of our knowledge, this problem is being solved using an EA for the first time.

A universal chain code compression method

A novel universal algorithm for various chain code compressions is presented.New chain code binarization scheme is proposed.The compression is based on RLE and variation of LZ77.The method achieves on average higher compression than state-of-the-art methods. This paper introduces a new approach for lossless chain code compression. Firstly, the chain codes are converted into the binary stream, independent on the input chain code. Then, the compression is done using three modes: RLE 0 , LZ 77 0 and COPY. RLE 0 compresses the runs of the 0-bits, LZ 77 0 is a simplified version of LZ 77 and handles the repetitions within the bit stream, whilst COPY is an escape mode used, when the other two methods are unsuccessful. This method has been tested on the Freeman chain code in eight and four directions, the Vertex chain code, the Three OrThogonal chain code, and the Normalized angle difference chain code. The experiments confirmed better compression ratios on various benchmark datasets in comparison to the state-of-the-art lossless chain code compression methods.

Efficient visualization of LIDAR datasets

The purpose of presented point-based rendering method is to visualize data retrieved from 3D scanners, especially from Light Detection and Ranging (LIDAR) scanners. LIDAR data scanners capture highly accurate and dense data and are used to gather vast spatial information about geographical areas. As a result, a lot of points are obtained and optimization techniques are needed for their real-time visualization. Fully interactive system with high frames-per-second (FPS) ratio is presented in this paper. The efficiency is achieved by reduction of a graphics workload, based on space subdivision, using a quad-tree. The points maintained in the leaves of the quad-tree are aligned evenly through nine or more depth-levels, depending on the number of points in the leaf. Such subdivision allows effective control of level-of-details (LOD) as it minimizes the number of calculations, needed for the visualization in real-time. The changes in the organization of data during the run-time are very rare and therefore, the LOD hierarchy can be established in the preprocessing. A method for achieving a detailed look of the scene, even at low LOD degree, is implemented, too.

Two-level evolutionary algorithm for discovering relations between nodes features in a complex network

Graphical abstractDisplay Omitted HighlightsA new approach to the analysis of complex networks.A functional definition of an edge-set that allows for examining the influences of the nodes features on the networks topology.Two-level evolutionary algorithm for finding an optimal function that explains the relations between nodes of a complex network.Superior results in comparison to traditional machine learning algorithms. Complex network theory offers an efficient mathematical framework for modelling natural phenomena. However, these studies focus mainly on the topological characteristics of networks, while the actual reasons behind the networks formation remain overlooked. This paper proposes a new approach to complex network analysis. By searching for the optimal functional definition of the networks edge set, it allows an examination of the influences of the physical properties of the nodes on the networks structure and behaviour (i.e. changes of the networks structure when the physical properties of nodes change). A two-level evolutionary algorithm is proposed for this purpose, whereby the search for a suitable function form is achieved at the first level, while the second level is used for optimal function fitting. In this way, not only the features with the largest influences are identified, but also the intensities of their influences are estimated. Synthetic networks are examined in order to show the superiority of the proposed approach over traditional machine learning algorithms, while the applicability of the proposed method is demonstrated on a real-world study of the behaviour of biological cells.

Unsigned Manhattan chain code

A new chain code named Unsigned Manhattan Chain Code (UMCC) is presented.UMCC is insensitive to rotation and mirroring, and enables shape magnification.UMCC is capable of detecting monotone parts of the shapes boundary.The proposed chain code achieves 50% better compression ratio than F8 code. This paper introduces a new chain code named Unsigned Manhattan Chain Code - UMCC. Although it exploits a pixels neighbourhood of 8-connectivity, only two coding symbols are used for navigating through the geometric shapes boundary pixels. For this, the movements in the x - and y -coordinate direction are separated, whilst the sign of the moving direction is controlled by two flags - one for each coordinate direction. UMCC is insensitive to rotation and mirroring and enables shape magnification. However, the more unique property is the UMCCs ability to explicitly separate the monotonic parts of the geometric shape. UMCCs properties have been compared against the properties of other chain codes including the Freeman chain code in eight and four directions, the Vertex Chain Code, and the Three OrThogonal chain code. It has been shown that the UMCC has superior properties in regards to the up-to-date chain codes.

Evaluation of Optimized Visualization of LiDAR Point Clouds, Based on Visual Perception

This paper presents a visual perception evaluation of efficient visualization for terrain data obtained by LiDAR technology. Firstly, we briefly summarize a proposed hierarchical data structure and discuss its advantages. Then two level-of-detail rendering algorithms are presented. The experimental results are then provided regarding the performance and rendering qualities for both approaches. The evaluation of the results is finally discussed in regard to the visual and spatial perceptions of human observers.

Segmentation schema for enhancing land cover identification: A case study using Sentinel 2 data

Abstract Land monitoring is performed increasingly using high and medium resolution optical satellites, such as the Sentinel-2. However, optical data is inevitably subjected to the variable operational conditions under which it was acquired. Overlapping of features caused by shadows, soft transitions between shadowed and non-shadowed regions, and temporal variability of the observed land-cover types require radiometric corrections. This study examines a new approach to enhancing the accuracy of land cover identification that resolves this problem. The proposed method constructs an ensemble-type classification model with weak classifiers tuned to the particular operational conditions under which the data was acquired. Iterative segmentation over the learning set is applied for this purpose, where feature space is partitioned according to the likelihood of misclassifications introduced by the classification model. As these are a consequence of overlapping features, such partitioning avoids the need for radiometric corrections of the data, and divides land cover types implicitly into subclasses. As a result, improved performance of all tested classification approaches were measured during the validation that was conducted on Sentinel-2 data. The highest accuracies in terms of F1-scores were achieved using the Naive Bayes Classifier as the weak classifier, while supplementing original spectral signatures with normalised difference vegetation index and texture analysis features, namely, average intensity, contrast, homogeneity, and dissimilarity. In total, an F1−score of nearly 95% was achieved in this way, with F1-scores of each particular land cover type reaching above 90%.

Efficient chain code compression with interpolative coding

Abstract This paper considers the use of interpolative coding for lossless chain code compression. The most popular chain codes are used, including Freeman chain code in eight (F8) and four directions (F4), Vertex Chain Code (VCC), and three-orthogonal chain code (3OT). The whole compression pipeline consists of the Burrows–Wheeler transform, Move-To-Front transform and the interpolative coding, which was improved by FELICS and new Ψ-coding. The approach was compared with the state-of-the-art chain code compression algorithms. For VCC, 3OT and F4, the obtained results are slightly better than the existing approaches. However, an important improvement was achieved with F8 chain code, where the presented approach is considerably better.