The three Rs of river ecosystem resilience: Resources,recruitment, and refugia

Resilience in river ecosystems requires that organisms must persist in the face of highly dynamic hydrological and geomorphological variations. Disturbance events such as floods and droughts are postulated to shape life history traits that support resilience, but river management and conservation would benefit from greater understanding of the emergent effects in communities of river organisms. We unify current knowledge of taxonomic‐, phylogenetic‐, and trait‐based aspects of river communities that might aid the identification and quantification of resilience mechanisms. Temporal variations in river productivity, physical connectivity, and environmental heterogeneity resulting from floods and droughts are highlighted as key characteristics that promote resilience in these dynamic ecosystems. Three community‐wide mechanisms that underlie resilience are (a) partitioning (competition/facilitation) of dynamically varying resources, (b) dispersal, recolonization, and recruitment promoted by connectivity, and (c) functional redundancy in communities promoted by resource heterogeneity and refugia. Along with taxonomic and phylogenetic identity, biological traits related to feeding specialization, dispersal ability, and habitat specialization mediate organism responses to disturbance. Measures of these factors might also enable assessment of the relative contributions of different mechanisms to community resilience. Interactions between abiotic drivers and biotic aspects of resource use, dispersal, and persistence have clear implications for river conservation and management. To support these management needs, we propose a set of taxonomic, phylogenetic, and life‐history trait metrics that might be used to measure resilience mechanisms. By identifying such indicators, our proposed framework can enable targeted management strategies to adapt river ecosystems to global change.     

Self-assembled polymeric solid films with temperature-induced large and reversible photonic-bandgap switching

In aqueous solutions the response of polymers and biological matter to external conditions, such as temperature and pH, is typically based on the hydrophobic/hydrophilic balance and its effects on the polymer conformation. In the solid state, related concepts using competing interactions could allow novel functions. In this work we demonstrate that polymeric self-assembly, reversibility of hydrogen bonding, and polymer-additive phase behaviour allow temperature response in the solid state with large and reversible switching of an optical bandgap. A complex of polystyrene-block-poly(4-vinylpyridinium methanesulphonate) and 3-n-pentadecylphenol leads to the supramolecular comb-shaped architecture with a particularly long lamellar period. The sample is green at room temperature, as an incomplete photonic bandgap due to a dielectric reflector is formed. On heating, hydrogen bonds are broken and 3-n-pentadecylphenol additionally becomes soluble in polystyrene, leading to a sharp and reversible transition at approximately 125 degrees C to uncoloured material due to collapse of the long period. This encourages further developments, for example, for functional coatings or sensors in the solid state.     

Speed‐up and performance evaluation of piecewise‐linear DC analysis

The good convergence properties of piecewise‐linear (PWL) DC analysis have been thoroughly discussed in many papers. This paper, in turn, concentrates on the speed of PWL DC analysis, where the boundary crossing of linear regions plays a crucial role. Fast methods are presented for performing the following boundary‐crossing computations: LU‐decomposition update, matrix‐equation solution, boundary‐crossing direction, and damping‐factor determination. Special attention is given to those PWL DC analysis methods that perform PWL modelling of the non‐linear components on the fly; an adaptive method is proposed for controlling the accuracy of PWL modelling and speeding up simulation. The computational efficiency of the accelerated PWL DC analysis is discussed and compared with that of conventional, Newton–Raphson iteration‐based, DC analysis. Finally, the performance evaluation is completed with realistic simulation examples: it is demonstrated that the speed of the accelerated PWL DC analysis is comparable with that of the conventional DC analysis. Copyright © 2006 John Wiley & Sons, Ltd.     

Nanoparticle-Aided Detection of Colorectal Cancer-Associated Glycoconjugates of Extracellular Vesicles in Human Serum

Extracellular vesicles (EVs) are found in all biological fluids, providing potential for the identification of disease biomarkers such as colorectal cancer (CRC). EVs are heavily glycosylated with specific glycoconjugates such as tetraspanins, integrins, and mucins, reflecting the characteristics of the original cell offering valuable targets for detection of CRC. We report here on europium-nanoparticle (EuNP)-based assay to detect and characterize different surface glycoconjugates of EVs without extensive purification steps from five different CRC and the HEK 293 cell lines. The promising EVs candidates from cell culture were clinically evaluated on small panel of serum samples including early-stage (n = 11) and late-stage (n = 11) CRC patients, benign condition (n = 11), and healthy control (n = 10). The majority of CRC cell lines expressed tetraspanin sub-population and glycovariants of integrins and conventional tumor markers. The subpopulation of CD151 having CD63 expression (CD151^CD63) was significantly (p = 0.001) elevated in early-stage CRC (8 out of 11) without detecting any benign and late-stage samples, while conventional CEA detected mostly late-stage CRC (p = 0.045) and with only four early-stage cases. The other glycovariant assays such as CEA^Con-A, CA125^WGA, CA 19.9^Ma696, and CA 19.9^Con-A further provided some complementation to the CD151^CD63 assay. These results indicate the potential application of CD151^CD63 assay for early detection of CRC patients in human serum.     

Global indoor self-localization based on the ambient magnetic field

There is evidence that animals utilize local anomalities of Earth’s magnetic field not just for orientation detection but also for true navigation, i.e., some animals are not only able to detect the direction of Earth’s magnetic field (compass heading), they are able to derive positional information from local cues arising from the local anomalities of Earth’s magnetic field. Similarly to Earth’s non-constant magnetic field, the magnetic field inside buildings can be highly non-uniform. The magnetic field fluctuations inside buildings arise from both natural and man-made sources, such as steel and reinforced concrete structures, electric power systems, electric and electronic appliances, and industrial devices. Assuming that the anomalities of the magnetic field inside a building are nearly static and they have sufficient local variability, the anomalies provide a unique magnetic fingerprint that can be utilized in global self-localization. Based on the evidence presented in this article it can be argued that this hypothesis is valid. In this article, a Monte Carlo Localization (MCL) technique based on the above hypothesis is proposed. The feasibility of the technique is demonstrated by presenting a series of global self-localization experiments conducted in four arbitrarily selected buildings, including a hospital. The experiment setup consists of a mobile robot instrumented with a 3-axis magnetometer and a computer. In addition to global robot self-localization experiments, successful person self-localization experiments were also conducted by using a wireless, wearable magnetometer. The reported experiments suggest that the ambient magnetic field may remain sufficiently stable for longer periods of time giving support for self-localization techniques utilizing the local deviations of the magnetic field.     

CAVE for collaborative patient room design: analysis with end-user opinion contrasting method

Several studies indicate that virtual reality (VR) systems are useful for end-user participation in an environmental design process. However, these systems can be costly and thus support for the decision whether to invest in a VR of some type is useful. This study presents a novel method for analysing the usefulness of a VR system for the purpose of end-user participation. We collected qualitative end-user opinion data in the real environment and then contrasted this data with the capabilities of a VR system. Additionally, to better understand the capabilities of the VR used, we examined how the end-users perceive the used virtual environment, which in this case was CAVE, an immersive VR system where projectors are directed to the walls of a room-sized cube. In this way, we analysed whether the same functions and elements identified by end-users on the actual wards could also be evaluated in the CAVE. Eleven nurses and 11 patients participated in the study by evaluating a bathroom and/or four patient rooms modelled by the CAVE and the actual hospital wards. The CAVE was convenient for evaluating most issues identified by the study participants in the actual hospital wards, i.e. aesthetics; correct location of equipment, supplies and materials; distraction by or the good companion of other patients as well as window position and size and living/workspace. However, it was not possible to evaluate with full certainty the possibilities for bracing against grab bars or other objects in the VR, and this was found to be relevant to the independent functioning of patients with limited mobility. Also, due to the relatively low luminance levels of projectors, evaluations regarding lighting were considered unreliable. Moreover, end-users were not always certain about the sizes and sufficiency of space in the CAVE. Solutions to overcome these limitations were proposed.     

Infinite factorization of multiple non-parametric views

Combined analysis of multiple data sources has increasing application interest, in particular for distinguishing shared and source-specific aspects. We extend this rationale to the generative and non-parametric clustering setting by introducing a novel non-parametric hierarchical mixture model. The lower level of the model describes each source with a flexible non-parametric mixture, and the top level combines these to describe commonalities of the sources. The lower-level clusters arise from hierarchical Dirichlet Processes, inducing an infinite-dimensional contingency table between the sources. The commonalities between the sources are modeled by an infinite component model of the contingency table, interpretable as non-negative factorization of infinite matrices, or as a prior for infinite contingency tables. With Gaussian mixture components plugged in for continuous measurements, the model is applied to two views of genes, mRNA expression and abundance of the produced proteins, to expose groups of genes that are co-regulated in either or both of the views. We discover complex relationships between the marginals (that are multimodal in both marginals) that would remain undetected by simpler models. Cluster analysis of co-expression is a standard method of screening for co-regulation, and the two-view analysis extends the approach to distinguishing between pre- and post-translational regulation.     

An industry experiment on the effects of test-driven development on external quality and productivity

Existing empirical studies on test-driven development (TDD) report different conclusions about its effects on quality and productivity. Very few of those studies are experiments conducted with software professionals in industry. We aim to analyse the effects of TDD on the external quality of the work done and the productivity of developers in an industrial setting. We conducted an experiment with 24 professionals from three different sites of a software organization. We chose a repeated-measures design, and asked subjects to implement TDD and incremental test last development (ITLD) in two simple tasks and a realistic application close to real-life complexity. To analyse our findings, we applied a repeated-measures general linear model procedure and a linear mixed effects procedure. We did not observe a statistical difference between the quality of the work done by subjects in both treatments. We observed that the subjects are more productive when they implement TDD on a simple task compared to ITLD, but the productivity drops significantly when applying TDD to a complex brownfield task. So, the task complexity significantly obscured the effect of TDD. Further evidence is necessary to conclude whether TDD is better or worse than ITLD in terms of external quality and productivity in an industrial setting. We found that experimental factors such as selection of tasks could dominate the findings in TDD studies.