Essential Constraints of Edge-Constrained Proximity Graphs

Given a plane forest $F = (V, E)$ of $|V| = n$ points, we find the minimum set $S \subseteq E$ of edges such that the edge-constrained minimum spanning tree over the set $V$ of vertices and the set $S$ of constraints contains $F$. We present an $O(n \log n )$-time algorithm that solves this problem. We generalize this to other proximity graphs in the constraint setting, such as the relative neighbourhood graph, Gabriel graph, $\beta$-skeleton and Delaunay triangulation. We present an algorithm that identifies the minimum set $S\subseteq E$ of edges of a given plane graph $I=(V,E)$ such that $I \subseteq CG_\beta(V, S)$ for $1 \leq \beta \leq 2$, where $CG_\beta(V, S)$ is the constraint $\beta$-skeleton over the set $V$ of vertices and the set $S$ of constraints. The running time of our algorithm is $O(n)$, provided that the constrained Delaunay triangulation of $I$ is given.Comment: 24 pages, 22 figures. A preliminary version of this paper appeared in the Proceedings of 27th International Workshop, IWOCA 2016, Helsinki, Finland. It was published by Springer in the Lecture Notes in Computer Science (LNCS) serie

Biasogram: visualization of confounding technical bias in gene expression data.

Gene expression profiles of clinical cohorts can be used to identify genes that are correlated with a clinical variable of interest such as patient outcome or response to a particular drug. However, expression measurements are susceptible to technical bias caused by variation in extraneous factors such as RNA quality and array hybridization conditions. If such technical bias is correlated with the clinical variable of interest, the likelihood of identifying false positive genes is increased. Here we describe a method to visualize an expression matrix as a projection of all genes onto a plane defined by a clinical variable and a technical nuisance variable. The resulting plot indicates the extent to which each gene is correlated with the clinical variable or the technical variable. We demonstrate this method by applying it to three clinical trial microarray data sets, one of which identified genes that may have been driven by a confounding technical variable. This approach can be used as a quality control step to identify data sets that are likely to yield false positive results

Approximating Mexican highways with slime mould

Plasmodium of Physarum polycephalum is a single cell visible by unaided eye. During its foraging behavior the cell spans spatially distributed sources of nutrients with a protoplasmic network. Geometrical structure of the protoplasmic networks allows the plasmodium to optimize transport of nutrients between remote parts of its body. Assuming major Mexican cities are sources of nutrients how much structure of Physarum protoplasmic network correspond to structure of Mexican Federal highway network? To find an answer undertook a series of laboratory experiments with living Physarum polycephalum. We represent geographical locations of major cities by oat flakes, place a piece of plasmodium in Mexico city area, record the plasmodium's foraging behavior and extract topology of nutrient transport networks. Results of our experiments show that the protoplasmic network formed by Physarum is isomorphic, subject to limitations imposed, to a network of principle highways. Ideas and results of the paper may contribute towards future developments in bio-inspired road planning

Assessment of health risks of policies

The assessment of health risks of policies is an inevitable, although challenging prerequisite for the inclusion of health considerations in political decision making. The aim of our project was to develop a so far missing methodological guide for the assessment of the complex impact structure of policies. The guide was developed in a consensual way based on experiences gathered during the assessment of specific national policies selected by the partners of an EU project. Methodological considerations were discussed and summarized in workshops and pilot tested on the EU Health Strategy for finalization. The combined tool, which includes a textual guidance and a checklist, follows the top-down approach, that is, it guides the analysis of causal chains from the policy through related health determinants and risk factors to health outcomes. The tool discusses the most important practical issues of assessment by impact level. It emphasises the transparent identification and prioritisation of factors, the consideration of the feasibility of exposure and outcome assessmentwith special focus on quantification. The developed guide provides useful methodological instructions for the comprehensive assessment of health risks of policies that can be effectively used in the health impact assessment of policy proposals.

Dynamics of amino acid metabolism of primary human liver cells in 3D bioreactors

The kinetics of 18 amino acids, ammonia (NH3) and urea (UREA) in 18 liver cell bioreactor runs were analyzed and simulated by a two-compartment model consisting of a system of 42 differential equations. The model parameters, most of them representing enzymatic activities, were identified and their values discussed with respect to the different liver cell bioreactor performance levels. The nitrogen balance based model was used as a tool to quantify the variability of runs and to describe different kinetic patterns of the amino acid metabolism, in particular with respect to glutamate (GLU) and aspartate (ASP)

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used

Investigation of Sf-9 Cell Metabolism Before and After Baculovirus Infection Using Biovolume: a Case for the Improvement of Adeno-Associated Viral Vector Production

Adeno-associated viral (AAV) vectors have been shown to be potential vectors for the treatment of diseases, including protocols using RNA interference (RNAi). AAV vector production in insect cells using the baculovirus vector expression system has been a major advance in furthering their use. A major limitation of AAV vector production at high cell densities is a reduction in cell specific yield, which is thought to be caused by nutrient limitations. Nutrient consumption profiles after infection, however, have still not been fully characterized, probably due to the difficulty of characterizing consumption patterns based on increases in cell density, which are minimal after infection. It is known, however, that cells increase in size after infection; therefore, the driving hypothesis of this thesis was that biovolume, or the total volume enclosed by the membrane of viable cells, which accounts for both cell density and cell size, could be used to characterize nutrient consumption patterns both before and after infection. The relationships between nutrient consumption and change in cell density and biovolume were examined by statistical correlation analysis. It was found that in uninfected cultures, no significant correlation differences, using either cell density or biovolume, were observed since cell size remained relatively constant; however, in infected cultures, more than half of the nutrients were found to be better correlated with biovolume than with cell density. When examining the nutrient and metabolite concentration data on a biovolume basis, nutrient consumption remained relatively constant. It is hypothesized that since it has been reported that the rate of cell respiration increases after infection, a more complete oxidation of nutrients occurs to satisfy increased energy needs during infection. By having a basis to base nutrient consumption, we can better assess the needs of the culture. This will allow the development of feeding strategies based on cellular requirements instead of supplying the cultures with generic nutrient cocktails. It is expected that different nutrient mixtures can be used to target different goals such as 1) enhancing cell growth (before infection) and 2) improving the production of recombinant products (after infection). This will not only increase the efficiency of AAV vector production, but will also reduce the cost of production and make the process more economical by eliminating the addition of unnecessary nutrients. Although promising, some limitations of using biovolume still exist. A first limitation is the biovolume measure itself. This measure requires a device that measures cell size, such as a Coulter Counter Multisizer (Beckman-Coulter, Miami, FL, USA), which can be expensive. Capacitance probes can be a more cost effective tool to estimate biovolume; however, the availability of capacitance probes is still not common. A second limitation is the interpretation of the biovolume profiles, which can depend strongly on the fraction of cells in culture that are infected. If the culture is infected asynchronously, then there will be many different cell populations in the culture. Future work may require separating the cell size distribution into populations of viable and non-viable cells to get a better biovolume measure as opposed to assuming that viability is well distributed over the entire range of cell sizes. In infected cultures where the viability may be low, it is likely that the cell size distribution of non-viable cells will be concentrated at the lower end of the distribution (smaller diameter) rather than being well distributed over the whole range. If this is the case, for the infected cultures with low viability, the mean cell diameter calculated will be underestimated, which will lead to an overestimation of nutrient consumption for cultures with low viability. This will certainly affect the accuracy of the nutrient consumption profiles. By separating cell size distribution data into different cell populations of viable and nonviable, the accuracy can be improved

Influence of head models on neuromagnetic fields and inverse source localizations

BACKGROUND: The magnetoencephalograms (MEGs) are mainly due to the source currents. However, there is a significant contribution to MEGs from the volume currents. The structure of the anatomical surfaces, e.g., gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the MEGs and the inverse source localizations. This was examined in detail with three different human head models. METHODS: Three finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissuetype model, (3) the Model 3 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. The lead fields and MEGs due to dipolar sources in the motor cortex were computed for all three models. The dipolar sources were oriented normal to the cortical surface and had a dipole moment of 100 μA meter. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. A set of 100 trial inverse runs was made covering the 3 cm cube motor cortex area in a random fashion. The Model 1 was used as a reference model. RESULTS: The reference model (Model 1), as expected, performed best in localizing the sources in the motor cortex area. The Model 3 performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The contour plots of the magnetic fields on top of the head were also different for all three models. The magnetic fields due to source currents were larger in magnitude as compared to the magnetic fields of volume currents. DISCUSSION: These results indicate that the complexity of head models strongly influences the MEGs and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces