Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis

Background Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study. Methodology/Principal Findings To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable. Conclusions/Significance We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses

Transfer Learning for Content-Based Recommender Systems using Tree Matching

In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information on the preferences exists in another domain. We show that training a system to use such information across domains can produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain. This mapping is considered a bridge between the two domains. Based on the correlation and content-attributes of the items, we train a machine learning model to predict users' ratings in the target domain. When we compare our approach to the popularity approach and KNN-cross-domain on a real world dataset, the results show that on an average of 83$$ of the cases our approach outperforms both methods

Magnetic structure of free cobalt clusters studied with Stern-Gerlach deflection experiments

We have studied the magnetic properties of free cobalt clusters in two semi-independent Stern-Gerlach deflection experiments at temperatures between 60 and 307 K. We find that clusters consisting of 13 to 200 cobalt atoms exhibit behavior that is entirely consistent with superparamagnetism, though complicated by finite-system fluctuations in cluster temperature. By fitting the data to the Langevin function, we report magnetic moments per atom for each cobalt cluster size and compare the results of our two measurements and all those performed previously. In addition to a gradual decrease in moment per atom with increasing size, there are oscillations that appear to be caused by geometrical shell structure. We discuss our observations in light of the two competing models for Langevin-like magnetization behavior in free clusters, superparamagnetism and adiabatic magnetization, and conclude that the evidence strongly supports the superparamagnetic model

High-resolution spatial mapping of a superconducting NbN wire using single-electron detection

Superconducting NbN wires have recently received attention as detectors for visible and infrared photons. We present experiments in which we use a NbN wire for high-efficiency (40 %) detection of single electrons with keV energy. We use the beam of a scanning electron microscope as a focussed, stable, and calibrated electron source. Scanning the beam over the surface of the wire provides a map of the detection efficiency. This map shows features as small as 150 nm, revealing wire inhomogeneities. The intrinsic resolution of this mapping method, superior to optical methods, provides the basis of a characterization tool relevant for photon detectors.Comment: 2009 IEEE Toronto International Conference, Science and Technology for Humanity (TIC-STH

Insights into the nature of northwest-to-southeast aligned ionospheric wavefronts from contemporaneous Very Large Array and ionosondes observations

The results of contemporaneous summer nighttime observations of midlatitude medium scale traveling ionospheric disturbances (MSTIDs) with the Very Large Array (VLA) in New Mexico and nearby ionosondes in Texas and Colorado are presented. Using 132, 20-minute observations, several instances of MSTIDs were detected, all having wavefronts aligned northwest to southeast and mostly propagating toward the southwest, consistent with previous studies of MSTIDs. However, some were also found to move toward the northeast. It was found that both classes of MSTIDs were only found when sporadic-E (Es) layers of moderate peak density (1.5<foEs<3 MHz) were present. Limited fbEs data from one ionosonde suggests that there was a significant amount of structure with the Es layers during observations when foEs>3 MHz that was not present when 1.5<foEs<3 MHz. No MSTIDs were observed either before midnight or when the F-region height was increasing at a relatively high rate, even when these Es layers were observed. Combining this result with AE indices which were relatively high at the time (an average of about 300 nT and maximum of nearly 700 nT), it is inferred that both the lack of MSTIDs and the increase in F-region height are due to substorm-induced electric fields. The northeastward-directed MSTIDs were strongest post-midnight during times when the F-region was observed to be collapsing relatively quickly. This implies that these two occurrences are related and likely both caused by rare shifts in F-region neutral wind direction from southwest to northwest.Comment: Accepted for publication in the Journal of Geophysical Researc

Connections between Classical and Parametric Network Entropies

This paper explores relationships between classical and parametric measures of graph (or network) complexity. Classical measures are based on vertex decompositions induced by equivalence relations. Parametric measures, on the other hand, are constructed by using information functions to assign probabilities to the vertices. The inequalities established in this paper relating classical and parametric measures lay a foundation for systematic classification of entropy-based measures of graph complexity

Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure

Biologic effects of light: An enlighting prospective

During the past several decades our knowledge on the effects of light on human health and its underlying mechanisms has been expanded exponentially. These findings have led to an enormous scientific progress including new concepts for prevention and treatment of many diseases such as autoimmune diseases, cardiovascular disease, skin cancer and other malignancies. To summarize our present knowledge on this topic and to stimulate new research initiatives, an international symposium entitled Biologic Effects of Light, that was organized by J. Reichrath, Th. Vogt and M.F. Holick, and that was supported by Deutsche Forschungsgemeinschaft (DFG), was held June 11/12, 2015 in Homburg/Saar, Germany. This meeting was specially designed to offer scientists and clinicians a platform to discuss the latest developments in this intriguing research area. Plenary and Keynote lectures as well as Round Table Discussions gave an update on carefully selected hot topics, including Vitamin D, skin cancer prevention, UVA radiation and cellular homeostasis, photocarcinogenesis, and photochemical internalization (PCI). Some of the relevant findings and conclusions of this meeting are published in this issue of Anticancer Research (1-13) and can be summarized as follows