QUANTIFYING UNCERTAINTY IN GENOTYPE CALLS

Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Mircorarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs)simultaneously. The starting point for the statistical analyses used by GWAS, to determine association between loci and disease, are genotype calls (AA, AB, or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays, and different sample batches has substantial inuence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability, GWAS run the risk of adversely affecting the quality of reported findings. In this paper we present solutions based on a multi-level mixed model. Software implementation of the method described in this paper is available as free and open source code in the crlmm R/BioConductor.

An integral conservative gridding--algorithm using Hermitian curve interpolation

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).

Simulation of four stroke engine cycle for a 4 - valve pentroof engine in KIVA 3VR2

The main purpose of this project is to understand the process of engine simulation using the open source CFD code called KIVA. This report mainly discusses the simulation of the 4-valve Pentroof engine through KIVA 3VR2. KIVA is an open source FORTRAN code which is used to solve the fluid flow field in the engines with the transient 2D and 3D chemically reactive flow with spray. It also focuses on the complete procedure to simulate an engine cycle starting from pre- processing until the final results. This report will serve a handbook for the using the KIVA code.

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Review of free-space optical communications with diverging beam

The report reviews the technology of Free-space Optical Communication (FSO) and simulation methods for testing the performance of diverged beam in the technology. In addition to the introduction, the theory of turbulence and its effect over laser is also reviewed. In the simulation revision chapter, on-off keying (OOK) and diverged beam is assumed in the transmitter, and in the receiver, avalanche photodiode (APD) is utilized to convert the photon stream into electron stream. Phase screens are adopted to simulate the effect of turbulence over the phase of the optical beam. Apart from this, the method of data processing is introduced and retrospected. In the summary chapter, there is a general explanation of different beam divergence and their performance.

Spatial and Temporal Evolution of the Volcanics and Sediments of the Kenya Rift

New volumetric and mass flux estimates have been calculated for the Kenya Rift. Spatial and temporal histories for volcanic eruptions, lacustrine deposition, and hominin fossil sites are presented, aided by the compilation of a new digital geologic map. Distribution of volcanism over time indicates several periods of southward expansion followed by relative positional stasis. Volcanism occurs throughout the activated rift length, with no obvious abandonment as the rift system migrated. The main exception is a period of volcanic concentration around 10 Ma, when activity was constrained within 2° of the equator. Volumes derived from seismic data indicate a total volume of c. 310,000 km3 (2.47 x 1010 kg/yr ), which is significantly more than the map-derived volumes found here or published previously. Map-based estimates are likely affected by a bias against recognizing small volume events in the older record. Such events are, however, the main driver of erupted volume over the last 5 Ma. A technique developed here to counter this bias results in convergence of the two volume estimation techniques. Relative erupted composition over time is variable. Overall, the erupted material has a mafic to silicic ratio of 0.9:1. Basalts are distinctly more common in the Turkana region, which previously experienced Mesozoic rifting. Despite the near equal ratio of mafic to silicic products, the Kenya Rift otherwise fits the definition of a SLIP. It is proposed that the compositions would better fit the published definition if the Turkana region was not twice-rifted. Lacustrine sedimentation post-dates initial volcanism by about 5 million years, and follows the same volcanic trends, showing south and eastward migration over time. This sedimentation delay is likely related to timing of fault displacements. Evidence of hominin habitation is distinctly abundant in the northern and southern sections of the Kenya Rift, but there is an observed gap in the equatorial rift between 4 and 0.5 million years ago. After 0.5 Ma, sites appear to progress towards the equator. The pattern and timing of hominid site distributions suggests that the equatorial gap in habitation may be the result of active volcanic avoidance.

PROBABILISTIC MODELING OF RAINFALL INDUCED LANDSLIDE HAZARD ASSESSMENT IN SAN JUAN LA LAGUNA, SOLOLÁ, GUATEMALA

The municipality of San Juan La Laguna, Guatemala is home to approximately 5,200 people and located on the western side of the Lake Atitlán caldera. Steep slopes surround all but the eastern side of San Juan. The Lake Atitlán watershed is susceptible to many natural hazards, but most predictable are the landslides that can occur annually with each rainy season, especially during high-intensity events. Hurricane Stan hit Guatemala in October 2005; the resulting flooding and landslides devastated the Atitlán region. Locations of landslide and non-landslide points were obtained from field observations and orthophotos taken following Hurricane Stan. This study used data from multiple attributes, at every landslide and non-landslide point, and applied different multivariate analyses to optimize a model for landslides prediction during high-intensity precipitation events like Hurricane Stan. The attributes considered in this study are: geology, geomorphology, distance to faults and streams, land use, slope, aspect, curvature, plan curvature, profile curvature and topographic wetness index. The attributes were pre-evaluated for their ability to predict landslides using four different attribute evaluators, all available in the open source data mining software Weka: filtered subset, information gain, gain ratio and chi-squared. Three multivariate algorithms (decision tree J48, logistic regression and BayesNet) were optimized for landslide prediction using different attributes. The following statistical parameters were used to evaluate model accuracy: precision, recall, F measure and area under the receiver operating characteristic (ROC) curve. The algorithm BayesNet yielded the most accurate model and was used to build a probability map of landslide initiation points. The probability map developed in this study was also compared to the results of a bivariate landslide susceptibility analysis conducted for the watershed, encompassing Lake Atitlán and San Juan. Landslides from Tropical Storm Agatha 2010 were used to independently validate this study’s multivariate model and the bivariate model. The ultimate aim of this study is to share the methodology and results with municipal contacts from the author's time as a U.S. Peace Corps volunteer, to facilitate more effective future landslide hazard planning and mitigation.

BEYOND ROOTS ALONE: NOVEL METHODOLOGIES FOR ANALYZING COMPLEX SOIL AND MINIRHIZOTRON IMAGERY USING IMAGE PROCESSING AND GIS TOOLS

Quantifying belowground dynamics is critical to our understanding of plant and ecosystem function and belowground carbon cycling, yet currently available tools for complex belowground image analyses are insufficient. We introduce novel techniques combining digital image processing tools and geographic information systems (GIS) analysis to permit semi-automated analysis of complex root and soil dynamics. We illustrate methodologies with imagery from microcosms, minirhizotrons, and a rhizotron, in upland and peatland soils. We provide guidelines for correct image capture, a method that automatically stitches together numerous minirhizotron images into one seamless image, and image analysis using image segmentation and classification in SPRING or change analysis in ArcMap. These methods facilitate spatial and temporal root and soil interaction studies, providing a framework to expand a more comprehensive understanding of belowground dynamics.

Automatic Labeling of Software Components and their Evolution using Log-Likelihood Ratio of Word Frequencies in Source Code

As more and more open-source software components become available on the internet we need automatic ways to label and compare them. For example, a developer who searches for reusable software must be able to quickly gain an understanding of retrieved components. This understanding cannot be gained at the level of source code due to the semantic gap between source code and the domain model. In this paper we present a lexical approach that uses the log-likelihood ratios of word frequencies to automatically provide labels for software components. We present a prototype implementation of our labeling/comparison algorithm and provide examples of its application. In particular, we apply the approach to detect trends in the evolution of a software system.

Stud.IP and ILIAS in an EU-Funded Project for the Middle East

In autumn 2005 InWEnt (Internationale Weiterbildung und Entwicklung/Capacity Building International gGmbH) on behalf of the EU invited to tender for three web based trainings (WBT). The precondition: either the open-source-platform Stud.IP or ILIAS should be used. The company data-quest decided not to offer the use of either Stud.IP or ILIAS, but both in combination - and won the contract. Several month later, the new learning environment with the combined powers of Stud.IP and ILIAS was ready to serve WBT-participants from all over the world. The following text describes the EU-Project "Efficient Management of Wastewater, its Treatment and Reuse in the Mediterranean Countries" (EMWater), the WBT concept and the experiences with the new Stud.IP-ILIAS-interface.

Stud.IP

Dieser Beitrag beschreibt die Konzeption, den Funktionsumfang und Erfahrungswerte der Open-Source-eLearning-Plattform Stud.IP. Der Funktionsumfang umfasst für jede einzelne Veranstaltung Ablaufpläne, das Hochladen von Hausarbeiten, Diskussionsforen, persönliche Homepages, Chaträume u.v.a. Ziel ist es hierbei, eine Infrastruktur des Lehrens und Lernens anzubieten, die dem Stand der Technik entspricht. Wissenschaftliche Einrichtungen finden zudem eine leistungsstarke Umgebung zur Verwaltung ihres Personals, Pflege ihrer Webseiten und der automatischer Erstellung von Veranstaltungs- oder Personallisten vor. Betreiber können auf ein verlässliches Supportsystem zugreifen, dass sie an der Weiterentwicklung durch die Entwickler- und Betreiber-Community teilhaben lässt.

Virtual Electrophysiology with SPatch

SPatch is an open source virtual laboratory designed to perform simulated electrophysiological experiments without the technical difficulties inherent to laboratory work. It provides the core equipment necessary for recording neuronal activity and allows the user to install the equipment, design their own protocols, prepare solutions to bathe the preparation or to fill the electrodes, and gather data. Assistance is provided for most steps with predefined components that are appropriate to a range of standard procedures. Experiments that can be performed with SPatch at present concern the study of voltage-gated channels in isolated neurons. This allows understanding the ionic mechanisms of Na+ and Ca2+ action potentials, after spike hyperpolarization, pacemaker tonic or bursting activity of neurons, delayed or sustained or adaptive firing of neurons in response to a depolarization, spontaneous depolarization of the membrane following an hyperpolarization, etc. In an educational context, the main interest of SPatch is to allow students to focus on the concepts and thought processes of electrophysiological investigation without the high equipment costs and extensive training required to perform laboratory work. It can be used to acquaint students with the relevant procedures before starting work in a real lab, or to give students an understanding of single neuron behavior and the ways it can be studied without requiring practical work. We illustrate the function and use of SPatch, explore educational issues arising from the inevitable differences between simulated and real laboratory work, and outline possible improvements.

Teaching Cognitive Modeling Using PDP++

PDP++ is a freely available, open source software package designed to support the development, simulation, and analysis of research-grade connectionist models of cognitive processes. It supports most popular parallel distributed processing paradigms and artificial neural network architectures, and it also provides an implementation of the LEABRA computational cognitive neuroscience framework. Models are typically constructed and examined using the PDP++ graphical user interface, but the system may also be extended through the incorporation of user-written C++ code. This article briefly reviews the features of PDP++, focusing on its utility for teaching cognitive modeling concepts and skills to university undergraduate and graduate students. An informal evaluation of the software as a pedagogical tool is provided, based on the author’s classroom experiences at three research universities and several conference-hosted tutorials.

Missing Links in the Proposed EU Data Protection Regulation and Cloud Computing Scenarios: A Brief Overview

Applying location-focused data protection law within the context of a location-agnostic cloud computing framework is fraught with difficulties. While the Proposed EU Data Protection Regulation has introduced a lot of changes to the current data protection framework, the complexities of data processing in the cloud involve various layers and intermediaries of actors that have not been properly addressed. This leaves some gaps in the regulation when analyzed in cloud scenarios. This paper gives a brief overview of the relevant provisions of the regulation that will have an impact on cloud transactions and addresses the missing links. It is hoped that these loopholes will be reconsidered before the final version of the law is passed in order to avoid unintended consequences.

A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest

Momentary brain electric field configurations are manifestations of momentary global functional states of the brain. Field configurations tend to persist over some time in the sub-second range (“microstates”) and concentrate within few classes of configurations. Accordingly, brain field data can be reduced efficiently into sequences of re-occurring classes of brain microstates, not overlapping in time. Different configurations must have been caused by different active neural ensembles, and thus different microstates assumedly implement different functions. The question arises whether the aberrant schizophrenic mentation is associated with specific changes in the repertory of microstates. Continuous sequences of brain electric field maps (multichannel EEG resting data) from 9 neuroleptic-naive, first-episode, acute schizophrenics and from 18 matched controls were analyzed. The map series were assigned to four individual microstate classes; these were tested for differences between groups. One microstate class displayed significantly different field configurations and shorter durations in patients than controls; degree of shortening correlated with severity of paranoid symptomatology. The three other microstate classes showed no group differences related to psychopathology. Schizophrenic thinking apparently is not a continuous bias in brain functions, but consists of intermittent occurrences of inappropriate brain microstates that open access to inadequate processing strategies and context information