Genome-wide non-CpG methylation of the host genome during M. tuberculosis infection

A mammalian cell utilizes DNA methylation to modulate gene expression in response to environmental changes during development and differentiation. Aberrant DNA methylation changes as a correlate to diseased states like cancer, neurodegenerative conditions and cardiovascular diseases have been documented. Here we show genome-wide DNA methylation changes in macrophages infected with the pathogen M. tuberculosis. Majority of the affected genomic loci were hypermethylated in M. tuberculosis infected THP1 macrophages. Hotspots of differential DNA methylation were enriched in genes involved in immune response and chromatin reorganization. Importantly, DNA methylation changes were observed predominantly for cytosines present in non-CpG dinucleotide context. This observation was consistent with our previous finding that the mycobacterial DNA methyltransferase, Rv2966c, targets non-CpG dinucleotides in the host DNA during M. tuberculosis infection and reiterates the hypothesis that pathogenic bacteria use non-canonical epigenetic strategies during infection

Mining Density Contrast Subgraphs

Dense subgraph discovery is a key primitive in many graph mining applications, such as detecting communities in social networks and mining gene correlation from biological data. Most studies on dense subgraph mining only deal with one graph. However, in many applications, we have more than one graph describing relations among a same group of entities. In this paper, given two graphs sharing the same set of vertices, we investigate the problem of detecting subgraphs that contrast the most with respect to density. We call such subgraphs Density Contrast Subgraphs, or DCS in short. Two widely used graph density measures, average degree and graph affinity, are considered. For both density measures, mining DCS is equivalent to mining the densest subgraph from a "difference" graph, which may have both positive and negative edge weights. Due to the existence of negative edge weights, existing dense subgraph detection algorithms cannot identify the subgraph we need. We prove the computational hardness of mining DCS under the two graph density measures and develop efficient algorithms to find DCS. We also conduct extensive experiments on several real-world datasets to evaluate our algorithms. The experimental results show that our algorithms are both effective and efficient.Comment: Full version of an ICDE'18 pape

On Constant Factors in Comparison-Based Geometric Algorithms and Data Structures

Many standard problems in computational geometry have been solved asymptotically optimally as far as comparison-based algorithms are concerned, but there has been little work focusing on improving the constant factors hidden in big-Oh bounds on the number of comparisons needed. In this thesis, we consider orthogonal-type problems and present a number of results that achieve optimality in the constant factors of the leading terms, including: - An output-sensitive algorithm that computes the maxima for a set of n points in two dimensions using 1n log(h) + O(n sqrt(log(h))) comparisons, where h is the size of the output. - A randomized algorithm that computes the maxima in three dimensions that uses 1n log(n) + O(n sqrt(log(n))) expected number of comparisons. - A randomized output-sensitive algorithm that computes the maxima in three dimensions that uses 1n log(h) + O(n log^(2/3)(h)) expected number of comparisons, where h is the size of the output. - An output-sensitive algorithm that computes the convex hull for a set of n points in two dimensions using 1n log(h) + O(n sqrt(log(h))) comparisons and O(n sqrt(log(h))) sidedness tests, where h is the size of the output. - A randomized algorithm for detecting whether of a set of n horizontal and vertical line segments in the plane intersect that uses 1n log(n) +O(n sqrt(log(n))) expected number of comparisons. - A data structure for point location among n axis-aligned disjoint boxes in three dimensions that answers queries using at most (3/2)log(n)+ O(log(log(n))) comparisons. The data structure can be extended to higher dimensions and uses at most (d/2)log(n)+ O(log(log(n))) comparisons. - A data structure for point location among n axis-aligned disjoint boxes that form a space-filling subdivision in three dimensions that answers queries using at most (4/3)log(n)+ O(sqrt(log(n))) comparisons. The data structure can be extended to higher dimensions and uses at most ((d+1)/3)log(n)+ O(sqrt(log(n))) comparisons. Our algorithms and data structures use a variety of techniques, including Seidel and Adamy's planar point location method, weighted binary search, and height-optimal BSP trees

Wind resource assessment using weather research and forecasting model. A case study of the wind resources at Havøygavlen wind farm

The need for energy increases globally due to rapid expansion of population and prosperity. To meet this demand while decreasing carbon emission and eventually transition out fossil fuel, efficient utilization of wind power is prominent. This study evaluates the performance of the Weather Research and Forecast model (WRF) with respect to wind speed and wind direction. The area of the study is the northernmost wind farm site in the world, Havøygavlen. It is located just about 50 kilometers southwest of the North cape, consisting of a complex and coastal terrain. The model simulation period was the entire year of 2017, and the resulting estimates where compared to on-site data measured at hub height at each of the 16 turbines located at the site. In terms of forecasting capability, the Model was evaluated using correlation, Root Mean Square Error and Bias. The assessment showed little agreement and implementing finer resolution displayed no apparent improvements. The estimate was particularly vulnerable to sudden changes in wind speed, and performed more accurately in periods of low to moderate wind speeds. Annual weather resource assessment of the site was performed using box plots, annual average wind maps and wind speed histograms. The model is unsuccessful at capturing the high complexity of the terrain, ultimately leading to an underestimation of the wind resources. However, enhanced domain resolution improved the predictive performance, which agreed adequately with the on-site measurements. Furthermore, the annual average wind maps provided valuable knowledge about the local wind patterns surrounding the site. Annual wind roses and wind fields at specific times of high wind speed occurrence was used to evaluate the model’s estimated wind direction. Enhanced domain resolution showed improved directional stability and ability to capture the terrain’s effect on the wind before arriving at the site. As a preliminary wind resource tool, the model performs sufficiently, despite the complex terrain of the studied area

Multimodal MRI characterization of visual word recognition: an integrative view

228 p.The ventral occipito-temporal (vOT) association cortex contributes significantly to recognize different types of visual patterns. It is widely accepted that a subset of this circuitry, including the visual word form area (VWFA), becomes trained to perform the task of rapidly identifying word forms. An important open question is the computational role of this circuitry: To what extent is part of a bottom-up hierarchical processing of information on visual word recognition and/or is involved in processing top-down signals from higher-level language regions. This doctoral dissertation thesis proposal is aimed at characterizing the vOT reading circuitry using behavioral, functional, structural and quantitative MRI indexes, and linking its computations to the other two important regions within the language network: the posterior parietal cortex (pPC) and the inferior frontal gyrus (IFG). Results revealed that two distinct word-responsive areas can be segregated in the vOT: one responsible for visual feature extraction that is connected to the intraparietal sulcus via the vertical occipital fasciculus and a second one responsible for semantic processing that is connected to the angular gyrus via the posterior arcuate fasciculus and to the IFG via the anterior arcuate fasciculus. Importantly, reading behavior was predicted by functional activation in regions identified along the vOT, pPC and IFG, as well as by structural properties of the white matter fiber tracts linking them. The present work constitutes a critical step in the creation of a highly detailed characterization of the early stages of reading at the individual-subject level and to establish a baseline model and parameter range that might serve to clarify functional and structural differences between typical, poor and atypical readers.BCBL: basque center on cognition, brain and languag

An expert system for integrated structural analysis and design optimization for aerospace structures

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided

Efficient Time of Arrival Calculation for Acoustic Source Localization Using Wireless Sensor Networks

Acoustic source localization is a very useful tool in surveillance and tracking applications. Potential exists for ubiquitous presence of acoustic source localization systems. However, due to several significant challenges they are currently limited in their applications. Wireless Sensor Networks (WSN) offer a feasible solution that can allow for large, ever present acoustic localization systems. Some fundamental challenges remain. This thesis presents some ideas for helping solve the challenging problems faced by networked acoustic localization systems. We make use of a low-power WSN designed specifically for distributed acoustic source localization. Our ideas are based on three important observations. First, sounds emanating from a source will be free of reflections at the beginning of the sound. We make use of this observation by selectively processing only the initial parts of a sound to be localized. Second, the significant features of a sound are more robust to various interference sources. We perform key feature recognition such as the locations of significant zero crossings and local peaks. Third, these features which are compressed descriptors, can also be used for distributed pattern matching. For this we perform basic pattern analysis by comparing sampled signals from various nodes in order to determine better Time Of Arrivals (TOA). Our implementation tests these ideas in a predictable test environment. A complete system for general sounds is left for future wor

A DECISION SUPPORT SYSTEM FOR THE SPATIAL CONTROL OF INVASIVE BIOAGENTS

A Decision Support System (DSS) is developed and applied to the spatial control of invasive bioagents, exemplified in this study by the resident Canada goose species (Branta Canadensis) in the Anacostia River system of the District of Columbia. The DSS incorporates a model of goose movement that responds to resource distribution; a twocompartment Expert System (ES) that identifies the causes of goose congregation in hotspots (Diagnosis ES) and prescribes strategies for goose population control (Prescription ES); and a Geographic Information System (GIS) that stores, analyzes, and displays geographic data. The DSS runs on an HP xw8600 64-bit Workstation running Window XP Operating System. The mathematical model developed in this study simulates goose-resource dynamics using partial differential equations - solved numerically using the Finite Element Method (FEM). MATLAB software (v. 7.1) performed all simulations. ArcGIS software (v. 9.3) produced by Environmental Systems Research Institute (ESRI) was used to store and manipulate georeferenced data for mapping, image processing, data management, and hotspot analysis. The rule-based Expert Systems (ES) were implemented within the GIS via ModelBuilder, a modular and intuitive Graphical User Interface (GUI) of ArcGIS software. The Diagnosis ES was developed in three steps. The first step was to acquire knowledge about goose biology through a literature search and discussions with human experts. The second step was to formalize the knowledge acquired in step 1 in the form of logical sentences (IF-THEN statements) representing the goose invasion diagnosis rules. Finally, in the third step, the rules were translated into decision trees. The Prescription ES was developed by following the same steps as in the development of the Diagnosis ES, the major difference being that, in this case, knowledge was acquired relative to goose control strategies rather than overpopulation causes; and additionally, knowledge was formalized based on the Diagnosis and on other local factors. Results of the DSS application indicate that high accessibility to food and water resources is the most likely cause of the congregation of geese in the critical areas identified by the model. Other causes include high accessibility to breeding and nesting habitats, and supplementary, artificial food provided by people in urban areas. The DSS prescribed the application of chemical repellents at feeding sites as a goose control strategy (GCS) to reduce the quality of the food resources consumed by resident Canada geese, and therefore the densities of geese in the infested locations. Two other prescribed GCSs are egg destruction and harvest of breeding adult geese, both of which have direct impacts on the goose populations by reducing their densities at hotspots or slowing down their increase. Enclosing small wetlands with fencing and banning the feeding of geese in urban areas are other GCSs recommended by the ES. Model simulations predicted that these strategies would reduce goose densities at hotspots by over 90%. It is suggested that further research is needed to investigate the use of similar systems for the management of other invasive bioagents in ecologically similar environments

Investigating the shift in the North-Atlantic storm track

This thesis presents results from an AGCM sensitivity study in which the response in the Northern Hemisphere storm tracks to an imposed SST anomaly is investigated. The study was motivated by observational studies cited in the IPCC Fourth Assessment Report indicating that the storm tracks have shifted northward during the second half of the 20th century, a shift which may be related to global warming. To perform sensitivity studies, the NCAR CAM3 model was applied using the data ocean model with an imposed 2 K SST anomaly in all oceanic grid points north of 45◦S. Additionally, the sensitivity to longitudinal and latitudinal variations in the SST anomaly domain was investigated by heating high-latitudes and low-latitudes, only high-latitudes and only low-latitudes in the Atlantic and Arctic Ocean in three different runs. To investigate the importance of a potential reduction in the ice cover, CAM3 was run without ice in the Northern Hemisphere. The storm tracks were represented in terms of bandpass variance using the bandpass filter method and cyclone count using the CCI method developed by Rasmus E. Benestad at the Norwegian Meteorological Institute. Warming the ocean by 2 K in all oceanic grid points north of 45◦S yields, in terms of bandpass variance, a northeastward shift in the North-Atlantic storm track and no latitudinal shift in the Pacific storm track, with corresponding changes in atmospheric baroclinicity and the mean circulation. The zonally averaged Eady parameter shifts upward and northward in response to an increased upper-level temperature gradient and a decreased lower-level temperature gradient, consistent with the findings of Yin (2005). As Yin (2005) performed a climate scenario study using a 15 member ensemble of coupled GCMs, while this study investigates the effects in a uncoupled AGCM where the only forcing is a positive SST anomaly, indications are that oceanic heating is the main driver of the observed storm track changes. Variations in the SST anomaly domain reveals that low-latitude heating is the primary driver of the observed storm track changes. Removing the ice cover yields changes of comparable magnitudes to highlatitude heating, as is therefore not as important as low-latitude heating