639,021 research outputs found

    Playing the wrong game: An experimental analysis of relational complexity and strategic misrepresentation

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    It has been suggested that players often produce simplified and/or misspecified mental representations of interactive decision problems (Kreps, 1990). We submit that the relational structure of players’ preferences in a game induces cognitive complexity, and may be an important driver of such simplifications. We provide a formal classification of order structures in two-person normal form games based on the two properties of monotonicity and projectivity, and present experiments in which subjects must first construct a representation of games of different relational complexity, and subsequently play the games according to their own representation. Experimental results support the hypothesis that relational complexity matters. More complex games are harder to represent, and this difficulty is correlated with measures of short term memory capacity. Furthermore, most erroneous representations are less complex than the correct ones. In addition, subjects who misrepresent the games behave consistently with such representations according to simple but rational decision criteria. This suggests that in many strategic settings individuals may act optimally on the ground of simplified and mistaken premises.pure motive, mixed motive, preferences, bi-orders, language, cognition, projectivity, monotonicity, short term memory, experiments

    Three in one-multiple faunal elements within an endangered european butterfly species

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    Ice ages within Europe forced many species to retreat to refugia, of which three major biogeographic basic types can be distinguished: "Mediterranean", "Continental" and "Alpine / Arctic" species. However, this classification often fails to explain the complex phylogeography of European species with a wide range of latitudinal and altitudinal distribution. Hence, we tested for the possibility that all three mentioned faunal elements are represented within one species. Our data was obtained by scoring 1,307 Euphydryas aurinia individuals (46 European locations) for 17 allozyme loci, and sequencing a subset of 492 individuals (21 sites) for a 626 base pairs COI fragment. Genetic diversity indices, F statistics, hierarchical analyses of molecular variance, individual-based clustering, and networks were used to explore the phylogeographic patterns. The COI fragment represented 18 haplotypes showing a strong geographic structure. All but one allozyme loci analysed were polymorphic with a mean F-ST of 0.20, supporting a pronounced among population structure. Interpretation of both genetic marker systems, using several analytical tools, calls for the recognition of twelve genetic groups. These analyses consistently distinguished different groups in Iberia (2), Italy, Provence, Alps (3), Slovenia, Carpathian Basin, the lowlands of West and Central Europe as well as Estonia, often with considerable additional substructures. The genetic data strongly support the hypothesis that E. aurinia survived the last glaciation in Mediterranean, extra-Mediterranean and perialpine refugia. It is thus a rare example of a model organism that combines attributes of faunal elements from all three of these sources. The observed differences between allozymes and mtDNA most likely result from recent introgression of mtDNA into nuclear allozyme groups. Our results indicate discrepancies with the morphologically-based subspecies models, underlining the need to revise the current taxonomy.Estonian targeted financing project [Sf0180122s08]; DFG graduate school [1319]; [Pest-C/MAR/LA0015/2011]info:eu-repo/semantics/publishedVersio

    Integral projection models for species with complex demography

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    Matrix projection models occupy a central role in population and conservation biology. Matrix models divide a population into discrete classes, even if the structuring trait exhibits continuous variation ( e. g., body size). The integral projection model ( IPM) avoids discrete classes and potential artifacts from arbitrary class divisions, facilitates parsimonious modeling based on smooth relationships between individual state and demographic performance, and can be implemented with standard matrix software. Here, we extend the IPM to species with complex demographic attributes, including dormant and active life stages, cross- classification by several attributes ( e. g., size, age, and condition), and changes between discrete and continuous structure over the life cycle. We present a general model encompassing these cases, numerical methods, and theoretical results, including stable population growth and sensitivity/ elasticity analysis for density- independent models, local stability analysis in density- dependent models, and optimal/ evolutionarily stable strategy life- history analysis. Our presentation centers on an IPM for the thistle Onopordum illyricum based on a 6- year field study. Flowering and death probabilities are size and age dependent, and individuals also vary in a latent attribute affecting survival, but a predictively accurate IPM is completely parameterized by fitting a few regression equations. The online edition of the American Naturalist includes a zip archive of R scripts illustrating our suggested methods

    Flexible Graph-based Learning with Applications to Genetic Data Analysis

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    With the abundance of increasingly complex and high dimensional data in many scientific disciplines, graphical models have become an extremely useful statistical tool to explore data structures. In this dissertation, we study graphical models from two perspectives: i) to enhance supervised learning, classification in particular, and ii) graphical model estimation for specific data types. For classification, the optimal classifier is often connected with the feature structure within each class. In the first project, starting from the Gaussian population scenario, we aim to find an approach to utilize the graphical structure information of the features in classification. With respect to graphical models, many existing graphical estimation methods have been proposed based on a homogeneous Gaussian population. Due to the Gaussian assumption, these methods may not be suitable for many typical genetic data. For instance, the gene expression data may come from individuals of multiple populations with possibly distinct graphical structures. Another instance would be the single cell RNA-sequencing data, which are featured by substantial sample dependence and zero-inflation. In the second and the third project, we propose multiple graphical model estimation methods for these scenarios respectively. In particular, two dependent count-data graphical models are introduced for the latter case. Both numerical and theoretical studies are performed to demonstrate the effectiveness of these methods.Doctor of Philosoph

    Playing the Wrong Game: An Experimental Analysis of Relational Complexity and Strategic Misrepresentation.

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    It has been suggested that players often produce simplified and/or misspecified mental models of strategic decisions [Kreps, D., 1990. Game Theory and Economic Modeling. Oxford Univ. Press, Oxford]. We submit that the relational structure of players’ preferences in a game is a source of cognitive complexity, and may be an important driver of such simplifications. We provide a classification of order structures in two- person games based on the properties of monotonicity and projectivity, and present experiments in which subjects construct representations of games of different relational complexity and subsequently play the games according to these representations. Experimental results suggest that relational complexity matters. More complex games are harder to represent, and this difficulty seems correlated with short term memory capacity. In addition, most erroneous representations are simpler than the correct ones. Finally, subjects who misrepresent the games behave consistently with such representations, suggesting that in many strategic settings individuals may act optimally on the ground of simplified and mistaken premises

    INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

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    Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

    Lightweight Ontologies

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    Ontologies are explicit specifications of conceptualizations. They are often thought of as directed graphs whose nodes represent concepts and whose edges represent relations between concepts. The notion of concept is understood as defined in Knowledge Representation, i.e., as a set of objects or individuals. This set is called the concept extension or the concept interpretation. Concepts are often lexically defined, i.e., they have natural language names which are used to describe the concept extensions (e.g., concept mother denotes the set of all female parents). Therefore, when ontologies are visualized, their nodes are often shown with corresponding natural language concept names. The backbone structure of the ontology graph is a taxonomy in which the relations are “is-a”, whereas the remaining structure of the graph supplies auxiliary information about the modeled domain and may include relations like “part-of”, “located-in”, “is-parent-of”, and many others

    Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network

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    Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.Comment: To Appear in the Efficient Deep Learning for Computer Vision (ECV) workshop at IEEE Computer Vision and Pattern Recognition (CVPR) 2018. Youtube demo at this: https://www.youtube.com/watch?v=zYypJPJipY

    Genetic Classification of Populations using Supervised Learning

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    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.Comment: Accepted PLOS On
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