Identification of spatial communities in the human genome graph to better understand HIV insertion

In this work, the 3D spatial organization of a human Jurkat cell, an immune cell who is one ofthe main targets of the human immunodeficiency virus (HIV), is analyzed through the clusteringof genome interactions networks provided by the Hi-C data, a 3D massive sequencing technologycapable of quantifying interactions among regions of the genome inside the nucleus of a cell. Thedata analysis approach consists on a graph theoretic modelling of these networks and the clusteringanalysis is performed by the use of spectral clustering methods, a family of clustering techniquesbased on the spectral decomposition of Laplacian matrices of graph networks. By inferring the3D structure of the Jurkat cell at the nuclear scale, the distribution of HIV integration sites onthe Jurkat genome is analyzed and contrasted with the current knowledge of the the integrationmechanisms and their relationship with the 3D genomic context. The clustering results are alsoevaluated through a common set of metrics, which serve to objectively asses the 3D structure ofthe nucleus of the Jurkat cell. With the proposed data analysis, the main findings are: the 3Dspatial structure is not prominent, the global interaction genomic network contains just a fewcommunities and the insertion pattern of HIV, contrasted on the detected communities, confirmsthe established knowledge of HIV integration mechanisms

Multi-level characterization and information extraction in directed and node-labeled functional brain networks

Current research in computational neuroscience puts great emphasis on the computation and analysis of the functional connectivity of the brain. The methodological developments presented in this work are concerned with a group-specific comprehensive analysis of networks that represent functional interaction patterns. Four application studies are presented, in which functional brain network samples of different clinical background were analyzed in different ways, using combinations of established approaches and own methodological developments. Study I is concerned with a sample-specific decomposition of the functional brain networks of depressed subjects and healthy controls into small functionally important and recurring subnetworks (motifs) using own developments. Study II investigates whether lithium treatment effects are reflected in the functional brain networks of HIV-positive subjects with diagnosed cognitive impairment. For it, microscopic and macroscopic structural properties were analyzed. Study III explores spatially highly resolved functional brain networks with regard to a functional segmentation given by identified module (community) structure. Also, ground truth networks with known module structure were generated using own methodological developments. They formed the basis of a comprehensive simulation study that quantified module structure quality and preservation in order to evaluate the effects of a novel approach for the identification of connectivity (lsGCI). Study IV tracks the time-evolution of module structure and introduces a newly developed own approach for the determination of edge weight thresholds based on multicriteria optimization. The methodological challenges that underly these different topological analyses, but also the various opportunities to gain an improved understanding of neural information processing among brain areas were highlighted by this work and the presented results

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing