Micro-, Meso- and Macro-Connectomics of the Brain

Neurosciences, Neurolog

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

2019 Oklahoma Research Day Full Program

Oklahoma Research Day 2019 - SWOSU Celebrating 20 years of Undergraduate Research Successes

Mining complex trees for hidden fruit : a graph–based computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.

The detection of crime is a complex and difficult endeavour. Public and private organisations – focusing on law enforcement, intelligence, and compliance – commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing ‘leads’ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality. The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the “GraphExtract” algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the “Super-broker” metric and attitude prediction. The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum

Neuronal Cultures: Exploring Biophysics, Complex Systems, and Medicine in a Dish

Neuronal cultures are one of the most important experimental models in modern interdisciplinary neuroscience, allowing to investigate in a control environment the emergence of complex behavior from an ensemble of interconnected neurons. Here, I review the research that we have conducted at the neurophysics laboratory at the University of Barcelona over the last 15 years, describing first the neuronal cultures that we prepare and the associated tools to acquire and analyze data, to next delve into the different research projects in which we actively participated to progress in the understanding of open questions, extend neuroscience research on new paradigms, and advance the treatment of neurological disorders. I finish the review by discussing the drawbacks and limitations of neuronal cultures, particularly in the context of brain-like models and biomedicine

A Model of the Network Architecture of the Brain that Supports Natural Language Processing

For centuries, neuroscience has proposed models of the neurobiology of language processing that are static and localised to few temporal and inferior frontal regions. Although existing models have offered some insight into the processes underlying lower-level language features, they have largely overlooked how language operates in the real world. Here, we aimed at investigating the network organisation of the brain and how it supports language processing in a naturalistic setting. We hypothesised that the brain is organised in a multiple core-periphery and dynamic modular architecture, with canonical language regions forming high-connectivity hubs. Moreover, we predicted that language processing would be distributed to much of the rest of the brain, allowing it to perform more complex tasks and to share information with other cognitive domains. To test these hypotheses, we collected the Naturalistic Neuroimaging Database of people watching full length movies during functional magnetic resonance imaging. We computed network algorithms to capture the voxel-wise architecture of the brain in individual participants and inspected variations in activity distribution over different stimuli and over more complex language features. Our results confirmed the hypothesis that the brain is organised in a flexible multiple core-periphery architecture with large dynamic communities. Here, language processing was distributed to much of the rest of the brain, together forming multiple communities. Canonical language regions constituted hubs, explaining why they consistently appear in various other neurobiology of language models. Moreover, language processing was supported by other regions such as visual cortex and episodic memory regions, when processing more complex context-specific language features. Overall, our flexible and distributed model of language comprehension and the brain points to additional brain regions and pathways that could be exploited for novel and more individualised therapies for patients suffering from speech impairments

Seeds of Transition. Essays on novelty production, niches and regimes in agriculture

Agriculture is confronted with changing societal expectations and demands regarding its role in food production and in the countryside. Complying with these expectations and demands will require a comprehensive, far-reaching and therefore far from easy and long-lasting transition of agriculture. This books seeks to explore the seeds of this transition by describing and analysing the production of promosing novelties in relation to to the dominant regime. On a theoretical level this books aims at the integration of hitherto largely disconneted disciplines and bodies of literature

The Effect of GATA6 Expression and Its Neighborhood Impact Factor on Regulating Cell Fate

abstract: A genetically engineered line of human induced pluripotent stem cells was used to study the effects of gene expression on cell fate. These cells were designed to activate expression of the gene GATA6 when exposed to the small molecule doxycycline. This gene was chosen because it plays an important role in the developmental biology stages of liver formation. Because of the way the cells were engineered, a given population would have a heterogeneous expression of GATA6 because each cell could have a different copy number of the exogenous gene. This variation allows for the differentiation of multiple cell types, and is used to grow liver organoids. The early liver organoid samples were studied via immunofluorescent staining, imaging, and quantitative image analysis. It was originally hypothesized that absolute gene expression was not the most important factor in determining cell fate, but relative gene expression was. This meant that the spatial location of the cells and their local environment were critical in determining cell fate. In other words, the level of GATA6 of a cell is important, but so is the level of GATA6 in the surrounding cells, or neighborhood, of that cell. This hypothesis was analyzed with the creation of various Neighborhood Impact Factor (NIF) methods. Multiple time points of growth were analyzed to study the temporal effect, in addition to the gene expression and NIF influence on a cell’s fate. Direct gene expression level showed correlation with certain cell fate markers. In addition to GATA6 expression levels, NIF results from early and late time point experiments show statistical significance with relatively small neighborhood radii. The NIF analysis was useful for examining the effect of neighboring cells and determining the size of the neighborhood – how far cells influence one another. While these systems are complex, the NIF analysis provides a way to look at gene expression and its influence in spatial context.Dissertation/ThesisPowerpoint presentation used in the defense.Masters Thesis Bioengineering 201