Cesar H. Comin

The aPKC-CBP Pathway Regulates Post-stroke Neurovascular Remodeling and Functional Recovery

Summary Epigenetic modifications have emerged as attractive molecular substrates that integrate extrinsic changes into the determination of cell identity. Since stroke-related brain damage releases micro-environmental cues, we examined the role of a signaling-induced epigenetic pathway, an atypical protein kinase C (aPKC)-mediated phosphorylation of CREB-binding protein (CBP), in post-stroke neurovascular remodeling. Using a knockin mouse strain (CbpS436A) where the aPKC-CBP pathway was defective, we show that disruption of the aPKC-CBP pathway in a murine focal ischemic stroke model increases the reprogramming efficiency of ischemia-activated pericytes (i-pericytes) to neural precursors. As a consequence of enhanced cellular reprogramming, CbpS436A mice show an increased transient population of locally derived neural precursors after stroke, while displaying a reduced number of i-pericytes, impaired vascular remodeling, and perturbed motor recovery during the chronic phase of stroke. Together, this study elucidates the role of the aPKC-CBP pathway in modulating neurovascular remodeling and functional recovery following focal ischemic stroke.

Archetypes and Outliers in the Neuromorphological Space

Neuromorphology has a long history of meticulous analysis and fundamental studies about the intricacies of neuronal shape. These studies converged to a plethora of information describing in detail many neuronal characteristics, as well as comprehensive data about cell localization, animal type, age, among others. Much of this information has notably been compiled through efforts of the Computational Neuroanatomy Group at the Krasnow Institute for Advanced Study, George Mason University, thus originating the NeuroMorpho.org repository, a resource that incorporates a large set of data and related tools. In the current work we present a methodology that can be used to search for novel relationships in cell morphology contained in databases such as the NeuroMorpho.org. More specifically, we try to understand which morphological characteristics can be considered universal for a given cell type, or to what extent we can represent an entire cell class through an archetypal shape. This analysis is done by taking a large number of characteristics from cells into account, and then applying multivariate techniques to analyze the data. The neurons are then classified as archetypes or outliers according to how close they are to the typical shape of the class. We find that granule and medium spiny neurons can be associated with a typical shape, and that different animals and brain regions show distinct distributions of shapes.

How integrated are theoretical and applied physics

How well integrated are theoretically and application oriented works in Physics currently? This interesting question, which has several relevant implications, has been approached mostly in a more subjective way. Recent concepts and methods from network science are used in the current work in order to develop a more principled, quantitative and objective approach to quantifying the integration and centrality of more theoretical/applied journals within the APS journals database, represented as a directed and undirected citation network. The results suggest a level of integration between more theoretical and applied journals, which are also characterized by remarkably similar centralities in the network.

Morphological Neuron Classification Based on Dendritic Tree Hierarchy

The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.

Topological characterization of world cities

The topological organization of several world cities are studied according to respective representations by complex networks. As a first step, the city maps are processed by a recently developed methodology that allows the most significant urban region of each city to be identified. Then, we estimate many topological measures on the obtained networks, and apply multivariate statistics and data analysis methods to study and compare the topologies. Remarkably, the obtained results show that cities from specific continents, especially Anglo-Saxon America, tend to have particular topological properties. Such developments should contribute to better understanding how cities are organized and related to different geographical locations worldwide.

Topology and dynamics in complex networks: The role of edge reciprocity

A key issue in complex systems regards the relationship between topology and dynamics. In this work, we use a recently introduced network property known as steering coefficient as a means to approach this issue with respect to different directed complex network systems under varying dynamics. Theoretical and real-world networks are considered, and the influences of reciprocity and average degree on the steering coefficient are quantified. A number of interesting results are reported that can assist the design of complex systems exhibiting larger or smaller relationships between topology and dynamics.

The dynamics of knowledge acquisition via self-learning in complex networks

Studies regarding knowledge organization and acquisition are of great importance to understand areas related to science and technology. A common way to model the relationship between different concepts is through complex networks. In such representations, networks nodes store knowledge and edges represent their relationships. Several studies that considered this type of structure and knowledge acquisition dynamics employed one or more agents to discover node concepts by walking on the network. In this study, we investigate a different type of dynamics adopting a single node as the network brain. Such a brain represents a range of real systems such as the information about the environment that is acquired by a person and is stored in the brain. To store the discovered information in a specific node, the agents walk on the network and return to the brain. We propose three different dynamics and test them on several network models and on a real system, which is formed by journal articles and their respective citations. The results revealed that, according to the adopted walking models, the efficiency of self-knowledge acquisition has only a weak dependency on topology and search strategy.