108 research outputs found
ITR/IM: Enabling the Creation and Use of GeoGrids for Next Generation Geospatial Information
The objective of this project is to advance science in information management, focusing in particular on geospatial information. It addresses the development of concepts, algorithms, and system architectures to enable users on a grid to query, analyze, and contribute to multivariate, quality-aware geospatial information. The approach consists of three complementary research areas: (1) establishing a statistical framework for assessing geospatial data quality; (2) developing uncertainty-based query processing capabilities; and (3) supporting the development of space- and accuracy-aware adaptive systems for geospatial datasets. The results of this project will support the extension of the concept of the computational grid to facilitate ubiquitous access, interaction, and contributions of quality-aware next generation geospatial information. By developing novel query processes as well as quality and similarity metrics the project aims to enable the integration and use of large collections of disperse information of varying quality and accuracy. This supports the evolution of a novel geocomputational paradigm, moving away from current standards-driven approaches to an inclusive, adaptive system, with example potential applications in mobile computing, bioinformatics, and geographic information systems. This experimental research is linked to educational activities in three different academic programs among the three participating sites. The outreach activities of this project include collaboration with U.S. federal agencies involved in geospatial data collection, an international partner (Brazil\u27s National Institute for Space Research), and the organization of a 2-day workshop with the participation of U.S. and international experts
Digital Government: Knowledge Management Over Time-Varying Geospatial Datasets
Spatially-related data is collected by many government agencies in various formats and for various uses. This project seeks to facilitate the integration of these data, thus providing new uses. This will require the development of a knowledge management framework to provide syntax, context, and semantics, as well as exploring the introduction of time-varying data into the framework. Education and outreach will be part of the project through the development of an on-line short courses related to data integration in the area of geographical information systems. The grantees will be working with government partners (National Imagery and Mapping Agency, the National Agricultural Statistics Service, and the US Army Topographic Engineering Center), as well as an industrial organization, Base Systems, and the non-profit OpenGIS Consortium, which works closely with vendors of GIS products
Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery
In today\u27s world of vast information availability users often confront large unorganized amounts of data with limited tools for managing them. Motion imagery datasets have become increasingly popular means for exposing and disseminating information. Commonly, moving objects are of primary interest in modeling such datasets. Users may require different levels of detail mainly for visualization and further processing purposes according to the application at hand. In this thesis we exploit the geometric attributes of objects for dataset summarization by using a series of image processing and neural network tools. In order to form data summaries we select representative time instances through the segmentation of an object\u27s spatio-temporal trajectory lines. High movement variation instances are selected through a new hybrid self-organizing map (SOM) technique to describe a single spatio-temporal trajectory. Multiple objects move in diverse yet classifiable patterns. In order to group corresponding trajectories we utilize an abstraction mechanism that investigates a vague moving relevance between the data in space and time. Thus, we introduce the spatio-temporal neighborhood unit as a variable generalization surface. By altering the unit\u27s dimensions, scaled generalization is accomplished. Common complications in tracking applications that include occlusion, noise, information gaps and unconnected segments of data sequences are addressed through the hybrid-SOM analysis. Nevertheless, entangled data sequences where no information on which data entry belongs to each corresponding trajectory are frequently evident. A multidimensional classification technique that combines geometric and backpropagation neural network implementation is used to distinguish between trajectory data. Further more, modeling and summarization of two-dimensional phenomena evolving in time brings forward the novel concept of spatio-temporal helixes as compact event representations. The phenomena models are comprised of SOM movement nodes (spines) and cardinality shape-change descriptors (prongs). While we focus on the analysis of MI datasets, the framework can be generalized to function with other types of spatio-temporal datasets. Multiple scale generalization is allowed in a dynamic significance-based scale rather than a constant one. The constructed summaries are not just a visualization product but they support further processing for metadata creation, indexing, and querying. Experimentation, comparisons and error estimations for each technique support the analyses discussed
Trends in the design and use of elastin-like recombinamers as biomaterials
Producción CientíficaElastin-like recombinamers (ELRs), which derive from one of the repetitive domains found in natural elastin, have been intensively studied in the last few years from several points of view. In this mini review, we discuss all the recent works related to the investigation of ELRs, starting with those that define these polypeptides as model intrinsically disordered proteins or regions (IDPs or IDRs) and its relevance for some biomedical applications. Furthermore, we summarize the current knowledge on the development of drug, vaccine and gene delivery systems based on ELRs, while also emphasizing the use of ELR-based hydrogels in tissue engineering and regenerative medicine (TERM). Finally, we show different studies that explore applications in other fields, and several examples that describe biomaterial blends in which ELRs have a key role. This review aims to give an overview of the recent advances regarding ELRs and to encourage further investigation of their properties and applications.Comisión Europea (project NMP-2014-646075)Ministerio de Economía, Industria y Competitividad (projects PCIN-2015-010 / MAT2016-78903-R / BES-2014-069763)Junta de Castilla y León (project VA317P18
Support Vector Methods for Higher-Level Event Extraction in Point Data
Phenomena occur both in space and time. Correspondingly, ability to model spatiotemporal behavior translates into ability to model phenomena as they occur in reality. Given the complexity inherent when integrating spatial and temporal dimensions, however, the establishment of computational methods for spatiotemporal analysis has proven relatively elusive. Nonetheless, one method, the spatiotemporal helix, has emerged from the field of video processing. Designed to efficiently summarize and query the deformation and movement of spatiotemporal events, the spatiotemporal helix has been demonstrated as capable of describing and differentiating the evolution of hurricanes from sequences of images. Being derived from image data, the representations of events for which the spatiotemporal helix was originally created appear in areal form (e.g., a hurricane covering several square miles is represented by groups of pixels). ii Many sources of spatiotemporal data, however, are not in areal form and instead appear as points. Examples of spatiotemporal point data include those from an epidemiologist recording the time and location of cases of disease and environmental observations collected by a geosensor at the point of its location. As points, these data cannot be directly incorporated into the spatiotemporal helix for analysis. However, with the analytic potential for clouds of point data limited, phenomena represented by point data are often described in terms of events. Defined as change units localized in space and time, the concept of events allows for analysis at multiple levels. For instance lower-level events refer to occurrences of interest described by single data streams at point locations (e.g., an individual case of a certain disease or a significant change in chemical concentration in the environment) while higher-level events describe occurrences of interest derived from aggregations of lower-level events and are frequently described in areal form (e.g., a disease cluster or a pollution cloud). Considering that these higher-level events appear in areal form, they could potentially be incorporated into the spatiotemporal helix. With deformation being an important element of spatiotemporal analysis, however, at the crux of a process for spatiotemporal analysis based on point data would be accurate translation of lower-level event points into representations of higher-level areal events. A limitation of current techniques for the derivation of higher-level events is that they imply bias a priori regarding the shape of higher-level events (e.g., elliptical, convex, linear) which could limit the description of the deformation of higher-level events over time. The objective of this research is to propose two newly developed kernel methods, support vector clustering (SVC) and support vector machines (SVMs), as means for iii translating lower-level event points into higher-level event areas that follow the distribution of lower-level points. SVC is suggested for the derivation of higher-level events arising in point process data while SVMs are explored for their potential with scalar field data (i.e., spatially continuous real-valued data). Developed in the field of machine learning to solve complex non-linear problems, both of these methods are capable of producing highly non-linear representations of higher-level events that may be more suitable than existing methods for spatiotemporal analysis of deformation. To introduce these methods, this thesis is organized so that a context for these methods is first established through a description of existing techniques. This discussion leads to a technical explanation of the mechanics of SVC and SVMs and to the implementation of each of the kernel methods on simulated datasets. Results from these simulations inform discussion regarding the application potential of SVC and SVMs
A Temporal GIS Approach to Characterizing Geographical Dynamics
Temporal GIS research has historically focused on change, motion, and events. This research introduces a framework to represent concepts of fluid kinematics with the emphasis on the concept of flows. General circulation models (GCMs) and other spatially explicit environmental models produce massive time series of geographic fields (e.g. temperature) that call for effective GIS approaches to elicit temporal information embedded in these model outputs. Common temporal GIS approaches with discrete constructs in space and time tend to overlook the spatiotemporal continuity that is fundamental to the understanding of geographic dynamic fields, such as temperature. Common methods of analyzing climatological characteristics center on trend analysis at fixed locations or monitoring meteorological phenomena, such as storm tracks, to evaluate circulation changes. The proposed temporal GIS framework, on the other hand, uses the velocity of virtual particles with fixed climatological values to capture changes in scalar continuous fields. The resulting spatiotemporal distributions of velocity suggest kinematic flows that can be used to recognize features indicative of geographic processes, such as divergence and convergence of isolines. Summative characterizations of these kinematic features highlight the embedded change and motion in these temporal sets of scalar fields and facilitate understanding and comparing model outputs
Estudios computacionales de mecanismos moleculares de la inmunidad innata
Tesis inédita de la Universidad Complutense de Madrid, Facultad de Farmacia, leída el 20-12-2022Antimicrobial Resistance (AMR) is a worldwide health emergency. ESKAPE pathogens include the most relevant AMR bacterial families. In particular, Gram-negative bacteria stand out due to their cell envelope complexity, which exhibits strong resistance to antimicrobials. A key element for AMR is the chemical structure of bacterial lipopolysaccharide (LPS), and the phospholipid composition of the membrane, inflecting the membrane permeability to antibiotics. We have applied coarse-grained molecular dynamics simulations to capture the role of the phospholipid composition and lipid A structure in the membrane properties and morphology of ESKAPE Gram-negative bacterial vesicles. Moreover, the reported antimicrobial peptides Cecropin B1, JB95, and PTCDA1-kf were used to unveil their implications for membrane disruption. This study opens a promising starting point for understanding the molecular keys of bacterial membranes and promoting the discovery of new antimicrobials to overcome AMR...La resistencia a los antimicrobianos (AMR) es una emergencia sanitaria mundial. Los patógenos ESKAPE incluyen las familias bacterianas más resistentes a antibióticos y son altamente virulentas. En particular, las bacterias Gram negativas destacan por la complejidad de su pared celular, que presenta una fuerte resistencia frente a los antibióticos. Un elemento clave para la AMR es la estructura química del lipopolisacárido bacteriano (LPS) y la composición de los fosfolípidos de la membrana bacteriana, que influyen en su permeabilidad a los antibióticos. Se han empleado simulaciones de dinámica molecular de grano grueso para captar el papel de la composición de los fosfolípidos y la estructura del LPS en las propiedades y morfología de modelos de vesículas bacterianas Gram negativas ESKAPE. Además, se han empleado los péptidos antimicrobianos Cecropin B1, JB95 y PTCDA1-kf para desvelar su mecanismo disrupción de la membrana bacteriana. Este estudio abre un prometedor punto de partida para comprender las claves moleculares de la resistencia en membranas bacterianas y acelerar el descubrimiento de nuevos antibióticos para hacer frente a la AMR...Fac. de FarmaciaTRUEunpu
Linking Moving Object Databases with Ontologies
This work investigates the supporting role of ontologies for supplementing the information contained in moving object databases. Details of the spatial representation as well as the sensed location of moving objects are frequently stored within a database schema. However, this knowledge lacks the semantic detail necessary for reasoning about characteristics that are specific to each object. Ontologies contribute semantic descriptions for moving objects and provide the foundation for discovering similarities between object types. These similarities can be drawn upon to extract additional details about the objects around us. The primary focus of the research is a framework for linking ontologies with databases. A major benefit gained from this kind of linking is the augmentation of database knowledge and multi-granular perspectives that are provided by ontologies through the process of generalization. Methods are presented for linking based on a military transportation scenario where data on vehicle position is collected from a sensor network and stored in a geosensor database. An ontology linking tool, implemented as a stand alone application, is introduced. This application associates individual values from the geosensor database with classes from a military transportation device ontology and returns linked value-class pairs to the user as a set of equivalence relations (i.e., matches). This research also formalizes a set of motion relations between two moving objects on a road network. It is demonstrated that the positional data collected from a geosensor network and stored in a spatio-temporal database, can provide a foundation for computing relations between moving objects. Configurations of moving objects, based on their spatial position, are described by motion relations that include isBehind and inFrontOf. These relations supply a user context about binary vehicle positions relative to a reference object. For example, the driver of a military supply truck may be interested in knowing what types of vehicles are in front of the truck. The types of objects that participate in these motion relations correspond to particular classes within the military transportation device ontology. This research reveals that linking a geosensor database to the military transportation device ontology will facilitate more abstract or higher-level perspectives of these moving objects, supporting inferences about moving objects over multiple levels of granularity. The details supplied by the generalization of geosensor data via linking, helps to interpret semantics and respond to user questions by extending the preliminary knowledge about the moving objects within these relations
Class III peroxidases PRX01, PRX44, and PRX73 potentially target extensins during root hair growth in Arabidopsis thaliana
Root hair cells are important sensors of soil conditions. Expanding several hundred times their original size, root hairs grow towards and absorb water-soluble nutrients. This rapid growth is oscillatory and is mediated by continuous remodelling of the cell wall. Root hair cell walls contain polysaccharides and hydroxyproline-rich glycoproteins including extensins (EXTs). Class-III peroxidases (PRXs) are secreted into the apoplastic space and are thought to trigger either cell wall loosening, mediated by oxygen radical species, or polymerization of cell wall components, including the Tyr-mediated assembly of EXT networks (EXT-PRXs). The precise role of these EXT-PRXs is unknown. Using genetic, biochemical, and modeling approaches, we identified and characterized three root hair-specific putative EXT-PRXs, PRX01, PRX44, and PRX73. The triple mutant prx01,44,73 and the PRX44 and PRX73 overexpressors had opposite phenotypes with respect to root hair growth, peroxidase activity and ROS production with a clear impact on cell wall thickness. Modeling and docking calculations suggested that these three putative EXT-PRXs may interact with non-O-glycosylated sections of EXT peptides that reduce the Tyr-to-Tyr intra-chain distances in EXT aggregates and thereby may enhance Tyr crosslinking. These results suggest that these three putative EXT-PRXs control cell wall properties during the polar expansion of root hair cells.Fil: Marzol, Eliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Borassi, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Ranocha, Philippe. Instituto National de Recherches Agronomiques. Centre de Recherches de Toulouse; FranciaFil: Aptekmann, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Bringas, Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Pennington, Janice. University of Wisconsin; Estados UnidosFil: Paez Valencia, Julio. University of Wisconsin; Estados UnidosFil: Martinez Pacheco, Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Rodriguez Garcia, Diana Rosa. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Rondon Guerrero, Yossmayer del Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Carignani Sardoy, Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Mangano, Silvina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Fleming, Margaret. State University of Colorado - Fort Collins; Estados UnidosFil: Mishler Elmore, John W.. Ohio University; Estados UnidosFil: Blanco Herrera, Francisca. Universidad Andrés Bello and Millennium Institute for Integrative Biology (iBio). Facultad de Ciencias de la Vida. Centro de Biotecnología Vegeta; ChileFil: Bedinger, Patricia. State University of Colorado - Fort Collins; Estados UnidosFil: Dunand, Christophe. Instituto National de Recherches Agronomiques. Centre de Recherches de Toulouse; FranciaFil: Capece, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Nadra, Alejandro Daniel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Biociencias, Biotecnología y Biología Traslacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Held, Michael. Ohio University; Estados UnidosFil: Otegui, Marisa S.. University of Wisconsin; Estados UnidosFil: Estevez, Jose Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Universidad Andrés Bello; Chil
Mechanism Of Nucleosome Targeting By Pioneer Transcription Factors
Transcription factors (TFs) forage the genome to instruct cell plasticity, identity, and differentiation. These developmental processes are elicited through TF engagement with chromatin. Yet, how and which TFs can engage with chromatin and thus, nucleosomes, remains largely unexplored. Pioneer TFs are TF that display a high affinity for nucleosomes. Extensive genetic and biochemical studies on the pioneer TF FOXA, a driver of fibroblast to hepatocyte reprogramming, revealed its nucleosome binding ability and chromatin targeting lead to chromatin accessibility and subsequent cooperative binding of TFs. Similarly, a number of reprogramming TFs have been suggested to have pioneering activity due to their ability to target compact chromatin and increase accessibility and enhancer formation in vivo. But whether these factors directly interact with nucleosomes remains to be assessed. Here we test the nucleosome binding ability of the cell reprogramming TFs, Oct4, Sox2, Klf4 and cMyc, that are required for the generation of induced pluripotent stem cells. In addition, we also test neuronal and macrophage reprogramming TFs. Our study shows that reprogramming TFs bind nucleosomes with a range of nucleosome binding affinities, indicating that although specific cocktails of TFs are required for reprogramming, mechanistically these TFs show differential nucleosome interacting behaviors. These results allowed us to assess differential features between TFs nucleosome binding ability and to correlate their binding with reprogramming potential.
To determine how general is nucleosome binding we extended our analysis to screen 593 of the 2,000 predicted human TFs in the genome for potential nucleosome binding and validated their binding in solution. Based on 3D structural analysis, we proposed that strong nucleosome binders anchor DNA through short -helixes and have a flexible and adaptable DNA binding domain while weak nucleosome binders use -sheets or unstructured regions and have a higher rigidity within their DNA binding domain. Through the experiments presented in this dissertation we present the first study revealing the shared structural features contributing to nucleosome binding potential of pioneer TFs and thus allow for predication of novel pioneer TFs with cell reprogramming potential
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