3D City Models and urban information: Current issues and perspectives

Considering sustainable development of cities implies investigating cities in a holistic way taking into account many interrelations between various urban or environmental issues. 3D city models are increasingly used in different cities and countries for an intended wide range of applications beyond mere visualization. Could these 3D City models be used to integrate urban and environmental knowledge? How could they be improved to fulfill such role? We believe that enriching the semantics of current 3D city models, would extend their functionality and usability; therefore, they could serve as integration platforms of the knowledge related to urban and environmental issues allowing a huge and significant improvement of city sustainable management and development. But which elements need to be added to 3D city models? What are the most efficient ways to realize such improvement / enrichment? How to evaluate the usability of these improved 3D city models? These were the questions tackled by the COST Action TU0801 “Semantic enrichment of 3D city models for sustainable urban development”. This book gathers various materials developed all along the four year of the Action and the significant breakthroughs

Transcriptional regulation of neurogenesis by the proneural factor Ascl1

Dissertação de mestrado BioinformaticsThis project aims to provide a better understanding of the transcriptional regulation of neurogenesis by the proneural factor Ascl1. The first genome-wide characterization of Ascl1 transcriptional program in the embryonic mouse brain was performed by ChIP-chip. However, the restriction to proximal promoter regions, excluding genes bound by Ascl1 to distal enhancers, and the need to validate the model with a more robust experimental approach, prompted the use of ChIP-seq. Genome-wide mapping of Ascl1 binding sites with higher resolution, reveals 3054 high confidence binding regions in ventral telencephalon. The chromatin states of genomic regions associated with Ascl1 recruitment were also characterised, concluding that these bear marks of distal enhancers, but also proximal promoter regions. Further integration of expression profiling data from Ascl1 LoF experiments identifies 643 target genes. Results from functional annotation of these targets corroborate previous findings, showing that Ascl1 coordinates neurogenesis by regulating a large number of target genes with a wide variety of biological functions, and associated with different stages of neurogenesis. Additional investigations should address how Ascl1 coordinates this complex transcriptional program along the neuronal lineage. This could explore a possible crosstalk with the Notch program, taking advantage of the 105 regulatory regions identified where Ascl1 is co-recruited by RBPJ, as assessed by ChIP-seq.O objetivo principal deste projeto consiste em compreender melhor a regulação transcricional da neurogénese pelo fator proneural Ascl1. A primeira caracterização à escala do genoma do programa de transcrição do Ascl1 no cérebro de embriões de ratinho foi realizada pela técnica de ChIP-chip. No entanto, a restrição a regiões próximas do promotor, com exclusão de genes ligados pelo Ascl1 a distal enhancers, e a necessidade de validar o modelo com uma abordagem experimental mais robusta, motivou o recurso à técnica de ChIP-seq. A análise de localização, com alta resolução, ao longo de todo o genoma para sítios de ligação do Ascl1, revelou 3054 regiões de ligação de elevada confiança no telencéfalo do ratinho. De seguida, caracterizaram-se os chromatin states de regiões genómicas associadas com o recrutamento do Ascl1. Desta análise conclui-se que estas regiões possuem marcas de distal enhancers, mas também de regiões próximas do promotor. A posterior integração de perfis de expressão em experiências de perda-de-função para o Ascl1 identificou 643 genes alvo. Os resultados da anotação funcional desses alvos corroboram as conclusões anteriormente publicadas, mostrando que o Ascl1 coordena a neurogénese através da regulação de um grande número de genes alvo, com uma ampla diversidade de funções biológicas, associados a diferentes fases da neurogénese. Estudos futuros deem abordar de que forma o Ascl1 coordena este programa de transcrição complexo ao longo da linhagem neuronal. Tal poderia explorar um possível crosstalk com o programa Notch, tirando partido das 105 regiões regulatórias identificadas por ChIP-seq, onde o Ascl1 é co-recrutado pelo RBPJ

Current trends on ICT technologies for enterprise information s²ystems

The proposed paper discusses the current trends on ICT technologies for Enterprise Information Systems. The paper starts by defining four big challenges of the next generation of information systems: (1) Data Value Chain Management; (2) Context Awareness; (3) Interaction and Visualization; and (4) Human Learning. The major contributions towards the next generation of information systems are elaborated based on the work and experience of the authors and their teams. This includes: (1) Ontology based solutions for semantic interoperability; (2) Context aware infrastructures; (3) Product Avatar based interactions; and (4) Human learning. Finally the current state of research is discussed highlighting the impact of these solutions on the economic and social landscape

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli.

A significant obstacle in training predictive cell models is the lack of integrated data sources. We develop semi-supervised normalization pipelines and perform experimental characterization (growth, transcriptional, proteome) to create Ecomics, a consistent, quality-controlled multi-omics compendium for Escherichia coli with cohesive meta-data information. We then use this resource to train a multi-scale model that integrates four omics layers to predict genome-wide concentrations and growth dynamics. The genetic and environmental ontology reconstructed from the omics data is substantially different and complementary to the genetic and chemical ontologies. The integration of different layers confers an incremental increase in the prediction performance, as does the information about the known gene regulatory and protein-protein interactions. The predictive performance of the model ranges from 0.54 to 0.87 for the various omics layers, which far exceeds various baselines. This work provides an integrative framework of omics-driven predictive modelling that is broadly applicable to guide biological discovery

Ontology mapping for semantically enabled applications

In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services

PREDICTING COMPLEX PHENOTYPE-GENOTYPE RELATIONSHIPS IN GRASSES: A SYSTEMS GENETICS APPROACH

It is becoming increasingly urgent to identify and understand the mechanisms underlying complex traits. Expected increases in the human population coupled with climate change make this especially urgent for grasses in the Poaceae family because these serve as major staples of the human and livestock diets worldwide. In particular, Oryza sativa (rice), Triticum spp. (wheat), Zea mays (maize), and Saccharum spp. (sugarcane) are among the top agricultural commodities. Molecular marker tools such as linkage-based Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWAS), Multiple Marker Assisted Selection (MMAS), and Genome Selection (GS) techniques offer promise for understanding the mechanisms behind complex traits and to improve breeding programs. These methods have shown some success. Often, however, they cannot identify the causal genes underlying traits nor the biological context in which those genes function. To improve our understanding of complex traits as well improve breeding techniques, additional tools are needed to augment existing methods. This work proposes a knowledge-independent systems-genetic paradigm that integrates results from genetic studies such as QTL mapping, GWAS and mutational insertion lines such as Tos17 with gene co-expression networks for grasses--in particular for rice. The techniques described herein attempt to overcome the bias of limited human knowledge by relying solely on the underlying signals within the data to capture a holistic representation of gene interactions for a species. Through integration of gene co-expression networks with genetic signal, modules of genes can be identified with potential effect for a given trait, and the biological function of those interacting genes can be determined