Physical Properties of Biological Entities: An Introduction to the Ontology of Physics for Biology

As biomedical investigators strive to integrate data and analyses across spatiotemporal scales and biomedical domains, they have recognized the benefits of formalizing languages and terminologies via computational ontologies. Although ontologies for biological entities—molecules, cells, organs—are well-established, there are no principled ontologies of physical properties—energies, volumes, flow rates—of those entities. In this paper, we introduce the Ontology of Physics for Biology (OPB), a reference ontology of classical physics designed for annotating biophysical content of growing repositories of biomedical datasets and analytical models. The OPB's semantic framework, traceable to James Clerk Maxwell, encompasses modern theories of system dynamics and thermodynamics, and is implemented as a computational ontology that references available upper ontologies. In this paper we focus on the OPB classes that are designed for annotating physical properties encoded in biomedical datasets and computational models, and we discuss how the OPB framework will facilitate biomedical knowledge integration

LinkedScales : bases de dados em multiescala

Orientador: André SantanchèTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: As ciências biológicas e médicas precisam cada vez mais de abordagens unificadas para a análise de dados, permitindo a exploração da rede de relacionamentos e interações entre elementos. No entanto, dados essenciais estão frequentemente espalhados por um conjunto cada vez maior de fontes com múltiplos níveis de heterogeneidade entre si, tornando a integração cada vez mais complexa. Abordagens de integração existentes geralmente adotam estratégias especializadas e custosas, exigindo a produção de soluções monolíticas para lidar com formatos e esquemas específicos. Para resolver questões de complexidade, essas abordagens adotam soluções pontuais que combinam ferramentas e algoritmos, exigindo adaptações manuais. Abordagens não sistemáticas dificultam a reutilização de tarefas comuns e resultados intermediários, mesmo que esses possam ser úteis em análises futuras. Além disso, é difícil o rastreamento de transformações e demais informações de proveniência, que costumam ser negligenciadas. Este trabalho propõe LinkedScales, um dataspace baseado em múltiplos níveis, projetado para suportar a construção progressiva de visões unificadas de fontes heterogêneas. LinkedScales sistematiza as múltiplas etapas de integração em escalas, partindo de representações brutas (escalas mais baixas), indo gradualmente para estruturas semelhantes a ontologias (escalas mais altas). LinkedScales define um modelo de dados e um processo de integração sistemático e sob demanda, através de transformações em um banco de dados de grafos. Resultados intermediários são encapsulados em escalas reutilizáveis e transformações entre escalas são rastreadas em um grafo de proveniência ortogonal, que conecta objetos entre escalas. Posteriormente, consultas ao dataspace podem considerar objetos nas escalas e o grafo de proveniência ortogonal. Aplicações práticas de LinkedScales são tratadas através de dois estudos de caso, um no domínio da biologia -- abordando um cenário de análise centrada em organismos -- e outro no domínio médico -- com foco em dados de medicina baseada em evidênciasAbstract: Biological and medical sciences increasingly need a unified, network-driven approach for exploring relationships and interactions among data elements. Nevertheless, essential data is frequently scattered across sources with multiple levels of heterogeneity. Existing data integration approaches usually adopt specialized, heavyweight strategies, requiring a costly upfront effort to produce monolithic solutions for handling specific formats and schemas. Furthermore, such ad-hoc strategies hamper the reuse of intermediary integration tasks and outcomes. This work proposes LinkedScales, a multiscale-based dataspace designed to support the progressive construction of a unified view of heterogeneous sources. It departs from raw representations (lower scales) and goes towards ontology-like structures (higher scales). LinkedScales defines a data model and a systematic, gradual integration process via operations over a graph database. Intermediary outcomes are encapsulated as reusable scales, tracking the provenance of inter-scale operations. Later, queries can combine both scale data and orthogonal provenance information. Practical applications of LinkedScales are discussed through two case studies on the biology domain -- addressing an organism-centric analysis scenario -- and the medical domain -- focusing on evidence-based medicine dataDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação141353/2015-5CAPESCNP

Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Adaptive multiscale processing for contrast enhancement

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space and frequency, results in a powerful methodology for image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations: (1) Dyadic wavelet transform (2) (phi) -transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by non-linear, logarithmic and constant scale-space weight functions. Multiscale edges identified within distinct levels of transform space provide a local support for enhancement throughout each decomposition. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

Recognition of compound characters in Kannada language

Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes