Sequence modelling and an extensible data model for genomic database

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models.

The 1000 Genomes Project: paving the way for personalized genomic medicine.

A survey of database management in engineering

1. Edward R.B. McCabe, MD, PhD* 1. 2. *Physician-in-Chief, Mattel Children’s Hospital at UCLA; Professor and Executive Chair, Department of Pediatrics, UCLA School of Medicine, Los Angeles, CA. Birth defects are the leading cause of infant mortality in the United States, representing more than 20% of all infant deaths. This infant mortality rate from birth defects exceeds that from sudden infant death syndrome, low-birthweight/short gestation, respiratory distress syndrome, and maternal complications. In addition, birth defects and genetic diseases represent major sources of morbidity for those who survive. As our ability increases to care effectively for those who have infectious diseases and other acute illnesses, individuals who have chronic illnesses due to genetic etiologies represent an increasing proportion of patients seen in the general pediatrician”s office. The Human Genome Project was initiated on October 1, 1990, and has a projected funding period of 15 years. The goal is to sequence the entire human genome, representing three billion base pairs that contain the coding sequences for approximately 75,000 genes. During the latter half of this century, investigations into the genetics of disease gathered increasing momentum. In addition to fundamental investigations into human genetics, technologic tools were developed that permitted large-scale genomic sequencing. These tools included the polymerase chain reaction (PCR), which permits amplification of hundreds of thousands or even millions of copies of DNA and requires only limited sequence data for its success; automated DNA sequencing, which allows increased sequence processing and decreased cost compared with manual methods; and improved information systems, which permit sophisticated analysis and assembly of the three billion base pairs of DNA in the human genome. Thus, the Human Genome Project represents the current chapter in our understanding, but it is neither the first nor the final chapter in this story. Once we know the sequences of all of the human genes, we must learn their functional roles in human development and disease pathogenesis. The Human Genome Project has been referred to as the “moon shot …

Chromosome 21 is the smallest human chromosome and represents a model for physical mapping of the entire human genome. Three copies of this autosome cause Down syndrome, the most frequent genetic disorder associated with significant mental retardation. Five years ago, in the framework of the international human genome project, and as an integrated part of the chromosome 21 community, a consortium, of academic groups from Japan and Germany was formed to map and sequence this chromosome. Several whole-genome and chromosome-specific DNA libraries were constructed for mapping purposes. Using a combination of nested-deletion [1] and shotgun sequencing approaches, our center determined and analyzed over 17 million bases of high-quality data. In total, over 33.5 million basepairs of DNA, distributed over four contigs, were sequenced with very high accuracy [2]. The largest of these contigs is nearly 25.5Mb long. Only three small clone gaps remain, which together comprise approximately 100kb. Thus, we achieved a coverage of 99.7% of 21q. In addition, 211,116 bp from the short arm were also sequenced. Analysis of the chromosome revealed far fewer genes than previously estimated. Instead of 800-1000 expected genes, we found only 225 genes, of which 127 are known genes and 98 are predicted genes. In addition, 59 pseudogenes were identified. The completion of the 21q sequence provides a unique resource for understanding the molecular pathophysiology of Down syndrome, as well as all other monogenic and complex disorders that map to this chromosome, including Alzheimer’s disease, leukemia, autoimmune disease, epilepsy and manic-depressive psychosis. It also stands as a structural framework from which the complete molecular architecture of the chromosome can be determined. With the announcement of the first “working draft” of the human genome by the international human genome sequencing consortium this past June, there has been a great interest in the total number of genes in the human genome[3]. Some of the current estimates range from as low as 34,000 genes to as high as 140,000 genes. The low gene-density of chromosome 21 was quite unexpected. Most striking is a 7-Mb region near the region near the centromere that contains only one gene. This region is much larger than the whole genome of some species, such as Escherichia coli, yet evolutionary processes permitted the existence of such a gene-poor SNA segment. This finding leads us to propose that there are additional large gene-less regions in other human/mammalian chromosomes. By combining the gene numbers of chromosomes 21 and 22 [4] (770genes; 2-3% of human genome) and assuming that together they represent the average gene content of the human genome, we estimate that the total number of human genes may be close to 40,000. As part of the international human genome sequencing project, we are also sequencing parts of chromosomes 11 and 18. Our high-throughput sequence production line generates about 10 Mb per day. In order to handle such a high volume of output, we have developed a system for automated data assembly, annotation and release. All the data is released through our website and through the DNA Database of Japan immediately according to the policies established by the international consortium. In addition to chromosomes 11, 18 and 21, we are also interested in the structure of the entire human genome. In collaboration with the University of Tokyo, we have developed a database called HGREP [5] (Human Genome Reconstruction Project) that contains working draft and finished sequences which cover more than 85% of the human genomic sequences. Sequence entries are aligned along the chromosomes based on sequence similarities to STS markers, BAC-end and other entry sequences. Further, biological features, such as genes, gene functions, repeats and CpG islands, are fully annotated on these entries. To identify evolutionally conserved and biologically important information in the genome, we are taking the strategy of comparative genomic sequence analysis between human and other related genomes such as those of rodents and primates. We are currently sequencing several regions of the mouse, including some regions that are counterpart to human chromosome 21, and are preparing genomic resources for sequencing of the chimpanzee. Through comparative methods, we are also trying to understand telomeric regions, structures at the end of chromosomes that play an important role in several biological functions and have been associated with human diseases like an oncogenesis and a cellular aging. The structure and function of all genes and their regulatory regions can only be fully understood by looking at how they interact under different circumstances and in several different backgrounds. Determining which genes and elements are human-specific will lead to a deeper understanding of who we are and will advance medical sciences drastically.

The niche concept has been recognised as a fundamental notion in biological and business systems, amongst others. Existing data models do not have adequate primitives that faithfully represent ecological niches for geographical information systems. This paper shows the limitations of the status quo, manifested in semantic heterogeneity and the meaningless integration of ecological data sets. We advocate an ontological engineering approach to ecological data modelling and propose two new data modelling primitives, Functional object granules [FOGs] and Functional Contexts, which can be used to represent niches. We present our data modelling environment, OntoCrucible, and illustrate our model and methodology in the context of a case study involving African smallholder farmer upliftment.KeywordsGeographical Information SystemSmallholder FarmerFunctional ContextOntological EngineeringSemantic HeterogeneityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

With the rapid advancement of science and technology and the gradual depletion of global energy resources, reducing energy consumption has emerged as a critical challenge. Hybrid and electric vehicles (HEVs and EVs) are playing an increasingly pivotal role in addressing this issue, necessitating further development and exploration. In this study, I employed AVL-CRUISE software to conduct vehicle simulations and data modeling, followed by a comprehensive performance evaluation of both hybrid and electric vehicles. Simulations were performed under the Highway driving cycle, revealing that the vehicle achieved a maximum speed of 134.88 km/h, a 0-100 km/h acceleration time of 11.49 seconds, and a maximum climbing capacity of 40.4%. These results demonstrate that hybrid and electric vehicles not only meet but also exceed performance expectations.Beyond the simulation outcomes, I also investigated key factors influencing vehicle performance, including vehicle mass, air resistance, and transmission ratio. Using MATLAB, I conducted a detailed analysis of the relationship between these factors and energy consumption, deriving correlation equations to describe these interactions. Through extensive data analysis and model fitting, this research has deepened our understanding of hybrid and electric vehicles. The findings not only contribute to the field but also hold significant potential for further advancements in the development of energy-efficient transportation solutions.

A bi-level object-oriented data model together with a user query language called OFQL is designed which can support applications like GIS (geographic information systems). The data model is divided into two layers, the higher-level data model and the lower-level data model. The higher-level data model or the geographic object data model primarily consists of the geographic objects and a set of semantic spatial functions through which the topological relationships of the geographic objects are defined or derived. The lower-level data model or the geometric object data model has geometric objects which are the actual spatial representations of the geographic objects in the higher-level data model. It also has a set of geometric functions that retrieve, manipulate, and compute for geometric objects. The general architecture of a GIS system using this data modeling approach consists of two modules: the query processor and the function implementor. With the OFQL user-interface, the user is able to pose queries of a geographic nature without knowing the details of the spatial representation and computation of the geographic objects.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A data model for moving objects is a blueprint that describes data of interest related to movements of objects and a set of operations represented in an information or database management system. Designing an appropriate data model fosters conceptual understanding of the dynamic phenomena of moving objects, yields efficient data storage and retrieval, and further facilitates spatiotemporal analytical processes. Various data models have been proposed to efficiently and effectively store, manage, query, and analyze data about moving objects. This entry starts with the description of fundamental elements of moving objects and their abstract data types. Then it reviews data modeling approaches for representing moving objects, including conventional spatiotemporal data model, constraint model, moving object data model, semantic trajectory data model, event‐based data model, and models for handling big moving object data.

In the field of environmental management, inefficient piecemeal sectorial approaches are still widespread, particularly in water resources management, where concerns and decisions are usually split among a set of rather weakly linked stakeholders. Planning and management problems to be addressed are in most cases complex and need to be tackled in a global, integrated and holistic way, including from the different areas of science and knowledge from the various stakeholders. Therefore, the sharing and integration of data, knowledge and models has become a major issue. A lot of effort has been made in recent years to develop such integrative tools, including systems (IS), decision support systems (DSS) or integrated modelling frameworks (IMF). Although these tools achieved great progress in terms of linkages, there is currently no environment providing a full integration of the various kinds of data, models, and their visualisation. This is the general issue tackled by the present thesis, with developments based on a semantic, systemic formalism, and applications focusing on water management issues. The first development step taken was the creation of a systemic description of water management. Albeit the various aspects of water management are abundantly documented and well-known, an integrated description including interconnections was lacking. Therefore, a graphic representation of interconnected elements annotated with definitions was created. This innovative result constitutes an ontology (a formal representation of a knowledge domain) providing a robust reference and a fairly exhaustive reminder of the complexity of the water management field, including transdisciplinary aspects. In the second step, a data model was created, based on the new SYSMOD language. The latter proposes a new graphical language uniting the ontological and systemic approaches, with a strong emphasis on interactions. Whereas existing data models are generally specific to a field or designed for a target application, this new data model is generic and transdisciplinary. It can handle any kind of systemically organised data, which potentially includes any sort of environmental data. To visualise and manage the systems-based, generic data stored within the database (created on the basis of the generic data model) a new kind of visualisation was developed, proposing diagrams of interconnected nodes. Whereas existing systems are often data warehouses without a user-friendly graphical user interface, or geographic systems focussing on spatial data, this new diagram visualisation proposes views including spatial and non-spatial elements (such as lakes, power plants, stakeholders or laws) interconnected by interactions (such as fluxes or influences). Bound to the database, the tool was called an information on the system (ISS). It provides a way of graphically managing and integrating the systemically organised, generic, transdisciplinary data. Furthermore, the systems approach fosters the apprehension and communication of complex situations in a holistic way (involving many different elements, influences and interactions from various fields), through their representation into thematic views. In complement to the diagram visualisation, a reporting tool was developed to display the data linked to elements (numeric values, comments, files, etc.) in a web-browser fashion. To interoperate with external models, another module was developed on the top of the ISS. Finally, a fully-fledged general user interface was created to integrate all the previous developments into a single environment. To visualise the different facets of elements, a geographic and a chart view were added, in complement to the reporting view. This final tool was called the generic decision support (GenDSS), as its application to given situations can give rise to a series of views and linkages to models, thereby providing specific DSS functionalities for these situations. The successive developments were tested in Birmingham (United Kingdom). Test results indicated that the GenDSS is currently limited to managing a few hundred elements; beyond this quantity, the loading times and the user interface reactivity can become long and unresponsive. On the other hand, the application also demonstrated the strengths of the GenDSS which provides an innovative integrated solution to centralise transdisciplinary data in various formats (e.g. numeric values, texts, geometries, files), to holistically describe situations using complementary visualisations, and to test scenarios using externally-linked models. Thus, the GenDSS may prove an adequately versatile solution for large institutions or enterprises dealing with complex situations, heterogeneous and transdisciplinary data, and where requirements are continuously evolving, such as: a car manufacturing corporation (which would involve several different production and research sites in different countries with different regulations), a hospital, or a strategic environmental planning network (where the tool could help to break the transdisciplinary, sectorial barriers). In the case of water, the GenDSS could prove very useful to achieve a truly integrated water management approach.

With approximately 17490 well-defined modeling objects, the SKO -Datenmodell is probably the most extensive reference data model in German for the banking area. So far, this reference data model has been used in about 30 projects describing different subject areas. The detailed project data models that have been derived from these projects have been reintegrated into the generic reference data model, as far as the results are applicable to the entire Sparkassen-organisation. The SKO-Datenmodell was initially developed approximately 15 years ago. It is derived from the Financial Services Data Model (FSDM), which has been provided by IBM. The FSDM is a reference data model which is generally valid for the banking area. In contrast to the FSDM, the SKO-Datenmodell is specialized for the requirements of the Sparkassenorganisation. The basic elements of the reference data model are a conceptual design of data model abstraction levels, an extensive Methods and Procedures Handbook with precise quality requirements and an integrated tool support by m1 and Rochade . The different levels of the SKO-Datenmodell and the use of these levels in practice are described in this chapter.

One of the goals in the design of database machines of the future is their generality. In addition to being capable of carrying out the common database management functions with high reliability and performance, some of these machines are intended to support more than one data model. A specific database machine, known as the DBC, is intended to support several existing data models. Although the DBC supports many data models, we single out the relational data model for this discussion. In particular, we have tried to concentrate mainly on the subject of database representation and query translation of System R-like database management systems. Some estimates of the storage requirements and performance gains are given in this paper. However, due to limited space, the detailed analysis is shown elsewhere in [22].

Activity-centric data gather feedback on students' learning to enhance learning effectiveness. The heterogeneity and multigranularity of such data require existing data models to perform complex on-the-fly computation when responding to queries of specific granularity. This, in turn, results in latency. In addition, existing data models are inefficient in storing computed results, which are often required for follow-up analysis. These follow-up analyses depend largely on stakeholder objectives, which often impose constraints to the analysis process. In this article, we propose a context-based data model that addresses two challenges associated with learning analytics: the increased processing time due to multiple data granularities required from various stakeholder objectives, and the lack of support for archiving and updating of new information that exist during iterative analysis. We demonstrate how the proposed model can help support analysis and visualization via the XuetangX and ASSISTments datasets. Results show that although our proposed data model requires preprocessing, it requires less time to process queries associated with learning-activity attributes of various granularities compared to existing data models.

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.