Dynamic integration of biological data sources using the data concierge Background: The ever-changing landscape of large-scale network environments and innovative biology technologies require dynamic mechanisms to rapidly integrate previously unknown bioinformatics sources at runtime. However, existing integration technologies lack sufficient flexibility to adapt to these changes, because the techniques used for integration are static, and sensitive to new or changing bioinformatics source implementations and evolutionary biologist requirements. To adapt to dynamic network environments and to meet diverse biologist's requirements, we propose an adaptive middleware, the Data Concierge, to easily and rapidly integrate heterogeneous biological data sources at runtime. In this innovative architecture, the Generic API Ontology is proposed to declaratively model the semantics of data source APIs. Based on the unified semantic metadata, XML-based state machines model sequences of requests to biological data services for complex biologist manipulations. This middleware provides adaptive functionalities for both the integration system and its client application to tackle the rapid changes of biological data sources without expensive and time-consuming software development and maintenance. The costs to integrate new biological data sources in the Data Concierge are significantly lower than that of static coding integration methods.. Our future work includes enabling the Data Concierge to represent the knowledge of the biological data functionalities' relations in the Generic API Ontology. According to classified metadata, the Data Concierge would be able to reason about the execution sequence for biologist manipulations. Thus, tasks of configuring and maintaining XML-based state machine models would be eased and even be avoided, which therefore would significantly reduce the integration costs further. For integrating new appearing biological data sources, traditional static integration techniques (Figure 23a ), which tightly couple with low level implementations, require program code changes along with subsequent testing and deployment. Although these static approaches achieve good performance, they are expensive in terms of engineering efforts.. Interface-based integration mechanisms such as CORBA and COM platforms decrease coupling with low level implementations according to their predefined Meta models (Figure 23b ). They can achieve polymorphism and can dynamically compose software components and change system behaviors at run-time. However, when data source interfaces are modified, code changes cannot be avoided.. In contrast, the Data Concierge deals with interface changes without application code changes. This is because the Generic API Ontology provides a declarative mechanism that can be modified and deployed when an interface change occurs. At run-time, the Data Concierge dynamically constructs biological data source API calls based on their classified API semantics and corresponding state machine context (Figure 23c ). The mechanism of dynamically accessing new data sources at run-time relieves developers from hard coded programming.. Our approach aims to use the Data Concierge to dynamically access new biological data sources at runtime without hard coded programming. This architecture provides sufficient flexibility to handle changes of biological data sources. Its dynamic call construction mechanism has several significant advantages over hard-coding calls to pre-fabricated wrappers:. No wrapper component is needed. In effect, the wrapper code is constructed dynamically from the information held in the meta-data repository. This potentially significantly reduces the software engineering effort needed to connect to a new type of biological data source. Ease of modification: If the underlying data source API changes, only changes to the meta-data repository are required. As the calls to the API are dynamically constructed, the changes will take effect as soon as the meta-data is updated. Hot swapping: As there is no wrapper, updating a data source API to provide bug fixes has no downstream implications. If the interface does not change, then the Data Concierge remains oblivious to the fact that a modified version of the API has been installed.. The Data Concierge relieves programmers from the routine maintenance tasks of integrating new biological data sources. However, this approach carries the costs of building and maintaining the Generic API Ontology for a given data source, and organizing classified API metadata to build state machine models. We use the Generic Wrappers, Data Source API Description Tool and the graphical State Machine Generator to minimize the maintenance costs.. In addition, some biological data sources provide abstract functionalities. The semantics of these biological data functionalities are implied in the parameter contents of data source APIs. Therefore, it is hard for the Data Concierge to classify these data source API semantics through their API syntax. One of our future targets is to find mechanism to extract the semantics of data source functionalities from the contents of their interface parameters. Huge amounts of biological data research projects put their efforts on the data source integration issue. Some of them target at providing flexible mechanisms for various biologist requirements and quick changing biological data source environment. TAMBIS [31] , BioMedoator [32] , [33] , and [34] cope with frequent modification of data source schemas. Based on a domain ontology, TAMBIS provides homogeneous views of various data sources. The ontology is designed to relieve biologists from heavy query tasks by shielding them from low data source details and is updated to cater for schema changes in the sources. To reduce the workload of database integration, the LIMBO architecture in [33] is designed as light-weight approach to overcome the problems with constantly evolving data warehousing schema. Both BioMedoator and [34] extract and represent metadata on the content of sources and relationships among sources. BioMedoator aims to facilitate the easy use of this tool with no informatics training required for the biologists, while [34] targets optimizing user's query plans according to semantic equivalence. Although these approaches provide significant flexibility to deal with user's queries and changes of data source contents, they lack dynamic mechanisms to deal with the rapid and frequent changes of biological data source functionalities.. IBM's DiscoveryLink [35] is a database middleware system that extracts data from multiple biology sources in response to a single query. This method promises there is no functionality lost in access the data source through DiscoveryLink. To make wrapper authoring as simple as possible, DiscoveryLink requires a small set of key services from a wrapper, which makes the cost of writing a basic wrapper small. Its DiscoveryLink Server compensates for missing functionality at data sources. This approach claims that data source schema can evolve without Figure 19 The performance results of state machine models with different null methods. requiring any change in the wrapper for the reason that the wrapper does not encode information on the schema used in the data source. However, the wrapper needs to be developed when its source API is changed.. The rapid integration of biological data web pages on the Internet is addressed by the approaches of [24] , [6] , [36] , and [37] . [24] highly depends on the description of service classes, which provides general descriptions of types of sources to integrate. However, the integrated biological data sources are restricted to biological data Web pages that must have a start page to include an HTML form with at least one text entry field. It also needs examples to analyze sources. Because of these limitations, the percentage of experimentally successful integrated web sources is lower than 70%. [6] aims to generate automatically the data source schema of each source by means of meta-information. The metainformation is extracted from output of a source query tool which identifies terms from tags of a set of Web pages examples. However, it cannot deal with some particular output formats which are provided by certain data source query tools. Based on its OWW Search View mechanism, [36] pursues no programming efforts for accessing any new integrated web pages. However, it cannot efficiently process user's complex queries which include multiple joints of several data sources. [37] proposes using reconfigurable web wrapper agents for user to represent Web browsing session. Based on sequential pattern mining techniques, web wrapper agents can automatically discover and extract patterns from structurally formatted biological data Web pages. However, the initial purposes of these methods limit the scale of integrated biological data sources. These approaches will not be available to other types of data sources except Web pages.. Biological data projects such as, [38] [39] [40] , and ISYS [41] , focus on component based integrations for biological data sources. [38] [39] [40] use CORBA to dynamically integrate biological data sources. These approaches aim to achieve flexible, scalable, and reconfigurable system architecture. However, these methodologies mainly focus on specifying syntactic interfaces of integrated components, while the semantics of these interfaces are implied in their implementations. ISYS emphasizes a decentralized integration mechanism for dynamically synchronizing component behaviour and exchanging their services without direct knowledge of one another. However, the programming tasks of building thick client components would be heavy for the communication with others. And biologists who are not familiar with information techniques have to resort assistance from IT expertise for building their client components.. SIBIOS [42] and Bio-Broker [9] are dynamic workflowbased systems. To achieve highly adaptability, SIBIOS separates individual service description from wrapper engine. The service description is stored in a service schema file. This file includes domain specific knowledge described by using ontology and a set of rules that describe how the data can be extracted from the services. The wrapper engine reads these service schema files and dynamically generates specific data source wrappers. However, this approach only targets services which are provided Web pages. Bio-Broker is an architecture for XML-based mediator systems. The system uses EVAS to construct mediator-services for the integration of heterogeneous data sources. The EVAS benefits user to easily construct workflows for their recurrent biological data processes. However, in this architecture, wrappers are created manually.. With the emergence of Web 2.0, Mashup applications such as Bio2RDF [43] and Damia [44] , provide mechanisms for users to customize new services through combining data and services from multiple Web sources. Bio2RDF converts various biological documents into standard RDF formats so that client applications can have unified access to various biological sources. Damia is a lightweight web style data integration platform which helps enterprise users to quickly combine data from different data sources and easily develop new enterprise applications. However when a new type of source appears, new wrappers such as rdfizers in Bio2RDF and connectors in Damia still need to be created.. The methods mentioned above provide solutions to some aspects of dynamic features in biological data source integration. The majority of these methodologies focus on the heterogeneity of data content. Some take the dynamic features of source functionalities into account. However, none of these methods uses ontology to represent the semantics of both biological data source functionality and data schema. As a result, they lack sufficient flexibility and adaptability to solve challenges arisen from dynamically integrating new previous unknown biological data sources at runtime. High throughput experimental processes in life science have led to a large variety of biological data sources continuously emerging on the Internet [1, 2] . These data sources provide great research potential for biology researchers to obtain data that support their new biological insights in areas such as gene prediction, proteomics analysis, mutations, and drug discovery. However, biology information is not easily and conveniently accessible [3, 4] . Even though most biological data source suppliers provide tools to access their own data sources, biologists have to switch to different interactive interfaces and manually seek and combine results from different resources. This manner of information collection is consequently tedious and time consuming [5, 6] . Hence a unified access mechanism to these various biological data sources has been necessitated to improve biology research processes [7] .. Many practical decisions have led to heterogeneous implementations of the existing biological data sources. Regardless, the resulting complexity makes the integration of biological data sources difficult. The lack of standardization also means biological data is available in a wide variety of formats. Various data schemas such as flat files, structured data (e.g. database), semi-structured data (e.g. XML [8] ), and arbitrary data structures, result in syntactical difficulties for data unification [9] .. Multifarious data access mechanisms such as web page navigation, web services, remote database access, FTP, Email, Wiki [10] , and so on, pose technical obstacles to unified schemes for data extraction and communication. In addition, semantic problems arise due to no standard terminology conventions in biological data. For instance, integration conflicts arise when different notions use the same terminology, or the same concept has different representations in different sources.. Data source integration is consequently a challenging research topic in biological data, and large amount of research efforts have been devoted into this area [11] . Early approaches focused on the integration of multiple biological relational databases. For instance, one popular early integration approach used multi-database query languages such as the Collection Programming Language (CPL) [12] , to enable biologists to specify complex queries for different biological databases. Mediation systems, for example Mediator-Wrapper [13] , Database Federation [14] , and data warehousing [15] , are another trend to provide a virtual or physical view of global biological data schema. In addition, some biological data centers such as EBI [16] , NCBI [17] , and DDBJ [18] , use navigation oriented methods, such as web browsers, to provide customized queries for researchers to access linked data sources. More recently, structured integration has gradually evolved to semantics based integration [19] and Web service based integration [20] . In these approaches, XML based Web services use Internet standards and protocols such as UDDI, SOAP, and WSDL, to offer interoperable and expandable integrations of biological data sources. Finally, targeting semantic heterogeneity in biological data sources, ontology-driven data integration [21, 22] has developed standardized biological vocabularies and naming convention [23] .. Although these existing technologies can partially solve the basic integration problems of the distribution, heterogeneity, and autonomy of biological data sources, they lack sufficient flexibility in adapting to the inherently dynamic and evolutionary environments. New research challenges emerge with the increase in scale and diversity of new biological data sources.. First of all, with the constant advance in bioinformatics techniques, for instance, microarray, new types of biology data with heterogeneous formats are continuously created and developed from different data sources. It is challenging to integrate new data schemas of these data types into existing integration systems at runtime. Furthermore, along with new types of biology data, new bioinformatics services for querying, translation, analysis, computation, and visualization, are continuously appearing for biologists to exploit their bioinformatics research processes. The collaboration of all these services in a uniform and automatic manner would greatly benefit bioinformatics research performance. However, in conventional integration approaches, these bioinformatics services are machine-manipulated but not machine-understandable. Once their invocation protocols have been hard-coded into the integration system, they cannot be simply modified if the logic sequence of operations required is updated. Adding new services or any changes of access interfaces would inevitably increase the burden of software development and maintenance.. Traditional integration techniques cannot solve the above dynamic challenges, because their static coding is sensitive to changes of biological data source functionality and schema. An integrated system has to be kept up-todate by manually modifying programs when new biological data sources must be integrated or the features of integrated biological data sources must be changed. The required software engineering tasks are time consuming, error prone, and expensive, and the inertia they introduce cannot accommodate to the growth of biological data sources on the Web [24] . Therefore, the next generation of integration technologies for bioinformatics should have the ability to rapidly respond to changing requirements and dynamic environments.. To address the above issues, in this paper we demonstrate how the Data Concierge adaptive middleware platform [25, 26] can be extended to integrate new biological data sources without the need for application-level programming. Our approach provides a comprehensive solution that can be used to dynamically connect to, access, and manipulate multiple biological data sources from a single client interface. The uniform access mechanism allows biologists to easily perform advanced and efficient research tasks in dynamic data environments.

O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Analysis of a spatial gene expression database for sea anemone Nematostella vectensis during early development Background: The spatial distribution of many genes has been visualized during the embryonic development in the starlet sea anemone Nematostella vectensis in the last decade. In situ hybridization images are available in the Kahi Kai gene expression database, and a method has been developed to quantify spatial gene expression patterns of N. vectensis. In this paper, gene expression quantification is performed on a wide range of gene expression patterns from this database and descriptions of observed expression domains are stored in a separate database for further analysis. Methods: Spatial gene expression from suitable in situ hybridization images has been quantified with the GenExp program. A correlation analysis has been performed on the resulting numerical gene expression profiles for each stage. Based on the correlated clusters of spatial gene expression and detailed descriptions of gene expression domains, various mechanisms for developmental gene expression are proposed. Our cluster analysis indicates that early gene expression domains in N. vectensis are spatially separated in a stable sequence along the primary body axis. An additional statistical analysis indicates that precise gene expression domains in N. vectensis are generally formed by two processes. Genes that are expressed in the blastula appear in broad expression regions. During gastrulation and planula development, the expression domains are refined within the boundaries of these broad regions.. It should be stressed that no additional experiments have been performed for our study. Spatial expression data in a public gene expression database have been quantified and analysed, and these analyses resulted in new hypotheses on cellular migration in N. vectensis. Spatial gene expression patterns have been collected in extensive databases for other model animals as well, and a similar computational approach can generate hypotheses about cellular migration before fate maps are available for these animals. A gene with expression profiles from multiple samples at one stage can belong to more than one cluster. This may be due to the noise in gene expression among individuals. Another possible explanation is that the expression patterns are different across individual in situs at the beginning and at the end of a developmental stage. This change could be caused by cells migrating from one region to another, or by dynamic regulatory interactions. In the current study, this issue is handled by performing cluster analyses to all expression profiles for all genes within a broad time window instead of analyzing blurred averages of each gene. A solution to this uncertainty would be an increased time resolution for the expression profiles, resulting in more precise regions for narrower time windows. In Drosophila embryos, the definition of narrow time classes allowed the observation of significant domain shifts [4] . In the sea urchin, precise timing resulted in a sequence of spatial regulatory states [16] .. Central domain expression is generally persistent in the endoderm, while expression in the central and external rings is often limited to the oral ectoderm during and after gastrulation. The first entry in Table 1 shows that the early gastrula expression pattern is known for 23 genes that are expressed exclusively in the central domain in the blastula stage. Out of these 23 genes, 21 are expressed in the Table 2 . Moreover, expression is observed exclusively in the same major domain in the next available stage for at least 69 % of all instances of expression limited to either of these two major domains, as indicated in the last column of Tables 4 and 5 . These observations are a strong indication that the central domain differentiates into the later endoderm and that the central and external rings become oral ectoderm. The vegetal hemisphere likely becomes aboral ectoderm, but this is based on a small number of expression patterns (Table 6 ). These differentiation regions are in agreement with the locations of a dye injected into an N. vectensis egg and recorded during embryonic development [11] . Injection of lineage tracers into individual cells of developing N. vectensis embryos results in contiguous clones of labeled cells without long distant migration of individual cells (Martindale, unpublished observations). Based on the statistical stability of the main gene expression regions, a regulation mechanism is proposed where genes in early regions activate genes in corresponding later regions, while genes in adjacent regions repress one another (Fig. 21) . If changes in expression patterns occur consistently in many genes, this might indicate a collective cellular motion in the embryo. During gastrulation, expression in the region between the oral and vegetal poles is observed for the quantified profiles of Anthox1, FGFRa, FoxD.1, Sox1, Sox3 and Rx. Due to intense Sox1 expression in the oral pole, the Sox1 profiles are outside the vegetal ring clusters. Anthox1, FGFRa and FoxD.1 are likewise classified as members of the vegetal pole cluster because their strongest expression occurs in the corresponding region. For these six genes, a For each period, the possible changes in gene expression (minor change:major change:vanished) are added in the second column and expressed as percentages in the third column. As an example, the period from the early gastrula to the mid gastrula (bold numbers) is included in the combinations of stages highlighted in Table 1 Table 5 This hybridization experiment is a weak indication that genes in the body wall ectoderm are first expressed in the external ring or vegetal ring of the blastula. This is basically the null hypothesis; in the absence of any known collective cell movements in the aboral ectoderm during gastrulation, expression in this region has likely remained stationary from the blastula. The body wall has been included in the proposed regulatory interactions among clustered gene expression regions (Fig. 21) . The vegetal domains of Anthox1, FGFRa and FoxD.1 become restricted to the aboral end in the planula stage, while Sox1 and Sox3 move towards the oral pole in the planula. Meanwhile, the Rx domain has significantly expanded in the late planula. These changes in gene expression domains may indicate that ectoderm elongation in the planula stage is most pronounced in the initially narrow Rx expression domain. Even though this hypothesis is based on few observations, it could be tested with fate mapping experiments. In the frog Xenopus laevis, the gene rax promotes cell proliferation in developing retinal tissue [17] ; Rx may similarly induce tissue growth in N. vectensis. Many hierarchical clusters include profiles with strong pairwise correlations to profiles in other clusters. This is caused by the partial overlap of the expression domains that characterize the clusters and by expression of some genes in both regions. The average cutoff value for the main clusters is 0.9; this value is quite high and indicates fuzzy boundaries between many expression clusters. These fuzzy boundaries between expression clusters are likely due to dynamic changes in gene expression. Out of the 25 genes expressed only in the central domain, 3 (12 %) will exhibit major expression changes, or terminate their expression (Table 4 , first row). Out of the 10 genes expressed in the central ring or external ring, 1 (10 %) is not expressed in the oral ectoderm (Table 5 , first row). This explains why some genes are expressed in multiple clusters at the same stage, and why some genes appear in clusters for different expression domains between developmental stages.. According to the hypothesis that the position of cells in the planula is largely determined by their position in the blastula, the change of gene expression patterns across major regions must be the result of regulatory action. The added percentages for possible major change and first appearance of gene expression in the early gastrula to early planula stages are 45, 48 and 55 % (Table 8 ). In comparison, these percentages are 37 and 25 % in the early planula to late planula stages. These observations suggest that more dynamic changes in expression occur during gastrulation than in the period that the planula transforms into a polyp. For the study of pattern formation in N. vectensis, recording gene expression during the relatively short period of gastrulation should therefore be more informative than monitoring the planula stages. In general, during gastrulation it is decided in which major region(s) the genes are expressed. Detailed expression patterns arise during the planula stages, generally within the bounds of the major regions determined for each gene at the end of gastrulation. The appearance of differential details explains the decrease in correlation among gene expression patterns after gastrulation (Figs. 10, 11, 12, 17, 18 and 19 ). This loss of correlation could also be caused by the lower number of quantified patterns in the planula stages; with additional gene expression quantifications in these stages of development it could be tested whether the expression domains diverge or whether they form a new set of clusters.. One aspect of gene expression that requires spatial localization, is the determination of the secondary axis in N. vectensis. Individual cells appear indistinguishable during the early cleavage cycles until the early blastula starts oscillating at the animal pole [18] . This oscillation stops at the mid blastula transition, when the synchrony of cell divisions is lost and the blastula remains spherical. The spherical blastula symmetry is permanently broken at the onset of gastrulation [19] . During gastrulation, Bmp ligands and antagonists are asymmetrically expressed along the secondary axis [13] . Based on these observations, determination of the secondary axis may coincide with the mid blastula transition. The asynchrony in cell divisions then produces a stochastic perturbation in a morphogen concentration which would define the secondary axis.. The gene expression database contains various genes that are differentially expressed along the secondary axis, although most are not identified in the expression summaries. From the incomplete list of genes included in the expression pattern overview in this study (Additional file 2), various genes involved in secondary axis formation can already be identified. The genes Anthox7, Anthox8b, Bmp2/4, chordin, Gbx, Hex, Msx, Msx2, NvHD060, Smad1/5 and Vent1 exhibit noncylindrical expression patterns. Quantification of these patterns requires a two-dimensional or three-dimensional template and eventually a three-dimensional detection method. Especially Vent1 asymmetric expression in the early gastrula stands out. In various animals, vent genes are involved in a Bmp signaling feedback loop [20] . Curiously, the genes Bmp2/4, chordin and Smad1/5 in this signaling pathway are still symmetrically expressed in the N. vectensis early gastrula (or at least their asymmetry is less pronounced).. The N. vectensis in situ hybridization collection contains expression data for less than half of all developmental stages for most genes stored. Despite the sparsity of this dataset, a large-scale analysis results in meaningful insights. Sparse data is common for databases that contain labor intensive measurements, and standardization allows global analysis approaches to be applied to incomplete biological databases. Developmental gene expression databases contain large sets of genes with many regulatory interactions. Our clustering-by-region approach is convenient to select genes from a large set for computational regulatory network modeling. Spatial gene expression assays can be used as a tool for verifying predicted regulatory interactions between genes and for predicting properties of missing components in a gene regulation network [1, 2] . The largest potential of spatial gene product distribution datasets, is in verifying numerical models of regulatory interaction networks, which has been demonstrated for the embryonic development of fruit fly Drosophila melanogaster [3] . Also the formation of digits in early mouse limbs has been replicated with mechanistic models with the help of gene expression patterns [4] .. To perform accurate simulations of such processes, the spatial gene expression patterns need to be digitally quantified and formatted to standardized profiles. A procedure for gene expression quantification has been described [5] and applied [6] for organisms with changing morphologies during embryonic development. Similarities among gene expression profiles can provide information about co-expression relationships [7] . Similarity metrics are a common tool for classifying time series expression data to identify correlating dynamics among genes. These similarity measures can identify correlating spatial expression among genes from quantified expression patterns as well. To use quantified gene expression patterns in dynamic simulations, reliable time points for gene expression patterns are required.. For example, in Drosophila, the spatial gene expression of even skipped (eve) has been measured precisely for many time points. The eve pattern is employed as a time reference: eve is assayed together with the queried gene to establish the development time for the sample [8] . For many other animal models, a time reference gene is not (yet) available and other embryo properties are applied to estimate the time of development. In these cases the subsequent stages of development can be qualitatively identified from the changing embryo morphology. These changes in morphology are caused by division and migration of cells, processes that are absent during the early cleavage cycles of flies.. In the comparative gene expression database Kahi Kai, in situ RNA hybridization assays are collected for many marine invertebrates [9] and are classified according to the embryo morphologies. This database thereby allows for an analysis of spatial expression features for all gene entries.. In this study, many genes from the Kahi Kai database are compared at various stages of development, based on their expression in different embryonic regions. First, the majority of in situ hybridization images are quantified and Table entries indicate the hours after fertilization derived from [11, 18, 21] . The annotations in the schematic morphologies are guidelines for researchers to describe expression domains in their hybridization images. AnHe = animal hemisphere, VeHe = vegetal hemisphere, An = animal pole, Cd = central domain, Cr = central ring, Er = external ring, Ve = vegetal pole, pEn = presumptive endoderm, bEc = blastoporal ectoderm, Ec = ectoderm, OrHe = oral hemisphere, AbHe = aboral hemisphere, OrEc = oral ectoderm, En = endoderm, AbEc = aboral ectoderm, PhEc = pharyngeal ectoderm, PhEn = pharyngeal endoderm, AtEn = apical tuft endoderm, AtEc = apical tuft ectoderm, At = apical tuft, M = mouth, BwEc = body wall ectoderm, BwEn = body wall endoderm, MeEc = mesentery ectoderm, MeEn = mesentery endoderm, TeB = tentacle bud, TeEc = tentacle ectoderm, TeEn = tentacle endoderm, Si = siphonoglyph, TeTi = tentacle tip, TeBa = tentacle base (The original nomenclature in [9] has been adapted to the more detailed denotations for the blastula stage in [15] .) the quantified gene expression patterns are collected in a list of digital expression profiles. Stage-specific correlation analyses are performed on these spatial gene expression profiles to discover the embryo's major division in expression domains.. Second, a subset of genes from the database is listed with a detailed description of the spatial expression in the stages for which data is available. This list provides an overview of the developmental stages with spatial hybridization images for each gene and allows a detailed description of expression properties beyond the general classifications. Progression of spatial expression is compared for subsequent available stages, and the main periods of gene expression dynamics are identified.. A large set of gene expression patterns in the starlet sea anemone Nematostella vectensis is analyzed. The determination of the secondary axis in N. vectensis is one aspect of gene expression that requires spatial localization. The database contains various genes that are expressed along this axis.. The change in N. vectensis morphology during development is schematically displayed in Fig. 1 . The nucleus in the egg is located at the future oral pole, which means that the primary (oral-aboral) axis is already determined before fertilization [10, 11] . The determination of the secondary axis, which is defined by the location of the syphonoglyph, is unclear. The first structures that appear along this axis are the primary mesenteries, but differential gene expression is already observed during gastrulation [12, 13] . Based on the early symmetry break in various gene Fig. 3 Gene expression quantification pipeline. A digital morphology is overlaid with the gene expression image (a) and the points are manually dragged over the embryo's cell layer (b). After the cell layer is decomposed into segments (c) and the intensity is plotted as a function of cell layer position (d), the profile is manually edited to correct for artifacts (e) Fig. 2 Workflow overview. The information stored in the Kahi Kai gene expression database has been processed into convenient formats for two partly overlapping sets of genes. These processing methods and the methods used for additional analyses are described in the text. While this workflow may seem to converge to a single final result, all intermediate results can be explored for multiple purposes Fig. 4) . Expression in the presumptive endoderm at the early gastrula is incorrect expression patterns and on early N. vectensis morphogenesis, a mechanism is proposed for secondary axis determination. Spatial gene expression patterns in early stages of development are necessary to study the determination and formation of the secondary axis.. We analyzed spatial gene expression patterns in various stages of development in N. vectensis. Changes in these patterns are due to gene expression dynamics within stationary cells or due to migrating cells that retain their gene expression state. Fate mapping experiments can conclusively determine migratory behavior, but these data are not available for N. vectensis. Our solution to deal with this lack of fate map data is the assumption that the expression state in migrating cells likely remains unchanged for many genes. In this fashion, we estimate major cell movements based on the spatial gene expression data, which are available.. In conclusion we demonstrate the application of correlation analysis to quantified spatial gene expression patterns in order to identify co-expressed spatial domains.. A possible application of these correlation matrices is the selection of gene clusters for regulatory experiments, functional studies [14] and computational gene regulation network models [6] , because co-expressed genes often share regulators or biological functions. The order of application for the described methods is displayed in the diagram in Fig. 2 . Note that the intermediate results provide new information on their own and can be subjected to additional analyses.

O O O O B I I O O O B I O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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A branch-and-price-and-cut approach for sustainable crop rotation planning In this paper, we study a multi-periodic production planning problem in agriculture. This problem belongs to the class of crop rotation planning problems, which have received increased attention in the literature in recent years. Crop cultivation and fallow periods must be scheduled on land plots over a given time horizon so as to minimize the total surface area of land used, while satisfying crop demands every period. This problem is proven strongly N P -hard. We propose a 0-1 linear programming compact formulation based on crop-sequence graphs. An extended formulation is then provided with a polynomial-time pricing problem, and a Branch-and-Priceand-Cut (BPC) algorithm is presented with adapted branching rules and cutting planes. The numerical experiments on instances varying the number of crops, periods and plots show the effectiveness of the BPC for the extended formulation compared to solving the compact formulation, even though these two formulations have the same linear relaxation bound. Although definitions of sustainable agriculture may vary, agricultural systems are generally considered as sustainable if they sustain themselves along a long period of time, that is, if they are economically viable, environmentally safe, and socially fair. In particular, sustainable agricultural practices are usually requested to incorporate alternatives to toxic fertilizers and pesticides, avoid excessive tillage and preserve soils. Many research papers about sustainable agriculture focus on the pollution and social side-effects of intensive agriculture, such as water spoiled by pesticides, crop diseases, and concentration of production in fewer and bigger farms that can afford large investments in costly automative production systems and technologies (e.g., [1; 2] ). Consistent with this diagnosis, recommendations on the need for new sustainable agricultural systems can be found in [3] . Crop rotations, combined with fallow periods where the land rests in order to recover its soil attributes after production, enable crop diversification on both space and time dimensions. Typical crop rotation problems usually focus on building rotations that maximize a profit or yield function, where the total surface area of land is either unbounded or fixed [4; 5; 6; 7] . This paper deals with an aspect of sustainability which is rarely considered in optimization of agricultural production systems: the minimization of the surface area needed to cover crop demands that vary over time. A compact formulation for a mixed-integer variant of the problem was originally introduced in [8] , following a communication in the EURO XXI Conference in 2006. Since then, a number of papers have addressed crop rotation planning in a sustainable development context. For example, a column generation approach was applied in [7] , where the objective is to maximize space occupation and the master problem includes adjacency constraints between plots. Column generation was also used in [9] for a crop rotation problem with land divided into plots, but with continuous variables representing the surface area assigned to a given rotation, hence requiring no branching. Another example can be found in [10] where harvested crops can be stocked for a limited period of time, and demands are subject to uncertainty. [11] presents multi-objective crop rotation models that take risk into account, converted into linear programs and solved with standard Linear Programming (LP) methods. A survey on crop rotation decisions exists [12] but does not include most recent papers in optimization. Dantzig-Wolfe decomposition [13] was applied to our problem in [14] , but with no inclusion in a branch-and-price approach to obtain opti-mal integer solutions. To our knowledge no branch-and-price algorithm has ever been designed so far for any crop rotation planning problem.. The original model presented in [8] was motivated by a Madagascan case study where the minimization of cultivated space contributed to the sustainable development of the primary forest in the long term. Indeed, farmers in Madagascar are used to clearing more and more primary forest areasalthough this is prevented by law -in order to extend their cultivation surface area to better cover their needs. A plot could be cultivated with several crops in the same period in this study. We direct the reader to [8] for more details on the agricultural Madagascan context. In this paper, we present a fully-combinatorial problem where a single crop can be cultivated on each plot at each period.. The paper is organized as follows. Section 2 introduces notation and cropsequence graphs. Section 3 describes a compact formulation of the problem. Section 4 proves N P -hardness. Section 5 provides a Covering Integer Programming extended formulation derived from a Dantzig-Wolfe decomposition approach. Section 6 presents the Branch-and-Price-and-Cut with branching rules and cutting planes. Section 7 presents computational experiments for various time horizons, number of crops and plot sizes. Section 8 concludes the paper. Crop rotation planning problems have received increasing attention from OR researchers in the past five years, especially for sustainable agriculture.

O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Scale Selection Properties of Generalized Scale-Space Interest Point Detectors Abstract Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition.This paper presents a theoretical analysis of the scale selection properties of a generalized framework for detecting interest points from scale-space features presented in Lindeberg (Int. J. Comput. Vis. 2010, under revision) and comprising:-an enriched set of differential interest operators at a fixed scale including the Laplacian operator, the determinant of the Hessian, the new Hessian feature strength measures I and II and the rescaled level curve curvature operator, as well as -an enriched set of scale selection mechanisms including scale selection based on local extrema over scale, complementary post-smoothing after the computation of nonlinear differential invariants and scale selection based on weighted averaging of scale values along feature trajectories over scale.It is shown how the selected scales of different linear and non-linear interest point detectors can be analyzed for Gaussian blob models. Specifically it is shown that for a rotationally symmetric Gaussian blob model, the scale estimates obtained by weighted scale selection will be similar to the scale estimates obtained from local extrema over scale ofThe support from the Swedish Research Council, Vetenskapsrådet (contracts 2004Vetenskapsrådet (contracts -4680, 2010 and from the Royal Swedish Academy of Sciences as well as the Knut and Alice Wallenberg Foundation is gratefully acknowledged.Institute of Technology, 100 44 Stockholm, Sweden e-mail: tony@csc.kth.se scale normalized derivatives for each one of the pure secondorder operators. In this respect, no scale compensation is needed between the two types of scale selection approaches. When using post-smoothing, the scale estimates may, however, be different between different types of interest point operators, and it is shown how relative calibration factors can be derived to enable comparable scale estimates for each purely second-order operator and for different amounts of self-similar post-smoothing. A theoretical analysis of the sensitivity to affine image deformations is presented, and it is shown that the scale estimates obtained from the determinant of the Hessian operator are affine covariant for an anisotropic Gaussian blob model. Among the other purely second-order operators, the Hessian feature strength measure I has the lowest sensitivity to nonuniform scaling transformations, followed by the Laplacian operator and the Hessian feature strength measure II. The predictions from this theoretical analysis agree with experimental results of the repeatability properties of the different interest point detectors under affine and perspective transformations of real image data. A number of less complete results are derived for the level curve curvature operator. The notion of scale selection is essential to adapt the scale of processing to local image structures. A computer vision system equipped with an automatic scale selection mechanism will have the ability to compute scale-invariant image features and thereby handle the a priori unknown scale variations that may occur in image data because of objects and substructures of different physical size in the world as well as objects at different distances to the camera. Computing local image descriptors at integration scales proportional to the detection scales of scale-invariant image features, moreover makes it possible to compute scale-invariant image descriptors (Lindeberg [35] ; Bretzner and Lindeberg [4] ; Mikolajczyk and Schmid [49] ; Lowe [48] ; Bay et al. [2] ; Lindeberg [38, 43] ).. A general framework for performing scale selection can be obtained by detecting local extrema over scale of γ -normalized derivative expressions (Lindeberg [35] ). This approach has been applied to a large variety of feature detection tasks (Lindeberg [34] ; Bretzner and Lindeberg [4] ; Sato et al. [54] ; Frangi et al. [11] ; Krissian et al. [22] ; Chomat et al. [5] ; Hall et al. [15] ; Mikolajczyk and Schmid [49] ; Lazebnik et al. [24] ; Negre et al. [52] ; Tuytelaars and Mikolajczyk [58] ). Specifically, highly successful applications can be found in image-based recognition (Lowe [48] ; Bay et al. [2] ). Alternative approaches for scale selection have also been proposed in terms of the detection of peaks over scale in weighted entropy measures (Kadir and Brady [18] ) or Lyapunov functionals (Sporring et al. [56] ), minimization of normalized error measures over scale (Lindeberg [36] ), determining minimum reliable scales for feature detection according a noise suppression model (Elder and Zucker [9] ), determining optimal stopping times in non-linear diffusionbased image restoration methods using similarity measurements relative to the original data (Mrázek and Navara [51] ), by applying statistical classifiers for texture analysis at different scales (Kang et al. [19] ) or by performing image segmentation from the scales at which a supervised classifier delivers class labels with the highest posterior (Loog et al. [47] ; Li et al. [25] ).. Recently, a generalization of the differential approach for scale selection based on local extrema over scale of γ -normalized derivatives has been proposed by linking image features over scale into feature trajectories over scale in a generalized scale-space primal sketch [39] . Specifically, two novel scale selection mechanisms have been proposed in terms of:. -post-smoothing of differential feature responses by performing a second-stage scale-space smoothing step after the computation of non-linear differential invariants, so as to simplify the task of linking feature responses over scale into feature trajectories, and -weighted scale selection where the scale estimates are computed by weighted averaging of scale-normalized feature responses along each feature trajectory over scale, in contrast to previous detection of local extrema or global extrema over scale.. The subject of this article is to perform an in-depth theoretical analysis of properties of these scale selection methods when applied to the task of computing scale-invariant interest points:. (i) When using a set of different types of interest point detectors that are based on different linear or non-linear combinations of scale-space derivatives, a basic question arises of how to relate thresholds on the magnitude values between different types of interest point detectors. By studying the responses of the different interest point detectors to unit contrast Gaussian blobs, we will derive a way of expressing mutually corresponding thresholds between different types of interest points detectors. Algorithmically, the resulting threshold relations lead to intuitively very reasonable results. (ii) The new scale selection method based on weighted averaging along feature trajectories over scale raises questions of how the properties of this scale selection method can be related to the previous scale selection method based on local extrema over scale of scalenormalized derivatives. We will show that for Gaussian blobs, the scale estimates obtained by weighted averaging over scale will be similar to the scale estimates obtained from local extrema over scale. If we assume that scale calibration can be performed based on the behaviour for Gaussian blobs, this result therefore shows that no relative scale compensation is needed between the two types of scale selection approaches. In previous work on scale selection based on γ -normalized derivatives [34, 35] a similar assumption of scale calibration based on Gaussian model signals has been demonstrated to lead to highly useful results for calibrating the value of the γ -parameter with respect to the problems of blob detection, corner detection, edge detection and ridge detection, with a large number of successful computer vision applications building on the resulting feature detectors. (iii) For the scale linking algorithm presented in [39] , which is based on local gradient ascent or gradient decent starting from local extrema in the differential responses at adjacent levels of scale, it turns out that a second post-smoothing stage after the computation of non-linear differential invariants is highly useful for increasing the performance of the scale linking algorithm, by suppressing spurious responses of low relative amplitude in the non-linear differential responses that are used for computing interest points. This selfsimilar amount of post-smoothing is determined as a constant times the local scale for computing the differential expressions, and may affect the scale estimates obtained from local extrema over scale or weighted averaging over scale. We will analyze how large this effect will be for different amounts of post-smoothing and also show how relative scale normalization factors can be determined for the different differential expressions to obtain scale estimates that are unbiased with respect to the effect of the post-smoothing operation, if we again assume that scale calibration can be performed based on the scale selection properties for Gaussian blobs. Notably, different scale compensation factors for the influence of post-smoothing will be obtained for the different differential expressions that are used for defining interest points. Without postsmoothing, the scale estimates obtained from the different differential expressions are, however, all similar for Gaussian blobs, which indicates the possibilities of using different types of differential expressions for performing combined interest point detection and scale selection, so that they can be interchangeably replaced in a modular fashion. (iv) When detecting interest points from images that are taken of an object from different viewing directions, the local image pattern will be deformed by the perspective projection. If the interest point corresponds to a point in the world that is located at a smooth surface of an object, this deformation can to first order of approximation be modelled by a local affine transformation (Gårding and Lindeberg [12] ). While the notion of affine shape adaptation has been demonstrated to be a highly useful tool for computing affine invariant interest points (Lindeberg and Gårding [46] ; Baumberg [1] ; Mikolajczyk and Schmid [49] ; Tuytelaars and van Gool [57] ), the success of such an affine shape adaptation process depends on the robustness of the underlying interest points that are used for initiating the iterative affine shape adaptation process. To investigate the properties of the different interest point detectors under affine transformations, we will perform a detailed analysis of the scale selection properties for affine Gaussian blobs, for which closed form theoretical analysis is possible. The analysis shows that the determinant of the Hessian operator and the new Hessian feature strength measure I do both have significantly better behaviour under affine transformations than the Laplacian operator or the new Hessian feature strength measure II. In comparison with experimental results [39] , the interest point detectors that have the best theoretical properties under affine transformations of Gaussian blob do also have significantly better repeatability properties under affine and perspective transformations than the other two. These results therefore show how experimental properties of interest points can be predicted by theoretical analysis, which contributes to an increased understanding of the relative properties of different types of interest point detectors.. In very recent work [42] , these generalized scale-space interest points have been integrated with local scale-invariant image descriptors and been demonstrated to lead to highly competitive results for image-based matching and recognition.

B O O O O B B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O B O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O B O O O O B O O O B O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O B O O O O O O O O O O O O B I O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O B O O O O O O O O B O O O O O O O B O O O O O O O O O O O B O O O O O O B O O O O O B O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O


An approach to designing and evaluating blended courses Abstract Recently, there has been an increased interest in producing electronic courses. However, literature shows that adopting e-learning does not guarantee improved learning. This is because mixing technology and content does not necessarily yield effective learning. This paper presents a systematic design process for developing blended courses for undergraduate higher education. The instructional design process is based on instructional design theories and utilized three taxonomies: Bloom Taxonomy, Redeker Taxonomy and Guerra scale. A mapping model is proposed and embedded in the design process to develop a blended course starting from the objectives and content of a traditional course. This paper also presents a evaluation process that estimates the effectiveness of the selected technologies in the design of the blended course. This effectiveness is evaluated in terms of three dimensions: course content formats, interaction and collaboration. A case study is presented to demonstrate the proposed design approach on a System Analysis and Design blended course under development. The lack of a clear systematic process to choose e-learning technology in designing blended courses led the authors to devise a design approach. An important aspect of this approach is that it blends with any e-learning development process. In this work, ID learning theories and taxonomies have been utilized to represent the three components of the technology-based learning design. A B-R-G mapping model has been proposed to assist developers to transform the contents and objectives of a traditional course into a number of suggested LOs for the blended course. This approach takes into consideration the learning objectives of the course while integrating technology with course content; thus assisting in improving the learning process.. In addition, an evaluation process has been proposed to measure the effectiveness of the suggested technologies of the designed blended course in terms of: course content format, interaction and collaboration in the course.. The approach has been applied to the System Analysis and Design course as a case study. Future work will perform an empirical study in order to evaluate the effectiveness and usability of different technologies in designing LOs. More specifically, an experiment will be designed to evaluate different modules of the System Analysis and Design course implemented using different e-learning technologies. The results of the experiment will assist in evaluating the weights of the evaluation process with user evaluation results. According to (Ally 2008) , "the development of effective online learning material should be based on proven learning theories". Bower proposed the following three components of "technology-based" learning design: content design, activity design and technology design (Bower 2008) . This research adopts these components in order to establish a systematic design process for developing a blended course. Thus, three taxonomies have been adopted in the proposed approach: (1) Bloom Taxonomy is utilized for representing learning objectives of the course content; (2) Redeker Taxonomy is employed to classify the activity component; (3) Guerra Scale is used to classify available learning tools and technologies. The next sections provide brief descriptions of Bloom Taxonomy, Redeker Taxonomy and Guerra Scale.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


R Package distrMod: S4 Classes and Methods for Probability Models This vignette is published as Kohl and Ruckdeschel (2010c) . Package distrMod provides an object oriented (more specifically S4-style) implementation of probability models. Moreover, it contains functions and methods to compute minimum criterion estimatorsin particular, maximum likelihood and minimum distance estimators.

O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I I


Reliable shop floor bottleneck detection for flow lines through process and inventory observations: the bottleneck walk Abstract Bottleneck detection in manufacturing is the key to improving production efficiency and stability in order to improve capacity. Yet, common bottleneck detection methods in industry and academia lack either accuracy or practicability, or both, for dynamic systems. The new methodology is conducted by the observation of processes and inventories. Blocked processes and full inventories indicate a downstream bottleneck. Starved processes and empty inventories indicate an upstream bottleneck. Through subsequent observations of multiple process states and inventory levels within a system, it is possible to determine the direction of the bottleneck at the given time and hence to find the momentary bottleneck in the system. The shifting of bottlenecks can be observed directly. Work sampling techniques can be used to obtain a long-term picture of the dynamically shifting bottleneck. The new methodology does not require any calculations, statistics, or time measurements. Hence, the method is suited for practical use by shop floor supervisors and clerks. The direct observation of the bottleneck also gives additional information about the underlying causes of the bottlenecks, simplifying the improvement of the system capacity. Extensive field testing of the method received positive feedback not only from management but also from shop floor operators. The method is already in use at the Robert Bosch GmbH, where it is known as the bottleneck walk. The summary above focused on methods used in industry. Overall, applicable methods lack the ability to detect the shifting bottleneck for dynamic and instable shop floor environments and are hence unsuitable for industry. Of course there are numerous other methods described in academic literature, although in our experience they are infrequently used in industry (see, e.g., [20, 21] , for a recent overview of methods). Other methods also look not only at throughput, but also at other objectives, including throughput time, reliability, WIP, and others [22] . 4 The bottleneck detection methodology Bottleneck detection is a critical part of the continuous improvement process. Unfortunately, commonly used bottleneck detection methods are woefully inadequate for practical use, lacking either validity or usability or both. In two-thirds of all bottleneck detections done by the authors, the bottleneck was in a process different from what the managers of the line believed. The presented bottleneck walk provides a framework for a simple yet accurate bottleneck detection method. For accurate bottleneck detection, it is necessary to determine the momentary bottleneck before making statistical conclusions. There are few methods that can detect the momentary bottleneck reliably, yet this is a key requisite for bottleneck detection in dynamic systems. Bottleneck detection methods that use averages overlook shifting bottlenecks. For a comparison of methods and their accuracy, see the forthcoming paper [25] , where the bottleneck walk outperformed all conventional methods and was second only to the mathematically more demanding average active period method. If the duration of the averages is reduced and the observation is repeated frequently, then the effect of inventory buffers will likely diminish the accuracy of the observations. The key advantage of the bottleneck walk, besides its accuracy, is its simplicity. No stopwatches or formulas are necessary for this approach. The bottleneck walk is effective and can be quickly applied. Furthermore, the bottleneck walk can also be easily taught to shop floor operators even without knowledge of mathematics. For this reason, it enables quick improvement cycles as demanded by the concept of lean production [26] and discussed in [24] . The method was thoroughly field tested in different manufacturing plants, providing a reliable and practical way to find the bottleneck.. The method has been developed based on research done at the Toyota Central Research and Development Laboratories, Japan, and the Robert Bosch GmbH, Germany.

O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


IPO: a tool for automated optimization of XCMS parameters


Fuzzy Based Packet Dropping Scheme in Wireless Cellular Networks Abstract: Wireless multimedia networks are becoming very popular owing to the user demands for multimedia services. Packet dropping in the event of buffer congestion is one of the important issue in wireless multimedia networks. A packet dropping scheme has to be flexible and adaptive such that acceptable quality of an application is maintained. The paper presents a fuzzy based packet dropping scheme for wireless multimedia networks. A buffer manager placed at the base station performs packet dropping depending upon the traffic conditions and type of an application. Packet dropping is performed by computing dropping factor by considering packet priority, queue length and adaptive queue length threshold. The adaptive queue length threshold is used to dynamically adjust the dropping factor. The queue length threshold is varied by using two fuzzy input parameters, channel condition and rate of flow of an application. The scheme has been extensively simulated to test the performance in terms of acceptance and dropping probability of real-time handoff and new calls. The use of wireless connectivity has been steadily growing over the recent years. New generation of mobile networks are opting for multimedia applications, such as video conferencing, e-commerce, e-learning, e-gaming, etc. These applications need good end-to-end quality of service (QoS) control even under the conditions of network overload. The QoS parameters are bandwidth, buffers, delays, jitters, packet loss, etc. The bandwidth and buffer management have become critical issues because of their scarcity. Whenever sufficient bandwidth is not available, buffers are employed to avoid packet losses. Due to excess traffic, buffers may become full and cause buffer congestion. The buffer congestion leads to dropping of packets randomly irrespective of application importance (real time or non real time). There is a need for employing flexible and 306 J.D. Mallapur, S.S. Manvi, D.H. Rao adaptable scheme for packet dropping, which may not deteriorate QoS of a real time application in wireless multimedia networks.. As per the literature survey, it is observed that most of the buffer management schemes in wireless networks, employ queue length as a threshold for dropping. Parameters such as round trip time and application priority are also used to make packet dropping decisions. Apart from these parameters, channel conditions and flow rates may also affect the dropping decisions. The rigid packet dropping schemes probably may not give flexibility to the buffer manager, hence soft packet dropping schemes by using fuzzy logic may be employed for wireless multimedia networks. Fuzzy system's characteristics are based on the concept of fuzzy partitioning of the information. The decision-making ability of the fuzzy model depends on the existence of rule base and fuzzy reasoning mechanism. The fuzzy logic based solutions are better for uncertain inputs which minimize the random outputs.. Some of the works done in the area of packet dropping in wireless networks are as follows. An algorithm called RED (random early detection), which drops the packets of all flows with the same probability depending on queue length is given in [1] . Flows with reduced transmission rates and shorter packet length are given unfair share in RED. The work presented in [2] proposes a packet dropping scheme that drops the packet based on the sharing index computed using differentiated services. In [3] , an adaptive fuzzy based control algorithm which computes the packet dropping probability according to pre-configured fuzzy logic using only queue length as input variable is presented.. A fair packet dropping algorithm is presented in [4] depending on channel condition and fairness so as to achieve trade off between throughput and fair service. An adaptive link layer retransmission and active drop mechanism based on fuzzy logic considering queue length as fuzzy parameter is discussed in [5] . A scheme for handoff calls is presented in [6] by using user population, used bandwidth and received power level as fuzzy parameters for handoff decision at the base station. The scheme given in [7] is based on the fuzzy timer which changes the transmission rate to avoid congestion. A fuzzy green controller is presented in [8] which is expected to act as congestion controller in the routers, for both conditions such as with background load and without back ground load. A congestion index has been proposed for intelligent packet dropping scheme by using fuzzy logic in [9] .. In [10] , average queue length is replaced by the product of delay and retransmission times of RTS (request to send) as congestion indicator to calculate dropping probability. The work presented in [11] is a new scheduling algorithm, named as Delay-sensitive Dynamic Fair Queuing (DSDFQ) algorithm which is designed to meet delay requirements of multimedia applications as well as maintain high network efficiency to adapt to load fluctuations of different traffic classes and varying wireless channel conditions caused by user mobility, fading and shadowing. In [12] , estimation of channel capacity in rayleigh fading environment and the gaussian noise environment is presented. Development of a systematic methodology of fuzzy logic modeling with three distinct features such as reasoning formation, fuzzy clustering and membership assignment for input and output is presented in [13] . A simple channel predictor is presented in [14] that responds to impairments in the channel at the packet transmission time scale and makes a decision on packet transmission.. The work given in [15] presents multihop relaying technology utilization for mitigating unfairness in QoS, which comes about due to the location-dependent signal quality and multihop system. The work presented in [16] depicts that cooperative relaying can improve both capacity and fairness in cellular network. The work proposed in [17] is an adaptive multirate auto rate fallback (AMARF) algorithm, in which each data rate is assigned a unique success threshold. This criterion is used to judge when to switch a rate to the next higher one, and the success thresholds can be adjusted dynamically in an adaptive manner according to the running condiFuzzy Based Packet Dropping Scheme in Wireless Cellular Networks 307 tions such as packet length and channel condition parameters. In wireless networks, the available bandwidth undergoes fast time-scale variations due to channel fading and error from physical obstacles [18] . In [20] , an adaptive QoS handoff priority scheme which reduces the probability of handoff call failures in a mobile multimedia network with a micro/picocellular architecture is presented. The scheme given in [21] supports voice and data calls with some grade of service guarantee in mobile multiservice networks.. The variations in the channel condition and data rate have motivated us to use fuzzy controller for developing a packet dropping scheme. The proposed scheme provides soft packet dropping, which minimizes the bursty losses and also provides better QoS for wireless multimedia network applications. Rest of the paper is organized as follows. Section 2 presents the proposed fuzzy based packet dropping scheme. Sections 3 and 4 present simulation model and results, respectively. Finally, section 5 concludes the work with some remarks. The main objective of the proposed work is to use the base station buffers efficiently and decrease the packet dropping especially for handoff and real-time calls during good channel conditions and high flow rate causing the buffer overflow. Extensive simulation results reveal that proposed scheme features very low call dropping, better handoff and new calls acceptance with different channel conditions. This is achieved because of tuning the queue length threshold by correction factor. The correction factor is derived by two fuzzy parameters such as channel condition and flow rate. The scheme can be extended for different fuzzy parameters such as device type, user priority, sustainable jitters, etc.

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