User−Producer Interaction in Housing Energy Innovations

Von Hippel and colleagues have highlighted the crucial role of users in innovation in different industries and types of products. They describe the innovation process in terms of the distinct domains of knowledge that producers and users possess. Producers have knowledge about technical solutions and users about their needs, the context of use, and their own capabilities as users. Both sets of knowledge are characterized by "stickiness": They move relatively freely within their own domain but are difficult to transfer outside of it.
In the case of radical innovations for sustainable consumption, the problem of "sticky information" is compounded. Both producers and consumers need to reach out of their conventional competencies and search for new solutions. "Societal actors," such as government bodies or environmental experts, can show the way to such solutions, but this new knowledge needs to be integrated with the "sticky" knowledge about everyday practices in production and consumption.
In the present article we attempt to conceptualize the role and interaction of user and producer knowledge with the knowledge of environmental experts in housing energy innovations. We do so by applying the user−producer interaction framework to a case study on the introduction of low-energy housing concepts in Finland. On the basis of this analysis, we draw conclusions on the potential and limitations of today's practices in the field. For example, we suggest that user involvement can help to enhance the acceptance of low-energy solutions but that the methods for involving users need to be adapted to the particular circumstances in each industry.     

From Umwelt to Soundtope: An Epistemological Essay on Cognitive Ecology

Capturing information means for every organism acquiring knowledge about the living and not living objects that exist in its surroundings. In this way, the “historical” concept of Umwelt, as a subjective surrounding has been recently integrated in the theory of landscape ecology where a landscape is not only a geographical entity but also a cognitive medium. The landscape may be considered a semiotic context used by the organisms to locate resources heterogeneously distributed in space and time. In particular, inside a landscape there are different eco-fields defined as spatial arrangements of objects carrier of meaning that organisms utilize to track resources. Along this epistemic path the sonic component of the landscape is an important carrier of information commonly used by the majority of animal species to managing many vital functions. In particular birds, which are animals with a complex system of acoustic communication, seem to organize acoustic centers for public information. These sonic patterns (soundtopes) are characterized by a great variability in space and time and function like a special eco-field that allows species to share information about the status of resources and the dynamics of populations. The availability of such public information avoids a deeper and more expensive exploration of the environment to assess its quality.     

An update on data standards for gel electrophoresis

The use of gel electrophoresis to separate and, in some instances, to quantify the abundance of large numbers of proteins from complex mixtures, has been well established for several decades. The quantity of publicly available data is still relatively modest due to a lack of community accepted data standards, tools to facilitate the data sharing process and controlled vocabularies to ensure that consistent terminology is used to describe the experimental methodology. It is becoming widely recognised that there are significant benefits in data sharing for proteomics, allowing results to be verified and new findings to be generated by re-analysis of published studies. We report on standards development by the Gel Analysis Workgroup of the Proteomics Standards Initiative. The workgroup develops reporting requirements, data formats and controlled vocabularies for experimental gel electrophoresis, and informatics performed on gel images. We present a tutorial on how such resources can be used and how the community should get involved with the on-going projects. Finally, we present a roadmap for future developments in this area.     

Knowledge networks in the age of the Semantic Web

The Web has become the major medium for various communities to share their knowledge. To this end, it provides an optimal environment for knowledge networks. The web offers global connectivity that is virtually instantaneous, and whose resources and documents can easily be indexed for easy searching. In the coupled realms of biomedical research and healthcare, this has become especially important where today many thousands of communities already exist that connect across academia, hospitals and industry. These communities also rely on several forms of knowledge assets, including publications, experimental data, domain-specific vocabularies and policies. Web-based communities will be one of the earlier beneficiaries of the emerging Semantic Web. With the new standards and technologies of the Semantic Web, effective utilization of knowledge networks will expand profoundly, fostering new levels of innovation and knowledge.     

Anatomical Entity Recognition with a Hierarchical Framework Augmented by External Resources

References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs) work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF) model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments) with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available.     

Techniques for integrating ‐omics data

The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present ‐omics community, because ‐omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.     

Linguistic measure of taxonomic and functional relatedness of nucleotide sequences

The frequencies of "words", oligonucleotides within nucleotide sequences, reflect the genetic information contained in the sequence "texts". Nucleotide sequences are characteristically represented by their contrast word vocabularies. Comparison of the sequences by correlating their contrast vocabularies is shown to reflect well the relatedness (unrelatedness) between the sequences. A single value, the linguistic similarity between the sequences, is suggested as a measure of sequence relatedness. Sequences as short as 1000 bases can be characterized and quantitatively related to other sequences by this technique. The linguistic sequence similarity value is used for analysis of taxonomically and functionally diverse nucleotide sequences. The similarity value is shown to be very sensitive to the relatedness of the source species, thus providing a convenient tool for taxonomic classification of species by their sequence vocabularies. Functionally diverse sequences appear distinct by their linguistic similarity values. This can be a basis for a quick screening technique for functional characterization of the sequences and for mapping functionally distinct regions in long sequences.     

Disclosure of Product System Models in Life Cycle Assessment: Achieving Transparency and Privacy

Many of the challenges facing knowledge synthesis from life cycle assessment (LCA) studies stem from the inability of study authors and readers to formally agree on the structure and content of the product system models used to perform LCA computations. This article presents a framework for formally disclosing the foreground of an LCA study in a way that permits the computations to be inspected, verified, and reproduced by a reader, provided that the reader has access to the same life cycle inventory and impact characterization resources as the author. The framework can also be used to partition a study into public and private portions, allowing both portions to be critically reviewed but omitting the private information from the disclosure. A disclosure is made up of six components, including three lists of entities in the model and three sparse matrices describing their interconnections. The entity lists make reference to previously‐published resources, including background inventory databases and characterized elementary flows, and the disclosure framework requires both author and reader to agree on the meaning of each of these references. The framework contributes to ongoing efforts within and beyond industrial ecology to improve the reproducibility and verifiability of scholarly works, and if implemented, plots a course toward distributed, platform‐independent computation and validation of LCA results.     

Free energy coupling within macromolecules. The chemical work of ligand binding at the individual sites in co-operative systems

Individual-site binding curves such as those obtainable from techniques of DNase footprinting or nuclear magnetic resonance spectroscopy can be used to monitor structurally localized events within biopolymers. This paper discusses thermodynamic aspects of individual-site ligand binding for co-operative systems where the binding of ligand at a local site is coupled to binding of the same ligand species at other sites within the macromolecule. Individual-site binding isotherms have the following properties. (1) They provide a direct indication of the role played by the particular site in the overall binding reaction. (2) They can be used to determine the energetic contribution of loading the site regardless of the complexity of the system. (3) They can be used to resolve microscopic equilibrium constants and co-operativity constants in cases where the classical isotherm is incapable of such resolution. The microscopic constants bear a complex relation to the chemical work of loading each individual site. For a system with two interacting sites we derive analytical relationships between the individual-site loading energies and the microscopic constants. These relationships prescribe, for any values of the microscopic constants, how the co-operative energy is partitioned between events at the two sites. At fixed ligand activity the binding free energy can be estimated directly from an individual-site isotherm. This quantity, which is also a composite of the microscopic constants, provides a useful measure of site--site interaction. Several examples and applications are discussed for these properties of individual-site binding reactions.     

Linguistic Measure of Taxonomic and Functional Relatedness of Nucleotide Sequences

Abstract

The frequencies of “words”, oligonucleotides within nucleotide sequences, reflect the genetic information contained in the sequence “texts”. Nucleotide sequences are characteristically represented by their contrast word vocabularies. Comparison of the sequences by correlating their contrast vocabularies is shown to reflect well the relatedness (unrelatedness) between the sequences. A single value, the linguistic similarity between the sequences, is suggested asa measure of sequence relatedness. Sequences as short as 1000 bases can be characterized and quantitatively related to other sequences by this technique. The linguistic sequence similarity value is used for analysis of taxonomically and functionally diverse nucleotide sequences. The similarity value is shown to be very sensitive to the relatedness of the source species, thus providing a convenient tool for taxonomic classification of species by their sequence vocabularies. Functionally diverse sequences appear distinct by their linguistic similarity values. This can be a basis for a quick screening technique for functional characterization of the sequences and for mapping functionally distinct regions in long sequences.     

Bloader--a batch loader application for MIAMExpress

SUMMARY: BLoader is a client server application for annotating and loading large amounts of Microarray data into a local installation of the MIAMExpress database. A set of nested spreadsheets is used to collect the required MIAME annotation. Controlled vocabularies are downloaded from MIAMExpress and ArrayExpress databases to guarantee MIAME compliance. AVAILABILITY: The application is available from the author at http://www.ansorge-group.embl.de/bloader CONTACT: schwager@embl.de SUPPLEMENTARY INFORMATION: For more details on BLoader visit the above web page.     

An upper-level ontology for the biomedical domain

At the US National Library of Medicine we have developed the Unified Medical Language System (UMLS), whose goal it is to provide integrated access to a large number of biomedical resources by unifying the vocabularies that are used to access those resources. The UMLS currently interrelates some 60 controlled vocabularies in the biomedical domain. The UMLS coverage is quite extensive, including not only many concepts in clinical medicine, but also a large number of concepts applicable to the broad domain of the life sciences. In order to provide an overarching conceptual framework for all UMLS concepts, we developed an upper-level ontology, called the UMLS semantic network. The semantic network, through its 134 semantic types, provides a consistent categorization of all concepts represented in the UMLS. The 54 links between the semantic types provide the structure for the network and represent important relationships in the biomedical domain. Because of the growing number of information resources that contain genetic information, the UMLS coverage in this area is being expanded. We recently integrated the taxonomy of organisms developed by the NLM's National Center for Biotechnology Information, and we are currently working together with the developers of the Gene Ontology to integrate this resource, as well. As additional, standard, ontologies become publicly available, we expect to integrate these into the UMLS construct.     

Recognizing names in biomedical texts: a machine learning approach

MOTIVATION: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition. RESULTS: In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential features: (1) word formation pattern; (2) morphological pattern, such as prefix and suffix; (3) part-of-speech; (4) head noun trigger; (5) special verb trigger and (6) name alias feature. All the features are integrated effectively and efficiently through a hidden Markov model (HMM) and a HMM-based named entity recognizer. In addition, a k-Nearest Neighbor (k-NN) algorithm is proposed to resolve the data sparseness problem in our system. Finally, we present a pattern-based post-processing to automatically extract rules from the training data to deal with the cascaded entity name phenomenon. From our best knowledge, PowerBioNE is the first system which deals with the cascaded entity name phenomenon. Evaluation shows that our system achieves the F-measure of 66.6 and 62.2 on the 23 classes of GENIA V3.0 and V1.1, respectively. In particular, our system achieves the F-measure of 75.8 on the "protein" class of GENIA V3.0. For comparison, our system outperforms the best published result by 7.8 on GENIA V1.1, without help of any dictionaries. It also shows that our HMM and the k-NN algorithm outperform other models, such as back-off HMM, linear interpolated HMM, support vector machines, C4.5, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem. Moreover, evaluation on GENIA V3.0 shows that the post-processing for the cascaded entity name phenomenon improves the F-measure by 3.9. Finally, error analysis shows that about half of the errors are caused by the strict annotation scheme and the annotation inconsistency in the GENIA corpus. This suggests that our system achieves an acceptable F-measure of 83.6 on the 23 classes of GENIA V3.0 and in particular 86.2 on the "protein" class, without help of any dictionaries. We think that a F-measure of 90 on the 23 classes of GENIA V3.0 and in particular 92 on the "protein" class, can be achieved through refining of the annotation scheme in the GENIA corpus, such as flexible annotation scheme and annotation consistency, and inclusion of a reasonable biomedical dictionary. AVAILABILITY: A demo system is available at http://textmining.i2r.a-star.edu.sg/NLS/demo.htm. Technology license is available upon the bilateral agreement.     

Sub-cellular localization of membrane proteins

In eukaryotes, numerous complex sub-cellular structures exist. The majority of these are delineated by membranes. Many proteins are trafficked to these in order to be able to carry out their correct physiological function. Assigning the sub-cellular location of a protein is of paramount importance to biologists in the elucidation of its role and in the refinement of knowledge of cellular processes by tracing certain activities to specific organelles. Membrane proteins are a key set of proteins as these form part of the boundary of the organelles and represent many important functions such as transporters, receptors, and trafficking. They are, however, some of the most challenging proteins to work with due to poor solubility, a wide concentration range within the cell and inaccessibility to many of the tools employed in proteomics studies. This review focuses on membrane proteins with particular emphasis on sub-cellular localization in terms of methodologies that can be used to determine the accurate location of membrane proteins to organelles. We also discuss what is known about the membrane protein cohorts of major organelles.     

Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation

MOTIVATION: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or 'semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repertoire of analyses. RESULTS: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. AVAILABILITY: Software available from http://www.russet.org.uk.     

Two types of division of labour in clonal plants: benefits, costs and constraints

Clonal plants spread vegetatively within their habitats by forming rooted ramets on stolons or rhizomes. Each of these ramets is capable of an independent existence after establishment. Nevertheless, ramets remain physically connected by stolon or rhizome internodes for variable periods of time, thereby allowing for resource movement and signal transduction within clones.Interconnected ramets of clonal plants, though potentially independent and totipotent, can specialize functionally in the performance of limited numbers of tasks such as the uptake of resources from above- vs below-ground sources, carbohydrate storage, vegetative spread and sexual reproduction. Such specialization and cooperation is comparable to a division of labour in economic systems or in colonies of social animals. The ecological significance of division of labour in clonal plants may be found in the increased efficiency of entire clones in exploiting their environments.Two different types of division of labour in clonal plants will be discussed in this review. The first type is an environmentally-induced specialization of ramets in the uptake of locally abundant resources (plastic division of labour), which can be found in several stoloniferous species. Evidence exists that this response increases resource uptake in spatially heterogeneous environments. The second type of division of labour, which occurs mainly in rhizomatous species, relates to a developmentally-programmed specialization and cooperation between interconnected ramets. This response pattern is thought to enhance plant performance by restricting the number of tasks for individual ramets and thereby significantly increasing the efficiency of task performance. In some plants, such an inherent division of labour is likely to contribute to nutrient extraction from poor and unpredictably variable sources.In this article not only benefits but also potential costs and constraints on division of labour in clonal plants are shown. The aim is to provide a review of existing knowledge and to develop concepts and hypotheses for future research.     

Identification of amino acid propensities that are strong determinants of linear B-cell epitope using neural networks

BACKGROUND: Identification of amino acid propensities that are strong determinants of linear B-cell epitope is very important to enrich our knowledge about epitopes. This can also help to obtain better epitope prediction. Typical linear B-cell epitope prediction methods combine various propensities in different ways to improve prediction accuracies. However, fewer but better features may yield better prediction. Moreover, for a propensity, when the sequence length is k, there will be k values, which should be treated as a single unit for feature selection and hence usual feature selection method will not work. Here we use a novel Group Feature Selecting Multilayered Perceptron, GFSMLP, which treats a group of related information as a single entity and selects useful propensities related to linear B-cell epitopes, and uses them to predict epitopes. METHODOLOGY/ PRINCIPAL FINDINGS: We use eight widely known propensities and four data sets. We use GFSMLP to rank propensities by the frequency with which they are selected. We find that Chou's beta-turn and Ponnuswamy's polarity are better features for prediction of linear B-cell epitope. We examine the individual and combined discriminating power of the selected propensities and analyze the correlation between paired propensities. Our results show that the selected propensities are indeed good features, which also cooperate with other propensities to enhance the discriminating power for predicting epitopes. We find that individually polarity is not the best predictor, but it collaborates with others to yield good prediction. Usual feature selection methods cannot provide such information. CONCLUSIONS/ SIGNIFICANCE: Our results confirm the effectiveness of active (group) feature selection by GFSMLP over the traditional passive approaches of evaluating various combinations of propensities. The GFSMLP-based feature selection can be extended to more than 500 remaining propensities to enhance our biological knowledge about epitopes and to obtain better prediction. A graphical-user-interface version of GFSMLP is available at: http://bio.classcloud.org/GFSMLP/.     

Resource Construction and Evaluation for Indirect Opinion Mining of Drug Reviews

Opinion mining is a well-known problem in natural language processing that has attracted increasing attention in recent years. Existing approaches are mainly limited to the identification of direct opinions and are mostly dedicated to explicit opinions. However, in some domains such as medical, the opinions about an entity are not usually expressed by opinion words directly, but they are expressed indirectly by describing the effect of that entity on other ones. Therefore, ignoring indirect opinions can lead to the loss of valuable information and noticeable decline in overall accuracy of opinion mining systems. In this paper, we first introduce the task of indirect opinion mining. Then, we present a novel approach to construct a knowledge base of indirect opinions, called OpinionKB, which aims to be a resource for automatically classifying people’s opinions about drugs. Using our approach, we have extracted 896 quadruples of indirect opinions at a precision of 88.08 percent. Furthermore, experiments on drug reviews demonstrate that our approach can achieve 85.25 percent precision in polarity detection task, and outperforms the state-of-the-art opinion mining methods. We also build a corpus of indirect opinions about drugs, which can be used as a basis for supervised indirect opinion mining. The proposed approach for corpus construction achieves the precision of 88.42 percent.