Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts. The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database. Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism-specific corpora of text. Textpresso can be accessed at http://www.textpresso.org or via WormBase at http://www.wormbase.org.We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts  and classes that relate two objects (e.g., association, regulation, etc.) or describe one . Together they form a catalog of types of objects and concepts called an ontology. After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories. The current ontology comprises 33 categories of terms. A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries. Full text access increases recall of biological data types from 45% to 95%. Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency. Textpresso currently focuses on  With the increasing availability of full-text scientific papers online, new tools, such as Textpresso, will help to extract information and knowledge from research literature Text-mining tools have become indispensable for the biomedical sciences. The increasing wealth of literature in biology and medicine makes it difficult for the researcher to keep up to date with ongoing research. This problem is worsened by the fact that researchers in the biomedical sciences are turning their attention from small-scale projects involving only a few genes or proteins to large-scale projects including genome-wide analyses, making it necessary to capture extended biological networks from literature. Most information of biological discovery is stored in descriptive, full text. Distilling this information from scientific papers manually is expensive and slow, if the full text is available to the researcher at all. We therefore wanted to develop a useful text-mining tool for full-text articles that allows an individual biologist to locate efficiently information of interest.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi). Information extraction is the process of obtaining pertinent information (facts) from documents. The facts can concern any type of biological object (entity), events, or relationships among entities. Useful measures of the performance of retrieval and extraction systems are recall and precision. In the case of retrieval, recall is the number of pertinent documents returned compared to all pertinent documents in the corpus of text. Precision is the number of pertinent documents compared to the total number of documents returned. A fully attentive reader would have complete recall, but low precision, because he has to read the whole body of text to find information. The emphasis for most applications is on recall, and we thus sought a system with high recall and as high precision as possible.The natural language processing field distinguishes information retrieval from information extraction. Information retrieval recovers a pertinent subset of documents. Most such retrieval systems use searches for keywords. Many Internet search engines are of this type, such as PubMed  in combination with the cell name. We therefore sought to develop a system that uses categories of terms such as “gene,” “cell,” or “biological process.” We established these categories of terms and organized them as an ontology, a catalog of types of objects and concepts and their relationships. The categories impart a semantic quality to searches, because the categories are based on the meaning of the entries.The precision of a keyword search can be increased by searching for combinations of keywords. For example, a researcher might construct a search for “anchor cell” and the gene name “lin-12” because he is interested in learning whether Caenorhabditis elegans literature as a test case for developing a useful information extraction system. C. elegans has a relatively small literature, so in principle we could use it to test a complete, well-defined corpus.In many cases literature databases only contain bibliographic information and abstracts. The latter suffer from the constraint of information compression and convolution imposed by a word limit. Access to the full text of articles is critical for sufficient coverage of facts and knowledge in the literature and for their retrieval ; our resWe also wanted to support a new database curation effort involving manual literature curation . LiteratOur system is defined by two key components: the introduction of an ontology and the searchability of full text. The ontology is organized into categories that facilitate broader searches of biological entities as illustrated above. To be useful, it should also contain other categories that are not composed of biological entities, but describe relationships between entities. We sought to offer the user an opportunity to query the literature in the framework of the ontology such that it returns sentences for inspection by the user. We hypothesized that searching the corpus of text with a combination of categories of an ontology could facilitate a query that contains the meaning of a question in a much better way than with keywords alone. For example, if there is a “gene” category containing all gene names and a “regulation” category that includes all terms (nouns, verbs, adjectives, etc.) describing regulation, searching for (at least) two instances of the category gene and one instance of the category regulation in a sentence increases the chance that the search engine will return a sentence describing a gene-gene regulation. The search could then be limited by using a particular gene name as a keyword to get a list of genes that regulate or are regulated by that particular gene.C. elegans papers, bibliographic information from WormBase, abstracts of C. elegans meetings and the Worm Breeder's Gazette, and some additional links and WormBase entities. See We have developed a text processing system, Textpresso, that splits papers into sentences, and sentences into words or phrases. Each word or phrase is then labeled using the eAbstracts, titles, and full texts in the Textpresso system are processed for the purpose of marking them up semantically by the ontology we constructed. An ontology is a catalog of types of objects and (abstract) concepts devised for the purpose of discussing a domain of interest. An ontology helps to clarify a domain's semantics for everyday use, as is nicely demonstrated by Gene Ontology GO; . AlthougThe first group of categories in the Textpresso ontology consists of biological entities: It contains the categories gene, transgene, allele, cell and cell group, cellular component, nucleic acid, organism, entity feature, life stage, phenotype, strain, sex, drugs and small molecules, molecular function, mutant, and clone. We have incorporated the GO molecular function category and proteins in the Textpresso molecular function category. A more detailed list with definitions can be found on the Textpresso Web site, and the most important ones are provided in The second group of categories comprises terms that characterize a biological entity or establish a relation between two of them. It includes physical association (in the sense of binding) and consort (abstract association), effect, purpose, pathway, regulation, comparison, spatial and time relation, localization in time and space, involvement, characterization (terms that express the characterization of something), method, biological process, action, and descriptor (words that describe the state or condition of an entity). These categories, while well defined, have somewhat delicate boundaries, and the common-sense aspects of our ontology apply more to this group. It is likely that its categories are going to be changed as we continue to develop the system. In some instances terms are attributed to one category, even though they might as well fit into another. As an example, the term “coexpress” is put in the “consort” category to emphasize the concurrent aspect of the process, while it could as well be classified as a biological process. However, we believe that in most cases the first sense of the word is used in the literature.The last group (auxiliary) contains categories that can be used for more involved semantic analysis of sentences. These categories are auxiliary (forms of the verbs “be” and “have”), bracket, determiner, conjunction , conjecture (could, might, should, suggests), negation, pronoun, preposition, and punctuation. Some of them overlap with the syntactic categories that the part-of-speech tagger , a dash (–), and a sequence of digits (\d+). As this example illustrates, the expressions can be made case sensitive. This is important as biological nomenclature becomes more elaborate, and the ability to distinguish subtle differences is pivotal for separating terms into the correct categories. Many of the regular expressions are generated automatically via scripts, taking a list of plain words as input and transforming them as shown in this example, to account for regular forms of verbs and nouns. The text-to-XML converter  regular expressions, each of which covers terms with a length from one to eight words. These expressions are contained in a lexicon. rter see  marks upThe marked-up text is stored in a database and can be queried. We built a user interface for general queries and another one for a specific type of query for WormBase curators . Textpresso is used in several related ways. Individual biologists use it to find specific information. Database curators, whose job is to extract information from papers or abstracts and to add this to a database, use it repeatedly to find all information of a particular type, in addition to using it for individual queries.http://www.textpresso.org/) includes a query interface, a side menu with links to informative pages about the ontology, a document type definition, a user guide, and example searches, as well as the two retrieval and customization interfaces. The Web site offers two different types of retrieval, simple and advanced. Options for the retrieval queries are offered: searching a combination of categories, subcategories, and keywords in a Boolean fashion, specifying the frequency of occurrences of particular items, and choosing where in the article to search (title, abstract, body). The user can also determine whether a query is to be met in the whole publication or in a sentence. These options make the search engine powerful; for example, if a query is met in the whole article, the search has the function of text categorization, while meeting it in a sentence aims at extracting facts, which can be viewed in the context of a paragraph. The specification of cooccurrence determines the character of a search. If a combination of keywords and categories is found in a sentence, the likelihood that a sentence contains a fact involving the chosen categories and keywords is quite high. If the user chooses cooccurrence within a document, he is more interested in finding a relevant document. The scope of a search can be confined to full text, abstract, title, author, year, or any combination thereof, for document searches as well as sentence searches. A typical result page shows a list of documents with all bibliographical information and the abstract as displayed in http://www.wormbase.org).The current Textpresso user interface , view a list of related articles that is provided by PubMed, export the bibliographical information into Endnote (two different links), or, if the user is accessing Textpresso internally , one can download the PDF of the paper.C. elegans gerontogene)?” can be answered by typing in the keyword “daf-16” and choosing the category “association.”The power of Textpresso's search engine unfolds when category searches are used. By searching for a category, the researcher is targeting all keywords that populate that category. For example, the researcher might be interested in facts about genetic regulation of cells. Assuming that many facts are expressed in one sentence, he would search for the categories “gene,” “regulation,” and “cell or cell group” in a sentence. He can then view the matches (and surrounding sentences) of the search return and decide which facts are relevant. If one is not interested in all genetic regulation instances mentioned in the literature, it might be more useful to combine keywords with categories. For example, the question “What entities interact with ‘daf-16'  allows the use of the subcategories of the ontology and the specification of Boolean operators, thereby concatenating categories and keywords with “or” or “not” to permit alternatives or exclude certain items. One special subdivision of terms is the distinction between named and unnamed entities: Categories can include both general terms and specific names of entities. For example, the word “gene” would be an unnamed term of the gene category, while “lin-11” is a named entity. The general terms will likely be used for fact extraction across several neighboring sentences, but they might also be useful for retrieval purposes, even though the rate of false positives might be much higher in the latter case. Lastly, the user can determine how a keyword or category term has to be matched numerically. The options “greater than,” “less than,” and “equal to” are available together with a drop-down menu for the number of occurrences.lin-11 is expressed, one would search sentences for a combination of the category “biological process” (subcategory “biosynthesis: expression”), the category “cell or cell group” (subcategory “type: name”) and the exact keyword “lin-11.” The subcategory “expression” filters out all words that relate to expression, the subcategory “name” limits the search to specific cells which have a name, such as “anchor cell,” “HO neurons,” “IL sensillum,” etc. Other subcategory options would be “group” (for example, “head,” “vulva,” “tail”) and “lineage” (“AB lineage,” “EMS lineage,” etc.). To better understand the following results, note that the term “cell(s)” has the type “name,” to gain the correct meaning of phrases such as “AB lineage cells.” The first two words of this phrase are marked as lineage, but the last word makes the whole phrase named cells.With these additional tools, document categorization can be made more effective. A detailed profile of which categories and keywords should occur a minimum, maximum, or exact number of times for triggering a match can be established. Similarly, searches on the sentence level acquire a semantic quality, i.e., they at least partially encompass a meaning. In many cases, the answers to questions, phrased in the form of a sophisticated query, can immediately be read off the result screen. If, for example, one were to ask in which cells vulva and uterine lineageP5.p and P7.p lineagelin-11 is expressed might be inferred by the knowledgeable reader, and not stated explicitly: “Our results demonstrate that the tissue-specific The system returns sentences of different quality. Some of them answer the question posed immediately . Note that there can be more than one data type per article.We examined the effectiveness of automatically identifying journal articles that contain particular types of data. A test set of 965 journal articles pertaining to We first measured the value of searching for keywords in the full text of an article as opposed to searching its abstracts . The oveWe tested the accuracy of a search combining word categories and keywords to retrieve sentences containing genetic interaction data. For this experiment we broadly defined genetic interaction as the effect of one or more genes on the function of another gene or genes (and thus it includes genetic interaction, regulation, and interaction of gene products). To directly assess how Textpresso performs, a human expert manually evaluated the text sentence by sentence .We formulated a Textpresso query that searched for the presence of at least two genes mentioned by name and at least one term belonging to the “regulation” or “association” word categories see . A totalFor this analysis we took the union of true sentences manually identified in the journal articles and the true sentences returned by Textpresso. The total number of true sentences identified by the two methods was 102. The recall of sentences containing genetic interaction was ∼62% using Textpresso compared to ∼71% for those sentences manually identified in journal articles. One-third of the sentences returned by Textpresso were true positives (35%).Although the numbers of true sentences retrieved by the automatic and manual methods were similar (63 and 73, respectively), only 34 of these sentences overlapped. To investigate this discrepancy, we manually extracted the genetic interactions described in both sets of sentences and determined the number of distinct genetic interactions found by each method . The senref-2 alone is insufficient to keep P(3–6).p unfused when lin-39 is absent.”). Approximately 8% of true sentences were missed because the genetic interaction information was discussed over a number of sentences. This is a limitation of the current Textpresso system, as search queries are matched per sentence (or per entire article).We analyzed the gene-gene interaction sentences missed by Textpresso. In many cases (65%) the word or phrase used to describe the genetic interaction belonged to neither the “association” nor the “regulation” word category and so the sentence was not returned. In some cases, the term or phrase that determined “genetic interaction” belonged to some other Textpresso word category . This type of analysis is useful for revising and updating the ontology. In other cases, due to the intricacies of natural language prose, it was difficult to isolate an interaction term in the sentence (e.g., “Thus pdk-1(gf) nor akt-1(gf) suppressed the Hyp phenotype of age-1(mg44).”). While we do have a “negation” category in our Textpresso ontology, we chose not to exclude negation terms from the posed query, to avoid missing true positives (in case the negation does not apply to the interaction term in a sentence, but to some other portion of it). Twenty-one percent of the false positive sentences were determined by inspection to suggest genetic interaction, but were too weakly phrased to extract the information in confidence without the context of the sentence. However, the majority of false positives (70%) were due to the lack of context of the search terms in the sentence, where they matched the query terms (underlined) but in a context that did not mention genetic interaction: “C. elegans pathway, encode proteins similar to Rb and its binding protein RbAp48.” This example strongly supports the idea that an information extraction method that considers semantic context of a search query would dramatically increase the precision of the return.Our analysis of the false positive sentences returned by Textpresso revealed that approximately 10% discussed gene-gene interactions that did not occur  transgene can supply sufficient Akt/PKB activity to weakly suppress the dauer arrest caused by age-1(mg44).” Some of the sentences strongly suggested genetic interaction but did not quite meet the genetic interaction criterion. These were grouped as “possible genetic interaction,” for example, if a phenotype was not mentioned: “For example, lin-15(lf) animals display a 54% penetrance of P11 to P12 fate transformation, while all egl-5(lf);lin-15(lf) double mutants show a P12 to P11 fate transformation.” Sometimes it is unclear exactly which genes are participating in the genetic interaction: “Evidently the effect of the sir-2.1 transgene alone is too subtle to trigger dauer formation without the sensitizing daf-1 or daf-4 mutations.” Another group was highlighted as discussing interaction, but fell outside the criterion set for genetic interaction. These were classified “non-genetic interaction.” Some examples of this are sentences that specify gene regulation: “These studies have shown that smg-3(Upf2) and smg-4(Upf3) are required for SMG-2 to become phosphorylated.” Finally, sentences that describe physical interaction were also put into the category “possible genetic interaction”: “For example, GLD-1 represses translation of tra-2, one of the sex-determination genes, by binding to the 3′-UTR or the tra-2 mRNA .”A typical sentence that was determined to be true for genetic interaction data is “Interestingly, at lower temperatures, the This analysis shows that there is a 1 in 200 chance of a sentence discussing genetic interaction (as defined above) randomly occurring in the full text of the journal articles analyzed. The odds increase to 7 in 100 if one looks at sentences containing at least two named genes. The returned matches from the Textpresso search are enriched 39-fold for genetic interaction compared to random chance, and there is a significant 3-fold enrichment when compared to sentences containing at least two named genes. There is a 1 in 5 chance that a returned Textpresso match is true. To date, 2,015 of the 17,851 returned sentences have been evaluated. Of these, 370 discuss genetic interaction, yielding 160 distinct gene-gene interactions mined from the literature. There are 213 sentences that mention nongenetic interactions, and 419 sentences are classified as possible genetic interactions.We have extracted gene-allele reference associations from the corpus of papers to populate the WormBase database by searching for the pattern <gene><bracket><allele> <bracket>. Of the 10,286 gene-allele associations extracted, 9,230 were already known by WormBase, while 1,056 associations were new and could be added to the database. In addition, 1,464 references could be added to the 2,504 allele reference associations in WormBase. Ninety-eight percent of the data extracted went into the database without any manual correction, and the last 2% were compromised because of typographical errors in the original paper or the inherent character of the data (i.e., gene name synonyms and changes).C. elegans literature. As of March 2004, the database contains full texts of 60% of all papers listed by the Caenorhabditis Genetics Center  and almost all abstracts that are information rich for C. elegans research. The introduction of semantic categories and subsequent marking up of the corpus of texts introduce powerful new ways of querying the literature, leading towards the formulation of meaningful questions that can be answered by the computer. We have demonstrated such queries with one example and have successfully tried many others. A more thorough evaluation of the system revealed that the availability of full text is crucial for building a retrieval system that covers many biological data types with a satisfying recall rate, and thus is truly useful for curators and researchers. For biologists, an automated system with high recall and even moderate precision (like the current Textpresso) confers a great advantage over skimming text by eye. Textpresso is already a useful system, and thus serves not only as proof of principle for ontology-based, full-text information retrieval, but also as motivation for further development of this and related systems to achieve higher precision and hence even greater time savings.We have developed a system to retrieve information from the full text of biological papers and applied it to the C. elegans literature (currently about 6,000) can be curated with the assistance of Textpresso, as it is much more efficient than when done by human readers alone. The larger the corpus of papers, the more useful Textpresso will become. We have shown this by calculating the frequencies of genetic interaction data in sentences in three different cases: random sentences, sentences that contain at least two genes, and sentences returned from a Textpresso advanced query. The efficiency was shown to increase dramatically (39-fold in the best case). We have outlined the first steps of how Textpresso helps the curation effort by extracting gene-gene interactions. Overall, we have shown that Textpresso has several uses for researchers and curators: It helps to identify relevant papers and facts and focuses information retrieval efforts. Indeed, Textpresso is used daily by C. elegans researchers and WormBase curators: The server sends 530 files to requests daily via the Web, a quarter of which are to WormBase curators.It is apparent that the number of articles available in the C. elegans literature and the fact that the ontology and its corresponding lexicon are still in their infancy. The preparation of full texts has to be better and more efficient. The conversion of PDF to plain texts was problematic because of the different layouts of each journal. Even with the software we developed, a layout template for each journal needs to be written to specify where different components of text can be found. Prior to the use of this software, we had to forgo the use of figure and table captions. Acquisition of processable text is a general problem for biologists. A new release of XPDF  eases this problem considerably  and SNOMED (http://www.snomed.org/, and to establish a more sophisticated tree structure. Our core lexicon recognizes 5.5 tags per sentence (out of an average of 23.7 tags per sentence) that are of scientific interest. This density results in a term coverage of 23.2%, while the maximum that could theoretically be added is 36.5%, assuming that all terms currently not marked up belong to relevant categories. An average of 9.5 tags per sentence are apparently of no interest for information retrieval; however, this is due to the nature of human language (and will be nonetheless useful for information extraction purposes). Reevaluation of the corpus of text for terms and their meanings that have been missed is necessary. This process will result in an expansion of our ontology, thus continually expanding the resulting lexicon, or revising the structure of the ontology. Ontology and lexicon revision is most efficiently done by a human, and a feasible automated approach seems out of reach. However, we have illustrated semiautomatic methods to help make this task easier in the future: The containment of words that are not covered in our lexicon with <text> tags serves several purposes. First, we are able to extract all words (or n-grams, which are represented as a consecutive sequence of words embedded in <text> tags), assemble a histogram of the most frequent terms, and add important ones to our lexicon. Second, having identified frequently occurring semantic patterns in the corpus, we are able to infer likely candidates of words for specific categories. For example, one popular pattern that indicates a gene-allele association is <gene><bracket><allele><bracket>. If one now searches for patterns such as <gene><bracket> <text><bracket> and extracts the word enveloped by the <text> tags, then a frequency-sorted list of words that are likely to be alleles can be assembled, presented to a curator for approval, and deposited into the lexicon. The alternative, <text><bracket><allele><bracket>, would give a list of possible gene names. Many other patterns, identified by statistical means and similarity measures, could be obtained and used in such a fashion. These two methods will help us to systematically and significantly reduce the number of terms not marked up in the corpus, making it more complete. The procedure can be repeated with every build of the Textpresso database and has the advantage that the list of words added to the lexicon is tailored to the literature for which it is used. In addition, shortcomings in the general structure of the ontology can be detected and corrected, if those issues have not been caught in the research and development of the information extraction aspects of the system. If the strategy outlined above is applied continually, we will be able to close this gap and reach saturation, even with the addition of new papers and abstracts.Advancing the Textpresso ontology will help to increase the specificity of the retrieval system. A deeper, meaningful structure is likely to make extraction easier and more stable. Possible improvements are to include other biological ontologies and language systems, such as UMLS (About 89% of current users take advantage primarily of the full text and multiple keywords. Some (11%) proceed to keyword plus category. Only 0.3% of users use the advanced retrieval search. It is clear that the implementation of a user test interface improvement/education cycle will greatly help the development of Textpresso and subsequently help users take full advantage of this system. More generally, biologists will become increasingly familiar with ontology-based search engines.Future development of Textpresso can be undertaken at many different levels. A synonym search could be enabled for keyword searches: After having compiled lists of them, an option could be given to automatically include synonyms for a given term (e.g., genes, cells, cellular component) in a search. Similarly, GO annotations could be used to search for and display sentences involving genes associated with gene ontology terms, after the latter ones have been queried first. As already mentioned, search targeting could be made more flexible: Papers could be subdivided into more sections (such as introduction, methods, results, conclusion, etc.), and a query could then be applied only to the specified sections. In addition, the limitation of searching criteria to just one sentence can be relaxed to a set number of neighboring sentences. Finally, one could improve on links to other databases of relevance besides WormBase and PubMed and increase the wealth of links to the latter ones.http://www.gmod.org, and a downloadable package with software will be made available on their Web site. For a different model organism, parts of the lexicon, and maybe also parts of the ontology, need to be modified. Language and jargon in each community differ, and terms need to be systematically collected to accommodate their specific usage in the respective communities. However, this is not too laborious, as we have been able to generate a yeast version in a few weeks (E. E. Kenny, Q. Dong, R. S. Nash, and J. M. Cherry, unpublished data).An important issue is the portability of the system to other model organism databases. This undertaking is part of the Generic Model Organism Database (GMOD) project  and the comparatively low degree of inflections and transformations certainly help. In addition, some scientific information is stored in a structured manner. We have already started to run simple pattern-matching scripts to populate gene-allele associations from the literature for WormBase, as many of them are written in the form “gene name,” such as “lin-3(n1058).”C. elegans database from four sources. A collection of articles in PDF format is compiled according to the canonical C. elegans bibliography maintained at the CGC (http://www.cbs.umn.edu/CGC/CGChomepage.htm ). As of March 2004 we had around 3,800 (60%) CGC papers in our database. Software developed by us (see below) converts the PDFs to plain text. We import additional bibliographical information from WormBase: titles of documents and author and citation information. WormBase data comprise additional C. elegans-related documents such as C. elegans meeting abstracts and Worm Breeder's Gazette articles. We also curate certain types of data ourselves. Some C. elegans-related papers are not found in the CGC bibliography or WormBase. We compile lists of URLs of journal Web sites and their articles, and links to related articles (provided by PubMed). Citations are prepared in Endnote format for download. Finally, as Textpresso returns scientific text to the user, we construct links to report pages of WormBase that display detailed information about biological entities, such as genes, cells, phenotypes, clones, and proteins. All data and links produced by us are referred as “Textpresso” data in Textpresso builds its The objective of an ontology is to make the concepts of a domain and the relationships and constraints between these concepts computable. For an ontology to be utilized in a search engine for biological literature, it has to include the language of everyday use and common sense. We have therefore assigned the most commonly used meaning to a word even though it has several meanings in different contexts. We have consequently adopted a strategy of devising an ontology drawing from our own knowledge. Our ontology includes all terms of the three major ontologies of GO, namely “cellular component,” “biological process,” and “molecular function.” The current ontology is unstructured for the sake of straightforward usability, our first priority.A variety of approaches were utilized to construct and populate the 33 categories of the Textpresso ontology. We first designed individual categories for well-defined biological units or concepts such as strain, phenotype, clone, or gene. The terms in some of these categories  were represented by a PERL regular expression designed to match any text that looked like that particular biological unit. This was possible where a conserved and unique nomenclature for that biological concept had been established in the literature. Any exceptions to the established nomenclature recorded in WormBase were also added to these categories.For other biological concepts , we extracted information from publicly accessible biological databases, such as WormBase, GO, and PubMed/NCBI to construct lists of terms. We supplemented these lists through primary literature and textbook surveys.C. elegans journal articles for analysis. From this list we flagged natural prose words that we felt had at least some defined meaning within the context of biological literature (for example, “expressed,” “lineage,” “bound,” “required for”). From this list we constructed 14 new categories designed to encapsulate the natural language used by biologists to describe biological events and the relationship between them . We made a second pass through the subset of flagged words from the list and assigned them to one of these categories according to what the sense of the word was in the biological literature for the majority of the time.Next, we conceived categories of terms that would describe the relationship between the biological categories. To structure these “relationship” categories, we listed words of the text of 400 Finally, a number of categories were designed to account for syntax and grammatical construction of text, such as “preposition,” “conjunction,” and “bracket.”C. elegans-specific implementation of Textpresso, the effort was helped by the fact that the C. elegans community is somewhat disciplined in choosing names and WormBase includes names of interest. Of course, there is the danger that entities not listed in WormBase (and therefore in our lexicon) will be missed in our system, and those cases are of special interest to curators (of WormBase) and researchers, such as newly defined genes or newly isolated alleles. Dictionaries tend to be incomplete and turn stale rapidly, because of the issues of synonyms, lack of naming conventions, and the rapid pace of scientific discovery. Thus, we do not rely only on WormBase, but maintain an independent, Textpresso-specific part of the lexicon.We have manually curated a lexicon of names because it has proved difficult in the past to automatically recognize names of biologically relevant entities . We therhttp://www.gmod.org). While this manuscript was being written, a new version (2.0.2) of XPDF (http://www.foolabs.com/xpdf/) was released. This version, unlike its predecessors, does a superb job in converting PDF into a congruent stream of plain text.C. elegans literature, and additional tagging rules are applied. For example, gene names are forced to be tagged as nouns. The grammatical tags are not further used in the current Textpresso system. After this preprocessing step, the corpus of titles, abstracts, and full texts is marked up using the lexicon of the ontology (PERL expressions), as explained in Results and exemplified in Additional bibliographic data of references for which PDFs are not available are imported from WormBase (symbolized as “WormBase data” in The corpus of searchable full texts, abstracts, and titles has 1,035,000 sentences. A total of 351,000 keywords have been indexed, covering 19,180,000 words in the texts. The semantic mark-up yields a total of 24,542,000 tags. When displaying sentences and paragraphs, Textpresso provides links to report pages of several biological entities, such as proteins, transgenes, alleles, cells, phenotypes, strains, clones, and loci. There are a total of 165,000 different entities in WormBase to which Textpresso links, including links to journal articles and PubMed. All these links are produced statically and again deposited on disk for fast retrieval, and these data are referred to as “Textpresso data” in We generated an exhaustive keyword and category index for the whole corpus. This index makes the search extremely fast, using rapid file access algorithms. All keywords and tags in the corpus are indexed. Also, all terms in the corpus that have a report page in WormBase are indexed. For 2,700 full-text articles and 16,300 abstracts, the index takes up 1.7 Gb.The interfaces for submitting queries and customizing display options are written as CGI scripts. They are supported by simple HTML pages that contain documentation. The Web site runs with a RedHat Linux operating system and an Apache http server. No special changes to the standard configuration are required. The Web interface accesses the custom-made Textpresso database; no commercial-grade database systems have been used. It takes 2–3 d to build the complete 6.9-Gb database.For the preliminary study, a query was formulated using three category rows of the Textpresso “advanced retrieval” interface to identify sentences containing gene-gene interaction data from a test set of eight full-text journal articles see : the PMIFor the semiautomatic information extraction from text, the same query was utilized as above. In addition, sentences that did not mention at least two uniquely named genes were eliminated.


The control of force and its between-trial variability are often taken as critical determinants of motor performance. Subjects performed isometric trunk flexion and extension forces without and with experiment pain to examine if pain yields changes in the control of trunk forces. The objective of this study is to determine if experimental low back pain modifies trunk isometric force production.Ten control subjects participated in this study. They were required to exert 50 and 75% of their isometric maximal trunk flexion and extension torque. In a learning phase preceding the non painful and painful trials, visual and verbal feedbacks were provided. Then, subjects were asked to perform 10 trials without any feedback. Time to peak torque, time to peak torque variability, peak torque variability as well as constant and absolute error in peak torque were calculated. Time to peak and peak dF/dt were computed to determine if the first peak of dF/dt could predict the peak torque achieved.Absolute and constant errors were higher in the presence of a painful electrical stimulation. Furthermore, peak torque variability for the higher level of force was increased with in the presence of experimental pain. The linear regressions between peak dF/dt, time to peak dF/dt and peak torque were similar for both conditions. Experimental low back pain yielded increased absolute and constant errors as well as a greater peak torque variability for the higher levels of force. The control strategy, however, remained the same between the non painful and painful condition. Cutaneous pain affects some isometric force production parameters but modifications of motor control strategies are not implemented spontaneously.It is hypothesized that adaptation of motor strategies to low back pain is implemented gradually over time. This would enable LBP patients to perform their daily tasks with presumably less pain and more accuracy. There are many possible explanations for the origin and consequences of acute and chronic low back pain but the transition from acute to chronic low back conditions needs to be clarified ,2. ThereFor example, Oddsson et al. used speClinical studies of chronic LBP patients involve heterogeneous populations and the effect of chronic pain cannot be differentiated from other degenerative and functional changes occurring in the lumbar spine. Experimentally induced LBP eliminates some of these uncertainties and could allow an examination of the effect of pain per se. To examIn a previous study, we have observed that two different control strategies were used by chronic LBP subjects to produce accurate trunk isometric forces. One subThe aim of the present study was to evaluate if a painful stimulation, induced by cutaneous electrical stimulation, would spontaneously yield a change in the control strategy or the variability of trunk isometric force production. To explore whether isometric trunk forces are similarly programmed with and without experimental pain, we used the model proposed by Gordon and Ghez. If motoForce production parameters were measured in 10 healthy subjects with no history of chronic or recurrent LBP (10 men, age: 25.9 years). Each subject gave their written inform consent and the study was approved by the local ethics committee. All subjects were university students. Force data (torque) were obtained from an isometric testing apparatus  and recorded at a sampling rate of 500 Hz. Torque data were digitally filtered with a seventh-order dual pass Butterworth filter (7 Hz low pass cut-off frequency). The first time derivative of torque was calculated using a finite difference algorithm (window 25 ms).Superficial pain was elicited by electrical stimulation of the skin over the spinous process of L3 using bipolar surface Ag-AgCl electrodes (Beckman electrodes, 1 cm diameter). This site of stimulation was chosen to ensure that no direct muscular activation could result from the electrical stimulus. The range of voltage used during our experiment was 135–140 Volts. This stimulation created a focal cutaneous painful stimulus with very limited current spread. The technique for inducing the pain stimulation was inspired from the work of Arendt-Nielsen et al.. Each stTesting was done in a neutral standing posture (no trunk flexion or extension). First, maximal isometric flexion and extension torques of trunk muscles were collected. The higher torque value obtained in three consecutive 4-second trials was used as the reference for maximal voluntary contraction. Then, four experimental conditions of trunk torque production were evaluated without and with experimental pain: 50 and 75% of the maximal isometric torque in both extension and flexion. Conditions were presented by block with the order of presentation being randomized across subjects. For each condition, trials without pain were presented first followed by the trials with pain. For each trial, subjects were instructed to produce a trunk isometric force as quickly as possible following an auditory signal. They were encouraged to produce a single impulse ("shoot and release") and to make no attempt at correcting the force once the contraction was initiated. For each condition, a learning phase was provided. During this phase, after each trial, subjects were given visual accuracy feedback through an oscilloscope located in front of them. Subjects were specifically asked to produce peak torques that were within 10% of the goal target. This learning phase stopped when five consecutive contractions were within the 10% margin. Following these learning trials, subjects performed 10 consecutive trials without any visual feedback. The pain condition followed. A second learning sequence with feedbacks and without pain was given to the subjects. This procedure was used to insure that no differences between the control and pain conditions would reflect a pain effect and not a loss of calibration after a block of trials without pain. Hence, if any differences between the control and pain conditions were found, these would reflect a pain effect and not a learning effect. For the pain trials, the stimulation was initiated 0.5 sec before an auditory tone indicating the subjects to initiate the contraction. All dependent variables were derived from the behavior observed for the 10 trials without feedback without and with the experimental pain.2, indicating that the first peak of dF/dt could predict the peak torque, was taken as an indication of a preprogrammed or open-loop mode of control[For each experimental trial, the onset of torque and peak torque were determined. Using this information, time to peak torque, time to peak torque variability, peak torque variability as well as the constant and absolute error in peak torque were calculated for each condition. Constant error represents the positive or negative difference between the peak torque reached and the target torque. Absolute error in peak torque represents the positive difference between the reached peak torque and the goal peak torque whereas time to peak represents the period of time between the beginning of rising torque and the maximal torque obtained in the trial. Peak dF/dt was also computed to examine if the first peak of dF/dt could predict the peak torque achieved. Linear regressions were calculated for each subject and a high rf control.p = 0.018). The main effects of Direction, Force level and all interactions were not significant (ps > 0.05). Similar observations were made for the constant errors as the ANOVA yielded a main effect of Pain (F1-9 = 6.22, p = 0.035). The main effects of Direction, Force level and all interactions also were not significant (ps > 0.05). The painful stimulus yielded increased constant and absolute errors indicating that subjects, on average, overshot the target by 25.9 Nm .On average, the maximal voluntary contraction in flexion and extension were 236.2 Nm and 346.5 Nm, respectively. Table Figure p = 0.021) and an interaction of Pain × Force (F 1,9  =  8.05, p = 0.020) but no main effect of Force (ps > 0.05). A decomposition of the interaction showed that peak torque variability increased with the painful stimulation only for the higher level of force. For the higher level of force, the variability was 12.6 Nm in the control condition and 18.6 Nm in the presence of electrical stimulation (Tukey: p = 0.027). For the lower level of force, peak torque variability was similar for both conditions .Figure ps > 0.05). The time to peak torque variability, however, was increased in the painful condition than in the normal condition . Again, the main effects of Direction, Force level and all interactions were not significant (ps > 0.05).The average time to peak torque was not affected by the lumbar electrical stimulation. On average, the time to peak torque was 240 ms. The main effects of Pain, Direction, Force level and all interactions were not significant . This suggests a similar control strategy for both the normal and the painful conditions.The presence of an experimental cutaneous lumbar pain altered the production of isometric trunk forces in various ways. Specifically, when exposed to the painful cutaneous electrical stimulation, subjects showed greater absolute and constant errors in isometric trunk torque production. The effects of a lumbar cutaneous painful stimulation on the isometric trunk force production yielded an overestimation of the learned level of force to be performed in both flexion and extension. This observation argues against a specific modification of the trunk flexor or extensor motoneuronal pool following the painful stimulation. Rather it appears that, independently of the direction of the force production required, the pain stimulation yielded an increased excitability of the agonist motor pathways. Our results, however, cannot discriminate if these modifications occurred at the programming stage or at the execution stage (upper and lower motorneurons). All of these mechanisms have previously been suggested to explain the modifications induced by experimental pain,14,18. ANumerous authors have showed that chronic LBP patients exhibit deficits in proprioception and trunk motor control -7. The lThe painful stimulus used in the present experiment consisted of trains of 1 ms electrical pulses applied to the spinous process of the third lumbar vertebra. Although the experimenter could not observe nor palpate any muscular contractions and the subjects did not report any involuntary muscular contractions, the possibility exists that spreading of the current over the surrounding tissues activated the neuromuscular junction of the nearby lumbar paraspinal muscles. Consequently, imperceptible muscular contractions could have occurred, particularly at the high level of Voltage used to elicit the perception of pain in our subjects. Muscular activation of the paraspinal muscles due to the current spreading while performing an isometric force reproduction task could certainly lead to a deterioration of the subjects' performance (both the accuracy and variability) under the experimental pain condition. However, the overestimation of the learned level of force was observed in both flexion and extension – whereas the painful stimulation was always applied to the spinous process  – argues against this factor playing a critical role in the findings reported in the present manuscript. Further work, however, is needed to quantify the performance of healthy subjects performing an isometric force reproduction task using a different experimental pain protocol .In a previous study, we observed that some chronic LBP patients, compared to healthy control subjects, adopted a more close loop control strategy of trunk isometric force production to maintain a particular level of performance. Both exThe present data indicate that trunk isometric force production can be affected by experimental cutaneous low back pain. While the motor control strategy remained the same between the non painful and painful condition, subjects showed less accuracy and more variability in the painful condition. Experimental cutaneous low back pain is different from deep tissue pain and the observed changes. This precludes any generalization to acute low back pain. It is hypothesized, however, that adaptation of motor strategies to low back pain is implemented gradually over time. This would enable LBP patients to perform their daily tasks with presumably less pain and more accuracy.The author(s) declare that they have no competing interests.MD and JS performed all the testing and data analyses. NT acted as the thesis director of MD and JS and participated in the design and coordination of the study. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


The announcement of the human genome sequence three years ago was widely hailed as one of the great scientific achievements in modern history, and with good reason. Determining the structure and nature of the genetic code promises to provide valuable insights into human evolution and the molecular basis of disease. But sequencing the genome is just the first step toward this decidedly worthy goal—the monumental task of ascribing biological meaning to those sequences has just begun. And while researchers know a great deal about some of the 30,000 or so genes in the human genome, they have yet to ascribe function to the majority of them. Takashi Gojobori and a large international team of collaborators have now taken a big step toward narrowing this knowledge gap.Deciphering the human genome presents such a daunting challenge in part because it's so huge, making it difficult to distinguish genetic signal from noise. Simpler organisms have much more compact genomes. In the case of brewer's yeast, for example, genes that encode proteins account for about 70% of the genome. In contrast, only about 1% to 2% of the human genome codes for proteins. That translates to about one gene for every 2,000 bases for yeast compared to about one gene for every 150,000 bases for humans.The low density of human genes makes identifying them difficult enough, but this process is further complicated by how genes are organized in the human genome. The functional parts are broken up into smaller segments called exons, which are separated in the genome by intervening sequences called introns. This configuration also occurs in simpler organisms, but since the number and size of introns is relatively small in simpler organisms, it's easier to tell what's a gene and what isn't. In humans, the introns are extremely long, as are the gaps between the genes, and the exons are tiny in comparison; plus, it takes many more of these short, scattered exons to make one gene.One approach to this problem is to use computer algorithms that scan the genome sequence looking for segments of DNA sequences that could potentially encode proteins. Gojobori and colleagues, however, used a different approach. They analyzed the sequences of 41,118 full-length cDNAs available from six sequencing centers around the world. These cDNAs are stretches of DNA that represent genes that have already been expressed and used by the cell for protein production. Since all the exons have been spliced together and the introns removed, these cDNAs correspond to the functional versions of these genes, allowing researchers to work backward, looking for the sequences in the genome.In order to process the 41,118 cDNAs, the researchers used a combination of computer algorithms and expert human analysis. To tackle such an enormous project, 158 genome scientists, representing 67 institutions from 12 countries, gathered in Japan in the summer of 2002. Over the course of a ten-day annotation marathon, the scientists validated, mapped, and annotated the cDNAs. As things stand, the team has been able to assemble the cDNAs into over 20,000 strong candidates for human genes.From just the initial analysis of the data generated by this group, several valuable findings about the human genome have emerged: there are over 5,000 candidates for new genes, including an exciting group of several hundred that do not appear to encode proteins; up to 4% of the genome appears not to be represented in the current human genome sequence; and several thousand DNA sequence variants have been uncovered that will be useful for disease mapping studies.But perhaps most important of all, the data from this study have been collected and assembled into a large searchable database called H-Invitational Database, which is linked to other functional databases around the world. This will be an invaluable resource for geneticists, and will serve as a starting point for further analyses. Future research on the human genome will be aimed at expanding the list of known genes and analyzing the properties of these genes. This study not only moves us closer to a complete functional description of the human genome, it also builds on the traditions of international cooperation and large-scale collaboration that played such an important part in deciphering the sequence itself.


This study was undertaken to investigate how different preferences of Learning Styles (LS) of psychiatrists might affect 49 psychiatrists  from 3 different clinics volunteered to participate in this study and to use the CDSS to diagnose a paper-based case . LS, attitudes to CDSS and complementary data were obtained via questionnaires and interviews. To facilitate the study, a special version of the CDSS was created, which automatically could log interaction details.The LS preferences (according to Kolb) of the 49 physicians turned out as follows: 37% were Assimilating, 31% Converging, 27% Accommodating and 6% Diverging.The CDSS under study seemed to favor psychiatrists with abstract conceptualization information perceiving mode (Assimilating and Converging learning styles).A correlation between learning styles preferences and computer skill was found. Positive attitude to computer-aided diagnostics and learning styles preferences was also found to correlate.Using the CDSS, the specialists produced only 1 correct diagnosis and the non-specialists 2 correct diagnoses  as compared to the three predetermined correct diagnoses of the actual case. Only 10% had all three diagnoses correct, 41 % two correct, 47 % one correct and 2 % had no correct diagnose at all.Our results indicate that the use of CDSS does not guarantee correct diagnosis and that LS might influence the results. Future research should focus on the possibility to create systems open to individuals with different LS preferences and possibility to create CDSS adapted to the level of expertise of the user. Different types of decision support (DS) methods have been used in medicine for long. Computerized decision support systems (CDSS) including so-called "expert systems" can be used in for example interpretation of medical images, medical diagnostics or other areas. Examples are MYCIN for antibiotic treatments  and deDoet al [For a more general reading on CDSS adoption in medical practice, see Fieschi et al  and for et al  and Millet al . Anotheret al  who compet al [et al [Various models, or mode of actions, of DS exists including textual guidelines based on if-then-else strategies that forces the decision maker to make decisions in a logical and sequential manner; more advanced systems using fuzzy logic; neural networks; and systems using so called Artificial Intelligence. Many DS systems have been developed with aims to enable more accurate, consistent diagnosis and faster diagnostic procedures. CDSS have been applied in many medical disciplines, but have also been discussed in terms of e.g. reliability, usefulness, and user-acceptance. For example, Lu et al  found thet al  who statet al  pointed l [et al  indicatel [et al ,14.It is unclear if the perceived usefulness of CDSS only is due to the DS model itself, or if the design and management of the computerized system is as important. Furthermore, there have been discussions about the design of computer systems, and how they might suit different users with different user-characteristics. Allen  argues tThe concept of learning style (LS) might be regarded as an important characteristic and independent variable when individual differences in perceiving and processing information are investigated. A deep understanding of the user, his tasks and his environment is required to design a well-accepted (useful) computer program. Learning styles and its importance for users of computer systems has been demonstrated in various areas, for example in Internet use , web-basThere are a number of learning style models and inventories -27. ClaxThis is a model of experience-based learning where all processes of the model are vital for the learning result. According to this model, the user/learner moves around the four modes in a circular direction Figure . First tThese four processes combine into four learning styles: Converging , Accommodating (concrete experience mode and active experimentation mode), Diverging (concrete experience mode and reflective observation mode) and Assimilating .Diverging learning style is associated with value generating skills: building relationships, helping others and sense making (reasoning). Assimilating learning style is associated with thinking skills: information gathering, information analysis and theory building. Converging learning style is associated with decision skills: quantitative analysis, technical device use and formulation of goals. Accommodating learning style is associated with action skills: leadership, initiative and action ,33. LearIn Psychiatry, the diagnostic process is mainly based on medical history, which is not often to be confirmed by lab tests or physical examinations. This leaves it more open for inter-personal differences in diagnostics, which might be a serious problem.To facilitate the diagnostic procedures, and increase certainty and consistency in Psychiatry, a system called DSM has been created . The DSM1. Which symptoms have currently forced the patient to seek help?2. How does the patient's overall pattern of experiences and behavior, compared with what is generally expected in the patients socio-cultural milieu, look like?3. Are there any somatic diseases, which have to be attended to?4. Have there occurred any stressful events in the patients' life along with the initial symptoms?5. How serious are the problems just now, how is the patient functioning?However, even DSM is not easily implemented in all situations, and therefore SCID, Structured Clinical Interview for DSM-diagnoses was created to further increase the reliability in psychiatric diagnostics . SCID isDSM training for physicians has been going on since 20 years during the Psychiatric course (9th semester) at Karolinska Institutet. The DSM-training is integrated in the course and the amount of time spent on DSM is approximately 4 hours. To our knowledge, there is no SCID-training in the Psychiatric course. However, in the training to become a specialist in Psychiatry at Karolinska Institutet, the DSM-framework is always used in teaching diagnostics and the amount of formal SCID-training is about 8 hours. Complementary to this, physicians becoming specialists in psychiatry are further trained in how to use SCID1 (axis 1 in DSM-IV) during their clinical training.During a SCID interview, "jigsaw puzzle bits" are gathered, where DSM functions as a method to sort and put together these puzzle bits to known clinical syndromes. DSM is criteria based. For each criteria the users have to consider if the patient's symptom reach clinical significance so that the criteria can be regarded as fulfilled or not. There is some help for the interviewer in the DSM system in the form of a "decision tree" for axis 1 diagnosis.CB-SCID1 (Computer-Based SCID for axis 1 diagnostics) is a software program that is reported to have advantages as compared to the ordinary SCID-interview  . The proacceptance, use and perceived usefulness of the CDSS CB-SCID1 for DSM-IV-diagnostics.This study was undertaken to investigate and describe how different learning style preferences among psychiatrists might affect A number of practicing Psychiatrists, working at three different clinics, with different degree of expertise were asked to participate in this study. A fourth clinic was invited to participate in the study but could not do this due to lack of time. Out of 67 invited physicians, 49 volunteered to participate in the study. Out of these, 31 were experts at a senior level , and 18 were non-specialists . In this study the groups are called "specialist group" (experts) and "non-specialist group" (not experts), respectively. They were all asked to complete a questionnaire regarding learning style preferences and to use the CB-SCID1 computer program for diagnostics of a real patient case (described in text) collected from the DSM-IV Case Book .To be able to relate the Learning Styles of the 49 physicians in the study to the general situation in Sweden, a random sample of 250 (out of 1900 practicing psychiatrists in Sweden) were asked to fill in the same Learning Style inventories as mentioned above. This part of the study was done by sending out a letter including details of the study, which was followed up by a second letter as a reminder some weeks after the first one. All data were kept unidentified.The learning style preferences for all participating physicians were measured according to the Kolb model  using thThe standardized terms and concepts of the DSM-system are the fundamentals of the CB-SCID1 system. CB-SCID1 uses logical inference of logical data , symbolically representing connections and dependency between components in the psychiatric knowledge base and presents questions according to the "paper" SCID-manual. CB-SCID1 takes care of the administration (for instance ordering of questions), correction possibility in criteria judgment, suggestion of answers according to previous in-data, summing up of fulfilled criteria, and also presentation of noted diagnoses. The system also handles some conflict control upon diagnoses. The user is asked to determine if various criteria are fulfilled or not and the system chooses how to go on, based on the user's input. If the number of fulfilled criteria reaches a certain level (according to DSM-IV) the program is automatically suggesting the corresponding DSM-IV diagnosis. The program is designed in a way that little training should be needed. CB-SCID1 also has a built-in, context sensitive help function in the consultation form, which put forward reminders and appropriate text information in tune with the decision problem at hand. The "assistance" from the system is based on the users input and also combined with the data driven rules derived from DSM-IV.The physicians were instructed to use the CB-SCID-1 program as a tool to find the correct DSM-IV diagnoses of the paper case, and use the system as if the case had been a real one.A special version of CB-SCID1, CB-SCID1_Log, was created, with a logging function that automatically stored a number of data in a separate log file, while the physician was using the program trying to make DSM-IV diagnosis on the patient case. The data logged (outcome log-file variables) were:• Total session time • Total decision time • Average decision time (mean time to decide about a criterion)• Total "non decision" time (time spent in the program not making decisions)• Total number of criteria judged • Total number of diagnoses , of proposed diagnoses by CB-SCID1)• Total number of correct diagnoses • Total number of incorrect diagnoses • Ratio between correct diagnoses and proposed number of diagnoses• Total number of regretted criteria-judgments • Total number of criteria judged unclear • Sum of numbers of regretted criteria-judgments and unclear criteria judgmentsThe CB-SCID1_Log system  was installed at the office computers of the clinicians and a short oral introduction of the system functionality was given, explaining both the use of the system, its online help system, and the aim of the study.The case used was picked from the DSM-IV Case Book . The casThe correct Axis 1 diagnoses  were in the actual case:• Major Depressive Disorder, recurrent, mild• Somatization Disorder• Alcohol Dependence, in sustained full remissionTo create a more realistic situation, the correct diagnoses were not revealed to the participating 49 psychiatrists, who all were given the same case with the aim to study the variation amongst the physicians using the CB-SCID1. They were not told what kind of case it was, neither the name of it ("Sickly"), nor where it came from.The investigation procedure was performed in four steps as follows:General information: The project leader gave oral information at each clinic on a regular meeting for psychiatrists about the study1. Individual information and questionnaire: Each participating physician was given further oral information about the study and was asked to fill in a form about gender, age, professional training, DSM/SCID training, computer skill and attitude towards computer-aided diagnostics. This was followed by filling in the Kolb Learning Style Inventory questionnaire LSI 3.2. CB-SCID1 test: The physicians received oral instructions on how to use the computer program and that it was more or less self-instructive compared to normal "paper" SCID-training, before using CB-SCID1. They were then instructed to diagnose the patient case with the help of the CB-SCID1-system. Ten minutes were offered reading the patient case before starting the CDSS-system3. Follow-up interview and questionnaire: A follow-up interview within a week from the first interview was done. There were open-ended questions about the pros and cons about the CB-SCID1 (pro and con categories were built on the basis of the answers content). Also structured questions using a four-graded scale were given about clinical interviewing skill (without computer aid), perceived usefulness of CB-SCID1 and computer anxiety, which all were graded using a four-graded scale.4. Structured questions, constructed by the authors, and graded on a four-grade scale were given in the pre-assessment survey about computer skill and attitude to computer-aided diagnostics. In the post-assessment survey, structured questions were given about computer anxiety, clinical interviewing skill (without computer aid) and perceived usefulness of CB-SCID1. The questions were put in a clear statement which they could agree to/not agree to in an ordered categorical scale (Strongly disagree = 1, Disagree = 2, Agree = 3, Strongly agree = 4). The subject areas asked about were well defined and familiar to the users, why standardized attitude scales were not used. An open-ended question about the pros and cons about the CB-SCID1 were also given in the post-assessment survey. The answers to this question were grouped into several categories.An analysis of correlations between the dimensions of LSI and the outcome log-file variables, as well as a comparison between specialists and non-specialists in this respect was also performed. Results were calculated as mean, standard deviation, median and lower – and upper quartile, where appropriate. Comparison between the two independent groups  was performed by the Mann-Whitney U Test and comparison between more than two independent groups (LS groups) was performed by the Kruskal-Wallis ANOVA by Ranks Test. Association between variables was calculated by Spearman Rank Order Correlations.All parts of this study have been approved by the ethical committee of Karolinska Institutet.All of the 49 psychiatrists volunteering to participate in the original study group fulfilled all phases of the study. The randomly sampled 250 psychiatrists had a response rate of 42% (95). Only 226 questionnaires could actually be sent out because 24 of the randomly chosen 250 psychiatrists could not be reached due to for example that they had moved abroad or retired. One of the 95 actual responses was incomplete. Demographic data of the study groups can be seen in Table The 49 physicians in the original study group were reporting a value of 3 for general computer skill and a value of 1 for computer anxiety, both median values on a four-graded scale (where 1 is very negative or very low and 4 is very positive or very high). There were no differences in computer skill between specialists and non-specialists. Computer skill median was 3 in the random sample group.The physicians were reporting to have a good clinical interviewing skill (without computer-aid), the median was 4 for specialists and 3 for non-specialists, which would predict high expected values on correct diagnosis and low values on incorrect diagnoses.In the original study group, the attitude to computer-aided diagnostics presented as medians and lower – and upper quartile were for, male specialists 3 (2–4), female specialists 3 (2–3) and male non-specialists 3 (3-3), female non-specialists 3 (3-3). In the random sample group, where all were specialists, the results were for males 3 (2–4) and females 3 (2–3).Other general variables like Gender, Age, Level of professional training, Computer skill, Attitude to Computer-aided diagnostics, DSM-IV/SCID-training were not found to be statistically correlated to the dependent log file variables .The Learning Styles of the 49 physicians (tested by the LSI for learning style preferences) are shown in Table In comparison, the random sample of the psychiatrists in Sweden  turned out as indicated in Table Among the 49 psychiatrists, it was found a correlation (p < .01) between learning styles and reported computer skill. The persons with highest score on computer skill were Converging, followed by Accommodating and Assimilating. Diverging styles were found to have the lowest computer skill.A positive attitude to computer-aided diagnostics and learning styles were also found to correlate (p = .04). Most positive were Assimilating, followed by Converging and Accommodating. Diverging had the lowest figures in terms of attitude to computer-aided diagnostics.Finally, it was also found that the distribution of learning styles and the Number of Criteria judged in the system significantly correlated (p < .01). The Accommodating group used the highest number of criteria, followed by the Converging, Diverging and Assimilating groups in that order.Interestingly, the 49 physicians gave a rather low percentage of correct diagnoses. The correct diagnoses Major Depressive Disorder was found by only 27%, Somatization Disorder by 55% and Alcohol Dependence by 78%. Only 10% had all three diagnoses correct. 41% two correct diagnoses, 47% one correct diagnose and 2% no correct diagnose at all. Median values of proposed, correct, incorrect and ratio correct/proposed diagnoses for specialist and non-specialists are shown in Table As seen in Table The indicator of acceptance, user's attitude to computer-aided diagnostics, was found to be 3 on a four-graded scale for both specialists and non-specialists in the original study group as well as for the random sample. No difference was found among genders. When correlating this to computer skill and computer anxiety it was found an overall negative significant correlation (-.48, p < .001) between positive attitude to computer-aided diagnostics and computer anxiety. Within the Accommodating group there was an even higher negative significant correlation (-.80, p < .001) between positive attitude to computer-aided diagnostics and computer anxiety.There was only a tendency to a correlation between computer skill and a positive attitude toward computer-aided diagnostics (.27, p = .06) in the original study group. However, within the Accommodating group, there was a positive significant correlation (.62, p = .02). In the random sample group, a significant correlation between computer skill and positive attitude to computer-aided diagnostics (.43, p < .001) was found.Finally, there was an overall negative significant correlation between computer skill and computer anxiety (-.51, p < .001).The use of CB-SCID1 was found to be varying among the 49 psychiatrists. For example, a positive significant correlation (.31, p = .03) was found between the active experimentation information processing mode (Converging and Accommodating) and the Total Number of Criteria judged in the program. No other significant correlations between the active experimentation-reflective observation dimension and the other 11 outcome log-file variables were found.The total number of criteria judged for the patient case used varied from 44 to 119.There were no significant correlations between the abstract conceptualization-concrete experience dimension and the 12 outcome log-file variables.The comparison between the specialist group and non-specialist group concerning the 12 log-file variables revealed no significant differences of medians  a significant correlation (-.32) between perceived future usefulness and the Abstract Conceptualization – Concrete Experience dimension (p = .02) was found. The Abstract Conceptualizations orientated group had more Pros while the Concrete Experience orientated had more Cons on perceived future usefulness. This indicates that the Assimilating and Converging learning styles, which perceive information by abstract conceptualizations, are favored by the CB-SCID1-system.According to the interviews, the perceived usefulness of the CB-SCID1-system was more negative than positive. 27 (55%) of the psychiatrists noted more Cons than Pros, 22 noted more Pros than Cons (45%).The responses were also categorized according to their general content. 6 positive and 7 more negative general categories were found, see Table Given the reported high clinical interviewing skill, high computer skill and relatively positive attitude to computer-aided diagnostics for the group, the low number of correct diagnoses and high number of incorrect diagnoses is very interesting. The DSM-IV-diagnosis is a symptom-diagnose derived from a deliberately limited amount of relevant diagnostic information, pattern of symptoms and development within predefined limits. This is not intended to be compared with the clinical diagnosis, which is building on patterns of symptoms, complete development, actual circumstances, anamnestic data, etiological discussion, laboratory tests, psychological tests etc. The symptom-diagnose and clinical diagnose perspective might be complementary to each other and it is usually recommended that a clinical interview always should be done before the SCID or CB-SCID1 interview. One possible explanation of our findings is that diagnosis in psychiatry is so complex that neither the DSM-system nor the "paper-SCID" or the CB-SCID1 systems might help. Another possible explanation is that CDSS have their limitations. This is in accordance with for example Dreyfus and Dreyfus who argued that computers could be good competent manipulators of symbols according to prepackaged algorithms, but they lack the type of intuition that real experts have .There are alternatives to the SCID and CB-SCID1 to make DSM-IV-diagnosis. For example, the SCAN  and the computerized version, the SCAN 2.1 system, are systems developed by the WHO  ,41. The The fact that this study did not diagnose living patients and that it was done with the help of a computer program may affect the results in that tacit knowing could not come into play as much as in a real situation. Polanyi  mentionsWe also have to keep in mind that the 49 physicians tried the CB-SCID1 for the first time and with limited training in the program (even if the system is said to be possible to be used with little training). When trying to evaluate the results, we have to consider the different needs, habits and working style of experts and non-experts of different levels. For example, any expert, with a possible intuitive way of thinking, might be confused when they use a program addressed to non-experts that emphasize rule-following and logical step-by-step working procedures.But our interpretation is still that the explanation is that CDSS do not suit all clinicians. This is in accordance with for example Ridderikhoff and van Herk  who founA limitation of this study is that only one case was used. The main reason for only using one single case was that the 49 physicians participating were very busy and reported that they had little time to spend for research projects like this. Furthermore, most of the 18 physicians not accepting to participate in the study reported lack of time as the main reason; asking the participants to use more cases, would most probably have resulted in more dropouts. The fact that physicians decided not to participate due to time constraints, could also raise a concern about selection bias since they might actually be less prone to use computer technology, differ in learning style or else. However, as the real case used is judged as being rather standardized and has been used successfully in training, we consider the risk of incorrect results being rather low.Another limitation is that the computer skill was self-reported, and that no standardized skill test was used.Finally, one limitation is that some of the psychiatrists participating in this study might have seen the used case before. However, since the DSM Case Book covers more than 200 cases, the chance that those psychiatrists remembered both the actual case and its three specific diagnoses is considered to be very low.et al [It has been reported that domain specialists might have different LS preferences, for example Baker III, et al  using thet al ,45 resulAnother possible explanation is, according to the Kolb theory, Diverging-preference persons with their Concrete perceiving and Reflective observation are predicted to be the least interested in computer work, and thus that there is a possibility that the result is due to selection bias and low response rate. Theoretically the nearly 60 % of the 226 Swedish psychiatrists who did not answer the questionnaire, might have a Diverging preference. If so psychiatrists would have a Diverging preference, and the CB-SCID1 perceived usefulness values would probably be even more negative. Divergers also had the lowest figures in terms of attitude to computer-aided diagnostics and computer skill. Could it be that the low number of Diverging preferences, 6% in the original study group is the result of "selection bias"? After all, they were volunteers to participate in the study. They were asked to try the CB-SCID1 and may have hesitated to participate due to lack of computer skill? The random sample group was not asked to try the CB-SCID1 and Diverging preferences in that group were 22%. The higher numbers with Converging preference (high computer skill) in the Original study group compared to the Random sample group might have been explained by the same reason.The fact that there were no significant correlations between the general variables and the dependent log-file variables indicate that further analyses of for example attitudes, use, perceived usefulness and learning style must be made in the future. This is supported by our findings that individual values regarding computer skill, computer anxiety and attitude to computer-aided diagnostics are related to learning styles and acceptance, use and perceived usefulness.Computer skill, computer anxiety and the attitude to computer-aided diagnostics are interpreted to be important variables in the acceptance of the CB-SCID1-system. Accommodating psychiatrists with computer anxiety have a very low value on the indicator of acceptance. This means that this group has difficulties accepting the CB-SCID1. Again within the Accommodating group, those with computer skill are positive to the indicator of acceptance.This highlights the importance of training as a means to increase computer skill and ease the acceptance of systems like CB-SCID1. Maybe the acceptance would increase if only targeting beginners or persons on "medium level" of training. As shown, a tendency for a more positive regard to computer-aided diagnostics exists in the non-expert group. The specialists, as proficient and on expert level, might not have a need of such computer-aid, or at least a different need in accordance with their way of working as experts with an intuitive frame of reference compared to the step by step, rule-following work of the novice.A possible explanation for the low number of significant results between LS and the outcome log-file variables might be that learning style is used with more flexibility in a real situation (using CB-SCID1) compared to more "attitude-like" variables like acceptance and perceived usefulness.It might be so that LS must be linked to learning skill and adaptivity/flexibility in different specific situations to give significant results against the log-file variables. The LSI is measuring learning style preferences not associated to specific situations.et al [Our findings that the program seems to be more attractive to psychiatrists with learning styles which prefer Abstract Conceptualizations (Assimilating and Converging learning styles) and in the majority negative to the Concrete Experience (Accommodating and Diverging learning styles) is in accordance with Lu et al  who founThe Cons in future perceived usefulness against the CB-SCID1 about the appropriateness of such a program, the conflict perspective about using it and the breaking up of dialogue contain an all or none view. However, the Cons arguments have much in similar with the Dreyfus and Dreyfus standpoint regarding the limitations of computer use and seems to be reflecting the expert view, for instance lack of appropriateness, empathy and intuition and underestimation of your own skill. The Pros arguments seem to meet the non-expert view of learning to diagnose, for instance the program as a structure, help and correction facility. How to use, by whom and when, in which situations the CB-SCID1 is to be used must be elaborated upon. Non-experts with Diverging preferences might increase their flexibility using learning styles as a result of training and maybe a more positive regard to computer-aided diagnostics. That is, going around the "Kolb circle" in a more flexible and balanced way, using all learning styles, and give up their strong Diverging preferences.Our results indicate that computer skill is of importance, and computer anxiety of negative impact on the attitude to computer-aided diagnostics. The highest computer skills were found within the Converging and Accommodating groups, which use Active Experimentation as information processing mode. Most positive to computer-aided diagnostics were the Assimilating and Converging groups, which in perceiving information are using Abstract Conceptualization.The results also indicate that the Active Experimentation information-processing mode (Accommodating and Converging learning styles) is significantly correlated to number of criteria judged in the program.Furthermore, the results also indicates, that although the 49 psychiatrists reported a positive attitude to computer-aided diagnostics, physicians with computer anxiety are less positive. Moreover, the CB-SCID1 CDSS seemingly invites the Accommodating and Converging learning styles to significantly adopt an Active Experimentation information-processing mode with a high number of criteria judged. However, this experimental mode may increase the number of incorrect diagnoses. The Assimilating and Converging learning styles also seems to be favored by the computer program concerning the Abstract Conceptualization mode in perceiving information.The results of this study suggests that a CDSS is no guarantee of improved diagnostic procedures in Psychiatry and that even a clinically experienced user might end up with several incorrect diagnoses using such a system. The results also indicate that the use of CDSS-tools seems to favor users with learning style preferences using abstract conceptualization information perceiving mode.Furthermore, our results indicate that future research on CDSS should focus on the possibility to create systems open to individuals with different LS preferences. Future research should also focus on the importance of computer training and different professional levels to optimize the usefulness of CDSS. The relationship between learning style preferences and working style habits at different professional levels might also be elaborated upon as well as the importance of learning style flexibility and decision modes in various diagnostic situations.CDSS Computerized decision support systemDS Decision supportDSM Diagnostic Statistical Manual, "DSM-system"DSM-IV Diagnostic Statistical Manual, version 4LS Learning StyleSCID Structured Clinical Interview for DSM-diagnosesCB-SCID1 Computer-Based Structured Clinical Interview for DSM-diagnoses (axis 1)Competing interests exists between the commercial CB-SCID1 system from Pilgrim Press and the SCAN-system developed by WHO, World Health Organization. However, the authors have no interests in any of these competitors.UF has supervised LB in the planning, carrying out and analysis of this research work. LB has carried out all experiments. The manuscript has been written on close collaboration between LB and UF.The pre-publication history for this paper can be accessed here:


Cardamine amara—to the enormous—124,000 Mb in the lily Fritillaria assyriaca. By comparison, fruitfly and human genomes have 180 Mb and 3,200 Mb, respectively. Genomes of important crops such as sorghum, soybean, maize, and wheat hover between 735 Mb and 16,900 Mb, and determining their complete sequences is daunting and costly.Plant genome sizes span the modest—54 million base pairs (Mb) in the bitter cress Sorghum bicolor), a staple crop in much of the developing world because of its resilience in arid climates.Wide size variations do not necessarily reflect differences in gene content, but rather reflect the presence of repetitive sequence elements that do not generally code for genes. Repetitive elements account for at least 75% of the maize and sorghum genomes. In a new study, Joseph Bedell and his colleagues describe a way to filter away repetitive elements when sequencing the genome of sorghum . BACs offer the most comprehensive representation of the genome because they contain large pieces of unmodified sorghum chromosomal DNA. Of the 148 genes identified on 14 sorghum BACs, 133 appeared in the filtered set. This means that the methylation filtration method captured at least 90% of the genes in the sorghum genome and 96% (131/137) if a repeat cluster of 11 known methylated genes is removed from the analysis.Arabidopsis—a plant model organism—predicted that shotgun approach would yield similar results (67% of the length of 96% of the genes) after sequencing the equivalent of 1.15 times its total length (rather than 1.15 times the length of just the coding regions). Thus, methylation filtration can provide as much information on coding sequences as the shotgun approach, with less investment in sequencing.Methylation filtration also compared favorably to shotgun sequencing, a method that reads the whole genome in small fragments that are progressively assembled into larger pieces by computer analysis. The authors reported that after sequencing 285 Mb of filtered sorghum DNA—approximately 1.15 times the length of the sorghum coding regions—they obtained on average 65% of the length of 96% of the genes. Theoretical calculations and simulation based on the genome of Methylation filtration does not yield a complete genome map, but it offers quicker, more affordable access to genes than most commonly used sequencing approaches. Sorghum is closely related to maize and sugar cane, and more distantly to rice. The availability of its genome sequence offers the chance for more in-depth experiments into the evolution of the grass family, and promises important insights into the genetic control of drought resistance.


Identifying the causative mutation for a disease can be the first step to a potential cure. This task is not always trivial. Often the initial strategy is to look for the variations within a mutated gene that alter its protein coding sequence, as these mutations often alter the gene's function. However, in a growing number of cases, the causative mutation is a “synonymous” mutation—a change in the coding sequence of a gene that doesn't change the sequence of the protein coded by the gene. This type of mutation may be responsible for Seckel syndrome, a human disease characterized by dwarfism. In Seckel syndrome, the mutation doesn't alter the protein sequence itself but instead results in the skipping over of a portion of the protein coding sequence (an exon), a process called altered splicing. The disease-causing potential of this type of splicing mutation has only recently gathered attention.Splicing assembles the exons of a transcribed gene (the RNA copy) into the right order while removing the non-coding sequences of the RNA (introns). This highly regulated process is coordinated by a number of sequences within a gene, including splice sites that precede and follow the exon, as well as by exonic splicing enhancers (ESEs), which help recruit the factors (proteins) necessary to insure proper splicing. Although splice sites have optimal (consensus) sequences, there is some variability amongst individual splice site sequences that allows splicing to take place to a greater or lesser extent. ESEs facilitate splicing, especially when a gene's splice sites vary from the consensus sequence. Candidate ESEs have previously been identified based on their more frequent occurrence in exons that are adjacent to non-consensus splice sites.PLoS Biology, William Fairbrother et al. investigated the functionality of these putative ESEs. If they are functional, the authors reasoned, then mutations that disrupt them would be selected against—that is, these mutations would tend to be discarded—in the human genome.In this issue of To this end Fairbrother et al. developed a computational method, which they call VERIFY , to evaluate the selective pressure on ESEs. They took advantage of a public database of all single nucleotide polymorphisms (DNA changes at a single point) within the human genome and compared them to the chimpanzee genome; this allowed the authors to infer the identity of the ancestral gene . By determining which allele is ancestral and which is the variant, the researchers could then distinguish the mutations that created ESEs from mutations that disrupted ESEs.Mutations that altered or disrupted ESEs were under-represented, leading the authors to conclude that predicted ESE sequences evolve under a more stringent level of selection than exonic sequences with no predicted ESEs. This selective pressure was greater for predicted ESEs located near the splice signals than for ESEs that were located within the exon. This result was consistent with experimental findings that ESE strength diminishes with distance from the splice site.As more vertebrate genomes are sequenced and the public database of single nucleotide polymorphisms continues to grow, this type of computational method will become increasingly valuable. It can help confirm the functionality or role of candidate regulatory elements thought to control various aspects of gene expression, and in so doing, offer insights into the complex machinations required to maintain the healthy operation of the human genome.