SOFTWARE AND KNOWLEDGE REuse have generated considerable interest because they reduce development time and the resources that projects require. For knowledge-based systems, in particular, the high cost of knowledge acquisition makes reuse essential. However, reuse involves these challenges: heterogeneity of representation formalisms, languages, and tools; lexical and semantic problems; assumptions implicit in each system; and commonsense-knowledge losses. Ontologies are a way around these obstacles. They are useful for unifying database, data-warehouse, and knowledge-base vocabularies and even for maintaining consistency when updating corporate memories used in knowledge management.To meet the challenge of building ontologies, we've developed Methontology, 1,2 a framework for specifying ontologies at the knowledge level, 3 and the Ontology Development Environment. This article presents our experience in using Methontology and ODE to build the Chemicals ontology. 4 The challenge of building ontologiesOntology building is a craft rather than an engineering activity. Each development team usually follows its own set of principles, design criteria, and phases. The absence of structured guidelines and methods hinders the development of shared and consensual ontologies within and between teams, the extension of an ontology by others, and its reuse in other ontologies and final applications. We believe that the source of these problems is the absence of an explicit and fully documented conceptual model upon which to formalize the ontology.Like knowledge-based-system development, ontology development faces a knowledge-acquisition bottleneck. Unlike KBS developers, ontology developers (ontologists) lack sufficiently tested and generalized methodologies recommending what activities should be performed and at what stage of ontology development. (For descriptions of related work, see the sidebar.)Ontology developers often switch directly from knowledge acquisition to implementation, which poses these problems:First, the conceptual models are implicit in the implementation codes. Making the conceptual models explicit usually requires reengineering.Second, ontological commitments 5 and design criteria are implicit and explicit in the ontology code.Third, domain experts and human end users have no understanding of formal ontologies codified in ontology languages. 6 Research has shown that, using the Ontology Server browser tools, 7 experts and users could gain a full understanding of and validate taxonomies and partially understand instances. However, they were unable to

BackgroundDisparate and contradictory results make studies necessary to investigate in more depth the relationship between diagnostic delay and survival in colorectal cancer (CRC) patients. The aim of this study is to analyse the relationship between the interval from first symptom to diagnosis (SDI) and survival in CRC.MethodsRetrospective study of n = 942 CRC patients. SDI was calculated as the time from the diagnosis of cancer and the first symptoms of CRC.Cox regression was used to estimate five-year mortality hazard ratios as a function of SDI, adjusting for age and gender. SDI was modelled according to SDI quartiles and as a continuous variable using penalized splines.ResultsMedian SDI was 3.4 months. SDI was not associated with stage at diagnosis (Stage I = 3.6 months, Stage II-III = 3.4, Stage IV = 3.2; p = 0.728). Shorter SDIs corresponded to patients with abdominal pain (2.8 months), and longer SDIs to patients with muchorrhage (5.2 months) and rectal tenesmus (4.4 months).Adjusting for age and gender, in rectum cancers, patients within the first SDI quartile had lower survival (p = 0.003), while in colon cancer no significant differences were found (p = 0.282). These results do not change after adjusting for TNM stage.The splines regression analysis revealed that, for rectum cancer, 5-year mortality progressively increases for SDIs lower than the median (3.7 months) and decreases as the delay increases until approximately 8 months. In colon cancer, no significant relationship was found between SDI and survival.ConclusionsShort diagnostic intervals are significantly associated with higher mortality in rectal but not in colon cancers, even though a borderline significant effect is also observed in colon cancer. Longer diagnostic intervals seemed not to be associated with poorer survival. Other factors than diagnostic delay should be taken into account to explain this “waiting-time paradox”.

The development of methods that can predict the metal-mediated biological activity based only on the 3D structure of metal-unbound proteins has become a goal of major importance. This work is dedicated to the amino terminal Cu(II)- and Ni(II)-binding (ATCUN) motifs that participate in the DNA cleavage and have antitumor activity. We have calculated herein, for the first time, the 3D electrostatic spectral moments for 415 different proteins, including 133 potential ATCUN antitumor proteins. Using these parameters as input for Linear Discriminant Analysis, we have found a model that discriminates between ATCUN-DNA cleavage proteins and nonactive proteins with 91.32% Accuracy (379 out of 415 of proteins including both training and external validation series). Finally, the model has predicted for the first time the DNA cleavage function of proteins from the pathogen parasites. We have predicted possible ATCUN-like proteins with a probability higher than 99% in nine parasite families such as Trypanosoma, Plasmodium, Leishmania, or Toxoplasma. The distribution by biological function of the ATCUN proteins predicted has been the following: oxidoreductases 70.5%, signaling proteins 62.5%, lyases 58.2%, membrane proteins 45.5%, ligases 44.4%, hydrolases 41.3%, transferases 39.2%, cell adhesion proteins 34.5%, metal binders 33.5%, translation proteins 25.0%, transporters 16.7%, structural proteins 9.1%, and isomerases 8.2%. The model is implemented at http://miaja.tic.udc.es/Bio-AIMS/ATCUNPred.php.

Early warning in egg production curves from commercial hens: A SVM approach. AbstractArtificial Intelligence allows the improvement of our daily life, for instance, speech and handwritten text recognition, real time translation and weather forecasting are common used applications. In the livestock sector, machine learning algorithms have the potential for early detection and warning of problems, which represents a significant milestone in the poultry industry. Production problems generate economic loss that could be avoided by acting in a timely manner.In the current study, training and testing of support vector machines are addressed, for an early detection of problems in the production curve of commercial eggs, using farm´s egg production data of 478,919 laying hens grouped in 24 flocks.Experiments using support vector machines with a 5 k-fold cross-validation were performed at different previous time intervals, to alert with up to 5 days of forecasting interval, whether a flock will experience a problem in production curve. Performance metrics such as accuracy, specificity, sensitivity, and positive predictive value were evaluated, reaching 0-day values of 0.9874, 0.9876, 0.9783 and 0.6518 respectively on unseen data (test-set).The optimal forecasting interval was from zero to three days, performance metrics decreases as the forecasting interval is increased. It should be emphasized that this technique was able to issue an alert a day in advance, achieving an accuracy of 0.9854, a specificity of 0.9865, a sensitivity of 0.9333 and a positive predictive value of 0.6135. This novel application embedded in a computer system of poultry management is able to provide significant improvements in early detection and warning of problems related to the production curve.