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Accurate and reliable part-of-speech tagging is useful for many Natural Language Processing (NLP) tasks that form the foundation of NLP-based approaches to information retrieval and data mining. In general, large annotated corpora are necessary to achieve desired part-of-speech tagger accuracy. We show that a large annotated general-English corpus is not sufficient for building a part-of-speech tagger model adequate for tagging documents from the medical domain. However, adding a quite small domain-specific corpus to a large general-English one boosts performance to over 92% accuracy from 87% in our studies. We also suggest a number of characteristics to quantify the similarities between a training corpus and the test data. These results give guidance for creating an appropriate corpus for building a part-of-speech tagger model that gives satisfactory accuracy results on a new domain at a relatively small cost.

Aims/hypothesis Genetic insulin receptoropathies are a rare cause of severe insulin resistance. We identified the Ile119Met missense mutation in the insulin receptor INSR gene, previously reported in a Yemeni kindred, in four unrelated patients with Somali ancestry. We aimed to investigate a possible genetic founder effect, and to study the mechanism of loss of function of the mutant receptor. Methods Biochemical profiling and DNA haplotype analysis of affected patients were performed. Insulin receptor expression in lymphoblastoid cells from a homozygous p.Ile119Met INSR patient, and in cells heterologously expressing the mutant receptor, was examined. Insulin binding, insulin-stimulated receptor autophosphorylation, and cooperativity and pH dependency of insulin dissociation were also assessed. Results All patients had biochemical profiles pathognomonic of insulin receptoropathy, while haplotype analysis revealed the putative shared region around the INSR mutant to be no larger than 28 kb. An increased insulin proreceptor to β subunit ratio was seen in patient-derived cells. Steady state insulin binding and insulin-stimulated autophosphorylation of the mutant receptor was normal; however it exhibited decreased insulin dissociation rates with preserved cooperativity, a difference accentuated at low pH. Conclusions/interpretation The p.Ile119Met INSR appears to have arisen around the Horn of Africa, and should be sought first in severely insulin resistant patients with ancestry from this region. Despite collectively compelling genetic, clinical and biochemical evidence for its pathogenicity, loss of function in conventional in vitro assays is subtle, suggesting mildly impaired receptor recycling only. Electronic supplementary material The online version of this article (doi:10.1007/s00125-011-2066-z) contains supplementary material, which is available to authorised users.

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97–1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82–0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.

The scattering amplitude from a strongly absorbing potential, perturbed by a parity-dependent term, is evaluated in a semiclassical limit. The perturbation causes the appearance of contributions from a new class of generalized trajectories in which sudden reversions of the relative position vector appear along the unperturbed trajectories. The contributions from these trajectories closely resemble those corresponding to trajectories refracted in the internal region.

A new model for heavy-ion elastic scattering at energies around the Coulomb barrier is presented. The model assumes that the reflection at the nuclear surface is the dominant scattering mechanism for the particles which hit the region of the nuclear interaction, and that the classical mechanics rules can be applied to estimate the cross-section. The predictions of the model are compared with a few experimental data and the radius of the reflecting region and the reflection properties of the nuclear surface are obtained.