Resistance of Reinforced Concrete Structures Under High Intensity Loads

John I. Parcel Fund

Writing clinical practice guidelines in controlled natural language

Clinicians could benefit from decision support systems incorporating the knowledge contained in clinical practice guidelines. However, the unstructured form of these guidelines makes them unsuitable for formal representation. To address this challenge we translated a complete set of pediatric guideline recommendations into Attempto Controlled English (ACE). One experienced pediatrician, one physician and a knowledge engineer assessed that a suitably extended version of ACE can accurately and naturally represent the clinical concepts and the proposed actions of the guidelines. Currently, we are developing a systematic and replicable approach to authoring guideline recommendations in ACE

Automatic extraction of candidate nomenclature terms using the doublet method

BACKGROUND: New terminology continuously enters the biomedical literature. How can curators identify new terms that can be added to existing nomenclatures? The most direct method, and one that has served well, involves reading the current literature. The scholarly curator adds new terms as they are encountered. Present-day scholars are severely challenged by the enormous volume of biomedical literature. Curators of medical nomenclatures need computational assistance if they hope to keep their terminologies current. The purpose of this paper is to describe a method of rapidly extracting new, candidate terms from huge volumes of biomedical text. The resulting lists of terms can be quickly reviewed by curators and added to nomenclatures, if appropriate. The candidate term extractor uses a variation of the previously described doublet coding method. The algorithm, which operates on virtually any nomenclature, derives from the observation that most terms within a knowledge domain are composed entirely of word combinations found in other terms from the same knowledge domain. Terms can be expressed as sequences of overlapping word doublets that have more specific meaning than the individual words that compose the term. The algorithm parses through text, finding contiguous sequences of word doublets that are known to occur somewhere in the reference nomenclature. When a sequence of matching word doublets is encountered, it is compared with whole terms already included in the nomenclature. If the doublet sequence is not already in the nomenclature, it is extracted as a candidate new term. Candidate new terms can be reviewed by a curator to determine if they should be added to the nomenclature. An implementation of the algorithm is demonstrated, using a corpus of published abstracts obtained through the National Library of Medicine's PubMed query service and using "The developmental lineage classification and taxonomy of neoplasms" as a reference nomenclature. RESULTS: A 31+ Megabyte corpus of pathology journal abstracts was parsed using the doublet extraction method. This corpus consisted of 4,289 records, each containing an abstract title. The total number of words included in the abstract titles was 50,547. New candidate terms for the nomenclature were automatically extracted from the titles of abstracts in the corpus. Total execution time on a desktop computer with CPU speed of 2.79 GHz was 2 seconds. The resulting output consisted of 313 new candidate terms, each consisting of concatenated doublets found in the reference nomenclature. Human review of the 313 candidate terms yielded a list of 285 terms approved by a curator. A final automatic extraction of duplicate terms yielded a final list of 222 new terms (71% of the original 313 extracted candidate terms) that could be added to the reference nomenclature. CONCLUSION: The doublet method for automatically extracting candidate nomenclature terms can be used to quickly find new terms from vast amounts of text. The method can be immediately adapted for virtually any text and any nomenclature. An implementation of the algorithm, in the Perl programming language, is provided with this article

Structural similarity assessment for drug sensitivity prediction in cancer

<p>Abstract</p> <p>Background</p> <p>The ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. Several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as NCI-60. The general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. Importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. However, one limitation is that the profiles can not be generated for untested drugs (i.e., drugs that are not part of an anti-cancer drug screen). In this work, we propose using an existing drug sensitivity profile for drug A as a substitute for an untested drug B given high structural similarities between drugs A and B.</p> <p>Results</p> <p>We first show that structural similarity between pairs of compounds in the NCI-60 dataset highly correlates with the similarity between their activities across the cancer cell lines. This result shows that structurally similar drugs can be expected to have a similar effect on cancer cell lines. We next set out to test our hypothesis that we can use existing drug sensitivity profiles as substitute profiles for untested drugs. In a cross-validation experiment, we found that the use of substitute profiles is possible without a significant loss of prediction accuracy if the substitute profile was generated from a compound with high structural similarity to the untested compound.</p> <p>Conclusion</p> <p>Anti-cancer drug screens are a valuable resource for generating omics-based drug sensitivity profiles. We show that it is possible to extend the usefulness of existing screens to untested drugs by deriving substitute sensitivity profiles from structurally similar drugs part of the screen.</p

Live slow-frozen human tumor tissues viable for 2D, 3D, ex vivo cultures and single-cell RNAseq

Biobanking of surplus human healthy and disease-derived tissues is essential for diagnostics and translational research. An enormous amount of formalin-fixed and paraffin-embedded (FFPE), Tissue-Tek OCT embedded or snap-frozen tissues are preserved in many biobanks worldwide and have been the basis of translational studies. However, their usage is limited to assays that do not require viable cells. The access to intact and viable human material is a prerequisite for translational validation of basic research, for novel therapeutic target discovery, and functional testing. Here we show that surplus tissues from multiple solid human cancers directly slow-frozen after resection can subsequently be used for different types of methods including the establishment of 2D, 3D, and ex vivo cultures as well as single-cell RNA sequencing with similar results when compared to freshly analyzed material

Germline MC1R status influences somatic mutation burden in melanoma

The major genetic determinants of cutaneous melanoma risk in the general population are disruptive variants (R alleles) in the melanocortin 1 receptor (MC1R) gene. These alleles are also linked to red hair, freckling, and sun sensitivity, all of which are known melanoma phenotypic risk factors. Here we report that in melanomas and for somatic C>T mutations, a signature linked to sun exposure, the expected single-nucleotide variant count associated with the presence of an R allele is estimated to be 42% (95% CI, 15-76%) higher than that among persons without an R allele. This figure is comparable to the expected mutational burden associated with an additional 21 years of age. We also find significant and similar enrichment of non-C>T mutation classes supporting a role for additional mutagenic processes in melanoma development in individuals carrying R alleles