Abstract:Telomeres and centromere are two essential features of all eukaryotic chromosomes. They provide function that is necessary for the stability of chromosomes. We developed a comprehensive database named TeCK, which covers a gamut of sequence and other related information about telomeric patterns, telomere repeat sequences, centromere sequences and centromeric patterns present in chromosomes. It also contains information about telomerase ribo-nucleoprotein complexes, centromere binding protein and centromere DNA-binding protein complexes. The database also includes a collection of all kinetochoreassociated proteins including inner, outer and central kinetochore proteins. The database can be searched using a userfriendly web interface.
An integral step in the discovery of new treatments for medical conditions is the matching of potential subjects with appropriate clinical trials. Eligibility criteria for clinical trials are typically specified as inclusion and exclusion criteria for each study in freetext form. While this is sufficient for a human to guide a recruitment interview, it cannot be reliably and computationally construed to identify potential subjects. Standardization of the representation of eligibility criteria can enhance the efficiency and accuracy of this process. This article presents a semantic framework that facilitates intelligent matchmaking by identifying a minimal set of eligibility criteria with maximal coverage of clinical trials. In contrast to existing top-down manual standardization efforts, a bottom-up data driven approach is presented to find a canonical nonredundant representation of an arbitrary collection of clinical trial criteria. The methodology has been validated with a corpus of 709 clinical trials related to Generalized Anxiety Disorder containing 2,760 inclusion and 4,871 exclusion eligibility criteria. This corpus is well represented by a relatively small number of 126 inclusion clusters and 175 exclusion clusters, each of which corresponds to a semantically distinct criterion. Internal and external validation measures provide an objective evaluation of the method. An eligibility criteria ontology has been constructed based on the clustering. The resulting model has been incorporated into the development of the MindTrial clinical trial recruiting system. The prototype for clinical trial recruitment illustrates the effectiveness of the methodology in characterizing clinical trials and subjects and accurate matching between them. ACM Reference Format:Lee, Y., Krishnamoorthy, S., and Dinakarpandian, D. 2013. A semantic framework for intelligent matchmaking for clinical trial eligibility criteria.
One of the common tasks in clinical natural language processing is medical entity linking (MEL) which involves mention detection followed by linking the mention to an entity in a knowledge base. One reason that MEL has not been solved is due to a problem that occurs in language where ambiguous texts can be resolved to several named entities. This problem is exacerbated when processing text found in electronic health records. Recent work has shown that deep learning models based on transformers outperform previous methods on linking at higher rates of performance. We introduce NeighBERT, a custom pre-training technique which extends BERT \citep{devlin-etal-2019-bert} by encoding how entities are related within a knowledge graph. This technique adds relational context that has been traditionally missing in original BERT, helping resolve the ambiguity found in clinical text. In our experiments, NeighBERT improves the precision, recall and F1-score of the state of the art by 1--3 points for named entity recognition and 10--15 points for MEL on two widely known clinical datasets.
Background The banana stem weevil, Odoiporus longicollis (Olivier), is a serious threat to banana cultivation world over. Since banana is a food crop, the use of naturally infecting biological control agents could be an effective alternative to manage the insect pest instead of harmful chemicals. Also, the efficacy of entomopathogenic fungi against O. longicollis was used in bioassay. Results Among the Beauveria bassiana isolates tested the median lethal concentration (LC50) 10.468 × 105 conidia ml−1 when treated with B. bassiana (NRCBEFPMP1), two other isolates of B. bassiana, namely NRCBEPF22 and NRCBEPF2, were also effective against O. longicollis and recorded LC50 of 12.617 × 105 and 12.891 × 105 conidia ml−1, respectively. The results of bioassay with different Metarhizium spp. showed variations in efficacy, where the most virulent isolate was M. quizhouense (NRCBEPF11) with LC50 8.050 × 105 conidia ml−1. Scanning electron microscopic analysis showed that B. bassiana and M. quizhouense caused infection by cuticle penetration and completed the infection process in 15 days. The composition of volatile organic compounds released by B. bassiana and M. anisopliae during pathogenesis showed that a significantly high number of known insect volatiles were present in infected insects. Consequently, these volatiles were emission in Insect attractant, Odorant receptor agonist, Plant hormone Plant, and Microbial Metabolites, through the biological activity, such as Methyl salicylate, Benzaldehyde, alpha-Terpineol, Limonene, Benzene, 1,2-dimethoxy, Phthalic acid, 1-Octadecene, Phenylacetaldehyde, 3-Octanone, Octanal, Methylheptenone and 2-Ethyl-1-hexyl alcohol. Conclusion Overall, the results show that EPF could significantly reduce damage by O. longicollis and produce a wide profile of secondary metabolites. Further, analysis was used for principal components to determine whether separated classes of fungi can be distinguished from one another based on their metabolite profiles.
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