PubMed · 14983635
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Michael D Brown. 2004-02-06. Coding credentials for your staff. Why and how.. https://pubmed.ncbi.nlm.nih.gov/14983635/
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An absorption and emission spectroscopic characterisation of the combined wild-type LOV1-LOV2 domain string (abbreviated LOV1/2) of phot from the green alga Chlamydomonas reinhardtii is carried out at pH 8. A LOV1/2-MBP fusion protein (MBP=maltose binding protein) and LOV1/2 with a His-tag at the C-terminus (LOV1/2-His) expressed in an Escherichia coli strain are investigated. Blue-light photo-excitation generates a non-fluorescent intermediate photoproduct (flavin-C(4a)-cysteinyl adduct with absorption peak at 390 nm). The photo-cycle dynamics is studied by dark-state absorption and fluorescence measurement, by following the temporal absorption and emission changes under blue and violet light exposure, and by measuring the temporal absorption and fluorescence recovery after light exposure. The fluorescence quantum yield, phi(F), of the dark adapted samples is phi(F)(LOV1/2-His) approximately 0.15 and phi(F)(LOV1/2-MBP) approximately 0.17. A bi-exponential absorption recovery after light exposure with a fast (in the several 10-s range) and a slow component (in the near 10-min range) are resolved. The quantum yield of photo-adduct formation, phi(Ad), is extracted from excitation intensity dependent absorption measurements. It decreases somewhat with rising excitation intensity. The behaviour of the combined wildtype LOV1-LOV2 double domains is compared with the behaviour of the separate LOV1 and LOV2 domains.
BACKGROUND: Many trials submitted to scientific meetings are not reported in peer-reviewed journals. Results may vary substantially and the lack of final publication constitutes a form of reporting bias. The authors sought to determine the presentation and publication rates of Phase I trials submitted to a major oncology meeting and the factors impeding their subsequent publication. METHODS: The authors identified all Phase I studies submitted to the annual meeting of the American Society of Clinical Oncology in 1997, categorizing them as novel (agents not approved by the Food and Drug Administration at the time of submission) or nonnovel (at least one agent approved), and as industry sponsored or not. MEDLINE searches and an E-mailed questionnaire confirmed publication in peer-reviewed literature and the reasons for nonpublication. RESULTS: Approximately 54% of the 275 Phase I studies were selected for presentation. Abstracts reporting novel agents were more likely selected for presentation than those reporting nonnovel compounds (68% vs. 38%; P < 0.0001). Seventy-two percent of the presented abstracts were subsequently published, compared with 62% of those not presented (P = 0.08). The overall publication rate was 67% at 7.5 years. Presentation status was associated with time to publication (P = 0.01), with abstracts chosen for presentation being published sooner. The median time from presentation to publication was found to be 3.4 years. Lack of time and author relocation were the major obstacles to publication. CONCLUSIONS: The underreporting of final results of Phase I oncology trials remains a serious problem. In the future, investigators must commit to the publication of final results in a timely manner. Journals should provide mechanisms for rapid reporting of Phase I trials.
In this paper, we present a biomedical name recognition system, called PowerBioNE. In order to deal with the special phenomena in the biomedical domain, various evidential features are proposed and integrated through a mutual information independence model (MIIM). In addition, a support vector machine (SVM) plus sigmoid is proposed to resolve the data sparseness problem in the MIIM. In this way, the data sparseness problem in MIIM-based biomedical name recognition can be resolved effectively and a biomedical name recognition system with better performance and better portability can be achieved. Finally, we present two post-processing modules to deal with the nested entity name and abbreviation phenomena in the biomedical domain to further improve the performance. Evaluation shows that our system achieves F-measures of 69.1 and 71.2 on the 23 classes of GENIA V1.1 and V3.0, respectively. In particular, our system achieves an F-measure of 77.8 on the "protein" class of GENIA V3.0. It also shows that our system outperforms the best-reported system on GENIA V1.1 and V3.0.