Abstract-Mechanistic dynamic models often contain unknown parameters whose values are difficult to determine even with highly specialized laboratory experiments. A practical approach is to estimate such parameters from available process data. Typically only a subset of the parameters can be estimated due to restrictions imposed by the model structure, lack of measurements, and limited data. We present a simple parameter selection method which accounts for the first two factors independent of the data available for parameter estimation. The magnitude of each parameter effect on the measured variables is quantified by applying principal-component analysis to the steady-state parameter-output sensitivity matrix. The uniqueness of each parameter effect is determined by computing the minimum distance between the sensitivity vector of the particular parameter and the vector spaces spanned by sensitivity vectors of the parameters already selected for estimation. A recursive algorithm that provides a tradeoff between the magnitude and linear independence of parameter effects yields a ranking of the parameters according to their inherent ease of estimation. The parameter-selection procedure is applied to the problem of kinetic parameter estimation for an industrial model of a polymerization reactor. For this specific example, the proposed method yields superior estimation results than those obtained with a parameter-selection technique based on the Fisher information matrix (FIM).

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Knee symptomatic osteoarthritis (SxOA) was associated with all-cause mortality. Walking disability and NSAIDs use have been postulated as potential mechanisms linking knee SxOA to all-cause mortality. Data were collected on ability of walking for 1 kilometer and use of NSAIDs at baseline and death information at follow-up. Subjects with knee SxOA were identified if at least one knee had both radiographic OA and pain. We first fitted a Cox proportional hazards model to examine the relation of knee SxOA to the risk of all-cause mortality. We then used marginal structural models to decompose total effect of knee SxOA on all-cause mortality into indirect and direct effects via walking disability and use of NSAIDs, respectively. Among 1025 subjects, 99 died over 8 years of follow-up. A multivariable adjusted hazard ratio of mortality for SxOA was 1.98 (95% CI: 1.09–3.62). The indirect effect of knee SxOA on all-cause mortality through either a walking disability or NSAIDs use was 1.92 (95% CI: 0.86–4.26) and 1.45 (95% CI: 0.72–2.92), respectively. The corresponding direct effect was 1.08 (95% CI: 0.55–1.12) and 1.35 (95% CI: 0.75–2.44). In this population-based cohort study, high all-cause mortality from knee SxOA was mediated mainly through a walking disability.

BackgroundEvidences indicate that inflammatory process plays pivotal role in tumor disease. Soluble epoxide hydrolase inhibitors (sEHIs) have been shown to participate in anti-inflammation and tumorigenesis by protecting epoxyeicosatrienoic acids (EETs). Although we have previously revealed some effects of t-AUCB on glioma in vitro, further investigations are needed to demonstrate its effects on glioblastoma growth in vivo and how to strengthen its antitumor effect.MethodsCCK-8 kit was used to test cell growth. Cell migration capacity was performed by wound healing assays. Transwell assay was used to test cell invasion potency. Cell-cycle analysis and cell apoptosis was performed by flow cytometry. The activity of caspase-3 in cells was measured using caspase-3 activity assay kits. Total RNA was extracted from cells lysated by TRIzol reagent. qRT-PCR was performed by ABI 7500 fast RT- PCR system. Lipofectamine RNAiMAX Transfection Reagent (Invitrogen) was used for siRNA transfection. Western blootting was used to test protein expression. Tumor cell xenograft mouse models were used for in vivo study. The SPSS version 17.0 software was applied for statistical analysis.ResultsOur data shown that t-AUCB inhibits cell proliferation, migration and invasion and induces cell cycle G1 phase arrest in vitro but induces no cell apoptosis; increased Hsp27 activation and following COX-2 overexpression confer resistance to t-AUCB treatment in glioblastoma both in vitro and in vivo; quercetin sensitizes glioblastoma to t-AUCB by dual inhibition of Hsp27 and COX-2 in vitro and in vivo.ConclusionsThese results indicate that combination of t-AUCB and quercetin may be a potential approach to treating glioblastoma.