PubMed · 14852128
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M W SCOTHORN. 1951. Veterinary service.. https://pubmed.ncbi.nlm.nih.gov/14852128/
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The contribution of tide-gauge data, which provide a unique monitoring of sea-level variability along the coasts of the world ocean, to operational oceanography is discussed in this paper. Two distinct applications that both demonstrate tide-gauge data utility when delivered in real-time are illustrated. The first case details basin-scale operational model validation of the French Mercator operational system applied to the North Atlantic. The accuracy of model outputs in the South Atlantic Bight both at coastal and offshore locations is evaluated using tide-gauge observations. These data enable one to assess the model's nowcasts and forecasts reliability which is needed in order for the model boundary conditions to be delivered to other coastal prediction systems. Such real-time validation is possible as long as data are delivered within a delay of a week. In the second application, tide-gauge data are assimilated in a storm surge model of the North Sea and used to control model trajectories in real-time. Using an advanced assimilation scheme that takes into account the swift evolution of model error statistics, these observations are shown to be very efficient to control model error, provided that they can be assimilated very frequently (i.e. available within a few hours).
Cigarette smoking is the leading cause of the respiratory diseases collectively known as chronic obstructive pulmonary disease (COPD). While the pathogenesis of COPD is complex, there is abundant evidence that alveolar macrophages (AM) play an important role. Based on the concept that COPD is a slow-progressing disorder likely involving multiple mediators released by AM activated by cigarette smoke, the present study focuses on the identification of previously unrecognized genes that may be linked to early events in the molecular pathogenesis of COPD, as opposed to factors associated with the presence of disease. To accomplish this, microarray analysis using Affymetrix microarrays was used to carry out an unbiased survey of the differences in gene expression profiles in the AM of phenotypically normal, approximately 20 pack-year smokers compared to healthy nonsmokers. Although smoking did not alter the global gene expression pattern of AM, 75 genes were modulated by smoking, with 40 genes up-regulated and 35 down-regulated in the AM of smokers compared to nonsmokers. Most of these genes belong to the functional categories of immune/inflammatory response, cell adhesion and extracellular matrix, proteolysis and antiproteolysis, lysosomal function, antioxidant-related function, signal transduction, and regulation of transcription. Of these 75 genes, 69 have not been previously recognized to be up- or down-regulated in AM in association with smoking or COPD, including genes coding for proteins belonging to all of the above categories, and others belonging to various functional categories or of unknown function. These observations suggest that gene expression responses of AM associated with the stress of cigarette smoking are more complex than previously thought, and offer a variety of new insights into the complex pathogenesis of smoking-induced lung diseases.
BACKGROUND: In real-time PCR data analysis, the cycle threshold (CT) method is currently the gold standard. This method is based on an assumption of equal PCR efficiency in all reactions, and precision may suffer if this condition is not met. Nonlinear regression analysis (NLR) or curve fitting has therefore been suggested as an alternative to the cycle threshold method for absolute quantitation. The advantages of NLR are that the individual sample efficiency is simulated by the model and that absolute quantitation is possible without a standard curve, releasing reaction wells for unknown samples. However, the calculation method has not been evaluated systematically and has not previously been applied to a TaqMan platform. AIM: To develop and evaluate an automated NLR algorithm capable of generating batch production regression analysis. RESULTS: Total RNA samples extracted from human gastric mucosa were reverse transcribed and analysed for TNFA, IL18 and ACTB by TaqMan real-time PCR. Fluorescence data were analysed by the regular CT method with a standard curve, and by NLR with a positive control for conversion of fluorescence intensity to copy number, and for this purpose an automated algorithm was written in SPSS syntax. Eleven separate regression models were tested, and the output data was subjected to Altman-Bland analysis. The Altman-Bland analysis showed that the best regression model yielded quantitative data with an intra-assay variation of 58% vs. 24% for the CT derived copy numbers, and with a mean inter-method deviation of x 0.8. CONCLUSION: NLR can be automated for batch production analysis, but the CT method is more precise for absolute quantitation in the present setting. The observed inter-method deviation is an indication that assessment of the fluorescence conversion factor used in the regression method can be improved. However, the versatility depends on the level of precision required, and in some settings the increased cost effectiveness of NLR may justify the lower precision.