Portable automated radio-frequency scanner for non-destructive testing of carbon-fibre-reinforced polymer composites

A portable automated scanner for non-destructive testing of carbon-fibre-reinforced polymer (CFRP) composites has been developed. Measurement head has been equipped with an array of newly developed radio-frequency (RF) inductive sensors mounted on a flexible arm, which allows the measurement of curved CFRP samples. The scanner is also equipped with vacuum sucks providing mechanical stability. RF sensors operate in a frequency range spanning from 10 up to 300 MHz, where the largest sensitivity to defects buried below the front CFRP surface is expected. Unlike to ultrasonic testing, which will be used for reference, the proposed technique does not require additional couplants. Moreover, negligible cost and high repeatability of inductive sensors allows developing large scanning arrays, thus, substantially speeding up the measurements of large surfaces. The objective will be to present the results of an extensive measurement campaign undertaken for both planar and curved large CFRP samples, pointing out major achievements and potential challenges that still have to be addressed

Correlations of EGFR mutations and increases in EGFR and HER2 copy number to gefitinib response in a retrospective analysis of lung cancer patients

<p>Abstract</p> <p>Background</p> <p>Gefitinib, a small molecule tyrosine kinase inhibitor of the Epidermal Growth Factor Receptor (<it>EGFR</it>), has shown limited efficacy in the treatment of lung cancer. Recognized clinical predictors of response to this drug, specifically female, non-smoker, Asian descent, and adenocarcinoma, together suggest a genetic basis for drug response. Recent studies have addressed the relationship between response and either sequence mutations or increased copy number of specific receptor tyrosine kinases. We set out to examine the relationship between response and the molecular status of two such kinases, <it>EGFR </it>and <it>HER2</it>, in 39 patients treated with gefitinib at the BC Cancer Agency.</p> <p>Methods</p> <p>Archival patient material was reviewed by a pathologist and malignant cells were selectively isolated by laser microdissection or manual recovery of cells from microscope slides. Genomic DNA was extracted from 37 such patient samples and exons 18–24, coding for the tyrosine kinase domain of <it>EGFR</it>, were amplified by PCR and sequenced. <it>EGFR </it>and <it>HER2 </it>copy number status were also assessed using FISH in 26 samples. Correlations between molecular features and drug response were assessed using the two-sided Fisher's exact test.</p> <p>Results</p> <p>Mutations previously correlated with response were detected in five tumours, four with exon 19 deletions and one with an exon 21 missense L858R point mutation. Increased gene copy number was observed in thirteen tumours, seven with <it>EGFR </it>amplification, three with <it>HER2 </it>amplification, and three with amplification of both genes. In our study cohort, a correlation was not observed between response and <it>EGFR </it>mutations (exon 19 deletion p = 0.0889, we observed a single exon 21 mutation in a non-responder) or increases in <it>EGFR </it>or <it>HER2 </it>copy number (p = 0.552 and 0.437, respectively).</p> <p>Conclusion</p> <p>Neither mutation of <it>EGFR </it>nor increased copy number of <it>EGFR </it>or <it>HER2 </it>was diagnostic of response to gefitinib in this cohort. However, validation of these features in a larger sample set is appropriate. Identification of additional predictive biomarkers beyond <it>EGFR </it>status may be necessary to accurately predict treatment outcome.</p

Assessment of predictive models for chlorophyll-a concentration of a tropical lake

<p>Abstract</p> <p>Background</p> <p>This study assesses four predictive ecological models; Fuzzy Logic (FL), Recurrent Artificial Neural Network (RANN), Hybrid Evolutionary Algorithm (HEA) and multiple linear regressions (MLR) to forecast chlorophyll- a concentration using limnological data from 2001 through 2004 of unstratified shallow, oligotrophic to mesotrophic tropical Putrajaya Lake (Malaysia). Performances of the models are assessed using Root Mean Square Error (RMSE), correlation coefficient (r), and Area under the Receiving Operating Characteristic (ROC) curve (AUC). Chlorophyll-a have been used to estimate algal biomass in aquatic ecosystem as it is common in most algae. Algal biomass indicates of the trophic status of a water body. Chlorophyll- a therefore, is an effective indicator for monitoring eutrophication which is a common problem of lakes and reservoirs all over the world. Assessments of these predictive models are necessary towards developing a reliable algorithm to estimate chlorophyll- a concentration for eutrophication management of tropical lakes.</p> <p>Results</p> <p>Same data set was used for models development and the data was divided into two sets; training and testing to avoid biasness in results. FL and RANN models were developed using parameters selected through sensitivity analysis. The selected variables were water temperature, pH, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and Secchi depth. Dissolved oxygen, selected through stepwise procedure, was used to develop the MLR model. HEA model used parameters selected using genetic algorithm (GA). The selected parameters were pH, Secchi depth, dissolved oxygen and nitrate nitrogen. RMSE, r, and AUC values for MLR model were (4.60, 0.5, and 0.76), FL model were (4.49, 0.6, and 0.84), RANN model were (4.28, 0.7, and 0.79) and HEA model were (4.27, 0.7, and 0.82) respectively. Performance inconsistencies between four models in terms of performance criteria in this study resulted from the methodology used in measuring the performance. RMSE is based on the level of error of prediction whereas AUC is based on binary classification task.</p> <p>Conclusions</p> <p>Overall, HEA produced the best performance in terms of RMSE, r, and AUC values. This was followed by FL, RANN, and MLR.</p