Increasing evidence has suggested that high expression level of cyclooxygenase-2 (Cox-2) is associated with the malignancies of non-small cell lung cancer (NSCLC), leading to a rationale of applying Cox-2 inhibitors as adjuvant therapy in the treatment of NSCLC. However, the addition of celecoxib, a selective Cox-2 inhibitor, to chemotherapy in clinical trials failed to benefit the survival of NSCLC patients, which urges the investigation to re-evaluate this strategy for NSCLC treatment. In this study, we observed that celecoxib treatment at clinically relevant concentrations induced epithelial-mesenchymal transition (EMT) in NSCLC cells regardless of Cox-2 status, which, however, was not recapitulated using another Cox-2 inhibitor, etodolac. Celecoxib-stimulated EMT in turn promoted cell invasion and rendered cells resistant to chemotherapy. Further mechanistic investigation by disrupting the integrity of signaling pathways using specific inhibitors or RNA interference revealed that celecoxib-induced EMT in NSCLC cells is indispensable of transforming growth factor-β1/Smad signaling. Instead, the activated MEK/ERK/SNAIL1 signaling largely accounted for celecoxib-induced EMT. Taken together, our study reveals the diverse impacts of Cox-2 inhibitors on EMT in NSCLC cells independent of Cox-2 inhibition, where celecoxib treatment leads to metastasis and chemoresistance via EMT induction. These findings reveal the increased risks of cancer metastasis and chemoresistance by applying Cox-2 inhibitors, celecoxib in particular, in clinical trials of NSCLC treatment and urge intensive preclinical assessment before proceeding to clinical application.

H .Q . Luo); Tel: +86 23 68253237; fax: +86 23 68253237. b. College of A nimal Science and Technolog y, Southwest Universi ty, Chongqing 400715, PR Chi na. †Electronic Sup plementary Info rmation (E SI) a vailable: [details of any supplementary info rmation avai lable shou ld be inc luded he re]. See www.rsc.org/ 5 15pH condition in vitro. The introduction of Fe 3+ can quench the fluorescence of MRFPs, and the fluorescence intensity of system decreases linearly with increasing concentration of Fe 3+ in the range of 0.05 − 50 μM with the detection limit of 4.6 nM at a signal-to-noise ratio of 3. Moreover, the recognition mechanism has been discussed, which is attributed to the charge transfer from excited-state MRFPs molecules to metal ions. In addition, the MRFPs have been successfully demonstrated as a good imaging probe for Fe 3+ sensing in living cells. This study shows that the biocompatible MRFPs 20 might hold great potential application in bioimaging, diagnosis, and therapy of intracellular diseases. 65 rea cti on in f ood a nd organis m, the rea cti on a nd associa ted products ha ve dra wn attenti on from many researchers . Especiall y, beca use the corres pondi ng fluores cence emissi on from the rea cti on can be us ed to as sess the a ging process es of The non-cytotoxic Maillard reaction fluorescent products were used as an imaging probe for Fe 3+ detection in living cells.

As the failure of a hydraulic pump is always instantaneous, the failure data are difficult to obtain. High-efficiency fault diagnosis under small-sample conditions for hydraulic pumps is urgently required in engineering applications. A fault diagnosis approach based on wavelet packet transform (WPT), singular value decomposition (SVD), and support vector machine (SVM) is proposed in this study. First, the nonlinear, non-stationary vibration signal of the hydraulic pump is decomposed into components by WPT. Second, singular value vectors are acquired as feature vectors by applying SVD to the components. Third, the health states of the hydraulic pumps are determined and classified with a SVM classifier. Furthermore, the SVM and Elman neural network classifiers are compared in terms of fault classification to demonstrate the superiority of SVM in dealing with small-sample problems. The results of the plunger pump rig test show that the proposed method can diagnose the faults of the hydraulic pump accurately even when the number of samples is small.

Due to the importance of the Airborne Equipment Software (AES), much more attentions have been drawn into here. Building a unified, standardized and effective management AES defect knowledge base with these data is a definitely valuable work. In this paper a framework of software quality integrate prediction has been established, which is highly essential to make accurate evaluations on the quality, predictions on the defects, identifications on the fault-prone modules. A framework on how to build an AES knowledge base is proposed, a combination mechanism is proposed by involving machine learning technology and production system, in which, in order to provide the instructions for defect prediction and quality assessment of AES.