A Class of Functional Methods for Error-Contaminated Survival Data Under Additive Hazards Models with Replicate Measurements

<p>Covariate measurement error has attracted extensive interest in survival analysis. Since Prentice, a large number of inference methods have been developed to handle error-prone data that are modulated with proportional hazards models. In contrast to proportional hazards models, additive hazards models offer a flexible tool to delineate survival processes. However, there is little research on measurement error effects under additive hazards models. In this article, we systematically investigate this important problem. New insights into measurement error effects are revealed, as opposed to well-documented results for proportional hazards models. In particular, we explore asymptotic bias of ignoring measurement error in the analysis. To correct for the induced bias, we develop a class of functional correction methods for measurement error effects. The validity of the proposed methods is carefully examined, and we investigate issues of model checking and model misspecification. Theoretical results are established, and are complemented with numerical assessments. Supplementary materials for this article are available online.</p

Characterization from Preparation of Cu-ZSM-5 catalysts by chemical vapour deposition for catalytic wet peroxide oxidation of phenol in a fixed bed reactor

TGA profiles ，N2 adsorption–desorption isotherms of the samples ，Elemental analysis results ，H2 temperature-programmed reduction profile

Characterization from Preparation of Cu-ZSM-5 catalysts by chemical vapour deposition for catalytic wet peroxide oxidation of phenol in a fixed bed reactor

TGA profiles ，N2 adsorption–desorption isotherms of the samples ，Elemental analysis results ，H2 temperature-programmed reduction profile

Telomere size and telomerase activity among pancreatic cancer cell lines.

<p>A) Telomere size measurements. Genomic DNA was digested with restriction enzymes, resolved by electrophoresis in agarose gels and detected by in situ hybridization to [³²P]-(CCCTAA)₄. B) Quantification of telomere size. Gel in A was scanned and the intensity of each lane was plotted as a function of telomere size, as described in the Material and Methods section. The median telomere size was estimated as the size corresponding to half the cumulative sum of the intensities. C) Detection of telomerase activity by the TRAP assay. Cell extracts containing 300 cells each were assayed using a Cy5-labeled TS oligo substrate. After PCR with the same oligo and a telomeric comeback primer, products were resolved by electrophoresis and detected with a Typhon PhosphoImager. Buffer was lysis buffer only. ITAS, Internal Telomerase Assay Standard. D) Quantification of relative telomerase activity. Relative telomerase activity was calculated as the ratio of the intensity of the telomerase ladder over the intensity of the ITAS. Each measurement is the Mean ± S.D. of triplicate samples (n = 3). Telomerase activity in each line is expressed as a percent of HeLa cells’ activity.</p

Immunofluorescence analysis of telomeres in the GRN163L-treated CD18 cells.

<p>CD18 treated with GRN163L (GRN) or with no drug (CTR) were harvested at the end of the growth curve presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085155#pone-0085155-g003" target="_blank">Figure 3</a>. Cells were fixed and stained with antibodies against TRF2 (green) and γ-H2AX (red) and were counter-stained with DAPI (blue). Images were visualized on a confocal microscope (Zeiss 510 Meta Confocal Laser Scanning Microscope). Three representative images of each sample are shown.</p

Effects of chronic GRN163L on telomere maintenance.

<p>Cells were treated every 2–3 days with no drug (CTR), mismatch oligo (MIS) or GRN163L (GRN). A–B) Southern blot analysis of the telomeres. At the indicated population doubling (PD), genomic DNA samples were collected and subsequently processed for telomere size measurements, as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085155#pone-0085155-g001" target="_blank">Figure 1A</a>. C–D) Telomere size measurements. Median telomere sizes are shown for the drug- and control-treated CAPAN1 and CD18 cells. E) Detection of ALT by quantitative PCR. Samples tested included CAPAN1 and CD18 cells treated with no drug (CTR), mismatch oligo (MIS) and GRN163L (GRN), all of which harvested at the end of the growth curves presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085155#pone-0085155-g003" target="_blank">Figure 3</a>. Also included were stocks of CAPAN1, CD18 and VA13 cells. Samples were tested in triplicate with (+) and without (−) the Φ29 DNA polymerase. A representative dot blot is shown.</p

The distribution of the Coulomb force on the interface.

<p>(1T)</p

Time–Frequency Analysis Based Flow Regime Identification Methods for Airlift Reactors

The flow regime transitions in an airlift reactor were investigated based on pressure fluctuation signals. Two time–frequency analysis methods, i.e., Wigner–Ville distribution and wavelet transform, were used to extract flow regime characteristics from pressure signals. The main frequency derived from the smoothed pseudo-Wigner–Ville distribution of the pressure signal was used to quantify flow regime transitions in the reactor. Two flow regime transition points were successfully detected from the evolution of main frequencies of pressure signals. In addition, the local dynamic characteristics of the pressure signal at different frequency bands were analyzed by use of the wavelet transform. A new flow regime identification method based on the wavelet entropy of the pressure signal was proposed. This method was confirmed to be reliable and efficient to detect flow regime transitions in the reactor

Markers of apoptosis, senescence and DNA damage response in the GRN163L-treated CAPAN1 and CD18 cells.

<p>At the end of the growth curves presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085155#pone-0085155-g003" target="_blank">Figure 3</a>, cells treated with no drug (CTR), mismatch oligo (MIS) and GRN163L (GRN) were analyzed for SA-β-galactosidase activity. A) Histological analysis of SA-β-galactosidase activity. Histochemical staining reveals SA-β-galactosidase activity in the GRN163L-treated CAPAN1 and CD18 cells, as evidenced by the deposition of insoluble blue pigments (Right panels). B) Percent of cells in each sample that stained positive for SA-β-galactosidase activity. Measurements were done in triplicates (Mean ± S.D., n = 3). C) Western blot analysis. Samples were probed with antibodies against histone H2AX, phosphorylated H2AX (γ-H2AX), PARP and actin. D) Flow cytometric analysis of DNA content. Cells were stained with propidium iodide and analyzed for DNA content. Measurements were made twice at one week interval for the CAPAN1 (Mean ± S.D., n = 2). In the case of the CD18, one measurement only could be made before the GRN163L-treated cells were lost to crisis (n = 1).</p

Effects of chronic GRN163L on cellular lifespan.

<p>A) Pancreatic cancer cell lines CAPAN1 and CD18 were selected for these studies. Every 2–3 days, each lines was given either no drug (vehicle), GRN163L (1 µM) or the Mismatched oligo (1 µM). Once a week, cells were counted and the results are plotted as the number of population doublings achieved as a function of time. Every other week, samples were put aside for TRF analysis (DNA) or frozen down as backup. B,C) Growth curves of the drug-treated CAPAN1 (B) and CD18 (C) cells are shown.</p