Given a passage classify if the passage has a definite objective/aim/goal or not. Output '1' if the passage has a defininte objective/aim/goal and output '0' if the passage does not have a definite objective/aim/goal.

[EX Q]: Treatment of rheumatoid arthritis (RA) with tumor necrosis factor (TNF)-blocking agents, including etanercept and infliximab, has resulted in reductions in the radiographic progression of RA. However, the exact mechanism by which this protection occurs has not been determined. In order to add to such knowledge, we investigated the effect of anti-TNF therapy on the expression of osteoprotegerin (OPG) and receptor activator of NF-kappaB ligand (RANKL) in synovial tissue.', "The expression of OPG and RANKL in synovial biopsy specimens was evaluated by immunohistochemistry. Serial synovial biopsy specimens were obtained from 18 patients with RA, before and after treatment with etanercept (9 patients) or infliximab (9 patients). Biopsy specimens were evaluated by double-blind semiquantitative analysis and image analysis. The in vitro effect of TNF antagonists on the RANKL/OPG expression in osteoblasts and endothelial cells was evaluated by Western blotting. Statistical analysis was performed using Wilcoxon's signed rank test, followed by the Bonferroni correction for multiple comparisons of paired samples. The results of in vitro experiments were evaluated by one-way analysis of variance, with Tukey's post hoc test.", 'Treatment with both infliximab and etanercept increased the expression of OPG in synovial tissue. After 8 weeks of treatment, neither infliximab nor etanercept influenced RANKL expression. In both groups of patients, the RANKL:OPG ratio decreased following therapy. In vitro, both of the TNF antagonists mimicked the in vivo effect, inducing a decrease in the RANKL:OPG ratio in TNF-primed osteoblasts and endothelial cells.
[EX A]: 1

[EX Q]: As a component of the outer membrane in Gram-negative bacteria, lipopolysaccharide (LPS)-induced proliferation and cell cycle progression of monocytes/macrophages. It has been suggested that the proapoptotic T-cell death-associated gene 51 (TDAG51) might be associated with cell proliferation and cell cycle progression; however, its role in the interaction between LPS and macrophages remains unclear.', 'We attempted to elucidate the role(s) of TDAG51 played in the interaction between LPS and macrophages.', 'We investigated TDAG51 expression in RAW264.7 cells stimulated with LPS and examined the effects of RNA interference-mediated TDAG51 down-regulation. We used CCK-8 assay and flow cytometry analysis to evaluate the interaction between TDAG51 and LPS-induced proliferation and cell cycle progression in RAW264.7 cells.', 'Our findings indicate that TDAG51 is up-regulated in LPS-stimulated RAW264.7 cells, the TDAG51 siRNA effectively reduced TDAG51 protein up-regulation following LPS stimulation in RAW264.7 cells, the significant changes of the proliferation and cell cycle progression of RAW264.7 cells in TDAG51 Knockdown RAW264.7 cells treated with LPS were observed.
[EX A]: 1

[EX Q]: Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested.', 'Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71-0.79) and moderately high for growth (r = 0.52-0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.
[EX A]:
0