instruction:
In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.
question:
Context: To assess how different imputation methods used to account for missing variance data in primary studies influence tests of heterogeneity and pooled results from a meta-analysis with continuous outcomes.', 'Point and variance estimates for changes in serum creatinine, glomerular filtration rate, systolic blood pressure, and diastolic blood pressure were variably reported among 48 primary longitudinal studies of living kidney donors (71%-78% of point estimates were reported, 8%-13% of variance data were reported). We compared the results of meta-analysis, which either were restricted to available data or used four methods to impute missing variance data. These methods used reported P-values, reported nonparametric summaries, results from other similar studies using multiple imputation, or results from estimated correlation coefficients.', 'Significant heterogeneity was present in all four outcomes regardless of the imputation methods applied. The random effects point estimates and 95% confidence intervals varied little across imputation methods, and the differences were not clinically significant.\Question: Do imputing variance estimates alter the conclusions of a meta-analysis with continuous outcomes : a case study of changes in renal function after living kidney donation?
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Different methods to impute the variance data in the primary studies did not alter the conclusions from this meta-analysis of continuous outcomes. Such reproducibility increases confidence in the results. However, as with most meta-analyses, there was no gold standard of truth, and results must be interpreted judiciously. The generalization of these findings to other meta-analyses, which differ in outcomes, missing data, or between-study heterogeneity, requires further consideration.


question:
Context: The yeast cell cycle is largely controlled by the cyclin-dependent kinase (CDK) Cdc28. Recent evidence suggests that both CDK complex stability as well as function during mitosis is determined by precise regulation of Swe1, a CDK inhibitory kinase and cyclin binding partner. A model of mitotic progression has been provided by study of filamentous yeast. When facing nutrient-limited conditions, Ras2-mediated PKA and MAPK signaling cascades induce a switch from round to filamentous morphology resulting in delayed mitotic progression.', 'To delineate how the dimorphic switch contributes to cell cycle regulation, temperature sensitive cdc28 mutants exhibiting constitutive filamentation were subjected to epistasis analyses with RAS2 signaling effectors. It was found that Swe1-mediated inhibitory tyrosine phosphorylation of Cdc28 during filamentous growth is in part mediated by Ras2 activation of PKA, but not Kss1-MAPK, signaling. This pathway is further influenced by Cks1, a conserved CDK-binding partner of elusive function with multiple proposed roles in CDK activation, transcriptional regulation and ubiquitin-mediated proteasome degradation.\Question: Do morphogenesis signaling components influence cell cycle regulation by cyclin dependent kinase?
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The dynamic balance between Cks1- and Swe1-dependent regulation of Cdc28 and, thereby, the timing of mitosis during yeast dimorphism is regulated in part by Ras2/cAMP-mediated PKA signaling, a key pathway controlling filamentous growth.


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Context: This study was conducted to gain insight into the relationship between expression profiles and underlying genetic changes, which are known to be important for the pathogenesis of lung cancers.', 'Expression profiles of 18,175 unique genes and three major targets for genetic changes, p53, epidermal growth factor receptor (EGFR), and K-ras, were investigated in 149 patients with non-small-cell lung cancer, including 90 patients with adenocarcinoma to determine their relationships with various clinicopathologic features and Gene Ontology (GO) terms.', 'This study successfully established a basis for expression profile-defined classification, which can classify adenocarcinomas into two major types, terminal respiratory unit (TRU) type and non-TRU type. Our GO term-based identifier of particular biologic processes, molecular functions, and cellular compartments clearly showed characteristic retention of normal peripheral lung features in TRU type, in sharp contrast to the significant association of non-TRU type with cell cycling and proliferation-related features. While significantly higher frequency of EGFR mutation was observed in TRU type, we found that the presence of EGFR mutations was a significant predictor of shorter postoperative survival for TRU type, independent of disease stage. We were also able to identify a set of genes in vivo with significant upregulation in the presence of EGFR mutations.\Question: Does expression profile-defined classification of lung adenocarcinoma show close relationship with underlying major genetic changes and clinicopathologic behaviors?
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This study has shed light on heterogeneity in lung cancers, especially in adenocarcinomas, by establishing a molecularly, genetically, and clinically relevant, expression profile-defined classification. Future studies using independent patient cohorts are warranted to confirm the prognostic significance of EGFR mutations in TRU-type adenocarcinoma.