Successional patterns of microfungi in fallen leaves of Ficus pleurocarpa (Moraceae) in an Australian tropical rain forest

Successional patterns of microfungi on decaying leaves of Ficus pleurocarpa were assessed as part of a study to enumerate microfungi in tropical rain forest leaf litter. Leaves degraded into fragments over a period of 3 mo. Two methods were applied, a direct observational method and a particle filtration protocol. Using a direct method, 104 species were observed, while 53 sporulating taxa and 100 sterile morphotaxa were isolated by particle filtration. Overall patterns of succession were confirmed by both methods, but the relative abundance of species detected differed between the two methods. Nonmetric Multidimensional Scaling identified at least four successional stages and suggested that microfungal communities increased in similarity with advancing decay. Data collected by the direct method indicated a slow but steady decline of diversity with advancing decay, whereas an increase in diversity was detected by particle filtration. Synecological succession studies provide a useful tool to identify patterns and generate hypotheses. Understanding the underlying causes of successional patterns will require further autecological studies

The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis. © 2010 Nature America, Inc. All rights reserved.0SCOPUS: ar.jinfo:eu-repo/semantics/publishe