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As people live longer, the population of older adults in need of support continues to expand relative to the available workforce of caregivers, necessitating new solutions to supplement caregiver availability for the physical, cognitive, and social needs of older adults. Robotics and automation present strong possible solutions. Past solutions have typically supported short-term rehabilitation and aging in place, yet many older adults live in skilled nursing facilities (SNFs), a setting reached by relatively little research to date. In this paper, we examine the unique needs of staff and residents at SNFs, after which we begin an iterative design process of robot-mediated wellness activities for the SNF space. We worked closely with domain experts in exercise science and physical therapy for older adults and a local SNF to design and test a series of robot-mediated activity prototypes with residents, visitors, and staff. We found that while both residents and staff highly value physical activity, there are nuanced challenges associated with supporting resident activity (one important element of overall wellbeing). As a result, we considered and tested a wide range of intervention options from usual approaches (e.g., mirroring movements) to creative approaches (e.g., social engagement via lewd humor). Our final design insights can inform practitioners who wish to use robots to support resident wellbeing in SNFs.

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.

Background: Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists., Results: Using the data sets generated by the MicroArray Quality Control (MAQC) project, we investigated the impact on the reproducibility of DEG lists of a few widely used gene selection procedures. We present comprehensive results from inter-site comparisons using the same microarray platform, cross-platform comparisons using multiple microarray platforms, and comparisons between microarray results and those from TaqMan - the widely regarded "standard" gene expression platform. Our results demonstrate that (1) previously reported discordance between DEG lists could simply result from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion with a non-stringent P-value cutoff filtering, the DEG lists become much more reproducible, especially when fewer genes are selected as differentially expressed, as is the case in most microarray studies; and (3) the instability of short DEG lists solely based on P-value ranking is an expected mathematical consequence of the high variability of the t-values; the more stringent the P-value threshold, the less reproducible the DEG list is. These observations are also consistent with results from extensive simulation calculations., Conclusion: We recommend the use of FC-ranking plus a non-stringent P cutoff as a straightforward and baseline practice in order to generate more reproducible DEG lists. Specifically, the P-value cutoff should not be stringent (too small) and FC should be as large as possible. Our results provide practical guidance to choose the appropriate FC and P-value cutoffs when selecting a given number of DEGs. The FC criterion enhances reproducibility, whereas the P criterion balances sensitivity and specificity.

External RNA controls (ERCs), although important for microarray assay performance assessment, have yet to be fully implemented in the research community. As part of the MicroArray Quality Control (MAQC) study, two types of ERCs were implemented and evaluated; one was added to the total RNA in the samples before amplification and labeling; the other was added to the copyRNAs (cRNAs) before hybridization. ERC concentration-response curves were used across multiple commercial microarray platforms to identify problematic assays and potential sources of variation in the analytical process. In addition, the behavior of different ERC types was investigated, resulting in several important observations, such as the sample-dependent attributes of performance and the potential of using these control RNAs in a combinatorial fashion. This multiplatform investigation of the behavior and utility of ERCs provides a basis for articulating specific recommendations for their future use in evaluating assay performance across multiple platforms.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.

We have assessed the utility of RNA titration samples for evaluating microarray platform performance and the impact of different normalization methods on the results obtained. As part of the MicroArray Quality Control project, we investigated the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, we determined the ability of each platform to detect the correct titration response across the samples. Global deviations from the response predicted by the titration ratios were observed. These differences could be explained by variations in relative amounts of messenger RNA as a fraction of total RNA between the two independent samples. Overall, both the qualitative and quantitative correspondence across platforms was high. In summary, titration samples may be regarded as a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

Background: Despite the widespread use of microarrays, much ambiguity regarding data analysis, interpretation and correlation of the different technologies exists. There is a considerable amount of interest in correlating results obtained between different microarray platforms. To date, only a few cross-platform evaluations have been published and unfortunately, no guidelines have been established on the best methods of making such correlations. To address this issue we conducted a thorough evaluation of two commercial microarray platforms to determine an appropriate methodology for making cross-platform correlations., Results: In this study, expression measurements for 10,763 genes uniquely represented on Affymetrix U133A/B GeneChips and Amersham CodeLink UniSet Human 20 K microarrays were compared. For each microarray platform, five technical replicates, derived from the same total RNA samples, were labeled, hybridized, and quantified according to each manufacturers' standard protocols. The correlation coefficient (r) of differential expression ratios for the entire set of 10,763 overlapping genes was 0.62 between platforms. However, the correlation improved significantly (r = 0.79) when genes within noise were excluded. In addition to levels of inter-platform correlation, we evaluated precision, statistical-significance profiles, power, and noise levels for each microarray platform. Accuracy of differential expression was measured against real-time PCR for 25 genes and both platforms correlated well with r values of 0.92 and 0.79 for CodeLink and GeneChip, respectively., Conclusions: As a result of this study, we recommend using only genes called 'present' in cross-platform correlations. However, as in this study, a large number of genes may be lost from the correlation due to differing levels of noise between platforms. This is an important consideration given the apparent difference in sensitivity of the two platforms. Data from microarray analysis need to be interpreted cautiously and therefore, we provide guidelines for making cross-platform correlations. In all, this study represents the most comprehensive and specifically designed comparison of short-oligonucleotide microarray platforms to date using the largest set of overlapping genes.

Hairpin ribozymes derived from the negative strand of satellite RNAs from the tobacco ringspot virus (sTRSV) can be engineered to target and cleave a variety of heterologous RNAs from both cellular and viral transcripts. Attention to design and targeting rules and optimization of helix 1 length and catalytic efficiency in vitro may increase the efficacy of hairpin ribozymes in reducing the expression of targeted transcripts. Here, principles for the design and targeting of sTRSV-derived hairpin ribozymes are described, as well as methods and materials for optimizing helix 1 length, and for conducting an initial screen of catalytic efficiency to identify promising candidates for further evaluation. Examples are provided for hairpin ribozymes that target human and mouse transforming growth-factor beta (TGF-beta), as well as human polycystic kidney disease gene 1 (PKD1) and JC virus large T-antigen. The tetraloop modification of the sTRSV hairpin ribozyme is considered superior to designs based on the native sTRSV hairpin ribozyme, given its potential to yield considerable improvements in stability and catalytic efficiency.

DNA microarrays enable users to obtain information on differences in transcript abundance on a massively parallel scale. Recently, however, data analyses have revealed potential pitfalls related to image acquisition, variability and misclassifications in replicate measurements, cross-hybridization and sensitivity limitations. We have generated a series of analytical tools to address the manufacturing, detection and data analysis components of a microarray experiment. Together, we have used these tools to optimize performance in an expression profiling study. We demonstrate three significant advantages of the Motorola CodeLink platform: sensitivity of one copy per cell, coefficients of variation of 10% in the hybridization signals across slides and across target preparations, and specificity in distinguishing highly homologous sequences. Slides where oligonucleotide probes are spotted in 6-fold redundancy were used to demonstrate the effect of replication on data quality. Lastly, the differential expression ratios obtained with the CodeLink expression platform were validated against those obtained with quantitative reverse transcription-PCR assays for 54 genes.

The hairpin ribozyme is a member of a family of small RNA endonucleases, which includes hammer-head, human hepatitis delta virus, Neurospora VS, and the lead-dependent catalytic RNAs. All these catalytic RNAs reversibly cleave the phosphodiester bond of substrate RNA to generate 5'-hydroxyl and 2',3'-cyclic phosphate termini. Whereas the reaction products from family members are similar, large structural and mechanistic differences exist. Structurally the hairpin ribozyme has two principal domains that interact to facilitate catalysis. The hairpin ribozyme uses a catalytic mechanism that does not require metals for cleavage or ligation of substrate RNA. In this regard it is presently unique among RNA catalysts. Targeting rules for cleavage of substrate have been determined and required bases for catalysis have been identified. The hairpin ribozyme has been developed and used for gene therapy and was the first ribozyme to be approved for human clinical trials.

A comprehensive analysis of base preferences for all positions in loops 1 and 5 of the hairpin ribozyme-substrate complex was carried out using a cis-ribozyme tethered to substrate by a pentapyrimidine loop. Ribozyme-substrate molecules were mutated to contain each of the three non-native base variations at each of the eight positions within these loops. Catalytic activity was measured for each mutant and compared to the activity of the original native sequence. This was the first time all base positions in these loops have been mutated to all variants and kinetically characterized. Various effects were found, ranging from invariant base positions to those with nearly complete tolerance of any base change. Two positions resulted in cleavage rates below the lower limit of accurate quantification for all non-wild-type base substitutions. These positions are G8 in the ribozyme and Gs6 in the substrate. When A10 was substituted with a pyrimidine, self-cleavage activity fell below the lower limit of detection while the remaining positions showed varying base preferences. The information reported here on loops 1 and 5 combined with previous mutagenesis data on loops 2 and 4 [Siwkowski, A., Shippy, R., and Hampel, A. (1997) Biochemistry 36, 3930-3940] completed a comprehensive mutational/kinetic analysis of every base position located within all the required loops of the hairpin ribozyme-substrate complex and allowed for the development of a mechanism for catalysis which is proposed.

In order to determine base requirements in loops 2 and 4 of the hairpin ribozyme, a comprehensive mutational analysis of the wild type sequence was done. Each base position in these two loops was mutated to contain each of the three non-wild type bases, and the effects of these mutations were analyzed using cis-cleavage assays. The method of data analysis allowed for the determination of self-cleavage rates as well as the fraction of transcripts produced which were uncleavable. Three positions in loop 2 (A22, A23, and C25) and one position in loop 4 (A38) resulted in no detectable self-cleavage when mutated to any of the non-wild type bases. The remainder of the base positions showed varying degrees of tolerance to base mutations with respect to their support of cis-cleavage. Evidence was obtained for the presence of a non-Watson-Crick base pair between A26 and G36 in the catalytic conformation of the hairpin ribozyme. On the basis of these results, a two-dimensional model for the hairpin ribozyme is presented.