Age-related hearing impairment (ARHI), or presbycusis, is a common condition of the elderly that results in significant communication difficulties in daily life. Clinically, it has been defined as a progressive loss of sensitivity to sound, starting at the high frequencies, inability to understand speech, lengthening of the minimum discernable temporal gap in sounds, and a decrease in the ability to filter out background noise. The causes of presbycusis are likely a combination of environmental and genetic factors. Previous research into the genetics of presbycusis has focused solely on hearing as measured by pure-tone thresholds. A few loci have been identified, based on a best ear pure-tone average phenotype, as having a likely role in susceptibility to this type of hearing loss; and GRM7 is the only gene that has achieved genome-wide significance. We examined the association of GRM7 variants identified from the previous study, which used an European cohort with Z-scores based on pure-tone thresholds, in a European–American population from Rochester, NY (N = 687), and used novel phenotypes of presbycusis. In the present study mixed modeling analyses were used to explore the relationship of GRM7 haplotype and SNP genotypes with various measures of auditory perception. Here we show that GRM7 alleles are associated primarily with peripheral measures of hearing loss, and particularly with speech detection in older adults.
The ex vivo challenge assay is being increasingly used as an efficacy endpoint during early human clinical trials of HIV prevention treatments. There is no standard methodology for the ex vivo challenge assay although the use of different data collection methods and analytical parameters may impact results and reduce the comparability of findings between trials. In this analysis we describe the impact of data imputation methods, kit type, testing schedule and tissue type on variability, statistical power and ex vivo HIV growth kinetics. Data were p24 antigen (pg/mL) measurements collected from clinical trials of candidate microbicides where rectal (n=502), cervical (n=88) and vaginal (n=110) tissues were challenged with HIV-1 BaL ex vivo. Imputation of missing data using a non-linear mixed effect model was found to provide an improved fit compared to imputation using half the limit of detection. The rectal virus growth period was found to be earlier and of a relatively shorter duration than the growth period for cervical and vaginal tissue types. On average, only four rectal tissue challenge assays in each treatment and control group would be needed to find a one log difference in p24 to be significant (alpha = 0.05) but a larger sample size was predicted to be needed for either cervical (n=21) or vaginal (n=10) tissue comparisons. Overall, the results indicated that improvements could be made in the design and analysis of the ex vivo challenge assay to provide a more standardized and powerful assay to compare efficacy of microbicide products.Advances in ex vivo challenge assay
Abstract:The purpose of this study is to validate the accuracy of abundance map reference data (AMRD) for three airborne imaging spectrometer (IS) scenes. AMRD refers to reference data maps ("ground truth") that are specifically designed to quantitatively assess the performance of spectral unmixing algorithms. While classification algorithms typically label whole pixels as belonging to certain ground cover classes, spectral unmixing allows pixels to be composed of fractions or abundances of each class. The AMRD validated in this paper were generated using our previously-proposed remotely-sensed reference data (RSRD) technique, which spatially aggregates the results of standard classification or unmixing algorithms from fine spatial-scale IS data to produce AMRD for co-located coarse-scale IS data. Validation of the three scenes was accomplished by estimating AMRD in 51 randomly-selected 10 m×10 m plots, using seven independent methods and observers. These independent estimates included field surveys by two observers, imagery analysis by two observers and RSRD by three algorithms. Results indicated statistically-significant differences between all versions of AMRD. Even AMRD from our two field surveys were significantly different for two of the four ground cover classes. These results suggest that all forms of reference data require validation prior to use in assessing the performance of classification and/or unmixing algorithms. Given the significant differences between the independent versions of AMRD, we propose that the mean of all (MOA) versions of reference data for each plot and class is most likely to represent true abundances. Our independent versions of AMRD were compared to MOA to characterize error and uncertainty. Best case results were achieved by a version of imagery analysis, which had mean coverage area differences of 2.0%, with a standard deviation of 5.6%. One of the RSRD algorithms was nearly as accurate, achieving mean differences of 3.0%, with a standard deviation of 6.3%. Further analysis of statistical equivalence yielded an overall zone of equivalence between [−7.0%, 7.2%] for this version of RSRD. The relative accuracy of RSRD methods is promising, given their potential to efficiently generate scene-wide AMRD. These results provide the first known validated abundance level reference data for airborne IS data.
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