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At least 1,009 records · Page 56Linked to original sources
PubMed2004

Development of a new standard laboratory protocol for estimation of the field attenuation of hearing protection devices: sample size necessary to provide acceptable reproducibility.

The mandate of ASA Working Group S12/WG11 has been to develop "laboratory and/or field procedure(s) that yield useful estimates of field performance" of hearing protection devices (HPDs). A real-ear attenuation at threshold procedure was selected, devised, tested for one earmuff and three earplugs via an interlaboratory study involving five laboratories and 147 subjects, and incorporated into a new standard that was approved in 1997 [Royster et al., "Development of a new standard laboratory protocol for estimating the field attenuation of hearing protection devices. Part I. Research of Working Group 11, Accredited Standards Committee S 12, Noise," J. Acoust. Soc. Am. 99, 1506-1526; ANSI, S12.6-1997, "American National Standard method for measuring real-ear attenuation of hearing protectors" (American National Standards Institute, New York, 1997)]. The subject-fit methodology of ANSI S12.6-1997 relies upon listeners who are audiometrically proficient, but inexperienced in the use of HPDs. Whenever a new method is adopted, it is important to know the effects of variability on the power of the measurements. In evaluation of protector noise reduction determined by experimenter-fit, informed-user-fit, and subject-fit methods, interlaboratory reproducibility was found to be best for the subject-fit method. Formulas were derived for determining the minimum detectable difference between attenuation measurements and for determining the number of subjects necessary to achieve a selected level of precision. For a precision of 6 dB, the study found that the minimum number of subjects was 4 for the Bilsom UF-1 earmuff, 10 for the E.A.R Classic earplug, 31 for the Willson EP100 earplug, and 22 for the PlasMed V-51R earplug.

Auditory Threshold↗
PubMed2005

Limitations of mRNA amplification from small-size cell samples.

BACKGROUND: Global mRNA amplification has become a widely used approach to obtain gene expression profiles from limited material. An important concern is the reliable reflection of the starting material in the results obtained. This is especially important with extremely low quantities of input RNA where stochastic effects due to template dilution may be present. This aspect remains under-documented in the literature, as quantitative measures of data reliability are most often lacking. To address this issue, we examined the sensitivity levels of each transcript in 3 different cell sample sizes. ANOVA analysis was used to estimate the overall effects of reduced input RNA in our experimental design. In order to estimate the validity of decreasing sample sizes, we examined the sensitivity levels of each transcript by applying a novel model-based method, TransCount. RESULTS: From expression data, TransCount provided estimates of absolute transcript concentrations in each examined sample. The results from TransCount were used to calculate the Pearson correlation coefficient between transcript concentrations for different sample sizes. The correlations were clearly transcript copy number dependent. A critical level was observed where stochastic fluctuations became significant. The analysis allowed us to pinpoint the gene specific number of transcript templates that defined the limit of reliability with respect to number of cells from that particular source. In the sample amplifying from 1000 cells, transcripts expressed with at least 121 transcripts/cell were statistically reliable and for 250 cells, the limit was 1806 transcripts/cell. Above these thresholds, correlation between our data sets was at acceptable values for reliable interpretation. CONCLUSION: These results imply that the reliability of any amplification experiment must be validated empirically to justify that any gene exists in sufficient quantity in the input material. This finding has important implications for any experiment where only extremely small samples such as single cell analyses or laser captured microdissected cells are available.

Analysis of Variance↗
PubMed2000

Determination of trace elements by inductively coupled plasma mass spectrometry of biomass and fuel oil reference materials using milligram sample sizes.

Most of the analytical techniques used to quantify elements associated with solid samples suffer from high detection limits and cannot be used for trace elements in biomass samples, particularly when only 20 mg are available for analysis. Inductively coupled plasma mass spectrometry (ICP-MS) can achieve detection limits of parts-per-trillion with liquid sample introduction by solution nebulisation. This technique was therefore tested with two standard biomass reference materials: oriental tobacco leaves and cabbage leaves. Two preparations successfully used on coal standards were used to digest the solid samples: a total digestion method (wet ashing digestion) and a partial leaching (microwave extraction). The concentrations of up to seventeen elements (As, Ba, Be, Cd, Co, Cr, Cu, Ga, Mn, Mo, Ni, Pb, Sb, Se, Sn, V and Zn) were measured after the two preparations. The accuracy and sensitivity of the measurements improved when the dilution factor decreased from 5000 to 1000 and to 500. Since the proportion of mineral matter in biomass samples is small (5%), the microwave digestion extracted elements that are generally not completely extracted from coal samples (e.g. Sb). However, some trace element concentrations were below the limit of quantification after microwave extraction, even with a reduced dilution factor (As, Se and Mo) and could not be quantified. A fuel oil was also digested. The trace element concentrations were very low (between 28 and 0.1 microgram g(-1)) but acceptable results were obtained by applying a dilution factor of 100. Only six elements in the fuel oil (As, Ba, Co, Ni, Se and V) had certified or indicated values. Factors affecting the accuracy and sensitivity of the analyses are discussed. The reproducibility of analysis of the tobacco leaf standard was checked over a period of nine months by both digestion methods. The wet ashing method gave acceptable reproducibility for Ba, Cd, Co, Cu, Ga, Mn, Mo, Ni, Pb, V and Zn but poor precision for Cr, Se and Sn and showed evidence of residual chloride interference for As. The microwave extraction gave good reproducibility for As, Ba, Cd, Co, Cr, Cu, Mo, Ni and Zn but poor precision for Se and low recoveries for Ga, Mn, Sn and V. In spite of the small quantities of material analysed, it proved possible to determine the trace elements at levels down to 0.1 microgram g(-1) in the reference materials.

Biomass↗
PubMed2006

Trainable fusion rules. I. Large sample size case.

A wide selection of standard statistical pattern classification algorithms can be applied as trainable fusion rules while designing neural network ensembles. A focus of the present two-part paper is finite sample effects: the complexity of base classifiers and fusion rules; the type of outputs provided by experts to the fusion rule; non-linearity of the fusion rule; degradation of experts and the fusion rule due to the lack of information in the design set; the adaptation of base classifiers to training set size, etc. In the first part of this paper, we consider arguments for utilizing continuous outputs of base classifiers versus categorical outputs and conclude: if one succeeds in having a small number of expert networks working perfectly in different parts of the input feature space, then crisp outputs may be preferable over continuous outputs. Afterwards, we oppose fixed fusion rules versus trainable ones and demonstrate situations where weighted average fusion can outperform simple average fusion. We present a review of statistical classification rules, paying special attention to these linear and non-linear rules, which are employed rarely but, according to our opinion, could be useful in neural network ensembles. We consider ideal and sample-based oracle decision rules and illustrate characteristic features of diverse fusion rules by considering an artificial two-dimensional (2D) example where the base classifiers perform well in different regions of input feature space.

Animals↗
PubMed2006

Improved variance estimation of classification performance via reduction of bias caused by small sample size.

BACKGROUND: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm that the classifier is robust with good generalization performance to new examples, or at least that it performs better than random guessing. A suggested alternative is to obtain a confidence interval of the error rate using repeated design and test sets selected from available examples. However, it is known that even in the ideal situation of repeated designs and tests with completely novel samples in each cycle, a small test set size leads to a large bias in the estimate of the true variance between design sets. Therefore different methods for small sample performance estimation such as a recently proposed procedure called Repeated Random Sampling (RSS) is also expected to result in heavily biased estimates, which in turn translates into biased confidence intervals. Here we explore such biases and develop a refined algorithm called Repeated Independent Design and Test (RIDT). RESULTS: Our simulations reveal that repeated designs and tests based on resampling in a fixed bag of samples yield a biased variance estimate. We also demonstrate that it is possible to obtain an improved variance estimate by means of a procedure that explicitly models how this bias depends on the number of samples used for testing. For the special case of repeated designs and tests using new samples for each design and test, we present an exact analytical expression for how the expected value of the bias decreases with the size of the test set. CONCLUSION: We show that via modeling and subsequent reduction of the small sample bias, it is possible to obtain an improved estimate of the variance of classifier performance between design sets. However, the uncertainty of the variance estimate is large in the simulations performed indicating that the method in its present form cannot be directly applied to small data sets.

Analysis of Variance↗
PubMed1989

Strategies and sample-size considerations for mapping a two-locus autosomal recessive disorder.

The problem of detecting linkage, by using the LOD-score method, of polymorphic marker loci to a disorder that is determined by recessive alleles at two independent autosomal diallelic loci has been considered. The expected LOD score and the distribution of the LOD score have been worked out for various scenarios. It is found that the expected numbers of families to be sampled for detection of linkage are within feasible limits if the recombination fractions between the marker loci and the disorder loci are less than or equal to .1. The strategy of studying affected offspring only is shown to be more efficient than the strategy of studying both affected and normal offspring. The efficiency of the "affecteds-only" strategy (1) increases with increase in sibship size, (2) decreases with increase in population prevalence of the disorder, and (3) increases with increase in recombination distances between the marker and the disorder loci. From various considerations, it is found that sampling families of sibship size three with at least one affected, and adopting the affecteds-only strategy for analysis, may be an optimal strategy.

Genes, Recessive↗