Logistic regression analysis of multiple interosseous hand-muscle activities using surface electromyography during finger-oriented tasks.

Intrinsic hand muscles are densely located in the hand, and the myoelectric observation from the surface is sometimes unreliable because of some outside influences that may interfere with the signals. In the present study, we evaluated the activities of multiple interosseous hand-muscles which densely located in the hand, through analyzing the surface electromyographic signals during finger-oriented tasks using univariate and multivariate logistic regression models. Ten healthy subjects participated in our experiment, and isometrically exercised each finger one by one in flexed form. The result of a univariate analysis with the power and amplitude domain predictor variables of the surface electromyographic signals showed significant consistency between the activated finger and the inserted finger of the dorsal interosseous muscles to the proximal phalanx (P < 0.001). Meanwhile, the results of a multivariate analysis showed a higher correlation of the regression model of the fourth dorsal interosseous muscle during the action of the ring finger using frequency-domain variables (the Nagelkerke R2 = 0.716 when the median frequency was used), compared to the model without the frequency-domain variables (the Nagelkerke R2 = 0.583). Our result showed that the logistic regression models have a particular possibility for the analysis of the surface electromyographic signals of densely located hand-muscle activities related to the finger-oriented tasks.

Localization of sperm whales in a group using clicks received at two separated short baseline arrays.

In this paper, a sperm whale click analysis scheme is proposed in order to calculate the position of individual sperm whales in a group using data received at two arrays deployed near the surface. The proposed method mainly consists of two parts: short baseline (SBL) with classification and long baseline (LBL) with class matching. In SBL with classification, a click is automatically detected, and its direction of arrival is calculated. The clicks are then classified based on their direction vectors. The class data are then sent together with direction data and matched to the other array's class data. LBL with class matching is used for localization. The classification algorithm can be used to estimate the number of whales clicking and to list potential candidates for LBL matching. As a result, the proposed method is able to localize the positions of the whales in a group. The performance of the proposed method is evaluated using data recorded off Ogasawara islands with two arrays near the surface. The three-dimensional underwater trajectories of six sperm whales are extracted to demonstrate the capability of the proposed method.

New correction algorithms for multiple comparisons in case-control multilocus association studies based on haplotypes and diplotype configurations.

The multiple comparison problem arises in population-based studies when the association between phenotypes and multilocus genotypes is examined. Although Bonferroni's correction is often used to cope with such a problem, it may yield too conservative conclusions because all of the tests are assumed to be independent. We have developed new correction algorithms for the test of independence between phenotypes and multilocus genotypes at loci in linkage disequilibrium. In one of the algorithms, the exact type I error rate is calculated for the independency test. We found that such exact probabilities can be calculated using a 128 CPU PC cluster if the numbers of cases and controls are not more than 50. As an alternative method, we developed algorithms to calculate asymptotically the type I error rates using a Markov-chain Monte Carlo sampler that provided a good approximation to values calculated by the exact method. When the new algorithms were applied to both simulation and real data, the real overall type I error rates for the loci in linkage disequilibrium were from one-third to half as high as those obtained by Bonferroni's correction. These algorithms are likely to be useful for multilocus association studies for data obtained by case-control and cohort studies.

Robust estimation of the arterial input function for Logan plots using an intersectional searching algorithm and clustering in positron emission tomography for neuroreceptor imaging.

The Logan plot is a powerful algorithm used to generate binding-potential images from dynamic positron emission tomography (PET) images in neuroreceptor studies. However, it requires arterial blood sampling and metabolite correction to provide an input function, and clinically it is preferable that this need for arterial blood sampling be obviated. Estimation of the input function with metabolite correction using an intersectional searching algorithm (ISA) has been proposed. The ISA seeks the input function from the intersection between the planes spanned by measured radioactivity curves in tissue and their cumulative integrals in data space. However, the ISA is sensitive to noise included in measured curves, and it often fails to estimate the input function. In this paper, we propose a robust estimation of the cumulative integral of the plasma time-activity curve (pTAC) using ISA (robust EPISA) to overcome noise issues. The EPISA reduces noise in the measured PET data using averaging and clustering that gathers radioactivity curves with similar kinetic parameters. We confirmed that a little noise made the estimation of the input function extremely difficult in the simulation. The robust EPISA was validated by application to eight real dynamic [(11)C]TMSX PET data sets used to visualize adenosine A(2A) receptors and four real dynamic [(11)C]PIB PET data sets used to visualize amyloid-beta plaque. Peripherally, the latter showed faster metabolism than the former. The clustering operation improved the signal-to-noise ratio for the PET data sufficiently to estimate the input function, and the calculated neuroreceptor images had a quality equivalent to that using measured pTACs after metabolite correction. Our proposed method noninvasively yields an alternative input function for Logan plots, allowing the Logan plot to be more useful in neuroreceptor studies.

Accurate automated clustering of two-dimensional data for single-nucleotide polymorphism genotyping by a combination of clustering methods: evaluation by large-scale real data.

MOTIVATION: The Invader assay is a fluorescence-based high-throughput genotyping technology. If the output data from the Invader assay were classified automatically, then genotypes for individuals would be determined efficiently. However, existing classification methods do not necessarily yield results with the same accuracy as can be achieved by technicians. Our clustering algorithm, Genocluster, is intended to increase the proportion of data points that need not be manually corrected by technicians. RESULTS: Genocluster worked well even when the number of clusters was unknown in advance and when there were only a few points in a cluster. The use of Genocluster enabled us to achieve an acceptance rate (proportion of assay results that did not need to be corrected by expert technicians) of 84.4% and a proportion of uncorrected points of 95.8%, as determined using the data from over 31 million points. AVAILABILITY: Information for obtaining the executable code, example data and example analysis are available at http://www.genstat.net/genocluster.