Objective: The ‘VA’ Windows program that we developed in 2011 for analyzing voice quality quantitatively uses zerocross picking to find individual basic pitch periods. It has a simple and user-friendly user interface and high accuracy. This program determines the fundamental frequency, jitter, shimmer, PPQ, APQ, and signal-to-noise ratio (Ra). It needs only a general-purpose Windows PC, USB audio interface and a microphone. The aim of this study is to improve the version of the VA Windows program in English and to develop a VA smart phone program to allow wider use of objective acoustic analysis. Study Design: Cross-sectional study. Methods: Sustained vowel /a/ sounds from 40 subjects without evident vocal problems, and 40 subjects with slight hoarseness, were examined. We compared the analyzed data with data from other software (MDVP and Praat). For a comparison between VA for Windows and VA for a smart phone, sustained vowel /a/ sounds from six subjects without hoarseness were recorded with each system simultaneously. Results: The normal voice and slightly hoarse voice data analyzed with VA showed a high correlation with most parameters from both MDVP and Praat. There was a strong correlation between the Windows and smart phone versions of VA in terms of the fundamental frequency and Ra. Conclusions: The results showed that the VA software was not inferior to the other acoustic analysis software tested. The simple and easy to use smart phone version may facilitate our goal of creating an objective, widely available method to evaluate hoarseness.

Traffic velocity is a fundamental metric for inferring traffic conditions. This paper proposes a new velocity estimation approach from temporal sequences of vehicle count that does not require tracking any vehicles or using any labeled data. It is useful for measuring traffic velocities with low quality and inexpensive sensors such as web cameras in general use. We formalize the task as a density estimation problem by introducing a new model for temporal sequences of vehicle counts wherein the correlation between the sequences is directly related to the traffic velocity. We also derive a sampling-based algorithm for the density estimation. We show the effectiveness of our method on artificial and real-world data sets.

Survival data analysis becomes complex when the proportional hazards assumption is violated at population level or when crude hazard rates are no longer estimators of marginal ones. We develop a Bayesian survival analysis method to deal with these situations, on the basis of assuming that the complexities are induced by latent cohort or disease heterogeneity that is not captured by covariates and that proportional hazards hold at the level of individuals. This leads to a description from which risk-specific marginal hazard rates and survival functions are fully accessible, 'decontaminated' of the effects of informative censoring, and which includes Cox, random effects and latent classmodels as special cases. Simulated data confirm that our approach can map a cohort's substructure and remove heterogeneity-induced informative censoring effects. Application to data from the Uppsala Longitudinal Study of Adult Men cohort leads to plausible alternative explanations for previous counter-intuitive inferences on prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to data from the Swedish Apolipoprotein Mortality Risk Study. Copyright (C) 2017 John Wiley & Sons, Ltd.

This paper addresses a problem in which we learn a regression model from sets of training data. Each of the sets has an only single label, and only one of the training data in the set reflects the label. This is particularly the case when the label is attached to a group of data, such as time-series data. The label is not attached to the point of the sequence but rather attached to particular time window of the sequence. As such, a small part of the time window likely reflects the label, whereas the other larger part of the time window likely does not reflect it. We design an algorithm for estimating which of the training data in each of the sets corresponds to the label, as well as for training the regression model on the basis of Bayesian modeling and posterior inference with variational Bayes. Our experimental results show that our approach perform better than baseline methods on an artificial dataset and on a real-world dataset.

Observing vocal fold vibration is essential when investigating the causes of hoarseness of voice and determining the appropriate treatment. However, in Japan, it is difficult at present to obtain commercial high-speed digital imaging (HSDI) systems for this purpose because of a lack of marketing routes. Accordingly, we devised an HSDI system using a high-speed camera (monochrome HiSpec1). We chose this camera as it has been an established instrument in industrial and laboratory use; it also provided excellent light sensitivity, and used a 400-watt xenon light source for the acquisition of bright and clear images. The HSDI system enabled video capture of up to 4.4 s with our usual setting of 422 × 384 pixels at 3000 frames per second (fps). It can also capture videos in various settings such as 396 × 256 pixels at 4000 fps. In addition, we equipped the system with devices for concurrent audio waveform acquisition. Our novel laryngeal HSDI system could be a useful clinical tool for vocal fold examination in patients with voice problems when access to commercially available devices is limited.

Purpose:The aim of this study was to establish a method to observe vocal fold vibration using a low-cost high-speed laryngeal imaging system. Procedures: We assembled a highspeed imaging system with a consumer digital camera and a rigid laryngeal endoscope. The camera can shoot digital images at a rate of 1,200 frames per second and be purchased for about USD 1,000 in Japan. Results: We examined the normal and pathological vocal folds of 215 subjects with our new system and analyzed the vocal fold vibration in these subjects by playback of a video and kymograph images. Conclusions: Our high-speed laryngeal imaging system is highly cost-effective and can be a useful tool for examining the vocal folds of patients with voice disorders. Copyright (C) 2012 S. Karger AG, Basel

We propose a Bayesian image super-resolution (SR) method with a causal Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from given multiple low-resolution images. An MRF model with the line process supplies a preferable prior for natural images with edges. We improve the existing image transformation model, the compound MRF model, and its hyperparameter prior model. We also derive the optimal estimator-not the joint maximum a posteriori (MAP) or the marginalized maximum likelihood (ML) but the posterior mean (PM)-from the objective function of the L2-norm-based (mean square error) peak signal-to-noise ratio. Point estimates such as MAP and ML are generally not stable in ill-posed high-dimensional problems because of overfitting, whereas PM is a stable estimator because all the parameters in the model are evaluated as distributions. The estimator is numerically determined by using the variational Bayesian method. The variational Bayesian method is a widely used method that approximately determines a complicated posterior distribution, but it is generally hard to use because it needs the conjugate prior. We solve this problem with simple Taylor approximations. Experimental results have shown that the proposed method is more accurate or comparable to existing methods.

This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from observed multiple low-resolution images. A compound Gaussian MRF model provides a preferable prior for natural images that preserves edges. PM is the optimal estimator for the objective function of peak signal-to-noise ratio (PSNR). This estimator is numerically determined by using variational Bayes (VB). We then solve the conjugate prior problem on VB and the exponential-order calculation cost problem of a compound Gaussian MRF prior with simple Taylor approximations. In experiments, the proposed method roughly overcomes existing methods.

The voice analyzer (VA) software program we developed for quantitatively analyzing voice quality uses zero-cross-picking to find individual basic pitch periods. Two such periods extracted from a voice waveform were analyzed to calculate the acoustic energy of harmonics separately from that of noise components. The program determines the ratio of harmonics to noise (Ra), FO, jitter, shimmer, APQ, and PPQ. Sustained vowel/a/from 28 subjects without evident vocal problems such as hoarseness and 49 subjects with hoarseness were examined. Average female and male Ra among the nonhoarse were 31. 9 dB and 34. 4 dB, jitter 0. 254% and 0. 246%, and shimmer 0.168% and 0.116%. Among those with hoarseness, all parameter scored worse than in the nonhoarse in proportion to auditory impression.

Statistical haplotype inference is an indispensable technique in the field of medical science. The method usually has two steps: inference of haplotype frequencies and inference of diplotype for each subject. The first step can be done by using the expectation-maximization (EM) algorithm, but it incurs an unreasonably large calculation cost when the number of single-nucleotide polymorphism (SNP) loci of concern is large. In this article, we describe an approximate probabilistic model of haplotype frequencies. The model is constructed by using several distributions of nearby local SNPs. This approximation seems good because SNPs are generally more strongly correlated when they are close to one another on a chromosome. To implement this approach, we use a log linear model, the Walsh-Hadamard transform, and a combinatorial optimization method. Artificial data suggested that the overall haplotype inference of our method is good if there are nine or more local consecutive SNPs. Some minor problems should be dealt with before this method can be applied to real data.

In the estimation of gene networks from microarray gene expression data, we propose a statistical method for quantification of the hidden confounders in gene networks, which were possibly removed from the set of genes on the gene networks or are novel biological elements that are not measured by microarrays. Due to high computational cost of the structural learning of Bayesian networks and the limited source of the microarray data, it is usual to perform gene selection prior to the estimation of gene networks. Therefore, there exist missing genes that decrease accuracy and interpretability of the estimated gene networks. The proposed method can identify hidden confounders based on the conflicts of the estimated local Bayesian network structures and estimate their ideal profiles based on the proposed Bayesian networks with hidden variables with an EM algorithm. From the estimated ideal profiles, we can identify genes which are missing in the network or suggest the existence of the novel biological elements if the ideal profiles are not significantly correlated with any expression profiles of genes. To the best of our knowledge, this research is the first study to theoretically characterize missing genes in gene networks and practically utilize this information to refine network estimation. © 2010 IEEE.

Belief propagation (BP) and the concave-convex procedure (CCCP) are algorithms that use the Bethe free energy as a cost function and are used to solve information processing tasks. We have developed a new algorithm that also uses the Bethe free energy but changes the roles of the master and slave variables. This is called the Bowman-Levin (BL) approximation in the domain of statistical physics. When we applied the BL approximation to decode the regular low-density parity-check (LDPC) codes over an additive white Gaussian noise (AWGN) channel, its average performance was roughly similar to that of either BP or CCCP, but slightly outperforms them if the vast calculation cost is not prohibitive. This implies that our algorithm based on the BL approximation can be successfully applied to other problems to which BP or CCCP have already been applied. We also found that the decoding dynamics of the BL algorithm particularly depend on the number of inner loops. These differences from BP may be important in understanding the complicated landscape of the Bethe free energy.

The method of statistical haplotype inference is an indispensable technique in the field of medical science The authors previously reported Hardy-Weinberg equilibrium-based haplotype inference that could manage single nucleotide polymorphism (SNP) data We recently extended the method to cover copy number variation (CNV) data Haplotype inference from mixed data is important because SNPs and CNVs are occasionally in linkage disequilibrium. The idea underlying the proposed method is simple, but the algorithm for it needs to be quite elaborate to reduce the calculation cost Consequently, we have focused on the details on the algorithm in this study. Although the main advantage of the method is accuracy, in that it does not use any approximation, its main disadvantage is still the calculation cost, which is sometimes intractable for large data sets with missing values.

Observing vibration of the vocal folds is essential to find causes of hoarseness. We assembled the high-speed movie system from a consumer digital video camera (EXILIM PRO EX-Fl, Casio Computer Co., LTD., Tokyo) and a conventional rigid laryngeal endoscope. This system enables us to observe each vocal fold vibration at a frame rate of 1, 200 images per second. We examined the vocal folds of 10 healthy subjects and two patients with hemilateral vocal fold paralysis with our system. The high-speed movies were saved in the camera as MOV format files. On slow playback of the movies and kymographic images constructed from the movie files, the vocal fold vibrations of healthy subjects showed regularity, symme-try, complete glottal closure, normal amplitude and normal mucosal waves. However, those of the sub-jects with vocal fold paralysis showed irregularity, asymmetry, incomplete glottal closure, abnormal am-plitude and abnormal mucosal waves. Our system will be useful for a routine clinical examination of the vocal folds.

Haplotype inference is an indispensable technique in medical science, especially in genome-wide association studies. Although the conventional method of inference using the expectation-maximization (EM) algorithm by Excoffier and Slatkin is one standard approach, as its calculation cost is an exponential function of the maximum number of heterozygous loci, it has not been widely applied. We propose a method of haplotype inference that can empirically accommodate up to several tens of single nucleotide polymorphism loci in a single haplotype block while maintaining criteria that are exactly equivalent to those of the EM algorithm. The idea is to reduce the cost of calculating the EM algorithm by using a haplotype-grouping preprocess exploiting the symmetrical and inclusive relationships of haplotypes based on the Hardy-Weinberg equilibrium. Testing of the proposed method using real data sets revealed that it has a wider range of applications than the EM algorithm.

Translational-symmetric filters provide a foundation for various kinds of image processing. When a filtered image containing noise is observed, the original one can be reconstructed by Bayesian inference. Furthermore, hyperparameters such as the smoothness of the image and the noise level in the communication channel through which the image observed can be estimated from the observed image by setting a criterion of maximizing marginalized likelihood. In this article we apply a diagonalization technique with the Fourier transform to this image reconstruction problem. This diagonalization not only reduces computational costs but also facilitates theoretical analyses of the estimation and reconstruction performances. We take as an example the Mexican-hat shaped neural cell receptive field seen in the early visual systems of animals, and we compare the reconstruction performances obtained under various hyperparameter and filter parameter conditions with each other and with the corresponding performances obtained under no-filter conditions. The results show that the using a Mexican-hat filter can reduce reconstruction error.

Low-density parity-check (LDPC) code has recently become of great interest. The statistical mechanics approach has been used to reveal some characteristics of LDPC in the thermodynamic limit. In this paper, we analyze this system for finite size rather than within the thermodynamic limit through a principal component analysis (PCA) approach. Specifically, both the decoding dynamics of belief propagation (BP) and the phases of the system are visualized and discussed. The result implies that the decoding dynamics roughly corresponds to the system temperature we introduced, and this system has several phases such as ferromagnetic, paramagnetic, and 1RSB spin-glass phases.

The replica method (RM) provides an accurate evaluation of a class of mean-field (MF) models in the thermodynamic limit. It is, however, not straightforward to extend its application to a class of non-MF models and finite-size models with sufficient accuracy. We previously proposed a numerical approach as an alternative, in which principal component analysis (PCA) is employed to analyze configurations sampled through Monte Carlo simulations. Using this method, we examine both two- and three-body mean-field Sourlas codes as a test board and compare our results with those of the RM. We confirm that the spin distribution map constructed using PCA axes has specific characteristics approximately corresponding to the phases given by RM. This result suggests that the PCA approach will be effective even with general non-MF finite-size models.

Transcriptional dysregulation as a result of sequestration of essential transcription factors into protein aggregates formed by polyglutamine (polyQ) expansions can lead to late-onset progressive neurodegeneration. DNA microarray analysis of Drosophila expressing polyQ in the compound eye over time revealed large numbers of transcriptional changes at the earliest stages of the disease including repression of the transient receptor potential calcium channels in a polyQ-induced cell death specific manner. While significant differences in expression profiles were found between the Drosophila compound eye and polyQ-sensitive neural cells, a number of possible key overlapping regulators were extracted. Among these, PDK1 was shown to act as a mediator for polyQ-toxicity, suggesting the involvement of the TOR pathway in polyQ-induced neurodegeneration.

Belief propagation (BP) and the concave convex procedure (CCCP) are both methods that utilize the Bethe free energy as a cost function and solve information processing tasks. We have developed a new algorithm that also uses the Bethe free energy, but changes the roles of the master variables and the slave variables. This is called the Bowman-Levin (BL) approximation in the domain of statistical physics. When we applied the BL algorithm to decode the Gallager ensemble of short-length regular low-density parity check codes (LDPCC) over an additive white Gaussian noise (AWGN) channel, its average performance was somewhat better than that of either BP or CCCP. This implies that the BL algorithm can also be successfully applied to other problems to which BP or CCCP has already been applied.

Recently, slow dynamics in learning of neural networks has been known to be closely related to singularities, which exist in parameter spaces of hierarchical learning models. To show the influence of singular structure on learning dynamics, we take statistical mechanical approaches and investigate online-learning dynamics under various learning scenario with different relationship between optimum and singularities. From the investigation, we found a quasi-plateau phenomenon which differs from the well known plateau. The quasi-plateau and plateau become extremely serious when an optimal point is in a neighborhood of a singularity. The quasi-plateau and plateau disappear in the natural gradient learning, which takes singular structures into account and uses Riemannian measure for the parameter space.

The statistical mechanical approach is a powerful method for understanding large degrees of freedom problems, such as those related to the spin-glass model, but its application is usually limited to the class of mean-field models. We try a new general and computational approach - instead of an exact calculation of the Boltzmann distribution, we use an empirical spin state distribution obtained through simulation and extract potentially useful axes by principal component analysis (PCA). We adopted a three-body Sourlas code to evaluate this PCA approach compared with existing replica theory. The empirical spin distribution projected to these PCA axes showed distinctive patterns corresponding to the phase given by the replica method. Moreover, the first principal component roughly coincided with one of the order parameters (averaged spin) under a certain condition. These results suggest that this PCA approach could be effective even in more complicated systems that we cannot investigate analytically.

A gene-expression microarray datum is modeled as an exponential expression signal (log-normal distribution) and additive noise. Variance-stabilizing transformation based on this model is useful for improving the uniformity of variance, which is often assumed for conventional statistical analysis methods. However, the existing method of estimating transformation parameters may not be perfect because of poor management of outliers. By employing an information normalization technique, we have developed an improved parameter estimation method, which enables statistically more straightforward outlier exclusion and works well even in the case of small sample size. Validation of this method with experimental data has suggested that it is superior to the conventional method. © Imperial College Press.

Adaptive natural gradient descent (ANGD) method realizes natural gradient descent (NGD) without needing to know the input distribution of learning data and reduces the calculation cost from a cubic order to a square order. However, no performance analysis of ANGD has been done. We have developed a statistical-mechanical theory of the simplified version of ANGD dynamics for soft committee machines in on-line learning; this method provides deterministic learning dynamics expressed through a few order parameters, even though ANGD intrinsically holds a large approximated Fisher information matrix. Numerical results obtained using this theory were consistent with those of a simulation, with respect not only to the learning curve but also to the learning failure. Utilizing this method, we numerically evaluated ANGD efficiency and found that ANGD generally performs as well as NGD. We also revealed the key condition affecting the learning plateau in ANGD.

Adaptive natural gradient descent (ANGD) method realizes natural gradient descent (NGD) without needing to know the input distribution of learning data and reduces the calculation cost from a cubic order to a square order. However, no performance analysis of ANGD has been done. We have developed a statistical-mechanical theory of the simplified version of ANGD dynamics for soft committee machines in on-line learning
this method provides deterministic learning dynamics expressed through a few order parameters, even though ANGD intrinsically holds a large approximated Fisher information matrix. Numerical results obtained using this theory were consistent with those of a simulation, with respect not only to the learning curve but also to the learning failure. Utilizing this method, we numerically evaluated ANGD efficiency and found that ANGD generally performs as well as NGD. We also revealed the key condition affecting the learning plateau in ANGD. © 2004 The American Physical Society.

Calcifying epithelioma (pilomatrixoma) is a relatively uncommon benign tumor. It usually presents as a slow-growing subcutaneous or intradermal nodule measuring less than 3 cm. In this report, we present a 20-year-old male with giant pilomatrixoma in the subauricular region. We performed en bloc resection of the tumor, which measured 12 x 9 cm. Histopathologically, this tumor did not show any malignant findings. There has not been any evidence of recurrence since the treatment.

In hierarchical models, such as neural networks, there exist complex singular structures. The singularity is known to affect estimation performances and learning dynamics of the models. Recently, there have been a number of studies on properties of obtained estimators for the models, but there are few studies on the dynamical properties of learning used for obtaining the estimators. Using two-layer neural networks, we investigate influences of singularities on dynamics of standard gradient learning and natural gradient learning under various learning conditions. In the standard gradient learning, we found a quasi-plateau phenomenon, which is severer than the well known plateau in some cases. The slow convergence due to the quasi-plateau and plateau becomes extremely serious when an optimal point is in a neighborhood of a singularity. In the natural gradient learning, however, the quasi-plateau and plateau are not observed and convergence speed is hardly affected by singularity.

We previously established two strains of Mongolian gerbil: a seizure-sensitive strain, established by selective inbreeding for motor seizures elicited by a stimulus called the S method and a seizure-resistant strain that does not exhibit inducible seizures. The behavior of the seizure-sensitive strain is characterized by a progressive increase in responsiveness to weekly application of the S method, from repetitive backward ear movements appearing after postnatal day 40, to a full-blown seizure, while the seizure-resistant strain is apparently unaffected by the stimulation. The difference between these two strains is presumably genetic. To determine the genetic factors underlying this difference, we first examined developmental changes in the stimulus-induced behavior of the F1 hybrids. When the S method was applied, most F1 hybrids had repetitive movements of the ears (and head) similar to the seizure-sensitive gerbils, but generalized seizures emerged considerably later than in seizure-sensitive gerbils. These results suggest that a half dose of the gene products involved renders most gerbils susceptible to the stimulus but is insufficient for the rapid accumulation of an as yet undefined change needed to spread the abnormal electrophysiologic activity to elicit generalized seizures.

In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures.

To elucidate cortical correlates of vestibulo-ocular reflex (VOR) modulation, we observed cortical activation during fixation suppression and habituation of caloric vestibular nystagmus in 12 normal subjects, using PET. Significant positive correlation between regional cerebral blood flow (rCBF) and slow phase eye velocity of caloric nystagmus was observed in the middle and posterior insula, inferior parietal lobule, temporal pole, right fusiform gyrus, lingual gyrus, and cerebellar vermis and hemisphere. The rCBF increase in the insular region and the inferior parietal lobule was lateralized depending on the direction of the nystagmus. Caloric nystagmus was suppressed as a result of visual fixation, during which time the area around the right frontal eye field, temporal pole, inferior temporal gyrus, a broad area in the visual cortex, including fusiform and lingual gyrus, cerebellar uvula/nodulus and flocculus, exhibited positive correlation with fixation suppression of caloric nystagmus, while vestibular cortices exhibited negative correlation. The caloric nystagmus habituated with repetition of stimulation. With habituation, we observed activation in the right anterior cingulate gyrus, left superior parietal lobule and right cuneus, and deactivation in the anterior insula, cingulate gyrus, inferior parietal lobule and occipito-temporal visual cortex. The region that showed significant co-activation with fixation suppression and habituation of caloric nystagmus was the right cuneus, and significant co-deactivation was observed in the anterior insula, cingulate gyrus, inferior parietal lobule and middle temporal visual cortex. The present results support previous observations that the parieto-insular cortex and inferior parietal lobule are involved in processing of vestibular information, and, in addition, suggest that activation may depend on the direction of nystagmus. Deactivation of vestibular cortices during visual fixation supports the concept of inhibitory visual-vestibular interaction in the cortex. Significant activation of the cingulate, superior parietal and visual cortices, and cerebellar vermis accompanying reduction of caloric response with repeated stimuli suggests possible involvement of these regions in vestibular habituation. Common activation of the cuneus in visual cortex and deactivation of vestibular and visuo-spatial association cortices by both visual suppression and habituation of VOR suggests that these two mechanisms are not completely independent but may share some cortical and subcortical regions.

Motivation: Given the vast amount of gene expression data, it is essential to develop a simple and reliable method of investigating the fine structure of gene interaction. We show how an information geometric measure achieves this.
Results: We introduce an information geometric measure of binary random vectors and show how this measure reveals the fine structure of gene interaction. In particular, we propose an iterative procedure by using this measure (called IPIG). The procedure finds higher-order dependencies which may underlie the interaction between two genes of interest. To demonstrate the method, we investigate the interaction between the two genes of interest in the data from human acute lymphoblastic leukemia cells. The method successfully discovered biologically known findings and also selected other genes as hidden causes that constitute the interaction.

The permutation symmetry of the hidden units in multilayer perceptrons causes the saddle structure and plateaus of the learning dynamics in gradient learning methods. The correlation of the weight vectors of hidden units in a teacher network is thought to affect this saddle structure, resulting in a prolonged learning time, but this mechanism is still unclear. In this paper, we discuss it with regard to soft committee machines and on-line learning using statistical mechanics. Conventional gradient descent needs more time to break the symmetry as the correlation of the teacher weight vectors rises. On the other hand, no plateaus occur with natural gradient descent regardless of the correlation for the limit of a low learning rate. Analytical results support these dynamics around the saddle point.

We examined cortical activation by speech in patients with moderate inner ear hearing loss using PET to investigate the response of the language network to insufficient speech input. We made two word lists, well-perceived words and poorly-perceived words, and measured rCBF during monaural presentation of these words. Well-perceived words activated bilateral temporal lobes, bilateral inferior frontal gyri (IFG) and left angular gyrus (AG) regardless of the ear stimulated, Poorly-perceived words activated contralateral temporal lobe and bilateral IFG, while little or no activation was observed in the ipsilateral temporal lobe and left AG. Insufficient activation of the temporal lobe ipsilateral to the ear stimulated might correlated with less accurate word comprehension in patients with inner ear hearing loss.

We designed a new type of nonlinear digital hearing aid (TD-1) with near-instantaneous amplitude compression for sensorineural hearing-impaired people. The amplification method utilized by the TD-1 normalizes the signal waveforms within a time window bounded by two adjacent positive going zero-crossing points. The signals within this window are amplified so that the maximum amplitude reaches a preset value, thus allowing a consistent peak amplitude to be maintained. To test this, we examined the efficacy of the TD-1 device in 31 patients with sensorineural hearing loss. When tested for recognition of Japanese sentences in a quiet environment, 7 of 25 (28.0%) subjects wearing the TD-1 showed a 20% or greater increase in the maximum intelligibility score than that obtained when wearing their own linear hearing aids. In addition, 7 of 31 (22.6%) subjects showed the same increase with the TD-1 relative to that obtained when they wore HA70 or HA73 devices (Rion Co., Ltd., Tokyo, Japan), which are conventional analogue hearing aids. These results indicate that TD-1 can be effective for patients with sensorineural hearing loss.

The etiology of spasmodic dysphonia (SD) is still unknown. In the present study, cortical function of a 59-year-old male patient with adductor type SD was examined during phonation with positron emission tomography (PET). Magnetic resonance imaging showed no organic abnormality in the brain. However, PET showed remarkable activities during phonation in the left motor cortex, Broca's area, the cerebellum, and the auditory cortices, whereas the supplementary motor area (SMA) was not activated. The SMA is known to function for motor planning and programming and is usually activated in normal phonation. Several previous reports have shown that the damage of the SMA caused a severe disturbance of voluntary vocalization, In the present case, it was suggested that the functional deficit of the SMA might be related to SD. (Am J Otolaryngol 2001;22:219-222. Copyright (C) 2001 by W.B. Saunders Company).

Background: It is believed that the number of neurons of the human cortex increases rapidly in the first postnatal year, and then decreases gradually towards adult level as their functions are revised up to 11 years of age ('synaptic revision'). It is also confirmed that regional cerebral blood flow (rCBF) at rest represents the density of the neurons and decreases in accordance with the synaptic revision in process. If synaptic revision does not occur, rCBF remains at high level. Thus, we can evaluate whether functional differentiation has occurred in the human cortex by measuring rCBF at resting state. Objective: To examine functional differentiation of the auditory association area (A2) in prelingually deaf subjects. Methods: Six postlingually and six prelingually deaf subjects who had undergone cochlear implant (CI) were involved in the current study. All prelingually deaf subjects underwent CI over 8 years old. The rCBF in A2 was examined during resting and listening to speech sounds using positron emission tomography (PET) and H2/15O intravenous injection. Twelve normal subjects' rCBFs were also measured as control. Furthermore, three prelingually deaf subjects underwent follow up PET studies in which cortical activities in A2 for listening and lipreading were examined. Results: In the examination of rCBF at rest, rCBFs of prelingually deaf subjects in A2 showed significantly higher than those of either the postlingually deaf subjects or normal subjects. During listening, rCBFs in A2 increased in postlingually deaf subjects and normal subjects, while there was no significant rCBF increase in the prelingually deaf. High rCBF level in A2 at rest observed in prelingually deaf subjects implied a lack of synaptic revision, and it was suggested that the functional differentiation for auditory processing was little in the A2 of prelingually deaf subjects. In the follow up study for three prelingually deaf subjects, activation of A2 was observed during lipreading but not during listening in two cases, who had developed the skill of lipreading while speech recognition was not improved by CI. In contrast, the other case had not used any visual clues in daily communication prior to CI, and the hearing acuity was well improved by CI. This case demonstrated an activity in A2 during listening, while lipreading induced no activation. Conclusion: It is suggested that functional differentiation of A2 should differ according to which of visual and auditory clue is chiefly used during critical periods for speech acquisition. The findings are thought to be important for us to schedule the education and treatment for prelingually deaf children. (C) 2000 Elsevier Science Ireland Ltd.

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation.<BR>The subjects were 12 normal volunteers. The following three types of images were constructed: 1) routine images by SPM, 2) three-dimensional static images, and 3) three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by selfmade C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system.<BR>Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method.<BR>To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact threedimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the surface model is the most common method of threedimensional display. However, the volume rendering method may be more effective for imaging regions such as the brain.

The effects of a nonlinear, digital hearing aid on the perception of sentences in a quiet environment were examined for 12 patients with profound sensorineural hearing impairments. This hearing aid was equipped with a compression circuit, which divides the waveform in the time domain by zero-cross points, and then amplifies each waveform to an fixed amplitude. To reduce the susceptibility to background noise, input signals less than a certain level were amplified linearly, and this level was easily adjustable. Three out of 12 (25.0%) patients understood more than 50% of the phrases in typical Japanese sentences at all of the pre-set sound level (50, 60, 70 and 80 dBSPL). Furthermore, this device offered better perception than linear amplification (Rion HA70 or 73) in 8 out of 12 (66.7%) subjects, and was superior to the subjects' own linear hearing aids in 5 out of 10 (50.0%) cases. These results indicate that this hearing aid is beneficial for some subjects.

The molecular basis of androgen resistance was investigated in a patient with undervirilized male syndrome. Binding studies of the androgen receptors in the patient's genital skin fibroblasts revealed a normal binding capacity of 5 alpha-dihydrotestosterone, although the affinity to androgen was slightly lower than the normal control value. The androgen binding of the patient's receptor showed a moderate thermal instability when the assay temperature was raised from 30 to 41 C.
Nucleotide sequencing analysis of the androgen receptor gene revealed a single nucleotide substitution in exon F, resulting in an amino acid alteration from leucine (CTC) to phenylalanine (TTC) at position 789 within the steroid-binding domain of androgen receptor. When expressed in COS-7 cells, the mutant androgen receptor harboring phenylalanine at position 789 showed thermolabile androgen-binding properties similar to those observed in the patient's genital skin fibroblasts. Cotransfection experiments with an androgen-inducible reporter gene demonstrated a decreased transactivational capability of the mutant receptor. These results indicate that this point mutation modified the receptor function and caused androgen resistance in this patient. This mutation caused the mildest form of all androgen insensitivity syndromes ever examined for mutations in the androgen receptor gene.