Antibodies to Encephalitozoon cuniculi were examined by enzyme-linked immunosorbent assay using E. cuniculi PTP2 recombinant protein and by Western blot analysis on a total of 472 dog serum samples that had been collected in Japan. Of these samples, 21.8% (103/472) had antibodies against E. cuniculi. Each of 5 serum samples that showed high (>1.0) or low (<0.1) OD value was selected randomly and further examined by Western blot using E. cuniculi-native antigens. All samples with high OD values reacted with specific E. cuniculi proteins, including an antigen of approximately 35 kDa corresponding with PTP2; sera with low OD values did not recognize this E. cuniculi band. This study is the first to demonstrate the prevalence of E. cuniculi infection in dogs in Japan.
Humans can detect various anomalies in a sound sequence without attending to each dimension explicitly. Event‐related potentials (ERPs) have been used to examine the processes of auditory deviance detection. Previous research has shown that music‐syntactic anomalies elicit early right anterior negativity (ERAN), whereas more general acoustic irregularities elicit mismatch negativity (MMN). Although these ERP components occur in a similar latency range with a similar scalp topography, the relationship between the detection processes they reflect remains unclear. This study compared these components by manipulating music‐syntactic (chord progression) and acoustic (intensity) irregularities orthogonally in two experiments. Non‐musicians (Experiment 1: N = 39; Experiment 2: N = 24) were asked to listen to chord sequences, each consisting of 5 four‐voice chords, as they watched a silent video clip. Standard, harmonic‐deviant, intensity‐deviant and double‐deviant chords occurred at the final position in each sequence. Deviant stimuli were presented infrequently (p = .10) in Experiment 1 and equiprobably (p = .25) in Experiment 2. Regardless of deviance probability, both harmonic and intensity deviants elicited similar negativities, which were indistinguishable in terms of latency or scalp distribution. When the two deviant types occurred simultaneously, the negativity increased in an additive manner; that is, the amplitude of the double‐deviant ERP was as large as the sum of the single‐deviant ERPs. These findings suggest that the detection of music‐syntactic and acoustic irregularities works independently, based on different regularity representations.
In this study, we developed a novel machine-learning model to estimate the carrier-to-noise ratio (CNR) of wireless medical telemetry (WMT) using time-domain waveform data measured by a low-cost software-defined radio. If the CNR can be estimated automatically, the management of the electromagnetic environment of WMT can be easier. Therefore, we proposed a machine-learning method for estimating CNR. According to the performance evaluation results by 5-segment cross-validation on 704 types of measured data, CNR was estimated with 99.5% R-square and 0.844 dB mean absolute error using a gradient boosting regression tree. The gradient boosting decision tree classifiers predicted whether the CNR exceeded 30 dB with 99.5% accuracy. The proposed method is effective for investigating electromagnetic environments in clinical settings.
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