Numerical investigation of effects of incisor angle on production of sibilant /s/

The effects of the inclination angle of the incisor on the speech production of the fricative consonant /s/ was investigated using an implicit compressible flow solver. The hierarchical structure grid was applied to reduce the grid generation time for the vocal tract geometry. The airflow and sound during the pronunciation of /s/ were simulated using the adaptively switched time stepping scheme, and the angle of the incisor in the vocal tract was changed from normal position up to 30°. The results showed that increasing the incisor angle affected the flow configuration and moved the location of the high turbulence intensity region thereby decreased the amplitudes of the sound in the frequency range from 8 to 12 kHz. Performing the Fourier transform on the velocity fluctuation, we found that the position of large magnitudes of the velocity at 10 kHz shifted toward the lip outlet when the incisor angle was increased. In addition, separate acoustic simulations showed that the shift in the potential sound source position decreased the far-field sound amplitudes above 8 kHz. These results provide the underlying insights necessary to design dental prostheses for the production of sibilant fricatives.

Quantitatively Measured Infrapatellar Fat Pad Signal Intensity Alteration is Associated with Joint Effusion-Synovitis in Knee Osteoarthritis

Background: Abnormal infrapatellar fat pad (IPFP) plays a detrimental role in knee osteoarthritis (OA) by producing pro-inflammatory cytokines. IPFP may interact with synovium because of their adjacent anatomical positions; however, whether abnormal IPFP can contribute to effusion-synovitis in knee OA is unclear.Methods: Among 255 knee OA patients, IPFP signal intensity alteration represented by four measurement parameters [standard deviation of IPFP signal intensity (IPFP sDev), upper quartile value of IPFP high signal intensity region (IPFP UQ (H)), ratio of IPFP high signal intensity region volume to whole IPFP volume (IPFP percentage (H)), and clustering factor of IPFP high signal intensity (IPFP clustering factor (H))] was measured quantitatively at baseline and two-year follow-up using magnetic resonance imaging (MRI). Effusion-synovitis of the suprapatellar pouch and other cavities were measured both quantitatively and semi-quantitatively as effusion-synovitis volume and effusion-synovitis score at baseline and two-year follow-up using MRI. Mixed-effects models were used to assess the associations between IPFP signal intensity alteration and effusion-synovitis over two years.Results: In multivariable analyses, all four parameters of IPFP signal intensity alteration were positively associated with total effusion-synovitis volume and effusion-synovitis volumes of the suprapatellar pouch and of other cavities over two years (all P＜0.05). They were also associated with the semi-quantitative measure of effusion-synovitis except for IPFP percentage (H) with effusion-synovitis in other cavities. Conclusion: Quantitatively measured IPFP signal intensity alteration is positively associated with joint effusion-synovitis in people with knee OA, suggesting that IPFP signal intensity alteration may contribute to effusion-synovitis and a coexistent pattern of these two imaging biomarkers could exist in knee OA patients.

Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia’s neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels’ intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.

STMO-01 Cerebral edema and perioperative epilepsy due to placement of BCNU wafer for malignant glioma

Introduction: Cerebral edema is the most frequent adverse event of BCNU wafer, which is used as local chemotherapy of malignant glioma. However, predictive factor of this event is unknown. Moreover, there is no consensus about cerebral edema and perioperative seizure, which is often observed in glioma. Here, we report risk factor of cerebral edema with BCNU placement and relationship with perioperative seizure in malignant glioma cases. Material and Method: Thirty-one case of adult malignant glioma who underwent BCNU placement in our institute between March 2013 to March 2019 were investigated. The patients were dichotomized to two groups; patient with postoperative transient cerebral edema (CE+ group) and patient without postoperative transient cerebral edema (CE- group). Result: Postoperative cerebral edema associated with placement of BCNU was observed in 9 out of 31 patients (29%). Tumor malignancy was significant parameter for postoperative cerebral edema (p=0.003). Other factors such as, age, gender, laterality, tumor location, primary or recurrent, number of BCNU wafers, duration of recurrence were not significant for postoperative cerebral edema. Seizure was seen in 14 patients (45%), and cerebral edema was not significant parameter for seizure. Tumor malignancy was significant parameters for postoperative cerebral edema. Tumor malignancy was significant parameters for seizure (p=0.0004). Although postoperative seizure was observed in 4 patients (44%) with CE+ group, neither maximum volume (mean 61.1 ml) nor change ratio (mean 354%) of FLAIR-high-intensity region were not related with postoperative seizure. Conclusions: Tumor malignancy was important factor for patients who underwent placement of BCNU wafer with postoperative cerebral edema and seizure. On the other hand, there were no relationship between postoperative cerebral edema and perioperative seizure in patients treated with BCNU wafer.

Energy flux of a vortex field focused using a secant gradient lens

In this paper we simulated the focusing of left circular polarized beam with a second order phase vortex and a second-order cylindrical vector beam by a gradient index Mikaelian lens. It was shown numerically, that there is an area with a negative Poynting vector projection on Z axis, that can be called an area with backward energy flow. Using a cylindrical hole in the output surface of the lens and optimizing it one can obtain a negative flow, which will be situated in the maximum intensity region, unlike to previous papers, in which such backward energy flow regions were situated in a shadow area. Thereby, this lens will work as an “optical magnet”, it will attract Rayleigh particles (with diameter about 1/20 of the wavelength) to its surface.

Intensity Factor Method for Segmenting Human Body Region in Gray-scale Infrared Image

The normalized intensity factor based on statistical first-order moment of gray-scale image is defined in this paper. The intensity factor can be used to distinguish the brightness level of a gray-scale image and to determine a threshold value for image segmentation. According to the intensity factor and the characteristic of human body in the gray-scale infrared image, a new algorithm of calculating the intensity-level threshold is designed which can be used for segmenting human body area in an infrared image. In the algorithm, based on the concept of intensity factor, a histogram of low brightness gray-scale image (LGIRI) is divided into three parts: a low-intensity region (0.25[Formula: see text][Formula: see text]), a medium-intensity region (0.25–0.75[Formula: see text][Formula: see text]), and a high-intensity region (0.75–1[Formula: see text][Formula: see text]), and then the intensity [Formula: see text] which satisfies the [Formula: see text] is selected as an intensity-level value [Formula: see text], and the intensity [Formula: see text] which satisfies [Formula: see text] is selected as an intensity-level value [Formula: see text], at last [Formula: see text] is the pixel classification threshold (the intensity-level threshold). It is noted that there is no preprocessing for image noise filtering and/or processing, and all images come from OTCBVS. Compared with the method of selecting trough points of the histogram as the intensity-level threshold, this algorithm avoids the problem of nonexistence of evident trough point at the high-intensity level of a histogram. Also, the experimental results show that the segmenting results of LGIRI processed by the algorithm are better than those of Otsu method.