Chronic Rhinosinusitis With Nasal Polyps Does Not Affect the Association Between the Nasal Provocation Test and Serum Allergen-Specific Immunoglobulin E Levels

Background and Objectives This study compared nasal provocation test (NPT) results between groups with and without chronic rhinosinusitis with nasal polyps (CRSwNP) to investigate whether CRSwNP affects the response to the intranasal allergen challenge. Methods We reviewed the medical records of patients who had undergone the NPT, multiple allergen simultaneous test (MAST), and paranasal sinus computed tomography. Patients were diagnosed with CRSwNP based on findings from nasal endoscopy and paranasal sinus computed tomography. The NPT for house dust mites was conducted, and a positive MAST diagnosis was determined when the levels of immunoglobulin E specific to Dermatophagoides farinae and Dermatophagoides pteronyssinus were equal to or greater than 2 positives or at least 0.70 IU/mL. We statistically analyzed the NPT results and their correlation with MAST outcomes, comparing the CRSwNP group to the non-CRSwNP group. Results Out of 99 participants, 30 had CRSwNP and 69 did not. There were no significant differences between the groups regarding MAST positivity, eosinophil count, eosinophil cationic protein levels, or responses to intranasal house dust mite challenges. The presence of CRSwNP did not significantly influence the correlation between NPT outcomes and MAST results. Conclusion The presence of CRSwNP did not influence the outcomes of the NPT or its correlation with the results of the MAST. Additional large-scale, longitudinal studies are warranted to validate these findings

Level of Contamination of Positive Airway Pressure Devices Used in Obstructive Sleep Apnea

Objectives. No study has yet evaluated the degree of contamination after the total disassembly of continuous positive airway pressure (CPAP) devices. We investigated the extent of contamination of CPAP devices used daily by patients with obstructive sleep apnea (OSA) by disassembling the systems and identifying the factors that influenced the degree of CPAP contamination. Methods. We conducted a chart review of the medical records of patients with OSA for whom the CPAP devices were disassembled and cleaned. Two skilled technicians photographed the levels of contamination of each component and scored them using a visual analog scale. Patients’ clinical characteristics and records of CPAP device usage were statistically analyzed to identify characteristics that were significantly associated with the degree of CPAP device contamination. Results. Among the 55 participants, both the external components, including the mask and tube, and the internal components, such as the humidifier and the interior of the main body, showed a substantial degree of contamination. The total and average daily duration of usage of the CPAP device did not show significant associations with the degree of contamination. Age was most consistently associated with the degree of contamination, such as in masks, humidifiers, and interior and exterior main parts. The degree of contamination of the internal components of the device was significantly correlated with the degree of contamination of the external components. Conclusion. Age-specific guidelines for managing the hygiene of external and internal CPAP components should be prepared

Experimental Study: Enhancing Voice Spoofing Detection Models with wav2vec 2.0

Conventional spoofing detection systems have heavily relied on the use of handcrafted features derived from speech data. However, a notable shift has recently emerged towards the direct utilization of raw speech waveforms, as demonstrated by methods like SincNet filters. This shift underscores the demand for more sophisticated audio sample features. Moreover, the success of deep learning models, particularly those utilizing large pretrained wav2vec 2.0 as a featurization front-end, highlights the importance of refined feature encoders. In response, this research assessed the representational capability of wav2vec 2.0 as an audio feature extractor, modifying the size of its pretrained Transformer layers through two key adjustments: (1) selecting a subset of layers starting from the leftmost one and (2) fine-tuning a portion of the selected layers from the rightmost one. We complemented this analysis with five spoofing detection back-end models, with a primary focus on AASIST, enabling us to pinpoint the optimal configuration for the selection and fine-tuning process. In contrast to conventional handcrafted features, our investigation identified several spoofing detection systems that achieve state-of-the-art performance in the ASVspoof 2019 LA dataset. This comprehensive exploration offers valuable insights into feature selection strategies, advancing the field of spoofing detection.Comment: 5 page

CNN-Based Acoustic Scene Classification System

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively

Particulate Matter 10 (PM10) Is Associated with Epistaxis in Children and Adults

The impact of atmospheric concentration of particulate matter ≤10 μm in diameter (PM10) continues to attract research attention. This study aimed to evaluate the effects of meteorological factors, including PM10 concentration, on epistaxis presentation in children and adults. We reviewed the data from 1557 days and 2273 cases of epistaxis between January 2015 and December 2019. Eligible patients were stratified by age into the children (age ≤17 years) and adult groups. The main outcome was the incidence and cumulative number of epistaxis presentations in hospital per day and month. Meteorological factors and PM10 concentration data were obtained from the Korea Meteorological Administration. Several meteorological factors were associated with epistaxis presentation in hospital; however, these associations differed between children and adults. Only PM10 concentration was consistently associated with daily epistaxis presentation in hospital among both children and adults. Additionally, PM10 concentration was associated with the daily cumulative number of epistaxis presentations in hospital in children and adults. Furthermore, the monthly mean PM10 concentration was significantly associated with the total number of epistaxis presentations in the corresponding month. PM10 concentration should be regarded as an important environmental factor that may affect epistaxis in both children and adults

A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance

Mapping Quantitative Trait Loci Underlying Function-Valued Traits Using Functional Principal Component Analysis and Multi-Trait Mapping

We previously proposed a simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. In order to better handle the case of noisy phenotype measurements and accommodate the correlation structure among time points, we propose an alternative approach that maintains much of the simplicity and speed of the regression-based method. We overcome noisy measurements by replacing the observed data with a smooth approximation. We then apply functional principal component analysis, replacing the smoothed phenotype data with a small number of principal components. Quantitative trait locus mapping is applied to these dimension-reduced data, either with a multi-trait method or by considering the traits individually and then taking the average or maximum LOD score across traits. We apply these approaches to root gravitropism data on Arabidopsis recombinant inbred lines and further investigate their performance in computer simulations. Our methods have been implemented in the R package, funqtl

On Robust Association Testing for Quantitative Traits and Rare Variants

With the advance of sequencing technologies, it has become a routine practice to test for association between a quantitative trait and a set of rare variants (RVs). While a number of RV association tests have been proposed, there is a dearth of studies on the robustness of RV association testing for nonnormal distributed traits, e.g., due to skewness, which is ubiquitous in cohort studies. By extensive simulations, we demonstrate that commonly used RV tests, including sequence kernel association test (SKAT) and optimal unified SKAT (SKAT-O), are not robust to heavy-tailed or right-skewed trait distributions with inflated type I error rates; in contrast, the adaptive sum of powered score (aSPU) test is much more robust. Here we further propose a robust version of the aSPU test, called aSPUr. We conduct extensive simulations to evaluate the power of the tests, finding that for a larger number of RVs, aSPU is often more powerful than SKAT and SKAT-O, owing to its high data-adaptivity. We also compare different tests by conducting association analysis of triglyceride levels using the NHLBI ESP whole-exome sequencing data. The QQ plots for SKAT and SKAT-O were severely inflated (λ = 1.89 and 1.78, respectively), while those for aSPU and aSPUr behaved normally. Due to its relatively high robustness to outliers and high power of the aSPU test, we recommend its use complementary to SKAT and SKAT-O. If there is evidence of inflated type I error rate from the aSPU test, we would recommend the use of the more robust, but less powerful, aSPUr test