Notes

Thoracolithiasis diagnosed through thoracoscopy underneath local sedation.
Deep learning methods for language recognition have achieved promising performance. However, most of the studies focus on frameworks for single types of acoustic features and single tasks. In this paper, we propose the deep joint learning strategies based on the Multi-Feature (MF) and Multi-Task (MT) models. First, we investigate the efficiency of integrating multiple acoustic features and explore two kinds of training constraints, one is introducing auxiliary classification constraints with adaptive weights for loss functions in feature encoder sub-networks, and the other option is introducing the Canonical Correlation Analysis (CCA) constraint to maximize the correlation of different feature representations. Correlated speech tasks, such as phoneme recognition, are applied as auxiliary tasks in order to learn related information to enhance the performance of language recognition. We analyze phoneme-aware information from different learning strategies, like joint learning on the frame-level, adversarial learning on the segment-level, and the combination mode. In addition, we present the Language-Phoneme embedding extraction structure to learn and extract language and phoneme embedding representations simultaneously. We demonstrate the effectiveness of the proposed approaches with experiments on the Oriental Language Recognition (OLR) data sets. Experimental results indicate that joint learning on the multi-feature and multi-task models extracts instinct feature representations for language identities and improves the performance, especially in complex challenges, such as cross-channel or open-set conditions.Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while labels are only available in the source domain. Lots of works in UDA focus on finding a common representation of the two domains via domain alignment, assuming that a classifier trained in the source domain can be generalized well to the target domain. Thus, most existing UDA methods only consider minimizing the domain discrepancy without enforcing any constraint on the classifier. However, due to the uniqueness of each domain, it is difficult to achieve a perfect common representation, especially when there is low similarity between the source domain and the target domain. As a consequence, the classifier is biased to the source domain features and makes incorrect predictions on the target domain. To address this issue, we propose a novel approach named reducing bias to source samples for unsupervised domain adaptation (RBDA) by jointly matching the distribution of the two domains and reducing the classifier's bias to source samples. Specifically, RBDA first conditions the adversarial networks with the cross-covariance of learned features and classifier predictions to match the distribution of two domains. Then to reduce the classifier's bias to source samples, RBDA is designed with three effective mechanisms a mean teacher model to guide the training of the original model, a regularization term to regularize the model and an improved cross-entropy loss for better supervised information learning. Comprehensive experiments on several open benchmarks demonstrate that RBDA achieves state-of-the-art results, which show its effectiveness for unsupervised domain adaptation scenarios.A challenging issue in the field of the automatic recognition of emotion from speech is the efficient modelling of long temporal contexts. Moreover, when incorporating long-term temporal dependencies between features, recurrent neural network (RNN) architectures are typically employed by default. GW788388 concentration In this work, we aim to present an efficient deep neural network architecture incorporating Connectionist Temporal Classification (CTC) loss for discrete speech emotion recognition (SER). Moreover, we also demonstrate the existence of further opportunities to improve SER performance by exploiting the properties of convolutional neural networks (CNNs) when modelling contextual information. Our proposed model uses parallel convolutional layers (PCN) integrated with Squeeze-and-Excitation Network (SEnet), a system herein denoted as PCNSE, to extract relationships from 3D spectrograms across timesteps and frequencies; here, we use the log-Mel spectrogram with deltas and delta-deltas as input. In addition, a self-attention Residual Dilated Network (SADRN) with CTC is employed as a classification block for SER. To the best of the authors' knowledge, this is the first time that such a hybrid architecture has been employed for discrete SER. We further demonstrate the effectiveness of our proposed approach on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) and FAU-Aibo Emotion corpus (FAU-AEC). Our experimental results reveal that the proposed method is well-suited to the task of discrete SER, achieving a weighted accuracy (WA) of 73.1% and an unweighted accuracy (UA) of 66.3% on IEMOCAP, as well as a UA of 41.1% on the FAU-AEC dataset.Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly debilitating and often co-morbid disorders. The disorders exhibit partly overlapping dysregulations on the behavioral and neurofunctional level. The determination of disorder-specific behavioral and neurofunctional dysregulations may therefore promote neuro-mechanistic and diagnostic specificity. In order to determine disorder-specific alterations in the domain of emotion-cognition interactions the present study examined emotional context-specific inhibitory control in treatment-naïve MDD (n = 37) and GAD (n = 35) patients and healthy controls (n = 35). On the behavioral level MDD but not GAD exhibited impaired inhibitory control irrespective of emotional context. On the neural level, MDD-specific attenuated recruitment of inferior/medial parietal, posterior frontal, and mid-cingulate regions during inhibitory control were found during the negative context. GAD exhibited a stronger engagement of the left dorsolateral prefrontal cortex relative to MDD. Overall the findings from the present study suggest disorder- and emotional context-specific behavioral and neurofunctional inhibitory control dysregulations in major depression and may point to a depression-specific neuropathological and diagnostic marker.Prescription opioid use disorder (POUD) has reached epidemic proportions in the United States, raising an urgent need for diagnostic biological tools that can improve predictions of disease characteristics. The use of neuroimaging methods to develop such biomarkers have yielded promising results when applied to neurodegenerative and psychiatric disorders, yet have not been extended to prescription opioid addiction. With this long-term goal in mind, we conducted a preliminary study in this understudied clinical group. Univariate and multivariate approaches to distinguishing between POUD (n = 26) and healthy controls (n = 21) were investigated, on the basis of structural MRI (sMRI) and resting-state functional connectivity (restFC) features. Univariate approaches revealed reduced structural integrity in the subcortical extent of a previously reported addiction-related network in POUD subjects. No reliable univariate between-group differences in cortical structure or edgewise restFC were observed. Contrasting these mixed univariate results, multivariate machine learning classification approaches recovered more statistically reliable group differences, especially when sMRI and restFC features were combined in a multi-modal model (classification accuracy = 66.7%, p less then .001). The same multivariate multi-modal approach also yielded reliable prediction of individual differences in a clinically relevant behavioral measure (persistence behavior; predicted-to-actual overlap r = 0.42, p = .009). Our findings suggest that sMRI and restFC measures can be used to reliably distinguish the neural effects of long-term opioid use, and that this endeavor numerically benefits from multivariate predictive approaches and multi-modal feature sets. This can serve as theoretical proof-of-concept for future longitudinal modeling of prognostic POUD characteristics from neuroimaging features, which would have clearer clinical utility.
The origin of vestibular symptoms in patients with vestibular schwannoma (VS) is uncertain. We used intratympanic gadolinium-enhanced magnetic resonance imaging (MRI) to confirm the labyrinthine lesions in patients with VS and to explore the features of endolymphatic hydrops (EH) in these patients.

In total, 66 patients diagnosed with unilateral VS were enrolled in this study and underwent intratympanic gadolinium-enhanced MRI. The borders of the vestibule and endolymph were mapped on the axial MRI images, and the area and volume of vestibule and endolymph were automatically calculated using Osirix software, and the area and volume percentage of vestibular endolymph were obtained.

The area and volume percentages of vestibular endolymph on the affected side were significantly larger than those on the healthy side (both p<0.001). Using Kendall's W test, we found that the area and volume percentages of vestibular endolymph on the affected side were consistent (p<0.001), but the consistency was moderaore accurate than the area percentage for assessing vestibular EH. Using 19.1% as the cut-off point to distinguish the presence or absence of vestibular EH, we found that 16.7% of patients with VS had varying degrees of vestibular EH. We believe that the vestibular symptoms in patients with VS may originate from the peripheral lesions.
Unobtrusive monitoring of sleep and sleep disorders in children presents challenges. We investigated the possibility of using Ultra-Wide band (UWB) radar to measure sleep in children.

Thirty-two children scheduled to undergo a clinical polysomnography participated; their ages ranged from 2 months to 14 years. During the polysomnography, the children's body movements and breathing rate were measured by an UWB-radar. A total of 38 features were calculated from the motion signals and breathing rate obtained from the raw radar signals. Adaptive boosting was used as machine learning classifier to estimate sleep stages, with polysomnography as gold standard method for comparison.

Data of all participants combined, this study achieved a Cohen's Kappa coefficient of 0.67 and an overall accuracy of 89.8% for wake and sleep classification, a Kappa of 0.47 and an accuracy of 72.9% for wake, rapid-eye-movement (REM) sleep, and non-REM sleep classification, and a Kappa of 0.43 and an accuracy of 58.0% for wake, REM sleep, light sleep and deep sleep classification.

Although the current performance is not sufficient for clinical use yet, UWB radar is a promising method for non-contact sleep analysis in children.
Although the current performance is not sufficient for clinical use yet, UWB radar is a promising method for non-contact sleep analysis in children.Short chain fatty acids (SCFAs), generated from microbial fermentation of dietary fibers, can regulate weight, appetite and energy homeostasis. Therefore, measuring SCFAs in fecal samples is important to understand the relationship between dietary patterns, gut microbial metabolism, and their impact on host metabolism homeostasis. However, due to the chemical complexity of fecal samples and the volatility of these SCFAs, the quantitative measurements of SCFAs remain challenging. In this study, we developed an absolute quantitation method for accurate and reliable analysis of SCFAs using an UPLC-Q Exactive HRMS system. Nine C2-C6 SCFAs were first derivatized and then separated on a reversed-phase CSH C18 column, and quantitated by UPLC-HRMS with targeted-selected ion monitoring (t-SIM) mode. Our calibration plots showed high linearity (R2>0.99) with high quantitation accuracy (from 91.24%-118.42%); additional analyses showed excellent precisions ranging from 1.12 % to 6.13 %, and accurate recoveries between 92.
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