Notes

Higher fee involving dyspareunia as well as possible vulvodynia throughout Ehlers-Danlos syndromes and hypermobility array problems: An online questionnaire.
This article intends to address an online optimal adaptive regulation of nonlinear discrete-time systems in affine form and with partially uncertain dynamics using a multilayer neural network (MNN). The actor-critic framework estimates both the optimal control input and value function. Instantaneous control input error and temporal difference are used to tune the weights of the critic and actor networks, respectively. The selection of the basis functions and their derivatives are not required in the proposed approach. The state vector, critic, and actor NN weights are proven to be bounded using the Lyapunov method. Our approach can be extended to neural networks with an arbitrary number of hidden layers. We have demonstrated our approach via a simulation example.Adversarial perturbations have demonstrated the vulnerabilities of deep learning algorithms to adversarial attacks. Existing adversary detection algorithms attempt to detect the singularities; however, they are in general, loss-function, database, or model dependent. To mitigate this limitation, we propose DAMAD--a generalized perturbation detection algorithm which is agnostic to model architecture, training data set, and loss function used during training. The proposed adversarial perturbation detection algorithm is based on the fusion of autoencoder embedding and statistical texture features extracted from convolutional neural networks. The performance of DAMAD is evaluated on the challenging scenarios of cross-database, cross-attack, and cross-architecture training and testing along with traditional evaluation of testing on the same database with known attack and model. Comparison with state-of-the-art perturbation detection algorithms showcase the effectiveness of the proposed algorithm on six databases ImageNet, CIFAR-10, Multi-PIE, MEDS, point and shoot challenge (PaSC), and MNIST. Performance evaluation with nearly a quarter of a million adversarial and original images and comparison with recent algorithms show the effectiveness of the proposed algorithm.Obstructive sleep apnea (OSA), as a highly prevalent sleep disorder, causes several serious health complaints. It has been proved that using intraoral mandibular advancement devices (MADs) during sleep is an efficient treatment for OSA. However, due to limited number of sleep study laboratories, effectiveness of MAD therapy is not regularly monitored. This paper proposes a smart MAD with the capability of continuously monitoring of cardiorespiratory parameters as well as sleeping postures and breathing routes. In this regard, a flexible hybrid wireless sensing platform based on the intraoral photoplethysmography (PPG), temperature and accelerometry monitoring is developed. It is qualitatively and quantitatively discussed that the intraorally captured PPG signals by the smart MAD have similar features as the ones received from the conventional anatomical position, i.e., the left index fingertip. Extensive experimental measurements indicate that the proposed smart MAD can estimate heart-rate (HR), respiration rate (RR) and blood oxygen saturation (SpO2) with the maximum mean-absolute-errors of 2.4 bpm, 2.52 breaths/min, and 0.8%, respectively, in comparison to the reference measurements, while such a capability is not dependent on subject's positions and breathing routes. It is also shown that the smart MAD can readily identify different sleeping postures, namely, supine, left, right, and prone and breathing routes. The reliability and stability of the proposed smart MAD's measurements are proved by examining a group of subjects. The proposed smart MAD has potential to monitor the effectiveness of MAD treatment and eliminate untreated OSA without the requirement of attaching an extra monitoring platform to the patient's body.In this work, In2O3 electrolyte gated thin film transistors (In2O3-EGTFTs) with integrated on-chip gate electrode were investigated as a label-free biosensor. The In2O3 channel works as sensitive membrane and the on-chip gate electrode replaces reference electrode. The pH sensitivity of the device is 64 mV/pH with the deviation of 10 mV. The In2O3 channel was coated by streptavidin/neutravidin receptors to detect the target biomolecules of biotin. The streptavidin modified device presents an ultra-low working voltage (VG=0 V, VDS=50 mV) and the detection limit as low as 50 pg·mL-1. And the neutravidin modified device presents apparent detecting performance even in low biotin concentration of 50 fg·mL-1 under a working voltage of VG=2 V and VDS=50 mV. This work provides a high-performance biosensor device with low power consumption and simple structure that is easy to integrate and package.Ultrasound elastography is a prominent non-invasive medical imaging technique which estimates tissue elastic properties to detect abnormalities in an organ. A common approximation to tissue elastic modulus is tissue strain induced after mechanical stimulation. To compute tissue strain, ultrasound radio-frequency (RF) data can be processed using energy-based algorithms. These algorithms suffer from ill-posedness to tackle. A continuity constraint along with the data amplitude similarity is imposed to obtain a unique solution to the time-delay estimation (TDE) problem. Existing energy-based methods exploit the first-order spatial derivative of the displacement field to construct a regularizer. This first-order regularization scheme alone is not fully consistent with the mechanics of tissue deformation while perturbed with an external force. As a consequence, state-of-the-art techniques suffer from two crucial drawbacks. First, the strain map is not sufficiently smooth in uniform tissue regions. Second, edges of the hard or soft inclusions are not well-defined in the image. Herein, we address these issues by formulating a novel regularizer taking both first- and second-order derivatives of the displacement field into account. The second-order constraint, which is the principal novelty of this work, contributes both to background continuity and edge sharpness by suppressing spurious noisy edges and enhancing strong boundaries. We name the proposed technique SOUL- Second Order Ultrasound eLastography. Comparative assessment of qualitative and quantitative results shows that SOUL substantially outperforms three recently developed TDE algorithms called Hybrid, GLUE and MPWC-Net++. SOUL yields 27.72%, 62.56% and 81.37% improvements of signal-to-noise ratio (SNR) and 72.35%, 54.03% and 65.17% improvements of contrast-to-noise ratio (CNR) over GLUE with data pertaining to simulation, phantom and in vivo tissue, respectively. FEN1-IN-4 The SOUL code can be downloaded from code.sonography.ai..
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