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Biceps tendon pathologies include a spectrum of injuries that range from mild tendinosis to complete tendon rupture.

Tendinosis, the most common pathology, occurs more frequently with age and is likely related to chronic degeneration. On the other side of the spectrum of severity lies a rupture of the long head of the biceps tendon (LHBT), which may be accompanied by injury to the glenoid labrum.

Superior labral anterior-posterior (SLAP) tears are frequently associated with biceps pathology. Surgical management for injuries of the bicipital-labral complex includes biceps tenodesis or tenotomy and SLAP repair. A consensus as to which of these procedures is the optimal choice has not been reached, and management may ultimately depend on patient-specific characteristics.

Due to the relatively low incidence of distal biceps tendon rupture, agreement on the optimal management strategy has not been reached. Surgical repair, or reconstruction in the case of a chronic rupture, is often chosen. However, nonoperative management has also been utilized in older, less-active patients.

Due to the relatively low incidence of distal biceps tendon rupture, agreement on the optimal management strategy has not been reached. Surgical repair, or reconstruction in the case of a chronic rupture, is often chosen. However, nonoperative management has also been utilized in older, less-active patients.

Physician burnout is a barrier to the patient-centered approach to health care.

One of the driving factors of resident burnout is the decreased level of control that residents have over their everyday lives.

Providing residents with a sense of control over their lives and their jobs increases job satisfaction and leads to a decrease in reports of negative effects on health, rest, participation in extracurricular activities, and time with family.

Providing residents with a sense of control over their lives and their jobs increases job satisfaction and leads to a decrease in reports of negative effects on health, rest, participation in extracurricular activities, and time with family.

Malignant hyperthermia (MH) is an inherited muscle disorder induced by volatile anesthetics and depolarizing muscle relaxants. While the incidence of MH is high in young, there are few reports on the clinical features of pediatric MH. In this study, we selected pediatric cases from an MH database and analyzed the clinical findings by age group. We hypothesized that there would be age-related differences in the clinical characteristics.

A retrospective analysis of MH data collected in our database during 1960 to 2020 was performed to identify pediatric subjects (≤18 years) with a Clinical Grading Scale of ≥35, indicating "very likely" or "almost certain" MH. We compared clinical characteristics among the 0 to 24 month, 2 to 12 year, and 13 to 18 year (youngest, middle, and oldest, respectively) age groups.

Data were available for 187 patients 15 in the youngest age group, 123 in the middle-aged group, and 49 in the oldest age group. Of these, 55 patients (29.4%) had undergone muscle biopsy and muscle conlar rigidity, P = .053; masseter spasm, P < .0001; dark urine, P < .0001). In particular, masseter spasm and dark urine were more common in the middle cohort when succinylcholine was administered (masseter spasm versus youngest cohort, P = .06, versus oldest cohort, P = .027; dark urine versus youngest cohort, P = .0072, versus oldest cohort, P = .0015).

The clinical characteristics of pediatric patients with MH vary according to age group. The difference in initial symptoms of MH depending on age group is noteworthy information for the early diagnosis of MH.

The clinical characteristics of pediatric patients with MH vary according to age group. The difference in initial symptoms of MH depending on age group is noteworthy information for the early diagnosis of MH.Neural dynamic surface control (NDSC) is an effective technique for the tracking control of nonlinear systems. The objective of this article is to improve closed-loop transient performance and reduce the number of learning parameters for a strict-feedback nonlinear system with unknown control gains. For this purpose, a predictor-based NDSC (PNDSC) approach is presented. It introduces Nussbaum functions and predictors into the traditional NDSC for nonlinear systems with unknown control gains. Unlike NDSC that uses surface errors to update the learning parameters of neural networks (NNs), the PNDSC employs prediction errors for the same purpose, leading to improved transient performance of closed-loop control systems. To reduce the number of learning parameters, the PNDSC is further embedded with the technique of the minimal number of learning parameters (MNLPs). This avoids the problem of the ``explosion of learning parameters as the order of the system increases. A Lyapunov-based stability analysis shows that all signals are bounded in the closed-loop systems under PNDSC embedded with MNLPs. Simulations are conducted to demonstrate the effectiveness of the PNDSC approach presented in this article.The automated analysis of electrocardiogram (ECG) signals plays a crucial role in the early diagnosis and management of cardiac arrhythmias. The diverse etiology of arrhythmia and the subtle variations in the pathological ECG characteristics pose challenges in designing reliable automated methods. Existing methods mostly use single deep convolutional neural networks (DCNN) based approaches for arrhythmia classification. Such approaches may not be adequate for effectively representing diverse pathological ECG characteristics. This paper presents a novel way of using an ensemble of multiple DCNN classifiers for effective arrhythmia classification named Deep Multi-Scale Convolutional neural network Ensemble (DMSCE). Specifically, we designed multiple scale-dependent DCNN expert classifiers with different receptive fields to encode the scale-specific pathological ECG characteristics and generate the local predictions. A convolutional gating network is designed to compute the dynamic fusion weights for the experts based on their competencies. These weights are used to aggregate the local predictions and generate final diagnosis decisions. Moreover, a new error function with a correlation penalty is formulated to enable interaction and optimal diversity among experts during the training process. The model is evaluated on the PTBXL-2020 12-lead ECG and the CinC-training2017 single-lead ECG datasets and delivers state-of-the-art performance. Average F1-score of 84.5% and 88.3% are obtained for the PTBXL-2020 and the CinC-training2017 datasets, respectively. Impressive performance across various cardiac arrhythmias and the elegant generalization ability for different leads make the method suitable for reliable remote or in-hospital arrhythmia monitoring applications.Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of epilepsy which is a life-threatening neurological disorder. Many algorithms have been proposed for the automatic detection of epileptic seizures using traditional machine learning and deep learning. Although deep learning methods have achieved great success in many fields, their performance in EEG analysis and classification is still limited mainly due to the relatively small sizes of available datasets. In this paper, we propose an automatic method for the detection of epileptic seizures based on deep metric learning which is a novel strategy tackling the few-shot problem by mitigating the demand for massive data. First, two one-dimensional convolutional embedding modules are proposed as a deep feature extractor, for single-channel and multichannel EEG signals respectively. Then, a deep metric learning model is detailed along with a stage-wise training strategy. Experiments are conducted on the publicly-available Bonn University dataset which is a benchmark dataset, and the CHB-MIT dataset which is larger and more realistic. Impressive averaged accuracy of 98.60% and specificity of 100% are achieved on the most difficult classification of interictal (subset D) vs ictal (subset E) of the Bonn dataset. On the CHB-MIT dataset, an averaged accuracy of 86.68% and specificity of 93.71% are reached. Y-27632 With the proposed method, automatic and accurate detection of seizures can be performed in real time, and the heavy burden of neurologists can be effectively reduced.Heart rate variability (HRV) has been used in assessing mental workload(MW) level. Compared with ECG, photoplethysmogram (PPG) provides convenient in assessing MW with wearable devices, which is more suitable for daily usage. However, PPG collected by smartwatches are prone to suffer from artifacts. Those signal corruptions cause invalid Inter-beat Intervals (IBI), making it challenging to evaluate the HRV feature. Hence, the PPG-based MW assessment system is difficult to obtain a sustainable and reliable assessment of MW. In this paper, we propose a pre- and post- processing technique, called outlier removal and uncertainty estimation, respectively, to reduce the negative influences of invalid IBIs. The proposed method helps to acquire accurate HRV features and evaluate the reliability of incoming IBIs, rejecting possibly misclassified data. We verified our approach in two open datasets, which are CLAS and MAUS. Experiment results show proposed method achieved higher accuracy (66.7% v.s. 74.2%) and lower variance (11.3% v.s. 10.8%) among users, which has comparable performance to an ECG-based MW system.Dense semantic forecasting anticipates future events in the video by inferring pixel-level semantics of an unobserved future image. We present a novel approach that is applicable to various single-frame architectures and tasks. Our approach consists of two modules. link2 The feature-to-motion (F2M) module forecasts a dense deformation field that warps past features into their future positions. link3 The feature-to-feature (F2F) module regresses the future features directly and is, therefore, able to account for emergent scenery. The compound F2MF model decouples the effects of motion from the effects of novelty in a task-agnostic manner. We aim to apply F2MF forecasting to the most subsampled and the most abstract representation of the desired single-frame model. Our design takes advantage of deformable convolutions and spatial correlation coefficients across neighboring time instants. We perform experiments on three dense prediction tasks semantic segmentation, instance-level segmentation, and panoptic segmentation. The results reveal state-of-the-art forecasting accuracy across three dense prediction tasks.Nonlinear model predictive control (NMPC) of industrial processes is changeling in part because the model of the plant may not be completely known but also for being computationally demanding. This work proposes an extremely efficient reservoir computing (RC)-based control framework that speeds up the NMPC of processes. In this framework, while an echo state network (ESN) serves as the dynamic RC-based system model of a process, the practical nonlinear model predictive controller (PNMPC) simplifies NMPC by splitting the forced and the free responses of the trained ESN, yielding the so-called ESN-PNMPC architecture. While the free response is generated by the forward simulation of the ESN model, the forced response is obtained by a fast and recursive calculation of the input-output sensitivities from the ESN. The efficiency not only results from the fast training inherited by RC but also from a computationally cheap control action given by the aforementioned novel recursive formulation and the computation in the reduced dimension space of input and output signals.