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No significant differences were found between the 2 groups, except for the fracture type. The operative treatment was performed with the patient under general anesthesia (n&nbsp;=&nbsp;12) or local anesthesia with sedation (n&nbsp;=&nbsp;3). Only 1 patient developed a complication (early hematoma). No other cases of early or delayed complications were reported. Two patients required a traditional surgical operation with the Gilles approach. The average hospitalization length and surgical time were 0.8 night and 18.4&nbsp;minutes for the type 1A group and 0.7 night and 19.0&nbsp;minutes for the type 1B group, respectively. Optimal esthetic and functional outcomes were obtained for all 15 patients. CONCLUSIONS The results of the present study suggest that optimal esthetic and functional results can be obtained, minimizing the effect on soft tissues and patient discomfort, with a short surgical time and low rate of complications. [https://www.selleckchem.com/products/pf-06650833.html Zimlovisertib] PURPOSE The relationship between edentulism and the severity of obstructive sleep apnea syndrome (OSAS) is not well established. The purpose of the present study was to evaluate the relationship of edentulism on the severity of OSAS compared with equally at-risk dentate subjects. PATIENTS AND METHODS We performed a retrospective matched cohort study of edentulous and dentate subjects with OSAS matched by age, gender, and body mass index (BMI). The primary predictor variable was dentate status (dentate vs edentulous) and the primary outcome variable was the OSAS severity measured using apnea hypopnea index (AHI). The secondary outcome variables were the Epworth sleepiness scale (ESS) score and nadir oxygen level. Inferential, univariate, and multivariate statistical analyses were completed. RESULTS A total of 26 subjects (13 edentulous and 13 dentate) were included in the present study. The edentulous subjects had more severe OSAS compared with the equally at-risk dentate subjects, with statistically significant differences between the 2 groups for AHI (47&nbsp;±&nbsp;25 vs 23&nbsp;±&nbsp;11; P&nbsp;=&nbsp;.0044) and ESS score (16&nbsp;±&nbsp;4.2 vs 11&nbsp;±&nbsp;5.2; P&nbsp;=&nbsp;.0094). A significant multivariate effect for OSAS was only found for the covariates of gender (female vs male; P&nbsp;=&nbsp;.016) and edentulism status (yes vs no; P&nbsp;=&nbsp;.01), with no significant interaction between them (P&nbsp;=&nbsp;.24). CONCLUSIONS Within the limits of the present study, edentulous subjects, compared with dentate subjects, and controlling for age, gender, and BMI, had more severe OSAS, as evidenced by the significantly increased AHI and ESS. Male gender and edentulism are significant risk factors for more severe OSAS compared with equally at-risk dentate patients. BACKGROUND Disease modifying agents (DMAs) are used to reduce relapses and decrease disability progression in Multiple Sclerosis (MS) patients. However, limited national level data exists regarding the prescribing patterns for MS, especially after the introduction of oral DMAs. OBJECTIVE This study examined the prescribing patterns and trends of DMAs using national level data and determined the factors associated with prescribing DMAs among MS patients in the United States. METHODS This cross-sectional study utilized 2006-2015 National Ambulatory Medical Care Survey (NAMCS) data to examine office-based visits involving MS (ICD-9-CM code 340). Descriptive weighted analyses were performed to assess the prescribing patterns of DMAs. Multivariable logistic regression model within the conceptual framework of Andersen Behavioral Model was used to determine the factors associated with prescribing of DMAs among MS patients. RESULTS An estimated 8.5 million (95% Confidence Interval [CI] 7.01-10.09 million) MS patient ents. A model predictive method is developed to tune an integral controller for uncertain systems subjected to constrained input signals. For this purpose, a stabilizing integral controller is firstly designed for linear time-invariant (LTI) systems with polytopic uncertainty. The integral controller gains can be determined via a feasible solution of a linear matrix inequality (LMI). Then, a predictive control is incorporated into the integral controller synthesis through an optimization problem subjected to some LMI constraints. The suggested control is successfully applied to a typical uncertain system and an uncertain chemical reactor. The effectiveness of the proposed technique will be shown in comparison with other control methods. This paper proposes a novel similarity-based algorithm for Remaining Useful Life (RUL) prediction and a methodology for machine prognostics. In the proposed RUL prediction algorithm, a Similarity Matching Procedure including the Kernel Two Sample Test (KTST) is developed to query similar run-to-failure (R2F) profiles from historical data library. Next, the preliminary predictions of RUL are obtained as remaining time-to-failure from the similar R2F records. In the last step, Weibull analysis is performed to fuse the preliminary predictions and to obtain the probability distribution of RUL. Moreover, a methodology for machine prognostics is developed based on the RUL prediction algorithm. Compared with existing similarity-based methods for RUL prediction, the proposed method holds several advantages 1) the similarities between sensor readings or feature matrices are directly measured without extra health assessment procedure; 2) the proposed method presents good probabilistic interpretations of the prediction uncertainties; 3) the estimated RUL distribution is statistically sound by applying KTST to prescreening the historical R2F records. The effectiveness and the superiority of the proposed method are justified based on the public aero-engine dataset. The framework of locally weighted learning (LWL) has established itself as a popular tool for developing nonlinear soft sensors in process industries. For LWL-based soft sensors, the key factor for achieving high performance is to construct accurate localized models. To this end, in this paper a nonlinear local model training algorithm called nonlinear Bayesian weighted regression (NBWR) is proposed. In the NBWR, the nonlinear features of process data are first extracted by the autoencoder; then, given a query sample a local dataset is selected on the feature space and a fully Bayesian regression model with differentiated sample weights is developed. The benefits of this approach, which include better consistency of correlation, stronger abilities to deal with process nonlinearities and uncertainties, overfitting and numerical issues, lead to superior performance. The NBWR is used for developing a soft sensor under the LWL framework, and a real-world industrial process is used to evaluate the performance of the NBWR-based soft sensor.