Posterior Distribution Of Weights Research Articles

Postural Stability and Regulation before and after High Tibial Osteotomy and Rehabilitation

Knee osteoarthrosis (OA) is a widespread orthopedic problem and a high tibial osteotomy (HTO) is a common treatment to minimize degeneration of the affected compartment. The primary aim of this study was to evaluate the postural regulation and stability among patients who underwent HTO and rehabilitation. This prospective study included 32 patients (55.3 ± 5.57 years) diagnosed with medial tibiofemoral OA. Each subject completed postural regulation and stability testing (Interactive Balance System), as well as pain intensity (visual analogue scale) and quality of life questionnaires (SF-36) prior to HTO (exam 1), and at six weeks (exam 2), twelve weeks (exam 3) and six months (exam 4) post HTO. For postural comparison, all patients were matched (sex, age, height) with asymptomatic subjects. Significant time effects (exam 1 vs. exam 4) were found for weight distribution index (WDI; ηp2 = 0.152), mediolateral weight distribution ηp2 = 0.163) and anterior–posterior weight distribution ηp2 = 0.131). The largest difference (exam 3: ηp2 = 0.251) and the most significant differences to the matched sample were calculated for the stability indicator (exam 1: ηp2 = 0.237; exam 2: ηp2 = 0.215; exam 3: ηp2 = 0.251; exam 4: ηp2 = 0.229). Pain intensity showed a significant reduction (ηp2 = 0.438) from exam 1 (50.7 ± 20.0 mm) to exam 4 (19.3 ± 16.0 mm). Physical pain was the quality of life parameter with the largest improvement between exams 1 and 4 (ηp2 = 0.560). HTO allows patients to improve their mediolateral weight distribution, whereas postural stability is consistently lower than in asymptomatic subjects. This surgery leads to marked improvements in quality of life and pain.

Prediction of Length of Stay on the Intensive Care Unit Based on Bayesian Neural Network

Predicting length of stay (LoS) accurately in the intensive care unit (ICU) is important to improve care quality and resource utilization. However, for LoS prediction, existing methods are facing main challenges, including uncertain prediction, generalization, interpretability, etc. In this paper, we utilize Bayesian neural network (BNN) to alleviate the above main challenges. The BNN introduces prior knowledge on the weights of neural networks and estimates outcomes from the predictive distribution after calculating the posterior distribution of weights. Extensive experiments on the eICU collaborative research database (eICU-CRD) show that the proposed method is competitive and more capable of anti-overfitting.

The effects of the application of low-dye taping on paretic side plantar pressure among patients with plantar fasciitis.

[Purpose] This study aimed to examine the effects of low-dye taping on paretic side plantar pressure in patients with plantar fasciitis. [Subjects] The 30 patients in this study were randomly allocated to a low-dye taping group (n = 15) or a conservative treatment group (n =15). [Methods] Both groups received treatment thrice a week for six weeks. BioRescue was used to measure the weight distribution of the patients’ paretic side. [Results] Within-group comparison showed that the posterior weight distribution significantly increased among patients in both groups. However, comparison between the two groups showed that the low-dye taping group’s posterior weight distribution was significantly higher than that of the conservative treatment group. [Conclusion] These findings show that the application of low-dye taping is an effective intervention for paretic-side plantar pressure among patients with plantar fasciitis.

Tongue and Posture

F. MARQUET. Before beginning I should like to thank you for agreeing to make a contribution to this issue devoted to posturology. In addition to tongue posture, a subject to which we’ll return later, what are the principal postural problems that should be considered in orthodontics? Maryvonne FOURNIER. The head hanging down in a forward position is one of the poor postures that are frequently encountered. It is associated with painful points in the occipital crest and sometimes prevents correction of breathing difficulties and problems with the tempero-mandibular joint. Mandibular deviations with which scoliosis, or a scoliatic tendency, are frequently associated with or without tendency to favor putting weight on one foot. Flat feet with disappearance of the inner arch of the foot, with weight placed purely on the posterior part of the foot in certain dental and skeletal Class III cases and exclusively on the anterior part of the foot in certain Class II dental and skeletal cases. F.M. You’re saying that poor weight distribution on the feet can cause dental or skeletal malformations? M.F. When too much weight is placed on the back f the foot, the child is always leaning backward, so much so that if we ask them to lean on the front part of the foot they might break their face and we need to place ourselves well in front of them to catch them if need be. In 50% of Class III cases we are going to find that in addition to this posterior weight distribution there is a recurvatum of the knees and a smoothing out of the lumbar, dorsal, and cervical curvatures. But, on the contrary, in Class II cases where patients place more weight on the front part of the foot there will be a flexum of the knee and an increase in the spinal curvatures. The patients whose thoracic cage tends to be caved in and under-developed usually have Class II Division 1 malocclsions and are mouth breathers. I cannot say that this type of poor development inevitably leads to dento-facial malformation. Some lateral mandibular deviations cause no postural problems whatever.

Bayesian neural network for rainfall‐runoff modeling

In this paper, a Bayesian learning approach is introduced to train a multilayer feed‐forward network for daily river flow and reservoir inflow simulation in a cold region river basin in Canada. In Bayesian approach, uncertainty about the relationship between inputs and outputs is initially taken care of by an assumed prior distribution of parameters (weights and biases). This prior distribution is updated to posterior distribution using a likelihood function following Bayes' theorem while data are observed. This posterior distribution is called the objective function of a network in the Bayesian learning approach. The objective function is maximized using a suitable optimization technique. Once the network is trained, the predictive distribution of the network outputs is obtained by integrating over the posterior distribution of weights. In this study, Gaussian prior distribution and a Gaussian noise model are used in defining posterior distribution. The network has been optimized using a scaled conjugate gradient technique. Posterior distribution of weights is approximated to Gaussian during prediction. Prediction performance of the Bayesian neural network (BNN) is compared with the results obtained from a standard artificial neural network (ANN) model and a widely used conceptual rainfall‐runoff model, namely, HBV‐96. The BNN model outperformed the conceptual model and slightly outperformed the standard ANN model in simulating mean, peak, and low river flows and reservoir inflows. The significant contribution of the Bayesian method over the conventional ANN approach, among others, is the uncertainty estimation of the outputs in the form of confidence intervals which are particularly needed in practical water resources applications. Prediction confidence limits (or intervals) indicate the extent to which one can rely on predictions for decision making. It is shown that the BNN can provide reliable streamflow and reservoir inflow forecasts without a loss in model prediction accuracy as compared to standard ANN and conceptual model HBV. Another significant advantage of BNN approach is that the overfitting and underfitting problems are automatically taken care of by the Bayesian learning algorithm, which conversely remain serious problems with conventional ANN learning algorithm.

Moderating the outputs of support vector machine classifiers

In this paper, we extend the use of moderated outputs to the support vector machine (SVM) by making use of a relationship between SVM and the evidence framework. The moderated output is more in line with the Bayesian idea that the posterior weight distribution should be taken into account upon prediction, and it also alleviates the usual tendency of assigning overly high confidence to the estimated class memberships of the test patterns. Moreover, the moderated output derived here can be taken as an approximation to the posterior class probability. Hence, meaningful rejection thresholds can be assigned and outputs from several networks can be directly compared. Experimental results on both artificial and real-world data are also discussed.

A review of Bayesian neural networks with an application to near infrared spectroscopy

MacKay's (1992) Bayesian framework for backpropagation is a practical and powerful means to improve the generalization ability of neural networks. It is based on a Gaussian approximation to the posterior weight distribution. The framework is extended, reviewed, and demonstrated in a pedagogical way. The notation is simplified using the ordinary weight decay parameter, and a detailed and explicit procedure for adjusting several weight decay parameters is given. Bayesian backprop is applied in the prediction of fat content in minced meat from near infrared spectra. It outperforms "early stopping" as well as quadratic regression. The evidence of a committee of differently trained networks is computed, and the corresponding improved generalization is verified. The error bars on the predictions of the fat content are computed. There are three contributors: The random noise, the uncertainty in the weights, and the deviation among the committee members. The Bayesian framework is compared to Moody's GPE (1992). Finally, MacKay and Neal's automatic relevance determination, in which the weight decay parameters depend on the input number, is applied to the data with improved results.