1,251 research outputs found

    Perturbation realization, potentials, and sensitivity analysis of Markov processes

    Get PDF
    Abstract — Two fundamental concepts and quantities, realization factors and performance potentials, are introduced for Markov processes. The relations among these two quantities and the group inverse of the infinitesimal generator are studied. It is shown that the sensitivity of the steady-state performance with respect to the change of the infinitesimal generator can be easily calculated by using either of these three quantities and that these quantities can be estimated by analyzing a single sample path of a Markov process. Based on these results, algorithms for estimating performance sensitivities on a single sample path of a Markov process can be proposed. The potentials in this paper are defined through realization factors and are shown to be the same as those defined by Poisson equations. The results provide a uniform framework of perturbation realization for infinitesimal perturbation analysis (IPA) and non-IPA approaches to the sensitivity analysis of steady-state performance; they also provide a theoretical background for the PA algorithms developed in recent years. Index Terms—Perturbation analysis, Poisson equations, samplepath analysis

    Editorial: changes at J-DEDS

    Full text link

    Perturbation realization, potentials, and sensitivity analysis of Markov processes

    Full text link

    Contributing Authors

    Full text link
    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45099/1/10626_2005_Article_1570.pd

    Myelin Activates FAK/Akt/NF-κB Pathways and Provokes CR3-Dependent Inflammatory Response in Murine System

    Get PDF
    Inflammatory response following central nervous system (CNS) injury contributes to progressive neuropathology and reduction in functional recovery. Axons are sensitive to mechanical injury and toxic inflammatory mediators, which may lead to demyelination. Although it is well documented that degenerated myelin triggers undesirable inflammatory responses in autoimmune diseases such as multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE), there has been very little study of the direct inflammatory consequences of damaged myelin in spinal cord injury (SCI), i.e., there is no direct evidence to show that myelin debris from injured spinal cord can trigger undesirable inflammation in vitro and in vivo. Our data showed that myelin can initiate inflammatory responses in vivo, which is complement receptor 3 (CR3)-dependent via stimulating macrophages to express pro-inflammatory molecules and down-regulates expression of anti-inflammatory cytokines. Mechanism study revealed that myelin-increased cytokine expression is through activation of FAK/PI3K/Akt/NF-κB signaling pathways and CR3 contributes to myelin-induced PI3K/Akt/NF-κB activation and cytokine production. The myelin induced inflammatory response is myelin specific as sphingomyelin (the major lipid of myelin) and myelin basic protein (MBP, one of the major proteins of myelin) are not able to activate NF-κB signaling pathway. In conclusion, our results demonstrate a crucial role of myelin as an endogenous inflammatory stimulus that induces pro-inflammatory responses and suggest that blocking myelin-CR3 interaction and enhancing myelin debris clearance may be effective interventions for treating SCI

    SR-POD : sample rotation based on principal-axis orientation distribution for data augmentation in deep object detection

    Get PDF
    Convolutional neural networks (CNNs) have outperformed most state-of-the-art methods in object detection. However, CNNs suffer the difficulty of detecting objects with rotation, because the dataset used to train the CCNs often does not contain sufficient samples with various angles of orientation. In this paper, we propose a novel data-augmentation approach to handle samples with rotation, which utilizes the distribution of the object's orientation without the time-consuming process of rotating the sample images. Firstly, we present an orientation descriptor, named as "principal-axis orientation" to describe the orientation of the object's principal axis in an image and estimate the distribution of objects’ principal-axis orientations (PODs) of the whole dataset. Secondly, we define a similarity metric to calculate the POD similarity between the training set and an additional dataset, which is built by randomly selecting images from the benchmark ImageNet ILSVRC2012 dataset. Finally, we optimize a cost function to obtain an optimal rotation angle, which indicates the highest POD similarity between the two aforementioned data sets. In order to evaluate our data augmentation method for object detection, experiments, conducted on the benchmark PASCAL VOC2007 dataset, show that with the training set augmented using our method, the average precision (AP) of the Faster RCNN in the TV-monitor is improved by 7.5%. In addition, our experimental results also demonstrate that new samples generated by random rotation are more likely to result in poor performance of object detection

    Prediction model of obstructive sleep apnea–related hypertension: Machine learning–based development and interpretation study

    Get PDF
    BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society

    Complete chloroplast genome sequence of Holoparasite Cistanche Deserticola (Orobanchaceae) reveals gene loss and horizontal gene transfer from Its host Haloxylon Ammodendron (Chenopodiaceae)

    Get PDF
    The central function of chloroplasts is to carry out photosynthesis, and its gene content and structure are highly conserved across land plants. Parasitic plants, which have reduced photosynthetic ability, suffer gene losses from the chloroplast (cp) genome accompanied by the relaxation of selective constraints. Compared with the rapid rise in the number of cp genome sequences of photosynthetic organisms, there are limited data sets from parasitic plants. The authors report the complete sequence of the cp genome of Cistanche deserticola, a holoparasitic desert species belonging to the family Orobanchaceae
    • …
    corecore