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Both periodontal disease and diabetes are common chronic inflammatory diseases. One of the major problems with type 2 diabetes is that unregulated blood glucose levels damage the vascular endothelium and cause complications. A bidirectional relationship between periodontal disease and diabetic complications has been reported previously. However, whether periodontal disease affects the presence of diabetic complications has not been clarified. Therefore, we examined the effect of the periodontal disease status on diabetic complications in patients with type 2 diabetes. Periodontal doctors examined the periodontal disease status of 104 type 2 diabetic patients who visited a private diabetes medical clinic once a month between 2016 and 2018. The subject's diabetic status was obtained from their medical records. Bayesian network analysis showed that bleeding on probing directly influenced the presence of diabetic retinopathy in type 2 diabetes patients. In addition, bleeding on probing was higher in the diabetic retinopathy group (n = 36) than in the group without diabetic retinopathy (n = 68, p = 0.006, Welch's t-test). Bleeding on probing represents gingival inflammation, which might affect the presence of diabetic retinopathy in type 2 diabetes patients who regularly visit diabetic clinics.

Background Supportive periodontal therapy (SPT) must take individual patient risk factors into account. We conducted a multicenter joint retrospective cohort study to investigate the value of modified periodontal risk assessment (MPRA) and therapy-resistant periodontitis (TRP) assessment as predictive factors for tooth loss due to periodontal disease in patients with severe periodontitis during SPT. Methods The subjects were 82 patients from 11 dental institutions who were diagnosed with severe periodontitis and continued SPT for at least 1 year (mean follow-up = 4.9 years) between 1981 and 2008. The outcome was tooth loss due to periodontal disease during SPT. The Cox proportional hazards model was used to analyze sex, age, diabetes status, smoking history, number of periodontal pockets measuring ≥6 mm, rate of bleeding on probing, bone loss/age ratio, number of teeth lost, MPRA, and TRP assessment as explanatory variables. Results Univariate analysis showed that loss of ≥8 teeth by the start of SPT [hazard ratio (HR) 2.86], MPRA score indicating moderate risk (HR 8.73) or high risk (HR 11.04), and TRP assessment as poor responsiveness to treatment (HR 2.79) were significantly associated with tooth loss (p

We investigate algorithms for efficiently detecting anomalies in real-valued one-dimensional time series. Past work has shown that a simple brute force algorithm that uses as an anomaly score the Euclidean distance between nearest neighbors of subsequences from a testing time series and a training time series is one of the most effective anomaly detectors. We investigate a very efficient implementation of this method and show that it is still too slow for most real world applications. Next, we present a new method based on summarizing the training time series with a small set of exemplars. The exemplars we use are feature vectors that capture both the high frequency and low frequency information in sets of similar subsequences of the time series. We show that this exemplar-based method is both much faster than the efficient brute force method as well as a prediction-based method and also handles a wider range of anomalies. We compare our algorithm across a large variety of publicly available time series and encourage others to do the same. Our exemplar-based algorithm is able to process time series in minutes that would take other methods days to process.

We study the problem of predicting travel times for links (road segments) using floating car data. We present four different methods for predicting travel times and discuss the differences in predicting on congested and uncongested roads. We show that estimates of the current travel time are mainly useful for prediction on links that get congested. Then we examine the problem of predicting link travel times when no recent probe car data is available for estimating current travel times. This is a serious problem that arises when using probe car data for prediction. Our solution, which we call geospatial inference, uses floating car data from nearby links to predict travel times on the desired link. We show that geospatial inference leads to improved travel time estimates for congested links compared to standard methods.