Novel semi-metrics for multivariate change point analysis and anomaly detection.

This paper proposes a new method for determining similarity and anomalies between time series, most practically effective in large collections of (likely related) time series, by measuring distances between structural breaks within such a collection. We introduce a class of semi-metric distance measures, which we term MJ distances. These semi-metrics provide an advantage over existing options such as the Hausdorff and Wasserstein metrics. We prove they have desirable properties, including better sensitivity to outliers, while experiments on simulated data demonstrate that they uncover similarity within collections of time series more effectively. Semi-metrics carry a potential disadvantage: without the triangle inequality, they may not satisfy a "transitivity property of closeness." We analyse this failure with proof and introduce an computational method to investigate, in which we demonstrate that our semi-metrics violate transitivity infrequently and mildly. Finally, we apply our methods to cryptocurrency and measles data, introducing a judicious application of eigenvalue analysis.

Micro-, meso-, macroscales: The effect of triangles on communities in networks.

Mesoscale structures (communities) are used to understand the macroscale properties of complex networks, such as their functionality and formation mechanisms. Microscale structures are known to exist in most complex networks (e.g., large number of triangles or motifs), but they are absent in the simple random-graph models considered (e.g., as null models) in community-detection algorithms. In this paper we investigate the effect of microstructures on the appearance of communities in networks. We find that alone the presence of triangles leads to the appearance of communities even in methods designed to avoid the detection of communities in random networks. This shows that communities can emerge spontaneously from simple processes of motiff generation happening at a microlevel. Our results are based on four widely used community-detection approaches (stochastic block model, spectral method, modularity maximization, and the Infomap algorithm) and three different generative network models (triadic closure, generalized configuration model, and random graphs with triangles).

Correction: Prognostic impact of systolic blood pressure variability in people with diabetes.

[This corrects the article DOI: 10.1371/journal.pone.0194084.].

Prognostic impact of systolic blood pressure variability in people with diabetes.

OBJECTIVE: Blood pressure variability (BPV) has been associated with risk of cardiovascular events in observational studies, independently of mean BP levels. In states with higher autonomic imbalance, such as in diabetes, the importance of BP variability may theoretically be even greater. We aimed to investigate the incremental value of BPV for prediction of cardiovascular and all-cause mortality in patients with type 2 diabetes. METHODS: We identified 9,855 patients without pre-existing cardiovascular disease who did not change BP-lowering treatment during the observation period from a Swedish primary health care cohort of patients with type 2 diabetes. BPV was summarized as the standard deviation (SD), coefficient of variation (CV), or variation independent of mean (VIM). Patients were followed for a median of 4 years and associations with cardiovascular and all-cause mortality were investigated using Cox proportional hazards models. RESULTS: BPV was not associated with cardiovascular specific or all-cause mortality in the total sample. In patients who were not on BP-lowering drugs during the observation period (n = 2,949), variability measures were associated with all-cause mortality: hazard ratios were 1.05, 1.04 and 1.05 for 50% increases in SD, CV and VIM, respectively, adjusted for Framingham risk score risk factors, including mean BP. However, the addition of the variability measures in this subgroup only led to very minimal improvement in discrimination, indicating they may have limited clinical usefulness (change in C-statistic ranged from 0.000-0.003 in all models). CONCLUSIONS: Although BPV was independently associated with all-cause mortality in diabetes patients in primary care who did not have pre-existing cardiovascular disease or BP-lowering drugs, it may be of minimal clinical usefulness above and beyond that of other routinely measured predictors, including mean BP.

Influence of the disease label 'polycystic ovary syndrome' on intention to have an ultrasound and psychosocial outcomes: a randomised online study in young women.

Study question: Does the disease label 'polycystic ovary syndrome' (PCOS) have an impact on desire for medical testing and psychosocial outcomes? Summary answer: When given the disease label PCOS in a hypothetical scenario, participants had higher intention to have an ultrasound, perceived the condition to be more severe and had lower self-esteem than those not given the disease label. What is known already: Widening diagnostic criteria and improved imaging sensitivity have increased the number of reproductive-aged women diagnosed with PCOS from 4% to 8% to up to 21%. The uncertain clinical benefit of knowing this diagnosis needs to be weighed against the potential for poor psychological outcomes in women labelled with PCOS. Study design, size, duration: This experimental online study randomised 181 young women to receive one of four hypothetical scenarios of a doctor's visit in a 2 (PCOS disease label versus no disease label) x 2 (information about unreliability of ultrasounds in clarifying diagnosis versus no information) design. Participants/materials, setting, methods: Participants were university students (mean age: 19.4). After presenting the scenario, intention to have an ultrasound, negative affect, self-esteem, perceived severity of condition, credibility of the doctor and interest in a second opinion were measured. Participants were then presented with a second scenario, where the possibility of PCOS overdiagnosis was mentioned. Change in intention and perceived severity were then measured. Main results and the role of chance: Participants given the PCOS label had significantly higher intention to have an ultrasound (mean = 6.62 versus mean = 5.76, P = 0.033, 95% CI(difference) = 0.069-1.599), perceived the condition to be more severe (17.17 versus 15.82, P = 0.019, 95% CI(difference) = 0.229-2.479) and had lower self-esteem (25.86 versus 27.56, P = 0.031, 95% CI(difference) = -3.187 to -0.157). After receiving overdiagnosis information, both intention and perceived severity decreased, regardless of condition (both P < 0.001). Limitations, reasons for caution: This study used hypothetical scenarios; it is likely that for women facing a real diagnosis of PCOS, outcomes would be more affected than in the current study. The hypothetical design, however, allowed the symptoms and risks of PCOS to be held constant across conditions, the impact on intention and psychosocial outcomes directly attributable to the effect of the disease label. Wider implications of the findings: These findings demonstrate the potential negative consequences of PCOS labelling. It is crucial we consider the impact of the label before diagnosing more women with PCOS when clinical benefit of this diagnosis is uncertain. Study funding/competing interest(s): This paper was written with support from a NHMRC grant awarded to the Screening and Test Evaluation Program. J.J. is supported by an NHMRC Early Career Fellowship. K.M. is supported by an NHMRC Career Development Fellowship. The authors declare that no competing interests exist. Trial registration number: ACTRN12617000111370. Trial registration date: 20/01/2017. Date of first patient's enrolment: 01/06/2015.

The Association of Surgical Margins and Local Recurrence in Women with Ductal Carcinoma In Situ Treated with Breast-Conserving Therapy: A Meta-Analysis.

PURPOSE: There is no consensus on adequate negative margins in breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS). We systematically reviewed the evidence on margins in BCS for DCIS. METHODS: A study-level meta-analysis of local recurrence (LR), microscopic margin status and threshold distance for negative margins. LR proportion was modeled using random-effects logistic meta-regression (frequentist) and network meta-analysis (Bayesian) that allows for multiple margin distances per study, adjusting for follow-up time. RESULTS: Based on 20 studies (LR: 865 of 7883), odds of LR were associated with margin status [logistic: odds ratio (OR) 0.53 for negative vs. positive/close (p < 0.001); network: OR 0.45 for negative vs. positive]. In logistic meta-regression, relative to >0 or 1 mm, ORs for 2 mm (0.51), 3 or 5 mm (0.42) and 10 mm (0.60) showed comparable significant reductions in the odds of LR. In the network analysis, ORs relative to positive margins for 2 (0.32), 3 (0.30) and 10 mm (0.32) showed similar reductions in the odds of LR that were greater than for >0 or 1 mm (0.45). There was weak evidence of lower odds at 2 mm compared with >0 or 1 mm [relative OR (ROR) 0.72, 95 % credible interval (CrI) 0.47-1.08], and no evidence of a difference between 2 and 10 mm (ROR 0.99, 95 % CrI 0.61-1.64). Adjustment for covariates, and analyses based only on studies using whole-breast radiotherapy, did not change the findings. CONCLUSION: Negative margins in BCS for DCIS reduce the odds of LR; however, minimum margin distances above 2 mm are not significantly associated with further reduced odds of LR in women receiving radiation.

Has the incidence of brain cancer risen in Australia since the introduction of mobile phones 29 years ago?

BACKGROUND: Mobile phone use in Australia has increased rapidly since its introduction in 1987 with whole population usage being 94% by 2014. We explored the popularly hypothesised association between brain cancer incidence and mobile phone use. STUDY METHODS: Using national cancer registration data, we examined age and gender specific incidence rates of 19,858 male and 14,222 females diagnosed with brain cancer in Australia between 1982 and 2012, and mobile phone usage data from 1987 to 2012. We modelled expected age specific rates (20-39, 40-59, 60-69, 70-84 years), based on published reports of relative risks (RR) of 1.5 in ever-users of mobile phones, and RR of 2.5 in a proportion of 'heavy users' (19% of all users), assuming a 10-year lag period between use and incidence. SUMMARY ANSWERS: Age adjusted brain cancer incidence rates (20-84 years, per 100,000) have risen slightly in males (p<0.05) but were stable over 30 years in females (p>0.05) and are higher in males 8.7 (CI=8.1-9.3) than in females, 5.8 (CI=5.3-6.3). Assuming a causal RR of 1.5 and 10-year lag period, the expected incidence rate in males in 2012 would be 11.7 (11-12.4) and in females 7.7 (CI=7.2-8.3), both p<0.01; 1434 cases observed in 2012, vs. 1867 expected. Significant increases in brain cancer incidence were observed (in keeping with modelled rates) only in those aged ≥70 years (both sexes), but the increase in incidence in this age group began from 1982, before the introduction of mobile phones. Modelled expected incidence rates were higher in all age groups in comparison to what was observed. Assuming a causal RR of 2.5 among 'heavy users' gave 2038 expected cases in all age groups. LIMITATIONS: This is an ecological trends analysis, with no data on individual mobile phone use and outcome. WHAT THIS STUDY ADDS: The observed stability of brain cancer incidence in Australia between 1982 and 2012 in all age groups except in those over 70 years compared to increasing modelled expected estimates, suggests that the observed increases in brain cancer incidence in the older age group are unlikely to be related to mobile phone use. Rather, we hypothesize that the observed increases in brain cancer incidence in Australia are related to the advent of improved diagnostic procedures when computed tomography and related imaging technologies were introduced in the early 1980s.

Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: the ICARE study.

BACKGROUND: The association between lung cancer and occupational exposure to organic solvents is discussed. Since different solvents are often used simultaneously, it is difficult to assess the role of individual substances. OBJECTIVES: The present study is focused on an in-depth investigation of the potential association between lung cancer risk and occupational exposure to a large group of organic solvents, taking into account the well-known risk factors for lung cancer, tobacco smoking and occupational exposure to asbestos. METHODS: We analysed data from the Investigation of occupational and environmental causes of respiratory cancers (ICARE) study, a large French population-based case-control study, set up between 2001 and 2007. A total of 2276 male cases and 2780 male controls were interviewed, and long-life occupational history was collected. In order to overcome the analytical difficulties created by multiple correlated exposures, we carried out a novel type of analysis based on Bayesian profile regression. RESULTS: After analysis with conventional logistic regression methods, none of the 11 solvents examined were associated with lung cancer risk. Through a profile regression approach, we did not observe any significant association between solvent exposure and lung cancer. However, we identified clusters at high risk that are related to occupations known to be at risk of developing lung cancer, such as painters. CONCLUSIONS: Organic solvents do not appear to be substantial contributors to the occupational risk of lung cancer for the occupations known to be at risk.

A predictive model for diagnosing stroke-related apraxia of speech.

Diagnosis of the speech motor planning/programming disorder, apraxia of speech (AOS), has proven challenging, largely due to its common co-occurrence with the language-based impairment of aphasia. Currently, diagnosis is based on perceptually identifying and rating the severity of several speech features. It is not known whether all, or a subset of the features, are required for a positive diagnosis. The purpose of this study was to assess predictor variables for the presence of AOS after left-hemisphere stroke, with the goal of increasing diagnostic objectivity and efficiency. This population-based case-control study involved a sample of 72 cases, using the outcome measure of expert judgment on presence of AOS and including a large number of independently collected candidate predictors representing behavioral measures of linguistic, cognitive, nonspeech oral motor, and speech motor ability. We constructed a predictive model using multiple imputation to deal with missing data; the Least Absolute Shrinkage and Selection Operator (Lasso) technique for variable selection to define the most relevant predictors, and bootstrapping to check the model stability and quantify the optimism of the developed model. Two measures were sufficient to distinguish between participants with AOS plus aphasia and those with aphasia alone, (1) a measure of speech errors with words of increasing length and (2) a measure of relative vowel duration in three-syllable words with weak-strong stress pattern (e.g., banana, potato). The model has high discriminative ability to distinguish between cases with and without AOS (c-index=0.93) and good agreement between observed and predicted probabilities (calibration slope=0.94). Some caution is warranted, given the relatively small sample specific to left-hemisphere stroke, and the limitations of imputing missing data. These two speech measures are straightforward to collect and analyse, facilitating use in research and clinical settings.

PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes.

PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership (Molitor, Papathomas, Jerrett, and Richardson 2010). The package allows binary, categorical, count and continuous response, as well as continuous and discrete covariates. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.

Identifying cell types from spatially referenced single-cell expression datasets.

Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid Platynereis dumereilii. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system.

A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer.

BACKGROUND: A common characteristic of environmental epidemiology is the multi-dimensional aspect of exposure patterns, frequently reduced to a cumulative exposure for simplicity of analysis. By adopting a flexible Bayesian clustering approach, we explore the risk function linking exposure history to disease. This approach is applied here to study the relationship between different smoking characteristics and lung cancer in the framework of a population based case control study. METHODS: Our study includes 4658 males (1995 cases, 2663 controls) with full smoking history (intensity, duration, time since cessation, pack-years) from the ICARE multi-centre study conducted from 2001-2007. We extend Bayesian clustering techniques to explore predictive risk surfaces for covariate profiles of interest. RESULTS: We were able to partition the population into 12 clusters with different smoking profiles and lung cancer risk. Our results confirm that when compared to intensity, duration is the predominant driver of risk. On the other hand, using pack-years of cigarette smoking as a single summary leads to a considerable loss of information. CONCLUSIONS: Our method estimates a disease risk associated to a specific exposure profile by robustly accounting for the different dimensions of exposure and will be helpful in general to give further insight into the effect of exposures that are accumulated through different time patterns.