In the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), women in the multimodal (MMS) arm had a serum CA125 test (first-line), with those at increased risk, having repeat CA125/ultrasound (second-line test). CA125 was interpreted using the "Risk of Ovarian Cancer Algorithm" (ROCA). We report on performance of other serial algorithms and a single CA125 threshold as a first-line screen in the UKCTOCS dataset. 50,083 post-menopausal women who attended 346,806 MMS screens were randomly split into training and validation sets, following stratification into cases (ovarian/tubal/peritoneal cancers) and controls. The two longitudinal algorithms, a new serial algorithm, method of mean trends (MMT) and the parametric empirical Bayes (PEB) were trained in the training set and tested in the blinded validation set and the performance characteristics, including that of a single CA125 threshold, were compared. The area under receiver operator curve (AUC) was significantly higher ( = 0.01) for MMT (0.921) compared with CA125 single threshold (0.884). At a specificity of 89.5%, sensitivities for MMT [86.5%; 95% confidence interval (CI), 78.4-91.9] and PEB (88.5%; 95% CI, 80.6-93.4) were similar to that reported for ROCA (sensitivity 87.1%; specificity 87.6%; AUC 0.915) and significantly higher than the single CA125 threshold (73.1%; 95% CI, 63.6-80.8). These findings from the largest available serial CA125 dataset in the general population provide definitive evidence that longitudinal algorithms are significantly superior to simple cutoff values for ovarian cancer screening. Use of these newer algorithms requires incorporation into a multimodal strategy. The results highlight the importance of incorporating serial change in biomarker levels in cancer screening/early detection strategies. .

We report on the discovery of a transition in rings of coupled electronic circuits in the chaotic regime to a collective periodic state characterized by a time scale that is between two and three orders faster than that corresponding to an isolated circuit. This transition arises from a linear instability in the uniform synchronized state of the ring through a symmetric Hopf bifurcation. The same type of transition is also observed for other coupled chaotic systems, e.g., a ring of Lorenz attractors.

We consider the behavior of rings of unidirectionally coupled chaotic systems. When the number of oscillators in the ring is below a certain critical number the behavior of the ring is chaotic synchronized, while above this threshold an instability appears. The novel feature is that this instability may yield a chaotic rotating wave if some conditions are fulfilled.

To date several algorithms for longitudinal analysis of ovarian cancer biomarkers have been proposed in the literature. An issue of specific interest is to determine whether the baseline level of a biomarker changes significantly at some time instant (change-point) prior to the clinical diagnosis of cancer. Such change-points in the serum biomarker Cancer Antigen 125 (CA125) time series data have been used in ovarian cancer screening, resulting in earlier detection with a sensitivity of 85% in the most recent trial, the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS, number ISRCTN22488978; NCT00058032). Here we propose to apply a hierarchical Bayesian change-point model to jointly study the features of time series from multiple biomarkers. For this model we have analytically derived the conditional probability distribution of every unknown parameter, thus enabling the design of efficient Markov chain Monte Carlo methods for their estimation. We have applied these methods to the estimation of change-points in time series data of multiple biomarkers, including CA125 and others, using data from a nested case-control study of women diagnosed with ovarian cancer in UKCTOCS. In this way we assess 1/21 whether any of these additional biomarkers can play a role in change-point detection and, therefore, aid in the diagnosis of the disease in patients for whom the CA125 time series does not display a change-point. We have also investigated whether the change-points for different biomarkers occur at similar times for the same patient. The main conclusion of our study is that the combined analysis of a group of specific biomarkers may possibly improve the detection of change-points in time series data (compared to the analysis of CA125 alone) which, in turn, are relevant for the early diagnosis of ovarian cancer.

Chaotic synchronization has been observed experimentally and numerically in arrays of Chua's circuits, arranged in both linear and ring geometries, that are coupled by using the method recently introduced by Güémez and Matías ͓Phys. Rev. E 52, R2145 ͑1995͔͒. For open linear geometries, the chaotic cells are seen to synchronize consecutively as a synchronization wave spreads through the array. Instead, for circular loops it is found that there is a critical number of cells above which the uniform synchronized state is not stable.

Longitudinal CA125 algorithms are the current basis of ovarian cancer screening. We report on longitudinal algorithms incorporating multiple markers. In the multimodal arm of United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), 50,640 postmenopausal women underwent annual screening using a serum CA125 longitudinal algorithm. Women (cases) with invasive tubo-ovarian cancer (WHO 2014) following outcome review with stored annual serum samples donated in the 5 years preceding diagnosis were matched 1:1 to controls (no invasive tubo-ovarian cancer) in terms of the number of annual samples and age at randomisation. Blinded samples were assayed for serum human epididymis protein 4 (HE4), CA72-4 and anti-TP53 autoantibodies. Multimarker method of mean trends (MMT) longitudinal algorithms were developed using the assay results and trial CA125 values on the training set and evaluated in the blinded validation set. The study set comprised of 1363 (2–5 per woman) serial samples from 179 cases and 181 controls. In the validation set, area under the curve (AUC) and sensitivity of longitudinal CA125-MMT algorithm were 0.911 (0.871–0.952) and 90.5% (82.5–98.6%). None of the longitudinal multi-marker algorithms (CA125-HE4, CA125-HE4-CA72-4, CA125-HE4-CA72-4-anti-TP53) performed better or improved on lead-time. Our population study suggests that longitudinal HE4, CA72-4, anti-TP53 autoantibodies adds little value to longitudinal serum CA125 as a first-line test in ovarian cancer screening of postmenopausal women.

Elements composing complex systems usually interact in several different ways, and as such, the interaction architecture is well modeled by a network with multiple layers-a multiplex network-where the system's complex dynamics is often the result of several intertwined processes taking place at different levels. However, only in a few cases can such multilayered architecture be empirically observed, as one usually only has experimental access to such structure from an aggregated projection. A fundamental challenge is thus to determine whether the hidden underlying architecture of complex systems is better modeled as a single interaction layer or if it results from the aggregation and interplay of multiple layers. Assuming a prior of intralayer Markovian diffusion, here we show that by using local information provided by a random walker navigating the aggregated network, it is possible to determine, in a robust manner, whether these dynamics can be more accurately represented by a single layer or if they are better explained by a (hidden) multiplex structure. In the latter case, we also provide Bayesian methods to estimate the most probable number of hidden layers and the model parameters, thereby fully reconstructing its architecture. The whole methodology enables us to decipher the underlying multiplex architecture of complex systems by exploiting the non-Markovian signatures on the statistics of a single random walk on the aggregated network. In fact, the mathematical formalism presented here extends above and beyond detection of physical layers in networked complex systems, as it provides a principled solution for the optimal decomposition and projection of complex, nonMarkovian dynamics into a Markov switching combination of diffusive modes. We validate the proposed methodology with numerical simulations of both (i) random walks navigating hidden multiplex networks (thereby reconstructing the true hidden architecture) and (ii) Markovian and non-Markovian continuous stochastic processes (thereby reconstructing an effective multiplex decomposition where each layer accounts for a different diffusive mode). We also state and prove two existence theorems guaranteeing that an exact reconstruction of the dynamics in terms of these hidden jump-Markov models is always possible for arbitrary * Corresponding author. l.lacasa@qmul.ac.uk PHYSICAL REVIEW X 8, 031038 (2018) 2160-3308=18=8(3)=031038 (36) 031038-1 Published by the American Physical Society finite-order Markovian and fully non-Markovian processes. Finally, we showcase the applicability of the method to experimental recordings from (i) the mobility dynamics of human players in an online multiplayer game and (ii) the dynamics of RNA polymerases at the single-molecule level.

HighlightsWe tackle the problem of early detection of ovariance cancer using longitudinal measurements of multiple biomarkers.We compare two different paradigms: Bayesian methods and deep learning.We provide evidence that using multiple biomarkers yields a performance boost as compared to the standard screening test using CA125 alone.