How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series

Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we present a new anomaly concept called "unicorn" or unique event and present a new, model-independent, unsupervised detection algorithm to detect unicorns. The Temporal Outlier Factor (TOF) is introduced to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily outlier in either pointwise or collective sense; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the standard Local Outlier Factor (LOF). TOF had superior performance compared to LOF even in recognizing traditional outliers and it also recognized unique events that LOF did not. Benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully recognized unique events in those cases where they were already known such as the gravitational waves of a black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years

Heme Oxygenase-1 (HMX1) Loss of Function Increases the In-Host Fitness of the Saccharomyces ‘boulardii’ Probiotic Yeast in a Mouse Fungemia Model

The use of yeast-containing probiotics is on the rise; however, these products occasionally cause fungal infections and possibly even fungemia among susceptible probiotic-treated patients. The incidence of such cases is probably underestimated, which is why it is important to delve deeper into the pathomechanism and the adaptive features of S. ‘boulardii’. Here in this study, the potential role of the gene heme oxygenase-1 (HMX1) in probiotic yeast bloodstream-derived infections was studied by generating marker-free HMX1 deletion mutants with CRISPR/Cas9 technology from both commercial and clinical S. ‘boulardii’ isolates. The six commercial and clinical yeasts used here represented closely related but different genetic backgrounds as revealed by comparative genomic analysis. We compared the wild-type isolates against deletion mutants for their tolerance of iron starvation, hemolytic activity, as well as kidney burden in immunosuppressed BALB/c mice after lateral tail vein injection. Our results reveal that the lack of HMX1 in S. ‘boulardii’ significantly (p < 0.0001) increases the kidney burden of the mice in most genetic backgrounds, while at the same time causes decreased growth in iron-deprived media in vitro. These findings indicate that even a single-gene loss-of-function mutation can, surprisingly, cause elevated fitness in the host during an opportunistic systemic infection. Our findings indicate that the safety assessment of S. ‘boulardii’ strains should not only take strain-to-strain variation into account, but also avoid extrapolating in vitro results to in vivo virulence factor determination

Causal relationship between local field potential and intrinsic optical signal in epileptiform activity in vitro

The directed causal relationship were examined between the local field potential (LFP) and the intrinsic optical signal (IOS) during induced epileptiform activity in in vitro cortical slices by the convergent cross-mapping causality analysis method. Two components of the IOS signal have been distinguished: a faster, activity dependent component (IOSh) which changes its sign between transmitted and reflected measurement, thus it is related to the reflectance or the scattering of the tissue and a slower component (IOSl), which is negative in both cases, thus it is resulted by the increase of the absorption of the tissue. We have found a strong, unidirectional, delayed causal effect from LFP to IOSh with 0.5- 1s delay, without signs of feedback from the IOSh to the LFP, while the correlation was small and the peaks of the cross correlation function did not reflect the actual causal dependency. Based on these observations, a model has been set up to describe the dependency of the IOSh on the LFP power and IOSh was reconstructed, based on the LFP signal. This study demonstrates that causality analysis can lead to better understanding the physiological interactions, even in case of two data series with drastically different time scales

Manifold-adaptive dimension estimation revisited

Data dimensionality informs us about data complexity and sets limit on the structure of successful signal processing pipelines. In this work we revisit and improve the manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator, making it one of the best nearest neighbor-based dimension estimators available. We compute the probability density function of local FSA estimates, if the local manifold density is uniform. Based on the probability density function, we propose to use the median of local estimates as a basic global measure of intrinsic dimensionality, and we demonstrate the advantages of this asymptotically unbiased estimator over the previously proposed statistics: the mode and the mean. Additionally, from the probability density function, we derive the maximum likelihood formula for global intrinsic dimensionality, if i.i.d. holds. We tackle edge and finite-sample effects with an exponential correction formula, calibrated on hypercube datasets. We compare the performance of the corrected median-FSA estimator with kNN estimators: maximum likelihood (Levina-Bickel), the 2NN and two implementations of DANCo (R and MATLAB). We show that corrected median-FSA estimator beats the maximum likelihood estimator and it is on equal footing with DANCo for standard synthetic benchmarks according to mean percentage error and error rate metrics. With the median-FSA algorithm, we reveal diverse changes in the neural dynamics while resting state and during epileptic seizures. We identify brain areas with lower-dimensional dynamics that are possible causal sources and candidates for being seizure onset zones

Strategic Positioning of Connexin36 Gap Junctions Across Human Retinal Ganglion Cell Dendritic Arbors

Connexin36 (Cx36) subunits form gap junctions (GJ) between neurons throughout the central nervous system. Such GJs of the mammalian retina serve the transmission, averaging and correlation of signals prior to conveying visual information to the brain. Retinal GJs have been exhaustively studied in various animal species, however, there is still a perplexing paucity of information regarding the presence and function of human retinal GJs. Particularly little is known about GJ formation of human retinal ganglion cells (hRGCs) due to the limited number of suitable experimental approaches. Compared to the neuronal coupling studies in animal models, where GJ permeable tracer injection is the gold standard method, the post-mortem nature of scarcely available human retinal samples leaves immunohistochemistry as a sole approach to obtain information on hRGC GJs. In this study Lucifer Yellow (LY) dye injections and Cx36 immunohistochemistry were performed in fixed short-post-mortem samples to stain hRGCs with complete dendritic arbors and locate dendritic Cx36 GJs. Subsequent neuronal reconstructions and morphometric analyses revealed that Cx36 plaques had a clear tendency to form clusters and particularly favored terminal dendritic segments