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The prediction of epileptic seizures has been an essential problem of epilepsy study. The calcium imaging video data images the whole brain-wide neurons activities with electrical discharge recorded by calcium fluorescence intensity (CFI). In this paper, using the zebrafish's brain-wide calcium image video data, we propose a data-driven approach to effectively detect the systemic change-point, and further predict the epileptic seizures. Our approach includes two phases: offline training and online testing. Specifically, during offline training, we extract features and confirm the existence of systemic change-point, then estimate the ratio of unchanged system duration to interictal period duration. For online testing, we implement a statistical model to estimate the change-point, and then predict the onset of epileptic seizure. The testing results show that our proposed approach could effectively predict the time range of future epileptic seizure. Furthermore, we explore the macroscopic patterns of epileptic and control cases, and extract features based on the pattern difference, then implement and compare the classification performance from four machine learning models. Based on the data structure, we also propose a new method to discretize related features, and combine with hierarchical clustering to better visualize and explain the pattern difference between epileptic and control cases.

The notion of dominance is ubiquitous across the animal kingdom, wherein some species/groups such relationships are strictly hierarchical and others are not. Modern approaches for measuring dominance have emerged in recent years taking advantage of increased computational power. One such technique, named Percolation and Conductance (Perc), uses both direct and indirect information about the flow of dominance relationships to generate hierarchical rank order that makes no assumptions about the linearity of these relationships. It also provides a new metric, known as ‘dominance certainty’, which is a complimentary measure to dominance rank that assesses the degree of ambiguity of rank relationships at the individual, dyadic and group levels. In this focused review, we will (i) describe how Perc measures dominance rank while accounting for both nonlinear hierarchical structure as well as sparsity in data—here we also provide a metric of dominance certainty estimated by Perc, which can be used to compliment the information dominance rank supplies; (ii) summarize a series of studies by our research team reflecting the importance of ‘dominance certainty’ on individual and societal health in large captive rhesus macaque breeding groups; and (iii) provide some concluding remarks and suggestions for future directions for dominance hierarchy research.This article is part of the theme issue ‘The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies’.

Large and densely sampled sensor datasets can contain a range of complex stochastic structures that are difficult to accommodate in conventional linear models. This can confound attempts to build a more complete picture of an animal’s behavior by aggregating information across multiple asynchronous sensor platforms. The Livestock Informatics Toolkit (LIT) has been developed in R to better facilitate knowledge discovery of complex behavioral patterns across Precision Livestock Farming (PLF) data streams using novel unsupervised machine learning and information theoretic approaches. The utility of this analytical pipeline is demonstrated using data from a 6-month feed trial conducted on a closed herd of 185 mix-parity organic dairy cows. Insights into the tradeoffs between behaviors in time budgets acquired from ear tag accelerometer records were improved by augmenting conventional hierarchical clustering techniques with a novel simulation-based approach designed to mimic the complex error structures of sensor data. These simulations were then repurposed to compress the information in this data stream into robust empirically-determined encodings using a novel pruning algorithm. Nonparametric and semiparametric tests using mutual and pointwise information subsequently revealed complex nonlinear associations between encodings of overall time budgets and the order that cows entered the parlor to be milked.

Clustering large and complex data sets whose partitions may adopt arbitrary shapes remains a difficult challenge. Part of this challenge comes from the difficulty in defining a similarity measure between the data points that captures the underlying geometry of those data points. In this paper, we propose an algorithm, DCG++ that generates such a similarity measure that is data-driven and ultrametric. DCG++ uses Markov Chain Random Walks to capture the intrinsic geometry of data, scans possible scales, and combines all this information using a simple procedure that is shown to generate an ultrametric. We validate the effectiveness of this similarity measure within the context of clustering on synthetic data with complex geometry, on a real-world data set containing segmented audio records of frog calls described by mel-frequency cepstral coefficients, as well as on an image segmentation problem. The experimental results show a significant improvement on performance with the DCG-based ultrametric compared to using an empirical distance measure.

ObjectivesPolicing is a conflict-limiting mechanism observed in many primate species. It is thought to require a skewed distribution of social power for some individuals to have sufficiently high social power to stop others' fights, yet social power has not been examined in most species with policing behavior. We examined networks of subordination signals as a source of social power that permits policing behavior in rhesus macaques.Materials and methodsFor each of seven captive groups of rhesus macaques, we (a) examined the structure of subordination signal networks and used GLMs to examine the relationship between (b) pairwise dominance certainty and subordination network pathways and (c) policing frequency and social power (group-level convergence in subordination signaling pathways).ResultsNetworks of subordination signals had perfect linear transitivity, and pairs connected by both direct and indirect pathways of signals had more certain dominance relationships than pairs with no such network connection. Social power calculated using both direct and indirect network pathways showed a heavy-tailed distribution and positively predicted conflict policing.ConclusionsOur results empirically substantiate that subordination signaling is associated with greater dominance relationship certainty and further show that pairs who signal rarely (or not at all) may use information from others' signaling interactions to infer or reaffirm the relative certainty of their own relationships. We argue that the network of formal dominance relationships is central to societal stability because it is important for relationship stability and also supports the additional stabilizing mechanism of policing.

With maritime transport of crude oil from Alaska to California, there is significant potential for a catastrophic spill which could impact migrating salmon. Therefore, this study compared the lethal and sublethal metabolic actions of the water-accommodated fraction (WAF) and the chemically enhanced WAF (CEWAF, via Corexit 9500) of Prudhoe Bay crude oil in smolts of Chinook salmon (Onchorhyncus tshawytscha). After 96-h exposure to the CEWAF, the resulting LC50 was some 20 times higher (i.e., less toxic) than that of the WAF. Muscle and liver samples from surviving fish were collected and low-molecular weight metabolites were analyzed using one-dimensional (1)H and projections of two-dimensional (1)H J-resolved NMR. Principal component analysis (PCA), employed to analyze NMR spectra and identify most variance from the samples, revealed age-related metabolic changes in the fish within the replicated studies, but few consistent metabolic effects from the treatments. However, ANOVA results demonstrated that the dose-response metabolite patterns are both metabolite- and organ-dependent. In general, exposure to either WAF or CEWAF resulted in an increase of amino acids (i.e., valine, glutamine and glutamate) and a decrease of both organic osmolytes (i.e., glycerophosphorylcholine) and energetic substrates (i.e., succinate). The simultaneous increase of formate and decrease of glycerophosphorylcholine in the liver, or the decrease of glycerophosphorylcholine in muscle, may serve as sensitive sublethal biomarkers for WAF or CEWAF exposures, respectively. In conclusion, dispersant treatment significantly decreased the lethal potency of crude oil to salmon smolts, and the NMR-based metabolomics approach provided a sensitive means to characterize the sublethal metabolic actions.

The maximum likelihood approach to jointly model the survival time and its longitudinal covariates has been successful to model both processes in longitudinal studies. Random effects in the longitudinal process are often used to model the survival times through a proportional hazards model, and this invokes an EM algorithm to search for the maximum likelihood estimates (MLEs). Several intriguing issues are examined here, including the robustness of the MLEs against departure from the normal random effects assumption, and difficulties with the profile likelihood approach to provide reliable estimates for the standard error of the MLEs. We provide insights into the robustness property and suggest to overcome the difficulty of reliable estimates for the standard errors by using bootstrap procedures. Numerical studies and data analysis illustrate our points.

We take a system point of view toward constructing any power or ranking hierarchy onto a society of human or animal players. The most common hierarchy is the linear ranking, which is habitually used in nearly all real-world problems. A stronger version of linear ranking via increasing and unvarying winning potentials, known as Bradley-Terry model, is particularly popular. Only recently non-linear ranking hierarchy is discussed and developed through recognition of dominance information contents beyond direct dyadic win-and-loss. We take this development further by rigorously arguing for the necessity of accommodating system's global pattern information contents, and then introducing a systemic testing on Bradley-Terry model. Our test statistic with an ensemble based empirical distribution favorably compares with the Deviance test equipped with a Chi-squared asymptotic approximation. Several simulated and real data sets are analyzed throughout our development.

Cten is a focal adhesion molecule and a member of the tensin family. Its expression is highly enriched in the prostate and placenta, suggesting that cten gene might be closely associated with mammalian species. Recent studies have reported that cten expression is frequently up-regulated in a variety of cancers and its levels appear to correlate with tumorigenicity. Here, we have (1) analyzed cten sequences of various species to build a phylogenetic tree, (2) examined cten mRNA levels in human and mouse tissues to establish its expression profiles, and (3) determined the promoter region of human CTEN gene in cell lines and in a mouse model to understand its transcriptional regulation. Our analyses indicate that all currently known cten genes are present in mammals. The prostate and placenta are the two most cten abundant tissues in human and mouse, meanwhile brain and lung also express low levels of cten. Results from cell culture reporter assays demonstrate that a 327-bp fragment is the shortest functional promoter. All functional promoter constructs produce 40- to 160-fold increases in luciferase reporter activities in normal prostate cells, whereas lower activities (< 40-fold) are detected in non-prostatic cell lines. To evaluate CTEN promoter activity in mice and develop a new tissue specific Cre recombinase mouse model, we have established pCTEN-Cre:R26R mice by crossing R26R β-galactosidase reporter mice with pCTEN-Cre transgenic mice, in which the 327-bp cten promoter drives the expression of Cre recombinase. X-gal analysis has shown strong β-galactosidase activities in the prostate, brain, and few other tissues in pCTEN-Cre:R26R mice. Altogether, we have identified the promoter region of human cten gene and provided a useful tool for investigating cell linages and generating tissue-specific knockout or knockin mice.

An important goal in neuroscience is to identify instances when EEG signals are coupled. We employ a method to measure the coupling strength between gamma signals (40–100 Hz) on a short time scale as the maximum cross-correlation over a range of time lags within a sliding variable-width window. Instances of coupling states among several signals are also identified, using a mixed multivariate beta distribution to model coupling strength across multiple gamma signals with reference to a common base signal. We first apply our variable-window method to simulated signals and compare its performance to a fixed-window approach. We then focus on gamma signals recorded in two regions of the rat hippocampus. Our results indicate that this may be a useful method for mapping coupling patterns among signals in EEG datasets.