The Breakdown of Synchronization in Systems of Non-identical Chaotic Oscillators: Theory and Experiment

The synchronization of chaotic systems has received a great deal of attention. However, most of the literature has focused on systems that possess invariant manifolds that persist as the coupling is varied. In this paper, we describe the process whereby synchronization is lost in systems of nonidentical coupled chaotic oscillators without special symmetries. We qualitatively and quantitatively analyze such systems in terms of the evolution of the unstable periodic orbit structure. Our results are illustrated with data from physical experiments

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition’s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data

Personalized glucose forecasting for type 2 diabetes using data assimilation

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges

Differential Impact of Serial Measurement of Nonplatelet Thromboxane Generation on Long-Term Outcome After Cardiac Surgery.

BACKGROUND: Systemic thromboxane generation, not suppressible by standard aspirin therapy and likely arising from nonplatelet sources, increases the risk of atherothrombosis and death in patients with cardiovascular disease. In the RIGOR (Reduction in Graft Occlusion Rates) study, greater nonplatelet thromboxane generation occurred early compared with late after coronary artery bypass graft surgery, although only the latter correlated with graft failure. We hypothesize that a similar differential association exists between nonplatelet thromboxane generation and long-term clinical outcome. METHODS AND RESULTS: Five-year outcome data were analyzed for 290 RIGOR subjects taking aspirin with suppressed platelet thromboxane generation. Multivariable modeling was performed to define the relative predictive value of the urine thromboxane metabolite, 11-dehydrothromboxane B CONCLUSIONS: Long-term nonplatelet thromboxane generation after coronary artery bypass graft surgery is a novel risk factor for 5-year adverse outcome, including death. In contrast, nonplatelet thromboxane generation in the early postoperative period appears to be driven predominantly by inflammation and did not independently predict long-term clinical outcome

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition’s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

A Murine Model to Study Epilepsy and SUDEP Induced by Malaria Infection.

One of the largest single sources of epilepsy in the world is produced as a neurological sequela in survivors of cerebral malaria. Nevertheless, the pathophysiological mechanisms of such epileptogenesis remain unknown and no adjunctive therapy during cerebral malaria has been shown to reduce the rate of subsequent epilepsy. There is no existing animal model of postmalarial epilepsy. In this technical report we demonstrate the first such animal models. These models were created from multiple mouse and parasite strain combinations, so that the epilepsy observed retained universality with respect to genetic background. We also discovered spontaneous sudden unexpected death in epilepsy (SUDEP) in two of our strain combinations. These models offer a platform to enable new preclinical research into mechanisms and prevention of epilepsy and SUDEP

Ancient Nursery Area for the Extinct Giant Shark Megalodon from the Miocene of Panama

BACKGROUND: As we know from modern species, nursery areas are essential shark habitats for vulnerable young. Nurseries are typically highly productive, shallow-water habitats that are characterized by the presence of juveniles and neonates. It has been suggested that in these areas, sharks can find ample food resources and protection from predators. Based on the fossil record, we know that the extinct Carcharocles megalodon was the biggest shark that ever lived. Previous proposed paleo-nursery areas for this species were based on the anecdotal presence of juvenile fossil teeth accompanied by fossil marine mammals. We now present the first definitive evidence of ancient nurseries for C. megalodon from the late Miocene of Panama, about 10 million years ago. METHODOLOGY/PRINCIPAL FINDINGS: We collected and measured fossil shark teeth of C. megalodon, within the highly productive, shallow marine Gatun Formation from the Miocene of Panama. Surprisingly, and in contrast to other fossil accumulations, the majority of the teeth from Gatun are very small. Here we compare the tooth sizes from the Gatun with specimens from different, but analogous localities. In addition we calculate the total length of the individuals found in Gatun. These comparisons and estimates suggest that the small size of Gatun's C. megalodon is neither related to a small population of this species nor the tooth position within the jaw. Thus, the individuals from Gatun were mostly juveniles and neonates, with estimated body lengths between 2 and 10.5 meters. CONCLUSIONS/SIGNIFICANCE: We propose that the Miocene Gatun Formation represents the first documented paleo-nursery area for C. megalodon from the Neotropics, and one of the few recorded in the fossil record for an extinct selachian. We therefore show that sharks have used nursery areas at least for 10 millions of years as an adaptive strategy during their life histories

Maternal Influences on the Transmission of Leukocyte Gene Expression Profiles in Population Samples from Brisbane, Australia

Two gene expression profiling studies designed to identify maternal influences on development of the neonate immune system and to address the population structure of the leukocyte transcriptome were carried out in Brisbane, Australia. In the first study, a comparison of 19 leukocyte samples obtained from mothers in the last three weeks of pregnancy with 37 umbilical cord blood samples documented differential expression of 7,382 probes at a false discovery rate of 1%, representing approximately half of the expressed transcriptome. An even larger component of the variation involving 8,432 probes, notably enriched for Vitamin E and methotrexate-responsive genes, distinguished two sets of individuals, with perfect transmission of the two profile types between each of 16 mother-child pairs in the study. A minor profile of variation was found to distinguish the gene expression profiles of obese mothers and children of gestational diabetic mothers from those of children born to obese mothers. The second study was of adult leukocyte profiles from a cross-section of Red Cross blood donors sampled throughout Brisbane. The first two axes in this study are related to the third and fourth axes of variation in the first study and also reflect variation in the abundance of CD4 and CD8 transcripts. One of the profiles associated with the third axis is largely excluded from samples from the central portion of the city. Despite enrichment of insulin signaling and aspects of central metabolism among the differentially expressed genes, there was little correlation between leukocyte expression profiles and body mass index overall. Our data is consistent with the notion that maternal health and cytokine milieu directly impact gene expression in fetal tissues, but that there is likely to be a complex interplay between cultural, genetic, and other environmental factors in the programming of gene expression in leukocytes of newborn children