The Nucleus of Comet 10P/Tempel 2 in 2013 and Consequences Regarding Its Rotational State: Early Science from the Discovery Channel Telescope

We present new lightcurve measurements of Comet 10P/Tempel 2 carried out with Lowell Observatory's Discovery Channel Telescope in early 2013 when the comet was at aphelion. These data represent some of the first science obtained with this new 4.3-m facility. With Tempel 2 having been observed to exhibit a small but ongoing spin-down in its rotation period for over two decades, our primary goals at this time were two-fold. First, to determine its current rotation period and compare it to that measured shortly after its most recent perihelion passage in 2010, and second, to disentangle the spin-down from synodic effects due to the solar day and the Earth's orbital motion and to determine the sense of rotation, i.e. prograde or retrograde. At our midpoint of 2013 Feb 24, the observed synodic period is 8.948+/-0.001 hr, exactly matching the predicted prograde rotation solution based on 2010 results, and yields a sidereal period of the identical value due to the solar and Earth synodic components just canceling out during the interval of the 2013 observations. The retrograde solution is ruled out because the associated sidereal periods in 2010 and 2013 are quite different even though we know that extremely little outgassing, needed to produce torques, occurred in this interval. With a definitive sense of rotation, the specific amounts of spin-down to the sidereal period could be assessed. The nominal values imply that the rate of spin-down has decreased over time, consistent with the secular drop in water production since 1988. Our data also exhibited an unexpectedly small lightcurve amplitude which appears to be associated with viewing from a large, negative sub-Earth latitude, and a lightcurve shape deviating from a simple sinusoid implying a highly irregularly shaped nucleus.Comment: Accepted by AJ; 12 pages of text (pre-print style), 3 tables, 2 figure

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

Ensemble Kalman Methods With Constraints

Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it

Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems

Annexin A2 at the interface between F-actin and membranes enriched in phosphatidylinositol 4,5,-bisphosphate

AbstractVesicle rocketing has been used as a model system for understanding the dynamics of the membrane-associated F-actin cytoskeleton, but in many experimental systems is induced by persistent, non-physiological stimuli. Localised changes in the concentration of phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) in membranes stimulate the recruitment of actin-remodelling proteins to their sites of action, regulate their activity and favour vesicle rocketing. The calcium and anionic phospholipid-binding protein annexin A2 is necessary for macropinocytic rocketing and has been shown to bind both PI(4,5)P2 and the barbed-ends of F-actin filaments. Here we show that annexin A2 localises to the comet tails which form constitutively in fibroblasts from patients with Lowe Syndrome. These fibroblasts are deficient in OCRL1, a phosphatidylinositol polyphosphate 5-phosphatase with specificity for PI(4,5)P2. We show that upon depletion of annexin A2 from these cells vesicle rocketing is reduced, and that this is also dependent upon PI(4,5)P2 formation. Annexin A2 co-localised with comet-tails induced by pervanadate and hyperosmotic shock in a basophilic cell line, and in an epithelial cell line upon activation of PKC. In vitro annexin A2 promoted comet formation in a bead-rocketing assay and was sufficient to link F-actin filaments to PI(4,5)P2 containing vesicles. These observations are consistent with a role for annexin A2 as an actin nucleator on PI(4,5)P2-enriched membranes

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