Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor

Accurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean ± standard deviation) absolute percentage errors of 25.2% ± 18.9% and 30.1% ± 33.8%, respectively, and mean ± standard deviation estimation errors of −17.7 ± 226.9 g and −6.1 ± 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.); DK10079604 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)Published versio

Phosphatidylserine dictates the assembly and dynamics of caveolae in the plasma membrane

Caveolae are bulb-shaped nanodomains of the plasma membrane that are enriched in cholesterol and sphingolipids. They have many physiological functions, including endocytic transport, mechanosensing, and regulation of membrane and lipid transport. Caveola formation relies on integral membrane proteins termed caveolins (Cavs) and the cavin family of peripheral proteins. Both protein families bind anionic phospholipids, but the precise roles of these lipids are unknown. Here, we studied the effects of phosphatidylserine (PtdSer), phosphatidylinositol 4-phosphate (PtdIns4P), and phosphatidylinositol 4,5-bisphosphate (PtdIns(4,5)P2) on caveolar formation and dynamics. Using live-cell, single-particle tracking of GFP-labeled Cav1 and ultrastructural analyses, we compared the effect of PtdSer disruption or phosphoinositide depletion with caveola disassembly caused by cavin1 loss. We found that PtdSer plays a crucial role in both caveola formation and stability. Sequestration or depletion of PtdSer decreased the number of detectable Cav1-GFP puncta and the number of caveolae visualized by electron microscopy. Under PtdSer-limiting conditions, the co-localization of Cav1 and cavin1 was diminished, and cavin1 degradation was increased. Using rapamycin-recruitable phosphatases, we also found that the acute depletion of PtdIns4P and PtdIns(4,5)P2 has minimal impact on caveola assembly but results in decreased lateral confinement. Finally, we show in a model of phospholipid scrambling, a feature of apoptotic cells, that caveola stability is acutely affected by the scrambling. We conclude that the predominant plasmalemmal anionic lipid PtdSer is essential for proper Cav clustering, caveola formation, and caveola dynamics and that membrane scrambling can perturb caveolar stability

Prognostic impact of human epidermal growth factor-like receptor 2 and hormone receptor status in inflammatory breast cancer (IBC): analysis of 2,014 IBC patient cases from the California Cancer Registry

IntroductionInflammatory breast cancer (IBC) is an aggressive form of breast cancer associated with overexpression of Her2/Neu (human epidermal growth factor-like receptor 2 (HER2)) and poor survival. We investigated survival differences for IBC patient cases based on hormone receptor status and HER2 receptor status using data from the California Cancer Registry, as contrasted with locally advanced breast cancer (LABC), metastatic breast cancer (MBC) and non-T4 breast cancer.MethodsA case-only analysis of 80,099 incident female breast cancer patient cases in the California Cancer Registry during 1999 to 2003 was performed, with follow-up through March 2007. Overall survival (OS) and breast cancer-specific survival (BC-SS) were analyzed using Kaplan-Meier methods and Cox proportional hazards ratios.ResultsA total of 2,014 IBC, 1,268 LABC, 3,059 MBC, and 73,758 non-T4 breast cancer patient cases were identified. HER2+ was associated with advanced tumor stage (P < 0.0001). IBC patient cases were more likely to be HER2+ (40%) and less likely to be hormone receptor-positive (HmR+) (59%) compared with LABC (35% and 69%, respectively), MBC (35% and 74%), and non-T4 patient cases (22% and 82%). HmR+ status was associated with improved OS and BC-SS for each breast cancer subtype after adjustment for clinically relevant factors. In multivariate analysis, HER2+ (versus HER2-) status was associated with poor BC-SS for non-T4 patient cases (hazards ratio = 1.16, 95% confidence interval 1.05 to 1.28) and had a borderline significant association with improved BC-SS for IBC (hazards ratio = 0.82, 95% confidence interval = 0.68 to 0.99).ConclusionsDespite an association with advanced tumor stage, HER2+ status is not an independent adverse prognostic factor for survival among IBC patient cases

Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models

Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18–34°C with maximal transmission occurring in a range from 26–29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones

Representing climatic uncertainty in agricultural models – an application of state-contingent theory

The state-contingent approach to production uncertainty presents a more general model than the conventional stochastic production approach. Here we investigate whether the state-contingent approach offers a tractable framework for representing climatic uncertainty at a farm level. We developed a discrete stochastic programming (DSP) model of a representative wheat–sheep (mixed) farm in the Central West of NSW. More explicit recognition of climatic states, and associated state-contingent responses, led to optimal farm plans that were more profitable on average and less prone to the effects of variations in climate than comparable farm plans based on the expected value framework. The solutions from the DSP model also appeared to more closely resemble farm land use than the equivalent expected value model using the same data. We conclude that there are benefits of adopting a state-contingent view of uncertainty, giving support to its more widespread application to other problems

Valuing seasonal climate forecasts in a state-contingent manner

We applied state-contingent theory to climate uncertainty at a farm level to assess the value of seasonal climate forecasts in the Central West region of NSW. We find that modelling uncertainty in a state-contingent manner results in a lower estimate of forecast value than the typical expected value approach. We attribute this finding to a more conservative long-term farm plan in the discrete stochastic programming (DSP) model, which is better balanced for climate uncertainty. Hence, a climate forecast, even though it still revises probabilities held by farmers, does not call forth such large changes in farm plans and associated farm incomes. We then use the DSP model to assess how attributes of a hypothetical forecasting system, particularly its skill and timeliness, as well as attributes of the decision environment, influence its value. Lastly, we assess the value of current operational forecast systems and show that the value derived from seasonal climate forecasts is relatively limited in the case study region largely because of low skill embodied in forecasts at the time when major farm decisions are being made

Valuing seasonal climate forecasts in a state-contingent manner

We applied state-contingent theory to climate uncertainty at a farm level to assess the value of seasonal climate forecasts in the Central West region of NSW. We find that modelling uncertainty in a state-contingent manner results in a lower estimate of forecast value than the typical expected value approach. We attribute this finding to a more conservative long-term farm plan in the discrete stochastic programming (DSP) model, which is better balanced for climate uncertainty. Hence, a climate forecast, even though it still revises probabilities held by farmers, does not call forth such large changes in farm plans and associated farm incomes. We then use the DSP model to assess how attributes of a hypothetical forecasting system, particularly its skill and timeliness, as well as attributes of the decision environment, influence its value. Lastly, we assess the value of current operational forecast systems and show that the value derived from seasonal climate forecasts is relatively limited in the case study region largely because of low skill embodied in forecasts at the time when major farm decisions are being made