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Retronasal Olfaction
Published in Alan R. Hirsch, Nutrition and Sensation, 2023
Jason J. Gruss, Alan R. Hirsch
A substance must be volatile enough to allow transportation of a stimulus to the olfactory sensory organs. Many things can alter a substance’s volatility. An example of this would be bread. Hot, baking bread, coming right out of the oven has a very strong and familiar odor. Two days later, the same bread is much less volatile and presents a much lower stimulus. Using this concept, modern chefs are preparing foods that have specific sensory effects. A chef might prepare a food with minimal aroma until it meets with saliva, or until it is chewed. An example of this would be crystallized or dehydrated concentration of specific flavors, such as powders that turn into fruit punch or lemonade with water. Another chef might add a complimentary aroma to a course that is not eaten but only smelled. An example of this would be a bouquet of pine needles. When hot water is added to the needles, the aroma is unmistakable, but the needles need not be eaten for the aroma to add to the flavor. In these ways, orthonasal aroma can be added to alter the flavor of food. Alternatively, retronasal flavors can be added. When a retronasal stimulus is added, there is a sudden change from minimal odor detection to strong odor detection. This contrast results in strong sensory perception, perhaps similar to stepping out of a darkened theater into sunlight.
Asthma Mortality Epidemics: The Problem Approached Epidemiologically
Published in Richard Beasley, Neil E. Pearce, The Role of Beta Receptor Agonist Therapy in Asthma Mortality, 2020
Infectious disease incidence rates can have extreme volatility from year to year, thus causing changes in the mortality rates. This is less often seen with so-called “chronic” diseases. However, the decline in death rates due to coronary heart disease has been shown to be partly due to a decline in the incidence rates since about 1965.3,4 Distinguishing between changes in incidence as contrasted to changes in the case:fatality ratio is an important step in the unraveling of the sequence of events when mortality rates change over time and a possible epidemic increase is suspected. Is the increase in death rates uniform in all groups at risk or concentrated among particular groups?
Falling Through the Safety Net
Published in Kant Patel, Mark Rushefsky, Healthcare Politics and Policy in America, 2019
Reproductive issues, such as abortion and family planning, are much older than the current debates over abortion acknowledge. From ancient civilizations through the twentieth century, ways were sought and used to control reproduction (Knowles 2012). We can see the volatility, intensity, and controversy surrounding healthcare in the passage of the Affordable Care Act as well as the elections of 2012. The issues of abortion and reproductive rights touch upon the values of order, including the protection of traditional social values, and liberty, including the right of privacy. In this section, we examine issues related to contraception. While the issue of contraception overlaps the issue of abortion, we discuss abortion as well as assisted reproductive technology in Chapter 9.
mHealth for pediatric chronic pain: state of the art and future directions
Published in Expert Review of Neurotherapeutics, 2020
Patricia A. Richardson, Lauren E. Harrison, Lauren C. Heathcote, Gillian Rush, Deborah Shear, Chitra Lalloo, Korey Hood, Rikard K. Wicksell, Jennifer Stinson, Laura E. Simons
Data generated by advancements in digital health technologies, in concert with other healthcare data, require sophisticated analyses. Such ‘Big Data’ approaches need to be able to process a multitude of data points on differing scales, including data from wearable sensors, apps, administrative healthcare data, clinical registries, electronic health record, diagnostic imaging, among others [116]. To process Big Data, machine learning algorithms and artificial intelligence (AI) are employed with the goal of predicting, preventing, and optimally treating target health outcomes. In one recent example within the context of chronic pain, a measure of pain volatility (i.e. variability in pain intensity over time) was developed for patients using a pain management app at 1 and 6 months (Manage My Pain app) [117]. A total of 130 demographic, clinical, and application usage variables were collected within the first month of app use. Machine learning algorithms were then employed to analyze the 130 variables to successfully predict pain volatility 6 months later. The above-described study by Rahman and colleagues [117] is a promising example of how mHealth and Big Data can be synthesized to assess and ultimately improve personalized care for patients with chronic pain. The healthcare industry is only beginning to explore the pragmatics of healthcare analytics [118]. A literature base is needed to evaluate if Big Data approaches will be able to improve upon existing assessment and interventional paradigms in the treatment of pediatric chronic pain.
Jumps beyond the realms of cricket: India's performance in One Day Internationals and stock market movements
Published in Journal of Applied Statistics, 2020
Konstantinos Gkillas, Rangan Gupta, Chi Keung Marco Lau, Muhammad Tahir Suleman
Note that financial market volatility is used as an important input in investment decisions, option pricing and financial market regulation [45]. In light of this, financial market participants care not only about the nature of volatility, but also about its level, with all traders making the distinction between good and bad volatilities [17]. Good volatility is directional, persistent and relatively easy to predict, while, bad volatility is jumpy and comparatively difficult to foresee. Therefore, good volatility is generally associated with the continuous and persistent part, while bad volatility captures the discontinuous and jump component of volatility. Given this, it has been stressed that studying jumps can improve the overall understanding of the latent process of volatility [29,30]. In light of this, we too in our study incorporate the impact of ODI match results involving India on the predictability of various types of volatility jumps (small, big, good, and bad), besides return and realized volatility, as well as good and bad versions of the latter.
A closed testing procedure for comparison between successive variances
Published in Journal of Applied Statistics, 2020
Navdeep Singh, Parminder Singh
Many statistical inference procedures assume equality of variances across the populations under comparison. For example, the classical analysis of variance procedure assumes the equality of variances of populations and the procedure is not robust when there is a deviation from the assumption. Also, in many studies, it is common to know whether there is equality among the variances of several populations. For example, in stock prices data analysis, one would like to know about the volatility in the prices over different periods of time. Bartlett [1] proposed a test procedure for testing the equality of 9] proposed a test procedure for testing the homogeneity of variances which is a robust test under the deviation from normality. In many experimental studies, it is natural to assume either increasing or decreasing trend in variances, see Noguchi and Gel [11]. In such a situation, an experimenter wishes to test the null hypothesis of homogeneity of variances against the alternative hypothesis of a monotonic trend (increasing or decreasing) in variances. Fujino [3] proposed a test procedure for testing the null hypothesis of equality of