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Since its enactment, the Affordable Care Act (ACA) has faced numerous legal challenges. Many of these lawsuits have focused on implementation of the law and the limits of executive power. Opponents challenged the ACA under the Obama Administration while supporters have turned to the courts to prevent the Trump Administration from undermining the law. In the meantime, Congress remains gridlocked over the ACA and many other critical health policy issues, leaving the executive branch to adopt its preferred policy approach and ultimately leading to lawsuits. This article briefly discusses the history of litigation over the ACA and some reasons why this litigation has been so enduring. The article then identifies other areas of health policy that are or could be future targets for litigation. Finally, the article comments on the potential impact of the courts on future health reform efforts.
Taylor's law (TL) originated as an empirical pattern in ecology. In many sets of samples of population density, the variance of each sample was approximately proportional to a power of the mean of that sample. In a family of nonnegative random variables, TL asserts that the population variance is proportional to a power of the population mean. TL, sometimes called fluctuation scaling, holds widely in physics, ecology, finance, demography, epidemiology, and other sciences, and characterizes many classical probability distributions and stochastic processes such as branching processes and birth-and-death processes. We demonstrate analytically for the first time that a version of TL holds for a class of distributions with infinite mean. These distributions, a subset of stable laws, and the associated TL differ qualitatively from those of light-tailed distributions. Our results employ and contribute to the methodology of Albrecher and Teugels (2006) and Albrecher et al. (2010). This work opens a new domain of investigation for generalizations of TL.
In a family, parameterized by θ, of non-negative random variables with finite, positive second moment, Taylor's law (TL) asserts that the population variance is proportional to a power of the population mean as θ varies: σ2 (θ) = a[μ(θ)]b, a > 0. TL, sometimes called fluctuation scaling, holds widely in science, probability theory, and stochastic processes. Here we report diverse examples of TL with b = 2 (equivalent to a constant coefficient of variation) arising from a difference of random variables in normed vector spaces of dimension 1 and larger. In these examples, we compute a exactly using, in some cases, a simple, new technique. These examples may prove useful in future models that involve differences of random variables, including models of the spatial distribution and migration of human populations.
Here is an exercise to try with your students or colleagues regarding wildlife conservation and management. Tell them they are managing an area containing a population of an endangered, charismatic, flagship wildlife species, say mountain nyala in Bale Mountains National Park, Ethiopia. Invite them to write down the one or two things they would most want to know in order to best manage the population. The answers will vary. Some may inquire into the population size or density; others may want to know what the nyala are eating; others may want to know about the nyalas’ levels of genetic heterozygosity. But what we really want to know is “what is the state of the population in terms of growth rate and relationship to resource density?” “what are the threats to the population?” and “what are the population's prospects for the future?” Are these questions we can answer? Will knowledge of population size or genetics or diet allow us to answer these? Or can answers best be obtained from other information? If so, how can such information be acquired? What are the best indicators?
Ideally, indicators of population well-being must be reliable. Further, they should be easy to measure, respond quickly to environmental change and forecast the future. Measurements of population sizes are frequently used in management decisions and may excel in identifying when small population issues are of concern, but are woefully inadequate as indicators of population processes. Such metrics do not necessarily respond quickly to environmental change. Most populations experience time-lagged dynamics. But time lags mean that density is a trailing indicator of current conditions. We must search elsewhere for leading indicators – indicators that predict the future rather than simply recapitulating the past. Perhaps we can find our indicators in the traits of organisms that have been shaped by evolution (Grafen 1982, Lucas & Grafen 1985, Mitchell & Valone 1990). One attractive class of characteristics comes from foraging theory and measures of behavior (Stephens & Krebs 1986). These can be classified into behavioral indicators based on diet, patch use or habitat selection.
Consider indicators of population well-being further. An example involving the Baltic tellin (Macoma balthica) illustrates this well. Baltic tellins, benthic bivalves from the Dutch Wadden Sea, suffer predation from red knots (Calidris canutus) (van Gils et al. 2009).
The 2013 Infection Prevention and Control (IP&C) Guideline for Cystic Fibrosis (CF) was commissioned by the CF Foundation as an update of the 2003 Infection Control Guideline for CF. During the past decade, new knowledge and new challenges provided the following rationale to develop updated IP&C strategies for this unique population:
1. The need to integrate relevant recommendations from evidence-based guidelines published since 2003 into IP&C practices for CF. These included guidelines from the Centers for Disease Control and Prevention (CDC)/Healthcare Infection Control Practices Advisory Committee (HICPAC), the World Health Organization (WHO), and key professional societies, including the Infectious Diseases Society of America (IDSA) and the Society for Healthcare Epidemiology of America (SHEA). During the past decade, new evidence has led to a renewed emphasis on source containment of potential pathogens and the role played by the contaminated healthcare environment in the transmission of infectious agents. Furthermore, an increased understanding of the importance of the application of implementation science, monitoring adherence, and feedback principles has been shown to increase the effectiveness of IP&C guideline recommendations.
2. Experience with emerging pathogens in the non-CF population has expanded our understanding of droplet transmission of respiratory pathogens and can inform IP&C strategies for CF. These pathogens include severe acute respiratory syndrome coronavirus and the 2009 influenza A H1N1. Lessons learned about preventing transmission of methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant gram-negative pathogens in non-CF patient populations also can inform IP&C strategies for CF.
Ice rises play key roles in buttressing the neighbouring ice shelves and potentially provide palaeoclimate proxies from ice cores drilled near their divides. Little is known, however, about their influence on local climate and surface mass balance (SMB). Here we combine 12 years (2001–12) of regional atmospheric climate model (RACMO2) output at high horizontal resolution (5.5 km) with recent observations from weather stations, ground-penetrating radar and firn cores in coastal Dronning Maud Land, East Antarctica, to describe climate and SMB variations around ice rises. We demonstrate strong spatial variability of climate and SMB in the vicinity of ice rises, in contrast to flat ice shelves, where they are relatively homogeneous. Despite their higher elevation, ice rises are characterized by higher winter temperatures compared with the flat ice shelf. Ice rises strongly influence SMB patterns, mainly through orographic uplift of moist air on the upwind slopes. Besides precipitation, drifting snow contributes significantly to the ice-rise SMB. The findings reported here may aid in selecting a representative location for ice coring on ice rises, and allow better constraint of local ice-rise as well as regional ice-shelf mass balance.
Corpus callosum malformation and dysfunction are increasingly recognized causes of cognitive and behavioral disability. Individuals with agenesis of the corpus callosum (AgCC) offer unique insights regarding the cognitive skills that depend specifically upon callosal connectivity. We examined the impact of AgCC on cognitive inhibition, flexibility, and processing speed using the Color-Word Interference Test (CWIT) and Trail Making Test (TMT) from the Delis-Kaplan Executive Function System. We compared 36 individuals with AgCC and IQs within the normal range to 56 matched controls. The AgCC cohort was impaired on timed measures of inhibition and flexibility; however, group differences on CWIT Inhibition, CWIT Inhibition/Switching and TMT Number-Letter Switching appear to be largely explained by slow performance in basic operations such as color naming and letter sequencing. On CWIT Inhibition/Switching, the AgCC group was found to commit significantly more errors which suggests that slow performance is not secondary to a cautious strategy. Therefore, while individuals with agenesis of the corpus callosum show real deficits on tasks of executive function, this impairment appears to be primarily a consequence of slow cognitive processing. Additional studies are needed to investigate the impact of AgCC on other aspects of higher order cortical function. (JINS, 2012, 18, 521–529)
Within the percolation and soaked facies of the Greenland ice sheet, the relationship between radar-derived internal reflection horizons and the layered structure of the firn column is unclear. We conducted two small-scale ground-penetrating radar (GPR) surveys in conjunction with 10 m firn cores that we collected within the percolation and soaked facies of the Greenland ice sheet. The two surveys were separated by a distance of ~50 km and ~340m of elevation leading to ~40 days of difference in the duration of average annual melt. At the higher site (~1997ma.s.l.), which receives less melt, we found that internal reflection horizons identified in GPR data were largely laterally continuous over the grid; however, stratigraphic layers identified in cores could not be traced between cores over any distance from 1.5 to 14.0 m. Thus, we found no correlation between firn core stratigraphy observed directly and radar-derived internal reflection horizons. At the lower site (~1660ma.s.l.), which receives more melt, we found massive ice layers >0.5m thick and stratigraphic boundaries that span >15m horizontally. Some ice layers and stratigraphic boundaries correlate well with internal reflection horizons that are laterally continuous over the area of the radar grid. Internal reflection horizons identified at ~1997ma.s.l. are likely annual isochrones, but the reflection horizons identified at ~1660ma.s.l. are likely multi-annual features. We find that mapping accumulation rates over long distances by tying core stratigraphy to radar horizons may lead to ambiguous results because: (1) there is no stratigraphic correlation between firn cores at the 1997 m location; and (2) the reflection horizons at the 1660m location are multi-annual features.
We present a characterization of the performance of an ultrashort laser pulse driven DC photoelectron gun based on the thermionic emission gun design of Togawa et al. [Togawa, K., Shintake, T., Inagaki, T., Onoe, K. & Tanaka, T. (2007). Phys Rev Spec Top-AC10, 020703]. The gun design intrinsically provides adequate optical access and accommodates the generation of ∼1 mm2 electron beams while contributing negligible divergent effects at the anode aperture. Both single-photon (with up to 20,000 electrons/pulse) and two-photon photoemission are observed from Ta and Cu(100) photocathodes driven by the harmonics (∼4 ps pulses at 261 nm and ∼200 fs pulses at 532 nm, respectively) of a high-power femtosecond Yb:KGW laser. The results, including the dependence of the photoemission efficiency on the polarization state of the drive laser radiation, are consistent with expectations. The implications of these observations and other physical limitations for the development of a dynamic transmission electron microscope with sub-1 nm·ps space-time resolution are discussed.
Matrix games, introduced in Subsection 3.1.2, formed the core of the early work on evolutionary games. Most game theoretic models, notions of strategy dynamics, solution concepts and applications of ESS definitions occurred explicitly in the context of matrix games. For continuous strategies, modelers relied on either Nash solutions (Auslander et al., 1978), or model-specific interpretations of the ESS concept (Lawlor and Maynard Smith, 1976; Eshel, 1983). The bulk of developments in evolutionary game theory associated with matrix games pre-date the G-function, strategy dynamics, and the ESS maximum principle. For a review of these developments see Hines (1987), Hofbauer and Sigmund (1988), and Cressman (2003). In this chapter, we place matrix games within the context of G-functions and the more general theory of continuous evolutionary games. We reformulate the ESS frequency maximum principle developed in Section 7.5 for application to matrix games. This reformulation requires some additional terminology and new definitions.
Fitness for a matrix game is expressed in terms of strategy frequency and a matrix of payoffs. As with continuous games, the G-function in the matrix game setting must take on a maximum value at all of the strategies which make up the ESS coalition vector. The reformulated maximum principle is applicable to both the traditional bi-linear matrix game and a more general non-linear matrix game.
All of life is a game, and evolution by natural selection is no exception. The evolutionary game theory developed in this 2005 book provides the tools necessary for understanding many of nature's mysteries, including co-evolution, speciation, extinction and the major biological questions regarding fit of form and function, diversity, procession, and the distribution and abundance of life. Mathematics for the evolutionary game are developed based on Darwin's postulates leading to the concept of a fitness generating function (G-function). G-function is a tool that simplifies notation and plays an important role developing Darwinian dynamics that drive natural selection. Natural selection may result in special outcomes such as the evolutionarily stable strategy (ESS). An ESS maximum principle is formulated and its graphical representation as an adaptive landscape illuminates concepts such as adaptation, Fisher's Fundamental Theorem of Natural Selection, and the nature of life's evolutionary game.
Because evolution occurs within an ecological setting, the concepts and models of population ecology are integral to evolutionary game theory. The organisms' environment and ecologies provide the “rules,” the context to which evolution responds. The transition from an ecological model to an evolutionary model can be made seamless. Examples include the Logistic growth model, Lotka–Volterra competition equations, models of predator–prey interactions, and consumer-resource models. In fact, any model or characterization of population dynamics can be reformulated as an evolutionary game. One need only identify evolutionary strategies that determine fitness and population growth rates. Conjoining an ecological model of population growth with heritable strategies puts the model in an evolutionary game setting. Not surprisingly, then, evolutionary game theory is well suited for addressing FF&F (fit of form and function) under all of nature's diverse ecological scenarios.
Games such as arms races, Prisoner's Dilemma, chicken, battle of the sexes, and wars of attrition have become standard bases for considering the evolution of many social behaviors (any issue of animal behavior offers examples of these or variants of these games). These games, however, are not unique to evolutionary ecology. They are products of and recurrent themes in economics, engineering, sociology, and political science. It is from these disciplines that game theory first emerged as the mathematical tools for understanding and solving conflicts of interest.
Natural selection produces strategies that are continually better than those discarded along the way to some evolutionary equilibrium. Intuitively this implies that eventually, natural selection should produce the “best” strategy for a given situation. The flowering time of a plant, the leg length of a coyote, or the filter feeding system of a clam should produce higher fitness than alternative strategies that are evolutionarily feasible (within the genetic, developmental, and physical constraints in the bauplan). In graphical form these products of natural selection should reside on peaks of the adaptive landscape. Yet we have seen in the previous chapter how, under Darwinian dynamics, natural selection may produce strategies that evolve to minimum points, maximum points, and saddlepoints on the adaptive landscape.
An evolutionary ecologist studying the traits of a species whose strategy has evolved to a convergent stable minimum on the adaptive landscape may, on reflection, be surprised. At this minimum, any individual with a strategy that deviates slightly from that produced by Darwinian dynamics has a higher, not lower, fitness than the resident strategy. While an evolutionarily stable minimum can result from Darwinian dynamics, this strategy is not the “correct” solution to the evolutionary game. In this chapter, we expand upon the original word definition of an evolutionarily stable strategy (ESS) as given by Maynard Smith: “An ESS is a strategy such that, if all members of a population adopt it, then no mutant strategy could invade the population under the influence of natural selection” (Maynard Smith, 1974).
The identity of plants and animals rendered by pre-historic artists in cave paintings, rock art, and sculpture is usually recognizable. With the development of language, plants and animals were given names, but a systematic categorization arose more recently when Carl Linnaeus introduced the idea of species as a binomial nomenclature of grouping organisms by genus and species. These early forms of pictorial, vernacular, and formal means of identifying groups of animals and plants were unaltered by later knowledge of evolution and phylogeny. Categorization simply recognizes the following three obvious properties of nature.
First, individuals (like matter in the universe) tend to be clumped rather than randomly or uniformly spread across the space of all imaginable morphologies, physiologies, and behaviors. This clumping of individuals around discrete types can to us be conspicuous – it's hard to misidentify an elephant. However, for some species, it can be really tricky. For example, both humans and male hummingbirds find it nigh impossible to distinguish the species identity of certain female hummingbirds. But, whether tightly clumped (as a planet or asteroid) or only vaguely clumped (more nebula-like), individuals can and seem to be naturally ordered as discrete kinds.
Second, long before Darwin, heritability was recognized by the fact that kinds tended to breed among themselves (assortative mating). Assortative mating can be socially or geographically imposed, or it can be due to physical constraints (elephants and hummingbirds cannot breed in any circumstance). Of course, hybrids occur; yet the very concept of a “hybrid” implies that organisms can be grouped by heritable characteristics and that crossings between groups produce novel, yet predictable, mixes of heritable traits.