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Reward Deficiency Syndrome (RDS) is an umbrella term for all drug and nondrug addictive behaviors, due to a dopamine deficiency, “hypodopaminergia.” There is an opioid-overdose epidemic in the USA, which may result in or worsen RDS. A paradigm shift is needed to combat a system that is not working. This shift involves the recognition of dopamine homeostasis as the ultimate treatment of RDS via precision, genetically guided KB220 variants, called Precision Behavioral Management (PBM). Recognition of RDS as an endophenotype and an umbrella term in the future DSM 6, following the Research Domain Criteria (RDoC), would assist in shifting this paradigm.
Predictive analytics in health is a complex, transdisciplinary field requiring collaboration across diverse scientific and stakeholder groups. Pilot implementation of participatory research to foster team science in predictive analytics through a partnered-symposium and funding competition. In total, 85 stakeholders were engaged across diverse translational domains, with a significant increase in perceived importance of early inclusion of patients and communities in research. Participatory research approaches may be an effective model for engaging broad stakeholders in predictive analytics.
The ways in which productivity, stability, population interactions, and community structure are regulated in ecosystems have been a central focus of ecology for over a century. At large spatial scales, major insights into these dynamics have been principally derived from analyses of changes induced from hunting, harvesting, and agricultural practices – so-called “natural experiments.” In terrestrial ecosystems estimates of the fraction of land transformed or degraded by human activity fall within the range of 39 to 75% (Vitousek et al., 1997; Ellis et al., 2010). Equally profound is the reality that up to 75% of the global oceans and in particular the continental shelf, transitional slope water areas, and reef habitats have been strongly impacted by human activity (Halpern et al., 2008).
One of the most widely studied human impacts has been the over-exploitation of large-bodied species. Berger et al. (2001) estimated that the spatial distribution of large mammalian carnivores that once played a dominant role in terrestrial ecosystems has declined by 95–99%. In the global oceans large predatory fish biomass may be as low as 10% of pre-industrial levels (Myers and Worm, 2003). These changes have created a vertical compaction and blunting of the trophic pyramid (Duffy, 2003; Chapter 14, this volume). On a global scale, these losses are attributable to a positive association between body size and sensitivity to population declines experienced by larger species which exhibit a greater susceptibility to decline or collapse as a consequence of their lower population densities, greater times to maturity, lower clutch sizes, and larger home ranges (Schipper et al., 2008). This reduction in the abundance of apex predators has led to abnormally high densities of their former prey in a wide range of ecosystems, which has, in turn, resulted in sometimes catastrophic changes in the ecosystems occupied. This has led some to conclude that large-bodied species are essential to the maintenance of ecosystem structure and stability (Hildrew et al., 2007; Estes et al., 2011).
We report on the analysis of virtual powder-diffraction patterns from serial femtosecond crystallography (SFX) data collected at an X-ray free-electron laser. Different approaches to binning and normalizing these patterns are discussed with respect to the microstructural characteristics which each highlights. Analysis of SFX data from a powder of Pr0.5Ca0.5MnO3 in this way finds evidence of other trace phases in its microstructure which was not detectable in a standard powder-diffraction measurement. Furthermore, a comparison between two virtual powder pattern integration strategies is shown to yield different diffraction peak broadening, indicating sensitivity to different types of microstrain. This paper is a first step in developing new data analysis methods for microstructure characterization from serial crystallography data.
As in the past, the primary activity of the IAU Working Group on Cartographic Coordinates and Rotational Elements has been to prepare and publish a triennial (“2009”) report containing current recommendations for models for Solar System bodies (Archinal et al. (2011a)). The authors are B. A. Archinal, M. F. A'Hearn, E. Bowell, A. Conrad, G. J. Consolmagno, R. Courtin, T. Fukushima, D. Hestroffer, J. L. Hilton, G. A. Krasinsky, G. Neumann, J. Oberst, P. K. Seidelmann, P. Stooke, D. J. Tholen, P. C. Thomas, and I. P. Williams. An erratum to the “2006” and “2009” reports has also been published (Archinal et al. (2011b)). Below we briefly summarize the contents of the 2009 report, a plan to consider requests for new recommendations more often than every three years, three general recommendations by the WG to the planetary community, other WG activities, and plans for our next report.
It can be complicated to attempt to understand how election mechanisms and other variables surrounding an election determine outcomes. This is because the variables of interest are often intertwined; thus, it is difficult to disentangle them to determine the cause and effect that variables have on each other. Formal models of elections are used to disentangle variables so that cause and effect can be isolated. Laboratory election experiments are conducted so that the causes and effects of these isolated variables from these formal models can be empirically measured. These types of experiments are conducted within a single location, where it is possible for a researcher to control many of the variables of the election environment and thus observe the behavior of subjects under different electoral situations. The elections are often carried out in computer laboratories via computer terminals, and the communication between the researcher and subjects occurs primarily through a computer interface. In these experiments, subjects are assigned as either voters or candidates, and, in some cases, they are given both roles. Voters are rewarded based on a utility function that assigns a preference for a particular candidate or party. Candidates are typically rewarded based on whether they win the election, but sometimes their rewards depend on the actions they take after the election.
Throughout this book we have attempted both to describe the methodology of experimental political science and to give advice to new experimentalists. In this appendix we provide an experimentalist's to do list that summarizes what an experimentalist needs to consider in designing and conducting an experiment as well as in analyzing experimental data. We reference parts of the book that are relevant for each item. Note that, although the items are numbered, they should not be viewed as chronological; that is, a researcher may address how he or she plans to evaluate a causal relationship before figuring out the specific target population for the experiment.
The Target Population and Subject Pools
An experimentalist should identify the target population for the study and how that target population relates to the subjects recruited for the experiment (see Definition 2.2).
Nonspecific Target Populations
If the target population is “humanity,” as discussed in Chapter 9, then the experimentalist can reasonably use any subject pool for a first investigation of a particular experimental design. In such a case, the target population for the experiment becomes the subject pool from which the subjects are drawn and the results are valid for that pool (assuming that the subjects are randomly drawn from that pool). The subject pool is defined as the set of recruited subjects who are willing to participate.
In Institutional Review Board (IRB) speak, one of the key aspects of determining whether an experiment is ethical is a consideration of the risks to subjects versus the benefits. But as the Office of Human Rights Protections (OHRP) notes in the 1993 IRB Guidebook, the use of the term benefit is inaccurate. It is essentially expected benefits that are considered, not known benefits, because we cannot know for sure what will be learned through the research (otherwise there would be no point to the study). Thus, we are concerned with the product of the two – the probability that benefits can occur times the value of those benefits – or expected benefits. Calculating expected benefits means calculating both the probability of benefit and the value of benefit. Correspondingly, the term risk is confusing as well. The guidebook states at one point that risk is a measure of the probability of harm, not mentioning the magnitude. But certainly the magnitude of harm is as important as the probability (and part of the aforementioned definition of minimal risk). Expected cost (probability of harm times the magnitude of harm) is a more accurate measure to compare to expected benefits. Most IRBs and the OHRP recognize that the comparison between risk and benefit is more accurately thought of as the comparison between the expected costs of the research for the subject (probability and magnitude of harm) and the expected benefits (probability and magnitude of benefits), with risk as shorthand for expected costs and benefits as shorthand for expected benefits.
When a researcher conducts an experiment, he or she intervenes in the data generating process, as we have discussed. Because as social scientists we are interested in studying human behavior, our interventions necessarily affect humans. Our interventions may mean that the humans affected – our subjects and, in some cases, those who are not directly considered our subjects – make choices that they would not have faced otherwise (or would have faced differently) or have experiences that they would not have been subject to otherwise. Thus, as experimentalists, we affect human lives. Of course, these are not the only ways our professional activities can affect human lives. We affect other humans in disseminating our research; in teaching students and training future scholars; in our interactions with our fellow scholars within our institutions, collaborative relationships, and professional organizations; and in our daily lives. In this way, political scientists are like members of other professions.
Most professions have codes of ethics, moral rules about how their members should conduct themselves in their interpersonal relations, which is also true for some political science professional societies. For example, the American Political Science Association (APSA) created a committee with a broad mandate to explore matters “relevant to the problems of maintaining a high sense of professional standards and responsibilities” in 1967. The committee was chaired by Marver H. Bernstein and prepared a written code of rules of professional conduct.