Brain Mechanisms Underlying Decision-Making: A Biological Perspective

A healthy lifestyle is fundamental for the prevention and treatment of cardiovascular disease and other noncommunicable diseases (NCDs). Investment in primary prevention, including modification of health risk behaviors, could result in a 4-fold improvement in health outcomes compared with secondary prevention based on pharmacological treatment. The American Heart Association (AHA) emphasized the importance of lifestyle in its 2020 goals for cardiovascular health promotion and disease reduction. In addition to defining “cardiovascular health” based on criteria for blood pressure and biochemical markers (lipids and glycemia), the AHA Strategic Planning Committee further identified lifestyle characteristics of central importance: nutrition, physical activity, smoking, and maintenance of a healthy body weight.1 The World Health Organization estimated that ≈80% of NCDs could be prevented if 4 key lifestyle practices were followed: a healthy diet, being physically active, avoidance of tobacco, and alcohol intake in moderation.2 To support healthy lifestyle initiatives, major changes are necessary at the societal level to improve population health. Numerous strategies might help to create a culture that promotes and facilitates healthy behaviors, including creating laws and regulations, mounting large-scale public awareness and education campaigns, implementing local community programs, and providing individual counseling.3 Physicians are uniquely positioned to encourage individuals to adopt healthy lifestyle behaviors: Approximately 80% of Americans visit their primary care physician at least once a year. Physicians directly communicate with their patients during clinical encounters across numerous settings, and research indicates that patients highly value recommendations provided by their physicians.4,5 However, data further indicate that lifestyle counseling does not routinely occur in physicians’ offices, thereby representing a lost opportunity. Physicians report that they perform lifestyle counseling during ≈34% of clinic visits.4 Patients, in turn, report an even lower frequency of physician lifestyle counseling. For example, obese patients reported receiving physical activity and …

Cognitive Mapping and the Origin of Language and Mind

A First Lesson in Econometrics

Attitudes of Junior High School Pupils toward Arithmetic

Previous articleNext article No AccessNew Biological BooksOrigins of the Modern Mind. Three Stages in the Evolution of Culture and Cognition. Merlin Donald Harry J. JerisonHarry J. Jerison Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Quarterly Review of Biology Volume 67, Number 4Dec., 1992 Published in association with Stony Brook University Article DOIhttps://doi.org/10.1086/417877 Views: 3Total views on this site Citations: 34Citations are reported from Crossref Copyright 1992 The University of ChicagoPDF download Crossref reports the following articles citing this article:Merlin Donald Précis of Origins of the modern mind: Three stages in the evolution of culture and cognition, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 737–748.https://doi.org/10.1017/S0140525X00032647Michael A. Arbib From cooperative computation to man/machine symbiosis, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 748–749.https://doi.org/10.1017/S0140525X00032659Derek Bickerton Putting cognitive carts before linguistic horses, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 749–750.https://doi.org/10.1017/S0140525X00032660C. Loring Brace “Pop science” versus understanding the emergence of the modern mind, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 750–751.https://doi.org/10.1017/S0140525X00032672Bruce Bridgeman, Margarita Azmitia Mimetic culture and modern sports: A synthesis, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 751–752.https://doi.org/10.1017/S0140525X00032684Philip G. Chase Archaeology and the cognitive sciences in the study of human evolution, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 752–753.https://doi.org/10.1017/S0140525X00032696Andy Clark Symbolic invention: The missing (computational) link?, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 753–754.https://doi.org/10.1017/S0140525X00032702Thomas L. Clarke A natural history of the mind: A guide for cognitive science, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 754–755.https://doi.org/10.1017/S0140525X00032714Alan Costall The place of cognition in human evolution, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 755–755.https://doi.org/10.1017/S0140525X00032726Vilmos Csányi Human evolution: Emergence of the group-self, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 755–756.https://doi.org/10.1017/S0140525X00032738Jeffrey Cynx, Stephen J. Clark Ethological foxes and cognitive hedgehogs, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 756–757.https://doi.org/10.1017/S0140525X0003274XJ. B. Deregowski What about pictures?, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 757–758.https://doi.org/10.1017/S0140525X00032751R. I. M. Dunbar The modern mind: Its missing parts?, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 758–759.https://doi.org/10.1017/S0140525X00032763Jerome A. Feldman From mimesis to synthesis, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 759–759.https://doi.org/10.1017/S0140525X00032775James H. Fetzer Evolution needs a modern theory of the mind, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 759–760.https://doi.org/10.1017/S0140525X00032787Liane M. Gabora Cultural transitions occur when mind parasites learn new tricks, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 760–761.https://doi.org/10.1017/S0140525X00032799K. J. Gilhooly Working memory and its extensions, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 761–762.https://doi.org/10.1017/S0140525X00032805John Halverson Mythos and logos, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 762–762.https://doi.org/10.1017/S0140525X00032817P. J. Hampson From mimetic to mythic culture: Stimulus equivalence effects and prelinguistic cognition, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 763–763.https://doi.org/10.1017/S0140525X00032829Harry J. Jerison The evolved mind, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 763–764.https://doi.org/10.1017/S0140525X00032830Leonard D. Katz The gradual evolution of enhanced control by plans: A view from below, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 764–765.https://doi.org/10.1017/S0140525X00032842Aarre Laakso Language equals mimesis plus speech, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 765–766.https://doi.org/10.1017/S0140525X00032854Rudi Lutz Lessons from evolution for artificial intelligence?, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 766–766.https://doi.org/10.1017/S0140525X00032866Alexander Marshack Correct data base: Wrong model?, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 767–768.https://doi.org/10.1017/S0140525X00032878Robert W. Mitchell, H. Lyn Miles Apes have mimetic culture, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 768–768.https://doi.org/10.1017/S0140525X0003288XH. C. Plotkin Hunting memes, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 768–769.https://doi.org/10.1017/S0140525X00032891Jocelyn Penny Small Memory, text and the Greek Revolution, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 769–770.https://doi.org/10.1017/S0140525X00032908Evan Thompson Language, thought and consciousness in the modern mind, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 770–771.https://doi.org/10.1017/S0140525X0003291XMichael Tomasello It's imitation, not mimesis, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 771–772.https://doi.org/10.1017/S0140525X00032921Jacques Vauclair, Joël Fagot Can a Saussurian ape be endowed with episodic memory only?, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 772–773.https://doi.org/10.1017/S0140525X00032933Christopher Wills Stages versus continuity, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 773–773.https://doi.org/10.1017/S0140525X00032945Thomas Wynn Archaeological evidence for mimetic mind and culture, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 774–774.https://doi.org/10.1017/S0140525X00032957Jiajie Zhang External representation: An issue for cognition, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 774–775.https://doi.org/10.1017/S0140525X00032969Merlin Donald On the evolution of representational capacities, Behavioral and Brain Sciences 16, no.44 (Feb 2010): 775–791.https://doi.org/10.1017/S0140525X00032970

Some years ago Hockett and Ascher (CA 5:135-68) developed several hypotheses as to how and when human language evolved from a primate call system. Their blending hypothesis for the opening of the call system has been criticized (Reynolds 1968), but to my mind no acceptable alternative has been offered. Carini's (CA 11:165-67) derivation of language from infant babbling is evolutionarily backward, as Washburn and Lancaster (CA 12:384-85) point out. The genetic propensity of human infants to babble is the result of selection by language. Carini's reply (CA 12:385) only emphasizes the differences in theoretical approach. His comparison of language with writing, by which he attempts to demonstrate that language had no communicative function but was purposeless at first, is implicitly antiDarwinian. Washburn and Lancaster outline the great many biological characteristics of man which are due to selection by language. Perhaps Carini would accept these as due to evolution, but he must postulate that babbling was a nonadaptive, purposeless, random behavior which simply existed for no reason prior to its role in the origin of language. This position is reminiscent of many previous postulations of nonadaptive traits in man, invariably discarded because of their inconsistency with genetic theory. Mutation, the source of all genetic variation, is indeed random; but if, for example, the sickle cell gene was ultimately a mutation from normal hemoglobin, nevertheless its widespread high frequencies are due to selection. A functionalist or Darwinian approach emphasizes deterministic, causative explanations for the existence of widespread, complex differ-

Understanding how plants are spatially and genetically distributed in the environment can be a challenging task given the difficulty to characterize ecological processes, such as gene flow, and to disentangle the relative importance of multiple factors underlying the generation of distinct patterns. In this dissertation, I study different populations of the understory plant Heliconia acuminata L.C. Richard (Heliconiaceae) distributed across 1-ha fragments and continuous forest sites in the Biological Dynamics of Forest Fragments Project (BDFFP), an experimentally fragmented landscape in central Amazonia. I characterize a set of ten microsatellite markers developed for Heliconia acuminata to first evaluate gene flow, which is one of the main processes influencing genetic structure and spatial patterns of plants, and second to assess the potential influence of endogenous (e.g., seed dispersal) and environmental factors on spatial patterns of plants and genetic relatedness distribution. I combine genetic and ecological data in a novel and comprehensive Bayesian model to estimate parentage to more fully characterize the contribution of pollen and seed dispersal to H. acuminata gene flow. I then compare metrics of gene flow between fragments and continuous forest, while taking in consideration the variation in abundance of reproductive plants in each population. I tested the conservation genetics prediction that gene flow is interrupted in fragmented landscapes. Contrary to this hypothesis, I found that that both fragmentation and low population densities were associated with greater immigration rates and longer pollination and seed dispersal distances. My results are one example of how fragmentation does not limit gene dispersal. I suggest that conservation genetics predictions are reformulated by taking in consideration the variation in the behavior of pollinators and seed dispersers across heterogeneous landscapes in response to habitat configuration and to the spatial and temporal availability of food resources. To investigate the influence of endogenous factors (plant - plant interactions) and environmental covariates (light, slope and soil characteristics) on spatial patterns of seedlings and adults, I use a new statistical methodology to model marked point patterns. Using this flexible approach, I also evaluate whether local spatial genetic structure is associated to spatial distribution of plants. The results show that H. acuminata seed dispersal is contagious, but not distance - restricted or genetically structured (presence of highly related plants). The absence of an association between spatial pattern and local genetic structure for adults also suggest the absence of genetic structuring in seedlings over time. Light and zinc availability are positively associated with spatial patterns of seedlings and adults, which may indicate carryover effects of seedlings on recruits over time. Carbon is negatively associated with adults, which may be evidence of competition with large dominant trees. I finally propose a new mechanistic framework to the studies of frugivore - mediated seed dispersal. I conduct a qualitative analysis of existent studies explicitly linking frugivores, fruiting plants and seed shadows and propose a frugivore - centered, process-based view of seed dispersal that integrates animal movement and seed dispersal ecology across multiple spatio - temporal scales. This critical analysis provides the empirical foundation over which we can build a more comprehensive, multi-scaled, research approach to the study of seed dispersal, process which is known to play a crucial role in the dynamics and evolution of plant populations.

Cancer diagnosis is a life-threatening traumatic event which can deeply affect the individual's psychological well-being, leading to depressive symptoms and anxiety, related to psychological distress (Gallagher et al., 2002). Recent times have seen an increase in the incidence of cancer, mainly attributed to urbanization, industrialization, lifestyle changes, population growth and many other unknown reasons. In spite of good advancements for diagnosis and treatment, cancer is still a big threat to the society (Kotnis et al., 2005). This is the second most common disease after cardiovascular disorders for maximum deaths in the world (Jemal et al., 2007). It accounts for about 23% and 7% deaths in USA and India, respectively. Projections based on the GLOBOCAN 2012 estimates predict a substantive increase to 19.3 million new cancer cases per year by 2025, due to growth and ageing of the global population. The myth that cancer affects people mostly in the developed countries is being broken by the fact that, more than half of all cancers (56.8%) and cancer deaths (64.9%) in 2012 occurred in less developed regions of the world, and these proportions will increase further by 2025. Deaths from cancer worldwide are projected to continue rising, with an estimated 13.1 million deaths in 2030. The cancer burden in developing countries is reaching pandemic proportions. Cancer is one of the leading causes of death in India, with about 2.5 million cancer patients, 1 million new cases added every year and with a chance of the disease rising five-fold by 2025. Indian Council of Medical Research (ICMR) has urged the Government of India to make cancer a disease requiring notification. There is a high probability of treating cancers if detected early - in Stage I or Stage II.Risk Factors for Cancer: According to epidemiological studies, 8090% of all cancers are due to environmental factors of which, lifestyle related factors are the most important and preventable. The major risk factors for cancer are tobacco, alcohol consumption, infections, dietary habits and behavioral factors. Tobacco consumption, either by way of chewing or smoking, accounts for 50% of all cancers in men. Studies have shown that appropriate changes in lifestyle will reduce the mortality and morbidity caused to cancer. About 30% of cancer deaths are due to the five leading behavioral and dietary risks: high body mass index, low fruit and vegetable intake, lack of physical activity, tobacco and alcohol use. The increasing trend of cancer in recent decades and its adverse effects on the physical, emotional, spiritual, social, and economic aspects, introduced cancer as a major health problem of the century. Most of these patients would experience more severe mental problems such as anxiety or depression, which can reduce their quality of life and their daily activities (Pedram, 2011 ).Cancer is primarily an environmental disease with 90-95% of cases attributed to environmental factors and 5-10% due to genetics. Environmental, as used by cancer researchers, means any cause that is not inherited genetically, not merely pollution. Common environmental factors that contribute to cancer death include tobacco (25-30%), diet and obesity (30-35%), infections (15-20%), radiation (both ionizing and non-ionizing, up to 10%), stress, lack of physical activity, and environmental pollutants. It is nearly impossible to prove what caused cancer in any individual, because most cancers have multiple possible causes. For example, if a person who uses tobacco heavily, develops lung cancer, then it was probably caused by the tobacco use, but since everyone has a small chance of developing lung cancer as a result of air pollution or radiation, then there is a small chance that the cancer developed because of air pollution or radiation.Cancer and Well Being: Well being is not just the absence of disease or illness but a complex combination of a person's physical, mental, emotional and social health status. …

Cutting the fat: the genetic dissection of body weight.

Classical economic theory always assumes that the individual will "act in his interest"; but it has never examined carefully the entity to which "his" refers. Often, as when households are taken as the unit for income and consumption, it is implicitly assumed that "the family" or "the household" is this entity whose interest is being maximized. Yet this is without theoretical foundation, merely a convenient but slipshod device. In this case, as in many others (e.g., when a man is willing to contribute much, even his life, to national defense, rather than use a strategy which will push the cost onto others), men act as if the "his" referred to some entity larger than themselves. That is, they appear to act in terms, not of their own interest, but of the interests of a collectivity or even of another person. Indeed, if they did not do so, the basis for society could hardly exist. Yet how can this be reconciled with the narrow premise of individual interest? . . . We could simply solve the problem by fiat, letting "his" refer to whatever entity the individual appeared to act in the interest of. This obviously would make the theory trivially true, and never disconfirmable. A more adequate solution is one which states the conditions under which the entity in whose interests he acts will be something other than himself.'

Previous articleNext article No AccessIdeologies and Attitudes, Academic and JudicialGlendon SchubertGlendon Schubert Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Journal of Politics Volume 29, Number 1Feb., 1967 Sponsored by the Southern Political Science Association Article DOIhttps://doi.org/10.2307/2127810 Views: 14Total views on this site Citations: 36Citations are reported from Crossref Copyright 1967 Southern Political Science AssociationPDF download Crossref reports the following articles citing this article:Wenzhang Zhou, Yi Peng, Haijun Bao Regular pattern of judicial decision on land acquisition and resettlement: An investigation on Zhejiang’s 901 administrative litigation cases, Habitat International 63 (May 2017): 79–88.https://doi.org/10.1016/j.habitatint.2017.03.013Glendon Schubert Two Versions of Pastoral, from A to Z, Politics and the Life Sciences 1, no.22 (May 2016): 114–124.https://doi.org/10.1017/S0730938400008546C. Neal Tate The methodology of judicial behavior research: A review and critique, Political Behavior 5, no.11 (Jan 1983): 51–82.https://doi.org/10.1007/BF00989986Charles J. Lumsden, Edward O. Wilson Précis of Genes, Mind, and Culture, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 1–7.https://doi.org/10.1017/S0140525X00010128David P. Barash From genes to mind to culture: Biting the bullet at last, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 7–8.https://doi.org/10.1017/S0140525X0001013XArthur L. Caplan Stalking the wild culturgen, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 8–9.https://doi.org/10.1017/S0140525X00010141William R. Charlesworth The epigenetic connection between genes and culture: Environment to the rescue, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 9–10.https://doi.org/10.1017/S0140525X00010153Robert Fagen Epigenesis and culture, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 10–10.https://doi.org/10.1017/S0140525X00010165Michael T. Ghiselin On mechanisms of cultural evolution, and the evolution of language and the common law, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 11–11.https://doi.org/10.1017/S0140525X00010177Howard E. Gruber Genes for general intellect rather than particular culture, Behavioral and Brain Sciences 5, no.0101 (Feb 2010): 11.https://doi.org/10.1017/S0140525X00010189C. R. Hallpike The “culturgen”: Science or science fiction?, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 12–13.https://doi.org/10.1017/S0140525X00010190Daniel L. Hartl A too simple view of population genetics, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 13–14.https://doi.org/10.1017/S0140525X00010207Timothy D. Johnston Concepts of development in the mathematics of cultural change, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 14–15.https://doi.org/10.1017/S0140525X00010219Joseph K. Kovach From genes to culture: The missing links, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 15–17.https://doi.org/10.1017/S0140525X00010220Geoffrey R. Loftus Top-down guidance from a bottom-up theory, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 17–18.https://doi.org/10.1017/S0140525X00010232Hubert Markl The power of reduction and the limits of compressibility, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 18–19.https://doi.org/10.1017/S0140525X00010244Roger D. Masters Toward a natural science of human culture, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 19–20.https://doi.org/10.1017/S0140525X00010256J. Maynard Smith Mind and the linkage between genes and culture, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 20–21.https://doi.org/10.1017/S0140525X00010268Robert Plutchik Genes, mind, and emotion, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 21–22.https://doi.org/10.1017/S0140525X0001027XAlexander Rosenberg Are there culturgens?, Behavioral and Brain Sciences 5, no.0101 (Feb 2010): 22.https://doi.org/10.1017/S0140525X00010281Glendon Schubert Epigenesis: The newer synthesis?, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 24–25.https://doi.org/10.1017/S0140525X00010293Joseph Shepher Collaboration between biology and the social sciences: A milestone, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 25–26.https://doi.org/10.1017/S0140525X0001030XL B. Slobodkin A bully pulpit, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 26–27.https://doi.org/10.1017/S0140525X00010311Pierre L. van den Berghe Resistance to biological self-understanding, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 27–27.https://doi.org/10.1017/S0140525X00010323Robert Van Gulick Information, feedback, and transparency, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 27–29.https://doi.org/10.1017/S0140525X00010335Thomas Rhys Williams Genes, mind, and culture; A turning point, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 29–30.https://doi.org/10.1017/S0140525X00010347Joachim F. Wohlwill The place of mind, and the limits of amplification, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 30–31.https://doi.org/10.1017/S0140525X00010359Charles J. Lumsden, Edward O. Wilson Genes and culture, protest and communication, Behavioral and Brain Sciences 5, no.11 (Feb 2010): 31–37.https://doi.org/10.1017/S0140525X00010360G. Schubert Infanticide by usurper hanuman langur males: A sociobiological myth, Social Science Information 21, no.22 (Mar 1982): 199–244.https://doi.org/10.1177/053901882021002002 Victor E. Flango , and Craig R. Ducat Toward an Integration of Public Law and Judicial Behavior, The Journal of Politics 39, no.11 (Oct 2015): 41–72.https://doi.org/10.2307/2129686Glendon Schubert Political Culture and Judicial Ideology, Comparative Political Studies 9, no.44 (Jan 1977): 363–408.https://doi.org/10.1177/001041407700900401 Sheldon Goldman and Thomas P. Jahnige Systems Analysis & Judicial Systems: Potential & Limitations, Polity 3, no.33 (Nov 2016): 334–359.https://doi.org/10.2307/3234115J. P. Nettl The State as a Conceptual Variable, World Politics 20, no.44 (Jul 2011): 559–592.https://doi.org/10.2307/2009684Glendon Schubert Political ideology on the high court, Politics 3, no.11 (Sep 2007): 21–40.https://doi.org/10.1080/00323266808401128J. Woodford Howard On the Fluidity of Judicial Choice, American Political Science Review 62, no.11 (Aug 2014): 43–56.https://doi.org/10.1017/S0003055400115631J. Woodford Howard On the Fluidity of Judicial Choice, American Political Science Review 62, no.11 (Aug 2014): 43–56.https://doi.org/10.2307/1953325

Ecological Determinants of Group Sizes of Foraging Lions

In the last two decades, advancements in artificial intelligence and data science have attracted researchers' attention to machine learning. Growing interests in applying machine learning algorithms can be observed in different scientific areas, including behavioral sciences. However, most of the research conducted in this area applied machine learning algorithms to imagining and physiological data such as EEG and fMRI and there are relatively limited non-imaging and non-physiological behavioral studies which have used machine learning to analyze their data. Therefore, in this perspective article, we aim to (1) provide a general understanding of models built for inference, models built for prediction (i.e., machine learning), methods used in these models, and their strengths and limitations; (2) investigate the applications of machine learning to categorical data in behavioral sciences; and (3) highlight the usefulness of applying machine learning algorithms to non-imaging and non-physiological data (e.g., clinical and categorical) data and provide evidence to encourage researchers to conduct further machine learning studies in behavioral and clinical sciences.

OBJECTIVES/GOALS: We have evaluated the primary factors behind successful expansion of the predoctoral TL1 training grant at OHSU to all research-intensive universities in the state, evaluating the precepts that were key to integration. We also evaluate inclusion of social and behavioral sciences in clinical and translational science training, METHODS/STUDY POPULATION: OHSU contains three professional schools (medicine, nursing, and dentistry) as well as graduate studies in science. There are also three research-intensive universities in Oregon: Portland State University (PSU); Oregon State University (OSU) in Corvallis; and the University of Oregon (UO). We report evaluation of our 7-year experience with a predoctoral TL1 program and the precepts behind successful implementation of statewide outreach. We have tracked applicants from each university and program, trainee feedback, and success of the applicants as measured by persistence in research, inclusion of translational methods, additional training acquired, and subsequent research funding. We also evaluate participation by behavioral and social scientists. RESULTS/ANTICIPATED RESULTS: The predoctoral TL1 has included 40 scholars over 7 years, with 65% PhD graduate students and 35% MD students who pursue an additional research degree (PhD, MCR). Of PhD graduate students, the distribution is similar among universities: OHSU 31%, PSU 19%, OSU 27%, UO 23%. 38% of all graduate student trainees are in behavioral or social science. Key precepts behind success include: concentration on specific graduate programs at each university; implementing a common curriculum based on the MCR curriculum; interinstitutional mentor teams; leadership team that spans universities; required core TL1 activities; and a competitive application process with an interinstitutional review committee. Applicants are required to identify how translational science education enhances their research career. DISCUSSION/SIGNIFICANCE: Creating a statewide resource for TL1 translational science training has increased opportunities to expand translational research throughout Oregon through providing new opportunities to enhance excellence through disseminating resources and training across the universities.

In the last two decades genetic research achieved very significant results. In 2015, only few years since the completion of the Human Genome Project, scientists launched new experimental studies to genetically modify the DNA sequence of human embryos. These scientific advances have raised several ethical concerns, promoting issues that involve both natural and human science– including legal studies. Law and ethics frequently have to address the issues originated from these innovations and finding a common toolset often proves to be an arduous task. Genetics has a transnational dimension, experiences a continuous and rapid evolution and spreads new unfamiliar concepts, which ask to be integrated in social and legal culture. Analyzing the path that conducted the US Congress to approve the Genetic Information Nondiscrimination Act of 2008, this essay assesses the most common problems in genetic information management in the legal field. In recent studies, the first issue concerns the qualification of genetic data, considering their legal value to identify possible suitable models of protection. With regard to this, U.S. genetic legislation originally presented two different complementary options — antidiscrimination and privacy laws. With the clear intent to foster genetic research and scientific investments, the Federal legislator decided to endorse the protection against discriminations — the most evident effects of the distort use of genetic information — moving privacy to the background. Nevertheless, the limited scope of the Act — health insurance and employment — and the rapid diffusion of genetic tests for extremely varied purposes stressed the loopholes of GINA, suggesting the need to address some issues through the different legal lens of data protection. For such reasons, the Congress, in 2016, introduced a new bill to handle the problem, considering, in particular, privacy in genetic research. Covering the misuses of genetic information safeguarding a lawful employment of data seems a possible way to encourage an aware usage of genetic tests and scientific techniques, taking advantage of their silver linings without harming fundamental rights.