Economic Freedom, Capital, and Growth: Evidence from the States

Hall et al. (2010) develop a growth model where the allocation and productivity of human and physical capital depend on the quality of institutions in a country. We apply their model to the US states from 1980 to 2000. Using the Economic Freedom of North America as our measure of institutional quality, we find evidence that increases in human capital lead to increases in output per worker only in states with average EFNA scores above 5.91. Physical capital, unlike in the cross-country case, always has a positive effect on output per worker

From long-lived batholith construction to giant porphyry copper deposit formation: petrological and zircon chemical evolution of the Quellaveco District, Southern Peru

Porphyry Cu ore deposits are a rare product of arc magmatism that often form spatiotemporal clusters in magmatic arcs. The petrogenetic evolution of igneous rocks that cover the temporal window prior to and during porphyry Cu deposit formation may provide critical insights into magmatic processes that are key in generating these systems. This study documents the magmatic evolution of the Palaeocene–Eocene Yarabamba Batholith, Southern Peru, that was incrementally assembled between ~ 67 and ~ 59 Ma and hosts three, nearly contemporaneous, giant porphyry Cu–Mo deposits that formed at 57–54 Ma (Quellaveco, Toquepala and Cuajone). Whole-rock geochemistry, U–Pb geochronology and zircon trace element chemistry are reported from Yarabamba rocks that span the duration of plutonic activity, and from six porphyry intrusions at Quellaveco that bracket mineralisation. A change in whole-rock chemistry in Yarabamba intrusive rocks to high Sr/Y, high La/Yb and high Eu/Eu* is observed at ~ 60 Ma which is broadly coincident with a change in vector of the converging Nazca plate and the onset of regional compression and crustal thickening during the first stage of the Incaic orogeny. The geochemical changes are interpreted to reflect a deepening of the locus of lower crustal magma evolution in which amphibole ± garnet are stabilised as early and abundant fractionating phases and plagioclase is suppressed. Zircons in these rocks show a marked change towards higher Eu/Eu* (> 0.3) and lower Ti (< 9 ppm) compositions after ~ 60 Ma. Numerical modelling of melt Eu systematics and zircon-melt partitioning indicates that the time series of zircon Eu/Eu* in these rocks can be explained by a transition from shallower, plagioclase-dominated fractionation to high-pressure amphibole-dominated fractionation at deep crustal levels from ~ 60 Ma. Our modelling suggests that any redox effects on zircon Eu/Eu* are subordinate compared to changes in melt composition controlled by the fractionating mineral assemblage. We suggest that growth and intermittent recharge of the lower crustal magma reservoir from ~ 60 Ma produced a significant volume of hydrous and metallogenically fertile residual melt which ascended to the upper crust and eventually generated the three giant porphyry Cu–Mo deposits at Quellaveco, Toquepala and Cuajone from ~ 57 Ma. Our study highlights the importance of high-pressure magma differentiation fostered by strongly compressive tectonic regimes in generating world-class porphyry Cu deposits

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Neuropsychosocial profiles of current and future adolescent alcohol misusers

A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Machine learning for geochemical exploration: classifying metallogenic fertility in arc magmas and insights into porphyry copper deposit formation

A current mineral exploration focus is the development of tools to identify magmatic districts predisposed to host porphyry copper deposits. In this paper, we train and test four, common, supervised machine learning algorithms: logistic regression, support vector machines, artificial neural networks and Random Forest to classify metallogenic “fertility” in arc magmas based on whole-rock geochemistry. We outline pre-processing steps that can be used to mitigate against the undesirable characteristics of geochemical data (high multicollinearity, sparsity, missing values, class imbalance and compositional data effects) and therefore produce more meaningful results. We evaluate the classification accuracy of each supervised machine learning technique using a 10-fold cross validation technique and by testing the models on deposits unseen during the training process. This yields 81-83% accuracy for all classifiers, and receiver operating characteristic (ROC) curves have mean area under curve (AUC) scores of 87-89% indicating the probability of ranking a “fertile” rock higher than an “unfertile” rock. By contrast, bivariate classification schemes show much lower performance, demonstrating the value of classifying geochemical data in high dimension space. Principal component analysis suggests that porphyry-fertile magmas fractionate deep in the arc crust, and that calc-alkaline magmas associated with Cu-rich porphyries evolve deeper in the crust than more alkaline magmas linked with Au-rich porphyries. Feature analysis of the machine learning classifiers suggests that the most important parameters associated with fertile magmas are low Mn, high Al, high Sr, high K and 1 listric REE patterns. These signatures further highlight the association of porphyry Cu deposits with hydrous arc magmas that undergo amphibole fractionation in the deep arc crust