Landscape response to Pleistocene-Holocene precipitation change in the Western Cordillera, Peru: 10Be concentrations in modern sediments and terrace fills

The landscape response to climate change is frequently investigated with models because natural experiments on geologic timescales are rare. In Quebrada Veladera, in the western Andes Mountains, the formation of alluvial terraces during periods of high precipitation presents opportunities for such an experiment. We compare drainage-average erosion rates during Pleistocene terrace deposition with Holocene rates, using cosmogenic 10Be samples for seven pairs of quartz sand taken from the trunk and tributaries of Quebrada Veladera and adjacent terraces. Each pair consists of sediment collected from the modern channel and excavated from an adjacent fill terrace. The terrace fill was deposited at ~16 ka and preserved an isotopic record of paleoerosion rates in the Late Pleistocene. Modern sands yield 10Be concentrations between 1.68 × 105 and 2.28 × 105 atoms/g, corresponding to Holocene erosion rates between 43 ± 3 and 58 ± 4 mm/kyr. The 10Be concentrations in terrace sands range from 9.46 × 104 to 3.73 × 105 atoms/g, corresponding to paleoerosion rates from 27 ± 2 to 103 ± 8 mm/kyr. Smaller, upstream tributaries show a substantial decline in erosion rate following the transition from a wet to dry climate, but larger drainage areas show no change. We interpret this trend to indicate that the wetter climate drove landscape dissection, which ceased with the return to dry conditions. As channel heads propagated upslope, erosion accelerated in low-order drainages before higher-order ones. This contrast disappeared when the drainage network ceased to expand; at that point, erosion rates became spatially uniform, consistent with the uniformity of modern hillslope gradients. Key Points Landscape response to climate change evaluated with 10Be erosion rates Wetter climate associated with rapid erosion in smaller, upstream drainages Drier, Holocene climate associated with spatially uniform erosion rates ©2013. American Geophysical Union. All Rights Reserved

Computational phenotyping of two-person interactions reveals differential neural response to depth-of-thought.

Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subject's depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-of-thought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans

Simulating future value in intertemporal choice

The laboratory study of how humans and other animals trade-off value and time has a long and storied history, and is the subject of a vast literature. However, despite a long history of study, there is no agreed upon mechanistic explanation of how intertemporal choice preferences arise. Several theorists have recently proposed model-based reinforcement learning as a candidate framework. This framework describes a suite of algorithms by which a model of the environment, in the form of a state transition function and reward function, can be converted on-line into a decision. The state transition function allows the model-based system to make decisions based on projected future states, while the reward function assigns value to each state, together capturing the necessary components for successful intertemporal choice. Empirical work has also pointed to a possible relationship between increased prospection and reduced discounting. In the current paper, we look for direct evidence of a relationship between temporal discounting and model-based control in a large new data set (n = 168). However, testing the relationship under several different modeling formulations revealed no indication that the two quantities are related

BOLD and its connection to dopamine release in human striatum: a cross-cohort comparison

Activity in midbrain dopamine neurons modulates the release of dopamine in terminal structures including the striatum, and controls reward-dependent valuation and choice. This fluctuating release of dopamine is thought to encode reward prediction error (RPE) signals and other value-related information crucial to decision-making, and such models have been used to track prediction error signals in the striatum as encoded by BOLD signals. However, until recently there have been no comparisons of BOLD responses and dopamine responses except for one clear correlation of these two signals in rodents. No such comparisons have been made in humans. Here, we report on the connection between the RPE-related BOLD signal recorded in one group of subjects carrying out an investment task, and the corresponding dopamine signal recorded directly using fast-scan cyclic voltammetry in a separate group of Parkinson's disease patients undergoing DBS surgery while performing the same task. The data display some correspondence between the signal types; however, there is not a one-to-one relationship. Further work is necessary to quantify the relationship between dopamine release, the BOLD signal and the computational models that have guided our understanding of both at the level of the striatum.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'

Reliability verification of mooring components for floating marine energy converters

PublishedThis paper was presented at SHF – conference on MRE – Brest (F), October 2013.Safety factors are critical to device reliability and are applied during device development to protect against early failures. At each stage of a development a designer may apply their own safety factor in relation to the criticality of the component or subassembly for which they are responsible. This paper seeks to understand how different assessment techniques can assist the design process by refining safety factors, with the aim of reducing device costs and improving economic viability. To achieve this, a methodology is presented to assess and verify the fatigue performance of mooring components. The paper draws on field data and introduces a combined approach of modelling, service simulation and field tests to validate the reliability of components. A shackle is used as a case study to demonstrate the methodology. Results from finite element analysis (FEA) and accelerated service simulation testing on the Dynamic Marine Component test facility (DMaC) are presented and discussed, including fatigue damage and failures. FEA is found to accurately predict areas of weakness within a component, however it underestimates component strength due to unrealistic stress concentrations at applied boundary conditions. Static and fatigue tests demonstrate the complex nature of reliability estimation, with static component safety factors of 8.6 being reduced to less than 3.7 under a fatigue loading regime. Service simulation testing is found to be important in refining initial reliability estimations from S-N curves and FEA models. The effect of mean stress on fatigue failure is also found to be significant.The authors would like to acknowledge the support of the UK Centre for Marine Energy Research (UKCMER) through the SuperGen programme funded by the Engineering and Physical Sciences Research Council

Loss Aversion Correlates With the Propensity to Deploy Model-Based Control

Reward-based decision making is thought to be driven by at least two different types of decision systems: a simple stimulus–response cache-based system which embodies the common-sense notion of “habit,” for which model-free reinforcement learning serves as a computational substrate, and a more deliberate, prospective, model-based planning system. Previous work has shown that loss aversion, a well-studied measure of how much more on average individuals weigh losses relative to gains during decision making, is reduced when participants take all possible decisions and outcomes into account including future ones, relative to when they myopically focus on the current decision. Model-based control offers a putative mechanism for implementing such foresight. Using a well-powered data set (N = 117) in which participants completed two different tasks designed to measure each of the two quantities of interest, and four models of choice data for these tasks, we found consistent evidence of a relationship between loss aversion and model-based control but in the direction opposite to that expected based on previous work: loss aversion had a positive relationship with model-based control. We did not find evidence for a relationship between either decision system and risk aversion, a related aspect of subjective utility

Protein O-Mannosylation in the Murine Brain: Occurrence of Mono-O-Mannosyl Glycans and Identification of New Substrates

Protein O-mannosylation is a post-translational modification essential for correct development of mammals. In humans, deficient O-mannosylation results in severe congenital muscular dystrophies often associated with impaired brain and eye development. Although various O-mannosylated proteins have been identified in the recent years, the distribution of O-mannosyl glycans in the mammalian brain and target proteins are still not well defined. In the present study, rabbit monoclonal antibodies directed against the O-mannosylated peptide YAT(α1-Man)AV were generated. Detailed characterization of clone RKU-1-3-5 revealed that this monoclonal antibody recognizes O-linked mannose also in different peptide and protein contexts. Using this tool, we observed that mono-O-mannosyl glycans occur ubiquitously throughout the murine brain but are especially enriched at inhibitory GABAergic neurons and at the perineural nets. Using a mass spectrometry-based approach, we further identified glycoproteins from the murine brain that bear single O-mannose residues. Among the candidates identified are members of the cadherin and plexin superfamilies and the perineural net protein neurocan. In addition, we identified neurexin 3, a cell adhesion protein involved in synaptic plasticity, and inter-alpha-trypsin inhibitor 5, a protease inhibitor important in stabilizing the extracellular matrix, as new O-mannosylated glycoproteins