Climatic controls on diffuse groundwater recharge across Australia

Reviews of field studies of groundwater recharge have attempted to investigate how climate characteristics control recharge, but due to a lack of data have not been able to draw any strong conclusions beyond that rainfall is the major determinant. This study has used numerical modelling for a range of Köppen-Geiger climate types (tropical, arid and temperate) to investigate the effect of climate variables on recharge for different soil and vegetation types. For the majority of climate types, the correlation between the modelled recharge and total annual rainfall is weaker than the correlation between recharge and the annual rainfall parameters reflecting rainfall intensity. Under similar soil and vegetation conditions for the same annual rainfall, annual recharge in regions with winter-dominated rainfall is greater than in regions with summer-dominated rainfall. The importance of climate parameters other than rainfall in recharge estimation is highest in the tropical climate type. Mean annual values of solar radiation and vapour pressure deficit show a greater importance in recharge estimation than mean annual values of the daily mean temperature. Climate parameters have the lowest relative importance in recharge estimation in the arid climate type (with cold winters) and the temperate climate type. For 75% of all soil, vegetation and climate types investigated, recharge elasticity varies between 2 and 4 indicating a 20% to 40% change in recharge for a 10% change in annual rainfall. Understanding how climate controls recharge under the observed historical climate allows more informed choices of analogue sites if they are to be used for climate change impact assessments

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

The meandering instability of a viscous thread

A viscous thread falling from a nozzle onto a surface exhibits the famous rope-coiling effect, in which the thread buckles to form loops. If the surface is replaced by a belt moving with speed $U$, the rotational symmetry of the buckling instability is broken and a wealth of interesting states are observed [See S. Chiu-Webster and J. R. Lister, J. Fluid Mech., {\bf 569}, 89 (2006)]. We experimentally studied this "fluid mechanical sewing machine" in a new, more precise apparatus. As $U$ is reduced, the steady catenary thread bifurcates into a meandering state in which the thread displacements are only transverse to the motion of the belt. We measured the amplitude and frequency $\omega$ of the meandering close to the bifurcation. For smaller $U$, single-frequency meandering bifurcates to a two-frequency "figure eight" state, which contains a significant $2\omega$ component and parallel as well as transverse displacements. This eventually reverts to single-frequency coiling at still smaller $U$. More complex, highly hysteretic states with additional frequencies are observed for larger nozzle heights. We propose to understand this zoology in terms of the generic amplitude equations appropriate for resonant interactions between two oscillatory modes with frequencies $\omega$ and $2\omega$. The form of the amplitude equations captures both the axisymmetry of the U=0 coiling state and the symmetry-breaking effects induced by the moving belt.Comment: 12 pages, 9 figures, revised, resubmitted to Physical Review

Agent-based Social Psychology: from Neurocognitive Processes to Social Data

Moral Foundation Theory states that groups of different observers may rely on partially dissimilar sets of moral foundations, thereby reaching different moral valuations. The use of functional imaging techniques has revealed a spectrum of cognitive styles with respect to the differential handling of novel or corroborating information that is correlated to political affiliation. Here we characterize the collective behavior of an agent-based model whose inter individual interactions due to information exchange in the form of opinions are in qualitative agreement with experimental neuroscience data. The main conclusion derived connects the existence of diversity in the cognitive strategies and statistics of the sets of moral foundations and suggests that this connection arises from interactions between agents. Thus a simple interacting agent model, whose interactions are in accord with empirical data on conformity and learning processes, presents statistical signatures consistent with moral judgment patterns of conservatives and liberals as obtained by survey studies of social psychology.Comment: 11 pages, 4 figures, 2 C codes, to appear in Advances in Complex System

Infrared spectrum and intermolecular potential energy surface of the CO-O2 dimer

Only a few weakly-bound complexes containing the O2 molecule have been characterized by high resolution spectroscopy, no doubt due to the complications added by the oxygen molecule's unpaired electron spin. Here we report an extensive infrared spectrum of CO-O2, observed in the CO fundamental band region using a tunable quantum cascade laser to probe a pulsed supersonic jet expansion. The rotational energy level pattern derived from the spectrum consists of stacks of levels characterized by the total angular momentum, J, and its projection on the intermolecular axis, K. Five such stacks are observed in the ground vibrational state, and ten in the excited state (v(CO) = 1). They are divided into two groups, with no observed transitions between groups. The groups correspond to different projections of the O2 electron spin, and correlate with the two lowest rotational states of O2, (N, J) = (1, 0) and (1, 2). The rotational constant of the lowest K = 0 stack implies an effective intermolecular separation of 3.82 Angstroms, but this should be interpreted with caution since it ignores possible effects of electron spin. A new high-level 4-dimensional potential energy surface is developed for CO-O2, and rotational energy levels are calculated for this surface, ignoring electron spin. By comparing calculated and observed levels, it is possible to assign detailed quantum labels to the observed level stacks.Comment: 35 pages and 8 figure

Voting and the Cardinal Aggregation of Judgments

The paper elaborates the idea that voting is an instance of the aggregation of judgments, this being a more general concept than the aggregation of preferences. To aggregate judgments one must first measure them. I show that such aggregation has been unproblematic whenever it has been based on an independent and unrestricted scale. The scales analyzed in voting theory are either context dependent or subject to unreasonable restrictions. This is the real source of the diverse 'paradoxes of voting' that would better be termed 'voting pathologies'. The theory leads me to advocate what I term evaluative voting. It can also be called utilitarian voting as it is based on having voters express their cardinal preferences. The alternative that maximizes the sum wins. This proposal operationalizes, in an election context, the abstract cardinal theories of collective choice due to Fleming and Harsanyi. On pragmatic grounds, I argue for a three valued scale for general elections

Functional Activation and Effective Connectivity Differences in Adolescent Marijuana Users Performing a Simulated Gambling Task

Background. Adolescent marijuana use is associated with structural and functional differences in forebrain regions while performing memory and attention tasks. In the present study, we investigated neural processing in adolescent marijuana users experiencing rewards and losses. Fourteen adolescents with frequent marijuana use (\u3e5 uses per week) and 14 nonuser controls performed a computer task where they were required to guess the outcome of a simulated coin flip while undergoing magnetic resonance imaging. Results. Across all participants, ?Wins? and ?Losses? were associated with activations including cingulate, middle frontal, superior frontal, and inferior frontal gyri and declive activations. Relative to controls, users had greater activity in the middle and inferior frontal gyri, caudate, and claustrum during ?Wins? and greater activity in the anterior and posterior cingulate, middle frontal gyrus, insula, claustrum, and declive during ?Losses.? Effective connectivity analyses revealed similar overall network interactions among these regions for users and controls during both ?Wins? and ?Losses.? However, users and controls had significantly different causal interactions for 10 out of 28 individual paths during the ?Losses? condition. Conclusions. Collectively, these results indicate adolescent marijuana users have enhanced neural responses to simulated monetary rewards and losses and relatively subtle differences in effective connectivity

The Chicken Yolk Sac IgY Receptor, a Mammalian Mannose Receptor Family Member, Transcytoses IgY across Polarized Epithelial Cells

In mammals the transfer of passive immunity from mother to young is mediated by the MHC-related receptor FcRn, which transports maternal IgG across epithelial cell barriers. In birds, maternal IgY in egg yolk is transferred across the yolk sac to passively immunize chicks during gestation and early independent life. The chicken yolk sac IgY receptor (FcRY) is the ortholog of the mammalian phospholipase A2 receptor, a mannose receptor family member, rather than an FcRn or MHC homolog. FcRn and FcRY both exhibit ligand binding at the acidic pH of endosomes and ligand release at the slightly basic pH of blood. Here we show that FcRY expressed in polarized mammalian epithelial cells functioned in endocytosis, bidirectional transcytosis, and recycling of chicken FcY/IgY. Confocal immunofluorescence studies demonstrated that IgY binding and endocytosis occurred at acidic but not basic pH, mimicking pH-dependent uptake of IgG by FcRn. Colocalization studies showed FcRY-mediated internalization via clathrin-coated pits and transport involving early and recycling endosomes. Disruption of microtubules partially inhibited apical-to-basolateral and basolateral-to-apical transcytosis, but not recycling, suggesting the use of different trafficking machinery. Our results represent the first cell biological evidence of functional equivalence between FcRY and FcRn and provide an intriguing example of how evolution can give rise to systems in which similar biological requirements in different species are satisfied utilizing distinct protein folds

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy