A Fellow\u27s Perspective on Technology in Healthcare

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Defining optimal DEM resolutions and point densities for modelling hydrologically sensitive areas in agricultural catchments dominated by microtopography

AbstractDefining critical source areas (CSAs) of diffuse pollution in agricultural catchments depends upon the accurate delineation of hydrologically sensitive areas (HSAs) at highest risk of generating surface runoff pathways. In topographically complex landscapes, this delineation is constrained by digital elevation model (DEM) resolution and the influence of microtopographic features. To address this, optimal DEM resolutions and point densities for spatially modelling HSAs were investigated, for onward use in delineating CSAs. The surface runoff framework was modelled using the Topographic Wetness Index (TWI) and maps were derived from 0.25m LiDAR DEMs (40 bare-earth points m−2), resampled 1m and 2m LiDAR DEMs, and a radar generated 5m DEM. Furthermore, the resampled 1m and 2m LiDAR DEMs were regenerated with reduced bare-earth point densities (5, 2, 1, 0.5, 0.25 and 0.125 points m−2) to analyse effects on elevation accuracy and important microtopographic features. Results were compared to surface runoff field observations in two 10km2 agricultural catchments for evaluation. Analysis showed that the accuracy of modelled HSAs using different thresholds (5%, 10% and 15% of the catchment area with the highest TWI values) was much higher using LiDAR data compared to the 5m DEM (70–100% and 10–84%, respectively). This was attributed to the DEM capturing microtopographic features such as hedgerow banks, roads, tramlines and open agricultural drains, which acted as topographic barriers or channels that diverted runoff away from the hillslope scale flow direction. Furthermore, the identification of ‘breakthrough’ and ‘delivery’ points along runoff pathways where runoff and mobilised pollutants could be potentially transported between fields or delivered to the drainage channel network was much higher using LiDAR data compared to the 5m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2m were identified for modelling HSAs, which balanced the need for microtopographic detail as well as surface generalisations required to model the natural hillslope scale movement of flow. Little loss of vertical accuracy was observed in 1–2m LiDAR DEMs with reduced bare-earth point densities of 2–5 points m−2, even at hedgerows. Further improvements in HSA models could be achieved if soil hydrological properties and the effects of flow sinks (filtered out in TWI models) on hydrological connectivity are also considered

Microtubule disruption targets HIF-1α mRNA to cytoplasmic P-bodies for translational repression

Active translation of HIF-1α requires association and trafficking of HIF mRNA on dynamic microtubules, whereas microtubule disruption represses HIF-1α translation by releasing its mRNA from polysomes and sequestering it to P-bodies

Qualia: The Geometry of Integrated Information

According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale—a shape that completely and univocally characterizes the quality of a conscious experience. Φ— the height of this shape—is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the “redness” of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Model averaging, optimal inference, and habit formation

Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior. Bayesian model averaging-which says that an agent should weight the predictions of different models according to their evidence-provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent's behavior should show an equivalent balance. We hypothesize that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realizable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behavior. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded) Bayesian inference, focusing particularly upon the relationship between goal-directed and habitual behavior

TESS Giants Transiting Giants. I.: A Noninflated Hot Jupiter Orbiting a Massive Subgiant

While the population of confirmed exoplanets continues to grow, the sample of confirmed transiting planets around evolved stars is still limited. We present the discovery and confirmation of a hot Jupiter orbiting TOI-2184 (TIC 176956893), a massive evolved subgiant (M ∗ = 1.53 ± 0.12 M o˙, R ∗ = 2.90 ± 0.14 R o˙) in the Transiting Exoplanet Survey Satellite (TESS) Southern Continuous Viewing Zone. The planet was flagged as a false positive by the TESS Quick-Look Pipeline due to periodic systematics introducing a spurious depth difference between even and odd transits. Using a new pipeline to remove background scattered light in TESS Full Frame Image data, we combine space-based TESS photometry, ground-based photometry, and ground-based radial velocity measurements to report a planet radius of R p = 1.017 ± 0.051 R J and mass of M p = 0.65 ± 0.16 M J . For a planet so close to its star, the mass and radius of TOI-2184b are unusually well matched to those of Jupiter. We find that the radius of TOI-2184b is smaller than theoretically predicted based on its mass and incident flux, providing a valuable new constraint on the timescale of post-main-sequence planet inflation. The discovery of TOI-2184b demonstrates the feasibility of detecting planets around faint (TESS magnitude > 12) post-main-sequence stars and suggests that many more similar systems are waiting to be detected in the TESS FFIs, whose confirmation may elucidate the final stages of planetary system evolution