Integrating visual and tactile information in the perirhinal cortex

By virtue of its widespread afferent projections, perirhinal cortex is thought to bind polymodal information into abstract object-level representations. Consistent with this proposal, deficits in cross-modal integration have been reported after perirhinal lesions in nonhuman primates. It is therefore surprising that imaging studies of humans have not observed perirhinal activation during visual–tactile object matching. Critically, however, these studies did not differentiate between congruent and incongruent trials. This is important because successful integration can only occur when polymodal information indicates a single object (congruent) rather than different objects (incongruent). We scanned neurologically intact individuals using functional magnetic resonance imaging (fMRI) while they matched shapes. We found higher perirhinal activation bilaterally for cross-modal (visual–tactile) than unimodal (visual–visual or tactile–tactile) matching, but only when visual and tactile attributes were congruent. Our results demonstrate that the human perirhinal cortex is involved in cross-modal, visual–tactile, integration and, thus, indicate a functional homology between human and monkey perirhinal cortices

The Potential For UK Portfolio Investors To Finance Sustainable Tropical Forestry

Environmental Economics and Policy, Resource /Energy Economics and Policy,

Particle size segregation in granular flow in silos

Segregation and layering of alumina in storage silos are investigated, with a view to predicting output quality versus time, given known variations in input quality on emplacement. A variety of experiments were conducted, existing relevant publications were reviewed, and the basis for an algorithm for predicting the effect of withdrawing from a central flowing region, in combination with variations in quality due to geometric, layering and segregation effects, is described in this report

Simulating the effects of mid- to upper-tropospheric clouds on microwave emissions in EC-Earth using COSP

In this work, the Cloud Feedback Model Intercomparison (CFMIP) Observation Simulation Package (COSP) is expanded to include scattering and emission effects of clouds and precipitation at passive microwave frequencies. This represents an advancement over the official version of COSP (version 1.4.0) in which only clear-sky brightness temperatures are simulated. To highlight the potential utility of this new microwave simulator, COSP results generated using the climate model EC-Earth's version 3 atmosphere as input are compared with Microwave Humidity Sounder (MHS) channel (190.311 GHz) observations. Specifically, simulated seasonal brightness temperatures (TB) are contrasted with MHS observations for the period December 2005 to November 2006 to identify possible biases in EC-Earth's cloud and atmosphere fields. The EC-Earth's atmosphere closely reproduces the microwave signature of many of the major large-scale and regional scale features of the atmosphere and surface. Moreover, greater than 60 % of the simulated TB are within 3 K of the NOAA-18 observations. However, COSP is unable to simulate sufficiently low TB in areas of frequent deep convection. Within the Tropics, the model's atmosphere can yield an underestimation of TB by nearly 30 K for cloudy areas in the ITCZ. Possible reasons for this discrepancy include both incorrect amount of cloud ice water in the model simulations and incorrect ice particle scattering assumptions used in the COSP microwave forward model. These multiple sources of error highlight the non-unique nature of the simulated satellite measurements, a problem exacerbated by the fact that EC-Earth lacks detailed micro-physical parameters necessary for accurate forward model calculations. Such issues limit the robustness of our evaluation and suggest a general note of caution when making COSP-satellite observation evaluations

Innovating for improved healthcare: Sociotechnical and innovation systems perspectives and lessons from the NHS

Healthcare systems with limited resources face rising demand pressures. Healthcare decision-makers increasingly recognise the potential of innovation to help respond to this challenge and to support high-quality care. However, comprehensive and actionable evidence on how to realise this potential is lacking. We adopt sociotechnical systems and innovation systems theoretical perspectives to examine conditions that can support and sustain innovating healthcare systems. We use primary data focussing on England (with 670 contributions over time) and triangulate findings against globally-relevant literature. We discuss the complexity of factors influencing an innovating healthcare system’s ability to support the development and uptake of innovations and share practical learning about changes in policy, culture, and behaviour that could support system improvement. Three themes are examined in detail: skills, capabilities, and leadership; motivations and accountabilities; and collaboration and coordination. We also contribute to advancing applications of sociotechnical systems thinking to major societal transformation challenges

Measurement Characteristics of Athlete Monitoring Tools in Professional Australian Football.

PURPOSE:To examine the measurement reliability and sensitivity of common athlete monitoring tools in professional Australian Football players. METHODS:Test-retest reliability (noise) and weekly variation (signal) data were collected from 42 professional Australian footballers from 1 club during a competition season. Perceptual wellness was measured via questionnaires completed before main training sessions (48, 72, and 96 h postmatch), with players providing a rating (1-5 Likert scale) regarding their muscle soreness, sleep quality, fatigue level, stress, and motivation. Eccentric hamstring force and countermovement jumps were assessed via proprietary systems once per week. Heart rate recovery was assessed via a standard submaximal run test on a grass-covered field with players wearing a heart rate monitor. The heart rate recovery was calculated by subtracting average heart rate during final 10 seconds of rest from average heart rate during final 30 seconds of exercise. Typical test error was reported as coefficient of variation percentage (CV%) and intraclass coefficients. Sensitivity was calculated by dividing weekly CV% by test CV% to produce a signal to noise ratio. RESULTS:All measures displayed acceptable sensitivity. Signal to noise ratio ranged from 1.3 to 11.1. Intraclass coefficients ranged from .30 to .97 for all measures. CONCLUSIONS:The heart rate recovery test, countermovement jump test, eccentric hamstring force test, and perceptual wellness all possess acceptable measurement sensitivity. Signal to noise ratio analysis is a novel method of assessing measurement characteristics of monitoring tools. These data can be used by coaches and scientists to identify meaningful changes in common measures of fitness and fatigue in professional Australian football

Comparison of Meteor Radar and Na Doppler Lidar Measurements of Winds in the Mesopause Region above Maui, Hawaii

The coincident measurements span 96 hours and altitudes between 80 and 100 km. Statistical comparisons are carried out on radar/lidar winds with 1 hour and 4 km time and height resolution, respectively. The RMS radar/lidar wind component differences observed in this study are in the range 12–17 m/s at altitudes below 96 km. This is smaller than the RMS differences observed in a previous Na lidar and meteor radar comparison. Lidar wind component variances exceed radar variances, and radar/lidar covariance, is nearly equal to the radar variance. Excess variance observed by the lidar is consistent with the fact that the meteor radar cannot resolve wind perturbations with horizontal scales smaller than ~200 km, whereas the lidar will respond to all horizontal scales. Close correspondence between the radar wind variance and radar/lidar covariance suggests that measurement errors associated with the radar winds are swamped by geophysical variation. Furthermore, the excess lidar variance exceeds lidar estimation errors by a large factor, indicating that the lidar measurement errors are also insignificant relative to geophysical variations. Together these observations suggest that the observed radar/lidar differences are a consequence of the different horizontal wave number filters associated with the techniques, and hence the differences are determined by the strength and shape of the horizontal wave number spectrum for wind perturbations at scales smaller than ~200 km