Sea surface temperature in global analyses: gains from the copernicus imaging microwave radiometer

Sea surface temperatures (SSTs) derived from passive microwave (PMW) observations benefit global ocean and SST analyses because of their near-all-weather availability. Present PMW SSTs have a real aperture-limited spatial resolution in excess of 50 km, limiting the spatial fidelity with which SST features, reflecting ocean dynamics, can be captured. This contrasts with the target resolution of global analyses of 5 to 10 km. The Copernicus Imaging Microwave Radiometer (CIMR) is a mission concept under consideration as a high-priority candidate mission for the expansion of the Copernicus space programme. This instrument would be capable of real aperture resolution < 15 km with low total uncertainties in the range 0.4–0.8 K for channels between 1.4 and 36.5 GHz, and a dual-view arrangement that further reduces noise. This paper provides a comparative study of SST uncertainty and feature resolution with and without the availability of CIMR in the future SST-observing satellite constellation based on a detailed simulation of CIMR plus infrared observations and the processing of global SST analyses with 0.05◦ final grid resolution. Simulations of CIMR data including structured errors were added to an observing system consisting of the Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel-3A and the Advanced Very High Resolution Radiometer (AVHRR) on MetOp-A. This resulted in a large improvement in the global root-mean-square error (RMSE) for SST from 0.37 K to 0.21 K for January and 0.40 K to 0.25 K for July. There was a particularly noticeable improvement in the performance of the analysis, as measured by the reduction in RMSE, for dynamical and persistently cloudy areas. Of these, the Aghulas Current showed an improvement of 43% in January and 48% in July, the Gulf Stream showed 70% and 44% improvements, the Southern Ocean showed 57% and 74% improvements, and the Maritime Continent showed 50% and 40% improvements, respectively

The dynamics of single spike-evoked adenosine release in the cerebellum

The purine adenosine is a potent neuromodulator in the brain, with roles in a number of diverse physiological and pathological processes. Modulators such as adenosine are difficult to study as once released they have a diffuse action (which can affect many neurones) and, unlike classical neurotransmitters, have no inotropic receptors. Thus rapid postsynaptic currents (PSCs) mediated by adenosine (equivalent to mPSCs) are not available for study. As a result the mechanisms and properties of adenosine release still remain relatively unclear. We have studied adenosine release evoked by stimulating the parallel fibres in the cerebellum. Using adenosine biosensors combined with deconvolution analysis and mathematical modelling, we have characterised the release dynamics and diffusion of adenosine in unprecedented detail. By partially blocking K+ channels, we were able to release adenosine in response to a single stimulus rather than a train of stimuli. This allowed reliable sub-second release of reproducible quantities of adenosine with stereotypic concentration waveforms that agreed well with predictions of a mathematical model of purine diffusion. We found no evidence for ATP release and thus suggest that adenosine is directly released in response to parallel fibre firing and does not arise from extracellular ATP metabolism. Adenosine release events showed novel short-term dynamics, including facilitated release with paired stimuli at millisecond stimulation intervals but depletion-recovery dynamics with paired stimuli delivered over minute time scales. These results demonstrate rich dynamics for adenosine release that are placed, for the first time, on a quantitative footing and show strong similarity with vesicular exocytosis

What approaches to social prescribing work, for whom, and in what circumstances? A protocol for a realist review

The use of non-drug, non-health-service interventions has been proposed as a cost-effective alternative to help those with long-term conditions manage their illness and improve their health and well-being. Interventions typically involve accessing activities run by the third sector or community agencies and may also be described as non-medical referral, community referral or social prescribing. To be effective, patients need to be “transferred” from the primary care setting into the community and to maintain their participation in activities. However, it is not currently known how and why these approaches enable which people under what circumstances to reach community services that may benefit their health and well-being.The use of non-drug, non-health-service interventions has been proposed as a cost-effective alternative to help those with long-term conditions manage their illness and improve their health and well-being. Interventions typically involve accessing activities run by the third sector or community agencies and may also be described as non-medical referral, community referral or social prescribing. To be effective, patients need to be “transferred” from the primary care setting into the community and to maintain their participation in activities. However, it is not currently known how and why these approaches enable which people under what circumstances to reach community services that may benefit their health and well-being

Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison

Background: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions. Objective: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?” Methods: We conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes. Results: We show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%). Conclusions: We propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents’ health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics

Isotopic analysis of faunal material from South Uist, Western Isles, Scotland

This paper reports on the results from stable isotope analysis of faunal bone collagen from a number of Iron Age and later sites on the island of South Uist, in the Western Isles, Scotland. This preliminary investigation into the isotopic signatures of the fauna is part of a larger project to model the interaction between humans, animals, and the broader environment in the Western Isles. The results demonstrate that the island fauna data fall within the range of expected results for the UK, with the terrestrial herbivorous diets of cattle and sheep confi rmed. The isotopic composition for pigs suggests that some of these animals had an omnivorous diet, whilst a single red deer value might be suggestive of the consumption of marine foods, such as by grazing on seaweed. However, further analysis is needed in order to verify this anomalous isotopic ratio

Identification of disease causing loci using an array-based genotyping approach on pooled DNA

BACKGROUND: Pooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs. RESULTS: We report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided. CONCLUSION: Our results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs

Managing healthcare budgets in times of austerity: the role of program budgeting and marginal analysis

Given limited resources, priority setting or choice making will remain a reality at all levels of publicly funded healthcare across countries for many years to come. The pressures may well be even more acute as the impact of the economic crisis of 2008 continues to play out but, even as economies begin to turn around, resources within healthcare will be limited, thus some form of rationing will be required. Over the last few decades, research on healthcare priority setting has focused on methods of implementation as well as on the development of approaches related to fairness and legitimacy and on more technical aspects of decision making including the use of multi-criteria decision analysis. Recently, research has led to better understanding of evaluating priority setting activity including defining ‘success’ and articulating key elements for high performance. This body of research, however, often goes untapped by those charged with making challenging decisions and as such, in line with prevailing public sector incentives, decisions are often reliant on historical allocation patterns and/or political negotiation. These archaic and ineffective approaches not only lead to poor decisions in terms of value for money but further do not reflect basic ethical conditions that can lead to fairness in the decision-making process. The purpose of this paper is to outline a comprehensive approach to priority setting and resource allocation that has been used in different contexts across countries. This will provide decision makers with a single point of access for a basic understanding of relevant tools when faced with having to make difficult decisions about what healthcare services to fund and what not to fund. The paper also addresses several key issues related to priority setting including how health technology assessments can be used, how performance can be improved at a practical level, and what ongoing resource management practice should look like. In terms of future research, one of the most important areas of priority setting that needs further attention is how best to engage public members

Epidemiology and Investigation of Melioidosis, Southern Arizona

Burkholderia pseudomallei is a bacterium endemic to Southeast Asia and northern Australia, but it has not been found to occur endemically in the United States. We report an ostensibly autochthonous case of melioidosis in the United States. Despite an extensive investigation, the source of exposure was not identified

Calmodulin-binding transcription activator 1 (CAMTA1) alleles predispose human episodic memory performance

Little is known about the genes and proteins involved in the process of human memory. To identify genetic factors related to human episodic memory performance, we conducted an ultra-high-density genome-wide screen at > 500000 single nucleotide polymorphisms (SNPs) in a sample of normal young adults stratified for performance on an episodic recall memory test. Analysis of this data identified SNPs within the calmodulin-binding transcription activator 1 (CAMTA1) gene that were significantly associated with memory performance. A follow up study, focused on the CAMTA1 locus in an independent cohort consisting of cognitively normal young adults, singled out SNP rs4908449 with a P-value of 0.0002 as the most significant associated SNP in the region. These validated genetic findings were further supported by the identification of CAMTA1 transcript enrichment in memory-related human brain regions and through a functional magnetic resonance imaging experiment on individuals matched for memory performance that identified CAMTA1 allele-specific upregulation of medial temporal lobe brain activity in those individuals harboring the ‘at-risk' allele for poorer memory performance. The CAMTA1 locus encodes a purported transcription factor that interfaces with the calcium-calmodulin system of the cell to alter gene expression patterns. Our validated genomic and functional biological findings described herein suggest a role for CAMTA1 in human episodic memor