The long-term prognostic significance of 6-minute walk test distance in patients with chronic heart failure

Background. The 6-minute walk test (6-MWT) is used to assess patients with chronic heart failure (CHF). The prognostic significance of the 6-MWT distance during long-term followup ( > 5 years) is unclear. Methods. 1,667 patients (median [inter-quartile range, IQR]) (age 72 [65-77] ; 75% males) with heart failure due to left ventricular systolic impairment undertook a 6-MWT as part of their baseline assessment and were followed up for 5 years. Results. At 5 years' followup, those patients who died (n = 959) were older at baseline and had a higher log NT pro-BNP than those who survived to 5 years (n = 708). 6-MWT distance was lower in those who died [163 (153) m versus 269 (160) m; P 360 m. 6-MWT distance was a predictor of all-cause mortality (HR 0.97; 95% CI 0.96-0.97; Chi-square = 184.1; P < 0.0001). Independent predictors of all-cause mortality were decreasing 6-MWT distance, increasing age, increasing NYHA classification, increasing log NT pro-BNP, decreasing diastolic blood pressure, decreasing sodium, and increasing urea. Conclusion. The 6-MWT is an important independent predictor of all-cause mortality following long-term followup in patients with CHF. © 2014 Lee Ingle et al

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation

Short-range force detection using optically-cooled levitated microspheres

We propose an experiment using optically trapped and cooled dielectric microspheres for the detection of short-range forces. The center-of-mass motion of a microsphere trapped in vacuum can experience extremely low dissipation and quality factors of $10^{12}$, leading to yoctonewton force sensitivity. Trapping the sphere in an optical field enables positioning at less than 1 $\mu$m from a surface, a regime where exotic new forces may exist. We expect that the proposed system could advance the search for non-Newtonian gravity forces via an enhanced sensitivity of $10^5-10^7$ over current experiments at the 1 $\mu$m length scale. Moreover, our system may be useful for characterizing other short-range physics such as Casimir forces.Comment: 4 pages, 3 figures, minor changes, Figs. 1 and 2 replace

Power-recycled weak-value-based metrology

We improve the precision of the interferometric weak-value-based beam deflection measurement by introducing a power recycling mirror, creating a resonant cavity. This results in \emph{all} the light exiting to the detector with a large deflection, thus eliminating the inefficiency of the rare postselection. The signal-to-noise ratio of the deflection is itself magnified by the weak value. We discuss ways to realize this proposal, using a transverse beam filter and different cavity designs.Comment: 5 pages, 1 figur

De-novo design of complementary (antisense) peptide mini-receptor inhibitor of interleukin 18 (IL-18).

Complementary (antisense) peptide mini-receptor inhibitors are complementary peptides designed to be receptor-surrogates that act by binding to selected surface features of biologically important proteins thereby inhibiting protein-cognate receptor interactions and subsequent biological effects. Previously, we described a complementary peptide mini-receptor inhibitor of interleukin-1beta (IL-1beta) that was designed to bind to an external surface loop (beta-bulge) of IL-1beta (Boraschi loop) clearly identified in the X-ray crystal structure of this cytokine. Here, we report the de-novo design and rational development of a complementary peptide mini-receptor inhibitor of cytokine interleukin-18 (IL-18), a protein for which there is no known X-ray crystal structure. Using sequence homology comparisons with IL-1beta, putative IL-18 surface loops are identified and used as a starting point for design, including a loop region 1 thought to be equivalent with the Boraschi loop of IL-1beta. Only loop region 1 complementary peptides are found to be promising leads as mini-receptor inhibitors of IL-18 but these are prevented from being properly successful owing to solubility problems. The application of "M-I pair mutagenesis" and inclusion of a C-terminal arginine residue are then sufficient to solve this problem and convert one lead peptide into a functional complementary peptide mini-receptor inhibitor of IL-18. This suggests that the biophysical and biological properties of complementary peptides can be improved in a rational and logical manner where appropriate, further strengthening the potential importance of complementary peptides as inhibitors of protein-protein interactions, even when X-ray crystal structural information is not readily available

Deleterious consequences of antioxidant supplementation on lifespan in a wild-derived mammal

Peer reviewedPublisher PD

The True Colours of Carbon

Carbon offset projects in developing countries are one of the principal mechanisms designed to reduce greenhouse gas emissions and promote sustainable development yet have critical limitations in both areas. Here we present a framework for categorizing carbon offset projects according to four general approaches to the reduction of greenhouse gas emissions: (1) efficiency (&#x27;Brown&#x27;); innovation (&#x27;Red&#x27;), terrestrial sequestration (&#x27;Green&#x27;) or sequestration in aquatic environments (&#x27;Blue&#x27;). Analysis of the 6109 CDM projects currently in the CDM &#x22;pipeline&#x22; reveals that 99% are Brown or Red, and only 1% are Green or Blue, yet Green and Blue projects typically offer a far greater range of benefits for ecosystems and society. The analysis concludes that the designers of emissions trading schemes should endorse Green and Blue offset projects as preferred forms of emissions offsetting, and that firms using offsets for compliance purposes be required to declare in public reports the colours of their offset acquisitions. Such reform will help redirect demand in carbon markets toward blue and green offset projects, increasing the sustainability outcomes of carbon offset developments

Monitoring sediment transfer processes on the desert margin

LANDSAT Thematic Mapper and Multispectral Scanner data have been used to construct change detection images for three playas in south-central Tunisia. Change detection images have been used to analyze changes in surface reflectance and absorption between wet and dry season (intra-annual change) and between different years (inter-annual change). Change detection imagery has been used to examine geomorphological changes on the playas. Changes in geomorphological phenomena are interpreted from changes in soil and foliar moisture levels, differences in reflectances between different salt and sediments and the spatial expression of geomorphological features. Intra-annual change phenomena that can be detected from multidate imagery are changes in surface moisture, texture and chemical composition, vegetation cover and the extent of aeolian activity. Inter-annual change phenomena are divisible into those restricted to marginal playa facies (sedimentation from sheetwash and alluvial fans, erosion from surface runoff and cliff retreat) and these are found in central playa facies which are related to the internal redistribution of water, salt and sediment

Investigating the effects of inter-annual weather variation (1968- 2016) on the functional response of cereal grain yield to applied nitrogen, using data from the Rothamsted Long-Term experiments

The effect of weather on inter-annual variation in the crop yield response to nitrogen (N) fertilizer for winter wheat (Triticum aestivvum L.) and spring barley (Hordeum vulgare L.) was investigated using yield data from the Broadbalk Wheat and Hoosfield Spring Barley long-term experiments at Rothamsted Research. Grain yields of crops from 1968 to 2016 were modelled as a function of N rates using a linear-plus-exponential (LEXP) function. The extent to which inter-annual variation in the parameters of these responses was explained by variations in weather (monthly summarized temperatures and rainfall), and by changes in the cultivar grown, was assessed. The inter-annual variability in rainfall and underlying temperature influenced the crop N response and hence grain yields in both crops. Asymptotic yields in wheat were particularly sensitive to mean temperature in November, April and May, and to total rainfall in October, February and June. In spring barley asymptotic yields were sensitive to mean temperature in February and June, and to total rainfall in April to July inclusive and September. The method presented here explores the separation of agronomic and environmental (weather) influences on crop yield over time. Fitting N response curves across multiple treatments can support an informative analysis of the influence of weather variation on the yield variability. Whilst there are issues of the confounding and collinearity of explanatory variables within such models, and that other factors also influence yields over time, our study confirms the considerable impact of weather variables at certain times of the year. This emphasizes the importance of including weather temporal variation when evaluating the impacts of climate change on crops