Aging is associated with an earlier arrival of reflected waves without a distal shift in reflection sites

Background-Despite pronounced increases in central pulse wave velocity (PWV) with aging, reflected wave transit time (RWTT), traditionally defined as the timing of the inflection point (T-INF) in the central pressure waveform, does not appreciably decrease, leading to the controversial proposition of a "distal-shift" of reflection sites. T-INF, however, is exceptionally prone to measurement error and is also affected by ejection pattern and not only by wave reflection. We assessed whether RWTT, assessed by advanced pressure-flow analysis, demonstrates the expected decline with aging. Methods and Results-We studied a sample of unselected adults without cardiovascular disease (n=48; median age 48 years) and a clinical population of older adults with suspected/established cardiovascular disease (n=164; 61 years). We measured central pressure and flow with carotid tonometry and phase-contrast MRI, respectively. We assessed RWTT using wave-separation analysis (RWTTWSA) and partially distributed tube-load (TL) modeling (RWTTTL). Consistent with previous reports, T-INF did not appreciably decrease with age despite pronounced increases in PWV in both populations. However, aging was associated with pronounced decreases in RWTTWSA (general population -15.0 ms/decade, P<0.001; clinical population -9.07 ms/decade, P=0.003) and RWTTTL (general -15.8 ms/decade, P<0.001; clinical -11.8 ms/decade, P<0.001). There was no evidence of an increased effective reflecting distance by either method. TINF was shown to reliably represent RWTT only under highly unrealistic assumptions about input impedance. Conclusions-RWTT declines with age in parallel with increased PWV, with earlier effects of wave reflections and without a distal shift in reflecting sites. These findings have important implications for our understanding of the role of wave reflections with aging

Functional kernel estimators of conditional extreme quantiles

We address the estimation of "extreme" conditional quantiles i.e. when their order converges to one as the sample size increases. Conditions on the rate of convergence of their order to one are provided to obtain asymptotically Gaussian distributed kernel estimators. A Weissman-type estimator and kernel estimators of the conditional tail-index are derived, permitting to estimate extreme conditional quantiles of arbitrary order.Comment: arXiv admin note: text overlap with arXiv:1107.226

The origin of the α-enhancement of massive galaxies Show affiliations

We study the origin of the stellar α-element-to-iron abundance ratio, [α/Fe]*, of present-day central galaxies, using cosmological, hydrodynamical simulations from the Evolution and Assembly of GaLaxies and their Environments (EAGLE) project. For galaxies with stellar masses of M* > 1010.5 M⊙, [α/Fe]* increases with increasing galaxy stellar mass and age. These trends are in good agreement with observations of early-type galaxies, and are consistent with a ‘downsizing’ galaxy formation scenario: more massive galaxies have formed the bulk of their stars earlier and more rapidly, hence from an interstellar medium that was mostly α-enriched by massive stars. In the absence of feedback from active galactic nuclei (AGNs), however, [α/Fe]* in M* > 1010.5 M⊙ galaxies is roughly constant with stellar mass and decreases with mean stellar age, extending the trends found for lower mass galaxies in both simulations with and without AGN. We conclude that AGN feedback can account for the α-enhancement of massive galaxies, as it suppresses their star formation, quenching more massive galaxies at earlier times, thereby preventing the iron from longer lived intermediate-mass stars (supernova Type Ia) from being incorporated into younger stars

Printing wet-on-wet: attraction and repulsion of drops on a viscous film

Wet-on-wet printing is frequently used in inkjet printing for graphical and industrial applications, where substrates can be coated with a thin liquid film prior to ink drop deposition. Two drops placed close together are expected to interact via deformations of the thin viscous film, but the nature of these capillary interactions is unknown. Here we show that the interaction can be attractive or repulsive depending on the distance separating the two drops. The distance at which the interaction changes from attraction to repulsion is found to depend on the thickness of the film, and increases over time. We reveal the origin of the non-monotonic interactions, which lies in the appearance of a visco-capillary wave on the thin film induced by the drops. Using the thin-film equation we identify the scaling law for the spreading of the waves, and demonstrate that this governs the range over which interaction is observed.Comment: 5 pages, 5 figure

An SPR based sensor for allergens detection

A simple, sensitive and label-free optical sensor method was developed for allergens analysis using α-casein as the biomarker for cow's milk detection, to be used directly in final rinse samples of cleaning in place systems (CIP) of food manufacturers. A Surface Plasmon Resonance (SPR) sensor chip consisting of four sensing arrays enabling the measurement of samples and control binding events simultaneously on the sensor surface was employed in this work. SPR offers several advantages in terms of label free detection, real time measurements and superior sensitivity when compared to ELISA based techniques. The gold sensor chip was used to immobilise α-casein-polyclonal antibody using EDC/NHS coupling procedure. The performance of the assay and the sensor was first optimised and characterised in pure buffer conditions giving a detection limit of 58 ng mL−1 as a direct binding assay. The assay sensitivity can be further improved by using sandwich assay format and amplified with nanoparticles. However, at this stage this is not required as the detection limit achieved exceeded the required allergens detection levels of 2 µg mL−1 for α-S1-casein. The sensor demonstrated good selectivity towards the α-casein as the target analyte and adequate recoveries from CIP final rinse wash samples. The sensor would be useful tool for monitoring allergen levels after cleaning procedures, providing additional data that may better inform upon wider food allergen risk management decision(s) that are made by food manufacturer. In particular, this sensor could potentially help validate or optimise cleaning practices for a given food manufacturing process

Patient-specific image-based computer simulation for theprediction of valve morphology and calcium displacement after TAVI with the Medtronic CoreValve and the Edwards SAPIEN valve

AIMS: Our aim was to validate patient-specific software integrating baseline anatomy and biomechanical properties of both the aortic root and valve for the prediction of valve morphology and aortic leaflet calcium displacement after TAVI. METHODS AND RESULTS: Finite element computer modelling was performed in 39 patients treated with a Medtronic CoreValve System (MCS; n=33) or an Edwards SAPIEN XT (ESV; n=6). Quantitative axial frame morphology at inflow (MCS, ESV) and nadir, coaptation and commissures (MCS) was compared between multislice computed tomography (MSCT) post TAVI and a computer model as well as displacement of the aortic leaflet calcifications, quantified by the distance between the coronary ostium and the closest calcium nodule. Bland-Altman analysis revealed a strong correlation between the observed (MSCT) and predicted frame dimensions, although small differences were detected for, e.g., Dmin at the inflow (mean±SD MSCT vs. MODEL: 21.6±2.4 mm vs. 22.0±2.4 mm; difference±SD: -0.4±1.3 mm, p<0.05) and Dmax (25.6±2.7 mm vs. 26.2±2.7 mm; difference±SD: -0.6±1.0 mm, p<0.01). The observed and predicted calcium displacements were highly correlated for the left and right coronary ostia (R2=0.67 and R2=0.71, respectively p<0.001). CONCLUSIONS: Dedicated software allows accurate prediction of frame morphology and calcium displacement after valve implantation, which may help to improve outcome

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).Industrial Ecolog