Direct 2D measurement of time-averaged forces and pressure amplitudes in acoustophoretic devices using optical trapping

Ultrasonic standing waves are increasingly applied in the manipulation and sorting of micrometer-sized particles in microfluidic cells. To optimize the performance of such devices, it is essential to know the exact forces that the particles experience in the acoustic wave. Although much progress has been made via analytical and numerical modeling, the reliability of these methods relies strongly on the assumptions used, e.g. the boundary conditions. Here, we have combined an acoustic flow cell with an optical laser trap to directly measure the force on a single spherical particle in two dimensions. While performing ultrasonic frequency scans, we measured the time-averaged forces on single particles that were moved with the laser trap through the microfluidic cell. The cell including piezoelectric transducers was modeled with finite element methods. We found that the experimentally obtained forces and the derived pressure fields confirm the predictions from theory and modeling. This novel approach can now be readily expanded to other particle, chamber, and fluid regimes and opens up the possibility of studying the effects of the presence of boundaries, acoustic streaming, and non-linear fluids.ISSN:1473-0197ISSN:1473-018

Contrasting local and long-range-transported warm ice-nucleating particles during an atmospheric river in coastal California, USA

Ice-nucleating particles (INPs) have been found to influence the amount, phase and efficiency of precipitation from winter storms, including atmospheric rivers.Warm INPs, those that initiate freezing at temperatures warmer than -10°C, are thought to be particularly impactful because they can create primary ice in mixed-phase clouds, enhancing precipitation efficiency. The dominant sources of warm INPs during atmospheric rivers, the role of meteorology in modulating transport and injection of warm INPs into atmospheric river clouds, and the impact of warm INPs on mixed-phase cloud properties are not well-understood. In this case study, time-resolved precipitation samples were collected during an atmospheric river in northern California, USA, during winter 2016. Precipitation samples were collected at two sites, one coastal and one inland, which are separated by about 35 km. The sites are sufficiently close that air mass sources during this storm were almost identical, but the inland site was exposed to terrestrial sources of warm INPs while the coastal site was not. Warm INPs were more numerous in precipitation at the inland site by an order of magnitude. Using FLEXPART (FLEXible PARTicle dispersion model) dispersion modeling and radar-derived cloud vertical structure, we detected influence from terrestrial INP sources at the inland site but did not find clear evidence of marine warm INPs at either site.We episodically detected warm INPs from long-range-transported sources at both sites. By extending the FLEXPART modeling using a meteorological reanalysis, we demonstrate that long-range-transported warm INPs were observed only when the upper tropospheric jet provided transport to cloud tops. Using radar-derived hydrometeor classifications, we demonstrate that hydrometeors over the terrestrially influenced inland site were more likely to be in the ice phase for cloud temperatures between 0 and -10°C. We thus conclude that terrestrial and long-rangetransported aerosol were important sources of warm INPs during this atmospheric river. Meteorological details such as transport mechanism and cloud structure were important in determining (i) warm INP source and injection temperature and (ii) ultimately the impact of warm INPs on mixed-phase cloud properties

Propranolol restricts the mobility of single EGF-receptors on the cell surface before their internalization

The epidermal growth factor receptor is involved in morphogenesis, proliferation and cell migration. Its up-regulation during tumorigenesis makes this receptor an interesting therapeutic target. In the absence of the ligand, the inhibition of phosphatidic acid phosphohydrolase activity by propranolol treatment leads to internalization of empty/inactive receptors. The molecular events involved in this endocytosis remain unknown. Here, we quantified the effects of propranolol on the mobility of single quantum-dot labelled receptors before the actual internalization took place. The single receptors showed a clear stop-and-go motion; their diffusive tracks were continuously interrupted by sub-second stalling events, presumably caused by transient clustering. In the presence of propranolol we found that: i) the diffusion rate reduced by 22 %, which indicates an increase in drag of the receptor. Atomic force microscopy measurements did not show an increase of the effective membrane tension, such that clustering of the receptor remains the likely mechanism for its reduced mobility. ii) The receptor got frequently stalled for longer periods of multiple seconds, which may signal the first step of the internalization process

Generation of Vorticity and Velocity Dispersion by Orbit Crossing

We study the generation of vorticity and velocity dispersion by orbit crossing using cosmological numerical simulations, and calculate the backreaction of these effects on the evolution of large-scale density and velocity divergence power spectra. We use Delaunay tessellations to define the velocity field, showing that the power spectra of velocity divergence and vorticity measured in this way are unbiased and have better noise properties than for standard interpolation methods that deal with mass weighted velocities. We show that high resolution simulations are required to recover the correct large-scale vorticity power spectrum, while poor resolution can spuriously amplify its amplitude by more than one order of magnitude. We measure the scalar and vector modes of the stress tensor induced by orbit crossing using an adaptive technique, showing that its vector modes lead, when input into the vorticity evolution equation, to the same vorticity power spectrum obtained from the Delaunay method. We incorporate orbit crossing corrections to the evolution of large scale density and velocity fields in perturbation theory by using the measured stress tensor modes. We find that at large scales (k~0.1 h/Mpc) vector modes have very little effect in the density power spectrum, while scalar modes (velocity dispersion) can induce percent level corrections at z=0, particularly in the velocity divergence power spectrum. In addition, we show that the velocity power spectrum is smaller than predicted by linear theory until well into the nonlinear regime, with little contribution from virial velocities.Comment: 27 pages, 14 figures. v2: reorganization of the material, new appendix. Accepted by PR

Spin alignment of dark matter haloes in filaments and walls

The MMF technique is used to segment the cosmic web as seen in a cosmological N-body simulation into wall-like and filament-like structures. We find that the spins and shapes of dark matter haloes are significantly correlated with each other and with the orientation of their host structures. The shape orientation is such that the halo minor axes tend to lie perpendicular to the host structure, be it a wall or filament. The orientation of the halo spin vector is mass dependent. Low mass haloes in walls and filaments have a tendency to have their spins oriented within the parent structure, while higher mass haloes in filaments have spins that tend to lie perpendicular to the parent structure.Comment: 4 pages, 2 figure

Nitrogen deposition shows no consistent negative nor positive effect on the response of forest productivity to drought across European FLUXNET forest sites

Atmospheric reactive nitrogen (N) deposition is an important driver of carbon (C) sequestration in forest ecosystems. Previous studies have focused on N-C interactions in various ecosystems; however, relatively little is known about the impact of N deposition on ecosystem C cycling during climate extremes such as droughts. With the occurrence and severity of droughts likely to be exacerbated by climate change, N deposition—drought interactions remain one of the key uncertainties in process-based models to date. This study aims to contribute to the understanding of N deposition-drought dynamics on gross primary production (GPP) in European forest ecosystems. To do so, different soil water availability indicators (Standardized Precipitation Evapotranspiration Index (SPEI), soil volumetric water) and GPP measurements from European FLUXNET forest sites were used to quantify the response of forest GPP to drought. The computed drought responses of the forest GPP to drought were linked to modelled N deposition estimates for varying edaphic, physiological, and climatic conditions. Our result showed a differential response of forest ecosystems to the drought indicators. Although all FLUXNET forest sites showed a coherent dependence of GPP on N deposition, no consistent or significant N deposition effect on the response of forest GPP to drought could be isolated. The mean response of forest GPP to drought could be predicted for forests with Pinus trees as dominant species (R2 = 0.85, RMSE = 8.1). After extracting the influence of the most prominent parameters (mean annual temperature and precipitation, forest age), however, the variability remained too large to significantly substantiate hypothesized N deposition effects. These results suggest that, while N deposition clearly affects forest productivity, N deposition is not a major nor consistent driver of forest productivity responses to drought in European forest ecosystems

Statistical methods used to combine the effective reproduction number, R(t), and other related measures of COVID-19 in the UK

In the COVID-19 pandemic, a range of epidemiological models have been used to predict the number of new daily infections, $I$, daily rate of exponential growth, $r$, and effective reproduction number, $R(t)$. These models differ in their approaches (e.g. mechanistic or empirical) and/or assumptions about spatial or age mixing, and some capture uncertainty in scientific understanding of disease dynamics, and/or have different simplifying assumptions. Combining estimates from multiple models to better understand the variation of these outcome measures is important to help inform decision making. We incorporate estimates of these outcome measures from a number of candidate models for specific UK nations/regions using meta analysis. Random effects models have been implemented to accommodate differing modelling approaches and assumptions between candidate models. Restricted maximum likelihood (REML) is used to estimate the heterogeneity variance parameter, with two approaches to calculate the confidence interval for the combined effect: standard Wald-type intervals and the Knapp and Hartung (KNHA) method. Approaches using REML alone and REML+KNHA provided similar ranges of variation for $R(t)$ and $r$. However, differences were observed when combining estimates on $I$, with the REML+KNHA approach providing more conservative confidence intervals. This is likely due to the limited number of candidate models contributing estimates for this outcome measure, coupled with the large variability observed between model estimates. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures, allowing an overall assessment of the current response measures with associated uncertainty. This in turn allows timely and informed decision making based on all available information