Ultra-fast sampling of terahertz pulses from a quantum cascade laser using superconducting antenna-coupled NbN and YBCO detectors

We demonstrate the ultra-fast detection of terahertz pulses from a quantum cascade laser (QCL) using superconducting NbN and YBCO detectors. This has enabled both the intrapulse and interpulse dynamics of a THz QCL to be measured directly, including interpulse heating effects on sub-μs timescales

Machine learning denoising of high-resolution X-ray nanotomography data

High-resolution X-ray nano­tomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nano­tomography data. The technique presented is applied to high-resolution nano­tomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields

A cross-ecosystem comparison of temporal variability in recruitment of functionally analogous fish stocks

As part of the international MENU collaboration, variability in temporal patterns of recruitment and spawning stock were compared among functionally analogous species from four marine ecosystems including the Gulf of Maine/Georges Bank, the Norwegian/Barents Seas, the eastern Bering Sea and the Gulf of Alaska. Variability was characterized by calculating coefficients of variation for each time series and by representing the time series as anomalies. Patterns of synchrony and asynchrony in recruitment and spawning stock indices were examined among and between ecosystems and related to observed patterns in biophysical properties (e.g. local trophodynamics, local hydrography and large scale climate indices) using a wide range of time series analyses, autocorrelation corrections, autoregressive processes, and multivariate cross-correlation analyses. Of all the commonalities, the relatively similar cross-ecosystem and within-species magnitude of variation was most notable. Of all the differences, the timing of high or low recruitment years across both species and ecosystems was most notable. However, many of the peaks in these indices of recruitment were synchronous across ecosystems for functionally analogous species. Yet the relationships (or lack thereof) between recruitment anomalies and key biophysical properties demonstrated that no one factor consistently caused large recruitment events. Our observations also suggested that there was no routine and common set of factors that influences recruitment; often multiple factors were of similar relative prominence. This work demonstrates that commonalities and synchronies in recruitment fluctuations can be found across geographically very distant ecosystems, but biophysical causes of the fluctuations are difficult to partition. Keywords: Ecosystem, recruitment, trophodynamics, variation

A comparison of community and trophic structure in five marine ecosystems based on energy budgets and system metrics

As part of the international MENU collaboration, energy budget models for five marine ecosystems were compared to identify differences and similarities in trophic and community characteristics across ecosystems. We examined the Gulf of Maine and Georges Bank in the Northwest Atlantic Ocean, the combined Norwegian/Barents Seas in the Northeast Atlantic Ocean, and the eastern Bering Sea and the Gulf of Alaska in the Northeast Pacific Ocean. Comparable energy budgets were constructed for each ecosystem by aggregating information for similar species groups into consistent functional groups across all five ecosystems. Several ecosystem metrics (including functional group production, consumption, and biomass ratios, ABC curves, cumulative biomass, food web macrodescriptors, and network metrics) were examined across the ecosystems. The comparative approach clearly identified data gaps for each ecosystem, an important outcome of this work. Commonalities across the ecosystems included overall high primary production and energy flow at low trophic levels, high production and consumption by carnivorous zooplankton, and similar proportions of apex predator to lower trophic level biomass. Major differences included distinct biomass ratios of pelagic to demersal fish, ranging from highest in the Norwegian/Barents ecosystem to lowest in the Alaskan systems, and notable gradients in primary production per unit area, highest in the Alaskan and Georges Bank/Gulf of Maine ecosystems, and lowest in the Norwegian ecosystems. While comparing a disparate group of organisms across a wide range of marine ecosystems is challenging, this work demonstrates that standardized metrics both elucidate properties common to marine ecosystems and identify key distinctions for fishery management

Proton acceleration enhanced by a plasma jet in expanding foils undergoing relativistic transparency

Ion acceleration driven by the interaction of an ultraintense (2x10^20 Wcm^-2) laser pulse with an ultrathin (40nm) foil target is experimentally and numerically investigated. Protons accelerated by sheath fields and via laser radiation pressure are angularly separated and identified based on their directionality and signature features (e.g. transverse instabilities) in the measured spatial-intensity distribution. A low divergence, high energy proton component is also detected when the heated target electrons expand and the target becomes relativistically transparent during the interaction. 2D and 3D particle-in-cell (PIC) simulations indicate that under these conditions a plasma jet is formed at the target rear, supported by a self-generated azimuthal magnetic field, which extends into the expanded layer of sheath-accelerated protons. Electrons trapped within this jet are directly accelerated to super-thermal energies by the portion of the laser pulse transmitted through the target. The resulting streaming of the electrons into the ion layers enhances the energy of protons in the vicinity of the jet. Through the addition of a controlled prepulse, the maximum energy of these protons is demonstrated experimentally and numerically to be sensitive to the picosecond rising edge prole of the laser pulse

Ion acceleration and plasma jet formation in ultra-thin foils undergoing expansion and relativistic transparency

At sufficiently high laser intensities, the rapid heating to relativistic velocities and resulting decompression of plasma electrons in an ultra-thin target foil can result in the target becoming relativistically transparent to the laser light during the interaction. Ion acceleration in this regime is strongly affected by the transition from an opaque to a relativistically transparent plasma. By spatially resolving the laser-accelerated proton beam at near-normal laser incidence and at an incidence angle of 30°, we identify characteristic features both experimentally and in particle-in-cell simulations which are consistent with the onset of three distinct ion acceleration mechanisms: sheath acceleration; radiation pressure acceleration; and transparency-enhanced acceleration. The latter mechanism occurs late in the interaction and is mediated by the formation of a plasma jet extending into the expanding ion population. The effect of laser incident angle on the plasma jet is explored

Influence of laser polarization on collective electron dynamics in ultraintense laser-foil interactions

The collective response of electrons in an ultrathin foil target irradiated by an ultraintense laser pulse is investigated experimentally and via 3D particle-in-cell simulations. It is shown that if the target is sufficiently thin that the laser induces significant radiation pressure, but not thin enough to become relativistically transparent to the laser light, the resulting relativistic electron beam is elliptical, with the major axis of the ellipse directed along the laser polarization axis. When the target thickness is decreased such that it becomes relativistically transparent early in the interaction with the laser pulse, diffraction of the transmitted laser light occurs through a so called 'relativistic plasma aperture', inducing structure in the spatial-intensity profile of the beam of energetic electrons. It is shown that the electron beam profile can be modified by variation of the target thickness and degree of ellipticity in the laser polarization

Towards optical polarization control of laser-driven proton acceleration in foils undergoing relativistic transparency

Control of the collective response of plasma particles to intense laser light is intrinsic to relativistic optics, the development of compact laser-driven particle and radiation sources, as well as investigations of some laboratory astrophysics phenomena. We recently demonstrated that a relativistic plasma aperture produced in an ultra-thin foil at the focus of intense laser radiation can induce diffraction, enabling polarization-based control of the collective motion of plasma electrons. Here we show that under these conditions the electron dynamics are mapped into the beam of protons accelerated via strong charge-separation-induced electrostatic fields. It is demonstrated experimentally and numerically via 3D particle-in-cell simulations that the degree of ellipticity of the laser polarization strongly influences the spatial-intensity distribution of the beam of multi-MeV protons. The influence on both sheath accelerated and radiation pressure accelerated protons is investigated. This approach opens up new routes to control laser-driven ion sources

Evasion of anti-growth signaling: a key step in tumorigenesis and potential target for treatment and prophylaxis by natural compounds

The evasion of anti-growth signaling is an important characteristic of cancer cells. In order to continue to proliferate, cancer cells must somehow uncouple themselves from the many signals that exist to slow down cell growth. Here, we define the anti-growth signaling process, and review several important pathways involved in growth signaling: p53, phosphatase and tensin homolog (PTEN), retinoblastoma protein (Rb), Hippo, growth differentiation factor 15 (GDF15), AT-rich interactive domain 1A (ARID1A), Notch, insulin-like growth factor (IGF), and Krüppel-like factor 5 (KLF5) pathways. Aberrations in these processes in cancer cells involve mutations and thus the suppression of genes that prevent growth, as well as mutation and activation of genes involved in driving cell growth. Using these pathways as examples, we prioritize molecular targets that might be leveraged to promote anti-growth signaling in cancer cells. Interestingly, naturally-occurring phytochemicals found in human diets (either singly or as mixtures) may promote anti-growth signaling, and do so without the potentially adverse effects associated with synthetic chemicals. We review examples of naturally-occurring phytochemicals that may be applied to prevent cancer by antagonizing growth signaling, and propose one phytochemical for each pathway. These are: epigallocatechin-3-gallate (EGCG) for the Rb pathway, luteolin for p53, curcumin for PTEN, porphyrins for Hippo, genistein for GDF15, resveratrol for ARID1A, withaferin A for Notch and diguelin for the IGF1-receptor pathway. The coordination of anti-growth signaling and natural compound studies will provide insight into the future application of these compounds in the clinical setting