Ionization of Rydberg atoms embedded in an ultracold plasma

We have studied the behavior of cold Rydberg atoms embedded in an ultracold plasma. We demonstrate that even deeply bound Rydberg atoms are completely ionized in such an environment, due to electron collisions. Using a fast pulse extraction of the electrons from the plasma we found that the number of excess positive charges, which is directly related to the electron temperature Te, is not strongly affected by the ionization of the Rydberg atoms. Assuming a Michie-King equilibrium distribution, in analogy with globular star cluster dynamics, we estimate Te. Without concluding on heating or cooling of the plasma by the Rydberg atoms, we discuss the range for changing the plasma temperature by adding Rydberg atoms.Comment: To be published in P.R.

The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators

Accurate, unbiased wetland inventories are critical to monitor and protect wetlands from future harm or land conversion. However, most wetland inventories are constructed through manual image interpretation or automated classification of multi-band imagery and are biased towards wetlands that are easy to directly detect in aerial and satellite imagery. Wetlands that are obscured by forest canopy, that occur ephemerally, and that have no visible standing water are, therefore, often missing from wetland maps. To aid in the detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential (WIP) tool, based on a wetland-indicator framework commonly used on the ground to detect wetlands through the presence of hydrophytic vegetation, hydrology, and hydric soils. Our tool uses a random forest model with spatially explicit input variables that represent all three wetland indicators, including novel multi-scale topographic indicators that represent the processes that drive wetland formation, to derive a map of wetland probability. With the ability to include multi-scale topographic indicators that help identify cryptic wetlands, the WIP tool can identify areas conducive to wetland formation while providing a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River watershed in western Washington, USA, classification of the output probability with a threshold of 0.5 provided an overall accuracy of 91.97 %. Compared to the National Wetlands Inventory, the classified WIP tool output identified over 2 times the wetland area and reduced errors of omission from 47.5 % to 14.1 % but increased errors of commission from 1.9 % to 10.5 %. The WIP tool is implemented as an ArcGIS toolbox using a combination of R and Python scripts.</p

Insight into partial agonism by observing multiple equilibria for ligand-bound and Gs-mimetic nanobody-bound β1-adrenergic receptor.

A complex conformational energy landscape determines G-protein-coupled receptor (GPCR) signalling via intracellular binding partners (IBPs), e.g., Gs and β-arrestin. Using 13C methyl methionine NMR for the β1-adrenergic receptor, we identify ligand efficacy-dependent equilibria between an inactive and pre-active state and, in complex with Gs-mimetic nanobody, between more and less active ternary complexes. Formation of a basal activity complex through ligand-free nanobody-receptor interaction reveals structural differences on the cytoplasmic receptor side compared to the full agonist-bound nanobody-coupled form, suggesting that ligand-induced variations in G-protein interaction underpin partial agonism. Significant differences in receptor dynamics are observed ranging from rigid nanobody-coupled states to extensive μs-to-ms timescale dynamics when bound to a full agonist. We suggest that the mobility of the full agonist-bound form primes the GPCR to couple to IBPs. On formation of the ternary complex, ligand efficacy determines the quality of the interaction between the rigidified receptor and an IBP and consequently the signalling level

Multispecies virial expansions

We study the virial expansion of mixtures of countably many different types of particles. The main tool is the Lagrange–Good inversion formula, which has other applications such as counting coloured trees or studying probability generating functions in multi-type branching processes. We prove that the virial expansion converges absolutely in a domain of small densities. In addition, we establish that the virial coefficients can be expressed in terms of two-connected graphs

Prospective Design, Rapid Prototyping, and Testing of Smart Dressings, Drug Delivery Patches, and Replacement Body Parts Using Microscopy Aided Design and ManufacturE (MADAME)

Natural materials exhibit smart properties including gradients in biophysical properties that engender higher order functions, as well as stimuli-responsive properties which integrate sensor and/or actuator capacities. Elucidation of mechanisms underpinning such smart material properties (i), and translation of that understanding (ii), represent two of the biggest challenges in emulating natural design paradigms for design and manufacture of disruptive materials, parts, and products. Microscopy Aided Design And ManufacturE (MADAME) stands for a computer-aided additive manufacturing platform that incorporates multidimensional (multi-D) printing and computer-controlled weaving. MADAME enables the creation of composite design motifs emulating e.g., patterns of woven protein fibers as well as gradients in different caliber porosities, mechanical, and molecular properties, found in natural tissues, from the skin on bones (periosteum) to tree bark. Insodoing, MADAME provides a means to manufacture a new genre of smart materials, products and replacement body parts that exhibit advantageous properties both under the influence of as well as harnessing dynamic mechanical loads to activate material properties (mechanoactive properties). This Technical Report introduces the MADAME technology platform and its associated machine-based workflow (pipeline), provides basic technical background of the novel technology and its applications, and discusses advantages and disadvantages of the approach in context of current 3 and 4D printing platforms

Evolution dynamics of a dense frozen Rydberg gas to plasma

Dense samples of cold Rydberg atoms have previously been observed to spontaneously evolve to a plasma, despite the fact that each atom may be bound by as much as 100 cm−1. Initially, ionization is caused by blackbody photoionization and Rydberg-Rydberg collisions. After the first electrons leave the interaction re- gion, the net positive charge traps subsequent electrons. As a result, rapid ionization starts to occur after 1 μs caused by electron-Rydberg collisions. The resulting cold plasma expands slowly and persists for tens of microseconds. While the initial report on this process identified the key issues described above, it failed to resolve one key aspect of the evolution process. Specifically, redistribution of population to Rydberg states other than the one initially populated was not observed, a necessary mechanism to maintain the energy balance in the system. Here we report new and expanded observations showing such redistribution and confirming theoretical predictions concerning the evolution to a plasma. These measurements also indicate that, for high n states of purely cold Rydberg samples, the initial ionization process which leads to electron trapping is one involving the interactions between Rydberg atoms

Evolution dynamics of a dense frozen Rydberg gas to plasma

Dense samples of cold Rydberg atoms have previously been observed to spontaneously evolve to a plasma, despite the fact that each atom may be bound by as much as 100 cm−1. Initially, ionization is caused by blackbody photoionization and Rydberg-Rydberg collisions. After the first electrons leave the interaction re- gion, the net positive charge traps subsequent electrons. As a result, rapid ionization starts to occur after 1 μs caused by electron-Rydberg collisions. The resulting cold plasma expands slowly and persists for tens of microseconds. While the initial report on this process identified the key issues described above, it failed to resolve one key aspect of the evolution process. Specifically, redistribution of population to Rydberg states other than the one initially populated was not observed, a necessary mechanism to maintain the energy balance in the system. Here we report new and expanded observations showing such redistribution and confirming theoretical predictions concerning the evolution to a plasma. These measurements also indicate that, for high n states of purely cold Rydberg samples, the initial ionization process which leads to electron trapping is one involving the interactions between Rydberg atoms

Snowpack Relative Permittivity and Density Derived from Near-Coincident Lidar and Ground-Penetrating Radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive \u3e20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to \u3e 30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged \u3e 400 mm, however, model uncertainty increased to \u3e 20% when SWE averaged \u3c 300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods

Proximity proteomics reveals UCH-L1 as an essential regulator of NLRP3-mediated IL-1β production in human macrophages and microglia

Activation of the NACHT, LRR, and PYD domains-containing protein 3 (NLRP3) inflammasome complex is an essential innate immune signaling mechanism. To reveal how human NLRP3 inflammasome assembly and activation are controlled, in particular by components of the ubiquitin system, proximity labeling, affinity purification, and RNAi screening approaches were performed. Our study provides an intricate time-resolved molecular map of different phases of NLRP3 inflammasome activation. Also, we show that ubiquitin C-terminal hydrolase 1 (UCH-L1) interacts with the NACHT domain of NLRP3. Downregulation of UCH-L1 decreases pro-interleukin-1β (IL-1β) levels. UCH-L1 chemical inhibition with small molecules interfered with NLRP3 puncta formation and ASC oligomerization, leading to altered IL-1β cleavage and secretion, particularly in microglia cells, which exhibited elevated UCH-L1 expression as compared to monocytes/macrophages. Altogether, we profiled NLRP3 inflammasome activation dynamics and highlight UCH-L1 as an important modulator of NLRP3-mediated IL-1β production, suggesting that a pharmacological inhibitor of UCH-L1 may decrease inflammation-associated pathologies