Slow light with integrated gain and large pulse delay

We demonstrate slow and stored light in Rb vapor with a combination of desirable features: minimal loss and distortion of the pulse shape, and large fractional delay (> 10). This behavior is enabled by: (i) a group index that can be controllably varied during light pulse propagation; and (ii) controllable gain integrated into the medium to compensate for pulse loss. Any medium with the above two characteristics should be able to realize similarly high-performance slow light.Comment: 5 pages, 4 figures; abstract is shortened, some typo correcte

Evaluation Of Model For The Contributin Of Phonon-induced Tunneling To Donor ESR Spectral Narrowing In Germanium

The model commonly assumed to explain the narrowing of the ESR spectra of donor impurities in germanium is examined in detail. This model combines the Anderson line-narrowing theory with the Miller and Abrahams theory for the phonon-induced tunneling (hopping) of an electron between impurities. The predictions of this model are found to be in drastic disagreement with experimental results now available. It is shown that the narrowed linewidth should depend strongly on donor concentration, acceptor concentration, and temperature. Future spin-resonance experiments in highly compensated samples may show the effects of hopping, but no evidence now exists which indicates that hopping is influencing the narrowing of the ESR spectra. © 1974 The American Physical Society

Diatom Proteomics Reveals Unique Acclimation Strategies to Mitigate Fe Limitation

Phytoplankton growth rates are limited by the supply of iron (Fe) in approximately one third of the open ocean, with major implications for carbon dioxide sequestration and carbon (C) biogeochemistry. To date, understanding how alteration of Fe supply changes phytoplankton physiology has focused on traditional metrics such as growth rate, elemental composition, and biophysical measurements such as photosynthetic competence (Fv/Fm). Researchers have subsequently employed transcriptomics to probe relationships between changes in Fe supply and phytoplankton physiology. Recently, studies have investigated longer-term (i.e. following acclimation) responses of phytoplankton to various Fe conditions. In the present study, the coastal diatom, Thalassiosira pseudonana, was acclimated (10 generations) to either low or high Fe conditions, i.e. Fe-limiting and Fe-replete. Quantitative proteomics and a newly developed proteomic profiling technique that identifies low abundance proteins were employed to examine the full complement of expressed proteins and consequently the metabolic pathways utilized by the diatom under the two Fe conditions. A total of 1850 proteins were confidently identified, nearly tripling previous identifications made from differential expression in diatoms. Given sufficient time to acclimate to Fe limitation, T. pseudonana up-regulates proteins involved in pathways associated with intracellular protein recycling, thereby decreasing dependence on extracellular nitrogen (N), C and Fe. The relative increase in the abundance of photorespiration and pentose phosphate pathway proteins reveal novel metabolic shifts, which create substrates that could support other well-established physiological responses, such as heavily silicified frustules observed for Fe-limited diatoms. Here, we discovered that proteins and hence pathways observed to be down-regulated in short-term Fe starvation studies are constitutively expressed when T. pseudonana is acclimated (i.e., nitrate and nitrite transporters, Photosystem II and Photosystem I complexes). Acclimation of the diatom to the desired Fe conditions and the comprehensive proteomic approach provides a more robust interpretation of this dynamic proteome than previous studies.This work was supported by National Science Foundation grants OCE1233014 (BLN) and the Office of Polar Programs Postdoctoral Fellowship grant 0444148 (BLN). DRG was supported by National Institutes of Health 5P30ES007033-10. AH and MTM were supported by Natural Sciences and Engineering Research Council of Canada. RFS and PWB were supported by the New Zealand Royal Society Marsden Fund and the Ministry of Science. This work is supported in part by the University of Washington's Proteomics Computer Resource Centre (UWPR95794). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Schematics of a Water Balloon Launcher Design and Reproducible Water-Balloon-Filling Procedures Used for a Middle School Summer Science Camp

We recently held a Science Summer Camp for middle school students, designed to infuse young people with increased excitement for STEM (Science, Technology, Engineering, and Math) subjects. Our efforts, which received nationally-syndicated news coverage,1 included the invention of a versatile water balloon launcher. This document contains: (1) detailed construction schematics and user operation guidelines for our balloon launcher; (2) data and instructions for reproducibly filling water balloons to specific volumes and weights, within used by students during the summer camp

Fault management for data systems

Issues related to automating the process of fault management (fault diagnosis and response) for data management systems are considered. Substantial benefits are to be gained by successful automation of this process, particularly for large, complex systems. The use of graph-based models to develop a computer assisted fault management system is advocated. The general problem is described and the motivation behind choosing graph-based models over other approaches for developing fault diagnosis computer programs is outlined. Some existing work in the area of graph-based fault diagnosis is reviewed, and a new fault management method which was developed from existing methods is offered. Our method is applied to an automatic telescope system intended as a prototype for future lunar telescope programs. Finally, an application of our method to general data management systems is described

A Low Temperature Nonlinear Optical Rotational Anisotropy Spectrometer for the Determination of Crystallographic and Electronic Symmetries

Nonlinear optical generation from a crystalline material can reveal the symmetries of both its lattice structure and underlying ordered electronic phases and can therefore be exploited as a complementary technique to diffraction based scattering probes. Although this technique has been successfully used to study the lattice and magnetic structures of systems such as semiconductor surfaces, multiferroic crystals, magnetic thin films and multilayers, challenging technical requirements have prevented its application to the plethora of complex electronic phases found in strongly correlated electron systems. These requirements include an ability to probe small bulk single crystals at the micron length scale, a need for sensitivity to the entire nonlinear optical susceptibility tensor, oblique light incidence reflection geometry and incident light frequency tunability among others. These measurements are further complicated by the need for extreme sample environments such as ultra low temperatures, high magnetic fields or high pressures. In this review we present a novel experimental construction using a rotating light scattering plane that meets all the aforementioned requirements. We demonstrate the efficacy of our scheme by making symmetry measurements on a micron scale facet of a small bulk single crystal of Sr$_2$IrO$_4$ using optical second and third harmonic generation.Comment: 8 pages, 5 figure

Mesenchymal Stem Cells for Treatment of CNS Injury

Brain and spinal cord injuries present significant therapeutic challenges. The treatments available for these conditions are largely ineffective, partly due to limitations in directly targeting the therapeutic agents to sites of pathology within the central nervous system (CNS). The use of stem cells to treat these conditions presents a novel therapeutic strategy. A variety of stem cell treatments have been examined in animal models of CNS trauma. Many of these studies have used stem cells as a cell-replacement strategy. These investigations have also highlighted the significant limitations of this approach. Another potential strategy for stem cell therapy utilises stem cells as a delivery mechanism for therapeutic molecules. This review surveys the literature relevant to the potential of mesenchymal stem cells for delivery of therapeutic agents in CNS trauma in humans

Analyzing machine learning models to accelerate generation of fundamental materials insights

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data

The Structure of Mouse Cytomegalovirus m04 Protein Obtained from Sparse NMR Data Reveals a Conserved Fold of the m02-m06 Viral Immune Modulator Family

SummaryImmunoevasins are key proteins used by viruses to subvert host immune responses. Determining their high-resolution structures is key to understanding virus-host interactions toward the design of vaccines and other antiviral therapies. Mouse cytomegalovirus encodes a unique set of immunoevasins, the m02-m06 family, that modulates major histocompatibility complex class I (MHC-I) antigen presentation to CD8+ T cells and natural killer cells. Notwithstanding the large number of genetic and functional studies, the structural biology of immunoevasins remains incompletely understood, largely because of crystallization bottlenecks. Here we implement a technology using sparse nuclear magnetic resonance data and integrative Rosetta modeling to determine the structure of the m04/gp34 immunoevasin extracellular domain. The structure reveals a β fold that is representative of the m02-m06 family of viral proteins, several of which are known to bind MHC-I molecules and interfere with antigen presentation, suggesting its role as a diversified immune regulation module