Anti-infective surface coatings: design and therapeutic promise against device-associated infections

Patient safety and well-being are under increasing threat from hospital-acquired infections [1]. The root cause of a large number of these infections arises from microbial biofilms that colonise on surfaces of medical devices such as the millions of catheters, endotracheal tubes, and prosthetics implanted every year [2]. Biofilm infections are accompanied by increased resistance to antimicrobial therapy and immune clearance, severely limiting treatment options and leading to life-threatening disease [3,4]. Device-associated infections are caused by both bacteria and fungi and, while most studies have focused on single-species biofilms, biofilm-related infections are often polymicrobial [5–8]. Multi-species biofilms, particularly those involving bacterial and fungal pathogens, are more challenging to treat, likely as a consequence of their combined architecture, protective extracellular matrix, and potential synergism in protecting against antimicrobials and host immunity [9–11]. Among the fungi, Candida species are the most important biofilm pathogens [12,13] and the fourth leading cause of blood-stream infections in United States hospitals [7]. Fungal diseases remain difficult to diagnose, mortality rates remain high, and antifungal drug resistance continues to limit therapeutic options [14,15]. We are in desperate need of innovative strategies that target the mechanisms of pathogenesis of polymicrobial biofilms on medical devices. This is a grand challenge because it requires multidisciplinary collaboration and breakthrough research involving physical chemistry, materials science, and microbiology. Communication between these disciplines has not been common, but recent advances show greater convergence in the development of anti-infective devices. At this nexus, we outline the therapeutic promise of anti-infective coatings for medical devices and discuss pitfalls and strategies for overcoming them.Bryan R. Coad, Hans J. Griesser, Anton Y. Peleg, Ana Trave

4MOST Consortium Survey 3: Milky Way Disc and Bulge Low-Resolution Survey (4MIDABLE-LR)

The mechanisms of the formation and evolution of the Milky Way are encoded in the orbits, chemistry and ages of its stars. With the 4MOST MIlky way Disk And BuLgE Low-Resolution Survey (4MIDABLE-LR) we aim to study kinematic and chemical substructures in the Milky Way disc and bulge region with samples of unprecedented size out to larger distances and greater precision than conceivable with Gaia alone or any other ongoing or planned survey. Gaia gives us the unique opportunity for target selection based almost entirely on parallax and magnitude range, hence increasing the efficiency in sampling larger Milky Way volumes with well-defined and effective selection functions. Our main goal is to provide a detailed chrono-chemo-kinematical extended map of our Galaxy and the largest Gaia follow-up down to $G = 19$ magnitudes (Vega). The complex nature of the disc components (for example, large target densities and highly structured extinction distribution in the Milky Way bulge and disc area), prompted us to develop a survey strategy with five main sub-surveys that are tailored to answer the still open questions about the assembly and evolution of our Galaxy, while taking full advantage of the Gaia data.Comment: Part of the 4MOST issue of The Messenger, published in preparation of 4MOST Community Workshop, see http://www.eso.org/sci/meetings/2019/4MOST.htm

Ginzburg-Landau theory of superconductors with short coherence length

We consider Fermions in two dimensions with an attractive interaction in the singlet d-wave channel of arbitrary strength. By means of a Hubbard-Stratonovich transformation a statistical Ginzburg-Landau theory is derived, which describes the smooth crossover from a weak-coupling BCS superconductor to a condensate of composite Bosons. Adjusting the interaction strength to the observed slope of H_c2 at T_c in the optimally doped high-T_c compounds YBCO and BSCCO, we determine the associated values of the Ginzburg-Landau correlation length xi and the London penetration depth lambda. The resulting dimensionless ratio k_F xi(0) approx 5-8 and the Ginzburg-Landau parameter kappa=lambda xi approx 90-100 agree well with the experimentally observed values. These parameters indicate that the optimally doped materials are still on the weak coupling side of the crossover to a Bose regime.Comment: 12 pages, RevTeX, 6 postscript figures, resubmitted with minor changes in section III, to appear in Physical Review

The K2-HERMES Survey: Age and Metallicity of the Thick Disc

Asteroseismology is a promising tool to study Galactic structure and evolution because it can probe the ages of stars. Earlier attempts comparing seismic data from the {\it Kepler} satellite with predictions from Galaxy models found that the models predicted more low-mass stars compared to the observed distribution of masses. It was unclear if the mismatch was due to inaccuracies in the Galactic models, or the unknown aspects of the selection function of the stars. Using new data from the K2 mission, which has a well-defined selection function, we find that an old metal-poor thick disc, as used in previous Galactic models, is incompatible with the asteroseismic information. We show that spectroscopic measurements of [Fe/H] and [$\alpha$/Fe] elemental abundances from the GALAH survey indicate a mean metallicity of $\log (Z/Z_{\odot})=-0.16$ for the thick disc. Here $Z$ is the effective solar-scaled metallicity, which is a function of [Fe/H] and [$\alpha$/Fe]. With the revised disc metallicities, for the first time, the theoretically predicted distribution of seismic masses show excellent agreement with the observed distribution of masses. This provides an indirect verification of the asteroseismic mass scaling relation is good to within five percent. Using an importance-sampling framework that takes the selection function into account, we fit a population synthesis model of the Galaxy to the observed seismic and spectroscopic data. Assuming the asteroseismic scaling relations are correct, we estimate the mean age of the thick disc to be about 10 Gyr, in agreement with the traditional idea of an old $\alpha$-enhanced thick disc.Comment: 21 pages, submitted to MNRA

Essential and checkpoint functions of budding yeast ATM and ATR during meiotic prophase are facilitated by differential phosphorylation of a meiotic adaptor protein, Hop1

A hallmark of the conserved ATM/ATR signalling is its ability to mediate a wide range of functions utilizing only a limited number of adaptors and effector kinases. During meiosis, Tel1 and Mec1, the budding yeast ATM and ATR, respectively, rely on a meiotic adaptor protein Hop1, a 53BP1/Rad9 functional analog, and its associated kinase Mek1, a CHK2/Rad53-paralog, to mediate multiple functions: control of the formation and repair of programmed meiotic DNA double strand breaks, enforcement of inter-homolog bias, regulation of meiotic progression, and implementation of checkpoint responses. Here, we present evidence that the multi-functionality of the Tel1/Mec1-to-Hop1/Mek1 signalling depends on stepwise activation of Mek1 that is mediated by Tel1/Mec1 phosphorylation of two specific residues within Hop1: phosphorylation at the threonine 318 (T318) ensures the transient basal level Mek1 activation required for viable spore formation during unperturbed meiosis. Phosphorylation at the serine 298 (S298) promotes stable Hop1-Mek1 interaction on chromosomes following the initial phospho-T318 mediated Mek1 recruitment. In the absence of Dmc1, the phospho-S298 also promotes Mek1 hyper-activation necessary for implementing meiotic checkpoint arrest. Taking these observations together, we propose that the Hop1 phospho-T318 and phospho-S298 constitute key components of the Tel1/Mec1- based meiotic recombination surveillance (MRS) network and facilitate effective coupling of meiotic recombination and progression during both unperturbed and challenged meiosis

Nonperturbative XY-model approach to strong coupling superconductivity in two and three dimensions

For an electron gas with delta-function attraction we investigate the crossover from weak- to strong-coupling supercoductivity in two and three dimensions. We derive analytic expressions for the stiffness of phase fluctuations and set up effective XY-models which serve to determine nonperturbatively the temperature of phase decoherence where superconductivity breaks down. We find the transition temperature T_c as a monotonous function of the coupling strength and carrier density both in two and three dimensions, and give analytic formulas for the merging of the temperature of phase decoherence with the temperature of pair formation in the weak-coupling limit.Comment: Few typos corrected. Emails that were sent to the address [email protected] in June and July 1999 were lost in a computer crash, so if your comments were not answered please send them once mor

Navy and the HARV: High angle of attack tactical utility issues

This presentation will highlight results from the latest Navy evaluation of the HARV (March 1994) and focus primarily on the impressions from a piloting standpoint of the tactical utility of thrust vectoring. Issue to be addressed will be mission suitability of high AOA flight, visual and motion feedback cues associated with operating at high AOA, and the adaptability of a pilot to effectively use the increased control power provided by the thrust vectoring system

Budding yeast ATM/ATR control meiotic double-strand break (DSB) levels by down-regulating Rec114, an essential component of the DSB-machinery

An essential feature of meiosis is Spo11 catalysis of programmed DNA double strand breaks (DSBs). Evidence suggests that the number of DSBs generated per meiosis is genetically determined and that this ability to maintain a pre-determined DSB level, or "DSB homeostasis", might be a property of the meiotic program. Here, we present direct evidence that Rec114, an evolutionarily conserved essential component of the meiotic DSB-machinery, interacts with DSB hotspot DNA, and that Tel1 and Mec1, the budding yeast ATM and ATR, respectively, down-regulate Rec114 upon meiotic DSB formation through phosphorylation. Mimicking constitutive phosphorylation reduces the interaction between Rec114 and DSB hotspot DNA, resulting in a reduction and/or delay in DSB formation. Conversely, a non-phosphorylatable rec114 allele confers a genome-wide increase in both DSB levels and in the interaction between Rec114 and the DSB hotspot DNA. These observations strongly suggest that Tel1 and/or Mec1 phosphorylation of Rec114 following Spo11 catalysis down-regulates DSB formation by limiting the interaction between Rec114 and DSB hotspots. We also present evidence that Ndt80, a meiosis specific transcription factor, contributes to Rec114 degradation, consistent with its requirement for complete cessation of DSB formation. Loss of Rec114 foci from chromatin is associated with homolog synapsis but independent of Ndt80 or Tel1/Mec1 phosphorylation. Taken together, we present evidence for three independent ways of regulating Rec114 activity, which likely contribute to meiotic DSBs-homeostasis in maintaining genetically determined levels of breaks

Flight validation of ground-based assessment for control power requirements at high angles of attack

A review is presented in viewgraph format of an ongoing NASA/U.S. Navy study to determine control power requirements at high angles of attack for the next generation high-performance aircraft. This paper focuses on recent flight test activities using the NASA High Alpha Research Vehicle (HARV), which are intended to validate results of previous ground-based simulation studies. The purpose of this study is discussed, and the overall program structure, approach, and objectives are described. Results from two areas of investigation are presented: (1) nose-down control power requirements and (2) lateral-directional control power requirements. Selected results which illustrate issues and challenges that are being addressed in the study are discussed including test methodology, comparisons between simulation and flight, and general lessons learned

Beyond Gaia DR3: tracing the [{\alpha}/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia RVS and Convolutional Neural-Networks

Gaia DR3 has provided the community with about one million RVS spectra covering the CaII triplet region. In the next Gaia data releases, we anticipate the number of RVS spectra to successively increase from several 10 million spectra to eventually more than 200M spectra. Thus, stellar spectra are produced on an "industrial scale" with numbers well above those for current and anticipated ground based surveys. However, many of these spectra have low S/N (from 15 to 25 per pixel), such that they pose problems for classical spectral analysis pipelines and therefore alternative ways to tap into these large datasets need to be devised. We aim to leverage the versatility/capabilities of machine learning techniques for supercharged stellar parametrization, by combining Gaia RVS spectra with the full set of Gaia products and high-resolution, high-quality spectroscopic reference data sets. We develop a hybrid Convolutional Neural-Network (CNN) which combines the Gaia DR3 RVS spectra, photometry (G, Bp, Rp), parallaxes, and XP coefficients to derive atmospheric parameters (Teff, log(g), and overall [M/H]) and chemical abundances ([Fe/H] and [$\alpha$/M]). We trained the CNN with a high-quality training sample based on APOGEE DR17 labels. With this CNN, we derived homogeneous atmospheric parameters and abundances for 841300 stars, that remarkably compared to external data-sets. The CNN is robust against noise in the RVS data, and very precise labels are derived down to S/N=15. We managed to characterize the [$\alpha$/M]-[M/H] bimodality from the inner regions to the outer parts of the Milky Way, which has never been done using RVS spectra or similar datasets. This work is the first to combine machine-learning with such diverse datasets (spectroscopy, astrometry, and photometry), and paves the way for the large scale machine-learning analysis of Gaia-RVS spectra from future data releases.Comment: 24 pages, 24 figures, submitted to A&