Asymptotically Matched Spacetime Metric for Non-Precessing, Spinning Black Hole Binaries

We construct a closed-form, fully analytical 4-metric that approximately represents the spacetime evolution of non-precessing, spinning black hole binaries from infinite separations up to a few orbits prior to merger. We employ the technique of asymptotic matching to join a perturbed Kerr metric in the neighborhood of each spinning black hole to a near-zone, post-Newtonian metric farther out. The latter is already naturally matched to a far-zone, post-Minkowskian metric that accounts for full temporal retardation. The result is a 4-metric that is approximately valid everywhere in space and in a small bundle of spatial hypersurfaces. We here restrict our attention to quasi- circular orbits, but the method is valid for any orbital motion or physical scenario, provided an overlapping region of validity or buffer zone exists. A simple extension of such a metric will allow for future studies of the accretion disk and jet dynamics around spinning back hole binaries

Patterned Protein Microarrays for Bacterial Detection

Abstract Patterned microarrays of antibodies were fabricated and tested for their ability to bind targeted bacteria. These arrays were used in a series of bacterial immunoassays to detect E. coli 0157:H7 and Renibacterium Salmoninarum (RS). Microarrays were fabricated using microcontact printing (µCP) and characterized using scanning probe microscopy (SPM). The high-resolution SPM imaging showed that targeted bacteria had a higher binding selectivity to complementary antibody patterns than to unfunctionalized regions of the substrate. Additional studies indicated a significant reduction in binding of bacteria when the microarrays were exposed to non-specific bacteria. These studies demonstrate how protein microarrays could be developed into useful platforms for sensing microorganisms.

Hydrogen adsorption on Pd(133) surface

In this study used is an approach based on measurements of the total energy distribution (TED) of field emitted electrons in order to examine the properties of Pd (133) from the aspect of both hydrogen adsorption and surface hydrides formation. The most favourable sites offered to a hydrogen atom to be adsorbed have been indicated and an attempt to describe the peaks of the enhancement factor R spectrum to the specific adsorption sites has also been made.Comment: to be submitted to the Centr. Eur. J. Phy

Evidence of d-wave Superconductivity in K_(1-x)Na_xFe_2As_2 (x = 0, 0.1) Single Crystals from Low-Temperature Specific Heat Measurements

From the measurement and analysis of the specific heat of high-quality K_(1-x)Na_xFe_2As_2 single crystals we establish the presence of large T^2 contributions with coefficients alpha_sc ~ 30 mJ/mol K^3 at low-T for both x=0 and 0.1. Together with the observed square root field behavior of the specific heat in the superconducting state both findings evidence d-wave superconductivity on almost all Fermi surface sheets with an average gap amplitude of Delta_0 in the range of 0.4 - 0.8 meV. The derived Delta_0 and the observed T_c agree well with the values calculated within the Eliashberg theory, adopting a spin-fluctuation mediated pairing in the intermediate coupling regime.Comment: 8 pages, 5 figures, field dependence of the specific heat added, slightly changed title, changed sequence of authors, one author added, accepted by Phys. Rev. B Rapid Communication

Growth landscape formed by perception and import of glucose in yeast

An important challenge in systems biology is to quantitatively describe microbial growth using a few measurable parameters that capture the essence of this complex phenomenon. Two key events at the cell membrane—extracellular glucose sensing and uptake—initiate the budding yeast’s growth on glucose. However, conventional growth models focus almost exclusively on glucose uptake. Here we present results from growth-rate experiments that cannot be explained by focusing on glucose uptake alone. By imposing a glucose uptake rate independent of the sensed extracellular glucose level, we show that despite increasing both the sensed glucose concentration and uptake rate, the cell’s growth rate can decrease or even approach zero. We resolve this puzzle by showing that the interaction between glucose perception and import, not their individual actions, determines the central features of growth, and characterize this interaction using a quantitative model. Disrupting this interaction by knocking out two key glucose sensors significantly changes the cell’s growth rate, yet uptake rates are unchanged. This is due to a decrease in burden that glucose perception places on the cells. Our work shows that glucose perception and import are separate and pivotal modules of yeast growth, the interaction of which can be precisely tuned and measured.National Institutes of Health (U.S.). Pioneer AwardNatural Sciences and Engineering Research Council of Canada (NSERC). Graduate Fellowshi

MicroRNA-mediated down-regulation of NKG2D ligands contributes to glioma immune escape

Malignant gliomas are intrinsic brain tumors with a dismal prognosis. They are well-adapted to hypoxic conditions and poorly immunogenic. NKG2D is one of the major activating receptors of natural killer (NK) cells and binds to several ligands (NKG2DL). Here we evaluated the impact of miRNA on the expression of NKG2DL in glioma cells including stem-like glioma cells. Three of the candidate miRNA predicted to target NKG2DL were expressed in various glioma cell lines as well as in glioblastomas in vivo: miR-20a, miR-93 and miR-106b. LNA inhibitor-mediated miRNA silencing up-regulated cell surface NKG2DL expression, which translated into increased susceptibility to NK cell-mediated lysis. This effect was reversed by neutralizing NKG2D antibodies, confirming that enhanced lysis upon miRNA silencing was mediated through the NKG2D system. Hypoxia, a hallmark of glioblastomas in vivo, down-regulated the expression of NKG2DL on glioma cells, associated with reduced susceptibility to NK cell-mediated lysis. This process, however, was not mediated through any of the examined miRNA. Accordingly, both hypoxia and the expression of miRNA targeting NKG2DL may contribute to the immune evasion of glioma cells at the level of the NKG2D recognition pathway. Targeting miRNA may therefore represent a novel approach to increase the immunogenicity of glioblastoma

Proteome-wide analysis reveals an age-associated cellular phenotype of in situ aged human fibroblasts

We analyzed an ex vivo model of in situ aged human dermal fibroblasts, obtained from 15 adult healthy donors from three different age groups using an unbiased quantitative proteome-wide approach applying label-free mass spectrometry. Thereby, we identified 2409 proteins, including 43 proteins with an age-associated abundance change. Most of the differentially abundant proteins have not been described in the context of fibroblasts' aging before, but the deduced biological processes confirmed known hallmarks of aging and led to a consistent picture of eight biological categories involved in fibroblast aging, namely proteostasis, cell cycle and proliferation, development and differentiation, cell death, cell organization and cytoskeleton, response to stress, cell communication and signal transduction, as well as RNA metabolism and translation. The exhaustive analysis of protein and mRNA data revealed that 77 % of the age-associated proteins were not linked to expression changes of the corresponding transcripts. This is in line with an associated miRNA study and led us to the conclusion that most of the age-associated alterations detected at the proteome level are likely caused post-transcriptionally rather than by differential gene expression. In summary, our findings led to the characterization of novel proteins potentially associated with fibroblast aging and revealed that primary cultures of in situ aged fibroblasts are characterized by moderate age-related proteomic changes comprising the multifactorial process of aging

Error-analysis and comparison to analytical models of numerical waveforms produced by the NRAR Collaboration

The Numerical-Relativity-Analytical-Relativity (NRAR) collaboration is a joint effort between members of the numerical relativity, analytical relativity and gravitational-wave data analysis communities. The goal of the NRAR collaboration is to produce numerical-relativity simulations of compact binaries and use them to develop accurate analytical templates for the LIGO/Virgo Collaboration to use in detecting gravitational-wave signals and extracting astrophysical information from them. We describe the results of the first stage of the NRAR project, which focused on producing an initial set of numerical waveforms from binary black holes with moderate mass ratios and spins, as well as one non-spinning binary configuration which has a mass ratio of 10. All of the numerical waveforms are analysed in a uniform and consistent manner, with numerical errors evaluated using an analysis code created by members of the NRAR collaboration. We compare previously-calibrated, non-precessing analytical waveforms, notably the effective-one-body (EOB) and phenomenological template families, to the newly-produced numerical waveforms. We find that when the binary's total mass is ~100-200 solar masses, current EOB and phenomenological models of spinning, non-precessing binary waveforms have overlaps above 99% (for advanced LIGO) with all of the non-precessing-binary numerical waveforms with mass ratios <= 4, when maximizing over binary parameters. This implies that the loss of event rate due to modelling error is below 3%. Moreover, the non-spinning EOB waveforms previously calibrated to five non-spinning waveforms with mass ratio smaller than 6 have overlaps above 99.7% with the numerical waveform with a mass ratio of 10, without even maximizing on the binary parameters.Comment: 51 pages, 10 figures; published versio

Molecular diagnostic tools for the World Health Organization (WHO) 2021 classification of gliomas, glioneuronal and neuronal tumors; an EANO guideline

In the 5th edition of the WHO CNS tumor classification (CNS5, 2021), multiple molecular characteristics became essential diagnostic criteria for many additional CNS tumor types. For those tumors, an integrated, 'histomolecular' diagnosis is required. A variety of approaches exists for determining the status of the underyling molecular markers. The present guideline focuses on the methods that can be used for assessment of the currently most informative diagnostic and prognostic molecular markers for the diagnosis of gliomas, glioneuronal and neuronal tumors. The main characteristics of the molecular methods are systematically discussed, followed by recommendations and information on available evidence levels for diagnostic measures. The recommendations cover DNA and RNA next-generation-sequencing, methylome profiling, and select assays for single/limited target analysis, including immunohistochemistry. Additionally, because of its importance as a predictive marker in IDH-wildtype glioblastomas, tools for the analysis of MGMT promoter status are covered. A structured overview of the different assays with their characteristics, especially their advantages and limitations, is provided, and requirements for input material and reporting of results are clarified. General aspects of molecular diagnostic testing regarding clinical relevance, accessibility, cost, implementation, regulatory and ethical aspects are discussed as well. Finally, we provide an outlook on new developments in the landscape of molecular testing technologies in neuro-oncology

Longitudinal stability of molecular alterations and drug response profiles in tumor spheroid cell lines enables reproducible analyses

The utility of patient-derived tumor cell lines as experimental models for glioblastoma has been challenged by limited representation of the in vivo tumor biology and low clinical translatability. Here, we report on longitudinal epigenetic and transcriptional profiling of seven glioblastoma spheroid cell line models cultured over an extended period. Molecular profiles were associated with drug response data obtained for 231 clinically used drugs. We show that the glioblastoma spheroid models remained molecularly stable and displayed reproducible drug responses over prolonged culture times of 30 in vitro passages. Integration of gene expression and drug response data identified predictive gene signatures linked to sensitivity to specific drugs, indicating the potential of gene expression-based prediction of glioblastoma therapy response. Our data thus empowers glioblastoma spheroid disease modeling as a useful preclinical assay that may uncover novel therapeutic vulnerabilities and associated molecular alterations