Human airway construct model is suitable for studying transcriptome changes associated with indoor air particulate matter toxicity

In vitro models mimicking the human respiratory system are essential when investigating the toxicological effects of inhaled indoor air particulate matter (PM). We present a pulmonary cell culture model for studying indoor air PM toxicity. We exposed normal human bronchial epithelial cells, grown on semi‐permeable cell culture membranes, to four doses of indoor air PM in the air‐liquid interface. We analyzed the chemokine interleukin‐8 concentration from the cell culture medium, protein concentration from the apical wash, measured tissue electrical resistance, and imaged airway constructs using light and transmission electron microscopy. We sequenced RNA using a targeted RNA toxicology panel for 386 genes associated with toxicological responses. PM was collected from a non‐complaint residential environment over 1 week. Sample collection was concomitant with monitoring size‐segregated PM counts and determination of microbial levels and diversity. PM exposure was not acutely toxic for the cells, and we observed up‐regulation of 34 genes and down‐regulation of 17 genes when compared to blank sampler control exposure. The five most up‐regulated genes were related to immunotoxicity. Despite indications of incomplete cell differentiation, this model enabled the comparison of a toxicological transcriptome associated with indoor air PM exposure

Quantitative nanoscale vortex-imaging using a cryogenic quantum magnetometer

Microscopic studies of superconductors and their vortices play a pivotal role in our understanding of the mechanisms underlying superconductivity. Local measurements of penetration depths or magnetic stray-fields enable access to fundamental aspects of superconductors such as nanoscale variations of superfluid densities or the symmetry of their order parameter. However, experimental tools, which offer quantitative, nanoscale magnetometry and operate over the large range of temperature and magnetic fields relevant to address many outstanding questions in superconductivity, are still missing. Here, we demonstrate quantitative, nanoscale magnetic imaging of Pearl vortices in the cuprate superconductor YBCO, using a scanning quantum sensor in form of a single Nitrogen-Vacancy (NV) electronic spin in diamond. The sensor-to-sample distance of ~10nm we achieve allows us to observe striking deviations from the prevalent monopole approximation in our vortex stray-field images, while we find excellent quantitative agreement with Pearl's analytic model. Our experiments yield a non-invasive and unambiguous determination of the system's local London penetration depth, and are readily extended to higher temperatures and magnetic fields. These results demonstrate the potential of quantitative quantum sensors in benchmarking microscopic models of complex electronic systems and open the door for further exploration of strongly correlated electron physics using scanning NV magnetometry.Comment: Main text (5 pages, 4 figures) plus supplementary material (5 pages, 6 figures). Comments welcome. Further information under http://www.quantum-sensing.c

Gravitational Waves from Gravitational Collapse

Gravitational wave emission from the gravitational collapse of massive stars has been studied for more than three decades. Current state of the art numerical investigations of collapse include those that use progenitors with realistic angular momentum profiles, properly treat microphysics issues, account for general relativity, and examine non--axisymmetric effects in three dimensions. Such simulations predict that gravitational waves from various phenomena associated with gravitational collapse could be detectable with advanced ground--based and future space--based interferometric observatories.Comment: 68 pages including 13 figures; revised version accepted for publication in Living Reviews in Relativity (http://www.livingreviews.org

Evolutionary distances in the twilight zone -- a rational kernel approach

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.Comment: to appear in PLoS ON

Specific serology for emerging human coronaviruses by protein microarray

We present a serological assay for the specific detection of IgM and IgG antibodies against the emerging human coronavirus hCoV-EMC and the SARS-CoV based on protein microarray technology. The assay uses the S1 receptor-binding subunit of the spike protein of hCoV-EMC and SARS-CoV as antigens. The assay has been validated extensively using putative cross-reacting sera of patient cohorts exposed to the four common hCoVs and sera from convalescent patients infected with hCoV-EMC or SARS-CoV

Effect of promoter architecture on the cell-to-cell variability in gene expression

According to recent experimental evidence, the architecture of a promoter, defined as the number, strength and regulatory role of the operators that control the promoter, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect noise in gene expression in a systematic rather than case-by-case fashion. In this article, we make such a systematic investigation, based on a simple microscopic model of gene regulation that incorporates stochastic effects. In particular, we show how operator strength and operator multiplicity affect this variability. We examine different modes of transcription factor binding to complex promoters (cooperative, independent, simultaneous) and how each of these affects the level of variability in transcription product from cell-to-cell. We propose that direct comparison between in vivo single-cell experiments and theoretical predictions for the moments of the probability distribution of mRNA number per cell can discriminate between different kinetic models of gene regulation.Comment: 35 pages, 6 figures, Submitte

Dopamine and inhibitory action control: evidence from spontaneous eye blink rates

The inhibitory control of actions has been claimed to rely on dopaminergic pathways. Given that this hypothesis is mainly based on patient and drug studies, some authors have questioned its validity and suggested that beneficial effects of dopaminergic stimulants on response inhibition may be limited to cases of suboptimal inhibitory functioning. We present evidence that, in carefully selected healthy adults, spontaneous eyeblink rate, a marker of central dopaminergic functioning, reliably predicts the efficiency in inhibiting unwanted action tendencies in a stop-signal task. These findings support the assumption of a modulatory role for dopamine in inhibitory action control