Quantum Gravity in Everyday Life: General Relativity as an Effective Field Theory

This article is meant as a summary and introduction to the ideas of effective field theory as applied to gravitational systems. Contents: 1. Introduction 2. Effective Field Theories 3. Low-Energy Quantum Gravity 4. Explicit Quantum Calculations 5. ConclusionsComment: 56 pages, 2 figures, JHEP style, Invited review to appear in Living Reviews of Relativit

Mineral Composition is Altered by Osteoblast Expression of an Engineered Gs-Coupled Receptor

Activation of the Gs G protein–coupled receptor Rs1 in osteoblasts increases bone mineral density by 5- to 15-fold in mice and recapitulates histologic aspects of fibrous dysplasia of the bone. However, the effects of constitutive Gs signaling on bone tissue quality are not known. The goal of this study was to determine bone tissue quality in mice resulting from osteoblast-specific constitutive Gs activation, by the complementary techniques of FTIR spectroscopy and synchrotron radiation micro-computed tomography (SRμCT). Col1(2.3)-tTA/TetO-Rs1 double transgenic (DT) mice, which showed osteoblast-specific constitutive Gs signaling activity by the Rs1 receptor, were created. Femora and calvariae of DT and wild-type (WT) mice (6 and 15 weeks old) were analyzed by FTIR spectroscopy. WT and DT femora (3 and 9 weeks old) were imaged by SRμCT. Mineral-to-matrix ratio was 25% lower (P = 0.010), carbonate-to-phosphate ratio was 20% higher (P = 0.025), crystallinity was 4% lower (P = 0.004), and cross-link ratio was 11% lower (P = 0.025) in 6-week DT bone. Differences persisted in 15-week animals. Quantitative SRμCT analysis revealed substantial differences in mean values and heterogeneity of tissue mineral density (TMD). TMD values were 1,156 ± 100 and 711 ± 251 mg/cm3 (mean ± SD) in WT and DT femoral diaphyses, respectively, at 3 weeks. Similar differences were found in 9-week animals. These results demonstrate that continuous Gs activation in murine osteoblasts leads to deposition of immature bone tissue with reduced mineralization. Our findings suggest that bone tissue quality may be an important contributor to increased fracture risk in fibrous dysplasia patients

“Candidatus Competibacter”-lineage genomes retrieved from metagenomes reveal functional metabolic diversity

The glycogen-accumulating organism (GAO) ‘Candidatus Competibacter’ (Competibacter) uses aerobically stored glycogen to enable anaerobic carbon uptake, which is subsequently stored as polyhydroxyalkanoates (PHAs). This biphasic metabolism is key for the Competibacter to survive under the cyclic anaerobic-‘feast’: aerobic-‘famine’ regime of enhanced biological phosphorus removal (EBPR) wastewater treatment systems. As they do not contribute to phosphorus (P) removal, but compete for resources with the polyphosphate-accumulating organisms (PAO), thought responsible for P removal, their proliferation theoretically reduces the EBPR capacity. In this study, two complete genomes from Competibacter were obtained from laboratory-scale enrichment reactors through metagenomics. Phylogenetic analysis identified the two genomes, ‘Candidatus Competibacter denitrificans’ and ‘Candidatus Contendobacter odensis’, as being affiliated with Competibacter-lineage subgroups 1 and 5, respectively. Both have genes for glycogen and PHA cycling and for the metabolism of volatile fatty acids. Marked differences were found in their potential for the Embden–Meyerhof–Parnas and Entner–Doudoroff glycolytic pathways, as well as for denitrification, nitrogen fixation, fermentation, trehalose synthesis and utilisation of glucose and lactate. Genetic comparison of P metabolism pathways with sequenced PAOs revealed the absence of the Pit phosphate transporter in the Competibacter-lineage genomes—identifying a key metabolic difference with the PAO physiology. These genomes are the first from any GAO organism and provide new insights into the complex interaction and niche competition between PAOs and GAOs in EBPR systems

Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other “omics” disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta