Vertex corrections in localized and extended systems

Within many-body perturbation theory we apply vertex corrections to various closed-shell atoms and to jellium, using a local approximation for the vertex consistent with starting the many-body perturbation theory from a DFT-LDA Green's function. The vertex appears in two places -- in the screened Coulomb interaction, W, and in the self-energy, \Sigma -- and we obtain a systematic discrimination of these two effects by turning the vertex in \Sigma on and off. We also make comparisons to standard GW results within the usual random-phase approximation (RPA), which omits the vertex from both. When a vertex is included for closed-shell atoms, both ground-state and excited-state properties demonstrate only limited improvements over standard GW. For jellium we observe marked improvement in the quasiparticle band width when the vertex is included only in W, whereas turning on the vertex in \Sigma leads to an unphysical quasiparticle dispersion and work function. A simple analysis suggests why implementation of the vertex only in W is a valid way to improve quasiparticle energy calculations, while the vertex in \Sigma is unphysical, and points the way to development of improved vertices for ab initio electronic structure calculations.Comment: 8 Pages, 6 Figures. Updated with quasiparticle neon results, extended conclusions and references section. Minor changes: Updated references, minor improvement

Detecting depression using an ensemble classifier based on Quality of Life scales

Major depressive disorder (MDD) is an issue that affects 350 million people worldwide. Traditional approaches have been to identify depressive symptoms in datasets, but recently, research is beginning to explore the association between psychosocial factors such as those on the quality of life scale and mental well-being, which will lead to earlier diagnosis and prediction of MDD. In this research, an ensemble binary classifier is proposed to analyse health survey data against ground truth from the SF-20 Quality of Life scales. The classifier aims to improve the performance of machine learning techniques on large datasets and identify depressed cases based on associations between items on the QoL scale and mental illness by increasing predictive performance. On the experimental evaluation on the National Health and Nutrition Examination Survey (NHANES), the classifier demonstrated an F1 score of 0.976 in the prediction, without any incorrectly identified depression instances. Only about 4% of instances had been mistakenly classified into depressed cases, with a significant accuracy of 95.4% comparing to the result from PHQ-9 mental screen inventory. The presented ensemble binary classifier performed comparably better than each baseline algorithm in all measures and all experiments. We trained the ensemble model on the processed NHANES dataset, tested and evaluated the results of its performance against mental screen inventory and discussed the comparable predictions. Finally, we provided future research directions

Effect of MWCNTs on Gastric Emptying in Mice

After making model of gastric functional disorder (FD), part of model mice were injected intravenously (i.v.) with oxide multi-walled carbon nanotubes (oMWCNTs) to investigate effect of carbon nanotubes on gastric emptying. The results showed that NO content in stomach, compared with model group, was decreased significantly and close to normal level post-injection with oMWCNTs (500 and 800 μg/mouse). In contrast to FD or normal groups, the content of acetylcholine (Ach) in stomach was increased obviously in injection group with 500 or 800 μg/mouse of oMWCNTs. The kinetic curve of emptying was fitted to calculate gastric motility factor k; the results showed that the k of injection group was much higher than FD and normal. In other words, the gastric motility of FD mice was enhanced via injection with oMWCNTs. In certain dosage, oMWCNTs could improve gastric emptying and motility

The impact of donor and recipient common clinical and genetic variation on estimated glomerular filtration rate in a European renal transplant population

Genetic variation across the HLA is known to influence renal‐transplant outcome. However, the impact of genetic variation beyond the HLA is less clear. We tested the association of common genetic variation and clinical characteristics, from both the donor and recipient, with post‐transplant eGFR at different time‐points, out to 5‐years post‐transplantation. We conducted GWAS meta‐analyses across 10,844 donors and recipients from five European ancestry cohorts. We also analysed the impact of polygenic risk scores (PRS), calculated using genetic variants associated with non‐transplant eGFR, on post‐transplant eGFR. PRS calculated using the recipient genotype alone, as well as combined donor and recipient genotypes were significantly associated with eGFR at 1‐year post‐transplant. 32% of the variability in eGFR at 1‐year post‐transplant was explained by our model containing clinical covariates (including weights for death/graft‐failure), principal components and combined donor‐recipient PRS, with 0.3% contributed by the PRS. No individual genetic variant was significantly associated with eGFR post‐transplant in the GWAS. This is the first study to examine PRS, composed of variants that impact kidney function in the general population, in a post‐transplant context. Despite PRS being a significant predictor of eGFR post‐transplant, the effect size of common genetic factors is limited compared to clinical variables

COMAP Early Science: III. CO Data Processing

We describe the first season COMAP analysis pipeline that converts raw detector readouts to calibrated sky maps. This pipeline implements four main steps: gain calibration, filtering, data selection, and map-making. Absolute gain calibration relies on a combination of instrumental and astrophysical sources, while relative gain calibration exploits real-time total-power variations. High efficiency filtering is achieved through spectroscopic common-mode rejection within and across receivers, resulting in nearly uncorrelated white noise within single-frequency channels. Consequently, near-optimal but biased maps are produced by binning the filtered time stream into pixelized maps; the corresponding signal bias transfer function is estimated through simulations. Data selection is performed automatically through a series of goodness-of-fit statistics, including $\chi^2$ and multi-scale correlation tests. Applying this pipeline to the first-season COMAP data, we produce a dataset with very low levels of correlated noise. We find that one of our two scanning strategies (the Lissajous type) is sensitive to residual instrumental systematics. As a result, we no longer use this type of scan and exclude data taken this way from our Season 1 power spectrum estimates. We perform a careful analysis of our data processing and observing efficiencies and take account of planned improvements to estimate our future performance. Power spectrum results derived from the first-season COMAP maps are presented and discussed in companion papers.Comment: Paper 3 of 7 in series. 26 pages, 23 figures, submitted to Ap

COMAP Early Science: IV. Power Spectrum Methodology and Results

We present the power spectrum methodology used for the first-season COMAP analysis, and assess the quality of the current data set. The main results are derived through the Feed-feed Pseudo-Cross-Spectrum (FPXS) method, which is a robust estimator with respect to both noise modeling errors and experimental systematics. We use effective transfer functions to take into account the effects of instrumental beam smoothing and various filter operations applied during the low-level data processing. The power spectra estimated in this way have allowed us to identify a systematic error associated with one of our two scanning strategies, believed to be due to residual ground or atmospheric contamination. We omit these data from our analysis and no longer use this scanning technique for observations. We present the power spectra from our first season of observing and demonstrate that the uncertainties are integrating as expected for uncorrelated noise, with any residual systematics suppressed to a level below the noise. Using the FPXS method, and combining data on scales $k=0.051-0.62 \,\mathrm{Mpc}^{-1}$ we estimate $P_\mathrm{CO}(k) = -2.7 \pm 1.7 \times 10^4\mu\textrm{K}^2\mathrm{Mpc}^3$, the first direct 3D constraint on the clustering component of the CO(1-0) power spectrum in the literature.Comment: Paper 4 of 7 in series. 18 pages, 11 figures, as accepted in Ap

COMAP Early Science: VII. Prospects for CO Intensity Mapping at Reionization

We introduce COMAP-EoR, the next generation of the Carbon Monoxide Mapping Array Project aimed at extending CO intensity mapping to the Epoch of Reionization. COMAP-EoR supplements the existing 30 GHz COMAP Pathfinder with two additional 30 GHz instruments and a new 16 GHz receiver. This combination of frequencies will be able to simultaneously map CO(1--0) and CO(2--1) at reionization redshifts ($z\sim5-8$) in addition to providing a significant boost to the $z\sim3$ sensitivity of the Pathfinder. We examine a set of existing models of the EoR CO signal, and find power spectra spanning several orders of magnitude, highlighting our extreme ignorance about this period of cosmic history and the value of the COMAP-EoR measurement. We carry out the most detailed forecast to date of an intensity mapping cross-correlation, and find that five out of the six models we consider yield signal to noise ratios (S/N) $\gtrsim20$ for COMAP-EoR, with the brightest reaching a S/N above 400. We show that, for these models, COMAP-EoR can make a detailed measurement of the cosmic molecular gas history from $z\sim2-8$, as well as probe the population of faint, star-forming galaxies predicted by these models to be undetectable by traditional surveys. We show that, for the single model that does not predict numerous faint emitters, a COMAP-EoR-type measurement is required to rule out their existence. We briefly explore prospects for a third-generation Expanded Reionization Array (COMAP-ERA) capable of detecting the faintest models and characterizing the brightest signals in extreme detail.Comment: Paper 7 of 7 in series. 19 pages, 10 figures, to be submitted to Ap

COMAP Early Science: VI. A First Look at the COMAP Galactic Plane Survey

We present early results from the COMAP Galactic Plane Survey conducted between June 2019 and April 2021, spanning $20^\circ<\ell<40^\circ$ in Galactic longitude and |b|<1.\!\!^{\circ}5 in Galactic latitude with an angular resolution of $4.5^{\prime}$. The full survey will span $\ell \sim 20^{\circ}$- $220^{\circ}$ and will be the first large-scale radio continuum survey at $30$ GHz with sub-degree resolution. We present initial results from the first part of the survey, including diffuse emission and spectral energy distributions (SEDs) of HII regions and supernova remnants. Using low and high frequency surveys to constrain free-free and thermal dust emission contributions, we find evidence of excess flux density at $30\,$GHz in six regions that we interpret as anomalous microwave emission. Furthermore we model UCHII contributions using data from the $5\,$GHz CORNISH catalogue and reject this as the cause of the $30\,$GHz excess. Six known supernova remnants (SNR) are detected at $30\,$GHz, and we measure spectral indices consistent with the literature or show evidence of steepening. The flux density of the SNR W44 at $30\,$GHz is consistent with a power-law extrapolation from lower frequencies with no indication of spectral steepening in contrast with recent results from the Sardinia Radio Telescope. We also extract five hydrogen radio recombination lines to map the warm ionized gas, which can be used to estimate electron temperatures or to constrain continuum free-free emission. The full COMAP Galactic plane survey, to be released in 2023/2024, will be an invaluable resource for Galactic astrophysics.Comment: Paper 6 of 7 in series. 28 pages, 10 figures, submitted to Ap