Abstract
Next generation radio experiments such as LOFAR, HERA, and SKA are expected to probe the Epoch of Reionization (EoR) and claim a first direct detection of the cosmic 21cm signal within the ...next decade. Data volumes will be enormous and can thus potentially revolutionize our understanding of the early Universe and galaxy formation. However, numerical modelling of the EoR can be prohibitively expensive for Bayesian parameter inference and how to optimally extract information from incoming data is currently unclear. Emulation techniques for fast model evaluations have recently been proposed as a way to bypass costly simulations. We consider the use of artificial neural networks as a blind emulation technique. We study the impact of training duration and training set size on the quality of the network prediction and the resulting best-fitting values of a parameter search. A direct comparison is drawn between our emulation technique and an equivalent analysis using 21CMMC. We find good predictive capabilities of our network using training sets of as low as 100 model evaluations, which is within the capabilities of fully numerical radiative transfer codes.
"The impacts of the Coronavirus Disease 2019 (COVID-19) pandemic and the shutdown it triggered at universities across the world, led to a great degree of social isolation among university staff and ...students. The aim of this study was to identify the perceived consequences of this on staff and their work and on students and their studies at universities.
The study used a variety of methods, which involved an on-line survey on the influences of social isolation using a non-probability sampling. More specifically, two techniques were used, namely a convenience sampling (i.e. involving members of the academic community, which are easy to reach by the study team), supported by a snow ball sampling (recruiting respondents among acquaintances of the participants). A total of 711 questionnaires from 41 countries were received. Descriptive statistics were deployed to analyse trends and to identify socio-demographic differences. Inferential statistics were used to assess significant differences among the geographical regions, work areas and other socio-demographic factors related to impacts of social isolation of university staff and students.
The study reveals that 90% of the respondents have been affected by the shutdown and unable to perform normal work or studies at their institution for between 1 week to 2 months. While 70% of the respondents perceive negative impacts of COVID 19 on their work or studies, more than 60% of them value the additional time that they have had indoors with families and others. .
While the majority of the respondents agree that they suffered from the lack of social interaction and communication during the social distancing/isolation, there were significant differences in the reactions to the lockdowns between academic staff and students. There are also differences in the degree of influence of some of the problems, when compared across geographical regions. In addition to policy actions that may be deployed, further research on innovative methods of teaching and communication with students is needed in order to allow staff and students to better cope with social isolation in cases of new or recurring pandemics.
This paper considers the impact of Lyman α coupling and X-ray heating on the 21-cm brightness-temperature one-point statistics (as predicted by seminumerical simulations). The X-ray production ...efficiency is varied over four orders of magnitude and the hardness of the X-ray spectrum is varied from that predicted for high-mass X-ray binaries, to the softer spectrum expected from the hot interstellar medium. We find peaks in the redshift evolution of both the variance and skewness associated with the efficiency of X-ray production. The amplitude of the variance is also sensitive to the hardness of the X-ray spectral energy distribution. We find that the relative timing of the coupling and heating phases can be inferred from the redshift extent of a plateau that connects a peak in the variance's evolution associated with Lyman α coupling to the heating peak. Importantly, we find that late X-ray heating would seriously hamper our ability to constrain reionization with the variance. Late X-ray heating also qualitatively alters the evolution of the skewness, providing a clean way to constrain such models. If foregrounds can be removed, we find that LOFAR, MWA and PAPER could constrain reionization and late X-ray heating models with the variance. We find that HERA and SKA (phase 1) will be able to constrain both reionization and heating by measuring the variance using foreground-avoidance techniques. If foregrounds can be removed they will also be able to constrain the nature of Lyman α coupling.
We explore the impact of reionization topology on 21-cm statistics. Four reionization models are presented which emulate large ionized bubbles around overdense regions (21cmfast/global-inside-out), ...small ionized bubbles in overdense regions (local-inside-out), large ionized bubbles around underdense regions (global-outside-in) and small ionized bubbles around underdense regions (local-outside-in). We show that first generation instruments might struggle to distinguish global models using the shape of the power spectrum alone. All instruments considered are capable of breaking this degeneracy with the variance, which is higher in outside-in models. Global models can also be distinguished at small scales from a boost in the power spectrum from a positive correlation between the density and neutral-fraction fields in outside-in models. Negative skewness is found to be unique to inside-out models and we find that pre-Square Kilometre Array (SKA) instruments could detect this feature in maps smoothed to reduce noise errors. The early, mid- and late phases of reionization imprint signatures in the brightness-temperature moments, we examine their model dependence and find pre-SKA instruments capable of exploiting these timing constraints in smoothed maps. The dimensional skewness is introduced and is shown to have stronger signatures of the early and mid-phase timing if the inside-out scenario is correct.
Little is known about the nature of genetic variation underlying complex diseases in humans. One popular view proposes that mapping efforts should focus on identification of susceptibility mutations ...that are relatively old and at high frequency. It is generally assumed—at least for modeling purposes—that selection against complex disease mutations is so weak that it can be ignored. In this article, I propose an explicit model for the evolution of complex disease loci, incorporating mutation, random genetic drift, and the possibility of purifying selection against susceptibility mutations. I show that, for the most plausible range of mutation rates, neutral susceptibility alleles are unlikely to be at intermediate frequencies and contribute little to the overall genetic variance for the disease. Instead, it seems likely that the bulk of genetic variance underlying diseases is due to loci where susceptibility mutations are mildly deleterious and where there is a high overall mutation rate to the susceptible class. At such loci, the total frequency of susceptibility mutations may be quite high, but there is likely to be extensive allelic heterogeneity at many of these loci. I discuss some practical implications of these results for gene mapping efforts.
There are numerous ways in which plants can influence the composition of soil communities. However, it remains unclear whether information on plant community attributes, including taxonomic, ...phylogenetic, or trait-based composition, can be used to predict the structure of soil communities. We tested, in both monocultures and field-grown mixed temperate grassland communities, whether plant attributes predict soil communities including taxonomic groups from across the tree of life (fungi, bacteria, protists, and metazoa). The composition of all soil community groups was affected by plant species identity, both in monocultures and in mixed communities. Moreover, plant community composition predicted additional variation in soil community composition beyond what could be predicted from soil abiotic characteristics. In addition, analysis of the field aboveground plant community composition and the composition of plant roots suggests that plant community attributes are better predictors of soil communities than root distributions. However, neither plant phylogeny nor plant traits were strong predictors of soil communities in either experiment. Our results demonstrate that grassland plant species form specific associations with soil community members and that information on plant species distributions can improve predictions of soil community composition. These results indicate that specific associations between plant species and complex soil communities are key determinants of biodiversity patterns in grassland soils.
The objectives of this study were to determine: 1) the prevalence of frailty using Fried's phenotype method and the Short Performance Physical Battery (SPPB), 2) agreement between frailty assessment ...methods, 3) the feasibility of assessing frailty using Fried's phenotype method and the SPPB.
This cross-sectional study was conducted at a geriatric out-patient clinic in Hamilton, Canada. A research assistant conducted all frailty assessments. Patients were classified as non-frail, pre-frail or frail according to Fried's phenotype method and the SPPB. Agreement among methods is reported using the Cohen kappa statistic (standard error). Feasibility data included the percent of eligible participants agreeing to attempt the frailty assessments (criterion for feasibility: ≥90% of patients agreeing to the frailty assessment), equipment required, and safety considerations. A p-value of <0.05 is considered significant.
A total of 110 participants (92%) and 109 participants (91%) agreed to attempt Fried's phenotype method and SPPB, respectively. No adverse events occurred during any assessments. According to Fried's phenotype method, the prevalence of frailty and pre-frailty was 35% and 56%, respectively, and according to the SPPB, the prevalence of frailty and pre-frailty was 50% and 35%, respectively. There was fair to moderate agreement between methods for determining which participants were frail (0.488 0.082, p < 0.001) and pre-frail (0.272 0.084, p = 0.002).
Frailty and pre-frailty are common in this geriatric outpatient population, and there is fair to moderate agreement between Fried's phenotype method and the SPPB. Over 90% of the patients who were eligible for the study agreed to attempt the frailty assessments, demonstrating that according to our feasibility criteria, frailty can be assessed in this patient population. Assessing frailty may help clinicians identify high-risk patients and tailor interventions based on baseline frailty characteristics.
Neutral atoms are a promising platform for scalable quantum computing, however, prior demonstration of high fidelity gates or low-loss readout methods have employed restricted numbers of qubits. ...Using randomized benchmarking of microwave-driven single-qubit gates, we demonstrate average gate errors of 7(2)×10^{-5} on a 225 site atom array using conventional, destructive readout. We further demonstrate a factor of 1.7 suppression of the primary measurement errors via low-loss, nondestructive, and state-selective readout on 49 sites while achieving gate errors of 2(9)×10^{-4}.
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