We develop a frame-covariant formulation of inflation in the slow-roll approximation by generalizing the inflationary attractor solution for scalar-curvature theories. Our formulation gives rise to new generalized forms for the potential slow-roll parameters, which enable us to examine the effect of conformal transformations and inflaton reparameterizations in scalar-curvature theories. We find that cosmological observables, such as the power spectrum, the spectral indices and their runnings, can be expressed in a concise manner in terms of the generalized potential slow-roll parameters which depend on the scalar-curvature coupling function, the inflaton wavefunction, and the inflaton potential. We show how the cosmological observables of inflation are frameinvariant in this generalized potential slow-roll formalism, as long as the end-of-inflation condition is appropriately extended to become frame-invariant as well. We then apply our formalism to specific scenarios, such as the induced gravity inflation, Higgs inflation and F (R) models of inflation, and obtain more accurate results, without making additional approximations to the potential. Our results are shown to be consistent to lowest order with those presented in the literature. Finally, we outline how our frame-covariant formalism can be naturally extended beyond the tree-level approximation, within the framework of the Vilkovisky-DeWitt effective action.

A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model’s performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature’s SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.

We revisit the calculation of matter quantum effects on the graviton self-energy on a flat Minkowski background, with the aim to acquire a deeper understanding of the mechanism that renders the graviton massless. To this end, we derive a low-energy theorem which directly relates the radiative corrections of the cosmological constant to those of the graviton mass to all orders in perturbation theory. As an illustrative example, we consider an Abelian Higgs model with minimal coupling to gravity and show explicitly how a suitable renormalization of the cosmological constant leads to the vanishing of the graviton mass at the one-loop level. In the same Abelian Higgs model, we also calculate the matter quantum corrections to the Newtonian potential and present analytical formulae in terms of modified Bessel and Struve functions of the particle masses in the loop. We show that the correction to the Newtonian potential exhibits an exponential fall-off dependence on the distance r, once the non-relativistic limit with respect to the non-zero loop mass is carefully considered. For massless scalars, fermions and gauge bosons in the loops, we recover the well-known results presented in the literature.

Clinical Microbiology and Infection xxx (xxxx) xxx Please cite this article as: Ptasinska A et al., Diagnostic accuracy of loop-mediated isothermal amplification coupled to nanopore sequencing (LamPORE) for the detection of SARS-CoV-2 infection at scale in symptomatic and asymptomatic populations, Clinical Microbiology and Infection,

Previous studies have described RT-LAMP methodology for the rapid detection of SARS-CoV-2 in nasopharyngeal (NP) and oropharyngeal (OP) swab and saliva samples. This study describes the validation of an improved sample preparation method for extraction free RT-LAMP and defines the clinical performance of four different RT-LAMP assay formats for detection of SARS-CoV-2 within a multisite clinical evaluation. Direct RT-LAMP was performed on 559 swabs and 86,760 saliva samples and RNA RT-LAMP on extracted RNA from 12,619 swabs and 12,521 saliva from asymptomatic and symptomatic individuals across healthcare and community settings. For Direct RT-LAMP, overall diagnostic sensitivity (DSe) of 70.35% (95% CI 63.48-76.60%) on swabs and 84.62% (79.50-88.88%) on saliva was observed, with diagnostic specificity (DSp) of 100% (98.98-100.00%) on swabs and 100% (99.72-100.00%) on saliva when compared to RT-qPCR; analysing samples with RT-qPCR ORF1ab C T values of < 25 and < 33, DSe of 100% (96.34-100%) and 77.78% (70.99-83.62%) for swabs were observed, and 99.01% (94.61-99.97%) and 87.61% (82.69-91.54%) for saliva, respectively. For RNA RT-LAMP, overall DSe and DSp were 96.06% (92.88-98.12%) and 99.99% (99.95-100%) for swabs, and 80.65% (73.54-86.54%) and 99.99% (99.95-100%) for saliva, respectively. These findings demonstrate that RT-LAMP is applicable to a variety of use-cases, including frequent, interval-based testing of saliva with Direct RT-LAMP from asymptomatic individuals that may otherwise be missed using symptomatic testing alone.