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Abstract We construct a high‐resolution shear‐wave velocity (VS) model for the uppermost 100 m using ambient noise tomography near the West Antarctic Ice Sheet Divide camp. This is achieved via joint inversion of Rayleigh wave phase velocity and H/V ratio, whose signal‐to‐noise ratios are boosted by three‐station interferometry and phase‐matched filtering, respectively. The VS shows a steep increase (0.04–0.9 km/s) in the top 5 m, with sharp interfaces at ∼8–12 m, followed by a gradual increase (1.2–1.8 km/s) between 10 and 45 m depth, and to 2 km/s at ∼65 m. The compressional‐wave velocity and empirically‐obtained density profile compares well with the results from Herglotz–Wiechert inversion of diving waves in active‐source shot experiments and ice core analysis. Our approach offers a tool to characterize high‐resolution properties of the firn and shallow ice column, which helps to infer the physical properties of deeper ice sheets, thereby contributes to improved understanding of Earth's cryosphere.

Abstract Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant’s vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.

BackgroundThe COVID-19 pandemic accelerated the interest in implementing mobile health (mHealth) in population-based health studies, but evidence is lacking on engagement and adherence in studies. We conducted a fully remote study for ≥6 months tracking COVID-19 digital biomarkers and symptoms using a smartphone app nested within an existing cohort of adults. ObjectiveWe aimed to investigate participant characteristics associated with initial and sustained engagement in digital biomarker collection from a bespoke smartphone app and if engagement changed over time or because of COVID-19 factors and explore participants’ reasons for consenting to the smartphone substudy and experiences related to initial and continued engagement. MethodsParticipants in the Fenland COVID-19 study were invited to the app substudy from August 2020 to October 2020 until study closure (April 30, 2021). Participants were asked to complete digital biomarker modules (oxygen saturation, body temperature, and resting heart rate [RHR]) and possible COVID-19 symptoms in the app 3 times per week. Participants manually entered the measurements, except RHR that was measured using the smartphone camera. Engagement was categorized by median weekly frequency of completing the 3 digital biomarker modules (categories: 0, 1-2, and ≥3 times per week). Sociodemographic and health characteristics of those who did or did not consent to the substudy and by engagement category were explored. Semistructured interviews were conducted with 35 participants who were purposively sampled by sex, age, educational attainment, and engagement category, and data were analyzed thematically; 63% (22/35) of the participants consented to the app substudy, and 37% (13/35) of the participants did not consent. ResultsA total of 62.61% (2524/4031) of Fenland COVID-19 study participants consented to the app substudy. Of those, 90.21% (2277/2524) completed the app onboarding process. Median time in the app substudy was 34.5 weeks (IQR 34-37) with no change in engagement from 0 to 3 months or 3 to 6 months. Completion rates (≥1 per week) across the study between digital biomarkers were similar (RHR: 56,517/77,664, 72.77%; temperature: 56,742/77,664, 73.06%; oxygen saturation: 57,088/77,664, 73.51%). Older age groups and lower managerial and intermediate occupations were associated with higher engagement, whereas working, being a current smoker, being overweight or obese, and high perceived stress were associated with lower engagement. Continued engagement was facilitated through routine and personal motivation, and poor engagement was caused by user error and app or equipment malfunctions preventing data input. From these results, we developed key recommendations to improve engagement in population-based mHealth studies. ConclusionsThis mixed methods study demonstrated both high initial and sustained engagement in a large mHealth COVID-19 study over a ≥6-month period. Being nested in a known cohort study enabled the identification of participant characteristics and factors associated with engagement to inform future applications in population-based health research.

IntroductionNitrate supplementation in the form of beetroot juice (BRJ) ingestion has been shown to improve exercise tolerance during acute hypoxia, but its effect on exercise physiology remains unstudied during sustained terrestrial high altitude exposure. We hypothesized that performing exercise at high altitude would lower circulating nitrate and nitrite levels and that BRJ ingestion would reverse this phenomenon while concomitantly improving key determinants of aerobic exercise performance.MethodsTwenty seven healthy volunteers (21 male) underwent a series of exercise tests at sea level (SL, London, 75 m) and again after 5–8 days at high altitude (HA, Capanna Regina Margherita or “Margherita Hut,” 4,559 m). Using a double-blind protocol, participants were randomized to consume a beetroot/fruit juice beverage (three doses per day) with high levels of nitrate (∼0.18 mmol/kg/day) or a nitrate-depleted placebo (∼11.5 μmoles/kg/day) control drink, from 3 days prior to the exercise trials until completion. Submaximal constant work rate cycle tests were performed to determine exercise efficiency and a maximal incremental ramp exercise test was undertaken to measure aerobic capacity, using breath-by-breath pulmonary gas exchange measurements throughout. Concentrations of nitrate, nitrite and nitrosation products were quantified in plasma samples collected at 5 timepoints during the constant work rate tests. Linear mixed modeling was used to analyze data.ResultsAt both SL and HA, plasma nitrate concentrations were elevated in the nitrate supplementation group compared to placebo (P < 0.001) but did not change throughout increasing exercise work rate. Delta exercise efficiency was not altered by altitude exposure (P = 0.072) or nitrate supplementation (P = 0.836). V̇O2peak decreased by 24% at high altitude (P < 0.001) and was lower in the nitrate-supplemented group at both sea level and high altitude compared to placebo (P = 0.041). Dietary nitrate supplementation did not alter other peak exercise variables or oxygen consumption at anaerobic threshold. Circulating nitrite and S-nitrosothiol levels unexpectedly rose in a few individuals right after cessation of exercise at high altitude.ConclusionWhilst regularly consumed during an 8 days expedition to terrestrial high altitude, nitrate supplementation did not alter exercise efficiency and other exercise physiological variables, except decreasing V̇O2peak. These results and those of others question the practical utility of BRJ consumption during prolonged altitude exposure.

AimCOVID-19 clinical presentation is heterogeneous, ranging from asymptomatic to severe cases. While there are a number of early publications relating to risk factors for COVID-19 infection, low sample size and heterogeneity in study design impacted consolidation of early findings. There is a pressing need to identify the factors which predispose patients to severe cases of COVID-19. For rapid and widespread risk stratification, these factors should be easily obtainable, inexpensive, and avoid invasive clinical procedures. The aim of our study is to fill this knowledge gap by systematically mapping all the available evidence on the association of various clinical, demographic, and lifestyle variables with the risk of specific adverse outcomes in patients with COVID-19.MethodsThe systematic review was conducted using standardized methodology, searching two electronic databases (PubMed and SCOPUS) for relevant literature published between 1st January 2020 and 9th July 2020. Included studies reported characteristics of patients with COVID-19 while reporting outcomes relating to disease severity. In the case of sufficient comparable data, meta-analyses were conducted to estimate risk of each variable.ResultsSeventy-six studies were identified, with a total of 17,860,001 patients across 14 countries. The studies were highly heterogeneous in terms of the sample under study, outcomes, and risk measures reported. A large number of risk factors were presented for COVID-19. Commonly reported variables for adverse outcome from COVID-19 comprised patient characteristics, including age >75 (OR: 2.65, 95% CI: 1.81-3.90), male sex (OR: 2.05, 95% CI: 1.39-3.04) and severe obesity (OR: 2.57, 95% CI: 1.31-5.05). Active cancer (OR: 1.46, 95% CI: 1.04-2.04) was associated with increased risk of severe outcome. A number of common symptoms and vital measures (respiratory rate and SpO2) also suggested elevated risk profiles.ConclusionsBased on the findings of this study, a range of easily assessed parameters are valuable to predict elevated risk of severe illness and mortality as a result of COVID-19, including patient characteristics and detailed comorbidities, alongside the novel inclusion of real-time symptoms and vital measurements.

Climate change poses an imminent physical risk to cultural heritage sites and their surrounding landscape through intensifying environmental processes such as damaging wetting and drying cycles that disrupt archaeological preservation conditions, and soil erosion which threatens to expose deposits and alter the archaeological context of sites. In the face of such threats, geospatial techniques such as GIS, remote sensing, and spatial modelling have proved invaluable tools for archaeological research and cultural heritage monitoring. This paper presents the application of secondary multi-source and multi-temporal geospatial data within a processing framework to provide a comprehensive assessment of geophysical risk to the Roman fort of Magna, Carvoran, UK. An investigation into the ancient hydraulic system at Magna was carried out with analysis of vegetation change over time, and spatio-temporal analysis of soil erosion risk at the site. Due to COVID-19 restrictions in place at the time of this study, these analyses were conducted using only secondary data with the aim to guide further archaeological research, and management and monitoring strategies for the stakeholders involved. Results guided inferences about the ancient hydraulic system, providing insights regarding how to better manage the site at Magna in the future. Analysis of soil erosion allowed the identification of hot spot areas, indicating a future increase in rates of erosion at Magna and suggesting a seasonal period of higher risk of degradation to the site. Results have proven that freely available multi-purpose national-scale datasets are sufficient to create meaningful insights into archaeological sites where physical access to the site is inhibited. This infers the potential to carry out preliminary risk assessment to inform future site management practices.

Rates of esophageal cancer have increased over the last 40 years. Recent clinical research has identified correlations between the esophageal microbiome and disease. However, mechanisms of action have been difficult to elucidate performing human experimentation. We propose an ex vivo model, which mimics the esophagus and is ideal for mechanistic studies on the esophageal microbiome and resultant transcriptome. To determine the microbiome and transcriptome profile of the human distal esophagus, the microbiome was assessed in 74 patients and the transcriptome profile was assessed in 37 patients with and without Barrett’s esophagus. Thereafter, an ex vivo model of the esophagus was created using an air–liquid interfaced (ALI) design. This design created a sterile apical surface and a nutrient-rich basal surface. An epithelial layer was grown on the apical surface. A normal microbiome and Barrett’s microbiome was harvested and created from patients during endoscopic examination of the esophagus. There was a distinct microbiome in patients with Barrett’s esophagus. The ex vivo model was successfully created with a squamous epithelial layer on the apical surface of the ex vivo system. Using this ex vivo model, multiple normal esophageal and Barrett’s esophageal cell lines will be created and used for experimentation. Each microbiome will be inoculated onto the sterile apical surface of each cell line. The resultant microbiome and transcriptome profile on each surface will be measured and compared to results in the human esophagus to determine the mechanism of the microbiome interaction.

The influence of surface ponding on the interior of ice shelves is currently unknown. Here, the authors combine surface and borehole geophysics on the Larsen C Ice Shelf, Antarctica, with remote sensing and modelling and show how pond refreezing increases ice shelf density and temperature.

Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km2 of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m2; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature.