Aerosol-cloud interactions contribute significant uncertainty to modern climate model predictions. Analysis of complex observed aerosol-cloud parameter relationships is a crucial piece of reducing this uncertainty. Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. These observations consist of flights over the Western North Atlantic Ocean, providing a large repository of data including aerosol, meteorological, and microphysical conditions in and out of clouds. We investigate this dataset using principal component analysis (PCA), a linear dimensionality reduction technique, and an autoencoder, a deep learning non-linear dimensionality reduction technique. We find that we can reduce the dimensionality of the parameter space by more than a factor of 2 and verify that the deep learning method outperforms a PCA baseline by two orders of magnitude. Analysis in the low dimensional space of both these techniques reveals two consistent physically interpretable regimes—a low pollution regime and an in-cloud regime. Through this work, we show that unsupervised machine learning techniques can learn useful information from in-situ atmospheric observations and provide interpretable results of low-dimensional variability.

To investigate the symptoms of SARS-CoV-2 infection, their dynamics and their discriminatory power for the disease using longitudinally, prospectively collected information reported at the time of their occurrence. We have analysed data from a large phase 3 clinical UK COVID-19 vaccine trial. The alpha variant was the predominant strain. Participants were assessed for SARS-CoV-2 infection via nasal/throat PCR at recruitment, vaccination appointments, and when symptomatic. Statistical techniques were implemented to infer estimates representative of the UK population, accounting for multiple symptomatic episodes associated with one individual. An optimal diagnostic model for SARS-CoV-2 infection was derived. The 4-month prevalence of SARS-CoV-2 was 2.1%; increasing to 19.4% (16.0%–22.7%) in participants reporting loss of appetite and 31.9% (27.1%–36.8%) in those with anosmia/ageusia. The model identified anosmia and/or ageusia, fever, congestion, and cough to be significantly associated with SARS-CoV-2 infection. Symptoms’ dynamics were vastly different in the two groups; after a slow start peaking later and lasting longer in PCR+ participants, whilst exhibiting a consistent decline in PCR- participants, with, on average, fewer than 3 days of symptoms reported. Anosmia/ageusia peaked late in confirmed SARS-CoV-2 infection (day 12), indicating a low discrimination power for early disease diagnosis.

The first demonstration of laser action in ruby was made in 1960 by T. H. Maiman of Hughes Research Laboratories, USA. Many laboratories worldwide began the search for lasers using different materials, operating at different wavelengths. In the UK, academia, industry and the central laboratories took up the challenge from the earliest days to develop these systems for a broad range of applications. This historical review looks at the contribution the UK has made to the advancement of the technology, the development of systems and components and their exploitation over the last 60 years.

Over the last 15 years, satellite-altimeter data have been used to produce surface-elevation maps of the Greenland and Antarctic ice sheets with a 2 m accuracy. Analysis of Seasat and Geosat cross-over points showed that satellite altimeters can measure changes in the mass balance of the ice sheets. The retracking algorithm used to extract surface elevations from Seasat and Geosat return wave forms is based upon a modified form of the Brown surface-scattering model. Recent work has shown that altimeter wave forms over higher-altitude regions of the ice sheets are affected by sub-surface volume-scattering. Here, we develop a theoretical model for altimeter return wave forms over the ice sheets that is based on a combination of surface-and volume-scattering. By approximating the altimeter’s antenna pattern and transmitted pulse shape with Gaussian functions, we derive a closed-form analytical solution for the return-power volume-scattered from beneath the ice-sheet surface. We then combine the volume-scattering model with the Brown model and apply it to average wave forms from the Greenland and Antarctic ice sheets. The results show that the combined model accurately describes variations in altimeter wave-form shapes that are produced by differing contributions of surface-and volume-scattering to the received power. The combined model is then used to simulate return wave forms from a dual-frequency altimeter. The simulation shows that a two-frequency system can provide quantitative estimates of the absorption and scattering coefficients for near-surface snow.

An X-band FM-CW radar was used to determine the feasibility of observing annual snow-accumulation layers in Antarctica with a high-resolution inexpensive radar system. The formation of layering boundaries, their resultant electromagnetic discontinuity and their detection by reflected energy are presented. Large returns from depths corresponding to reasonable positions for annual layers were found. The average accumulation rates calculated from the radar returns agree with those measured in a previous pit study done in the same area. The detection of the annual accumulation layers with this system implies a simple, inexpensive mobile radar could be used to profile large areas allowing the distorting effects of local topography to be removed.This type of system with a concurrent pit study could provide insight into the effect of sub-surface strata on spaceborne or airborne microwave remote sensing.

Physical activity is influenced by genetic factors whose expression may change with age. We employed an extension to the classical twin model that allows a modifier variable, age, to interact with the effects of the latent genetic and environmental factors. The model was applied to self-reported data from twins aged 19 to 50 from seven countries that collaborated in the GenomEUtwin project: Australia, Denmark, Finland, Norway, Netherlands, Sweden and United Kingdom. Results confirmed the importance of genetic influences on physical activity in all countries and showed an age-related decrease in heritability for 4 countries. In the other three countries age did not interact with heritability but those samples were smaller or had a more restricted age range. Effects of shared environment were absent, except in older Swedish participants. The study confirms the importance of taking age effects into account when exploring the genetic and environmental contribution to physical activity. It also suggests that the power of genome-wide association studies to identify the genetic variants contributing to physical activity may be larger in young adult cohorts.

The effect of air-dry storage environment on the longevity of conidia from seven isolates of Beauveria bassiana produced at different times and locations was determined by estimating the parameters of a viability equation. Conidia were stored hermetically at six to 11 moisture contents between 2.3 and 32.0% with one (50±0.5 °C) to five constant temperatures (10, 20, 30, 40 and 50±0.5 °) for various periods up to 372 d and then tested for viability. All isolates behaved similarly (P > 0.25) in terms of the relative effect of moisture content (CW) and temperature (CH and CQ) on conidial longevity; common values were CW = 3.05 (SE = 0.07), CH = 0.0293 (SE = 0.0078), and CQ = 0.00081 (SE = 0.00011). Estimates of the low-moisture-content limit to the negative logarithmic relation between conidial moisture content and longevity were 4.6 and 5.0% at 50° and 40°, respectively, for isolate I98-1140ss, and 5.2 and 5.1% moisture content, respectively, for isolate I97-1111. Absolute longevity (KE) varied considerably (P < 0.005) among isolates, even within an isolate when conidia were produced at different locations. Among the eight samples of seven isolates, two cohorts were identified with respect to KE (P < 0.005): conidia of three isolates which were produced at Ascot had a common estimate of KE of 6.696 (SE = 0.170), whereas those produced at Nairobi or Carolina provided a lower estimate (6.203, SE = 0.029). This difference in KE means that for any given viability period in any given environment, the conidia produced in Ascot provided about three times the longevity of the other samples.