Genetic analysis reveals the complex structure of HIV-1 transmission within defined risk groups

We explored the epidemic history of HIV-1 subtype B in the United Kingdom using statistical methods that infer the population history of pathogens from sampled gene sequence data. Phylogenetic analysis of HIV-1 pol gene sequences from Britain showed at least six large transmission chains, indicating a genetically variable, but epidemiologically homogeneous, epidemic among men having sex with men. Through coalescent-based analysis we showed that these chains arose through separate introductions of subtype B strains into the United Kingdom in the early-to-mid 1980s. After an initial period of exponential growth, the rate of spread generally slowed in the early 1990s, which is more likely to correlate with behaviour change than with reduced infectiousness resulting from highly active antiretroviral therapy. Our results provide new insights into the complexity of HIV-1 epidemics that must be considered when developing HIV monitoring and prevention initiatives

Tropical Peatland Vegetation Structure and Biomass: Optimal Exploitation of Airborne Laser Scanning

Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and Ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R2 = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed

Evidence of a link between the evolution of clusters and their AGN fraction

‘The definitive version is available at www3.interscience.wiley.com .' Copyright Blackwell Publishing / Royal Astronomical Society. DOI: 10.1111/j.1365-2966.2009.14513.xPeer reviewe

Improving the performance of National Centre for Earth Observation (NCEO) code using GPUs

The aim of this study was to investigate the advantages of different tools designed for running code on Graphics Processing Units (GPUs) and specify the types of code best suited for GPU acceleration. • Three examples of code were obtained from NCEO scientists for three distinct applications and ported to GPUs on the Natural Environment Research Council (NERC) Earth Observation Data Acquisition and Analysis Service (NEODAAS) MAGEO computing cluster. • Comparisons were made between the time taken to run the code on the original Central Processing Unit (CPU) and the MAGEO CPU and GPU. • Accelerations of between x30 and x1800 were achieved: more details are provided below. • In terms of energy saving this relates to an estimated 93.9% to 99.8% reduction in electricity usage. • The study highlighted the value of expertise in GPUs and coding such as provided by NEODAA

Kinematic Structure in the Galactic Halo at the North Galactic Pole: RR Lyrae and BHB Stars show different kinematics

Space motions are given for 38 RR Lyrae (RRL) stars and 79 blue horizontal branch (BHB) stars in a ~200 deg2 area around the North Galactic Pole (NGP) using a homogeneous distance scale consistent with (m-M)0=18.52 for the LMC. The kinematics of the 26 RRL and 52 BHB stars in the 10.4 cubic kpc volume that have Z<8 kpc are not homogeneous. Our BHB sample (like that of Sirko et al. 2004b) has a zero galactic rotation (V_phi) and roughly isotropic velocity dispersions. The RRL sample shows a definite retrograde rotation (V_phi = -95+/-29 km/s) and non-isotropic velocity dispersions. The combined BHB and RRL sample has a retrograde galactic rotation (V) that is similar to that found by Majewski (1992) for his sample of subdwarfs in SA 57. The velocity dispersion of the RRL stars that have a positive W motion is significantly smaller than the dispersion of those "streaming down" with a negative W. One component of our sample (rich in RRL's) shows retrograde rotation and the streaming motion that we associate with the accretion process. The other (traced by the BHB stars) shows essentially no rotation and less evidence of streaming. These two components have HB morphologies that suggest that they may be the field star equivalents of the young and old halo globular clusters respectively.Comment: Accepted for publication on MNRAS. 20 pages, 7 figures, 12 table

A Method for Upscaling In Situ Soil Moisture Measurements to Satellite Footprint Scale Using Random Forests

Geophysical products generated from remotely sensed data require validation to evaluate their accuracy. Typically in situ measurements are used for validation, as is the case for satellite-derived soil moisture products. However, a large disparity in scales often exists between in situ measurements (covering meters to 10 s of meters) and satellite footprints (often hundreds of meters to several kilometers), making direct comparison difficult. Before using in situ measurements for validation, they must be “upscaled” to provide the mean soil moisture within the satellite footprint. There are a number of existing upscaling methods previously applied to soil moisture measurements, but many place strict requirements on the number and spatial distribution of soil moisture sensors difficult to achieve with permanent/semipermanent ground networks necessary for long-term validation efforts. A new method for upscaling is presented here, using Random Forests to fit a model between in situ measurements and a number of landscape parameters and variables impacting the spatial and temporal distributions of soil moisture. The method is specifically intended for validation of the NASA soil moisture active passive (SMAP) products at 36-, 9-, and 3-km scales. The method was applied to in situ data from the SoilSCAPE network in California, validated with data from the SMAPVEX12 campaign in Manitoba, Canada with additional verification from the TxSON network in Texas. For the SMAPVEX12 site, the proposed method was compared to extensive field measurements and was able to predict mean soil moisture over a large area more accurately than other upscaling approaches

Characterization of Unstable Blinking Pixels in the AisaOWL Thermal Hyperspectral Imager

The AisaOWL thermal hyperspectral instrument, manufactured by Specim, is a relatively new push-broom sensor well suited to airborne environmental surveys. The sensor covers the 7.6-12.6 μm part of the long-wave infrared region with 102 continuous bands, and is capable of imaging in low-light conditions. The detector array is a mercury cadmium telluride (MCT) semiconductor, which has an inherent randomly varying dark current for random pixels. This manifests in the raw data as a pixel switching between different intensity levels. These pixels are termed ``blinkers' by the manufacturer. For each data acquisition, the pixels need to be tested for blinking behavior as different pixels are affected during each acquisition. However, little is known about the number of blink events, the duration of frames, or the optimal length of data acquisition. This paper presents the characterization of the blinking nature of pixels in the MCT detector array to provide guidance on data acquisition and processing. This paper finds that blinking behavior is not completely random, with some pixels more prone to blinking behavior than others. Most blinking pixels have only a few short blinks; therefore, there is still a considerable amount of good data in a blinking pixel

Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution

Shipping regulations lead to large reduction in cloud perturbations

Global shipping accounts for 13% of global emissions of SO2, which, once oxidized to sulfate aerosol, acts to cool the planet both directly by scattering sunlight and indirectly by increasing the albedo of clouds. This cooling due to sulfate aerosol offsets some of the warming effect of greenhouse gasses and is the largest uncertainty in determining the change in the Earth’s radiative balance by human activity. Ship tracks—the visible manifestation of the indirect of effect of ship emissions on clouds as quasi-linear features—have long provided an opportunity to quantify these effects. However, they have been arduous to catalog and typically studied only in particular regions for short periods of time. Using a machine-learning algorithm to automate their detection we catalog more than 1 million ship tracks to provide a global climatology. We use this to investigate the effect of stringent fuel regulations introduced by the International Maritime Organization in 2020 on their global prevalence since then, while accounting for the disruption in global commerce caused by COVID-19. We find a marked, but clearly nonlinear, decline in ship tracks globally: An 80% reduction in SOx emissions causes only a 25% reduction in the number of tracks detected