Impact of second-trip echoes for space-borne high-pulse-repetition-frequency nadir-looking W-band cloud radars

The appearance of second-trip echoes generated by mirror images over the ocean and by multiple scattering tails in correspondence with deep convective cores has been investigated for space-borne nadir-looking W-band cloud radar observations. Examples extracted from the CloudSat radar are used to demonstrate the mechanisms of formation and to validate the modelling of such returns. A statistical analysis shows that, for CloudSat observations, second-trip echoes are rare and appear only above 20 km (thus easy to remove). CloudSat climatology is then used to estimate the occurrence of second-trip echoes in the different configurations envisaged for the operations of the EarthCARE radar, which will adopt pulse repetition frequencies much higher than the one used by the CloudSat radar in order to improve its Doppler capabilities. Our findings predict that the presence of such echoes in EarthCARE observations cannot be neglected: in particular, over the ocean, mirror images will tend to populate the EarthCARE sampling window with a maximum frequency at its upper boundary. This will create an additional fake cloud cover in the upper troposphere (of the order of 3 % at the top of the sampling window and steadily decreasing moving downwards), and, in much less frequent instances, it will cause an amplification of signals in areas where clouds are already present. Multiple scattering tails will also produce second-trip echoes but with much lower frequencies: less than 1 profile out of 1000 in the tropics and practically no effects at high latitudes. At the moment, level-2 algorithms of the EarthCARE radar do not account for such occurrences. We recommend to properly remove these second-trip echoes and to correct for reflectivity enhancements, where needed. More generally this work is relevant for the design of future space-borne Doppler W-band radar missions.

Impact of second trip echoes for space-borne high PRF nadir-looking W-band cloud radars

The appearance of second trip echoes generated by mirror images over the ocean and by multiple scattering tails in correspondence of deep convective cores has been investigated for space-borne nadir-looking W-band cloud radar observations. Examples extracted from the CloudSat radar are used to demonstrate the mechanisms of formation and to validate the modeling of such returns. A statistical analysis shows that, for CloudSat observations, second trip echoes are rare and appear only above 20 km (thus easy to remove). CloudSat climatology is then used to estimate the occurrence of second trip echoes in the different configurations envisaged for the operations of the EarthCARE radar, which will adopt pulse repetition frequencies much higher than the one used by the CloudSat radar in order to improve its Doppler capabilities. Our findings predict that the presence of such echoes in EarthCARE observations cannot be neglected: in particular, over the ocean, mirror images will tend to populate the EarthCARE sampling window with a maximum frequency at its upper boundary. This will create an additional fake cloud cover in the upper troposphere (of the order of 3 % at the top of the sampling window and steadily decreasing moving downwards) and, in much less frequent instances, it will cause an amplification of signals in areas where clouds are already present. Multiple scattering tails will produce also second trip echoes but with much lower frequencies: less than one profile out of 1000 in the Tropics and practically no effects at high latitudes. At the moment Level-2 algorithms of the EarthCARE radar do not account for such occurrences. We recommend to properly remove these second trip echoes and to correct for reflectivity enhancements, where needed. More generally this work is relevant for the design of future space-borne Doppler W-band radar missions.

Persistence to Biologic Therapy among Patients with Spondyloarthritis: An Observational Study using the OPAL Dataset

Objective To describe the treatment response and persistence to biologic DMARD (bDMARD) therapy in patients with ankylosing spondylitis (AS) in a real-world Australian cohort. Methods This was a retrospective, non-interventional cohort study that extracted data for patients with AS from the Optimising Patient outcomes in Australian rheumatology (OPAL) dataset for the period Aug-2006 to Sep-2019. Patients were classified as either bDMARD initiators if they commenced a bDMARD during the sampling window, or bDMARD naïve if they did not. Results were summarised descriptively. Treatment persistence was calculated using Kaplan-Meier methods. Differences in treatment persistence were explored using log-rank tests. Results 5048 patients with AS were identified. 2597 patients initiated bDMARDs and 2451 remained bDMARD naïve throughout the study window. Treatment with first, second and third line bDMARDs significantly reduced disease activity. Median persistence on first line bDMARDs was 96 months (95% CI 85 to 109), declining to 19 months (95% CI 16 to 22) in second line, and 14 months (95% CI 11 to 18) in third line therapy. Median persistence was longest for the golimumab treated group in all lines of therapy and shortest for the etanercept group. Differences in persistence rates according to the time-period that bDMARDs were prescribed (pre-and post-2012) were also seen for etanercept and adalimumab. Conclusion In this cohort all bDMARDs effectively reduced disease activity. Patients remained on their first bDMARD longer than subsequent agents. Median persistence was longest for the golimumab treated group in all lines of therapy and shortest for the etanercept group.

Purifying selection determines the short-term time dependency of evolutionary rates in SARS-CoV-2 and pH1N1 influenza

High throughput sequencing enables rapid genome sequencing during infectious disease outbreaks and provides an opportunity to quantify the evolutionary dynamics of pathogens in near real-time. One difficulty of undertaking evolutionary analyses over short timescales is the dependency of the inferred evolutionary parameters on the timespan of observation. Here, we characterise the molecular evolutionary dynamics of SARS-CoV-2 and 2009 pandemic H1N1 (pH1N1) influenza during the first 12 months of their respective pandemics. We use Bayesian phylogenetic methods to estimate the dates of emergence, evolutionary rates, and growth rates of SARS-CoV-2 and pH1N1 over time and investigate how varying sampling window and dataset sizes affects the accuracy of parameter estimation. We further use a generalised McDonald-Kreitman test to estimate the number of segregating non-neutral sites over time. We find that the inferred evolutionary parameters for both pandemics are time-dependent, and that the inferred rates of SARS-CoV-2 and pH1N1 decline by ~50% and ~100%, respectively, over the course of one year. After at least 4 months since the start of sequence sampling, inferred growth rates and emergence dates remain relatively stable and can be inferred reliably using a logistic growth coalescent model. We show that the time-dependency of the mean substitution rate is due to elevated substitution rates at terminal branches which are 2-4 times higher than those of internal branches for both viruses. The elevated rate at terminal branches is strongly correlated with an increasing number of segregating non-neutral sites, demonstrating the role of purifying selection in generating the time-dependency of evolutionary parameters during pandemics.

Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (<30 m) satellites together with modern machine learning algorithms provide new prospects for spaceborne large area forest inventory. In this study, we investigated the capability of Sentinel-2 (S2) image and metadata, topography data, and canopy height model (CHM), as well as their combinations, to predict growing stock volume with deep neural networks (DNN) in four forestry districts in Central Finland. We focused on investigating the relevance of different input features, the effect of DNN depth, the amount of training data, and the size of image data sampling window to model prediction performance. We also studied model transfer between different silvicultural districts in Finland, with the objective to minimize the amount of new field data needed. We used forest inventory data provided by the Finnish Forest Centre for model training and performance evaluation. Leaving out CHM features, the model using RGB and NIR bands, the imaging and sun angles, and topography features as additional predictive variables obtained the best plot level accuracy (RMSE% = 42.6%, |BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept.

Gene- and genome-centric dynamics shape the diversity of oral bacterial populations

Two major viewpoints have been put forward for how microbes adapt to a niche, differing in whether adaptation is driven principally by gene-centric or genome-centric processes. Longitudinal sampling at microbially-relevant timescales, i.e., days to weeks, is critical for distinguishing these mechanisms. Because of its significance for both microbial ecology and human health and its accessibility and high level of curation, we used the oral microbiota to evaluate evolutionary mechanisms. Metagenomes were generated by shotgun sequencing of total community DNA from the healthy tongues of 17 volunteers at four to seven timepoints obtained over intervals of days to weeks. We obtained 390 high-quality metagenome-assembled genomes (MAGs) defining population genomes from 55 genera, the majority of which were temporally stable at the MAG level. Decomposing MAG-defined populations by single nucleotide variant frequencies revealed MAGs were composed of up to 5 haplotypes, putatively distinct strain- or subpopulation-level genotypes. Most haplotypes were stable over time, yet we found examples of individual haplotypes sweeping from low abundance to dominance in a population over a period of days, a pattern suggestive of genome-centric adaptation. At the gene level, the vast majority of genes in each MAG were tightly linked over the two-week sampling window based on their frequency in the metagenomes of different mouths. The few genes that changed in abundance independently from nearby genes did not change in a directional manner, nor did nonsynonymous codon variants within such genes. Altogether, these observations characterize the intrapopulation genomic dynamics of the oral microbiota at microbially-relevant timescales. Our results demonstrate that both gene- and genome-wide sweeps occur on daily timescales but likely with different ecological ramifications. We infer that genome-wide selection of ecotypes is the dominant mode of adaptation in the oral populations, with short-term changes in gene frequency also occurring.

Spatial sampling heterogeneity limits the detectability of deep time latitudinal biodiversity gradients

The latitudinal biodiversity gradient (LBG), in which species richness decreases from tropical to polar regions, is a pervasive pattern of the modern biosphere. Although the distribution of fossil occurrences suggests this pattern has varied through deep time, the recognition of palaeobiogeographic patterns is hampered by geological and anthropogenic biases. In particular, spatial sampling heterogeneity has the capacity to impact upon the reconstruction of deep time LBGs. Here we use a simulation framework to test the detectability of three different types of LBG (flat, unimodal and bimodal) over the last 300 Myr. We show that heterogeneity in spatial sampling significantly impacts upon the detectability of genuine LBGs, with known biodiversity patterns regularly obscured after applying the spatial sampling window of fossil collections. Sampling-standardization aids the reconstruction of relative biodiversity gradients, but cannot account for artefactual absences introduced by geological and anthropogenic biases. Therefore, we argue that some previous studies might have failed to recover the ‘true’ LBG type owing to incomplete and heterogeneous sampling, particularly between 200 and 20 Ma. Furthermore, these issues also have the potential to bias global estimates of past biodiversity, as well as inhibit the recognition of extinction and radiation events.