Tropospheric carbon monoxide: satellite observations and their applications

Carbon monoxide (CO) is present in the troposphere as a product of fossil fuel combustion, biomass burning and the oxidation of volatile hydrocarbons. It is the principal sink of the hydroxyl radical (OH), thereby affecting the concentrations of greenhouse gases such as CH4 and O3. Consequently, CO has an atmospheric lifetime of 1-3 months, making it a good tracer for studying the long range transport of pollution. Satellite observations present a valuable tool to investigate tropospheric CO. The Atmospheric InfraRed Sounder (AIRS), onboard the Aqua satellite, is sensitive to tropospheric CO in ~50 of its 2378 channels. This sensitivity to CO, combined with the daily global coverage provided by AIRS, makes AIRS a potentially useful instrument for observing CO sources and transport. An optimal estimation retrieval scheme has been developed for AIRS, to provide CO profiles from near-surface altitudes to 150 hPa. Through a validation study, using CO profiles from in-situ aircraft measurements, this retrieval scheme has been shown to provide CO observations with strong correlations to in situ measurements. Compared to the operational AIRS v4 CO product this retrieval scheme is shown to provide total column CO retrievals with a reduced bias relative to the in situ measurements (~ -10% to ~ -1%). In addition, the optimal estimation retrieval is shown to provide improved estimation and characterization of the retrieval errors. Further validation work has been carried out through comparison with the established CO observations from the MOPITT instrument, onboard the Terra satellite. Good agreement (correlation coefficient > 0.9, and bias < 1.0 ppbv) between the instruments is observed in the mid-troposphere. At this level, the optimal estimation scheme is shown to remove a positive bias of ~10 ppbv, relative to MOPITT, that is present in the AIRS v4 CO product. The AIRS instrument is also shown to be less sensitive to CO in the lower troposphere than MOPITT. AIRS is also demonstrated to provide fewer pieces of independent information about the vertical structure of CO at tropical latitudes, where higher thermal contrast increases the sensitivity of MOPITT. Through time series analysis, the capability of AIRS to detect seasonal trends in CO is demonstrated. The potential of AIRS to be used to track, both horizontal and vertical, CO transport is explored. AIRS is shown to be capable of tracking horizontal transport, and to have potential to track vertical transport when combined with another satellite sensor

Determining lake surface water temperatures worldwide using a tuned one-dimensional lake model (FLake, v1)

A tuning method for FLake, a one-dimensional (1-D) freshwater lake model, is applied for the individual tuning of 244 globally distributed large lakes using observed lake surface water temperatures (LSWTs) derived from along-track scanning radiometers (ATSRs). The model, which was tuned using only three lake properties (lake depth, snow and ice albedo and light extinction coefficient), substantially improves the measured mean differences in various features of the LSWT annual cycle, including the LSWTs of saline and high altitude lakes, when compared to the observed LSWTs. Lakes whose lake-mean LSWT persists below 1 °C for part of the annual cycle are considered to be seasonally ice-covered. For trial seasonally ice-covered lakes (21 lakes), the daily mean and standard deviation (2σ) of absolute differences between the modelled and observed LSWTs are reduced from 3.07 °C ± 2.25 °C to 0.84 °C ± 0.51 °C by tuning the model. For all other trial lakes (14 non-ice-covered lakes), the improvement is from 3.55 °C ± 3.20 °C to 0.96 °C ± 0.63 °C. The post tuning results for the 35 trial lakes (21 seasonally ice-covered lakes and 14 non-ice-covered lakes) are highly representative of the post-tuning results of the 244 lakes. For the 21 seasonally ice-covered lakes, the modelled response of the summer LSWTs to changes in snow and ice albedo is found to be statistically related to lake depth and latitude, which together explain 0.50 (R2adj, p = 0.001) of the inter-lake variance in summer LSWTs. Lake depth alone explains 0.35 (p = 0.003) of the variance. Lake characteristic information (snow and ice albedo and light extinction coefficient) is not available for many lakes. The approach taken to tune the model, bypasses the need to acquire detailed lake characteristic values. Furthermore, the tuned values for lake depth, snow and ice albedo and light extinction coefficient for the 244 lakes provide some guidance on improving FLake LSWT modelling

A simple proposal for the publication of journal citation distributions

Although the Journal Impact Factor (JIF) is widely acknowledged to be a poor indicator of the quality of individual papers, it is used routinely to evaluate research and researchers. Here, we present a simple method for generating the citation distributions that underlie JIFs. Application of this straightforward protocol reveals the full extent of the skew of these distributions and the variation in citations received by published papers that is characteristic of all scientific journals. Although there are differences among journals across the spectrum of JIFs, the citation distributions overlap extensively, demonstrating that the citation performance of individual papers cannot be inferred from the JIF. We propose that this methodology be adopted by all journals as a move to greater transparency, one that should help to refocus attention on individual pieces of work and counter the inappropriate usage of JIFs during the process of research assessment

Business networks and localization effects for new Swedish technology-based firms’ innovation performance

This study examines the business networks and localization effects for new technology-based firms (NTBFs) in the context of innovation performance (the number of patents and product differentiation). In this regard, the study includes 28 variables. A survey was conducted in 2016 with 401 Swedish NTBFs that were small and young (the employment mean was 1.80 and the average age of each firm was 28.3\ua0months). The biggest category of NTBFs was knowledge-intensive high-technology services, followed by medium high-technology manufacturing, and high-technology manufacturing. Hypotheses on how business networks and localization are related to innovation performance were tested using principal component analysis, correlation analysis, and regression analysis. The results show that the primary significant factor for innovation performance regarding business networks and localization dimensions are professional network services, while industrial and regional areas also have a positive relationship on product differentiation. Our study also shows that innovation performance enhances firms’ abilities to access external financing through professional network services (e.g., venture capital companies)

Asymmetric reproductive isolation between terminal forms of the salamander ring species Ensatina eschscholtzii revealed by fine-scale genetic analysis of a hybrid zone

<p>Abstract</p> <p>Background</p> <p>Ring species, exemplified by salamanders of the <it>Ensatina eschscholtzii </it>complex, represent a special window into the speciation process because they allow the history of species formation to be traced back in time through the geographically differentiated forms connecting the two terminal forms of the ring. Of particular interest is the nature and extent of reproductive isolation between the geographically terminal forms, in this case <it>E. e. eschscholtzii </it>and <it>E. e. klauberi</it>. Previous studies have documented infrequent hybridization at the end of the ring. Here, we report the first fine-scale genetic analysis of a hybrid zone between the terminal forms in southern California using individual-based Bayesian analyses of multilocus genetic data to estimate levels and direction of hybridization and maximum-likelihood analysis of linkage disequilibrium and cline shape to make inferences about migration and selection in the hybrid zone.</p> <p>Results</p> <p>The center of the hybrid zone has a high proportion of hybrids, about half of which were classified as F1s. Clines are narrow with respect to dispersal, and there are significant deviations from Hardy-Weinberg equilibrium as well as nonrandom associations (linkage disequilibria) between alleles characteristic of each parental type. There is cytonuclear discordance, both in terms of introgression and the geographic position of mitochondrial versus nuclear clines. Genetic disequilibrium is concentrated on the <it>eschscholtzii </it>side of the zone. Nearly all hybrids possess <it>klauberi </it>mtDNA, indicating that most hybrids are formed from female <it>klauberi </it>mating with male <it>eschscholtzii </it>or male hybrids (but not vice versa).</p> <p>Conclusions</p> <p>Our results are consistent with a tension zone trapped at an ecotone, with gene combinations characteristic of <it>klauberi </it>showing up on the <it>eschscholtzii </it>side of the zone due to asymmetric hybridization. We suggest that the observed asymmetry is best explained by increased discriminatory power of <it>eschscholtzii </it>females, or asymmetric postzygotic isolation. The relatively high frequency of hybrids, particularly F1s, contrasts with other contacts between the terminal forms, and with other contacts between other divergent <it>Ensatina </it>lineages, highlighting the diverse outcomes of secondary contact within a single species complex.</p

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023.

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded by the National Institutes of Health with additional funding from the Wellcome Trust. VEuPathDB supports >600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Since 2004, VEuPathDB has analyzed omics data from the public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, and integrated the analysis results with analysis tools, visualizations, and advanced search capabilities. The unique data mining platform coupled with >3000 pre-analyzed data sets facilitates the exploration of pertinent omics data in support of hypothesis driven research. Comparisons are easily made across data sets, data types and organisms. A Galaxy workspace offers the opportunity for the analysis of private large-scale datasets and for porting to VEuPathDB for comparisons with integrated data. The MapVEu tool provides a platform for exploration of spatially resolved data such as vector surveillance and insecticide resistance monitoring. To address the growing body of omics data and advances in laboratory techniques, VEuPathDB has added several new data types, searches and features, improved the Galaxy workspace environment, redesigned the MapVEu interface and updated the infrastructure to accommodate these changes

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Lake Surface Water Temperature ARC-Lake v3 (1995-2012)

The dataset consists of satellite observations of Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) for major lakes, globally, from 1995 to 2012. The dataset was generated within the European Space Agency (ESA) ‘ATSR Reprocessing for Climate: Lake Surface Water Temperature & Ice Cover’ (ARC-Lake) project in 2014 and data were/are available for downloading at www.laketemp.net. The project aimed to use the exceptional radiometric qualities and dual-view scanning capability of the ATSRs to derive observations of LSWT contained in this dataset. This can be considered to be a major contribution to monitoring of lakes within the Global Climate Observing System, because the ATSRs are in principle capable of being a highly accurate source of information on LSWTs on a systematic global basis, in comparison to in situ measurements which may not be avaialable. The method is based on surface water temperature retrieval from the thermal channels using the following instruments: ATRS2 on the ERS-2 platform and AATSR on the Envisat platform. LSWT and LIC observations have potential environmental and meteorological applications for inland water management and numerical weather prediction (NWP). They also offer the basis of a long term record of the physical state of lakes