Mechanisms of seawater acclimation in a primitive, anadromous fish, the green sturgeon

Relatively little is known about salinity acclimation in the primitive groups of fishes. To test whether physiological preparative changes occur and to investigate the mechanisms of salinity acclimation, anadromous green sturgeon, Acipenser medirostris (Chondrostei) of three different ages (100, 170, and 533 dph) were acclimated for 7 weeks to three different salinities (<3, 10, and 33 ppt). Gill, kidney, pyloric caeca, and spiral intestine tissues were assayed for Na+, K+-ATPase activity; and gills were analyzed for mitochondria-rich cell (MRC) size, abundance, localization and Na+, K+-ATPase content. Kidneys were analyzed for Na+, K+-ATPase localization and the gastro-intestinal tract (GIT) was assessed for changes in ion and base content. Na+, K+-ATPase activities increased in the gills and decreased in the kidneys with increasing salinity. Gill MRCs increased in size and decreased in relative abundance with fish size/age. Gill MRC Na+, K+-ATPase content (e.g., ion-pumping capacity) was proportional to MRC size, indicating greater abilities to regulate ions with size/age. Developmental/ontogenetic changes were seen in the rapid increases in gill MRC size and lamellar length between 100 and 170 dph. Na+, K+-ATPase activities increased fourfold in the pyloric caeca in 33 ppt, presumably due to increased salt and water absorption as indicated by GIT fluids, solids, and ion concentrations. In contrast to teleosts, a greater proportion of base (HCO3− and 2CO32−) was found in intestinal precipitates than fluids. Green sturgeon osmo- and ionoregulate with similar mechanisms to more-derived teleosts, indicating the importance of these mechanisms during the evolution of fishes, although salinity acclimation may be more dependent on body size

Economic Analysis of Knowledge: The History of Thought and the Central Themes

Following the development of knowledge economies, there has been a rapid expansion of economic analysis of knowledge, both in the context of technological knowledge in particular and the decision theory in general. This paper surveys this literature by identifying the main themes and contributions and outlines the future prospects of the discipline. The wide scope of knowledge related questions in terms of applicability and alternative approaches has led to the fragmentation of research. Nevertheless, one can identify a continuing tradition which analyses various aspects of the generation, dissemination and use of knowledge in the economy

Detection of High-Energy Gamma-Ray Emission from the Globular Cluster 47 Tucanae with Fermi

Gamma-Ray Pulsar Bonanza Most of the pulsars we know about were detected through their radio emission; a few are known to pulse gamma rays but were first detected at other wavelengths (see the Perspective by Halpern ). Using the Fermi Gamma-Ray Space Telescope, Abdo et al. (p. 840 , published online 2 July; see the cover) report the detection of 16 previously unknown pulsars based on their gamma-ray emission alone. Thirteen of these coincide with previously unidentified gamma-ray sources, solving the 30-year-old mystery of their identities. Pulsars are fast-rotating neutron stars. With time they slow down and cease to radiate; however, if they are in a binary system, they can have their spin rates increased by mass transfer from their companion stars, starting a new life as millisecond pulsars. In another study, Abdo et al. (p. 845 ) report the detection of gamma-ray emission from the globular cluster 47 Tucanae, which is coming from an ensemble of millisecond pulsars in the cluster's core. The data imply that there are up to 60 millisecond pulsars in 47 Tucanae, twice as many as predicted by radio observations. In a further companion study, Abdo et al. (p. 848 , published online 2 July) searched Fermi Large Area Telescope data for pulsations from all known millisecond pulsars outside of stellar clusters, finding gamma-ray pulsations for eight of them. Their properties resemble those of other gamma-ray pulsars, suggesting that they share the same basic emission mechanism. Indeed, both sets of pulsars favor emission models in which the gamma rays are produced in the outer magnetosphere of the neutron star

A Population of Gamma-Ray Millisecond Pulsars Seen with the Fermi Large Area Telescope

Gamma-Ray Pulsar Bonanza Most of the pulsars we know about were detected through their radio emission; a few are known to pulse gamma rays but were first detected at other wavelengths (see the Perspective by Halpern ). Using the Fermi Gamma-Ray Space Telescope, Abdo et al. (p. 840 , published online 2 July; see the cover) report the detection of 16 previously unknown pulsars based on their gamma-ray emission alone. Thirteen of these coincide with previously unidentified gamma-ray sources, solving the 30-year-old mystery of their identities. Pulsars are fast-rotating neutron stars. With time they slow down and cease to radiate; however, if they are in a binary system, they can have their spin rates increased by mass transfer from their companion stars, starting a new life as millisecond pulsars. In another study, Abdo et al. (p. 845 ) report the detection of gamma-ray emission from the globular cluster 47 Tucanae, which is coming from an ensemble of millisecond pulsars in the cluster's core. The data imply that there are up to 60 millisecond pulsars in 47 Tucanae, twice as many as predicted by radio observations. In a further companion study, Abdo et al. (p. 848 , published online 2 July) searched Fermi Large Area Telescope data for pulsations from all known millisecond pulsars outside of stellar clusters, finding gamma-ray pulsations for eight of them. Their properties resemble those of other gamma-ray pulsars, suggesting that they share the same basic emission mechanism. Indeed, both sets of pulsars favor emission models in which the gamma rays are produced in the outer magnetosphere of the neutron star

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

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)

Screening out irrelevant cell-based models of disease

The common and persistent failures to translate promising preclinical drug candidates into clinical success highlight the limited effectiveness of disease models currently used in drug discovery. An apparent reluctance to explore and adopt alternative cell-and tissue-based model systems, coupled with a detachment from clinical practice during assay validation, contributes to ineffective translational research. To help address these issues and stimulate debate, here we propose a set of principles to facilitate the definition and development of disease-relevant assays, and we discuss new opportunities for exploiting the latest advances in cell-based assay technologies in drug discovery, including induced pluripotent stem cells, three-dimensional (3D) co-culture and organ-on-a-chip systems, complemented by advances in single-cell imaging and gene editing technologies. Funding to support precompetitive, multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies could have a key role in improving their clinical relevance, and ultimately increase clinical success rates