Feeding, Respiration and Excretion of the Copepod Calanus hyperboreus from Baffin Bay, Including Waters Contaminated by Oil Seeps

Metabolic processes in eastern arctic copepods Calanus hyperboreus were analyzed during the post-bloom period (August-September). Mixed adult and subadult copepods were collected from 12 stations in Baffin Bay (Davis Strait to Lancaster Sound) by trawling from 0-300 m. Measurements were made of clearance rate, O2-consumption and NH3 excretion. The cruise track included 6 stations in oil-seep contaminated waters of Scott Inlet and Buchan Gulf. Physiological parameters for populations of C. hyperboreus from the latter stations were compared with those from non-seep stations. Mean O2 consumption rates (0.309 - 0.907 µl O2 / mg dry wt / h) for all stations were similar to those described for Antarctic calanoid species but were higher than reported for more northern arctic waters. Mean ammonia excretion rates (0.023 - 0.071 µg N / mg dry wt / h) were somewhat lower than reported for comparable Antarctic species and were similar to values from other eastern arctic studies. O:N ratios for 11 of the 12 stations occupied ranged between 8.4 and 22.1, indicative of protein-based metabolism. The single exception was a High Arctic station with O:N ratio 43.6. Clearance rates were low to nonexistant for all stations. Most of the non-feeding values came from the Scott Inlet-Buchan Gulf region of western Baffin Bay. At those stations in this region a strong negative correlation (P<.01) exists between clearance rate and hydrocarbon contamination. This suggests that in the oil-seep region of Baffin Bay feeding may be suppressed in Calanus hyperboreus by low concentrations of petroleum hydrocarbons derived from sub-sea seepage.Key words: zooplankton, Calanus hyperboreus, Arctic, metabolism, oil seep, petroleum, hydrocarbons, oil pollutionMots clés: zooplancton, Calanus hyperboreus, Arctique, métabolisme, suitements de pétrole, pétrole, hydrocarbures, pollution par les hydrocarbure

Consensus on Aquatic Primary Productivity Field Protocols for Satellite Validation and Model Synthesis

The NASA PACE project, in conjunction with the IOCCG, EUMETSAT, and JAXA, have initiated an Aquatic Primary Productivity working group, with the aim to develop community consensus on multiple methods for measuring aquatic primary productivity used for satellite validation and model synthesis. A workshop to commence the working group efforts was held December 05-07, 2018 at the University Space Research Association headquarters in Columbia, MD U.S.A., bringing together 26 active researchers from 16 institutions. The group discussed the primary differences, nuances, scales, uncertainties, definitions, and best practices for measurements of primary productivity derived from in situ/on-deck/laboratory radio/stable isotope incubations, dissolved oxygen concentrations (from incubations or autonomous platforms such as floats or gliders), oxygen-argon ratios, triple oxygen isotope, natural fluorescence, and FRRF/ETR/kinetic analysis. These discussions highlighted the necessity to move the community forward towards the establishment of climate-quality primary productivity measurements that follow uniform protocols, which is imperative to ensure that existing and future measurements can be compared, assimilated, and their uncertainties determined for model development and validation. The specific deliverable resulting from of this activity will be a protocol document, published in coordination with the IOCCG. This presentation will discuss the findings of the meeting, and address future activities of the working group

Δ-9,11 modification of glucocorticoids dissociates nuclear factor-κ B inhibitory efficacy from glucocorticoid response element-associated side effects

Glucocorticoids are standard of care for many inflammatory conditions, but chronic use is associated with a broad array of side effects. This has led to a search for dissociative glucocorticoids—drugs able to retain or improve efficacy associated with transrepression [nuclear factor-κB (NF-κB) inhibition] but with the loss of side effects associated with transactivation (receptor-mediated transcriptional activation through glucocorticoid response element gene promoter elements). We investigated a glucocorticoid derivative with a Δ-9,11 modification as a dissociative steroid. The Δ-9,11 analog showed potent inhibition of tumor necrosis factor-α-induced NF-κB signaling in cell reporter assays, and this transrepression activity was blocked by 17β-hydroxy-11β-[4-dimethylamino phenyl]-17α-[1-propynyl]estra-4,9-dien-3-one (RU-486), showing the requirement for the glucocorticoid receptor (GR). The Δ-9,11 analog induced the nuclear translocation of GR but showed the loss of transactivation as assayed by GR-luciferase constructs as well as mRNA profiles of treated cells. The Δ-9,11 analog was tested for efficacy and side effects in two mouse models of muscular dystrophy: mdx (dystrophin deficiency), and SJL (dysferlin deficiency). Daily oral delivery of the Δ-9,11 analog showed a reduction of muscle inflammation and improvements in multiple muscle function assays yet no reductions in body weight or spleen size, suggesting the loss of key side effects. Our data demonstrate that a Δ-9,11 analog dissociates the GR-mediated transcriptional activities from anti-inflammatory activities. Accordingly, Δ-9,11 analogs may hold promise as a source of safer therapeutic agents for chronic inflammatory disorders

Author Correction: GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality

An author of the paper was omitted in the original version (Ted Conroy, University of Waikato, New Zealand). This has been corrected in the pdf and HTML versions of the paper, and the associated metadata

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data