The use of algorithms to predict surface seawater dimethyl sulphide concentrations in the SE Pacific, a region of steep gradients in primary productivity, biomass and mixed layer depth

Dimethyl sulphide (DMS) is an important precursor of cloud condensation nuclei (CCN), particularly in the remote marine atmosphere. The SE Pacific is consistently covered with a persistent stratocumulus layer that increases the albedo over this large area. It is not certain whether the source of CCN to these clouds is natural and oceanic or anthropogenic and terrestrial. This unknown currently limits our ability to reliably model either the cloud behaviour or the oceanic heat budget of the region. In order to better constrain the marine source of CCN, it is necessary to have an improved understanding of the sea-air flux of DMS. Of the factors that govern the magnitude of this flux, the greatest unknown is the surface seawater DMS concentration. In the study area, there is a paucity of such data, although previous measurements suggest that the concentration can be substantially variable. In order to overcome such data scarcity, a number of climatologies and algorithms have been devised in the last decade to predict seawater DMS. Here we test some of these in the SE Pacific by comparing predictions with measurements of surface seawater made during the Vamos Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) in October and November of 2008. We conclude that none of the existing algorithms reproduce local variability in seawater DMS in this region very well. From these findings, we recommend the best algorithm choice for the SE Pacific and suggest lines of investigation for future work

Toward Improved Observing of the Rapidly Changing Arctic Ocean

Arctic Observing Summit (April 30 – May 2, 2013, Vancouver, Canada); AON statementIn order to observe and understand the Arctic Ocean and its response to climate change, the traditional approach of acquiring observations when and where the Arctic is accessible has to be enhanced with multi-faceted measurement systems operating autonomously to provide year-round information in real time. The major goal of such a network of autonomous sensors is to measure and monitor physical, chemical and biological parameters in the atmosphere, sea ice and ocean on at least daily intervals

Taxonomic and Environmental Variability in the Elemental Composition and Stoichiometry of Individual Dinoflagellate and Diatom Cells from the NW Mediterranean Sea

Here we present, for the first time, the elemental concentration, including C, N and O, of single phytoplankton cells collected from the sea. Plankton elemental concentration and stoichiometry are key variables in phytoplankton ecophysiology and ocean biogeochemistry, and are used to link cells and ecosystems. However, most field studies rely on bulk techniques that overestimate carbon and nitrogen because the samples include organic matter other than plankton organisms. Here we used X-ray microanalysis (XRMA), a technique that, unlike bulk analyses, gives simultaneous quotas of C, N, O, Mg, Si, P, and S, in single-cell organisms that can be collected directly from the sea. We analysed the elemental composition of dinoflagellates and diatoms (largely Chaetoceros spp.) collected from different sites of the Catalan coast (NW Mediterranean Sea). As expected, a lower C content is found in our cells compared to historical values of cultured cells. Our results indicate that, except for Si and O in diatoms, the mass of all elements is not a constant fraction of cell volume but rather decreases with increasing cell volume. Also, diatoms are significantly less dense in all the measured elements, except Si, compared to dinoflagellates. The N:P ratio of both groups is higher than the Redfield ratio, as it is the N:P nutrient ratio in deep NW Mediterranean Sea waters (N:P = 20–23). The results suggest that the P requirement is highest for bacterioplankton, followed by dinoflagellates, and lowest for diatoms, giving them a clear ecological advantage in P-limited environments like the Mediterranean Sea. Finally, the P concentration of cells of the same genera but growing under different nutrient conditions was the same, suggesting that the P quota of these cells is at a critical level. Our results indicate that XRMA is an accurate technique to determine single cell elemental quotas and derived conversion factors used to understand and model ocean biogeochemical cycles

Differentiated neuroprogenitor cells incubated with human or canine adenovirus, or lentiviral vectors have distinct transcriptome profiles

Several studies have demonstrated the potential for vector-mediated gene transfer to the brain. Helper-dependent (HD) human (HAd) and canine (CAV-2) adenovirus, and VSV-G-pseudotyped self-inactivating HIV-1 vectors (LV) effectively transduce human brain cells and their toxicity has been partly analysed. However, their effect on the brain homeostasis is far from fully defined, especially because of the complexity of the central nervous system (CNS). With the goal of dissecting the toxicogenomic signatures of the three vectors for human neurons, we transduced a bona fide human neuronal system with HD-HAd, HD-CAV-2 and LV. We analysed the transcriptional response of more than 47,000 transcripts using gene chips. Chip data showed that HD-CAV-2 and LV vectors activated the innate arm of the immune response, including Toll-like receptors and hyaluronan circuits. LV vector also induced an IFN response. Moreover, HD-CAV-2 and LV vectors affected DNA damage pathways - but in opposite directions - suggesting a differential response of the p53 and ATM pathways to the vector genomes. As a general response to the vectors, human neurons activated pro-survival genes and neuron morphogenesis, presumably with the goal of re-establishing homeostasis. These data are complementary to in vivo studies on brain vector toxicity and allow a better understanding of the impact of viral vectors on human neurons, and mechanistic approaches to improve the therapeutic impact of brain-directed gene transfer

Introducing the construct of risky cannabis use: designing and piloting a co-created educational intervention on cannabis health literacy among adolescents and young adults. The CAHLY (CAnabis Health LiteracY) study.

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DSYB catalyses the key step of dimethylsulfoniopropionate biosynthesis in many phytoplankton

Dimethylsulfoniopropionate (DMSP) is a globally important organosulfur molecule and the major precursor for dimethyl sulfide. These compounds are important info-chemicals, key nutrients for marine microorganisms, and are involved in global sulfur cycling, atmospheric chemistry and cloud formation1,2,3. DMSP production was thought to be confined to eukaryotes, but heterotrophic bacteria can also produce DMSP through the pathway used by most phytoplankton4, and the DsyB enzyme catalysing the key step of this pathway in bacteria was recently identified5. However, eukaryotic phytoplankton probably produce most of Earth’s DMSP, yet no DMSP biosynthesis genes have been identified in any such organisms. Here we identify functional dsyB homologues, termed DSYB, in many phytoplankton and corals. DSYB is a methylthiohydroxybutryate methyltransferase enzyme localized in the chloroplasts and mitochondria of the haptophyte Prymnesium parvum, and stable isotope tracking experiments support these organelles as sites of DMSP synthesis. DSYB transcription levels increased with DMSP concentrations in different phytoplankton and were indicative of intracellular DMSP. Identification of the eukaryotic DSYB sequences, along with bacterial dsyB, provides the first molecular tools to predict the relative contributions of eukaryotes and prokaryotes to global DMSP production. Furthermore, evolutionary analysis suggests that eukaryotic DSYB originated in bacteria and was passed to eukaryotes early in their evolution

DMSP synthesis and exudation in phytoplankton:a modeling approach

In the marine environment, phytoplankton are the fundamental producers of dimethylsulfoniopropionate (DMSP), the precursor of the climatically active gas dimethylsulfide (DMS). DMSP is released by exudation, cell autolysis, and zooplankton grazing during phytoplankton blooms. In this study, we developed a model of phytoplankton DMSP and DMS production allowing quantification of the exudation rates of these compounds during different growth phases. The model was tested on published data from axenic cultures of Prorocentrum minimum and Phaeocystis sp.; DMSP exudation rates vary considerably between the 2 species. Model results show that P. minimum exudes around 1% d(-1) of its DMSP quota during the latent, exponential and senescent phases. This is comparable to the average exudation rate estimated from previous laboratory experiments. However, Phaeocystis sp. exudes from 3 to 11% d(-1) of its DMSP quota. For this species, DMSP exudation rates apparently show an inverse relationship with the population growth rate. The maximum DMSP exudation rate in Phaeocystis sp. is 10 times higher than previously reported DMSP or DMS exudation rates. Our results suggest that exudation may be as important as cell autolysis in the release of DMSP during Phaeocystis sp. blooms. We conclude that exudation should be incorporated in models of DMS cycling in the marine environment. Moreover, our results for Phaeocystis sp. suggest that a low and constant exudation rate, as sometimes used in models, is not suitable for all conditions

Parameterizing the impact of seawater temperature and irradiance on dimethylsulfide (DMS) in the Great Barrier Reef and the contribution of coral reefs to the global sulfur cycle

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jackson, R. L., Gabric, A. J., Matrai, P. A., Woodhouse, M. T., Cropp, R., Jones, G. B., Deschaseaux, E. S. M., Omori, Y., McParland, E. L., Swan, H. B., & Tanimoto, H. Parameterizing the impact of seawater temperature and irradiance on dimethylsulfide (DMS) in the Great Barrier Reef and the contribution of coral reefs to the global sulfur cycle. Journal of Geophysical Research:Oceans, 126(3), (2021): e2020JC016783, https://doi.org/10.1029/2020JC016783.Biogenic emissions of dimethylsulfide (DMS) are an important source of sulfur to the atmosphere, with implications for aerosol formation and cloud albedo over the ocean. Natural aerosol sources constitute the largest uncertainty in estimates of aerosol radiative forcing and climate and thus, an improved understanding of DMS sources is needed. Coral reefs are strong point sources of DMS; however, this coral source of biogenic sulfur is not explicitly included in climatologies or in model simulations. Consequently, the role of coral reefs in local and regional climate remains uncertain. We aim to improve the representation of tropical coral reefs in DMS databases by calculating a climatology of seawater DMS concentration (DMSw) and sea-air flux in the Great Barrier Reef (GBR), Australia. DMSw is calculated from remotely sensed observations of sea surface temperature and photosynthetically active radiation using a multiple linear regression model derived from field observations of DMSw in the GBR. We estimate that coral reefs and lagoon waters in the GBR (∼347,000 km2) release 0.03–0.05 Tg yr−1 of DMS (0.02 Tg yr−1 of sulfur). Based on this estimate, global tropical coral reefs (∼600,000 km2) could emit 0.08 Tg yr−1 of DMS (0.04 Tg yr−1 of sulfur), with the potential to influence the local radiative balance.Australian Research Council. Grant Number: DP150101649 National Science Foundation (NSF). Grant Number: 1543450 Ministry of Education, Culture, Sports, Science and Technology Grants-in-Aid for Scientific Research. Grant Number: 23310016,16H02967,24241010,15H01732 Ministry of Education, Culture, Sports, Science and Technology Grant-in-Aid for Young Scientists. Grant Number: 17K1281

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters