Oil pollution in the North Sea: the impact of governance measures on oil pollution over several decades

Oil pollution entering the marine environment has been an issue of concern for many decades. It can come from riverine or land-based sources, accidental and intentional discharges from ships, or as a by-product of offshore oil extraction. Growing awareness of the impact of oil pollution on the marine environment has led, since the late 1960s, to the introduction of measures to reduce or eliminate pollution from shipping and the offshore oil industry. A framework for environmental protection of the North Sea has developed over many decades through international agreements, regional cooperation, and national measures, while education has also played an important role with modern-day sailors being given due training to understand that dumping waste at sea is illegal in many areas, and is harmful to the marine environment. This paper presents data on trends in pollution from ships and oil installations. While significant reductions in oil pollution have been identified over more than two decades, there remain some areas where action is needed to reduce inputs still further, especially from oil and gas platforms

Assessing the cost of global biodiversity and conservation knowledge

Knowledge products comprise assessments of authoritative information supported by standards, governance, quality control, data, tools, and capacity building mechanisms. Considerable resources are dedicated to developing and maintaining knowledge products for biodiversity conservation, and they are widely used to inform policy and advise decision makers and practitioners. However, the financial cost of delivering this information is largely undocumented. We evaluated the costs and funding sources for developing and maintaining four global biodiversity and conservation knowledge products: The IUCN Red List of Threatened Species, the IUCN Red List of Ecosystems, Protected Planet, and the World Database of Key Biodiversity Areas. These are secondary data sets, built on primary data collected by extensive networks of expert contributors worldwide. We estimate that US$160 million (range: US$116-204 million), plus 293 person-years of volunteer time (range: 278-308 person-years) valued at US$14 million (range US$12-16 million), were invested in these four knowledge products between 1979 and 2013. More than half of this financing was provided through philanthropy, and nearly three-quarters was spent on personnel costs. The estimated annual cost of maintaining data and platforms for three of these knowledge products (excluding the IUCN Red List of Ecosystems for which annual costs were not possible to estimate for 2013 ) is US$6.5 million in total (range: US$6.2-6.7 million). We estimated that an additional US$114 million will be needed to reach pre-defined baselines of data coverage for all the four knowledge products, and that once achieved, annual maintenance costs will be approximately US$12 million. These costs are much lower than those to maintain many other, similarly important, global knowledge products. Ensuring that biodiversity and conservation knowledge products are sufficiently up to date, comprehensive and accurate is fundamental to inform decision-making for biodiversity conservation and sustainable development. Thus, the development and implementation of plans for sustainable long-term financing for them is critical

Phytoplankton evolution during the creation of a biofloc system for shrimp culture

[EN] Microalgae play a key role in the dynamics of biofloc technology aquaculture systems. Some phytoplankton groups, such as diatoms, are desired for their high nutritional value and contribution to water quality. Other groups, such as cyanobacteria, are undesired because of their low nutritional value and capacity of producing toxins. So, monitoring the phytoplankton community structure and succession is key for managing biofloc systems. However, research on phytoplankton in these systems is scarce and mostly done by microscopy. The primary objective of this research was to estimate phytoplankton community structure in shrimp biofloc system water samples, using high-performance liquid chromatography methods and CHEMTAX software. The major groups present in our system were diatoms, euglenophytes, cyanobacteria and chlorophytes, while dinoflagellates were only remarkable at the initial period. We observed a clear dominance of diatoms all along the 5 months that comprised a complete biofloc system culture. The characteristic succession of autotrophic processes by heterotrophs of the biofloc systems, was observed by the reduction of net primary production. Light intensity played a key role in determining the phytoplankton composition and abundance. Algal pigment analyses using high-performance liquid chromatography and subsequent CHEMTAX analysis in water samples was useful for estimating the phytoplankton community structure in the biofloc systems. However, we found some limitations when the biofloc system was in heterotrophic mode. Under these conditions, some dinoflagellates and cyanobacteria behaved as heterotrophs and lost or decreased their biomarkers pigments. So, further research is needed to increase knowledge on the accuracy of high-performance liquid chromatography /CHEMTAX under these conditions.Financial support for this research was provided by Conselleria d’Educació, Investigació, Cultura i Esport of the Generalitat Valenciana, through the program VALi+D, fle number ACIF/2014/244. We would like to express our deepest thanks to Professor Luis Henrique da Silva Poersch of FURG (Universidade Federal do Rio Grande) and Ivan Vidal (Langostinos el Real) for his support. Finally, the authors wish to thank Le Gouessant and Michaël Metz for providing the commercial feed.Llario-Sempere, F.; Rodilla, M.; Escrivá-Perales, J.; Falco, S.; Sebastiá-Frasquet, M. (2018). Phytoplankton evolution during the creation of a biofloc system for shrimp culture. 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The Mycobacterium tuberculosis Drugome and Its Polypharmacological Implications

We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale. The approach has been applied to the genome of Mycobacterium tuberculosis (M.tb), the causative agent of one of today's most widely spread infectious diseases. The resulting drug-target interaction network for all structurally characterized approved drugs bound to putative M.tb receptors, we refer to as the ‘TB-drugome’. The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets. Furthermore, a detailed analysis of the TB-drugome has shed new light on the controversial issues surrounding drug-target networks [1]–[3]. Indeed, our results support the idea that drug-target networks are inherently modular, and further that any observed randomness is mainly caused by biased target coverage. The TB-drugome (http://funsite.sdsc.edu/drugome/TB) has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics

Para-infectious brain injury in COVID-19 persists at follow-up despite attenuated cytokine and autoantibody responses

Data Availability Statement: The individual-level data from these studies is not publicly available to main confidentiality. Data generated by the ISARIC4C consortium is available for collaborative analysis projects through an independent data and materials access committee at isaric4c.net/sample_access. Data and samples from the COVID-Clinical Neuroscience Study are available through collaborative research by application through the NIHR bioresource at https://bioresource.nihr.ac.uk/using-our-bioresource/apply-for-bioresource-data-access/. Brain injury marker and immune mediator data are present in the paper and in the source data file. Source data are provided with this paper.To understand neurological complications of COVID-19 better both acutely and for recovery, we measured markers of brain injury, inflammatory mediators, and autoantibodies in 203 hospitalised participants; 111 with acute sera (1–11 days post-admission) and 92 convalescent sera (56 with COVID-19-associated neurological diagnoses). Here we show that compared to 60 uninfected controls, tTau, GFAP, NfL, and UCH-L1 are increased with COVID-19 infection at acute timepoints and NfL and GFAP are significantly higher in participants with neurological complications. Inflammatory mediators (IL-6, IL-12p40, HGF, M-CSF, CCL2, and IL-1RA) are associated with both altered consciousness and markers of brain injury. Autoantibodies are more common in COVID-19 than controls and some (including against MYL7, UCH-L1, and GRIN3B) are more frequent with altered consciousness. Additionally, convalescent participants with neurological complications show elevated GFAP and NfL, unrelated to attenuated systemic inflammatory mediators and to autoantibody responses. Overall, neurological complications of COVID-19 are associated with evidence of neuroglial injury in both acute and late disease and these correlate with dysregulated innate and adaptive immune responses acutely.National Institute for Health and Care Research (NIHR) (CO-CIN-01) and jointly by NIHR and UK Research and Innovation (CV220-169, MC_PC_19059). B.D.M. is supported by the UKRI/MRC (MR/V03605X/1), the MRC/UKRI (MR/V007181/1), MRC (MR/T028750/1) and Wellcome (ISSF201902/3). C.D. is supported by MRC (MC_PC_19044). We would like to thank the University of Liverpool GCP laboratory facility team for Luminex assistance and the Liverpool University Biobank team for all their help, especially Dr. Victoria Shaw, Lara Lavelle-Langham, and Sue Holden. We would like to acknowledge the Liverpool Experimental Cancer Medicine Centre for providing infrastructure support for this research (Grant Reference: C18616/A25153). We acknowledge the Liverpool Centre for Cell Imaging (CCI) for provision of imaging equipment (Dragonfly confocal microscope) and excellent technical assistance (BBSRC grant number BB/R01390X/1). Tom Solomon is supported by The Pandemic Institute and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool. D.K.M. and E.N. are supported by the NIHR Cambridge Biomedical Centre and by NIHR funding to the NIHR BioResource (RG94028 and RG85445), and by funding from Brain Research UK 201819-20. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. We thank the National Institute for Health and Care Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute. The authors would like to acknowledge the eDRIS team (Public Health Scotland) for their support in obtaining approvals, the provisioning and linking of data and facilitating access to the National Safe Haven. The views expressed are those of the author(s) and not necessarily those of the UKRI, NHS, the NIHR or the Department of Health and Social Care

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion