The geological record provides hints at a link between the variations in the shape of the Earth's orbit and pulses of evolution or adaptation of marine calcifying phytoplankton (i.e., coccolithophores) during the last 2 Ma of Earth's history. These pulses are expressed as a reduction in diversity and increased proliferation of certain morphotypes belonging to the Noelaerhabdaceae family, recorded almost ubiquitously in sedimentary sequences ranging from low to high-mid latitudes of the global ocean. Such eccentricity-forced changes in the biological pump and the production and export of carbon by phytoplankton populations could be an important mediator between orbital forcing and the global carbon cycle, possibly modifying the geochemistry of the global ocean reservoir by accelerating deep dissolution processes. To test whether this dynamic occurs consistently in time and across all latitudes, observations at high latitudinal extremes, where the involvement of siliceous phytoplankton populations could be susceptible to the forcing, are critically required. Applying an innovative combination of micropaleontological, geochemical and image analysis techniques on nannofossil assemblages over selected intervals from the sediments retrieved during Expedition 403, with a sampling of sufficient resolution for orbital scale features, we aim to characterise the patterns of variability of phytoplankton populations (amount and diversity) and net production and export of carbon through time. This will provide critical notions to elucidate the co-evolution of phytoplankton dynamics and environment at the Fram Strait, its drivers and feedbacks on the regional production of carbon and a plausible connection with the global content.

Studies investigating the effects of nanoplastics (NPs) on aquatic organisms used concentrations between 2 to 7 order-of-magnitudes higher than those predicted in the open ocean in order to be able to track NPs. These studies divided the community between those sounding the alarm due to the observed ecotoxicological effects, and those predicting that NP concentrations in the environment are far below any threshold-effect. In reality most experiments were inadequately designed, and thus the results unsatisfying. Fit-to-purpose experimental designs have been hindered by a lack of appropriate NP models, tracking methods, and monitoring strategies for environmentally realistic concentrations. Using 14C-labelled NPs and conventional nuclear techniques, we have recently modelled that scallops, chronically exposed (over a year) to environmentally realistic NP concentrations (15 ug/L) might accumulate and reach NPs concentrations in body tissue where effects have been observed by those sounding the alarm. Astonishingly, this suggests that NPs might already be beyond threshold-effects in organisms and harming the marine biota. Here, we will deliver an innovative approach that will overcome the analytical limitations for detecting, mapping and quantifying NPs in realistic environmental settings. By combining 14C-labelling of NPs with the ultimate sensitivity of Accelerator Mass Spectrometry (AMS), METABOLISM will allow to investigate whether NPs in the oceans are already beyond "threshold-effect" concentrations in tissues. METABOLISM will: i) provide representative intrinsically radiolabelled NP models; ii) perform chronic NP exposures with a model organism (i.e. mussels) at environmentally realistic NP concentrations (ppt-levels); iii) develop the combustion AMS to generate toxicokinetic data; iv) explore the LA-AMS to produce spatially-resolve 14C measurement to quantify tissue distribution of NPs. The approach proposed here is essential and will produce unique, valuable and fundamental knowledge on the combined long-term accumulation of NPs in aquatic environments. This is critical for developing appropriate management strategies regarding plastic litter. If successful, METABOLISM will indeed support policy makers in improving environmental risk assessments of NPs and other contaminants of emerging concerns (CEC). It is envisioned that the approach proposed herein will enable a step-change in the research on CECs and will allow the study of many different aspects of their fates (e.g., transformation, fragmentation, biomineralization, biodistribution). METABOLISM chooses a highly innovative approach to address its research questions. It combines radiochemistry and unlock the power of the AMS to resolve important environmental questions. It will establish 14C-labelled NPs as a gold standard for performing realistic laboratory-based studies. It is fundamental research that will have a critical impact beyond its overall goal. The research proposed will, for instance, have a huge impact on the use of 14C as low-level tracer in biomedical studies (i.e. micro-dosing), where appropriate methods are often missing. The approach proposed is unique and will allow to perform ground-breaking science that goes beyond the state-of-the-art. METABOLISM builds an inter-disciplinary research team that integrates the relevant expertise in environmental analytical chemistry, radiochemistry and physics.

Cells respond to stress via rapid signalling through post-translational modifications (PTMs) of proteins. Protein phosphorylation is by far the most studied PTM, although other ones are being increasingly studied as well. Mass spectrometry (MS)-based proteomics techniques are becoming increasingly central in the life sciences and personalised medicine studies, and represent the most used experimental approach for studying PTMs. PTM-enriched proteomics datasets are complex to analyse. There is still a significant fraction of the generated mass spectra that cannot be assigned to a peptide sequence, and then remain unidentified. Regrettably then, generated data in these studies cannot be used yet to its full potential. Therefore, there is the need to develop novel analysis approaches for proteomics datasets. Beyond data analysis, a common challenge is to extract biologically and functionally relevant information from the proteomics results, including e.g. a list of detected PTMs (e.g. phosphosites). However, currently it is hard to prioritise the detected PTMs for downstream analysis, which can involve expensive follow-on studies. Artificial Intelligence (AI) approaches including Machine Learning (ML) and Deep Learning (DL) are revolutionising proteomics, enabling improvements in many steps of the proteomics analysis workflow. These developments in AI approaches for proteomics have largely been enabled by the wide availability of datasets in the public domain. The PRIDE database (European Bioinformatics Institute, EMBL-EBI, UK) is the world-leading proteomics data repository, accounting for >80% of stored datasets worldwide. UniProt (EMBL-EBI) is the most used protein knowledge-base and it is increasingly incorporating PTM data, including information about their functional relevance. In this proposal called PTM-AI we will use AI to further leverage the huge amount of public proteomics datasets to improve the detection and functional characterization of PTMs. PTM-AI includes the teams in charge of the world-leading resources PRIDE and UniProt, and two International groups active in AI approaches for proteomics: the Beltrao (Switzerland) and Renard/Schlaffner groups (Germany).

Some of the most spectacular data in the recent history of earth science have been derived from the drilling of the polar ice caps. Foremost amongst these is the revelation that the atmospheric CO2 content was about one-third lower (roughly 80ppmV) during the Last Glacial Maximum than during the warmer period of the past 10 thousand years. Thus, it is widely believed that changes in atmospheric CO2 strongly amplified glacial-interglacial climate change. Although a clear explanation has yet to emerge for the observed CO2 decline during glacials and rise during interglacials, mass balance arguments clearly point to the ocean exchange as the primary modulator of the CO2 changes on these time scales. Recent studies have pointed to the Southern Ocean due to the tight coupling between carbon dioxide levels and climate in the southern hemisphere high latitudes. One prevailing model involving the SO envisions that at the end of the last glacial cycle (deglacial) climate reorganisation, the reduction in sea ice cover and strengthening wind fields may have stirred up deep ocean waters rich in carbon and nutrients to the surface releasing CO2 that has been stored in the deep ocean during the glacial period. However, this model presents a paradox. In the modern SO, the physical release of CO2 is roughly compensated by the uptake of carbon by algae during photosynthesis at the sea surface utilising the nutrients that accompany CO2 in the resurfacing deep waters. Therefore for the CO2 release model to work conditions in SO should have been unfavourable for the biological uptake allowing globally significant CO2 efflux to occur. In the proposal we hypothesize that one potential factor that could have constrained biological CO2 uptake in the SO is the dearth of Fe during algal growth. The substantial decline in dust inputs (important source of Fe) during the deglacial recorded in Anatrctic ice cores lends support to this idea. Therefore, we propose to investigate the role of productivity on CO2 efflux from the SO during the last deglaciation by investigating the nature and magnitude of marine productivity, relative macronutrient utilisation (nitrate and silicic acid), micronutrient (Fe & Zn) bio-availability and in a carefully selected set of marine sediment cores covering this period. We propose to apply state-of-art geochemical and isotopic tools recently developed including silicon and nitrogen isotopes as proxies for macronutrient utilisation and diatom-bound trace metals as tracers of Fe and Zn biological availability in combination with more conventional proxies of productivity and dust inputs. By doing so, we propose to address a fundamental and lingering question in Earth System Science- that is "What are the controls on glacial-interglacial CO2 change?"

Density Functional Theory is an atomic scale tool which can be used to learn about the structure and behaviour of substances, especially when atoms or molecules react with one another. For instance, it has been used to tell us about how the structure of water changes when it becomes acid or alkali. It is true to say that it has revolutionised our understanding in many aspects of science, and one of the originators (Walter Kohn) was awarded the Nobel Prize (in 1998) for developing the underlying theory which is at the heart of DFT computer simulation software. Since the first implementation of DFT, several flavours of DFT have been developed that have generally increase accuracy of this method, allowing scientists to calculate energies for chemical reactions with amazing accuracy. The usefulness of this method is increased when computer processors can be utilised in parallel to divide up the calculation into small sub-calculations. Currently Intel are marketing their Duo core processors for desktop and notebook computers where the computer is able to split the computational burden over two processors. The same principle is used on national supercomputers, where over 1000 processors can be used to make very demanding calculations (that would take 1000 years on one processor) into a far more manageable task, taking one year on 1000 processors, assuming the program was perfectly efficient. In reality, it is very difficult to obtain such efficient parallelism / special tricks need to be used to use the computer processor performance. This application seeks funding to develop a popular new piece of software that it can run far more efficiently on the new national supercomputer.Once the develpoment has taken place, we will look in detail at the structure and nanoscopic defects in ice. Understanding the structure and behaviour of microscopic imperfections in the ice structure will lead us to better understand how it conducts but more generally, how these defects influence the stability of ice. The latter is becoming ever more topical and important as we seek to understand how ice melts in order to better estimate the influence of temperature on glacial ice sheet.