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Modern microbial and ecosystem sciences require diverse interdisciplinary teams that are often challenged in “speaking” to one another due to different languages and data product types. Here we introduce the IsoGenie Database (IsoGenieDB; https://isogenie-db.asc.Ohio-state.edu/), a de novo developed data management and exploration platform, as a solution to this challenge of accurately representing and integrating heterogenous environmental and microbial data across ecosystem scales. The IsoGenieDB is a public and private data infrastructure designed to store and query data generated by the IsoGenie Project, a ~10 year DOE-funded project focused on discovering ecosystem climate feedbacks in a thawing permafrost landscape. The IsoGenieDB provides (i) a platform for IsoGenie Project members to explore the project's interdisciplinary datasets across scales through the inherent relationships among data entities, (ii) a framework to consolidate and harmonize the datasets needed by the team's modelers, and (iii) a public venue that leverages the same spatially explicit, disciplinarily integrated data structure to share published datasets. The IsoGenieDB is also being expanded to cover the NASA-funded Archaea to Atmosphere (A2A) project, which scales the findings of IsoGenie to a broader suite of Arctic peatlands, via the umbrella A2A Database (A2A-DB). The IsoGenieDB's expandability and flexible architecture allow it to serve as an example ecosystems database.

Methane is a potent greenhouse gas, and there are concerns that its natural emissions from the Arctic could act as a substantial positive feedback to anthropogenic global warming. Determining the sources of methane emissions and the biogeochemical processes controlling them is important for understanding present and future Arctic contributions to atmospheric methane budgets. Here we apply measurements of multiply-substituted isotopologues, or clumped isotopes, of methane as a new tool to identify the origins of ebullitive fluxes in Alaska, Sweden and the Arctic Ocean. When methane forms in isotopic equilibrium, clumped isotope measurements indicate the formation temperature. In some microbial methane, however, non-equilibrium isotope effects, probably related to the kinetics of methanogenesis, lead to low clumped isotope values. We identify four categories of emissions in the studied samples: thermogenic methane, deep subsurface or marine microbial methane formed in isotopic equilibrium, freshwater microbial methane with non-equilibrium clumped isotope values, and mixtures of deep and shallow methane (i.e., combinations of the first three end members). Mixing between deep and shallow methane sources produces a non-linear variation in clumped isotope values with mixing proportion that provides new constraints for the formation environment of the mixing end-members. Analyses of microbial methane emitted from lakes, as well as a methanol-consuming methanogen pure culture, support the hypothesis that non-equilibrium clumped isotope values are controlled, in part, by kinetic isotope effects induced during enzymatic reactions involved in methanogenesis. Our results indicate that these kinetic isotope effects vary widely in microbial methane produced in Arctic lake sediments, with non-equilibrium δ18 values spanning a range of more than 5‰.

Northern lakes are important sources of the climate forcing trace gases methane (CH4) and carbon dioxide (CO2). A substantial portion of lakes' annual emissions can take place immediately after ice melt in spring. The drivers of these fluxes are neither well constrained nor fully understood. We present a detailed carbon gas budget for three subarctic lakes, using 6 years of eddy covariance and 9 years of manual flux measurements. We combine measurements of temperature, dissolved oxygen, and CH4 stable isotopologues to quantify functional relationships between carbon gas production and conversion, energy inputs, and the redox regime. Spring emissions were regulated by the availability of oxygen in winter, rather than temperature as during ice‐free conditions. Under‐ice storage increased predictably with ice‐cover duration, and CH4 accumulation rates (25 ± 2 mg CH4‐C·m−2·day−1) exceeded summer emissions (19 ± 1 mg CH4‐C·m−2·day−1). The seasonally ice‐covered lakes emitted 26–59% of the annual CH4 flux and 15–30% of the annual CO2 flux at ice‐off. Reduced spring emissions were associated with winter snowmelt events, which can transport water downstream and oxygenate the water column. Stable isotopes indicate that 64–96% of accumulated CH4 escaped oxidation, implying that a considerable portion of the dissolved gases produced over winter may evade to the atmosphere. Plain Language Summary: Northern lakes are globally significant sources of greenhouse gases methane and carbon dioxide, but the seasonal pattern of emissions from lakes that are ice covered in winter remains poorly resolved. Our multiyear, multilake study reveals the importance of emissions during the ice‐off period, when carbon gas that had accumulated under ice is released. We show that more than half of the annual methane emissions and a third of the yearly carbon dioxide flux can be released at spring ice‐out. Unlike emissions during summer, which tend to be closely correlated with temperature, spring emissions of both gases are in part regulated by the availability of oxygen under ice. The quantity of carbon gas accumulating under ice, and therefore the amount that can be emitted in spring, scales predictably with ice‐cover duration. Parameterizations of winter and spring carbon cycling processes, such as those described in this study, may contribute to more accurate models of annual, ecosystem‐level carbon fluxes and are essential tools to improve freshwater emission inventories and quantify critical Arctic warming feedbacks. Key Points: Multiyear flux measurements show that annual CH4 emissions from northern lakes can be dominated by emissions during ice‐off in springCH4 accumulates under ice at rates exceeding summer emissions, in part because prolonged anoxia limits methanotrophyUnder‐ice carbon gas storage scales predictably with ice‐cover season length, but meltwater advection or dilution can limit fluxes in spring [ABSTRACT FROM AUTHOR]

We have developed neural network models that predict Kp from upstream solar wind data. We study the importance of various input parameters, starting with the magnetic component Bz, particle density n, and velocity V and then adding total field B and the By component. As we also notice a seasonal and UT variation in average Kp we include functions of day-of-year and UT. Finally, as Kp is a global representation of the maximum range of geomagnetic variation over 3-hour UT intervals we conclude that sudden changes in the solar wind can have a big effect on Kp, even though it is a 3-hour value. Therefore, 3-hour solar wind averages will not always appropriately represent the solar wind condition, and we introduce 3-hour maxima and minima values to some degree address this problem. We find that introducing total field B and 3-hour maxima and minima, derived from 1-minute solar wind data, have a great influence on the performance. Due to the low number of samples for high Kp values there can be considerable variation in predicted Kp for different networks with similar validation errors. We address this issue by using an ensemble of networks from which we use the median predicted Kp. The models (ensemble of networks) provide prediction lead times in the range 20–90 min given by the time it takes a solar wind structure to travel from L1 to Earth. Two models are implemented that can be run with real time data: (1) IRF-Kp-2017-h3 uses the 3-hour averages of the solar wind data and (2) IRF-Kp-2017 uses in addition to the averages, also the minima and maxima values. The IRF-Kp-2017 model has RMS error of 0.55 and linear correlation of 0.92 based on an independent test set with final Kp covering 2 years using ACE Level 2 data. The IRF-Kp-2017-h3 model has RMSE = 0.63 and correlation = 0.89. We also explore the errors when tested on another two-year period with real-time ACE data which gives RMSE = 0.59 for IRF-Kp-2017 and RMSE = 0.73 for IRF-Kp-2017-h3. The errors as function of Kp and for different years are also studied.

Empirical models are developed to provide 10–30-min forecasts of the magnitude of the time derivative of local horizontal ground geomagnetic field (|dBh/dt|) over Europe. The models are driven by ACE solar wind data. A major part of the work has been devoted to the search and selection of datasets to support the model development. To simplify the problem, but at the same time capture sudden changes, 30-min maximum values of |dBh/dt| are forecast with a cadence of 1 min. Models are tested both with and without the use of ACE SWEPAM plasma data. It is shown that the models generally capture sudden increases in |dBh/dt| that are associated with sudden impulses (SI). The SI is the dominant disturbance source for geomagnetic latitudes below 50° N and with minor contribution from substorms. However, at occasions, large disturbances can be seen associated with geomagnetic pulsations. For higher latitudes longer lasting disturbances, associated with substorms, are generally also captured. It is also shown that the models using only solar wind magnetic field as input perform in most cases equally well as models with plasma data. The models have been verified using different approaches including the extremal dependence index which is suitable for rare events.

Previous studies have demonstrated a close relationship between the time derivative of the horizontal geomagnetic field vector (dH/dt) and geomagnetically induced currents (GIC) at a nearby location in a power grid. Similarly, a high correlation exists between GIC and the local horizontal geoelectric field (E), typically modelled from a measured magnetic field. Considering GIC forecasting, it is not feasible to assume that detailed prediction of time series will be possible. Instead, other measures summarising the activity level over a given period are preferable. In this paper, we consider the 30-min maximum of dH/dt or E as a local activity indicator (|dH/dt|30 or |E|30). Concerning GIC, we use the sum of currents through the neutral leads at substations and apply its 30-min maximum as a regional activity measure (GIC30). We show that |dH/dt|30 at a single point yields a proxy for GIC activity in a larger region. A practical consequence is that if |dH/dt|30 can be predicted at some point then it is also possible to assess the expected GIC level in the surrounding area. As is also demonstrated, |E|30 and GIC30 depend linearly on |dH/dt|30, so there is no saturation with increasing geomagnetic activity contrary to often used activity indices.

Statistics of geomagnetically induced currents (GIC) in the European high-voltage power grids based on 1-min geomagnetic recordings in 1996–2008 and on 1-D models of the ground conductivity have been derived in the EURISGIC project (European Risk from Geomagnetically Induced Currents). The simplified yet realistic power grid model indicates that large GIC can occur anywhere in Europe. However, geomagnetic variations are clearly larger in North Europe, so it is the likely region of significant GIC events. Additionally, there are areas in the North with especially low ground conductivities, which further tend to increase GIC. The largest modelled GIC values at single substations in 1996–2008 are about 400 A in the Nordic Countries, about 100 A in the British Isles, about 80 A in the Baltic Countries, and less than 50 A in Central and South Europe. The largest GIC event in the period studied is the Halloween storm on 29–30 October 2003, and the next largest ones occurred on 15 July 2000 and 9 November 2004.

The EURISGIC project (European Risk from Geomagnetically Induced Currents) aims at deriving statistics of geomagnetically induced currents (GIC) in the European high-voltage power grids. Such a continent-wide system of more than 1500 substations and transmission lines requires updates of the previous modelling, which has dealt with national grids in fairly small geographic areas. We present here how GIC modelling can be conveniently performed on a spherical surface with minor changes in the previous technique. We derive the exact formulation to calculate geovoltages on the surface of a sphere and show its practical approximation in a fast vectorised form. Using the model of the old Finnish power grid and a much larger prototype model of European high-voltage power grids, we validate the new technique by comparing it to the old one. We also compare model results to measured data in the following cases: geoelectric field at the Nagycenk observatory, Hungary; GIC at a Russian transformer; GIC along the Finnish natural gas pipeline. In all cases, the new method works reasonably well.

Abstract Background During the transmission route from poultry to the human host, the major foodborne pathogen C. jejuni may experience many types of stresses, including low pH caused by different acids. However, not all strains are equally sensitive to the stresses. The aim of this study was to investigate the response to acid stress of three sequenced C. jejuni strains with different acid tolerances using HCl and acetic acid. Results Two-dimensional gel electrophoresis was used for proteomic analysis and proteins were radioactively labelled with methionine to identify proteins only related to acid exposure. To allow added radioactive methionine to be incorporated into induced proteins, a modified chemically defined broth was developed with the minimal amount of methionine necessary for satisfactory growth of all strains. Protein spots were analyzed using image software and identification was done with MALDI-TOF-TOF. The most acid-sensitive isolate was C. jejuni 327, followed by NCTC 11168 and isolate 305 as the most tolerant. Overall, induction of five proteins was observed within the pI range investigated: 19 kDa periplasmic protein (p19), thioredoxin-disulfide (TrxB), a hypothetical protein Cj0706 (Cj0706), molybdenum cofactor biosynthesis protein (MogA), and bacterioferritin (Dps). Strain and acid type dependent differences in the level of response were observed. For strain NCTC 11168, the induced proteins and the regulator fur were analysed at the transcriptomic level using qRT-PCR. In this transcriptomic analysis, only up-regulation of trxB and p19 was observed. Conclusions A defined medium that supports the growth of a range of Campylobacter strains and suitable for proteomic analysis was developed. Mainly proteins normally involved in iron control and oxidative stress defence were induced during acid stress of C. jejuni. Both strain and acid type affected sensitivity and response.

Dietary intake characteristics were studied among 3833 adults of the second Dutch National Food Consumption Survey held in 1992. The subjects were classified into three groups based on their intake of total fat (% energy), saturated fatty acids (% energy), dietary fibre (g/MJ), and fruit and vegetables (g/d). All the classifications resulted in differences in energy intake. Except for dietary fibre, the mean energy intake was higher in the higher-intake tertiles. For the classification based on total fat, saturated fatty acids and dietary fibre the more prudent diets were accompanied with a lower energy-intake. As to the consumption of food groups, differences existed in both the proportion of consumers and in the mean consumption among users. It is concluded that the trends observed are probably more important than the actual figures. [ABSTRACT FROM PUBLISHER]