Dem Leben der Meerforelle auf der Spur

Einzigartiges Forschungsprojekt an der Lippingau: Geomar-Wissenschaftler pflanzen Fischen Überwachungs-Chips ei

Electromagnetic characteristics of ENSO

The motion of electrically conducting sea water through Earth's magnetic field induces secondary electromagnetic fields. Due to its periodicity, the oceanic tidally induced magnetic field is easily distinguishable in magnetic field measurements and therefore detectable. These tidally induced signatures in the electromagnetic fields are also sensitive to changes in oceanic temperature and salinity distributions. We investigate the impact of oceanic heat and salinity changes related to the El Niño–Southern Oscillation (ENSO) on oceanic tidally induced magnetic fields. Synthetic hydrographic data containing characteristic ENSO dynamics have been derived from a coupled ocean–atmosphere simulation covering a period of 50 years. The corresponding tidally induced magnetic signals have been calculated with the 3-D induction solver x3dg. By means of the Oceanic Niño Index (ONI), based on sea surface temperature anomalies, and a corresponding Magnetic Niño Index (MaNI), based on anomalies in the oceanic tidally induced magnetic field at sea level, we demonstrate that evidence of developing ENSO events can be found in the oceanic magnetic fields statistically 4 months earlier than in sea surface temperatures. The analysis of the spatio-temporal progression of the oceanic magnetic field anomalies offers a deeper understanding on the underlying oceanic processes and is used to test and validate the initial findings

Author Correction: Identification of a novel cAMP dependent protein kinase A phosphorylation site on the human cardiac calcium channel

The original version of this Article contained a typographical error in the spelling of the author A. Harvey Millar, which was incorrectly given as Harvey A. Millar. This has now been corrected in the PDF and HTML versions of the Article and in the Supplementary Information

Daisychain Search and Interactive Visualisation of Homologs in Genome Assemblies

Daisychain is an interactive graph visualisation and search tool for custom-built gene homology databases. The main goal of Daisychain is to allow researchers working with specific genes to identify homologs in other annotation releases. The gene-centric representation includes local gene neighborhood to distinguish orthologs and paralogs by local synteny. The software supports genome sequences in FASTA format and GFF3 formatted annotation files, and the process of building the homology database requires a minimum amount of user interaction. Daisychain includes an integrated web viewer that can be used for both data analysis and data publishing. The web interface extends KnetMaps.js and is based on JavaScript

Simulating High-Dimensional Multivariate Data using the bigsimr R Package

It is critical to accurately simulate data when employing Monte Carlo techniques and evaluating statistical methodology. Measurements are often correlated and high dimensional in this era of big data, such as data obtained in high-throughput biomedical experiments. Due to the computational complexity and a lack of user-friendly software available to simulate these massive multivariate constructions, researchers resort to simulation designs that posit independence or perform arbitrary data transformations. To close this gap, we developed the Bigsimr Julia package with R and Python interfaces. This paper focuses on the R interface. These packages empower high-dimensional random vector simulation with arbitrary marginal distributions and dependency via a Pearson, Spearman, or Kendall correlation matrix. bigsimr contains high-performance features, including multi-core and graphical-processing-unit-accelerated algorithms to estimate correlation and compute the nearest correlation matrix. Monte Carlo studies quantify the accuracy and scalability of our approach, up to $d=10,000$. We describe example workflows and apply to a high-dimensional data set -- RNA-sequencing data obtained from breast cancer tumor samples.Comment: 22 pages, 10 figures, https://cran.r-project.org/web/packages/bigsimr/index.htm

On the characterization of tidal ocean-dynamo signals in coastal magnetic observatories

Periodic tidal ocean currents induce electric currents and, therefore, magnetic field signals that are observable using spaceborne and ground-based observation techniques. In theory, the signals can be used to monitor oceanic temperature and salinity variations. Tidal magnetic field amplitudes and phases have been extracted from magnetometer measurements in the past. However, due to uncertainties caused by a plentitude of influencing factors, the shape and temporal variation of these signals are only known to a limited extent. This study uses past extraction methods to characterize seasonal variations and long-term trends in the ten year magnetometer time series of three coastal island observatories. First, we assess data processing procedures used to prepare ground-based magnetometer observations for tidal ocean dynamo signal extraction to demonstrate that existing approaches, i.e., subtraction of core field models or first-order differencing, are unable to reliably remove low-frequency contributions. We hence propose low-frequency filtering using smoothing splines and demonstrate the advantages over the existing approaches. Second, we determine signal and side peak magnitudes of the M2 tide induced magnetic field signal by spectral analysis of the processed data. We find evidence for seasonal magnetic field signal variations of up to 25% from the annual mean. Third, to characterize the long-term behavior of tidal ocean dynamo signal amplitudes and phases, we apply different signal extraction techniques to identify tidal ocean-dynamo signal amplitudes and phases in sub-series of the ten-year time series with incrementally increasing lengths. The analyses support three main findings: (1) trends cause signal amplitude changes of up to ~1 nT and phase changes are in the order of O(10°) within the observation period; (2) at least four years of data are needed to obtain reliable amplitude and phase values with the extraction methods used and (3) signal phases are a less dependent on the chosen extraction method than signal amplitudes

Artificial gravity partially protects space-induced neurological deficits in Drosophila melanogaster

Spaceflight poses risks to the central nervous system (CNS), and understanding neurological responses is important for future missions. We report CNS changes in Drosophila aboard the International Space Station in response to spaceflight microgravity (SFμg) and artificially simulated Earth gravity (SF1g) via inflight centrifugation as a countermeasure. While inflight behavioral analyses of SFμg exhibit increased activity, postflight analysis displays significant climbing defects, highlighting the sensitivity of behavior to altered gravity. Multi-omics analysis shows alterations in metabolic, oxidative stress and synaptic transmission pathways in both SFμg and SF1g; however, neurological changes immediately postflight, including neuronal loss, glial cell count alterations, oxidative damage, and apoptosis, are seen only in SFμg. Additionally, progressive neuronal loss and a glial phenotype in SF1g and SFμg brains, with pronounced phenotypes in SFμg, are seen upon acclimation to Earth conditions. Overall, our results indicate that artificial gravity partially protects the CNS from the adverse effects of spaceflight