Nanoscale mapping of ultrafast magnetization dynamics with femtosecond Lorentz microscopy

Novel time-resolved imaging techniques for the investigation of ultrafast nanoscale magnetization dynamics are indispensable for further developments in light-controlled magnetism. Here, we introduce femtosecond Lorentz microscopy, achieving a spatial resolution below 100 nm and a temporal resolution of 700 fs, which gives access to the transiently excited state of the spin system on femtosecond timescales and its subsequent relaxation dynamics. We demonstrate the capabilities of this technique by spatio-temporally mapping the light-induced demagnetization of a single magnetic vortex structure and quantitatively extracting the evolution of the magnetization field after optical excitation. Tunable electron imaging conditions allow for an optimization of spatial resolution or field sensitivity, enabling future investigations of ultrafast internal dynamics of magnetic topological defects on 10-nanometer length scales

Early Predictability of Grasping Movements by Neurofunctional Representations: A Feasibility Study

Human grasping is a relatively fast process and control signals for upper limb prosthetics cannot be generated and processed in a sufficiently timely manner. The aim of this study was to examine whether discriminating between different grasping movements at a cortical level can provide information prior to the actual grasping process, allowing for more intuitive prosthetic control. EEG datasets were captured from 13 healthy subjects who repeatedly performed 16 activities of daily living. Common classifiers were trained on features extracted from the waking-state frequency and total-frequency time domains. Different training scenarios were used to investigate whether classifiers can already be pre-trained by base networks for fine-tuning with data of a target person. A support vector machine algorithm with spatial covariance matrices as EEG signal descriptors based on Riemannian geometry showed the highest balanced accuracy (0.91 ± 0.05 SD) in discriminating five grasping categories according to the Cutkosky taxonomy in an interval from 1.0 s before to 0.5 s after the initial movement. Fine-tuning did not improve any classifier. No significant accuracy differences between the two frequency domains were apparent (p > 0.07). Neurofunctional representations enabled highly accurate discrimination of five different grasping movements. Our results indicate that, for upper limb prosthetics, it is possible to use them in a sufficiently timely manner and to predict the respective grasping task as a discrete category to kinematically prepare the prosthetic hand

The CO2Image mission: retrieval studies and performance analysis

The CO2Image satellite mission, led by the German Aerospace Center (DLR), aims to demonstrate the feasibility of quantifying carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. Several DLR institutes are currently working on the reliminary design phase (Phase B) of the mission. Here we present a performance analysis based on the current instrument specifications. The Beer InfraRed Retrieval Algorithm (BIRRA), the line-by-line radiative transfer model Py4CAtS (Python for Computational ATmospheric Spectroscopy) and a COSIS (Carbon dioxide Sensing Imaging Spectrometer) instrument model are employed to infer CO2 and CH4 concentrations from synthetic COSIS spectra. We evaluate the instrument's performance and determine if it meets the intended requirements. The study assesses uncertainties in the retrieved concentrations as well as errors in point source emission estimates caused by instrument noise. First results suggest that the detection and quantification limits stated in the mission requirements document are justified. The analysis also demonstrates that retrieval errors tend to increase when the signal-to-noise ratio is low, complicating the distinction between emission sources and background concentrations. Furthermore, we discuss non-instrumental effects and demonstrate that the fit quality significantly improves if a low-level plume is scaled instead of a background reference profile that covers the atmosphere's full vertical extent. The analysis on heterogeneous scenes (high albedo contrast) reveals that the various instrument setups perform similarly for both molecules

CO2Image retrieval studies and performance analysis

Current and planned satellite missions such as the Japanese GOSAT (Greenhouse Gases Observing Satellite) and NASA's OCO (Orbiting Carbon Observatory) series and the upcoming Copernicus Carbon Dioxide Monitoring (CO2M) mission aim to constrain national and regional-scale emissions down to scales of urban agglomerations and large point sources. The CO2Image demonstrator mission of the German Aerospace Center (DLR) is specifically designed to detect and quantify carbon dioxide (CO2) and methane (CH4) emissions from medium-size point sources. To this end its COSIS (Carbon dioxide Sensing Imaging Spectrometer) push-broom grating spectrometer measures reflected solar radiation with a high spatial resolution of 50x50 m2, covering tiles of ~50x50 km2 extent. The instrument has a moderate spectral resolution of approximately ~1 nm and observes in a single spectral window in the 2 µm region. Here we present and discuss the impact of the expected COSIS performance on the retrieved level-2 data. The level-1 data (spectra) are generated using the Py4CAtS (Python for Computational ATmospheric Spectroscopy) line-by-line radiative transfer model and the COSIS SIMulator (COSIS-SIM). Based on the COSIS instrument parameters the analysis examines the retrieval errors related to noise which allows to estimate the detection and quantification limit of CO2 and CH4 emission rates at the instrument's spatial and spectral resolution. We further discuss the effect of heterogeneous scenes, i.e. high contrast surfaces that cause an effective distortion of the spectral response function by non-uniform illumination of the entrance slit. Finally, we assess the influence of initial guess values for the plume's vertical extent and shape on the retrieval

MEMOTE for standardized genome-scale metabolic model testing

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-yReconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).info:eu-repo/semantics/publishedVersio

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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Expression of Toll-Like Receptors in Chronic Histiocytic Intervillositis of the Placenta

<div><p>Chronic histiocytic intervillositis of the placenta (CHI) shows monocytic/histiocytic infiltration of the intervillous space. Placental malaria has a CHI-like histopathology and induces an aberrant expression of Toll-like receptors (TLR) 3, 7–9. We hypothesized that, similar to placental malaria, CHI could be associated with increased TLR expression. TLR1-10 and other inflammation-associated factors were analyzed by real-time PCR and immunohistochemistry. A total of 31 formalin-fixed and paraffin–embedded placenta samples were evaluated: CHI (<i>n</i> = 9), and for control purposes, villitis of unknown etiology (VUE, <i>n</i> = 8) and placentas without inflammation (<i>n</i> = 14). CHI shows increased expression of monocytic TLR1, a receptor which is involved in bacterial lipopolysaccharide (LPS)-induced inflammation. This could indicate a TLR1-mediated immune mechanism in the placenta (e.g. triggered by transient, clinically inapparent maternal bacteraemia) which leads to massive monocytic/histiocytic accumulation in the intervillous space. The increased expression of TLR1 with no increased expression of TLR3 and TLR7-9 is different from that in malaria.</p></div