Modelling managed forest ecosystems in Sweden : Poster presentation:

In this work, the forestry-enabled dynamic vegetation model LPJ-GUESS was used to simulate forest standing volume for the three main regions of Sweden. At the regional scale, the model results were evaluated against observational data from the Swedish National Forest Inventory. Carbon fluxes of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco) were simulated at the local scale on a daily time step for two sites in Sweden and results were evaluated against data from the Integrated Carbon Observation System (ICOS). The model produced adequate results of standing volume in monocultures of Norway spruce and Scots pine for southern and central Sweden, after an updated parameterization of the species. Stand-scale simulations of carbon fluxes produced mixed results after an evaluation against EC data from ICOS

Mass- and field-shift isotope parameters for the 2s−2p2s - 2p resonance doublet of lithium-like ions

It was recently shown that dielectronic recombination measurements can be used for accurately inferring changes in the nuclear mean-square charge radii of highly-charged lithium-like neodymium [Brandau et al., Phys. Rev. Lett. 100 073201 (2008)]. To make use of this method to derive information about the nuclear charge distribution for other elements and isotopes, accurate electronic isotope shift parameters are required. In this work, we calculate and discuss the relativistic mass- and field-shift factors for the two $2s ^{2}S_{1/2} - 2p ^{2}P^{o}_{1/2,3/2}$ transitions along the lithium isoelectronic sequence. Based on the multiconfiguration Dirac-Hartree-Fock method, the electron correlation and the Breit interaction are taken into account systematically. The analysis of the isotope shifts for these two transitions along the isoelectronic sequence demonstrates the importance and competition between the mass shifts and the field shifts.Comment: Accepted by Phys. Rev.

An epidermis-driven mechanism positions and scales stem cell niches in plants.

How molecular patterning scales to organ size is highly debated in developmental biology. We explore this question for the characteristic gene expression domains of the plant stem cell niche residing in the shoot apical meristem. We show that a combination of signals originating from the epidermal cell layer can correctly pattern the key gene expression domains and notably leads to adaptive scaling of these domains to the size of the tissue. Using live imaging, we experimentally confirm this prediction. The identified mechanism is also sufficient to explain de novo stem cell niches in emerging flowers. Our findings suggest that the deformation of the tissue transposes meristem geometry into an instructive scaling and positional input for the apical plant stem cell niche.This work was funded by grants from the Gatsby Charitable Foundation (GAT3395/PR4) and the Swedish Research Council (VR2013-4632) to HJ; and by Gatsby Charitable Foundation grants GAT3272/C and GAT3273-PR1, the US National Institutes of Health (R01 GM104244), the Howard Hughes Medical Institute, and the Gordon and Betty Moore Foundation (GBMF3406) to EMM).This is the final version of the article. It first appeared from the American Association for the Advancement of Science via http://dx.doi.org/10.1126/sciadv.150098

Layering and temperature-dependent magnetization and anisotropy of naturally produced Ni/NiO multilayers

Ni/NiO multilayers were grown by magnetron sputtering at room temperature, with the aid of the natural oxidation procedure. That is, at the end of the deposition of each single Ni layer, air is let to flow into the vacuum chamber through a leak valve. Then, a very thin NiO layer (~1.2nm) is formed. Simulated x-ray reflectivity patterns reveal that layering is excellent for individual Ni-layer thickness larger than 2.5nm, which is attributed to the intercalation of amorphous NiO between the polycrystalline Ni layers. The magnetization of the films, measured at temperatures 5–300K, has almost bulk- like value, whereas the films exhibit a trend to perpendicular magnetic anisotropy (PMA) with an unusual significant positive interface anisotropy contribution, which presents a weak temperature dependence. The power-law behavior of the multilayers indicates a non-negligible contribution of higher order anisotropies in the uniaxial anisotropy. Bloch-law fittings for the temperature dependence of the magnetization in the spin-wave regime show that the magnetization in the multilayers decreases faster as a function of temperature than the one of bulk Ni. Finally, when the individual Ni-layer thickness decreases below 2nm, the multilayer stacking vanishes, resulting in a dramatic decrease of the interface magnetic anisotropy and consequently in a decrease of the perpendicular magnetic anisotropy

Microfluidic devices for photo-and spectroelectrochemical applications

The review presents recent developments in electrochemical devices for photo- and spectroelectrochemical investigations, with the emphasis on miniaturization (i.e., nanointerdigitated complementary metal-oxide-semiconductor devices, micro- and nano-porous silicon membranes or microoptoelectromechanical systems), silica glass/microreactors (i.e., plasmonic, Raman spectroscopy or optical microcavities) or polymer-based devices (i.e., 3D-printed, laser-engraved channels). Furthermore, we have evaluated inter alia the efficiency of various fabrication approaches for bioelectrochemical systems, biocatalysis, photochemical synthesis, or single nanoparticle spectroelectrochemistry. We envisioned the miniaturization of applied techniques such as cathodoluminescence, surface plasmon resonance, surface-enhanced Raman spectroscopy, voltametric and amperometric methods in the spectroelectrochemical microdevices. The research challenges and development perspectives of microfluidic, and spectroelectrochemical devices were also elaborated on.publishedVersio

Transient motion classification through turbid volumes via parallelized single-photon detection and deep contrastive embedding

Fast noninvasive probing of spatially varying decorrelating events, such as cerebral blood flow beneath the human skull, is an essential task in various scientific and clinical settings. One of the primary optical techniques used is diffuse correlation spectroscopy (DCS), whose classical implementation uses a single or few single-photon detectors, resulting in poor spatial localization accuracy and relatively low temporal resolution. Here, we propose a technique termed Classifying Rapid decorrelation Events via Parallelized single photon dEtection (CREPE)}, a new form of DCS that can probe and classify different decorrelating movements hidden underneath turbid volume with high sensitivity using parallelized speckle detection from a $32\times32$ pixel SPAD array. We evaluate our setup by classifying different spatiotemporal-decorrelating patterns hidden beneath a 5mm tissue-like phantom made with rapidly decorrelating dynamic scattering media. Twelve multi-mode fibers are used to collect scattered light from different positions on the surface of the tissue phantom. To validate our setup, we generate perturbed decorrelation patterns by both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as well as a vessel phantom containing flowing fluid. Along with a deep contrastive learning algorithm that outperforms classic unsupervised learning methods, we demonstrate our approach can accurately detect and classify different transient decorrelation events (happening in 0.1-0.4s) underneath turbid scattering media, without any data labeling. This has the potential to be applied to noninvasively monitor deep tissue motion patterns, for example identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates within a compact and static detection probe.Comment: Journal submissio

A Time Projection Chamber with GEM-Based Readout

For the International Large Detector concept at the planned International Linear Collider, the use of time projection chambers (TPC) with micro-pattern gas detector readout as the main tracking detector is investigated. In this paper, results from a prototype TPC, placed in a 1 T solenoidal field and read out with three independent GEM-based readout modules, are reported. The TPC was exposed to a 6 GeV electron beam at the DESY II synchrotron. The efficiency for reconstructing hits, the measurement of the drift velocity, the space point resolution and the control of field inhomogeneities are presented.Comment: 22 pages, 19 figure

Parallelized computational 3D video microscopy of freely moving organisms at multiple gigapixels per second

To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to 230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae

The remarkably low affinity of CD4/peptide-major histocompatibility complex class II protein interactions

The αβ T-cell co-receptor CD4 enhances immune responses more than one million-fold in some assays, and yet the affinity of CD4 for its ligand, peptide-major histocompatibility class II (pMHC II) on antigen-presenting cells, is so weak that it was previously unquantifiable. Here, we report that a soluble form of CD4 failed to bind detectably to pMHC II in surface plasmon resonance-based assays, establishing a new upper limit for the solution affinity at 2.5 mM. However, when presented multivalently on magnetic beads, soluble CD4 bound pMHC II-expressing B cells, confirming that it is active and allowing mapping of the native co-receptor binding site on pMHC II. Whereas binding was undetectable in solution, the affinity of the CD4/pMHC II interaction could be measured in two dimensions (2D) using CD4- and adhesion molecule-functionalized, supported lipid bilayers, yielding a 2D dissociation constant, Kd, of ~5000 molecules/μm2. This value is 2-3 orders of magnitude higher than previously measured 2D Kd values for interacting leukocyte surface proteins. Calculations indicated, however, that CD4/pMHC II binding would increase rates of T-cell receptor (TCR) complex phosphorylation by three-fold via the recruitment of Lck, with only a small, 2-20% increase in the effective affinity of the TCR for pMHC II. The affinity of CD4/pMHC II therefore appears to be set at a value that increases T-cell sensitivity by enhancing phosphorylation, without compromising ligand discrimination.This work was supported by the Wellcome Trust and the UK Medical Research Council. PJ was supported by grants from the Swedish Research Council (number: 623-2014- 6387 and 621-2014-3907). OD is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number: 098363)

Improving land cover classification using input variables derived from a geographically weighted principal components analysis

This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested