Improved boundary layer flow modelling by enhanced land surface characterization

In this study the influence of surface roughness and land use type on the simulation of boundary layer (BL) flows is investigated. For wind energy applications the correct prediction of winds in the BL is essential. This requires that the interaction of the flow with the rough surface is captured accurately in numerical models. Several studies show that mesoscale models frequently overestimate wind speeds near the surface especially over complex terrain and fail to reproduce realistic vertical profiles of wind speed including e.g. low level jets (LLJs). It is assumed that state-of-the-art land use data sets don’t represent the surface roughness due to forests, buildings and cropland correctly in models with horizontal resolutions of less than 1km. In addition, land use data sets should be time dependent to consider (seasonal) changes in surface roughness, e.g. due to forest clearances or cultivation of crop. The WRF model is used in this study to run high resolution real-case simulations of BL flows. The case studies are performed over both complex terrain (double-hill site of the Perdigão 2017 campaign, Portugal) and over flat terrain (Comet campaign 2018, Poland). It is shown that aerodynamic roughness lengths from state-of-the-art land use data sets underestimate real surface roughness, which leads to overestimated surface winds. The implementation of a forest parameterization in WRF leads to a considerable improvement and reduction of surface winds due to additional friction terms on the lowermost model levels. This results in a better representation of LLJs.</p

Figure 2

<p>(a) As for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002509#pcbi-1002509-g001" target="_blank">Fig. 1c</a>, but with only main-chain atoms (N, O, C, C, H) included in the force calculation. (b) Mean inter-residue forces (main-chain only) for beta strands 1 and 5. (c) & (d) Distributions of main-chain–main-chain force vs C separation for the residue pairs Gln2-Glu64 (c) and Ile3-Ser65 (c). Each dot corresponds to a single frame from the MD trajectory. The vertical red line shows the mean separation, and the blue curve is a local polynomial fit to the data using the Loess method. The inset highlights the relevant residue pair in the beta sheet structure.</p

Figure 5

<p>(a) Left: Effective force profile for the side-chains of Asp52 and Arg72. Right: Snapshots of various hydrogen binding states observed for the trio of Asp39, Asp52, and Arg72. Sometimes two of the amides in the Asp side-chain are simultaneously hydrogen bonded to the two oxygens in the Asp side-chain (upper left); sometimes an amide is bound to a side-chain oxygen, and another to the backbone carbonyl oxygen (upper right); sometimes only a single bond is formed, to the backbone oxygen (lower left); and sometimes the Asp39-Arg72 salt bridge breaks entirely, and Arg72 instead forms a short-lived salt bridge with Asp52, which is simultaneously bound to Lys27 (lower right). (b) As for (a), but for the residues Leu8 and Val70. In one state (the ‘out’ state), the side-chain of Leu8 is oriented outwards, above that of Val70. In the other (the ‘in’ state), the Leu8 side-chain is buried underneath Val70. Superimposed on the scatterplot are two density plots, which show the density of points belonging to each of the ‘in’ (red) and ‘out’ (blue) states. The classification of points into two states is described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002509#pcbi.1002509.s004" target="_blank">Fig. S4</a>.</p

Dynamic Prestress in a Globular Protein

<div><p>A protein at equilibrium is commonly thought of as a fully relaxed structure, with the intra-molecular interactions showing fluctuations around their energy minimum. In contrast, here we find direct evidence for a protein as a molecular tensegrity structure, comprising a balance of tensed and compressed interactions, a concept that has been put forward for macroscopic structures. We quantified the distribution of inter-residue prestress in ubiquitin and immunoglobulin from all-atom molecular dynamics simulations. The network of highly fluctuating yet significant inter-residue forces in proteins is a consequence of the intrinsic frustration of a protein when sampling its rugged energy landscape. In beta sheets, this balance of forces is found to compress the intra-strand hydrogen bonds. We estimate that the observed magnitude of this pre-compression is enough to induce significant changes in the hydrogen bond lifetimes; thus, prestress, which can be as high as a few 100 pN, can be considered a key factor in determining the unfolding kinetics and pathway of proteins under force. Strong pre-tension in certain salt bridges on the other hand is connected to the thermodynamic stability of ubiquitin. Effective force profiles between some side-chains reveal the signature of multiple, distinct conformational states, and such static disorder could be one factor explaining the growing body of experiments revealing non-exponential unfolding kinetics of proteins. The design of prestress distributions in engineering proteins promises to be a new tool for tailoring the mechanical properties of made-to-order nanomaterials.</p> </div

Average forces for each of the cross-strand hydrogen bonds in beta sheet 1–5.

<p>Error bars show the standard error in the mean.</p

(a) X-ray structure of ubiquitin (PDB accession code 1UBI).

<p>The lower view is rotated 90 around the horizontal axis with respect to the upper view. Helices are colored red, and beta strands green. Note that the beta strands 1 and 5 are closest to the N- and C- termini respectively, and thus the interaction between them is the primary determinant of the protein's mechanical stability against stretching from the termini. (b) The network representing the inter-residue forces for ubiquitin, averaged over 100 ns of molecular dynamics simulations, superimposed on the 3D structure of the protein. The color and width of cylinders connecting residue pairs correspond to the direction and magnitude of the mean inter-residue force: blue for repulsive force, red for attractive force, and the maximum width corresponding to a force magnitude of pN. (c) A circle graph representation of the prestress network in (b). Numbers around the circumference are residue indices. Colored arcs correspond to secondary structure: alpha helix (red), beta strand (green), hydrogen-bonded loop (purple) and 3–10 helix (cyan).</p

(a) and (b) Networks representing the inter-residue forces for ubiquitin, as for <b>Fig. 1c</b>, but accounting for side-chain atoms only; (a) inward-pointing side-chains, (b) outward-facing side-chains.

<p>(a) and (b) Networks representing the inter-residue forces for ubiquitin, as for <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002509#pcbi-1002509-g001" target="_blank"><b>Fig. 1c</b></a>, but accounting for side-chain atoms only; (a) inward-pointing side-chains, (b) outward-facing side-chains.</p

Additional file 1: of Assessing cell-specific effects of genetic variations using tRNA microarrays

Figure S1. Biological replicates of the comparative tRNA microarrays are highly reproducible. a-e Data for HBE (a), organoids (b), CFBE41o- (c), FRT (d) and HNE from a male (NL124) or female (NL117) individual (e) are shown as fold-enrichment (gradient ruler at the bottom) of tRNAs compared with HEK293 cells. Global reproducibility between each two replicates was assessed by Kolmogorov-Smirnov test (for all arrays p ≥ 0.9, i.e. very similar). The reproducibility for each tRNA probe was assessed by variability analysis of each two replicates and is presented as coefficient of variance. tRNA isoacceptors are depicted with their anticodon and corresponding amino acid; Meti-CAU, initiator tRNAMet pairing to the AUG codon. Figure S2. Correlation of tRNA abundance between tested models. a Pairwise correlation of tRNA isoacceptor abundancies. For iPSCs, only the zero time point was considered. R, Pearson correlation coefficient. b Correlation of tRNA isoacceptor abundancies for iPCSs over the course of differentiation. Stem cells at days 5, 10, 15 and 21 (gradient ruler) were compared to non-differentiated cells at time zero. Figure S3. Standard deviations of computed ribosome occupancies per codon among models. In these calculations, only the zero time point was considered for iPSCs. (DOCX 1932 kb

Additional file 2: of Assessing cell-specific effects of genetic variations using tRNA microarrays

Source data tRNA microarrays. (XLSX 18 kb

Additional file 3: of Targeted capture-based NGS is superior to multiplex PCR-based NGS for hereditary BRCA1 and BRCA2 gene analysis in FFPE tumor samples

Table S2 Pathogenic BRCA1/2 variants in investigated blood and FFPE samples. (XLSX 12 kb