Oriented clonal cell dynamics enables accurate growth and shaping of vertebrate cartilage.

Cartilaginous structures are at the core of embryo growth and shaping before the bone forms. Here we report a novel principle of vertebrate cartilage growth that is based on introducing transversally-oriented clones into pre-existing cartilage. This mechanism of growth uncouples the lateral expansion of curved cartilaginous sheets from the control of cartilage thickness, a process which might be the evolutionary mechanism underlying adaptations of facial shape. In rod-shaped cartilage structures (Meckel, ribs and skeletal elements in developing limbs), the transverse integration of clonal columns determines the well-defined diameter and resulting rod-like morphology. We were able to alter cartilage shape by experimentally manipulating clonal geometries. Using in silico modeling, we discovered that anisotropic proliferation might explain cartilage bending and groove formation at the macro-scale

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar

Argyroploce arbutella (Tortricidae) associated with a montane peat bog in the Sumava Mountains, Czech Republic

Volume: 21Start Page: 283End Page: 28

Apotomis Fraterculana Krogerus, 1946, a Northern Tortricid Moth in Central Europe (Tortricidae)

Volume: 27Start Page: 89End Page: 9

The geographic range of Rhyacionia hafneri (Rebel, 1937) (Tortricidae)

Volume: 28Start Page: 69End Page: 6

Acceleration of grammatical evolution using graphics processing units

Presented at the CIGPU Workshop at GECCO '11, the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible application and/or the extent of experimentation undertaken. This paper deals with utilizing mainstream graphics processing units (GPU) for acceleration of GE solving symbolic regression. GPU optimization details are discussed and the NVCC compiler is analyzed. We design an effective mapping of the algorithm to the CUDA framework, and in so doing must tackle constraints of the GPU approach, such as the PCI-express bottleneck and main memory transactions. This is the first occasion GE has been adapted for running on a GPU. We measure our implementation running on one core of CPU Core i7 and GPU GTX 480 together with a GE library written in JAVA, GEVA. Results indicate that our algorithm offers the same con- vergence, and it is suitable for a larger number of regression points where GPU is able to reach speedups of up to 39 times faster when compared to GEVA on a serial CPU code written in C. In conclusion, properly utilized, GPU can offer an interesting performance boost for GE tackling symbolic regression.Science Foundation IrelandOther funderCzech Science FoundationFaculty of Information Technology, Brno University of Technologyti, sp, ke, ab, co, li- TS 23.02.1

Comparison and practical review of segmentation approaches for label-free microscopy

<p>This dataset contains microscopic images of PNT1A cell line captured by multiple microcopic without use of any labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation. </p> <p><strong>Materials and methods </strong></p> <p>Cells were cultured in RPMI-1640 medium supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml) with 10% fetal bovine serum. Prior microscopy acquisition, cells were maintained at 37 cenigrade in a humidified incubator with 5% CO2. Intentionally, high passage number of cells was used (>30) in order to describe distinct morphological heterogeneity of cells (rounded and spindle-shaped, relatively small to large polyploid cells). For acquisition purposes, cells were cultivated in Flow chambers µ-Slide I Luer Family (Ibidi, Martinsried, Germany).</p> <p>Quantitative phase imaging (QPI) microscopy was performed on Tescan Q-PHASE (Tescan, Brno, Czech republic), with objective Nikon CFI Plan Fluor 10x/0.30 captured by Ximea MR4021MC (Ximea, Münster, Germany). Imaging is based on the original concept of coherence-controlled holographic microscope \cite{Kolman:10,Slaby:13}, images are shown in grayscale with units of pg/µm2.</p> <p>DIC microscopy was performed on microscope Nikon A1R (Nikon, Tokyo, Japan), with objective Nikon CFI Plan Apo VC 20x/0.75 captured by CCD camera Jenoptik ProgRes MF (Jenoptik, Jena, Germany). </p> <p>HMC microscopy was performed on microscope Olympus IX71 (Olympus, Tokyo, Japan), with objective Olympus CplanFL N 10x/0.3 RC1 captured by CCD camera Hamamatsu Photonics ORCA-R2 (Hamamatsu Photonics K.K., Hamamatsu, Japan).</p> <p>PC microscopy was performed on a Nikon Eclipse TS100-F microscope, with a Nikon CFI Achro ADL 10x/0.25 objective captured by CCD camera Jenoptik ProgRes MF.</p> <p><strong>Folder structure and file and filename description</strong><br> <br> <em>folder "source data+groundtruth"</em><br> - includes raw microscopic data <br>   (uncompressed 16-bit for DIC, HMC and PC, 32-bit for QPI)<br> - includes manualy annotated groundtruth (zip file - imageJ ROI file, 1bit png mask)</p> <p>e.g. <br> DIC_01_raw.tif<br> DIC_01_groundtruth_imagejROI.zip<br> DIC_01_groundtruth_mask.png</p> <p><br> <em>folder "reconstructions"</em></p> <p>includes reconstructed images using reconstructions with highest dice coefficient achieved. </p> <p>for DIC and HMC: rDIC-Koos, rDIC-Yin, and rWeka<br> for PC: rPC-Top-Hat, rDIC-Yin, and rWeka<br> for QPI: rWeka</p> <p>note that for rWeka images numbered 01 for DIC, HMC and PC and 01-03 for QPI were used for learning.</p> <p><strong>Abbreviations</strong><br> DIC, differential image contrast<br> HMC, Hoffman modulation contrast<br> PC, phase contrast<br> QPI, quantitative phase imaging<br> rDIC-Koos, DIC/HMC image reconstruction according to Koos et al, Sci Rep. 2016;6:30420<br> rDIC-Yin, DIC/HMC image reconstruction according to Yin et al, Inf Process Med Imaging. 2011;22:384-97.<br> rPC-Yin, PC image reconstruction according to Yin et al,  Med Im Anal. 2012; 16(5):1047<br> rPC-Top-Hat, Top-Hat filter according to Dewan et al, IEEE Transactions on Biomedical Circuits and<br> Systems.2014;8(5):716-728<br> rWeka, probability map using Trainable Weka segmentation according to Arganda-Carreras et al. Bioinformatics. 2017</p