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Microeconomic data provided by Amadeus database, and contextual variables provided by World Tourism Organization, OECD, and Eurostat. Two measures of corporate profitability are considered in this dataset: Return on equity (ROE) and Return of assets (ROA). Additional financial indicators are included as control variables. Country heterogeneity may be checked using macroeconomic variables and tourism indicators.
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Supplementary material to Olierook et al., 2020 Precambrian Research
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Results of wood decay assays involving known wood-decaying fungi and bark beetle-associated fungi.
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Total RNA was purified from E. pacifica using with an RNeasy Lipid tissue mini kit. The library of E. pacifica for next generation sequencing was made using with a TruSeq RNA library prep kit v2 (Illumina). RNA purification and library preparation were performed according to the manufacturers’ instructions. The library was analyzed by Miseq using a Miseq reagent kit v3 (600 cycle) (Illumina). The fastaq data was assembled by Trinity.
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Table S2 Normalised signal values from arrays in this study
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This data was collected in the Maasai Mara National Reserve in Kenya. A total of 1,170 cattle were sampled and screened for antibodies against foot-and-mouth disease (FMD). A metafile describing the data is attached.
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Four excel files in supplementary materials presents all fault data. Each file of “along-fault displacement” contain two sheets. One sheet show the displacements of segments, and the other sheet show the summed displacements. The file of “D_max and fault length” contain two sheets to show the max displacements and fault length of segments in the DF2 and the DF3, respectively.
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We addressed the accuracy of Lung Ultrasound (LUS) to detect pneumothorax in children with acute chest pain evaluated in the pediatric Emergency Department (pED). Methods We prospectively analyzed patients from 5 to 17 years of age with acute chest pain and clinical suspicion of pneumothorax (PNX) evaluated at a tertiary level pediatric hospital. After clinical examination and before Chest X-Ray (CXR), children underwent LUS to evaluate the presence of PNX. Results We enrolled 77 children, 44 (57,1 %) male, with median age of 10 years and 3 months (IQR 6 years and 9 months - 14 years and 2 months). Thirty (39%) children had interstitial lung disease; 20/77 (26%) had pneumonia with or without pleural effusions; 7/77 (9,1%) had thoracic trauma; 7/77 (9,1%) had a final diagnosis of myo/pericarditis and 13 (16,9%) received a final diagnosis of PNX. In all 13 patients LUS showed the “bar-code sign” while in 12 (92,3%) there was the lung point, giving a diagnosis of PNX. All cases were confirmed by CXR. The lung point had a sensitivity of 92,3% and a specificity of 100%, a positive predictive value of 100% and a negative predictive value of 98,4 % for the detection of PNX. The “bar-code sign” had a sensitivity of 100% and a specificity of 100%, a positive predictive value of 100% and a negative predictive value of 100% for the detection of PNX. Conclusions LUS is highly accurate in detecting or excluding pneumothorax in children with acute chest pain evaluated in the pediatric emergency department. Importantly, both lung-point and M-mode need to be performed when PNX is suspected.
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In view of the irregular trace distribution of rock discontinuities, rock mass appears as both a statistical distribution and a texture distribution in the spatial image. This paper proposes a new method on statistical texture analysis for automated demarcating the homogeneous domains of trace distribution within a rock mass. Grey-Level Co-occurrence Matrix (GLCM) is used to quantify the statistical texture features of trace distribution. Relativity, Inverse Difference Moment and Entropy are screened from ten texture parameters of GLCM using robustness analysis and using principal components analysis. The reliability of three screened texture parameters is verified by comparing the Chebyshev polynomials fitting of three screened texture parameters with Normal distribution, Fisher distribution, and Exponent distribution using χ2 testing. Automated demarcation of the homogeneous domains is implemented by means of classifying three texture parameters of Relativity, Inverse Difference Moment and Entropy in a moving window using the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA). The screening process of texture parameters and a case study indicates that texture parameters and automated demarcation method is so robust, reliable, and efficient that it could replace the traditional representation of the probability statistics in trace distribution and greatly save a lot of manual labor in a large-scale domain.
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Figures and Tables for the manuscript, 'Proteome- and genome- wide profiling of early duodenal cancer'.
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