Wave Impact Study on a Residential Building

Recent natural disasters around the world including both tsunamis and hurricanes, have highlighted the inability of wood buildings to withstand wave and surge loading during these extreme events. Little is known about the interaction between coastal residential light-frame wood buildings and wave and surge loading because often little is left of the buildings. This leaves minimal opportunity for forensic investigations. This paper summarizes the results of a study whose objective was to begin to better understand the interaction between North American style residential structures and wave loading. To do this, one-sixth scale residential building models typical of North American coastal construction, were subjected to tsunami wave bores generated from waves of heights varying from 10 cm to 60 cm. The lateral force produced by the wave bores were, as expected, found to vary nonlinearly with parent wave height.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Fuji Technology Press. The published article can be found at: http://www.fujipress.jp/JDR/.Keywords: hurricane, residential building, light-frame wood, tsunami, bore, wav

GREP^DR5 accurately translates across model systems (<i>in vitro</i> to <i>in vivo</i>) and platforms (microarray to RNA-seq).

<p>GREP^DR5 predictions trained on cell lines, has been applied to pancreatic primary tumor xenograft (PTX) samples using microarray and RNA-seq data. <i>(A)</i> Performance of DR5+Casp8 predictor on 11 PTX models results in a PPV = 50%. <i>(B)</i> GREP^DR5 prediction probability accurately predicts %T/C in 11 pancreatic PTX models (PPV = 100%). Additionally, GREP^DR5 predictions correlates linearly with the anti-tumor activity (%T/C or %Regression) (Pearson’s R = -0.91, p = 10⁻⁵). <i>(C)</i> GREP^DR5 predictions between the microarray and RNA-seq platforms are highly correlated (R = 0.87), showing that GREP can be readily used for translation to another platform. It should be noted that the data was not transformed (e.g. scaled or batch corrected) before applying the predictions.</p

GREP improves predictions and translatability over standard approaches for feature selection and classification.

<p>(<i>A</i>) GREP^DR5 compared to ratio classifiers with random gene sets (N = 100) of the same size as the hypothesis gene set. 90% of the models were not significantly better than random (Fisher’s test p-value>0.05). Examination of some random models that performed significantly showed that they included DR5-related genes. (<i>B</i>) Challenging the GREP modeling assumptions in predicting <i>in vitro</i> response. GREP^DR5 was compared to four models, each built without one or more of its key assumptions (error bars show 95% confidence from cross-validation). GREP^DR5 outperforms random and 2-gene classifiers, but a standard gene expression predictor that used ratios for feature selection performed just as well in cell lines. (<i>C</i>) Challenging the GREP modeling assumptions in predicting <i>in vivo</i> response. Validation of the classifier predictions in pancreatic patient-derived tumor xenograft models (PTX) compared to classifiers built with single genes. Assuming a 30% margin of error on the PPV calculation for 11 samples (95% confidence), GREP outperforms both the 2-gene classifier and a standard gene expression predictor that used ratios for feature selection. Vertical dotted line denotes AUC of random classifier (0.5).</p

DR5Nb1-tetra is selective with responses in multiple tumor lineages.

<p>(<i>A</i>) Schematic diagram of DR5Nb1-tetra Nanobody. <i>(B)</i> Composition of <i>in vitro</i> pan-cancer screen tested for response to DR5Nb1-tetra. <i>(C)</i> DR5Nb1-tetra response in the CLiP, CRXX and Lab screens. Response is shown as A_max relative to IC₅₀. A_max cut-offs for sensitive, intermediate, and insensitive classes are drawn. (<i>D</i>) Consistency of A_max values across the three screens. A_max values for each screen (CLIP, CRXX and Lab) are shown as a heatmap colored to represent sensitive (red), intermediate (yellow) and insensitive (blue) categories defined using the same thresholds for A_max across the three screens. Missing values are shown in gray. (<i>E</i>) Response rates (% sensitives) are plotted for each of the lineages (#cell lines ≥10). Lineages with significant (p<0.05 using Fisher’s exact test) enrichment are denoted by *.</p

Gene expression ratio predictor (GREP).

<p>(<i>A</i>) Comparison of DR5 versus cFLIP expression in sensitive (red) and insensitive (blue) pancreatic cell lines shows their opposing effects on response. DR5>cFLIP explains sensitivity better than high DR5 alone. (<i>B</i>) Flowchart of analysis steps used for our GREP^DR5 analysis. CLIP, CRXX and Lab indicate independent <i>in vitro</i> screens described in text. <i>(C)</i> Comparison of GREP performance in cross-validation (CV) between cell lines from a screen (training set) and tested in a completely independent screen (Test), using the CLiP and combined CRXX+Lab screens. Each chart shows the reciever-operator curves for prediction fidelity, for CV and Test runs, first using CLiP and then using CRXX+Lab as the training set. To preserve complete independence for the “Test” analyses, all lines common to the two screen sets were removed from the test set. (<i>D</i>) GREP^DR5 prediction probability across sensitive (red), intermediate (yellow) and insensitive (blue) cell lines defined by a prediction threshold of 0.5.</p

DR5 and CASP8 expression are top ranking features correlated with sensitivity and together improve predictions.

<p>(<i>A</i>) Differential association of gene expression using p-values to assess the significance of gene expression features correlated with DR5Nb1-tetra sensitivity. Inset shows the top 20 genes (dotted line = FDR < 0.05). Colors are based on association with sensitives (red) and insensitives (blue). (<i>B</i>) Comparison of relative surface protein levels of DR5 to mRNA expression in 25 pancreatic cancer cell lines. Points are colored based on sensitivity to DR5Nb1-tetra: sensitives (red), intermediates (orange) and insensitives (blue). <i>(C)</i> Induction of Casp8 activity compared to DR5 gene expression in 27 pancreatic cell lines. (<i>D</i>) DR5+Casp8 expression compared to sensitivity. DR5 and Casp8 are individually significantly associated with response, as shown in the marginal histograms for sensitives (red), insensitives (blue), with overlap shown in purple (DR5: p = 10⁻¹², Casp8: p = 10⁻⁹). Scatterplot of DR5 and Casp8 shows that cell lines with high expression of both genes (marked by red box) are enriched in sensitives (PPV = 44%) compared to high DR5 (PPV = 34%) and unselected (PPV = 34%).</p