Ceramic matrix composite turbine engine vane

A vane has an airfoil shell and a spar within the shell. The vane has an outboard shroud at an outboard end of the shell and an inboard platform at an inboard end of the shell. The shell includes a region having a depth-wise coefficient of thermal expansion and a second coefficient of thermal expansion transverse thereto, the depth-wise coefficient of thermal expansion being greater than the second coefficient of thermal expansion

Relationship of Topography to the Distribution of Soils and to Loess Thickness on the Galva-Primghar Experimental Farm

The preparation of a highly detailed soil map and a contour map of the Galva-Primghar Experimental Farm has provided an opportunity to study the relationship of topography to the distribution of soils and to the loess thickness pattern on the farm. A report on these relationships is given in this paper

Free Iron Distribution in Some Poorly Drained Prairie Soils in Iowa

In classification and mapping of soils an interpretation of the natural drainage characteristics of the soil types is usually made. Some standard natural drainage classes used are poorly drained, imperfectly drained, moderately well- drained, and well-drained (1). Interpretation of the natural drainage of the soils is important from the agronomic standpoint, and also is basic to the soil classification scheme in present use. The natural drainage of a soil is interpreted mainly by inferences from the color and mottling of hydrated iron oxides in the subsoil. Few studies have been made of the nature and quantity of these iron oxides in soils. Extractable iron or free iron has been determined in a few well-drained Brunizem and Gray Brown Podzolic soils, and in several poorly drained Forested Planosols (2) (3) (4) (5). The purpose of this paper is to report data on free iron in several poorly drained prairie (Wiesenboden) soils and to compare these data with available data of other great soil groups in Iowa

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

Influence of Subslab Aggregate Permeability of SSV Performance

The effectiveness of the technique of subslab ventilation (SSV) for limiting radon entry into basements was investigated through complementary experimentation and numerical modeling. Determination of the impact of subslab aggregate permeability on SSV performance was a primary objective. Subslab pressure fields resulting from SSV were measured in six well-characterized basements, each with a different combination of soil and aggregate permeability. The relationship between air velocity and pressure gradient within the three types of aggregate installed beneath the basement slabs was measured in the laboratory. A new numerical model of SSV was developed and verified with the field data. This model simulates non-Darcy flow in the aggregate. We demonstrate that non-Darcy effects significantly impact SSV performance. Field data and numerical simulations indicate that increasing the aggregate permeability within the investigated range of 2 x 10{sup -8} m{sup 2} to 3 x 10{sup -7} m{sup 2} substantially improves the extension of the subslab pressure field due to SSV operation. Subslab pressure field extension also improves as soil permeability decreases between 10{sup -9} m{sup 2} and 10{sup -10} m{sup 2}. With a slab-wall gap thickness of 1 mm and the range of aggregate permeability investigated, further reductions in soil permeability do not significantly improve the subslab pressure field extension. Sealing of cracks in the slab and excavation of a small pit where the SSV pipe penetrates the slab also dramatically improve this pressure field extension. A large ratio of aggregate permeability to soil permeability reduces the need for large depressurizations at the SSV pit. Our findings are consistent with the results of prior field studies; however, our understanding of SSV is improved and the dependence of SSV performance on the relevant parameters can now be quantified with the model

Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available

Network 'small-world-ness': a quantitative method for determining canonical network equivalence

Background: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model-the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. Methodology/Principal Findings: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S. 1-an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. Conclusions/Significance: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing

The TM6SF2 E167K genetic variant induces lipid biosynthesis and reduces apolipoprotein B secretion in human hepatic 3D spheroids

There is a high unmet need for developing treatments for nonalcoholic fatty liver disease (NAFLD), for which there are no approved drugs today. Here, we used a human in vitro disease model to understand mechanisms linked to genetic risk variants associated with NAFLD. The model is based on 3D spheroids from primary human hepatocytes from five different donors. Across these donors, we observed highly reproducible differences in the extent of steatosis induction, demonstrating that inter-donor variability is reflected in the in vitro model. Importantly, our data indicates that the genetic variant TM6SF2 E167K, previously associated with increased risk for NAFLD, induces increased hepatocyte fat content by reducing APOB particle secretion. Finally, differences in gene expression pathways involved in cholesterol, fatty acid and glucose metabolism between wild type and TM6SF2 E167K mutation carriers (N = 125) were confirmed in the in vitro model. Our data suggest that the 3D in vitro spheroids can be used to investigate the mechanisms underlying the association of human genetic variants associated with NAFLD. This model may also be suitable to discover new treatments against NAFLD

Differential Forms on Log Canonical Spaces

The present paper is concerned with differential forms on log canonical varieties. It is shown that any p-form defined on the smooth locus of a variety with canonical or klt singularities extends regularly to any resolution of singularities. In fact, a much more general theorem for log canonical pairs is established. The proof relies on vanishing theorems for log canonical varieties and on methods of the minimal model program. In addition, a theory of differential forms on dlt pairs is developed. It is shown that many of the fundamental theorems and techniques known for sheaves of logarithmic differentials on smooth varieties also hold in the dlt setting. Immediate applications include the existence of a pull-back map for reflexive differentials, generalisations of Bogomolov-Sommese type vanishing results, and a positive answer to the Lipman-Zariski conjecture for klt spaces.Comment: 72 pages, 6 figures. A shortened version of this paper has appeared in Publications math\'ematiques de l'IH\'ES. The final publication is available at http://www.springerlink.co