Nine regions involved in the computation of the local descriptor.

<p>The red arrow indicates the canonical orientation.</p

Superoleophilic and Flexible Thermoplastic Polymer Nanofiber Aerogels for Removal of Oils and Organic Solvents

Chemical cross-linked poly­(vinyl alcohol-<i>co</i>-ethylene) (EVOH) nanofiber aerogels (NFAs) were fabricated employing an economical and facile freeze-drying process. The manufactured chemical cross-linking nanofiber aerogel was successfully confirmed by scanning electron microscopy, attenuated total reflection-Fourier transform infrared spectrometer, and X-ray diffraction. The resulting aerogels showed high porosity (>99%), superior elasticity, elastic durability, high hydrophobicity, and superoleophilicity without any other hydrophobic modification. The cross-linked EVOH NFAs exhibited excellent absorption capacity (ranging from 45 to 102 times their own weight) when exposed to various oils and organic solvents, which was observed to be higher than that for most sorbents reported in the literature. Consequently, it is envisaged that the cross-linked EVOH NFA would play an important role in many fields of pollution removal

The overall flowchart of 3DMKDSRC.

<p>The overall flowchart of 3DMKDSRC.</p

Gene sets associated with the two-dimensional clinical outcome based on MeDiA.

<p>^* Superscripts by the GO terms are for easy reference from the main text.</p><p>Gene sets associated with the two-dimensional clinical outcome based on MeDiA.</p

Random samples generated from independent bivariate normal distribution (left), and mixture bivariate normal distribution with ±0.8 covariates (right).

<p>The dashed lines connects two observations if they are nearest neighbors.</p

MeDiA: Mean Distance Association and Its Applications in Nonlinear Gene Set Analysis

<div><p>Probabilistic association discovery aims at identifying the association between random vectors, regardless of number of variables involved or linear/nonlinear functional forms. Recently, applications in high-dimensional data have generated rising interest in probabilistic association discovery. We developed a framework based on functions on the observation graph, named MeDiA (<u>M</u>ean <u>D</u>istance <u>A</u>ssociation). We generalize its property to a group of functions on the observation graph. The group of functions encapsulates major existing methods in association discovery, e.g. mutual information and Brownian Covariance, and can be expanded to more complicated forms. We conducted numerical comparison of the statistical power of related methods under multiple scenarios. We further demonstrated the application of MeDiA as a method of gene set analysis that captures a broader range of responses than traditional gene set analysis methods.</p></div

Summary of methods on Probabilistic association discovery discussed in this paper.

<p>Summary of methods on Probabilistic association discovery discussed in this paper.</p

Rank-1 recognition rates on Bosphorus.

<p>Rank-1 recognition rates on Bosphorus.</p

Comparison between the independent bivariate normal distribution and mixture normal distribution in Fig 1.

<p>Comparison between the independent bivariate normal distribution and mixture normal distribution in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124620#pone.0124620.g001" target="_blank">Fig 1</a>.</p

An example of a coefficient vector which is not sparse.

<p>An example of a coefficient vector which is not sparse.</p