A novel prestack sparse azimuthal AVO inversion

In this paper we demonstrate a new algorithm for sparse prestack azimuthal AVO inversion. A novel Euclidean prior model is developed to at once respect sparseness in the layered earth and smoothness in the model of reflectivity. Recognizing that methods of artificial intelligence and Bayesian computation are finding an every increasing role in augmenting the process of interpretation and analysis of geophysical data, we derive a generalized matrix-variate model of reflectivity in terms of orthogonal basis functions, subject to sparse constraints. This supports a direct application of machine learning methods, in a way that can be mapped back onto the physical principles known to govern reflection seismology. As a demonstration we present an application of these methods to the Marcellus shale. Attributes extracted using the azimuthal inversion are clustered using an unsupervised learning algorithm. Interpretation of the clusters is performed in the context of the Ruger model of azimuthal AVO

MOESM1 of Immunohistochemical prognostic markers of esophageal squamous cell carcinoma: a systematic review

Additional file 1: Table S1. Description of original studies included in the systematic review

MOESM2 of Immunohistochemical prognostic markers of esophageal squamous cell carcinoma: a systematic review

Additional file 2: Table S2. Assessment of prognostic biomarker studies for risk of bias using the “Quality Assessment in Prognostic studies” (QUIPS) tool

A Modularity-Based Method Reveals Mixed Modules from Chemical-Gene Heterogeneous Network

<div><p>For a multicomponent therapy, molecular network is essential to uncover its specific mode of action from a holistic perspective. The molecular system of a Traditional Chinese Medicine (TCM) formula can be represented by a 2-class heterogeneous network (2-HN), which typically includes chemical similarities, chemical-target interactions and gene interactions. An important premise of uncovering the molecular mechanism is to identify mixed modules from complex chemical-gene heterogeneous network of a TCM formula. We thus proposed a novel method (MixMod) based on mixed modularity to detect accurate mixed modules from 2-HNs. At first, we compared MixMod with Clauset-Newman-Moore algorithm (CNM), Markov Cluster algorithm (MCL), Infomap and Louvain on benchmark 2-HNs with known module structure. Results showed that MixMod was superior to other methods when 2-HNs had promiscuous module structure. Then these methods were tested on a real drug-target network, in which 88 disease clusters were regarded as real modules. MixMod could identify the most accurate mixed modules from the drug-target 2-HN (normalized mutual information 0.62 and classification accuracy 0.4524). In the end, MixMod was applied to the 2-HN of Buchang naoxintong capsule (BNC) and detected 49 mixed modules. By using enrichment analysis, we investigated five mixed modules that contained primary constituents of BNC intestinal absorption liquid. As a matter of fact, the findings of <i>in vitro</i> experiments using BNC intestinal absorption liquid were found to highly accord with previous analysis. Therefore, MixMod is an effective method to detect accurate mixed modules from chemical-gene heterogeneous networks and further uncover the molecular mechanism of multicomponent therapies, especially TCM formulae.</p></div

Tests of five methods on benchmark 2-HNs with varying <i>μ</i>_A and <i>μ</i>_B.

<p>(<b>a</b>). Normalized Mutual Informations (NMIs) of five methods on benchmarks with <i>p</i> = 0.5 and <i>μ</i>_B = 0.2. (<b>b</b>). NMIs when <i>p</i> = 0.5 and <i>μ</i>_B = 0.8. (<b>c</b>). NMIs when <i>μ</i>_A = 0.2 and <i>p</i> = 0.5. (<b>d</b>). NMIs when <i>μ</i>_A = 0.8 and <i>p</i> = 0.5. (<b>e</b>)(<b>f</b>)(<b>g</b>)(<b>h</b>). CAs of five methods on 2-HNs with different parameters. In these figures, the variation curve of each method is marked by a unique color as shown in (<b>f</b>).</p

Performance of five methods on real drug-target heterogeneous network.

<p>All modules include mixed modules and modules of single-class nodes.</p><p>Performance of five methods on real drug-target heterogeneous network.</p

An illustration of a chemical-gene heterogeneous network.

<p>The blue nodes are chemical constituents and the red nodes represent potential gene targets. This network is an instance of 2-class heterogeneous network [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125585#pone.0125585.ref009" target="_blank">9</a>], which is more than a simple chemical-gene bipartite graph by including additional interactions between chemicals and between genes. Obviously, there are three mixed modules (1, 2, and 3) in this heterogeneous network. Each mixed module is a highly-interconnected unit in which chemicals directly or indirectly regulate the expression of corresponding genes. Additionally, module A and B are also considered as special cases of mixed module. Such modules may influence the final partition of module detection methods, but make little contribution to uncovering particular molecular mechanism.</p

Tests of four methods on weighted benchmarks.

<p>(<b>a</b>). Normalized Mutual Informations (NMIs) of four methods on 2-HNs with different <i>μ</i>_A, <i>μ</i>_B and <i>p</i>. The subnetwork <i>G</i>_A of each 2-HN is weighted according to the weighting scheme of LFR benchmark. (<b>b</b>). NMIs of four methods on 2-HNs with weighted subnetwork <i>G</i>_Π. (<b>c</b>). NMIs of four methods on 2-HNs with weighted <i>G</i>_B.</p

Three Dimensional and Homogenous Single Cell Cyclic Stretch within a Magnetic Micropillar Array (mMPA) for a Cell Proliferation Study

The physical properties of the extracellular matrix (ECM) are a key aspect of the cell microenvironment. A biological system is a highly dynamic organization. In our study, we designed and prepared a large area of magnetic PDMS elastomer micropillar array (mMPA) with robust and tunable movement for cell mechanics study. The rotational movement frequency of the micropillars could be precisely controlled by a home-built magnetic actuation apparatus. Cells cultured in the mMPA could be suspended in between two micropillars in a single level and exhibited a 3D structure. With the rotational movement of the micropillar, a homogeneous stretchable force could be applied to a single cell along it long axis with various frequencies. We exclusively studied the influence of dynamic properties of the micropillar movement on cell behaviors. We found that, by fixing the amplitude of the stretchable force, the frequency-based properties of the cell microenvironment could significantly change cell functions. The cell behaviors are dependent on the micropillar movement frequency and a transition from proliferation to apoptosis/death exhibited with the increment of the force application frequency

Novel Carbonothioate-Based Colorimetric and Fluorescent Probe for Selective Detection of Mercury Ions

The development of probes for selective detection of mercury ions (Hg²⁺) is an important mission to accomplish because of the toxicity and ubiquity of Hg²⁺. Herein, we designed and synthesized a novel fluorescent probe <i>O</i>-(<i>N</i>-butyl-1,8-naphthalimide)-4-yl-<i>O</i>-phenyl carbonothioate (<b>CBONT</b>) for selective and sensitive detection of Hg²⁺ by turn-on fluorescence spectroscopy. Probe <b>CBONT</b> exhibited a fast response for Hg²⁺ with excellent sensitivity (limit of detection = 1.9 nM, 3σ/slope), and it might be attributed to the adoption of a new recognition receptor of carbonothioate moiety. Additionally, probe <b>CBONT</b> could serve as a “naked-eye” indicator for Hg²⁺. Finally, probe <b>CBONT</b> could be successfully applied to detect the concentrations of Hg²⁺ in real water samples. Our proposed recognition receptor would open up new, exciting opportunities for designing highly selective and ultrasensitive fluorescent probes for the determination of Hg²⁺ in real water samples