A Simplified Version of Wnt/Wg Signaling

<p>In the absence of the Wnt ligand, Arm/ β-catenin (β) is phosphorylated by a complex of Axin, APC, and Zw3/GSK3 β and rapidly degraded. Upon Wnt (W) signaling through the Frizzled receptor and Dsh, this complex is inhibited: as a consequence, β-catenin accumulates and binds to LEF/TCF proteins to stimulate transcription of Wnt target genes.</p

Automatic Breast Mass Detection and Classification for Mammograms with Deep Learning

Breast cancer has become one of the most concerning cancers that are well known for its high incidence rate and mortality. Integration of novel deep learning techniques in computer aided systems for quick diagnosis of breast cancer caused by breast mass is of great importance. In thesis, I aimed at developing a breast mass detection and classification system for mammography images based on deep learning techniques.The developed system is comprised of three modules including pre-processing, breast mass detection, and breast mass classification. The pre-processing module mainly focuses on standardizing the input images for following computation. The main contribution within pre-processing module is that I developed a novel attention mechanism called global channel attention module, which helps the deep learning network to learn inter-channel attention and thus improves the segmentation performance of deep learning networks. To develop a breast mass detection module with higher detection performance but with lower training cost, I then developed a new patch-based breast mass detection system, within which I improved the patch extraction algorithm and developed a new false positive suppression algorithm. Thanks to proposed algorithms, the developed breast mass detection system can achieve the state-of-the-art performance. In the newly developed breast mass classification module, I proposed to feed classifiers with fused deep learning features. By doing so, the overall classification performance can be improved thanks to better usage of information. Moreover, the training efficiency of the novel classifier is much higher than the back propagation based learning algorithms thanks to the novel architectures introduced. The experiments on public datasets showed high performance of the three developed modules against the state-of-the-art methods while some of them showed even higher performance than the state-of-the-art methods.</p

Final single-objective best solutions obtained by the swarms of MSCLPSO on all the benchmark problems.

<p>Final single-objective best solutions obtained by the swarms of MSCLPSO on all the benchmark problems.</p

Implementation of dynamic coupling in hybrid Molecular Dynamics–Lattice Boltzmann approach: Modeling aggregation of amphiphiles

Amphiphile-based aggregates are extensively used in numerous applications for encapsulation, storage, transport and delivery of toxic, active molecules due to the structural properties of the aggregates. The properties of the aggregates in turn are dictated by the molecular architecture of the amphiphiles. A complete understanding of the multiscale architecture–structure–function relationship for amphiphile-based aggregates requires the simultaneous resolution of the self-assembly of amphiphilic molecules along with an understanding of the role of various long range physical interactions including hydrodynamics. A multiscale computational approach such as the hybrid Molecular Dynamics–Lattice Boltzmann technique is able to fulfill most of those requirements. However, existing implementations only account for static coupling between the Molecular Dynamics technique and the Lattice Boltzmann method, and hence are unable to resolve the changes in the solvent-amphiphile interface during processes such as self-assembly and interfacial adsorption. In this study, a new implementation incorporating a dynamic coupling scheme between the Molecular Dynamics technique and the Lattice Boltzmann method is introduced so as to resolve dynamical changes in interfaces. The application of the new implementation to the self-assembly of phospholipids yields results which are in good agreement with computation, experiments and theory. In particular, we found the scaling exponent α of the cluster number (N(t) = C t^α) to be ~1. The previous version of this program (AEPH_v1_0) may be found at http://dx.doi.org/10.1016/j.cpc.2013.03.024

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems

<div><p>Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.</p></div

Number of function evaluations on all the benchmark problems.

Number of function evaluations on all the benchmark problems.</p

Conformational energy landscapes with respect to backbone RMSD and Rg for (A) DWT25, (B) DWT265, (C) DWT330, and (D) DMT25.

<p>Labels of 1 and 2 in the maps represent the initial (left) and the final (right) structures at 0 ns and 40 ns, respectively. Color codes: negatively charged residues (<i>red</i>), positively charged residues (<i>blue</i>), hydrophilic residues (<i>green</i>), and hydrophobic residues (<i>white</i>). C_β atoms of Met35 are shown by VDW spheres to guide eyes.</p

Time evolution of globulomer sphericity for DWT25 (<i>black</i>), DWT265 (<i>red</i>), DWT330 (<i>green</i>), MWT0 (<i>yellow</i>), MWT90 (<i>blue</i>), MWT170 (<i>pink</i>), D25M (<i>cyan</i>), and M0M (<i>gray</i>).

<p>Time evolution of globulomer sphericity for DWT25 (<i>black</i>), DWT265 (<i>red</i>), DWT330 (<i>green</i>), MWT0 (<i>yellow</i>), MWT90 (<i>blue</i>), MWT170 (<i>pink</i>), D25M (<i>cyan</i>), and M0M (<i>gray</i>).</p

Averaged structures of monomer-based Aβ globulomers of (A) MWT0, (B) MWT90, (C) MWT170, and (D) MMT0 and dimer-based Aβ globulomers of (E) DWT25, (F) DWT265, (G) DWT330, and (H) DMT25 from the last 5-ns MD simulations.

<p>The residue-based RMSF is imposed on each averaged structure using a <i>blue-white-red</i> scale, with low RMSF of <3 Å (<i>blue</i>), intermediate RMSF of 3∼6 Å (<i>white</i>), and high RMSF of >6 Å (<i>red</i>).</p

Conformational energy landscapes with respect to backbone RMSD and Rg for (A) MWT0, (B) MWT90, (C) MWT170, and (D) MMT0.

<p>Labels of 1 and 2 in the landscapes represent the initial (left) and the final (right) structures at 0 ns and 40 ns, respectively. Color codes: negatively charged residues (<i>red</i>), positively charged residues (<i>blue</i>), hydrophilic residues (<i>green</i>), and hydrophobic residues (<i>white</i>). C_β atoms of Met35 are shown by VDW spheres to guide eyes. All cartoon structures are rendered by VMD <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020575#pone.0020575-Humphrey1" target="_blank">[62]</a>.</p