Multiquark picture for Lambda(1405) and Sigma(1620)

We propose a new QCD sum rule analysis for the Lambda(1405) and the Sigma(1620). Using the I=0 and I=1 multiquark sum rules we predict their masses.Comment: 5 pages, 3 ps files. Talk given at 11th International Light-Cone School and Workshop : New Directions in Quantum Chromodynamics, and 12th Nuclear Physics Summer School and Symposium (NuSS'99), Seoul, Korea, 26 May - 26 Jun, 199

Lambda(1405) as a multiquark state

In the QCD sum rule approach we predict the $\Lambda$ (1405) mass by choosing the $\pi^0\Sigma^0$ multiquark interpolating field. It is found that the mass is about 1.419 GeV from $\Pi_1 (q^2)$ sum rule which is more reliable than $\Pi_q (q^2)$ sum rule, where $\Pi_q (q^2)$ and $\Pi_1 (q^2)$ are two invariant functions of the correlator $\Pi (q^2)$. We also present the sum rules for the $K^+ p$ and the $\pi^+\Sigma^+$ multiquark states, and compare to those for the $\pi^0\Sigma^0$ multiquark state. The mass of the $\Lambda$ (1600) can be also reproduced in our approach.Comment: revtex, 12 pages, 13 ps figures; considerable revision, accepted in Eur. Phys. J. (A

Molecular dynamics studies of interactions between Arg9(nona-arginine) and a DOPC/DOPG(4:1) membrane

It has been known that the uptake mechanisms of cell-penetrating peptides(CPPs) depend on the experimental conditions such as concentration of peptides, lipid composition, temperature, etc. In this study we investigate the temperature dependence of the penetration of Arg9s into a DOPC/DOPG(4:1) membrane using molecular dynamics(MD) simulations at two different temperatures, T = 310 K and T = 288 K. Although it is difficult to identify the temperature dependence because of having only one single simulation at each temperature and no evidence of translocation of Arg9s across the membrane at both temperatures, our simulations suggest that followings are strongly correlated with the penetration of Arg9s: a number of water molecules coordinated by Arg9s, electrostatic energy between Arg9s and the lipids molecules. We also present how Arg9s change a bending rigidity of the membrane and how a collective behavior between Arg9s enhances the penetration and the membrane bending. Our analyses can be applicable to any cell-penetrating peptides(CPPs) to investigate their interactions with various membranes using MD simulations.Comment: 10 pages, 12 figure

Cubical homology-based Image Classification - A Comparative Study

Persistent homology is a powerful tool in topological data analysis (TDA) to compute, study and encode efficiently multi-scale topological features and is being increasingly used in digital image classification. The topological features represent number of connected components, cycles, and voids that describe the shape of data. Persistent homology extracts the birth and death of these topological features through a filtration process. The lifespan of these features can represented using persistent diagrams (topological signatures). Cubical homology is a more efficient method for extracting topological features from a 2D image and uses a collection of cubes to compute the homology, which fits the digital image structure of grids. In this research, we propose a cubical homology-based algorithm for extracting topological features from 2D images to generate their topological signatures. Additionally, we propose a score, which measures the significance of each of the sub-simplices in terms of persistence. Also, gray level co-occurrence matrix (GLCM) and contrast limited adapting histogram equalization (CLAHE) are used as a supplementary method for extracting features. Machine learning techniques are then employed to classify images using the topological signatures. Among the eight tested algorithms with six published image datasets with varying pixel sizes, classes, and distributions, our experiments demonstrate that cubical homology-based machine learning with deep residual network (ResNet 1D) and Light Gradient Boosting Machine (lightGBM) shows promise with the extracted topological features.Master of Science in Applied Computer Scienc

A Continuum Method for Determining Membrane Protein Insertion Energies and the Problem of Charged Residues

Continuum electrostatic approaches have been extremely successful at describing the charged nature of soluble proteins and how they interact with binding partners. However, it is unclear whether continuum methods can be used to quantitatively understand the energetics of membrane protein insertion and stability. Recent translation experiments suggest that the energy required to insert charged peptides into membranes is much smaller than predicted by present continuum theories. Atomistic simulations have pointed to bilayer inhomogeneity and membrane deformation around buried charged groups as two critical features that are neglected in simpler models. Here, we develop a fully continuum method that circumvents both of these shortcomings by using elasticity theory to determine the shape of the deformed membrane and then subsequently uses this shape to carry out continuum electrostatics calculations. Our method does an excellent job of quantitatively matching results from detailed molecular dynamics simulations at a tiny fraction of the computational cost. We expect that this method will be ideal for studying large membrane protein complexes

CMB Spectral μ\mu-Distortion of Multiple Inflation Scenario

In multiple inflation scenario having two inflations with an intermediate matter-dominated phase, the power spectrum is estimated to be enhanced on scales smaller than the horizon size at the beginning of the second inflation, $k > k_{\rm b}$. We require $k_{\rm b} > 10 {\rm Mpc}^{-1}$ to make sure that the enhanced power spectrum is consistent with large scale observation of cosmic microwave background (CMB). We consider the CMB spectral distortions generated by the dissipation of acoustic waves to constrain the power spectrum. The $\mu$-distortion value can be $10$ times larger than the expectation of the standard $\Lambda$CDM model ($\mu_{\Lambda\mathrm{CDM}} \simeq 2 \times 10^{-8}$) for $k_{\rm b} \lesssim 10^3 {\rm Mpc}^{-1}$, while the $y$-distortion is hardly affected by the enhancement of the power spectrum.Comment: 16 pages, 5 figure

MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-Mixer

We propose a novel architecture called MLP-SRGAN, which is a single-dimension Super Resolution Generative Adversarial Network (SRGAN) that utilizes Multi-Layer Perceptron Mixers (MLP-Mixers) along with convolutional layers to upsample in the slice direction. MLP-SRGAN is trained and validated using high resolution (HR) FLAIR MRI from the MSSEG2 challenge dataset. The method was applied to three multicentre FLAIR datasets (CAIN, ADNI, CCNA) of images with low spatial resolution in the slice dimension to examine performance on held-out (unseen) clinical data. Upsampled results are compared to several state-of-the-art SR networks. For images with high resolution (HR) ground truths, peak-signal-to-noise-ratio (PSNR) and structural similarity index (SSIM) are used to measure upsampling performance. Several new structural, no-reference image quality metrics were proposed to quantify sharpness (edge strength), noise (entropy), and blurriness (low frequency information) in the absence of ground truths. Results show MLP-SRGAN results in sharper edges, less blurring, preserves more texture and fine-anatomical detail, with fewer parameters, faster training/evaluation time, and smaller model size than existing methods. Code for MLP-SRGAN training and inference, data generators, models and no-reference image quality metrics will be available at https://github.com/IAMLAB-Ryerson/MLP-SRGAN.Comment: 14 pages, 10 figure