Finite element modeling of energy-absorbing materials in blast-loaded structures

Energy absorbing materials such as foam or honeycomb are of interest in blast protection because of their ability to absorb energy through plastic deformation. They absorb a considerable amount of energy relative to their low density, and are investigated to determine if their energy absorbing abilities can be used to mitigate blast damage. Ballistic pendulum experiments show that energy absorbing materials increase the energy transferred from a blast. This behavior was contrary to expected results so computational models were created in LS-DYNA to understand the phenomenon that causes an increase in transferred energy. Many models using ConWep and Arbitrary-Lagrangian-Eulerian (ALE) techniques were created to test the loading methods available in LS-DYNA. Additional ConWep models were created to directly compare simulations against ballistic pendulum experiments. The ConWep model results correlate with the experiments, showing that energy absorbing materials cause an increase in energy transferred to the system

There are many paths to solvation

Two ab initio studies are presented herein. The first is of the addition of successive water molecules to the amino acid L-alanine in both the neutral and zwitterion forms. The main focus is on the number of water molecules needed to stabilize the zwitterion form, and how the solvent affects conformational preference. The solvent is modeled by ab initio, EFP (Effective Fragment Potential), and the isotropic dielectrics PCM (Polarizable Continuum Method) bulk solvation techniques. The EFP discrete solvation model uses a Monte Carlo algorithm to sample the configuration space to find the global minimum. The study is undertaken at the EFP2(General Effective Fragment Potential), RHF (Restricted Hartree-Fock), DFT (Density Functional Theory), and MP2 (Moller-Plesset) levels of theory with a 6-31++G(d,p) atomic basis set. A Second study is presented of substrates for a pentameric ligand gated ion-channel, or Cys-loop receptor, which mediate chemical signals across the cellular membrane. Blocking specific signaling receptors may induce death in agricultural pests such as nematodes. Many biological signaling proteins have inter-membrane domains that cause difficulty in obtaining an x-ray diffraction structure. Further, those that have been elucidated with x-ray diffraction studies are static structures that do not capture the structural dynamics. Since there is a known set of competitive binding molecules, an ab initio study of the competitive binding molecules was employed. From this study one may be able to design new competitive binding molecules that will mitigate resistance to current methods of pest control. The ab initio methods include Moller-Plesset second order perturbation theory with the 6-31G(d,p) basis set, adding diffuse functions when solvents are used. The solvent model employs a discrete ab initio and effective fragment part along with the polarizable continuum method. A thorough understanding of the geometries and electron densities of known substrates can lead to the design and synthesis of competitive inhibitors that could improve crop yields and animal health. Calculations have been performed with the GAMESS (General Atomic and Molecular Electronic Structure System) suite of programs

What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing

ROCK signaling promotes collagen remodeling to facilitate invasive pancreatic ductal adenocarcinoma tumor cell growth

Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer death; identifying PDAC enablers may reveal potential therapeutic targets. Expression of the actomyosin regulatory ROCK1 and ROCK2 kinases increased with tumor progression in human and mouse pancreatic tumors, while elevated ROCK1/ROCK2 expression in human patients, or conditional ROCK2 activation in a KrasG12D/p53R172H mouse PDAC model, was associated with reduced survival. Conditional ROCK1 or ROCK2 activation promoted invasive growth of mouse PDAC cells into three‐dimensional collagen matrices by increasing matrix remodeling activities. RNA sequencing revealed a coordinated program of ROCK‐induced genes that facilitate extracellular matrix remodeling, with greatest fold‐changes for matrix metalloproteinases (MMPs) Mmp10 and Mmp13. MMP inhibition not only decreased collagen degradation and invasion, but also reduced proliferation in three‐dimensional contexts. Treatment of KrasG12D/p53R172H PDAC mice with a ROCK inhibitor prolonged survival, which was associated with increased tumor‐associated collagen. These findings reveal an ancillary role for increased ROCK signaling in pancreatic cancer progression to promote extracellular matrix remodeling that facilitates proliferation and invasive tumor growth

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders

A central tenet of network science states that the structure of the network influences processing. In this study of a phonological network of English words we asked: how does damage alter the network structure (Study 1)? How does the damaged structure influence lexical processing (Study 2)? How does the structure of the intact network “protect” processing with a less efficient algorithm (Study 3)? In Study 1, connections in the network were randomly removed to increasingly damage the network. Various measures showed the network remained well-connected (i.e., it is resilient to damage) until ~90% of the connections were removed. In Study 2, computer simulations examined the retrieval of a set of words. The performance of the model was positively correlated with naming accuracy by people with aphasia (PWA) on the Philadelphia Naming Test (PNT) across four types of aphasia. In Study 3, we demonstrated another way to model developmental or acquired disorders by manipulating how efficiently activation spread through the network. We found that the structure of the network “protects” word retrieval despite decreases in processing efficiency; words that are relatively easy to retrieve with efficient transmission of priming remain relatively easy to retrieve with less efficient transmission of priming. Cognitive network science and computer simulations may provide insight to a wide range of speech, language, hearing, and cognitive disorders

Systematic Fragmentation Method and the Effective Fragment Potential: An Efficient Method for Capturing Molecular Energies

The systematic fragmentation method fragments a large molecular system into smaller pieces, in such a way as to greatly reduce the computational cost while retaining nearly the accuracy of the parent ab initio electronic structure method. In order to attain the desired (sub-kcal/mol) accuracy, one must properly account for the nonbonded interactions between the separated fragments. Since, for a large molecular species, there can be a great many fragments and therefore a great many nonbonded interactions, computations of the nonbonded interactions can be very time-consuming. The present work explores the efficacy of employing the effective fragment potential (EFP) method to obtain the nonbonded interactions since the EFP method has been shown previously to capture nonbonded interactions with an accuracy that is often comparable to that of second-order perturbation theory. It is demonstrated that for nonbonded interactions that are not high on the repulsive wall (generally \u3e2.7 Å), the EFP method appears to be a viable approach for evaluating the nonbonded interactions. The efficacy of the EFP method for this purpose is illustrated by comparing the method to ab initio methods for small water clusters, the ZOVGAS molecule, retinal, and the α-helix. Using SFM with EFP for nonbonded interactions yields an error of 0.2 kcal/mol for the retinal cis−trans isomerization and a mean error of 1.0 kcal/mol for the isomerization energies of five small (120−170 atoms) α-helices

Continuous in-line virus inactivation for next generation bioprocessing

The shift in industry toward connected and continuous monoclonal antibody (mAb) processing has necessitated the development of novel approaches to improve or replace traditional unit operations. A bottleneck in connected processing is the viral inactivation step, which is typically accomplished by holding the Protein A elution material in a large vessel for a fixed period of time. There are multiple factors to consider when translating this inherently batch operation into a continuous mode. In this presentation, we will describe our efforts to develop a comprehensive understanding of virus inactivation kinetics and the impact of buffer/mAb composition on the virus inactivation process. Based on this knowledge, a flow-through system can be designed to achieve the desired virus clearance capabilities. We will also describe how such in-line virus inactivation processes may lead to shorter processing times, reduced facility footprint, and simpler integration with adjacent processing operations. Technologies such as in-line virus inactivation are expected to play an important role in the next generation mAb processing toolbox