Enhancement of EMAT’s efficiency by using silicon steel laminations back-plate

Silicon steel laminations are introduced as the back-plate to an electromagnetic acoustic transducer (EMAT) to increase the efficiency of the EMAT by increasing the magnitude of the EMAT coil's dynamic magnetic field and the eddy current in the sample surface. A two-dimensional, non-linear finite element model is developed to quantify the effectiveness of the back-plate’s different maximum permeability and saturation flux density, on increasing the eddy current density and the dynamic magnetic flux density in the specimen. A three-dimensional FE model is also developed, and confirms the expected result that the laminated structure of silicon steel (SiFe) markedly reduces the eddy current induced in the back-plate, when compared to a continuous slab of the steel. Experimental results show that silicon steel lamination can increase the efficiency of the EMAT in the cases both with and without a biasing magnetic field

Spindle pole body component 25 and platelet-derived growth factor mediate crosstalk between tumor-associated macrophages and prostate cancer cells

Tumor-associated macrophages (TAMs) are involved in the growth of prostate cancer (PrC), while the molecular mechanisms underlying the interactive crosstalk between TAM and PrC cells remain largely unknown. Platelet-derived growth factor (PDGF) is known to promote mesenchymal stromal cell chemotaxis to the tumor microenvironment. Recently, activation of spindle pole body component 25 (SPC25) has been shown to promote PrC cell proliferation and is associated with PrC stemness. Here, the relationship between SPC25 and PDGF in the crosstalk between TAM and PrC was investigated. Significant increases in both PDGF and SPC25 levels were detected in PrC specimens compared to paired adjacent normal prostate tissues. A significant correlation was detected between PDGF and SPC25 levels in PrC specimens and cell lines. SPC25 increased PDGF production and tumor cell growth in cultured PrC cells and in xenotransplantation. Mechanistically, SPC25 appeared to activate PDGF in PrC likely through Early Growth Response 1 (Egr1), while the secreted PDGF signaled to TAM through PDGFR on macrophages and polarized macrophages, which, in turn, induced the growth of PrC cells likely through their production and secretion of transforming growth factor β1 (TGFβ1). Thus, our data suggest that SPC25 triggers the crosstalk between TAM and PrC cells via SPC25/PDGF/PDGFR/TGFβ1 receptor signaling to enhance PrC growth

Evolutionary History and Phylodynamics of Influenza A and B Neuraminidase (NA) Genes Inferred from Large- Scale Sequence Analyses

Background: Influenza neuraminidase (NA) is an important surface glycoprotein and plays a vital role in viral replication and drug development. The NA is found in influenza A and B viruses, with nine subtypes classified in influenza A. The complete knowledge of influenza NA evolutionary history and phylodynamics, although critical for the prevention and control of influenza epidemics and pandemics, remains lacking. Methodology/Principal findings: Evolutionary and phylogenetic analyses of influenza NA sequences using Maximum Likelihood and Bayesian MCMC methods demonstrated that the divergence of influenza viruses into types A and B occurred earlier than the divergence of influenza A NA subtypes. Twenty-three lineages were identified within influenza A, two lineages were classified within influenza B, and most lineages were specific to host, subtype or geographical location. Interestingly, evolutionary rates vary not only among lineages but also among branches within lineages. The estimated tMRCAs of influenza lineages suggest that the viruses of different lineages emerge several months or even years before their initial detection. The dN/dS ratios ranged from 0.062 to 0.313 for influenza A lineages, and 0.257 to 0.259 for influenza B lineages. Structural analyses revealed that all positively selected sites are at the surface of the NA protein, with a number of sites found to be important for host antibody and drug binding. Conclusions/Significance: The divergence into influenza type A and B from a putative ancestral NA was followed by the divergence of type A into nine NA subtypes, of which 23 lineages subsequently diverged. This study provides a better understanding of influenza NA lineages and their evolutionary dynamics, which may facilitate early detection of newly emerging influenza viruses and thus improve influenza surveillance

GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation

Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products. Despite the traditional machine learning models, Graph Neural Networks (GNNs), by design, can understand complex relations like similarity between products. However, in contrast to their wide usage in retrieval tasks and their focus on optimizing the relevance, the current GNN architectures are not tailored toward maximizing revenue-related objectives such as Gross Merchandise Value (GMV), which is one of the major business metrics for e-Commerce companies. In addition, defining accurate edge relations in GNNs is non-trivial in large-scale e-Commerce systems, due to the heterogeneity nature of the item-item relationships. This work aims to address these issues by designing a new GNN architecture called GNN-GMVO (Graph Neural Network - Gross Merchandise Value Optimizer). This model directly optimizes GMV while considering the complex relations between items. In addition, we propose a customized edge construction method to tailor the model toward similar item recommendation task and alleviate the noisy and complex item-item relations. In our comprehensive experiments on three real-world datasets, we show higher prediction performance and expected GMV for top ranked items recommended by our model when compared with selected state-of-the-art benchmark models.Comment: 9 pages, 3 figures, 43 citation

Auroral electrojets variations caused by recurrent high-speed solar wind streams during the extreme solar minimum of 2008

We present a small statistical data set, where we investigate energy conversion at the magnetopause using Cluster measurements of magnetopause crossings. The Cluster observations of magnetic field, plasma velocity, current density and magnetopause orientation are needed to infer the energy conversion at the magnetopause. These parameters can be inferred either from accurate multispacecraft methods, or by using single-spacecraft methods. Our final aim is a large statistical study, for which only single-spacecraft methods can be applied. The Cluster mission provides an opportunity to examine and validate single-spacecraft methods against the multispacecraft methods. For single-spacecraft methods, we use the Generic Residue Analysis (GRA) and a standard one-dimensional current density method using magnetic field measurements. For multispacecraft methods, we use triangulation (Constant Velocity Approach - CVA) and the curlometer technique. We find that in some cases the single-spacecraft methods yield a different sign for the energy conversion than compared to the multispacecraft methods. These sign ambiguities arise from the orientation of the magnetopause, choosing the interval to be analyzed, large normal current and time offset of the current density inferred from the two methods. By using the Finnish Meteorological Institute global MHD simulation GUMICS-4, we are able to determine which sign is likely to be correct, introducing an opportunity to correct the ambiguous energy conversion values. After correcting the few ambiguous cases, we find that the energy conversion estimated from single-spacecraft methods is generally lower by 70% compared to the multispacecraft methods.Peer reviewe

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering betteror best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (ΔG≠ off). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated.We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/ mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect thismethodology to be widely applicable to drug discovery and development