Correlated Prompt Fission Data in Transport Simulations

Detailed information on the fission process can be inferred from the observation, modeling and theoretical understanding of prompt fission neutron and $\gamma$-ray~observables. Beyond simple average quantities, the study of distributions and correlations in prompt data, e.g., multiplicity-dependent neutron and \gray~spectra, angular distributions of the emitted particles, $n$-$n$, $n$-$\gamma$, and $\gamma$-$\gamma$~correlations, can place stringent constraints on fission models and parameters that would otherwise be free to be tuned separately to represent individual fission observables. The FREYA~and CGMF~codes have been developed to follow the sequential emissions of prompt neutrons and $\gamma$-rays~from the initial excited fission fragments produced right after scission. Both codes implement Monte Carlo techniques to sample initial fission fragment configurations in mass, charge and kinetic energy and sample probabilities of neutron and $\gamma$~emission at each stage of the decay. This approach naturally leads to using simple but powerful statistical techniques to infer distributions and correlations among many observables and model parameters. The comparison of model calculations with experimental data provides a rich arena for testing various nuclear physics models such as those related to the nuclear structure and level densities of neutron-rich nuclei, the $\gamma$-ray~strength functions of dipole and quadrupole transitions, the mechanism for dividing the excitation energy between the two nascent fragments near scission, and the mechanisms behind the production of angular momentum in the fragments, etc. Beyond the obvious interest from a fundamental physics point of view, such studies are also important for addressing data needs in various nuclear applications. (See text for full abstract.)Comment: 39 pages, 57 figure files, published in Eur. Phys. J. A, reference added this versio

Genetic and Epigenetic Alterations of Lysophosphatidic Acid Receptor Genes in Rodent Tumors by Experimental Models

Lysophosphatidic acid (LPA) is a bioactive mediator and induces several biological effects, including cell proliferation, migration, morphogenesis and differentiation. LPA interacts with at least six G protein-coupled receptors (GPCRs), including LPA receptor-1 (LPA1), LPA2, LPA3, LPA4, LPA5 and LPA6. These receptors show different biological functions through the binding of LPA, depending on the type of cells. In human malignancies, a high level of LPA production was found in plasma and ascites in ovarian cancer cases. Moreover, aberrant expression levels of LPA receptor genes were detected in some cancer cells. Therefore, it is suggested that LPA receptors may be involved in the pathogenesis of tumor cells as well as LPA per se. Recently, we have reported that alterations of LPA receptor genes also occur in rodent tumors. In this review, we summarize the recent evidence in the investigations of LPA receptor alterations in rodent tumors by experimental models

Analysis of Structure Destroyed Metal after Diffusion Heat Treatment

It was accomplished research of the structure steel which carbonitriding and subsequent heat treatment was exposed for its cause's destruction to discover. For measure quality field of metal were used methods optical, appearing electronic microscopy and X-ray diffraction. Therefore one of the principal problems were research phase composition, grain and dislocation structure of a metal the gear teeth. Mechanism of rising hear cracks in the gear teeth on different stages her making and their trajectories of evolution were determined

Forkhead Transcription Factors (FoxOs) Promote Apoptosis of Insulin-Resistant Macrophages During Cholesterol-Induced Endoplasmic Reticulum Stress

OBJECTIVE—Endoplasmic reticulum stress increases macrophage apoptosis, contributing to the complications of atherosclerosis. Insulin-resistant macrophages are more susceptible to endoplasmic reticulum stress–associated apoptosis probably contributing to macrophage death and necrotic core formation in atherosclerotic plaques in type 2 diabetes. However, the molecular mechanisms of increased apoptosis in insulin-resistant macrophages remain unclear

Constitutive Expression of Insulin Receptor Substrate (IRS)-1 Inhibits Myogenic Differentiation through Nuclear Exclusion of Foxo1 in L6 Myoblasts

Insulin-like growth factors (IGFs) are well known to play essential roles in enhancement of myogenic differentiation. In this report we showed that initial IGF-I signal activation but long-term IGF-1 signal termination are required for myogenic differentiation. L6 myoblast stably transfected with myc-epitope tagged insulin receptor substrate-1, myc-IRS-1 (L6-mIRS1) was unable to differentiate into myotubes, indicating that IRS-1 constitutive expression inhibited myogenesis. To elucidate the molecular mechanisms underlying myogenic inhibition, IGF-I signaling was examined. IGF-I treatment of control L6 cells for 18 h resulted in a marked suppression of IGF-I stimulated IRS-1 association with the p85 PI 3-kinase and suppression of activation of Akt that correlated with a down regulation of IRS-1 protein. L6-mIRS1 cells, in contrast, had sustained high levels of IRS-1 protein following 18 h of IGF-I treatment with persistent p85 PI 3-kinase association with IRS-1, Akt phosphorylation and phosphorylation of the downstream Akt substrate, Foxo1. Consistent with Foxo1 phosphorylation, Foxo1 protein was excluded from the nuclei in L6-mIRS1 cells, whereas Foxo1 was localized in the nuclei in control L6 cells during induction of differentiation. In addition, L6 cells stably expressing a dominant-interfering form of Foxo1, Δ256Foxo1 (L6-Δ256Foxo1) were unable to differentiate into myotubes. Together, these data demonstrate that IGF-I regulation of Foxo1 nuclear localization is essential for the myogenic program in L6 cells but that persistent activation of IGF-1 signaling pathways results in a negative feedback to prevent myogenesis

Optimal In Silico Target Gene Deletion through Nonlinear Programming for Genetic Engineering

Optimal selection of multiple regulatory genes, known as targets, for deletion to enhance or suppress the activities of downstream genes or metabolites is an important problem in genetic engineering. Such problems become more feasible to address in silico due to the availability of more realistic dynamical system models of gene regulatory and metabolic networks. The goal of the computational problem is to search for a subset of genes to knock out so that the activity of a downstream gene or a metabolite is optimized.Based on discrete dynamical system modeling of gene regulatory networks, an integer programming problem is formulated for the optimal in silico target gene deletion problem. In the first result, the integer programming problem is proved to be NP-hard and equivalent to a nonlinear programming problem. In the second result, a heuristic algorithm, called GKONP, is designed to approximate the optimal solution, involving an approach to prune insignificant terms in the objective function, and the parallel differential evolution algorithm. In the third result, the effectiveness of the GKONP algorithm is demonstrated by applying it to a discrete dynamical system model of the yeast pheromone pathways. The empirical accuracy and time efficiency are assessed in comparison to an optimal, but exhaustive search strategy.Although the in silico target gene deletion problem has enormous potential applications in genetic engineering, one must overcome the computational challenge due to its NP-hardness. The presented solution, which has been demonstrated to approximate the optimal solution in a practical amount of time, is among the few that address the computational challenge. In the experiment on the yeast pheromone pathways, the identified best subset of genes for deletion showed advantage over genes that were selected empirically. Once validated in vivo, the optimal target genes are expected to achieve higher genetic engineering effectiveness than a trial-and-error procedure

Neuroinflammation, Mast Cells, and Glia: Dangerous Liaisons

The perspective of neuroinflammation as an epiphenomenon following neuron damage is being replaced by the awareness of glia and their importance in neural functions and disorders. Systemic inflammation generates signals that communicate with the brain and leads to changes in metabolism and behavior, with microglia assuming a pro-inflammatory phenotype. Identification of potential peripheral-to-central cellular links is thus a critical step in designing effective therapeutics. Mast cells may fulfill such a role. These resident immune cells are found close to and within peripheral nerves and in brain parenchyma/meninges, where they exercise a key role in orchestrating the inflammatory process from initiation through chronic activation. Mast cells and glia engage in crosstalk that contributes to accelerate disease progression; such interactions become exaggerated with aging and increased cell sensitivity to stress. Emerging evidence for oligodendrocytes, independent of myelin and support of axonal integrity, points to their having strong immune functions, innate immune receptor expression, and production/response to chemokines and cytokines that modulate immune responses in the central nervous system while engaging in crosstalk with microglia and astrocytes. In this review, we summarize the findings related to our understanding of the biology and cellular signaling mechanisms of neuroinflammation, with emphasis on mast cell-glia interactions