Somatostatin Receptor Subtype 2 is Functionally Expressed in RAW264.7 Cells

Chronic inflammation left unchecked can be quite harmful to the tissue with the pro-inflammatory stimulus. It is marked by the recruitment and activation of leukocytes, including lymphocytes and macrophages with their subsequent proliferation and reactive oxygen species release. Macrophages are also one of the primary players in propagating the inflammatory response as they secrete pro-inflammatory cytokines to sustain local tissue responses. Current therapies for chronic inflammation include non-steroidal anti­ inflammatory drugs and glucocorticoids; however, both have various side effects and setbacks. Somatostatin is an endogenous hormone which inhibits cellular secretion and proliferation throughout the body. Somatostatin receptor activation is mediated through a family of heterotrimeric guanine nucleotide coupled proteins (G-proteins) belonging to the Gi and G0 family of G-proteins. In this study, we show that a murine macrophage cell line, RA W264.7, transcribes the mRNA and expresses the protein of the somatostatin receptor 2B subtype. We also demonstrate that this receptor reduces cytokine-induced phosphorylation of the ST A T-3 transcription factor. Taken together, these data suggest the functional presence of a somatostatin receptor in the RAW 264. 7 macrophage cell, a cellular model of the murine macrophage

Somatostatin Receptor Subtype 2 is Functionally Expressed in RAW264.7 Cells

Chronic inflammation left unchecked can be quite harmful to the tissue with the pro-inflammatory stimulus. It is marked by the recruitment and activation of leukocytes, including lymphocytes and macrophages with their subsequent proliferation and reactive oxygen species release. Macrophages are also one of the primary players in propagating the inflammatory response as they secrete pro-inflammatory cytokines to sustain local tissue responses. Current therapies for chronic inflammation include non-steroidal anti­ inflammatory drugs and glucocorticoids; however, both have various side effects and setbacks. Somatostatin is an endogenous hormone which inhibits cellular secretion and proliferation throughout the body. Somatostatin receptor activation is mediated through a family of heterotrimeric guanine nucleotide coupled proteins (G-proteins) belonging to the Gi and G0 family of G-proteins. In this study, we show that a murine macrophage cell line, RA W264.7, transcribes the mRNA and expresses the protein of the somatostatin receptor 2B subtype. We also demonstrate that this receptor reduces cytokine-induced phosphorylation of the ST A T-3 transcription factor. Taken together, these data suggest the functional presence of a somatostatin receptor in the RAW 264. 7 macrophage cell, a cellular model of the murine macrophage

Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments

The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatly limited in practice, particularly for large-scale and many-input computer experiments that have become typical. We propose a multi-resolution functional ANOVA model as a computationally feasible emulation alternative. More generally, this model can be used for large-scale and many-input non-linear regression problems. An overlapping group lasso approach is used for estimation, ensuring computational feasibility in a large-scale and many-input setting. New results on consistency and inference for the (potentially overlapping) group lasso in a high-dimensional setting are developed and applied to the proposed multi-resolution functional ANOVA model. Importantly, these results allow us to quantify the uncertainty in our predictions. Numerical examples demonstrate that the proposed model enjoys marked computational advantages. Data capabilities, both in terms of sample size and dimension, meet or exceed best available emulation tools while meeting or exceeding emulation accuracy

Searching for Dark Photons with Maverick Top Partners

In this paper, we present a model in which an up-type vector-like quark (VLQ) is charged under a new $U(1)_d$ gauge force which kinetically mixes with the SM hypercharge. The gauge boson of the $U(1)_d$ is the dark photon, $\gamma_d$. Traditional searches for VLQs rely on decays into Standard Model electroweak bosons $W,Z$ or Higgs. However, since no evidence for VLQs has been found at the Large Hadron Collider (LHC), it is imperative to search for other novel signatures of VLQs beyond their traditional decays. As we will show, if the dark photon is much less massive than the Standard Model electroweak sector, $M_{\gamma_d}\ll M_Z$, for the large majority of the allowed parameter space the VLQ predominately decays into the dark photon and the dark Higgs that breaks the $U(1)_d$ . That is, this VLQ is a `maverick top partner' with nontraditional decays. One of the appeals of this scenario is that pair production of the VLQ at the LHC occurs through the strong force and the rate is determined by the gauge structure. Hence, the production of the dark photon at the LHC only depends on the strong force and is largely independent of the small kinetic mixing with hypercharge. This scenario provides a robust framework to search for a light dark sector via searches for heavy colored particles at the LHC.Comment: 40 pages and 11 figure

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

The Chemical Abundances Of Stars In The Halo (CASH) Project. II. A Sample Of 14 Extremely Metal-Poor Stars

We present a comprehensive abundance analysis of 20 elements for 16 new low-metallicity stars from the Chemical Abundances of Stars in the Halo (CASH) project. The abundances have been derived from both Hobby-Eberly Telescope High Resolution Spectrograph snapshot spectra (R similar to 15,000) and corresponding high-resolution (R similar to 35,000) Magellan Inamori Kyocera Echelle spectra. The stars span a metallicity range from [Fe/H] from -2.9 to -3.9, including four new stars with [Fe/H] < -3.7. We find four stars to be carbon-enhanced metal-poor (CEMP) stars, confirming the trend of increasing [C/Fe] abundance ratios with decreasing metallicity. Two of these objects can be classified as CEMP-no stars, adding to the growing number of these objects at [Fe/H]< -3. We also find four neutron-capture-enhanced stars in the sample, one of which has [Eu/Fe] of 0.8 with clear r-process signatures. These pilot sample stars are the most metal-poor ([Fe/H] less than or similar to -3.0) of the brightest stars included in CASH and are used to calibrate a newly developed, automated stellar parameter and abundance determination pipeline. This code will be used for the entire similar to 500 star CASH snapshot sample. We find that the pipeline results are statistically identical for snapshot spectra when compared to a traditional, manual analysis from a high-resolution spectrum.Physics Frontier Center/Joint Institute for Nuclear Astrophysics (JINA) PHY 02-16783, PHY 0822648Carnegie Institution of WashingtonNSF AST-0908978Astronom