A Spatial Stochastic Model of AMPAR Trafficking and Subunit Dynamics

In excitatory neurons, the ability of a synaptic connection to strengthen or weaken is known as synaptic plasticity and is thought to be the cellular basis for learning and memory. Understanding the mechanism of synaptic plasticity is an important step towards understanding and developing treatment methods for learning and memory disorders. A key molecular process in synaptic plasticity for mammalian glutamatergic neurons is the exocytosis (delivery to the synapse) of AMPA-type glutamate receptors (AMPARs). While the protein signaling pathways responsible for exocytosis have long been investigated with experimental methods, it remains unreasonable to study the system in its full complexity via only in vitro and in vivo studies. A large number of protein interaction states are observed, creating a system both difficult to monitor and limited in spatiotemporal resolution in an experimental setting. Thus, a computational modeling approach could be employed to help elucidate the underlying protein interaction mechanisms. Here we develop a systematic model to investigate the spatiotemporal patterning of AMPARs. We replicate in silico two distinct mechanisms of AMPAR trafficking related to variation in AMPAR subunit functionality. This model is validated against current knowledge of AMPAR trafficking and used to explore spatial localization of AMPARs to specific synaptic sites, as well as to describe the differences in the spatiotemporal dynamics between the two interacting pathways. These findings help to explain how AMPAR trafficking occurs and can serve as a step towards understanding the role it plays in synaptic plasticity

Monte Carlo calibration of the SMM gamma ray spectrometer for high energy gamma rays and neutrons

The Gamma Ray Spectrometer (GRS) on the Solar Maximum Mission spacecraft was primarily designed and calibrated for nuclear gamma ray line measurements, but also has a high energy mode which allows the detection of gamma rays at energies above 10 MeV and solar neutrons above 20 MeV. The GRS response has been extrapolated until now for high energy gamma rays from an early design study employing Monte Carlo calculations. The response to 50 to 600 MeV solar neutrons was estimated from a simple model which did not consider secondary charged particles escaping into the veto shields. In view of numerous detections by the GRS of solar flares emitting high energy gamma rays, including at least two emitting directly detectable neutrons, the calibration of the high energy mode in the flight model has been recalculated by the use of more sophisticated Monte Carlo computer codes. New results presented show that the GRS response to gamma rays above 20 MeV and to neutrons above 100 MeV is significantly lower than the earlier estimates

Legislative Actions to Repeal, Defund, or Delay the Affordable Care Act

This report summarizes legislative and other actions taken to repeal, defund, or delay the Affordable Care Act (ACA), since the law's enactment. The information is presented in four appendixes

Quantitative Models of Protein Dynamics in Synaptic Plasticity: Analysis of Spatial and Stochastic Effects

Memory formation within neurons depends on complex protein signaling networks, which become dysregulated in neurological disorders such as Alzheimer’s disease. To characterize therapeutic strategies for these disorders, we require a better understanding of the how the protein interactions are regulated. Conventionally, protein interactions are studied by experimental techniques and complemented by computational models. However, most models are deterministic, limiting their biophysical accuracy. First, deterministic models exclude the stochastic effects necessitated by the small protein concentrations often observed within neurons. Second, deterministic models exclude the effects of spatial localizations on neuronal protein binding and activation. Third, many different models exclude an explicit representation of competition for binding to the essential protein calmodulin when multiple calmodulin-binding proteins are known to simultaneously coordinate the regulation of synaptic plasticity. Therefore, here we present a highly detailed model that explicitly accounts for stochastic effects, spatial localizations, and competitive binding, using the open source software MCell. Using our model, we compare against previous models and experimental data to analyze how spatial and stochastic effects determine the dynamics observed. These conclusions will be drawn from the concentrations of various neuronal protein activations and chemical modifications. In the future, our model may be used as a tool to identify and characterize therapeutic targets for neurological disorders

Is the High-Energy Emission from Centaurus A Compton-Scattered Jet Radiation?

We consider whether the hard X-ray and soft gamma-ray emission from Centaurus A is beamed radiation from the active nucleus which is Compton-scattered into our line-of-sight. We derive the spectrum and degree of polarization of scattered radiation when incident beamed radiation is scattered from a cold ($kT<<m_ec^2$) electron cloud moving with bulk relativistic motion along the jet axis, and calculate results for an unpolarized, highly-beamed incident power-law photon source. We fit the OSSE data from Centaurus A with this model and find that if the scatterers are not moving relativistically, then the angle the jet makes with respect to our line-of-sight is $61^\circ\pm 5^\circ$. We predict a high degree of polarization of the scattered radiation below $\sim300$ keV. Future measurements with X-ray and gamma-ray polarimeters could be used to constrain or rule out such a scenario.Comment: 12 pages, Postscript file with 3 Figures, NRL 017-331-09

Legislative Actions to Repeal, Defund, or Delay the Affordable Care Act

This report summarizes legislative and other actions taken to repeal, defund, or delay the Affordable Care Act (ACA), since the law's enactment. The information is presented in four appendixes

Measurement of the 0.3-8.5 MeV Galactic Gamma-Ray Spectrum from the Galactic Center Direction

The low-energy gamma-ray spectrum from the direction of the Galactic center is determined using data obtained with the SMM Gamma-Ray Spectrometer. It is found that the diffuse gamma-ray spectrum from the Galactic center region can be interpreted in a straightforward way as the sum of five components of a presented equation. The components include a hard power law dominating the continuum at high energies caused principally by cosmic ray electron bremsstrahlung radiation, two narrow lines due to Al-26 decay and positron annihilation, an excess continuum component below 0.511 MeV consistent with the annihilation of positrons by formation of Ps, and a soft power law at low energies which is consistent with an extrapolation upward in energy of known hard X-ray sources in the Galactic center region

An Automated Workflow for Quantifying RNA Transcripts in Individual Cells in Large Data-sets

Advanced molecular probing techniques such as single molecule fluorescence in situ hybridization (smFISH) or RNAscope can be used to assess the quantity and spatial location of mRNA transcripts within cells. Quantifying mRNA expression in large image sets usually involves automated counting of fluorescent spots. Though conventional spot counting algorithms may suffice, they often lack high-throughput capacity and accuracy in cases of crowded signal or excessive noise. Automatic identification of cells and processing of many images is still a challenge. We have developed a method to perform automatic cell boundary identification while providing quantitative data about mRNA transcript levels across many images. Comparisons of mRNA transcript levels identified by the method highly correlate to qPCR measurements of mRNA expression in Drosophila genotypes with different levels of Rhodopsin 1 transcript. We also introduce a graphical user interface to facilitate analysis of large data sets. We expect these methods to translate to model systems where automated image processing can be harnessed to obtain single-cell data. The described method: Provides relative intensity measurements that scale directly with the number of labeled transcript probes within individual cells. Allows quantitative assessment of single molecule data from images with crowded signal and moderate signal to noise ratios

An Automated Workflow for Quantifying RNA Transcripts in Individual Cells in Large Data-sets

Advanced molecular probing techniques such as single molecule fluorescence in situ hybridization (smFISH) or RNAscope can be used to assess the quantity and spatial location of mRNA transcripts within cells. Quantifying mRNA expression in large image sets usually involves automated counting of fluorescent spots. Though conventional spot counting algorithms may suffice, they often lack high-throughput capacity and accuracy in cases of crowded signal or excessive noise. Automatic identification of cells and processing of many images is still a challenge. We have developed a method to perform automatic cell boundary identification while providing quantitative data about mRNA transcript levels across many images. Comparisons of mRNA transcript levels identified by the method highly correlate to qPCR measurements of mRNA expression in Drosophila genotypes with different levels of Rhodopsin 1 transcript. We also introduce a graphical user interface to facilitate analysis of large data sets. We expect these methods to translate to model systems where automated image processing can be harnessed to obtain single-cell data. The described method: • Provides relative intensity measurements that scale directly with the number of labeled transcript probes within individual cells.• Allows quantitative assessment of single molecule data from images with crowded signal and moderate signal to noise ratios