Overview of the Main Propulsion System for a Nuclear Thermal Propulsion Flight Demonstrator

A demonstration of a Nuclear Thermal Propulsion (NTP) engine has not been conducted in over 50 years. Several tests were conducted during the NERVA program but no NTP engine was ever flown in space. In the last several years there has been a considerable amount of conceptual design work on NTP engines conducted. With the prospect of human Mars missions in the 2030s there has been a renewed interest in NTP engines. A concept design study was conducted with the intent to design 2 flight demonstrator vehicles that would buy down programmatic and technical risks associated with launching and operating nuclear reactors in space. The intent of the first demonstrator mission would be to employ a simplified NTP engine and buy down programmatic risks whereas the second demonstrator would buy down technical risks with a NTP engine designed to be similar to an operational NTP model. The results of the study showed that a simplified NTP engine demonstrator could be feasibly built and flown in the near term with mostly high TRL, commercial off-the-shelf components

Fast Predictive Image Registration

We present a method to predict image deformations based on patch-wise image appearance. Specifically, we design a patch-based deep encoder-decoder network which learns the pixel/voxel-wise mapping between image appearance and registration parameters. Our approach can predict general deformation parameterizations, however, we focus on the large deformation diffeomorphic metric mapping (LDDMM) registration model. By predicting the LDDMM momentum-parameterization we retain the desirable theoretical properties of LDDMM, while reducing computation time by orders of magnitude: combined with patch pruning, we achieve a 1500x/66x speed up compared to GPU-based optimization for 2D/3D image registration. Our approach has better prediction accuracy than predicting deformation or velocity fields and results in diffeomorphic transformations. Additionally, we create a Bayesian probabilistic version of our network, which allows evaluation of deformation field uncertainty through Monte Carlo sampling using dropout at test time. We show that deformation uncertainty highlights areas of ambiguous deformations. We test our method on the OASIS brain image dataset in 2D and 3D

Expression of Cancer/Testis genes in ductal carcinoma in situ and benign lesions of the breast.

Cancer/testis (CT) genes represent a unique class of genes, which are expressed by germ cells, normally silenced in somatic cells, but activated in various cancers. CT proteins can elicit spontaneous immune responses in cancer patients and this feature makes them attractive targets for immunotherapy-based approaches. We have previously reported that CTs are relatively commonly expressed in estrogen receptor (ER) negative, high risk carcinomas. In this study, we examined the expression of selected CT genes in ductal carcinoma in situ (DCIS), lobular carcinoma in situ (LCIS) and benign proliferative lesions of the breast. ER negative DCIS were found to be associated with significant CT gene expression together with HER2 positivity and a marked stromal immune response

Mathematical Model of Innate and Adaptive Immunity of Sepsis: A Modeling and Simulation Study of Infectious Disease

Citation: Shi, Z. Z., Wu, C. H. J., Ben-Arieh, D., & Simpson, S. Q. (2015). Mathematical Model of Innate and Adaptive Immunity of Sepsis: A Modeling and Simulation Study of Infectious Disease. Biomed Research International, 31. doi:10.1155/2015/504259Sepsis is a systemic inflammatory response (SIR) to infection. In this work, a system dynamics mathematical model (SDMM) is examined to describe the basic components of SIR and sepsis progression. Both innate and adaptive immunities are included, and simulated results in silico have shown that adaptive immunity has significant impacts on the outcomes of sepsis progression. Further investigation has found that the intervention timing, intensity of anti- inflammatory cytokines, and initial pathogen load are highly predictive of outcomes of a sepsis episode. Sensitivity and stability analysis were carried out using bifurcation analysis to explore system stability with various initial and boundary conditions. The stability analysis suggested that the system could diverge at an unstable equilibrium after perturbations if r(t2max) (maximum release rate of Tumor Necrosis Factor- (TNF-) alpha by neutrophil) falls below a certain level. This finding conforms to clinical findings and existing literature regarding the lack of efficacy of anti- TNF antibody therapy

Structural variability, coordination and adaptation of a native photosynthetic machinery

Cyanobacterial thylakoid membranes represent the active sites for both photosynthetic and respiratory electron transport. We used high-resolution atomic force microscopy to visualize the native organization and interactions of photosynthetic complexes within the thylakoid membranes from the model cyanobacterium Synechococcus elongatus PCC 7942. The thylakoid membranes are heterogeneous and assemble photosynthetic complexes into functional domains to enhance their coordination and regulation. Under high light, the chlorophyll-binding proteins IsiA are strongly expressed and associate with Photosystem I (PSI), forming highly variable IsiA-PSI supercomplexes to increase the absorption cross-section of PSI. There are also tight interactions of PSI with Photosystem II (PSII), cytochrome b6f, ATP synthase and NAD(P)H dehydrogenase complexes. The organizational variability of these photosynthetic supercomplexes permits efficient linear and cyclic electron transport as well as bioenergetic regulation. Understanding the organizational landscape and environmental adaptation of cyanobacterial thylakoid membranes may help inform strategies for engineering efficient photosynthetic systems and photo-biofactories

A Note on Charge Quantization Through Anomaly Cancellation

In a minimal extension of the Standard Model, in which new neutral fermions have been introduced, we show that the requirement of vanishing anomalies fixes the hypercharges of all fermions uniquely. This naturally leads to electric charge quantization in this minimal scenario which has features similar to the Standard Model: invariance under the gauge group $SU(2)_L\otimes U(1)_Y$, conservation of the total lepton number and masslessness for the ordinary neutrinos. Such minimal models might arise as low-energy realizations of some heterotic superstring models or $SO(10)$ grand unified theories.Comment: 14p., TeX, (final version

Strong negative self regulation of Prokaryotic transcription factors increases the intrinsic noise of protein expression

Background Many prokaryotic transcription factors repress their own transcription. It is often asserted that such regulation enables a cell to homeostatically maintain protein abundance. We explore the role of negative self regulation of transcription in regulating the variability of protein abundance using a variety of stochastic modeling techniques. Results We undertake a novel analysis of a classic model for negative self regulation. We demonstrate that, with standard approximations, protein variance relative to its mean should be independent of repressor strength in a physiological range. Consequently, in that range, the coefficient of variation would increase with repressor strength. However, stochastic computer simulations demonstrate that there is a greater increase in noise associated with strong repressors than predicted by theory. The discrepancies between the mathematical analysis and computer simulations arise because with strong repressors the approximation that leads to Michaelis-Menten-like hyperbolic repression terms ceases to be valid. Because we observe that strong negative feedback increases variability and so is unlikely to be a mechanism for noise control, we suggest instead that negative feedback is evolutionarily favoured because it allows the cell to minimize mRNA usage. To test this, we used in silico evolution to demonstrate that while negative feedback can achieve only a modest improvement in protein noise reduction compared with the unregulated system, it can achieve good improvement in protein response times and very substantial improvement in reducing mRNA levels. Conclusions Strong negative self regulation of transcription may not always be a mechanism for homeostatic control of protein abundance, but instead might be evolutionarily favoured as a mechanism to limit the use of mRNA. The use of hyperbolic terms derived from quasi-steady-state approximation should also be avoided in the analysis of stochastic models with strong repressors