ARTSN: An Automated Real-Time Spacecraft Navigation System

As part of the Deep Space Network (DSN) advanced technology program an effort is underway to design a filter to automate the deep space navigation process.The automated real-time spacecraft navigation (ARTSN) filter task is based on a prototype consisting of a FORTRAN77 package operating on an HP-9000/700 workstation running HP-UX 9.05. This will be converted to C, and maintained as the operational version. The processing tasks required are: (1) read a measurement, (2) integrate the spacecraft state to the current measurement time, (3) compute the observable based on the integrated state, and (4) incorporate the measurement information into the state using an extended Kalman filter. This filter processes radiometric data collected by the DSN. The dynamic (force) models currently include point mass gravitational terms for all planets, the Sun and Moon, solar radiation pressure, finite maneuvers, and attitude maintenance activity modeled quadratically. In addition, observable errors due to troposphere are included. Further data types, force and observable models will be ncluded to enhance the accuracy of the models and the capability of the package. The heart of the ARSTSN is a currently available continuous-discrete extended Kalman filter. Simulated data used to test the implementation at various stages of development and the results from processing actual mission data are presented

Assessment of Environments for Mars Science Laboratory Entry, Descent, and Surface Operations

The Mars Science Laboratory mission aims to land a car-sized rover on Mars’ surface and operate it for at least one Mars year in order to assess whether its field area was ever capable of supporting microbial life. Here we describe the approach used to identify, characterize, and assess environmental risks to the landing and rover surface operations. Novel entry, descent, and landing approaches will be used to accurately deliver the 900-kg rover, including the ability to sense and “fly out” deviations from a best-estimate atmospheric state. A joint engineering and science team developed methods to estimate the range of potential atmospheric states at the time of arrival and to quantitatively assess the spacecraft’s performance and risk given its particular sensitivities to atmospheric conditions. Numerical models are used to calculate the atmospheric parameters, with observations used to define model cases, tune model parameters, and validate results. This joint program has resulted in a spacecraft capable of accessing, with minimal risk, the four finalist sites chosen for their scientific merit. The capability to operate the landed rover over the latitude range of candidate landing sites, and for all seasons, was verified against an analysis of surface environmental conditions described here. These results, from orbital and model data sets, also drive engineering simulations of the rover’s thermal state that are used to plan surface operations.Keywords: Mars, Mars’ surface, Mars’ atmosphere, Spacecraf

A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from multiple unrelated cell types (mosaic data) or from purified enzyme data. In this work, we develop a model of the PI cycle informed by experimental and omics data taken from a single cell type, namely platelets. We were able to make a number of predictions regarding the regulation of PI cycle enzymes, the importance of the number of receptors required for successful GPCR signaling and the importance of lipid- and protein-binding proteins in regulating second messenger outputs. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain consistent. However, when informed by mosaic experimental data model predictions greatly vary illustrating the risks of using mosaic datasets from unrelated cell types

Cloud structure of three Galactic infrared dark star-forming regions from combining ground and space based bolometric observations

We have modified the iterative procedure introduced by Lin et al. (2016), to systematically combine the submm images taken from ground based (e.g., CSO, JCMT, APEX) and space (e.g., Herschel, Planck) telescopes. We applied the updated procedure to observations of three well studied Infrared Dark Clouds (IRDCs): G11.11-0.12, G14.225-0.506 and G28.34+0.06, and then performed single-component, modified black-body fits to derive $\sim$10$"$ resolution dust temperature and column density maps. The derived column density maps show that these three IRDCs exhibit complex filamentary structures embedding with rich clumps/cores. We compared the column density probability distribution functions (N-PDFs) and two-point correlation (2PT) functions of the column density field between these IRDCs with several OB cluster-forming regions. Based on the observed correlation and measurements, and complementary hydrodynamical simulations for a 10$^{4}$ $\rm M_{\odot}$ molecular cloud, we hypothesize that cloud evolution can be better characterized by the evolution of the (column) density distribution function and the relative power of dense structures as a function of spatial scales, rather than merely based on the presence of star-forming activity. Based on the small analyzed sample, we propose four evolutionary stages, namely: {\it cloud integration, stellar assembly, cloud pre-dispersal and dispersed-cloud.} The initial {\it cloud integration} stage and the final {\it dispersed cloud} stage may be distinguished from the two intermediate stages by a steeper than $-$4 power-law index of the N-PDF. The {\it cloud integration} stage and the subsequent {\it stellar assembly} stage are further distinguished from each other by the larger luminosity-to-mass ratio ($>$40 $\rm L_{\odot}/M_{\odot}$) of the latter

Inhibition of p38 MAPK Suppresses Inflammatory Cytokine Induction by Etoposide, 5-Fluorouracil, and Doxorubicin without Affecting Tumoricidal Activity

Cancer patients undergoing treatment with systemic cancer chemotherapy drugs often experience debilitating fatigue similar to sickness behavior, a normal response to infection or tissue damage caused by the production of the inflammatory cytokines IL-1β, TNF-α, and IL-6. The p38 mitogen activated protein kinase (p38 MAPK) plays a central role in the production of these cytokines and consequently the development of sickness behavior. Targeted inhibitors of p38 MAPK can reduce systemic inflammatory cytokine production and the development of sickness behavior. Several systemic cancer chemotherapy drugs have been shown to stimulate inflammatory cytokine production, yet whether this response is related to a common ability to activate p38 MAPK is not known and is the focus of this study. This understanding may present the possibility of using p38 MAPK inhibitors to reduce chemotherapy-induced inflammatory cytokine production and consequently treatment-related fatigue. One caveat of this approach is a potential reduction in chemotherapeutic efficacy as some believe that p38 MAPK activity is required for chemotherapy-induced cytotoxicity of tumor cells. The purpose of this study was to demonstrate proof of principal that p38 MAPK inhibition can block chemotherapy- induced inflammatory cytokine production without inhibiting drug-induced cytotoxicity using murine peritoneal macrophages and Lewis Lung Carcinoma (LLC1) cells as model cell systems. Using these cells we assessed the requirement of etoposide, doxorubicin, 5-flourouracil, and docetaxel for p38 MAPK in inflammatory cytokine production and cytotoxicity. Study findings demonstrate that clinically relevant doses of etoposide, doxorubicin, and 5-FU activated p38 MAPK in both macrophages and LLC1 cells. In contrast, docetaxel failed to activate p38 MAPK in either cell type. Activation of p38 MAPK mediated the drug's effects on inflammatory cytokine production in macrophages but not LLC1 cytotoxicity and this was confirmed with inhibitor studies

PAX8 promotes tumor cell growth by transcriptionally regulating E2F1 and stabilizing RB protein

The retinoblastoma protein (RB)–E2F1 pathway has a central role in regulating the cell cycle. Several PAX proteins (tissue-specific developmental regulators), including PAX8, interact with the RB protein, and thus regulate the cell cycle directly or indirectly. Here, we report that PAX8 expression is frequent in renal cell carcinoma, bladder, ovarian and thyroid cancer cell lines, and that silencing of PAX8 in cancer cell lines leads to a striking reduction in the expression of E2F1 and its target genes, as well as a proteasome-dependent destabilization of RB protein, with the RB1 mRNA level remaining unaffected. Cancer cells expressing PAX8 undergo a G1/S arrest and eventually senesce following PAX8 silencing. We demonstrate that PAX8 transcriptionally regulates the E2F1 promoter directly, and E2F1 transcription is enhanced after RB depletion. RB is recruited to the PAX8-binding site, and is involved in PAX8-mediated E2F1 transcription in cancer cells. Therefore, our results suggest that, in cancer, frequent and persistent expression of PAX8 is required for cell growth control through transcriptional activation of E2F1 expression and upregulation of the RB–E2F1 pathway