Observational analysis of cumulus and stratocumulus entrainment using ozone

October 1987.Also issued as Clark Jay Weaver's dissertation (Ph.D.) -- Colorado State University, 1987.Includes bibliographical references.This study demonstrates that ozone mixing ratio (03) is conserved during moist convection and can be used as a tracer for cloud entrainment studies. The approach in Part I is to apply mixing line analysis to pairs of Oe, 01, total water mixing ratio and 03 derived from aircraft penetrations of growing cumulus congestus. Conclusions about entrainment from the mixing diagrams employing 03 agree with those using thermodynamic quantities. Any disagreement uncovered deficiencies in the water substance measurement technique. Ozone is conserved / and recommended for future entrainments studies. Other conclusions were that strong updrafts, thought to be a diluted adiabatic core, entrained laterally from the environment at the observation level. In contrast, the downshear region of the cloud entrained air from above the observation level as well as laterally. Entrainment instability is thought to be a cause of stratocumulus break up. At the cloud-overlying air interface, mixtures may form which are negatively buoyant due to cloud droplet evaporation. In Part II, quantities devised to predict breakup, ~ 2 , X and ~m, are obtained from aircraft observations and are tested against cloud observations from satellite. Often, the parameters indicate that breakup should occur but the clouds remain, sometimes for several days. One possible explanation for break up is vertical motion from passing synoptic cyclones. Several cases suggest that break up is associated with the downward vertical motion from the cold air advection behind a eastward moving cyclone.Sponsored by the National Science Foundation ATM-8114575, ATM-8312615, ATM-8311405, and ATM-8614956

Hypothesis Generation Using Network Structures on Community Health Center Cancer-Screening Performance

RESEARCH OBJECTIVES: Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS: To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS: This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments

Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers

AbstractOur conceptual model demonstrates our goal to investigate the impact of clinical decision support (CDS) utilization on cancer screening improvement strategies in the community health care (CHC) setting. We employed a dual modeling technique using both statistical and computational modeling to evaluate impact. Our statistical model used the Spearman’s Rho test to evaluate the strength of relationship between our proximal outcome measures (CDS utilization) against our distal outcome measure (provider self-reported cancer screening improvement). Our computational model relied on network evolution theory and made use of a tool called Construct-TM to model the use of CDS measured by the rate of organizational learning. We employed the use of previously collected survey data from community health centers Cancer Health Disparities Collaborative (HDCC). Our intent is to demonstrate the added valued gained by using a computational modeling tool in conjunction with a statistical analysis when evaluating the impact a health information technology, in the form of CDS, on health care quality process outcomes such as facility-level screening improvement. Significant simulated disparities in organizational learning over time were observed between community health centers beginning the simulation with high and low clinical decision support capability

The human GCOM1 complex gene interacts with the NMDA receptor and internexin-alpha

The known functions of the human GCOM1 complex hub gene include transcription elongation and the intercalated disk of cardiac myocytes. However, in all likelihood, the gene's most interesting, and thus far least understood, roles will be found in the central nervous system. To investigate the functions of the GCOM1 gene in the CNS, we have cloned human and rat brain cDNAs encoding novel, 105 kDa GCOM1 combined (Gcom) proteins, designated Gcom15, and identified a new group of GCOM1 interacting genes, termed Gints, from yeast two-hybrid (Y2H) screens. We showed that Gcom15 interacts with the NR1 subunit of the NMDA receptor by co-expression in heterologous cells, in which we observed bi-directional co-immunoprecipitation of human Gcom15 and murine NR1. Our Y2H screens revealed 27 novel GCOM1 interacting genes, many of which are synaptic proteins and/or play roles in neurologic diseases. Finally, we showed, using rat brain protein preparations, that the Gint internexin-alpha (INA), a known interactor of the NMDAR, co-IPs with GCOM1 proteins, suggesting a GCOM1-GRIN1-INA interaction and a novel pathway that may be relevant to neuroprotection

Evidence of Fragmenting Dust Particles from Near-Simultaneous Optical and Near-IR Photometry and Polarimetry of Comet 73P/Schwassmann-Wachmann 3

We report imaging polarimetry of segments B and C of the Jupiter-family Comet 73P/Schwassmann-Wachmann 3 in the I and H bandpasses at solar phase angles of approximately 35 and 85deg. The level of polarization was typical for active comets, but larger than expected for a Jupiter-family comet. The polarimetric color was slightly red (dP/dL = +1.2 +/- 0.4) at a phase angle of ~ 35deg and either neutral or slightly blue at a phase angle of ~ 85deg. Observations during the closest approach from 2006 May 11-13 achieved a resolution of 35 km at the nucleus. Both segments clearly depart from a 1/rho surface brightness for the first 50 - 200 km from the nucleus. Simulations of radiation driven dust dynamics can reproduce some of the observed coma morphology, but only with a wide distribution of initial dust velocities (at least a factor of 10) for a given grain radius. Grain aggregate breakup and fragmentation are able to reproduce the observed profile perpendicular to the Sun-Comet axis, but fit the observations less well along this axis (into the tail). The required fragmentation is significant, with a reduction in the mean grain aggregate size by about a factor of 10. A combination of the two processes could possibly explain the surface brightness profile of the comet.Comment: 40 pages including 11 figure

Dynamic evolution in the key honey bee pathogen deformed wing virus: Novel insights into virulence and competition using reverse genetics

The impacts of invertebrate RNA virus population dynamics on virulence and infection out- comes are poorly understood. Deformed wing virus (DWV), the main viral pathogen of honey bees, negatively impacts bee health, which can lead to colony death. Despite previ- ous reports on the reduction of DWV diversity following the arrival of the parasitic mite Var- roa destructor, the key DWV vector, we found high genetic diversity of DWV in infested United States honey bee colonies. Phylogenetic analysis showed that divergent US DWV genotypes are of monophyletic origin and were likely generated as a result of diversification after a genetic bottleneck. To investigate the population dynamics of this divergent DWV, we designed a series of novel infectious cDNA clones corresponding to coexisting DWV genotypes, thereby devising a reverse-genetics system for an invertebrate RNA virus qua- sispecies. Equal replication rates were observed for all clone-derived DWV variants in single infections. Surprisingly, individual clones replicated to the same high levels as their mixtures and even the parental highly diverse natural DWV population, suggesting that complemen- tation between genotypes was not required to replicate to high levels. Mixed clone–derived infections showed a lack of strong competitive exclusion, suggesting that the DWV geno- types were adapted to coexist. Mutational and recombination events were observed across clone progeny, providing new insights into the forces that drive and constrain virus diversifi- cation. Accordingly, our results suggest that Varroa influences DWV dynamics by causing an initial selective sweep, which is followed by virus diversification fueled by negative fre- quency-dependent selection for new genotypes. We suggest that this selection might reflect the ability of rare lineages to evade host defenses, specifically antiviral RNA interference (RNAi). In support of this hypothesis, we show that RNAi induced against one DWV strain is less effective against an alternate strain from the same population

Computational and transcriptional evidence for microRNAs in the honey bee genome

A total of 68 non-redundant candidate honey bee miRNAs were identified computationally; several of them appear to have previously unrecognized orthologs in the Drosophila genome. Several miRNAs showed caste- or age-related differences in transcript abundance and are likely to be involved in regulating honey bee development

Insights from a Convocation: Integrating Discovery-Based Research into the Undergraduate Curriculum

The National Academies of Sciences, Engineering, and Medicine organized a convocation in 2015 to explore and elucidate opportunities, barriers, and realities of course-based undergraduate research experiences, known as CUREs, as a potentially integral component of undergraduate science, technology, engineering, and mathematics education. This paper summarizes the convocation and resulting report

Sulforaphane induces cell cycle arrest by protecting RB-E2F-1 complex in epithelial ovarian cancer cells

<p>Abstract</p> <p>Background</p> <p>Sulforaphane (SFN), an isothiocyanate phytochemical present predominantly in cruciferous vegetables such as brussels sprout and broccoli, is considered a promising chemo-preventive agent against cancer. In-vitro exposure to SFN appears to result in the induction of apoptosis and cell-cycle arrest in a variety of tumor types. However, the molecular mechanisms leading to the inhibition of cell cycle progression by SFN are poorly understood in epithelial ovarian cancer cells (EOC). The aim of this study is to understand the signaling mechanisms through which SFN influences the cell growth and proliferation in EOC.</p> <p>Results</p> <p>SFN at concentrations of 5 - 20 μM induced a dose-dependent suppression of growth in cell lines MDAH 2774 and SkOV-3 with an IC50 of ~8 μM after a 3 day exposure. Combination treatment with chemotherapeutic agent, paclitaxel, resulted in additive growth suppression. SFN at ~8 μM decreased growth by 40% and 20% on day 1 in MDAH 2774 and SkOV-3, respectively. Cells treated with cytotoxic concentrations of SFN have reduced cell migration and increased apoptotic cell death via an increase in Bak/Bcl-2 ratio and cleavage of procaspase-9 and poly (ADP-ribose)-polymerase (PARP). Gene expression profile analysis of cell cycle regulated proteins demonstrated increased levels of tumor suppressor retinoblastoma protein (RB) and decreased levels of E2F-1 transcription factor. SFN treatment resulted in G1 cell cycle arrest through down modulation of RB phosphorylation and by protecting the RB-E2F-1 complex.</p> <p>Conclusions</p> <p>SFN induces growth arrest and apoptosis in EOC cells. Inhibition of retinoblastoma (RB) phosphorylation and reduction in levels of free E2F-1 appear to play an important role in EOC growth arrest.</p