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

Kinetic and structural mechanism for DNA unwinding by a non-hexameric helicase

UvrD, a model for non-hexameric Superfamily 1 helicases, utilizes ATP hydrolysis to translocate stepwise along single-stranded DNA and unwind the duplex. Previous estimates of its step size have been indirect, and a consensus on its stepping mechanism is lacking. To dissect the mechanism underlying DNA unwinding, we use optical tweezers to measure directly the stepping behavior of UvrD as it processes a DNA hairpin and show that UvrD exhibits a variable step size averaging ~3 base pairs. Analyzing stepping kinetics across ATP reveals the type and number of catalytic events that occur with different step sizes. These single-molecule data reveal a mechanism in which UvrD moves one base pair at a time but sequesters the nascent single strands, releasing them non-uniformly after a variable number of catalytic cycles. Molecular dynamics simulations point to a structural basis for this behavior, identifying the protein-DNA interactions responsible for strand sequestration. Based on structural and sequence alignment data, we propose that this stepping mechanism may be conserved among other non-hexameric helicases

Testing Newtonian Gravity with AAOmega: Mass-to-Light Profiles of Four Globular Clusters

Testing Newtonian gravity in the weak-acceleration regime is vital to our understanding of the nature of the gravitational interaction. It has recently been claimed that the velocity dispersion profiles of several globular clusters flatten out at large radii, reminiscent of galaxy rotation curves, even though globular clusters are thought to contain little or no dark matter. We investigate this claim, using AAOmega observations of four globular clusters, namely M22, M30, M53 and M68. M30, one such cluster that has had this claim made for its velocity dispersion, was included for comparison with previous studies. We find no statistically significant flattening of the velocity dispersion at large radii for any of our target clusters and therefore we infer the observed dynamics do not require that globular clusters are dark matter dominated, or a modification of gravity. Furthermore, by applying a simple dynamical model we determine the radial mass-to-light profiles for each cluster. The isothermal rotations of each cluster are also measured, with M22 exhibiting clear rotation, M68 possible rotation and M30 and M53 lacking any rotation, within the uncertainties.Comment: 7 pages, 4 figures and two tables. Accepted by MNRA

Two Stellar Components in the Halo of the Milky Way

The halo of the Milky Way provides unique elemental abundance and kinematic information on the first objects to form in the Universe, which can be used to tightly constrain models of galaxy formation and evolution. Although the halo was once considered a single component, evidence for its dichotomy has slowly emerged in recent years from inspection of small samples of halo objects. Here we show that the halo is indeed clearly divisible into two broadly overlapping structural components -- an inner and an outer halo -- that exhibit different spatial density profiles, stellar orbits and stellar metallicities (abundances of elements heavier than helium). The inner halo has a modest net prograde rotation, whereas the outer halo exhibits a net retrograde rotation and a peak metallicity one-third that of the inner halo. These properties indicate that the individual halo components probably formed in fundamentally different ways, through successive dissipational (inner) and dissipationless (outer) mergers and tidal disruption of proto-Galactic clumps.Comment: Two stand-alone files in manuscript, concatenated together. The first is for the main paper, the second for supplementary information. The version is consistent with the version published in Natur

The Century Survey Galactic Halo Project III: A Complete 4300 deg^2 Survey of Blue Horizontal Branch Stars in the Metal-Weak Thick Disk and Inner Halo

We present a complete spectroscopic survey of 2414 2MASS-selected blue horizontal branch (BHB) candidates selected over 4300 deg^2 of the sky. We identify 655 BHB stars in this non-kinematically selected sample. We calculate the luminosity function of field BHB stars and find evidence for very few hot BHB stars in the field. The BHB stars located at a distance from the Galactic plane |Z|<4 kpc trace what is clearly a metal-weak thick disk population, with a mean metallicity of [Fe/H]= -1.7, a rotation velocity gradient of dv_{rot}/d|Z|= -28+-3.4 km/s in the region |Z|<6 kpc, and a density scale height of h_Z= 1.26+-0.1 kpc. The BHB stars located at 5<|Z|<9 kpc are a predominantly inner-halo population, with a mean metallicity of [Fe/H]= -2.0 and a mean Galactic rotation of -4+-31 km/s. We infer the density of halo and thick disk BHB stars is 104+-37 kpc^-3 near the Sun, and the relative normalization of halo to thick-disk BHB stars is 4+-1% near the Sun.Comment: 12 pages in emulateapj format, accepted for publication in February A

The Most Metal-Poor Stars. IV : The Two Populations with [Fe/H] <~ -3.0

We discuss the carbon-normal and carbon-rich populations of Galactic halo stars having [Fe/H] lsim -3.0, utilizing chemical abundances from high-resolution, high signal-to-noise model-atmosphere analyses. The C-rich population represents ~28% of stars below [Fe/H] = -3.1, with the present C-rich sample comprising 16 CEMP-no stars, and two others with [Fe/H] ~ -5.5 and uncertain classification. The population is O-rich ([O/Fe] gsim +1.5); the light elements Na, Mg, and Al are enhanced relative to Fe in half the sample; and for Z > 20 (Ca) there is little evidence for enhancements relative to solar values. These results are best explained in terms of the admixing and processing of material from H-burning and He-burning regions as achieved by nucleosynthesis in zero-heavy-element models in the literature of "mixing and fallback" supernovae (SNe); of rotating, massive, and intermediate-mass stars; and of Type II SNe with relativistic jets. The available (limited) radial velocities offer little support for the C-rich stars with [Fe/H] < -3.1 being binary. More data are required before one could conclude that binarity is key to an understanding of this population. We suggest that the C-rich and C-normal populations result from two different gas cooling channels in the very early universe of material that formed the progenitors of the two populations. The first was cooling by fine-structure line transitions of C II and O I (to form the C-rich population); the second, while not well defined (perhaps dust-induced cooling?), led to the C-normal group. In this scenario, the C-rich population contains the oldest stars currently observed.Peer reviewe

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN