The Value of Evidence-Based Computer Simulation of Oral Health Outcomes for Management Analysis of the Alaska Dental Health Aide Program

Objectives: To create an evidence‐based research tool to inform and guide policy and program managers as they develop and deploy new service delivery models for oral disease prevention and intervention. Methods: A village‐level discrete event simulation was developed to project outcomes associated with different service delivery patterns. Evidence‐ based outcomes were associated with dental health aide activities, and projected indicators (DMFT, F+ST, T‐health, SiC, CPI, ECC) were proxy for oral health outcomes. Model runs representing the planned program implementation, a more intensive staffing scenario, and a more robust prevention scenario, generated 20‐year projections of clinical indicators; graphs and tallies were analyzed for trends and differences. Results: Outcomes associated with alternative patterns of service delivery indicate there is potential for substantial improvement in clinical outcomes with modest program changes. Not all segments of the population derive equal benefit when program variables are altered. Children benefit more from increased prevention, while adults benefit more from intensive staffing. Conclusions: Evidence‐ based simulation is a useful tool to analyze the impact of changing program variables on program outcome measures. This simulation informs dental managers of the clinical outcomes associated with policy and service delivery variables. Simulation tools can assist public health managers in analyzing and understanding the relationship between their policy decisions and long‐term clinical outcomes.The Ford Foundation

Revealing cancer subtypes with higher-order correlations applied to imaging and omics data

Figure S9. Screenshot of the interactive Tumor Map visualization, showing HOCUS applied to the TCGA Pancan-12 mutation data. Each point is one tumor sample, which we have color-coded by tissue type. A dotted box highlights the cluster of samples that have both PIK3CA and TP53 mutations, which are usually mutually exclusive. (EPS 751 kb

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inference and reconstruction of the heimdallarchaeial ancestry of eukaryotes

In the ongoing debates about eukaryogenesis—the series of evolutionary events leading to the emergence of the eukaryotic cell from prokaryotic ancestors—members of the Asgard archaea play a key part as the closest archaeal relatives of eukaryotes1. However, the nature and phylogenetic identity of the last common ancestor of Asgard archaea and eukaryotes remain unresolved2–4. Here we analyse distinct phylogenetic marker datasets of an expanded genomic sampling of Asgard archaea and evaluate competing evolutionary scenarios using state-of-the-art phylogenomic approaches. We find that eukaryotes are placed, with high confidence, as a well-nested clade within Asgard archaea and as a sister lineage to Hodarchaeales, a newly proposed order within Heimdallarchaeia. Using sophisticated gene tree and species tree reconciliation approaches, we show that analogous to the evolution of eukaryotic genomes, genome evolution in Asgard archaea involved significantly more gene duplication and fewer gene loss events compared with other archaea. Finally, we infer that the last common ancestor of Asgard archaea was probably a thermophilic chemolithotroph and that the lineage from which eukaryotes evolved adapted to mesophilic conditions and acquired the genetic potential to support a heterotrophic lifestyle. Our work provides key insights into the prokaryote-to-eukaryote transition and a platform for better understanding the emergence of cellular complexity in eukaryotic cells

Heterologous Expression of Membrane Proteins: Choosing the Appropriate Host

International audienceBACKGROUND: Membrane proteins are the targets of 50% of drugs, although they only represent 1% of total cellular proteins. The first major bottleneck on the route to their functional and structural characterisation is their overexpression; and simply choosing the right system can involve many months of trial and error. This work is intended as a guide to where to start when faced with heterologous expression of a membrane protein. METHODOLOGY/PRINCIPAL FINDINGS: The expression of 20 membrane proteins, both peripheral and integral, in three prokaryotic (E. coli, L. lactis, R. sphaeroides) and three eukaryotic (A. thaliana, N. benthamiana, Sf9 insect cells) hosts was tested. The proteins tested were of various origins (bacteria, plants and mammals), functions (transporters, receptors, enzymes) and topologies (between 0 and 13 transmembrane segments). The Gateway system was used to clone all 20 genes into appropriate vectors for the hosts to be tested. Culture conditions were optimised for each host, and specific strategies were tested, such as the use of Mistic fusions in E. coli. 17 of the 20 proteins were produced at adequate yields for functional and, in some cases, structural studies. We have formulated general recommendations to assist with choosing an appropriate system based on our observations of protein behaviour in the different hosts. CONCLUSIONS/SIGNIFICANCE: Most of the methods presented here can be quite easily implemented in other laboratories. The results highlight certain factors that should be considered when selecting an expression host. The decision aide provided should help both newcomers and old-hands to select the best system for their favourite membrane protein

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Comprehensive Molecular Characterization of Pheochromocytoma and Paraganglioma

SummaryWe report a comprehensive molecular characterization of pheochromocytomas and paragangliomas (PCCs/PGLs), a rare tumor type. Multi-platform integration revealed that PCCs/PGLs are driven by diverse alterations affecting multiple genes and pathways. Pathogenic germline mutations occurred in eight PCC/PGL susceptibility genes. We identified CSDE1 as a somatically mutated driver gene, complementing four known drivers (HRAS, RET, EPAS1, and NF1). We also discovered fusion genes in PCCs/PGLs, involving MAML3, BRAF, NGFR, and NF1. Integrated analysis classified PCCs/PGLs into four molecularly defined groups: a kinase signaling subtype, a pseudohypoxia subtype, a Wnt-altered subtype, driven by MAML3 and CSDE1, and a cortical admixture subtype. Correlates of metastatic PCCs/PGLs included the MAML3 fusion gene. This integrated molecular characterization provides a comprehensive foundation for developing PCC/PGL precision medicine

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201