Emulation of a chemical transport model to assess air quality under future emission scenarios for the southwest of Western Australia

Simulation outputs from chemical transport models (CTMs) are essential to plan effective air quality policies. A key strength of these models is their ability to separate out source-specific components which facilitate the simulation of the potential impact of policy on future air quality. However, configuring and running these models is complex and computationally intensive, making the evaluation of multiple scenarios less accessible to many researchers and policy experts. The aim of this work is to present how Gaussian process emulation can provide a top-down approach to interrogating and interpreting the outputs from CTMs at minimal computational cost. A case study is presented (based on fine particle sources in the southwest of Western Australia) to illustrate how an emulator can be constructed to simultaneously evaluate changes in emissions from on-road transport and electricity sectors. This study demonstrates how emulation provides a flexible way of exploring local impacts of electric vehicles and wider regional effects of emissions from electricity generation. The potential for emulators to be applied to other settings involving air quality research is discussed

Learning through 'prosuming': Insights from media literacy programmes in Asia

10.1177/097172180801300205Science, Technology and Society132259-27

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

The PHENIX Experiment at RHIC

The physics emphases of the PHENIX collaboration and the design and current status of the PHENIX detector are discussed. The plan of the collaboration for making the most effective use of the available luminosity in the first years of RHIC operation is also presented.Comment: 5 pages, 1 figure. Further details of the PHENIX physics program available at http://www.rhic.bnl.gov/phenix

Age at symptom onset and death and disease duration in genetic frontotemporal dementia : an international retrospective cohort study

Background: Frontotemporal dementia is a heterogenous neurodegenerative disorder, with about a third of cases being genetic. Most of this genetic component is accounted for by mutations in GRN, MAPT, and C9orf72. In this study, we aimed to complement previous phenotypic studies by doing an international study of age at symptom onset, age at death, and disease duration in individuals with mutations in GRN, MAPT, and C9orf72. Methods: In this international, retrospective cohort study, we collected data on age at symptom onset, age at death, and disease duration for patients with pathogenic mutations in the GRN and MAPT genes and pathological expansions in the C9orf72 gene through the Frontotemporal Dementia Prevention Initiative and from published papers. We used mixed effects models to explore differences in age at onset, age at death, and disease duration between genetic groups and individual mutations. We also assessed correlations between the age at onset and at death of each individual and the age at onset and at death of their parents and the mean age at onset and at death of their family members. Lastly, we used mixed effects models to investigate the extent to which variability in age at onset and at death could be accounted for by family membership and the specific mutation carried. Findings: Data were available from 3403 individuals from 1492 families: 1433 with C9orf72 expansions (755 families), 1179 with GRN mutations (483 families, 130 different mutations), and 791 with MAPT mutations (254 families, 67 different mutations). Mean age at symptom onset and at death was 49\ub75 years (SD 10\ub70; onset) and 58\ub75 years (11\ub73; death) in the MAPT group, 58\ub72 years (9\ub78; onset) and 65\ub73 years (10\ub79; death) in the C9orf72 group, and 61\ub73 years (8\ub78; onset) and 68\ub78 years (9\ub77; death) in the GRN group. Mean disease duration was 6\ub74 years (SD 4\ub79) in the C9orf72 group, 7\ub71 years (3\ub79) in the GRN group, and 9\ub73 years (6\ub74) in the MAPT group. Individual age at onset and at death was significantly correlated with both parental age at onset and at death and with mean family age at onset and at death in all three groups, with a stronger correlation observed in the MAPT group (r=0\ub745 between individual and parental age at onset, r=0\ub763 between individual and mean family age at onset, r=0\ub758 between individual and parental age at death, and r=0\ub769 between individual and mean family age at death) than in either the C9orf72 group (r=0\ub732 individual and parental age at onset, r=0\ub736 individual and mean family age at onset, r=0\ub738 individual and parental age at death, and r=0\ub740 individual and mean family age at death) or the GRN group (r=0\ub722 individual and parental age at onset, r=0\ub718 individual and mean family age at onset, r=0\ub722 individual and parental age at death, and r=0\ub732 individual and mean family age at death). Modelling showed that the variability in age at onset and at death in the MAPT group was explained partly by the specific mutation (48%, 95% CI 35\u201362, for age at onset; 61%, 47\u201373, for age at death), and even more by family membership (66%, 56\u201375, for age at onset; 74%, 65\u201382, for age at death). In the GRN group, only 2% (0\u201310) of the variability of age at onset and 9% (3\u201321) of that of age of death was explained by the specific mutation, whereas 14% (9\u201322) of the variability of age at onset and 20% (12\u201330) of that of age at death was explained by family membership. In the C9orf72 group, family membership explained 17% (11\u201326) of the variability of age at onset and 19% (12\u201329) of that of age at death. Interpretation: Our study showed that age at symptom onset and at death of people with genetic frontotemporal dementia is influenced by genetic group and, particularly for MAPT mutations, by the specific mutation carried and by family membership. Although estimation of age at onset will be an important factor in future pre-symptomatic therapeutic trials for all three genetic groups, our study suggests that data from other members of the family will be particularly helpful only for individuals with MAPT mutations. Further work in identifying both genetic and environmental factors that modify phenotype in all groups will be important to improve such estimates. Funding: UK Medical Research Council, National Institute for Health Research, and Alzheimer's Society

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Regulatory Architecture of the Neuronal Cacng2/Tarpγ2 Gene Promoter: Multiple Repressive Domains, a Polymorphic Regulatory Short Tandem Repeat, and Bidirectional Organization with Co-regulated lncRNAs

CACNG2 (TARPγ2, Stargazin) is a multi-functional regulator of excitatory neurotransmission and has been implicated in the pathological processes of several brain diseases. Cacng2 function is dependent upon expression level, but currently, little is known about the molecular mechanisms that control expression of this gene. To address this deficit and investigate disease-related gene variants, we have cloned and characterized the rat Cacng2 promoter and have defined three major features: (i) multiple repressive domains that include an array of RE-1 silencing transcription factor (REST) elements, and a calcium regulatory element-binding factor (CaRF) element, (ii) a (poly-GA) short tandem repeat (STR), and (iii) bidirectional organization with expressed lncRNAs. Functional activity of the promoter was demonstrated in transfected neuronal cell lines (HT22 and PC12), but although selective removal of REST and CaRF domains was shown to enhance promoter-driven transcription, the enhanced Cacng2 promoter constructs were still about fivefold weaker than a comparable rat Synapsin-1 promoter sequence. Direct evidence of REST activity at the Cacng2 promoter was obtained through co-transfection with an established dominant-negative REST (DNR) construct. Investigation of the GA-repeat STR revealed polymorphism across both animal strains and species, and size variation was also observed in absence epilepsy disease model cohorts (Genetic Absence Epilepsy Rats, Strasbourg [GAERS] and non-epileptic control [NEC] rats). These data provide evidence of a genotype (STR)-phenotype correlation that may be unique with respect to proximal gene regulatory sequence in the demonstrated absence of other promoter, or 3′ UTR variants in GAERS rats. However, although transcriptional regulatory activity of the STR was demonstrated in further transfection studies, we did not find a GAERS vs. NEC difference, indicating that this specific STR length variation may only be relevant in the context of other (Cacna1h and Kcnk9) gene variants in this disease model. Additional studies revealed further (bidirectional) complexity at the Cacng2 promoter, and we identified novel, co-regulated, antisense rat lncRNAs that are paired with Cacng2 mRNA. These studies have provided novel insights into the organization of a synaptic protein gene promoter, describing multiple repressive and modulatory domains that can mediate diverse regulatory inputs

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field