Three Roses, Three Sweet Roses of Mine

https://digitalcommons.library.umaine.edu/mmb-vp/6326/thumbnail.jp

The Star Of Glory

https://digitalcommons.library.umaine.edu/mmb-vp/2522/thumbnail.jp

How\u27d You Like To Be My Wife

https://digitalcommons.library.umaine.edu/mmb-vp/3887/thumbnail.jp

In The Moonlight

https://digitalcommons.library.umaine.edu/mmb-vp/1876/thumbnail.jp

Three Roses

Illustration of three red roses, two in full bloom and one bud, is located in the upper left corner of the cover. Three roses brighten my garden today, Each rose is a part of my life, My first rose is Mother, fast and fading away, and the rose in full bloom is my Wife. The tiny rosebud that has blossomed today is an answer to a prayer divine. May God\u27s tender care guard all flowers fair And these three sweet roses of mine is located in the upper right corner of the page with the title Three Roses: Three Sweet Roses of Mine in the center of the page. The background colors are various shades of light green.https://scholarsjunction.msstate.edu/cht-sheet-music/1116/thumbnail.jp

Development and validation of a generalised engineering methodology for thermal analysis of structural members in fire

Presented at 5th International Seminar on Fire & Explosion Hazards, Edinburgh 23-27 April 2007A novel methodology for generalising CFD-based approaches for thermal analysis of protected steelwork in fire has been developed, known as GeniSTELA. This is a quasi-3D approach with computation of a "steel temperature field" parameter in each computational cell. The methodology accommodates both uncertainties in the input parameters and possible variants to the specification by means of parallel calculations. A framework for the inclusion of temperature/time-dependent thermal properties, including the effects of moisture and intumescence, has been established. Indicative values of intumescent material properties have been obtained by means of cone calorimeter testing. These are dependent on initial thickness and exposure heat flux. GeniSTELA has been implemented as a submodel within the SOFIE RANS CFD code. The model is validated against measurements from the BRE large compartment fire tests, which involved well-instrumented post-flashover fires in a 12 x 12m compartment, including steel indicatives with and without protection. Sensitivity studies reveal the expected strong dependencies on structural member specification and properties of protection materials. The computational requirements are addressed, considering aspects such as the number of simultaneous cases and frequency of GeniSTELA call, in order to achieve a reasonable balance between fluid and solid-phase analyses. It is established that the model can be a practical tool, performing c. 10-100 simultaneous thermal calculations before becoming dominant. These steel temperature field predictions provided by GeniSTELA can provide far more flexibility in assessing the thermal response of structures to fire than is available via existing methods

Prostate cancer risk related to foods, food groups, macronutrients and micronutrients derived from the UK Dietary Cohort Consortium food diaries.

BACKGROUND/OBJECTIVES: The influence of dietary factors remains controversial for screen-detected prostate cancer and inconclusive for clinically detected disease. We aimed to examine these associations using prospectively collected food diaries. SUBJECTS/METHODS: A total of 1,717 prostate cancer cases in middle-aged and older UK men were pooled from four prospective cohorts with clinically detected disease (n=663), with routine data follow-up (means 6.6-13.3 years) and a case-control study with screen-detected disease (n=1054), nested in a randomised trial of prostate cancer treatments (ISCTRN 20141297). Multiple-day food diaries (records) completed by men prior to diagnosis were used to estimate intakes of 37 selected nutrients, food groups and items, including carbohydrate, fat, protein, dairy products, fish, meat, fruit and vegetables, energy, fibre, alcohol, lycopene and selenium. Cases were matched on age and diary date to at least one control within study (n=3528). Prostate cancer risk was calculated, using conditional logistic regression (adjusted for baseline covariates) and expressed as odds ratios in each quintile of intake (±95% confidence intervals). Prostate cancer risk was also investigated by localised or advanced stage and by cancer detection method. RESULTS: There were no strong associations between prostate cancer risk and 37 dietary factors. CONCLUSIONS: Prostate cancer risk, including by disease stage, was not strongly associated with dietary factors measured by food diaries in middle-aged and older UK men.Medical Research Council (Grant ID: MC_UU_12019/1), Medical Research Council Population Health Sciences Research Network, British Heart Foundation, Cancer Research UK (Grant ID: C8221/A19170), Department of Health, Food Standards Agency, Stroke Association, WCRF, National Institute for Health Research Health Technology Assessment Programme (Project IDs: 96/20/06, 96/20/99), National Cancer Research Institute (formed by Cancer Research UK, Medical Research Council, Department of Health)This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ejcn.2016.16

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