We don't know how long we have to wait for Dragon Age: Dreadwolf, but BioWare has shared a few codex entries to keep us occupied.
We've been getting dribs and drabs of Dragon Age: Deadwolf, but BioWare's latest community update shares a look at some codex entries while explaining that the game is aimed at both"veteran fans" and new players.devs explain that the new game"returns us to Thedas, where we can revisit friends and places that are familiar," while also revealing a few codex entries offering a glimpse into the writing behind Dreadwolf.
"Unlike the vast galaxies we explore in our other franchises, Dragon Age: Dreadwolf returns us to Thedas, where we can revisit friends and places that are familiar," adds narrative editor Ryan Cormier."Some fans haven’t spent time with Dragon Age since the 2014 release of Inquisition, while others have read every comic and story published since. Others never played a Dragon Age game at all and have no idea who the bald guy is .
BioWare also gives us a look at three codex entries: one titled"misconceptions about the Necropolis," one about a dragon named Vinsomer, and one from The Randy Dowager Quarterly. What do you think about our latest glimpse of Dragon Age: Dreadwolf? How long of a wait do you think we still have ahead of us? Let us know what you think in the comments, and don't forget to check out our list of some of the
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Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study - BMC MedicineBackground Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age. Methods Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997–1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston’s R2, Akaike’s information criterion, sensitivity, specificity, C-statistic and calibration. Results Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p|0.05. Conclusions Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.
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