In the town of Thneedville, twelve-year-old Ted (Zac Efron) wishes to win the heart of Audrey (Taylor Swift), but he must first find her favorite tree: a Truffula. Ted explores the tale of Danny DeVito, a former gruff forest guardian and of the Once-ler who allowed greed to destroy his love for the natural world.
The Story
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The Lorax is a mythological creature that appears from a stump and speaks for the trees. The Once-ler is a greedy businessman that cut down Truffula trees to create yarn. Lorax tells him that you can’t make things better unless your take a lot of care. The Lorax encourages him to get involved and restore the land, planting Truffula trees as well as creating a forest. The story ends with a quote from the Lorax that says, “Unless someone like you cares a whole awful lot, nothing is going to get better.” This is a reminder of the responsibility we have to improve our planet’s conditions.
The characters
The lprax characters are vibrant, fun and sometimes a little crazy. Grammy Norma is a lively and enthusiastic character who isn’t afraid to voice her opinions on things that she believes in. She tells Ted about her powerful story and encourages him to be bold enough to defend what he believes. Below is a list of all the characters in lprax, organized alphabetically by name. Click on the names of these characters to see more details.
The Message
L-PRx is a long-sampling pressure reactivity index based on longer (1 min) averaged values of MAP and ICP that has recently been shown to be significantly associated with outcome after TBI. Compared to PRx, however, L-PRx might be less affected by high-frequency fluctuations and/or noise and can also display pressure reactivity changes that occur during slow MAP waves [1].
Nevertheless, the ability of both PRx and L-PRx to predict outcome over the temporal course after TBI was similar and discrimination tended to be higher for PRx than for L-PRx. Moreover, a recently developed multi-window CPPopt algorithm built on the data of these two indices (LPRx and PRx) was successful in predicting mortality over the entire post-traumatic time period.
To further enhance the performance of CPPopt, we examined whether a low-resolution, minute-by-minute signal monitoring approach for obtaining LPRx signals would suffice to calculate appropriate CPPopt values. Ultimately, we found that both indices were significantly associated with outcome after TBI but that they lost significance when adjusting for ICP and CPP.
