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His connection to the Julii Caesares was further consolidated through a marriage between him and Agrippina the Elder, a granddaughter of Augustus. As a result, Germanicus became an official member of the gens Julia, another prominent family, to which he was related on his mother's side. In AD 4 he was adopted by his paternal uncle Tiberius, who succeeded Augustus as Roman emperor a decade later. The agnomen Germanicus was added to his full name in 9 BC when it was posthumously awarded to his father in honor of his victories in Germania. The son of Nero Claudius Drusus and Antonia the Younger, Germanicus was born into an influential branch of the patrician gens Claudia. Germanicus Julius Caesar (24 May 15 BC – 10 October AD 19) was an ancient Roman general and politician most famously known for his campaigns in Germania. ![]() Results: All 20 patients with normal pancreas presented a TSIC-type 1. MR perfusion images were processed using a dedicated software by two experienced reviewers in conference that classified five TSIC shapes: type 1 (quick enhancement and quick decay followed by slowly decaying) type 2 (slow enhancement followed by slow constant enhancement) type 3 (fast enhancement followed by signal plateau) type 4 (fast enhancement followed by slowly decaying plateau) and type 5 (quick and marked enhancement followed by slow constant decay). A dose of 7 mL gadobenate-dimeglumine (Gd-BOPTA MultiHance, Bracco) with a 20 mL saline flush was injected with a flow rate of 4 mL/sec. Dynamic contrast-enhanced MR perfusion consisted of a 3D axial free-breathing T1w LAVA sequence (TR/TE, 2.28/1.05 ms 10.0 mmthk/-0.0 mmsp field-of-view, 35-42 cm matrix,128x128 0.75 NEX 1 second) repeated up to 5 minutes to cover the pancreatic head, body, and tail. Methods and Materials: Twenty patients without pancreatic disease and twenty with pathologically confirmed pancreatic lesions (ductal adenocarcinoma, n=10 endocrine tumor, n=4 with 7 lesions focal chronic pancreatitis, n=5 autoimmune pancreatitis, n=1), underwent MR imaging at 1.5 T device. Falaschi Pisa/IT Purpose: To evaluate the usefulness of time-signal-intensity curves (TSIC) by using dynamic contrast-enhanced MR perfusion of pancreatic lesions. 265 S127 Scientific Sessions S128 Scientific Sessions Thursday Thursday, March 4 S129 Scientific Sessions 14:00 - 15:30 Abdominal Viscera (Solid Organs) Room A MR perfusion of pancreatic lesions: Usefulness of time-signal intensity curves P. Insights Imaging DOI 10.1007/s1324-1 Scientific Sessions (SS) Thursday. ![]() B - Scientific Sessions B - Scientific Sessions Now those who are want to enjoy any kinds of YouTube content without having a single ad they can use the YouTube Vanced APK. 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Support for high definition (720p), 8 bits per color channel, and monitors bigger than 9 inches across The TPM Trusted Platform Module, version 2.0Ĭompatible with the WDDM 2.0 driver and DirectX 12 or newer 1GHz or faster dual-core processor with at least two cores that is 64-bit compatible Sample Data data <- structure(list(SPECIES = structure(c(1L, 1L, 1L, 1L, 1L, 1L). # SPECIES LOCATION X.COLONIZATION SPORE_DENSITY pH NO3 NH4 P Organic_C K Cu Mn Zn BD X.Sand X.Silt X.Clay Note that columns currently being group_by-ed are not included in everything(). Here's a simple approach with the new across functionality. I have tried using reshape, tapply, summarize but all to no avail. ![]() same with row 4 to 6 reducing to 1 row and so on. So i want to have the row 1 to 3 reduced to 1 row by taking average of each variable for the specie (and location). ![]() Here is the first few rows of my data SPECIES LOCATION X.COLONIZATION SPORE_DENSITY pH NO3 NH4 P Organic_C K Cu Mn Zn BD X.Sandġ C. How can i reduce the dataframe to 1 reading of each variable by taking the mean of the 3 readings. In my dataframe, i have 3 readings of each variable for a each observation. |
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