br Experimental design materials and methods Animal procedur

Experimental design, materials and methods Animal procedures were approved by the Bioethical Committee at the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences. An adult male Wistar rat was imaged in vivo on a 11.7T horizontal-bore animal MRI scanner (BioSpec 117/16 USR; Bruker BioSpin, Ettlingen, Germany) with a four-channel surface phased-array coil. The protocol included three spoiled 3D gradient echo sequences enabling MT, T1, and PD nampt inhibitor weightings. Images were acquired with whole-brain coverage, isotropic voxel size of 170µm3, and the total scan time of about 1.5h. Details of the imaging protocol can be found elsewhere [1]. The MPF map was reconstructed using custom-written C-language software according to the single-point method [2] with the synthetic reference image for data normalization [3] and two-pool model parameter constraints specifically determined for 11.7T magnetic field [1].
Acknowledgements The authors acknowledge financial support from the Russian Science Foundation [Grant no. 14-45-00040] for experimental studies, data analysis, and manuscript preparation and National Institutes of Health [Grant no. R21EB016135] for software development. Animal procedures were carried out at the Center for Genetic Resources of Laboratory Animals at the Institute of Cytology and Genetics (Siberian Branch, Russian Academy of Sciences) operating under support of the Ministry of Education and Science [projects RFMEFI61914X0005 and RFMEFI62114X0010].
Data The dataset includes images, videos, and plots from experiments combining ex vivo DTI and two-photon microscopy of CLARITY mouse brains. Figs. 1–3 show both raw and analyzed data from these experiments indicating the relationship between various diffusivity measures and MBP immunofluorescence. Table 1 shows DTI-derived measures and MBP immunofluorescence values for major myelinated white matter tracts of the mouse brain. The following is Supplementary material related to this article Video 1, Video 2..
Experimental design, materials and methods
Acknowledgements The authors would like to thank Dr. Amanda Chan for valuable technical assistance. This work was supported by Center for Intervention Development and Applied Research (P50MH080173). Funding agencies had no role in the study design, acquisition or interpretation of data, or in writing the manuscript.
Data This article contains infodemiological data on silicosis searched in the USA in the study period 2004–2010, obtained from Google Trends (GT) (Fig. 1). These data well correlated with “real-world” data obtained from the Centers for Disease Control and Prevention (CDC) site for the same study period (Tables 1–3).
Experimental design, materials and methods GT (available at https://www.google.com/trends) was exploited in order to capture Internet activities and interest related to silicosis. GT was mined in the USA, looking for “silicosis” as keyword, and using both “search term” (data directly available at https://www.google.com/trends/explore?date=2004-01-01%202010-12-31&geo=US&q=Silicosis) and “search topic” [Disease] (data directly available at https://www.google.com/trends/explore?date=2004-01-01%202010-12-31&geo=US&q=%2Fm%2F02yw8n) as search strategy options, from 2004 to 2010. Data downloadable from GT are available as monthly data, in comma-separated values (CSV) format. “Real-world” statistical data, both raw and adjusted, were collected from the CDC site for the same study period 2004–2010 [1–5]. All statistical analyses were carried out using the Statistical Package for Social Science version 23.0 (SPSS, IBM, IL, USA) and STATISTICA version 12 (StatSoft Inc., Tulsa, OK, USA). Figures with a p-value <0.05 were considered significant. For further details, the reader is referred to [6].
Data The data set consists of 267,215 Twitter posts, each of which contains at least one drug-related keyword. Two sets of language models accompany the raw data—the first is a set of models based on distributional semantics, which encapsulate semantic properties by representing word tokens as dense vectors, while the second set of models is based on n-gram sequences, capturing sequential patterns. All the data are available via our webpage, along with download/usage instructions: http://diego.asu.edu/Publications/Drugchatter.html. We will release more data and resources in the future via this link.