Multi-satellite tracking of solar wind dynamic pressure pulse observations through the Earth's magnetosphere enables us to distinguish local changes with propagation signatures.
Vidal-Luengo, S. E., & Moldwin, M. B. (2021). Global magnetosphere response to solar wind dynamic pressure pulses during northward IMF using the heliophysics system observatory. Journal of Geophysical Research: Space Physics, 126, e2020JA028587. https://doi.org/10.1029/2020JA028587
The research that produced this data focused on conducting a statistical comparison between horizontal winds modeled with GITM and those derived from the accelerometer aboard the GOCE satellite. The winds from GITM and GOCE were compared by constructing their respective probability densities under different levels of geomagnetic activity, and by distributing them as a function of geomagnetic activity, magnetic latitude, magnetic local time, day-of-the-year, and solar radio flux.
There are three experimental outputs from Seq-Scope. (1) High definition map coordinate identifier (HDMI) sequence, tile and spatial coordinate information from 1st-Seq, (2) HDMI sequence, coupled with cDNA sequence from 2nd-Seq, and (3) Histological image obtained from Hematoxylin and Eosin (H&E) staining of the tissue slice. (1) and (2) were uploaded to GEO ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE186601). (3) is deposited here. In addition, this deposit includes the processed RDS (single R object) data files.
Citation to related publication:
Do TH, Ma F, Andrade PR, Teles R, de Andrade Silva BJ, Hu C, Espinoza A, Hsu JE, Cho CS, Kim M, Xi J, Xing X, Plazyo O, Tsoi LC, Cheng C, Kim J, Bryson BD, O'Neill AM, Colonna M, Gudjonsson JE, Klechevsky E, Lee JH, Gallo RL, Bloom BR, Pellegrini M, Modlin RL. TREM2 macrophages induced by human lipids drive inflammation in acne lesions. Sci Immunol. 2022 Jul 22;7(73):eabo2787. doi: 10.1126/sciimmunol.abo2787. Epub 2022 Jul 22. PMID: 35867799; PMCID: PMC9400695.
This publication contains anonymized SPECT/CT scans of two patients. Patient scans were taken at 4 different time points in the week following a therapeutic dose of Lu-177 DOTATATE. Both the SPECT and the co-registered CT are provided. All images are in DICOM format.
The data sources and methods used to process the raw data are described in the paper
www.doi.org/10.1073/pnas.2118046119 and the associated Supplementary Information.
These data are anonymized (see Methodology for details). Consequently, running the same code on these data vs. the data in the paper does not yield *identical* results but qualitatively similar ones.
Using the statistical programming package R ( https://cran.r-project.org/), and JAGS (Just Another Gibbs Sampler, http://mcmc-jags.sourceforge.net/), we processed multiple estimates of the Laurentian Great Lakes water balance components -- over-lake precipitation, evaporation, lateral tributary runoff, connecting channel flows, and diversions -- feeding them into prior distributions (using data from 1950 through 1979), and likelihood functions. The Bayesian Network is coded in the BUGS language. Water balance computations assume that monthly change in storage for a given lake is the difference between beginning of month water levels surrounding each month. For example, the change in storage for June 2015 is the difference between the beginning of month water level for July 2015 and that for June 2015., More details on the model can be found in the following summary report for the International Watersheds Initiative of the International Joint Commission, where the model was used to generate a new water balance historical record from 1950 through 2015: https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf. Large Lake Statistical Water Balance Model (L2SWBM): https://www.glerl.noaa.gov/data/WaterBalanceModel/, and This data set has a shorter timespan to accommodate a prior which uses data not used in the likelihood functions.
Smith, J., Gronewald, A. et al. Summary Report: Development of the Large Lake Statistical Water Balance Model for Constructing a New Historical Record of the Great Lakes Water Balance. Submitted to: The International Watersheds Initiative of the International Joint Commission. Accessible at https://www.glerl.noaa.gov/pubs/fulltext/2018/20180021.pdf, Large Lake Statistical Water Balance Model (L2SWBM). https://www.glerl.noaa.gov/data/WaterBalanceModel/, and Gronewold, A.D., Smith, J.P., Read, L. and Crooks, J.L., 2020. Reconciling the water balance of large lake systems. Advances in Water Resources, p.103505.
Interest in quantitative imaging of Y-90 is growing because transarterial radioembolization (RE) with Y-90 loaded microspheres is a promising and minimally invasive treatment that is FDA approved for unresectable primary and metastatic liver tumors. These cancers are a leading cause of cancer mortality and morbidity. Radioembolization is a therapy that irradiates liver tumors with radioactive microspheres administered through a microcatheter placed in the hepatic arterial vasculature. Radioembolization is based on the principle that healthy liver and tumor are mainly vascularized by the portal vein and the hepatic artery respectively. As a result, radioactive microspheres are preferentially located in the lesions after they are administered via the hepatic artery.
Van, B. J., Dewaraja, Y. K., Sangogo, M. L., & Mikell, J. K. (2021). Y-90 SIRT: Evaluation of TCP variation across dosimetric models. EJNMMI Physics, 8(1), 45. https://doi.org/10.1186/s40658-021-00391-6
In a broad sense, this project explores morphological and phonological processing in English monolinguals and two bilingual populations, Chinese-English and Spanish-English, using a battery of standardized and self-developed behavioral measures, as well as fNIRS neuroimaging. (T1=NEW PARTICIPANTES -TESTED BEHAVIORAL AND fNIRS-, T2= RETURNING PARTICIPANTS -JUST TESTED WITH BEHAVIORAL ASSESSMENTS)
In a broad sense, this dataset explores morphological and phonological processing in English monolinguals and two bilingual populations, Chinese-English and Spanish-English, using a battery of standardized and self-developed behavioral measures.
Language: English - Spanish - Chinese
Student capital is the set of skills, traits, and resources that an individual can draw upon to be successful in school. With dropout rates around 50%, community college students often don't have enough student capital to achieve their goals. The R code in this dataset estimates the average student capital of a group of community college students using data on their total credits and academic outcomes. It also contains R code to create figures, as found in the paper "The Shape of Educational Inequality" by Quarles, Budak & Resnick.
