Reshaping commensal gut microbiota in early life with amoxicillin presents with lower blood pressure
Contributors: Saroj Chakraborty
... Pediatric hypertension is recognized as an emerging global health concern. While new guidelines are developed for facilitating clinical management, the reasons for the prevalence of hypertension in children remain unknown. Genetics and environmental factors do not fully account for the growing incidence of pediatric hypertension. Because stable bacterial flora in early life are linked with health outcomes later in life, we hypothesized that reshaping of gut microbiota in early developmental stages of life affects blood pressure (BP) of pediatric subjects. To test this hypothesis, we administered amoxicillin, the most commonly prescribed pediatric antibiotic, to alter gut microbiota of young, genetically hypertensive rats (study 1) and dams during gestation and lactation to reshape microbiota of offspring (study 2). Reshaping of microbiota, with reductions in Firmicutes/Bacteriodetes ratio observed in Amoxicillin treated young rats and in dams. Amoxicillin treated rats also had lower blood pressure compared to the untreated rats. In the young rats treated with amoxicillin, the lowering effect on blood pressure persisted even after the antibiotics were discontinued. Similarly, the offspring from the dams treated with amoxicillin also showed lower systolic blood pressure compared to the control rats. Remarkably, in all cases, a decrease in BP was associated with lowering of Veillonellaceae, which are succinate-producing bacteria. Elevated plasma succinate is reported in hypertension. Accordingly, serum succinate was measured and found lower in animals treated with amoxicillin. Our results demonstrate a direct correlation between succinate-producing gut microbiota and early development of hypertension, and indicate that reshaping gut microbiota, especially by depleting succinate-producing microbiota early in life may have long-term benefits for hypertension-prone individuals.
Contributors: Nikola Tošić, Adam Knaack, Yahya Kurama
... This dataset contains supporting documentation explaining the use of time-dependent concrete material models TDConcrete, TDConcreteEXP, TDConcreteMC, and TDConcreteMC10NL in OpenSees. The dataset contains a manual explaining the use and features of the models, an Excel table for calculating model input parameters and example files using the material model TDConcreteMC10NL on a specific example.
Contributors: Matthias Sinnesael, Sietske J. Batenburg, Christian Zeeden, Jiri Laurin, Damien Pas, Linda Hinnov, Johannes Monkenbusch, Maximilian Vahlenkamp, Mingsong Li, Sébastien Wouters
... Supplementary materials for Submission: Sinnesael et al., Earth-Science Reviews "The Cyclostratigraphy Intercomparison Project (CIP): consistency, merits and pitfalls ".
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Contributors: Mei-hui Liu
... This set of supplementary data is related to the article “Blended Cross-tier Teacher Development: Designing Online Video-based Pragmatic Assessment.” It contains 1) survey data, 2) data collection methods, and 3) samples of edited video clips retrieved from Youtube links and deigned discourse completion tests. The participants’ interview content and reflection entries cannot be exposed to the public under the ethics guidelines of National Cheng Kung University Human Research Ethics Committee in Taiwan.
Contributors: Susanne Moskalski, France Floc'h, Romaric Verney
... Timeseries of water level, tidal velocity, backscatter, suspended sediment concentration, and sediment flux derived from acoustic doppler current profilers. Sediment fluxes calculated according to Dyer decomposition equation and Yu et al. (2012) analytical model. Two deployments in 2013: February and September. Two locations in the estuary: Site 1 near the mouth of the river (48° 16.842’ N, 004° 16.009’ W), and Site 3 in the central estuary (48° 14.851’ N, 004° 10.140’ W). Yu, Q., Wang, Y.P., Flemming, B., Gao, S., 2012. Tide-induced suspended sediment transport: Depth-averaged concentrations and residual fluxes. Continental Shelf Research 34, 53-63.
Contributors: Guillermo Toriz, Carmen Miramontes-Corona, Alfredo Escalante, Ezequiel Delgado, Corona Gonzalez Rosa Isela, Humberto Vázquez-Torres
... Processed Data
Bridging sensory evaluation and consumer research for strategic leafy Brassica (Brassica oleracea) improvement
Contributors: Hannah Swegarden, Phillip Griffiths, Alina Stelick, Robin Dando
... Research data uploaded here provided the foundation for analysis of Qualitative Multivariate Analysis (QMA_FocusGroupObservations), descriptive analysis (QDA_Sessions123 and ConsumerClusterDemo_Kale2019), and consumer testing (ConsumerKale_FinalData). These data have been cleaned and formatted from raw datasets; they are ready for downstream analysis. Participant journal entries and raw data are available upon request.
Contributors: Nemanja Stanisic
... The data from 13,561 complete sets of annual financial statements for 4,701 companies are combined with the data from the corresponding audit reports, forming an unbalanced panel data set. The client companies included in the sample represent a supermajority of medium- and large-sized companies registered in the Republic of Serbia. The information on the auditor firm name and the type of audit opinion is hand-collected from the audit reports issued by 64 audit firms (Big 4 plus 60 other audit firms), which, again, represents a supermajority of all the auditor firms registered in this country. To the best of our knowledge, this is the largest data set used in the literature devoted to predicting the type of audit opinion. In the total sample of audit opinions (13,561), the following frequencies of the four main types of audit opinions are observed: adverse opinion (71), disclaimer of opinion (644), qualified opinion (3,706), and unqualified opinion (9,140). Feel free to use it for research purposes or to reproduce the results presented in the article. For a detailed description of the variables and their descriptive statistics, please read the article: Stanišić, N., Radojević, T., Stanić, N. (2019). Predicting the Type of Auditor Opinion: Statistics, Machine Learning, or a Combination of the Two?. The European Journal of Applied Economics, 16(2), 1-58. doi:10.5937/EJAE16-21832 that is available at: http://journal.singidunum.ac.rs/paper/predicting-the-type-of-auditor-opinion-statistics-machine-learning-or-a-combination-of-the-two.html When referring to the data set in publications please cite the data as follows: Stanisic, Nemanja (2019), “Predicting the Type of Auditor Opinion: Statistics, Machine Learning, or a Combination of the Two?”, Mendeley Data, V1, doi: 10.17632/mmcczp3g3y.1 Also, consider citing the related research paper. These data are used in a research study and may not be redistributed or used for commercial purposes. If you have any questions please feel free to contact me at email@example.com
A Mathematical Model for the Berth Allocation Problem with Variable Service Time and Continuous Time Horizon
Contributors: Bruno Luís Hönigmann Cereser
... Tests, Results and Codes of the paper "A Mathematical Model for the Berth Allocation Problem with Variable Service Time and Continuous Time Horizon".
Parvimico materdei gen. et sp. nov.: A new platyrrhine from the Early Miocene of the Amazon Basin, Peru
Contributors: Richard F Kay
... SOM File S1. NEXUS-formatted file for phylogenetic analysis. (.nex file) SOM File S2. Nexus-formatted file with branch lengths for PGLS analysis. (.nex file) SOM File S3. R code for PGLS run. (R script) SOM Table S1. Extant platyrrhine frugivore mean upper first molar mesiodistal length, and sum of shearing (includes both real values and natural logs). (csv file) SOM Table S2. Molar shearing and mesiodistal lengths for platyrrhine specimens. (xlsx-formatted Excel spreadsheet) SOM Table S3. Procrustes-aligned data matrix and ancillary results from principal component analysis reported in the text. (xlsx-formatted Excel spreadsheet) SOM S1. Age determinations using U/Pb dating of detrital zircons. SOM S2. Characters and character states used in the phylogenetic analysis. SOM S3. Landmark Analyses: Additional Data and Results.