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: Samadi Samadi, Sitti Wajizah, Agus Arip Munawar
... Dataset contain Infrared spectral data in form of Absorbance spectrum for a total of 25 Animal feed samples from agricultural residues (Sago, coffee pulp, cocoa pod and corncob). Nutritive values were measured: IVOMD, IVDMD, NDF and ADF.
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Contributors: Gabriela Dávila
... The CrunchTope input file and the excel result file for the simulation related to this article can be found here
Data belonging to: "Direct visualization of native CRISPR target search in live bacteria reveals Cascade DNA surveillance mechanism"
Contributors: Jochem Vink
... Dataset includes D* coefficient lists, all localizations/tracks, interference data for different conditions and further data used in all figures of the manuscript. For raw image files (due to size restrictions not uploaded here) ask one of the corresponding authors of the paper. For further details see STAR methods of the paper.
Contributors: Pranav Pandya, Kartikey Hadiya
... This project is one of the academic projects given to us in the Geographic Information System (GIS) Course. Created by: Pranav Pandya (Me) and Kartikey Hadiya We sampled information for pollution emission in Delhi, India. Pollution data was obtained from https://data.gov.in/resources/real-time-air-quality-index-various-locations Pollution index data can be obtained from https://cpcb.nic.in/RealTimeAirQualityData.php Pollution data only had address of Indian Meteorological Department, so each station was located in Google Earth and pin points were added at each station. Then in the sidebar containing those pins on right-click, a new folder was added and all the pins were added in that new folder in google earth. Then that folder was saved as kml file. This kml file was uploaded to Mygeodata: https://mygeodata.cloud/converter/kml-to-csv and was converted into csv. Then the csv file was opened and coordinates were copied in the pollution data file. That file was later saved as CSV and imported in ArcGIS and xy data was displayed. Shapefile was obtained from web search, which is attached as well. That shapefile was imported in ArcGIS and the final view was generated which is shown in the picture.
Contributors: Sheikh R. Ahmed
... The online repository of research data to provide an insight into the statistical test.
Contributors: Janis Brammer, Bernhard Lutz, Dirk Neumann
... Contains the datasets used in our study "Permutation Flow Shop Scheduling with Multiple Lines and Demand Plans Using Reinforcement Learning". Two datasets are provided. The main dataset (folder data) contains 1050 problem instances for the multi-line permutation flow shop problem. The generation follows the method of Taillard (1993) and generates random processing times in the interval [1,99]. To create the demand plan we draw randomly from a multinomial distribution with equal probability for each job type. The additional dataset (folder data_disturbed) contains 150 problem instances for the multi-line permutation flow shop problem with short term-disturbances. Dataset structure Each PFSP dataset is structured in 15 subfolders. Each folder contains problem instances for a combination of line layout and processing time variation. Notation: Tai_PFSP_AL_B / Tai_D_PFSP_AL_B A: Number of Lines (1-3) B: Number of processing time variation (1-5) Each folder contains 70 problem instances. A problem file is a combination of one problem characteristic (number of jobs, machines and stations) and a demand plan variation. The processing times are fixed for one problem characteristic. Notation: tCD_E_F_G_H.mix C: Number of Lines (1-3) D: Number of problem characteristic (1-7) E: Number of jobs (20,100,500) F: Number of machines (5,10,20) G: Number of sorts (5,10,20) H: Number of demand plan variation (1-10) Each file represents a different problem in text format. Line Notation: L1: Demand plan L2: Layout Type L3: Number of machines L4: Number of machines per line L5: Number of total machines with synchronization machine L6: Number of sorts L7-end: Processing times matrix for the combination of machine (row) and job type (column) 1. Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64 (2), 278-285.
On the Decomposition of Austenite in a High-Silicon Medium-Carbon Steel During Quenching and Isothermal Holding Above and Below the Martensite Start Temperature
Contributors: shima pashangeh
... These data refer to the paper that submitted to the Journal of Materials characterization.
Contributors: Rafael Sanchez-Marquez
... Contains: Raw data transformed, Statistical analyses with transformed data, and time series charts with transformed data