2,788 research outputs found
Metabolomics data from A universal metabolite repair enzyme removes a strong inhibitor of the TCA cycle
No full textMetabolomics was performed on wild-type and OAT1 knockout Escherichia coli. Here we provide all data collected as .mzXML files.Metabolomics data was prepared as following. Freezer stocks of wt (BW25113) and ΔycgM E. coli were streaked on LB plates and 4 single colonies from each strain were grown for 24 h in M9 minimal medium (0.2% glucose). Cells were washed with 1x M9 salts and used to inoculate 5 mL culture tubes containing M9 medium with either 0.2% glucose (natural isotopic abundance) or 0.2% 13C6-glucose (fully labeled) to an initial OD600 = 0.05. Four replicate cultures of each genotype were grown in each media formulation by shaking at 37°C for ~4 h until cells reached mid-log growth phase (OD600 ~0.6; determined with a Thermo Scientific Genesys 30 spectrophotometer that can measure OD inside culture tubes). To minimize metabolic disturbances, a rapid harvesting protocol was used. Briefly, an equivalent of 1 mL at OD600 = 1.0 was quickly transferred to 1.5 mL polypropylene tubes, cells were pelleted in a microcentrifuge at full speed for 30 s, the supernatant was quickly aspirated and collection tubes were immediately snap frozen in liquid nitrogen. Samples were stored at -80°C prior to extraction. After collecting the mid-log samples, cultures were shaken for an additional 2 h until early stationary growth phase (OD600 ~1.2) and samples were harvested as above. Collection tubes were placed on dry ice, 0.2 mL of cold 90% methanol was added, and tubes were incubated at -80°C for 72 h. Samples were removed from the freezer, vortexed for 15 s, and incubated on ice for 3 h with vortexing every ~30 min. Afterwards samples were spun for 15 min in a microcentrifuge (16,000 g) at 4°C and supernatants were collected for analysis.
Metabolomic data were obtained using an ultra-performance liquid chromatography-electrospray ionization-hybrid quadrupole-orbitrap mass spectrometer (Ultimate® 3000 HPLC, Q Exactive™, Thermo Scientific) with an autosampler and with a sample vial block maintained at 4°C. Chromatographic separations were carried out on an SeQuant® ZIC®-cHILIC 3µm, 100Å, 100 x 2.1 mm column (Merck, Darmstadt, Germany) with column temperature 40°C, flow rate 0.40 mL/min, and a 2 μL injection volume. Mobile phases A: 0.1% formic acid in water and B: 0.1% formic acid in acetonitrile were delivered over a 23 min. gradient according to the following profile: initial 98% B, 2 min 98% B, 20 min 40% B, 22 min 98% B, 23 min 98% B. The MS conditions used were full scan (mass range 50-750 m/z, and 115-1000 m/z in separate analyses), resolution 70,000, desolvation temperature 350°C, spray voltage 3800 V, auxiliary gas flow rate 20, sheath gas flow rate 50, sweep gas flow rate 1, S-Lens RF level 50, and auxiliary gas heater temperature 300°C. Xcalibur™ software version 2.1 (Thermo Scientific) was used for data collection.
Tandem MS data were obtained using data dependent Top N acquisition (Full MS & dd-MS/MS). Precursor ions (top 5 most abundant ions per scan) were sequentially fragmented in the HCD collision cell with normalized collision energies (NCE) of 10, 20, 30, 40, 50, and 60 for six independent injections of each sample. MS/MS scans were acquired with 17,500 resolution, target value 1.0 × 105, 50 ms maximum injection time, and isolation window of 4.0 m/z. Data files were converted from .RAW to .mzML and .mgf formats using the ProteoWizard tool MSConvertGUI. MZmine 2.53 was utilized for extracting exact-mass chromatographic data for isotope ratio calculations, for generating untargeted metabolite feature tables, and for tabulating peak heights from targeted exact masses for calculating relative signal intensity ratios.
Key for sample names:
1st position letter: A = wild-type, B = OAT1 KO (ΔycgM)
2nd position number: 2 = natural glucose, 3 = 13C-glucose
3rd position letter: M = mid-log phase, S = early stationary phase
4th position number: 1-4 = biological replicates (mid-log and early stationary samples are paired; i.e., A2M1 and A2S1 were collected from the same culture tube)
example: A2M1 = wild type E. coli grown with natural glucose and harvested at mid-log, replicate 1Niehaus, Thomas D; Hegeman, Adrian D. (2024). Metabolomics data from A universal metabolite repair enzyme removes a strong inhibitor of the TCA cycle. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/fjt8-ed44
Cervical length measurement for the prediction of preterm birth in multiple pregnancies: a systematic review and bivariate meta-analysis
No full textArticle first published online: 20 JUN 2011Abstract not availableA. C. Lim, M. A. Hegeman, M. A. Huis In, T Veld, B. C. Opmeer, H. W. Bruinse and B. W. J. Mo
Reise durch Italien und Sicilien vom Jahre 1828 bis 1830 : (ein richtiges und in's Einzelne führende Handbuch für Reisende in Italien und Sicilien)
No full textvon J. B. Hegeman
Reise durch Italien und Sicilien vom Jahre 1828 bis 1830 : (ein richtiges und in's Einzelne führende Handbuch für Reisende in Italien und Sicilien)
No full textvon J. B. Hegeman
Assisted overtaking: An assessment of overtaking on two-lane rural roads
No full textAt the start of the 21st century overtaking on two-lane rural roads is a major traffic safety problem. However, this dissertation research demonstrates that most drivers are perfectly able to safely perform these manoeuvres. Their time spent in the left lane is about eight seconds. Preparing subtasks of the manoeuvre, such as checking surrounding vehicles' behaviours, changing gear and starting accelerations are started in the right lane. In this way, available overtaking gap in the oncoming traffic stream are optimally used. This anticipative behaviour is accounted for in the proposed overtaking assistant design, which informs the driver about overtaking gaps three seconds before they become available. This system is developed to assist less daring drivers with overtaking and to prevent risk-taking drivers to perform overtaking manoeuvres in unsafe situations. A driving simulator experiment demonstrated that this assistant does not much affect overtaking efficiency, drivers' comfort or safety, as long as the threshold for a safe gap is chosen such that the safety margin with the first oncoming vehicle remains above three seconds. A microscopic traffic simulation study shows that when this threshold is eleven seconds, traffic system efficiency remains similar or increases slightly due to increased number of overtaking manoeuvres. Drivers' safety during overtaking increases, because the time-to-collision with oncoming vehicles will not become smaller than three seconds. And, drivers' comfort is improved in terms of higher overtaking frequencies and less time spent following. To introduce the proposed overtaking assistant to the market, vehicle-to-vehicle communication is necessary. This technology enables vehicles to localise all other surrounding vehicles. The automotive industry works hard on the development of vehicle-to-vehicle communication, however, it will take at least another ten years before the first overtaking assistant as proposed in this dissertation, will become available.Civil Engineering and Geoscience
Hegeman, Joseph (Death, 1880-12-16)
No full textAddress: 99 Avery St.Age at death: 54 yrsPg 117/1880/300/M N M/N. Y./Dr. A. Carrick/J. Habig/Colored AmericanOriginal record filed in drawer labeled 'HEGEL-HEISS'
Hegeman, Nathaniel (Death, 1876-02-17)
No full textAddress: 232 Court St.Age at death: 4moPg 104/1876/437/M N S/City/Dr. T. Humot/J. Habig/Colored AmericanOriginal record filed in drawer labeled 'HEGEL-HEISS'
DISTRIK MELINGKAR: Circular housing in Indonesia
No full textIndonesia is currently facing a housing deficit of 15 million units. In order to try and resolve this problem, 800.000 units of formal housing are being build every year and the most popular typology in this fast growing housing market is the Indonesian housing cluster. The goal within this project is to do a redesign of the housing cluster typology and create a sustainable neighbourhood using building principles derived from the learnings of circular economy.Architecture, Urbanism and Building Sciences | Explorela
Predicting the air quality by combining model simulations with machine learning
No full textCombating air pollution has proven to be a difficult task for countries with rapidly developing economies. Poor air quality can be hazardous to people doing any outdoor activities. So being able to make accurate, short term air quality predictions can be very useful. However, making these predictions has proven to be quite difficult, since there are a lot of different physical and chemical processes involved in the emission and transport of the various aerosols that contribute to air pollution. So instead of the more traditional Chemical Transport Models (CTMs) we will be using neural networks in order to make predictions of one of these aerosols, PM2.5. In particular, we will be using a Long Short Term Memory (LSTM) network. In addition, we will include the simulations results from a CTM, LOTOS-EUROS, as input data to the LSTM network to improve the performance of the neural network. One of the main drawbacks of the LSTM approach is that whenever the PM2.5 concentration changes a lot, the predictions made by the LSTM network take some time to change as well, causing a visible time delay when looking at the measurements and predictions in the same time series plot. We will also try a simpler type of neural network, a Feedforward Neural Network (FNN) and compare its performance to that of LSTM. We found that using the simulation data does indeed improve the LSTM network. Not only in terms of the loss function used by the neural network and, but in particular in the amount gross overestimations by the network, which we use to quantify the LSTM time delay problem. We also found that FNN outperforms the LSTM approach, in particular on samples of high PM2.5 concentrations, which we argue is primarily caused by a low amount of samples in our dataset
LDBC Graphalytics graphs (2023)
No full textData sets for the LDBC Graphalytics benchmark. Stored in zstd-compressed CSV files. Compared to the previous version released in 2021, this data set fixes issues with incorrect/unavailable validation data sets.
Website: https://ldbcouncil.org/benchmarks/graphalytics/
Related publication: Alexandru Iosup, Ahmed Musaafir, Alexandru Uta, Arnau Prat-Pérez, Gábor Szárnyas, Hassan Chafi, Ilie Gabriel Tanase, Lifeng Nai, Michael J. Anderson, Mihai Capota, Narayanan Sundaram, Peter A. Boncz, Siegfried Depner, Stijn Heldens, Thomas Manhardt, Tim Hegeman, Wing Lung Ngai, Yinglong Xia: The LDBC Graphalytics Benchmark. CoRR abs/2011.15028 (2020). https://arxiv.org/abs/2011.1502
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