KU Leuven Research Data Repository
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Replication data for: Wheat gluten structure and (non-)covalent network formation during deep-fat frying
No full textThe aim of this work was to investigate wheat gluten protein network structure throughout the deep-frying process and evaluate its contribution to frying-induced microstructure development. Gluten polymerization, gluten-water interactions, and molecular mobility were assessed as a function of the deep-frying time (0 – 180 s) for gluten-water model systems of differing hydration levels (40 – 60 % moisture content). Results showed that protein extractability decreased considerably upon deep frying (5 s) due to glutenin polymerization by disulfide covalent cross-linking. Stronger protein-protein interactions were attributed to the breaking of hydrogen bonds and evaporation of water interacting with protein chains. Longer deep-frying resulted in progressively lower protein extractabilities in part due to gliadin co-polymerization by thiol-disulfide exchange reactions. The molecular mobility of gluten polymers was substantially reduced and gluten proteins gradually transitioned from the rubbery to the glassy state. The findings of this work demonstrate that the extent of gluten structural expansion as a result of deep-frying is dictated by the polymerization of the proteins and reduction in their molecular mobility
Gastrointestinal physiology in aging populations
No full textGastrointestinal (GI) changes can significantly influence drug absorption, potentially affecting the efficacy and safety of oral pharmacotherapy. Despite this, GI physiology remains understudied in the aging population. This dataset includes information on transit times and intraluminal pH across different GI segments (stomach, small intestine, and colon), providing valuable insights into GI physiology among geriatric patients and older adults
Enactment and complex stance-taking in Flemish Sign Language: a multimodal approach
No full textThis dataset was analyzed as part of the dissertation project "Enactment and complex stance-taking in Flemish Sign Language: a multimodal approach" (Andries, 2025). The dataset contains the video clips that served as examples in the dissertation as well as an ELAN template for the annotation and analysis of co-enactment in signed interactions. The study investigates how VGT signers use enactment in complex stance acts. Drawing on a dataset of five hours of dyadic conversations between VGT signers, the dissertation presents a bundle of six studies shedding light on the various uses of enactment for stance-taking, focussing on the semiotic composition of enactments, their temporal embedding and the co-constructed nature of enactment in the context of stance-taking. This dissertation touches on two key components of complex stance acts: 1) the temporal relation of multiple stance acts, and 2) the combination of multiple viewpoints to express complex stances. A systematic multimodal analysis of enactments in dialogic interactions has provided novel insights into their potential for co-construction and the semiotic composition when combined with narration in VGT
Residual stiffness and fatigue cycle test data for six types of glass fiber-reinforced composites, along with the probabilistic random forest algorithm
No full textThe composite material random alternating loads data and model code has been used in the paper “Prediction of residual stiffness of composite materials under random vibration loading using a combined probabilistic random forest and probabilistic stiffness model” in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering.
The codes include the data used for Fig. 6, Fig. 8, and Fig. 10.
Random forest.ipynb is used in this paper for probabilistic random forest stiffness prediction
Supplementary materials for: From Microstructure to Mechanics: A Multi-constituent Micro-scale Computational Model of Arterial Tissue
No full textThis dataset in KU Leuven's research data repository is the supplementary materials for the research paper entitled "From Microstructure to Mechanics: A Multi-constituent Micro-scale Computational Model of Arterial Tissue". Authors: Maïté Pétré (UCLouvain & KULeuven), Hannes Wolfs (KULeuven), Lauranne Maes (KULeuven), Greet Kerckhofs (UCLouvain) and Nele Famaey (KULeuven). The following supplementary files enables the creation of a representative volume element (RVE) of arterial tissue based on microstructural information. Microstructural parameters were derived from 3D contrast-enhancing computed tomography images of the medial porcine aortic layer and relevant literature studies using scanning electron microscopy (SEM). The 360 x 360 x 360 μm³ RVE consists of twelve 15μm thick elastic lamellae spaced by 15μm.
Collagen fibers between elastic lamellae are periodic and follow a preferred orientation and dispersion determined by probability density functions. In addition, elastin struts and interlamellar elastin fibers (IEFs) are introduced between the lamellae, connecting the lower plane to the upper. The different constituents of the model are embedded in the ground matrix. Truss elements (T3D2) are selected for collagen fibers, elastin struts, and IEFs, shell elements (S4R) for the elastic lamellae, and solid elements (C3D8RH) for the ground matrix. The same neo-Hookean material model was applied for the elastin constituents, while the ground matrix is also modeled with a neo-Hookean material. Finally, analogous to Dalbosco et al. 2021, a user-defined material model (UMAT) for the collagen fibers with periodic boundary conditions is implemented in Abaqus. A tutorial presentation explaining the code structure and its different steps is available under the name "rve-generation-code-training.pdf".
INPUT DATA - optional
*ID01. Name input datafile(s)
cube_60.inp, cube_60_PBC.inp
cube_120.inp, cube_120_PBC.inp
cube_240.inp, cube_240_PBC.inp
cube_360.inp, cube_360_PBC.inp
cube_480.inp, cube_480_PBC.inp
cube_600.inp, cube_600_PBC.inp
cube_660.inp, cube_660_PBC.inp
cube_720.inp, cube_720_PBC.inp
smc_ellipsoid.inp
*ID02. Description input datafile(s)
Pyvista cannot create C3D8H elements used for the ground matrix and C3D10H elements for the smooth muscle cells in ellipsoid shape.
Therefore, these parts need to be created beforehand in abaqus so that Pyvista can use them and merge the elements and nodes in the big .inp file
that is generated by the main.py and that can be used to run in Abaqus.
cube_size.inp are the Abaqus input files created beforehand in Abaqus and imported in the main.py script.
Few sizes has already been created for the user such as size [60, 120, 240, 360, 480, 600, 660, 720].
If the user want another size, the user will need to create a solid cube in Abaqus with C3D8H elements and save the .inp file.
cube_size_PBC.inp are the Abaqus input files defining all the nodes of the different faces of the cube for periodic bounadry conditions. If the user want another cube size, the user will need to first create the new cube in Abaqus and then run the periodic_boundary_conditions_assignement.py
smc_ellipsoid.inp is the Abaqus input file for one smooth muscle cell (smc) having an ellipsoid shape and having C3D10H elements. The code main.py uses
this one instance of ellipsoid to orient and assemble many other instances of smcs in the volume of the cube.
CODE
CA01. Name and description of code file(s)
main.py : main python file to create a RVE of arterial tissue.
Several parameters can be modified by the user such as the fiber diameter d, the uniaxial stiffness modulus of collagen fibers,
the neohookean material properties (C10, D) for elastin constituents and ground matrix, the size of the RVE...
geometry.py: contains all geometrical function to generate collagen fibers, elastic lammellae, elastin struts, interlammellar elastin fibers
and smooth muscle cells. The cube of the ground matrix is first generated, then the elastic lamellae are generated based on their thickness
and the interlammellar spacing. Then, in between the elastic lammellae, like a sandwich formation, all other constituents are created.
distribution.py: create all the distribution (von-mises, beta) used for generating the in-plane and out-of-plane angle for collagen fibers
as well as the distribution for the recruitment stretch values assigned to collagen fibers.
mathematics.py : mathematical functions implemented for vectorial used in the geometry.py
recruitment_stretch_collagen_fibers: create from a beta distribution all the recruitment stretch values assigned to all the elements
of collagen fibers. These recruitment stretches are needed for the UMAT.
write_input_files: functions to create the main abaqus input file that can be run in Abaqus. It also creates a .ssh file if want to run
the main abaqus input file on a cluster (super computer).
arguments.for: arguments used in the fortran user material subroutine (UMAT). Needed only when running the main abaqus input file on Abaqus.
UMAT_fiber.for: the user material subroutine used for the collagen fibers to implement the concept of recruitment stretch
(see reference [1] or the tutorial presentation for more information). Needed only when running the main abaqus input file on Abaqus.
periodic_boundary_conditions_assignement.py : create an Abaqus .inp file with all the nodes belonging to the different face of the cube to implement periodic
boundary conditions.
USAGE
US01. Installation instructions - add relevant links
Intsall pyvista, meshio in your python environment as well as numpy, scipy and math.
pyvista: https://docs.pyvista.org/
meshio: https://pypi.org/project/meshio/2.3.5/
scipy: https://pypi.org/project/scipy/
US02. How to run
After installation of the relevant packages, adapt the path of directory in main.py as well as the path to the input files (cube_size.inp, cube_size_PBC.inp, smc_ellipsoid.inp).
Run the main.py and it will automatically generate all the .inp file needed for Abaqus to run it. The typical output files it will generate are:
RVE_size_60.inp
RVE_size_60.ssh
collagen_renumbering_size_60.inp
recruitment_stretch_size_60.inp
ief_renumbering_size_60.inp
smc_renumbering_size_60.inp
elstrut_renumbering_size_60.inp
planes_renumbering_size_60.in
Replication Data for: Nitrogen recovery from digestate via stripping-scrubbing using citric acid: Potential effects of recirculation and postdigestion on additional biogas recovery, and assessment of the fertilizer potential of end-products
No full textIn this Excel data file, the authors share the data from the publication 'Nitrogen recovery from digestate via stripping-scrubbing using citric acid: Potential effects of recirculation and postdigestion on additional biogas recovery, and assessment of the fertilizer potential of end-products'. The data encompasses results obtained with the anaerobic digestion and post-aerobic digestion after stripping or scrubbing
Replication Data for: Renal Resistance During Hypothermic Machine Perfusion: A Scoping Review of Variability and Determinants, with a Meta-Analysis of Predictive Value for Transplant Outcomes
No full textThis dataset contains the data extraction tables of a scoping review performed to assess variability, determinants, and predictive value for transplant outcomes. The final search was carried out on July 15, 2024
Dataset for the manuscript "Room for renewables: A GIS-based agrivoltaics site suitability analysis in urbanized landscapes"
No full textDataset for the manuscript "Room for renewables: A GIS-based agrivoltaics site suitability analysis in urbanized landscapes". The dataset includes input geodatasets as well as result geodatasets per crop category. The dataset also includes a numerical summary of results per score in excel
Effect of COVID-19 restrictions on the sleep of adolescents with and without ADHD
No full textThis dataset contains the sleep data (objective actigraphy measured sleep, subjective sleep diary measured sleep, Chronic Sleep Reduction Questionnaire, revised Adolescent Sleep Hygiene Scale) of four groups (two with ADHD and two without ADHD (NT)) of in total 100 adolescents (50 ADHD and 50 NT). One ADHD and NT group were tested during many COVID-19 restrictions, the other during few
Intego-II database December 2024
No full textThe Intego-II database builds on three decades of primary care data collection in Flanders, Belgium. Since 1994 pseudonymized electronic medical record (EMR) data from participating general practices are collected within Intego. Its integration with Healthdata.be provides scalable linkage to mortality, environmental, and disease-specific datasets at the national level.
Intego-II incorporates substantial advancements in the database’s structure, operations, and accessibility. A robust two-step Extract-Transform-Load (ETL) process ensures data security, privacy, tidiness, and quality. To enhance international research interoperability, the database is aligned with the OMOP Common Data Model.
Intego-II is organized into three key modules: Patient Information, Medical History, and Clinical Encounters, enabling longitudinal analyses across diverse healthcare domains covering, among others, demographic variables, diagnoses, prescriptions, and laboratory test results.
Structured quarterly releases with detailed metadata ensure findability and reusability. Researchers can access the full Intego-II database via a secure research environment provided by Healthdata.be, following submission and approval of a study protocol. The data access process can be found on www.intego.be