1,721,025 research outputs found
Onco* tutorial
No full textOnco* tutorial
This repository contains a tutorial for the following software packages:
OncoFEM
OncoSTR
OncoTUM
OncoGEN
The goal is to demonstrate all functions and provide users with easier access to the tools.
After successfully installing one or all of the packages, the respective tutorial can be started with Python, for example:
python oncofem_tut_01_quick_start.py
Legacy versions can be found in the OncoFEM data repository, respectively in virtual box for OncoFEM
Onco* version 0.1.0
No full textOnco* version 0.1.0
Onco* is a module based umbrella software project for numerical simulations of patient-specific cancer diseases, see following figure. From given input states of medical images the disease is modelled and its evolution is simulated giving possible predictions. In this way, a digital cancer patient is created, which could be used as a basis for further research, as a decision-making tool for doctors in diagnosis and treatment and as an additional illustrative demonstrator for enabling patients understand their individual disease. All parts resolve to an open-access framework, that is ment to be an accelerator for the digital cancer patient. Each module can be installed and run independently. The current state of development comprises the following modules:
OncoFEM
OncoGEN
OncoTUM
OncoSTR
Content
The uploaded virtual box is a virtual machine of a linux mint 21.2 cinnamon, 64 bit system and 8 GB RAM. The machine contains the pre-installed version of OncoFEM version 1.0, that corresponds to its related publication. The virtual machine need to be imported in Oracle VM VirtualBox.
Username: onco
password: 0000
The pre-installed version of onco* is implemented in a conda environment that is activated with the terminal command
python3 oncofem_tut_01_quick_start
Of course, the tutorials will only run, if the system meets the necessary requirements. The tutorials (1, 2, 3, 4, 5, 6) where performed on a local machine (intel cpu i7-9700k with 3.6 GHz, 128 GB RAM). The tumor segmentation (tutorial 7, 8) have been tested on a different machine with a gpu (Nvidia a40, 48 GB VRAM, 32 core AMD epyc type 7452). For testing, the discretisation in space is decreased, compared to the results published in the respective paper.
This is the software state at version 0.1.0 and the actual development can be found in the respective github.
OncoFEM
OncoGEN
OncoTUM
OncoSTR
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OncoTUM models
No full textOncoTUM models
This repository hosts pretrained neural network models for OncoTUM,
a key software package within the umbrella project Onco* for modelling and numerical simulations of tumours. OncoTUM is designed to facilitate tumour segmentations from medical images, leveraging state-of-the-art deep learning techniques.
Purpose
The pretrained models in this repository are required for using the inference function of OncoTUM. These models have been trained on relevant datasets (BraTS 2020) to ensure
high accuracy and performance in tumor segmentation and its classification.
Usage
To utilise the inference functionality of OncoTUM, download the appropriate pretrained models from this repository and ensure they are correctly linked to the OncoTUM software package.
Detailed instructions for setup and integration can be found in the
OncoTUM documentation.
Content
In order to remain with most possible flexibility, the modality agnostic implementation allows to perform segmentation with a subset of the gold standard modalities.
Brain tumour segmentation
Full modal model: trained to all gold standard modalities (t1, t1gd, t2, flair).
Single modality model: trained to single modalities of the gold standard.
Null image: Empty image for training.
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Data for: A continuum mechanical porous media model for simulating vertebroplasty: Numerical simulations and experimental validation
No full textThis dataset includes experiment and simulation data used in the research described in the paper "A continuum mechanical porous media model for simulating vertebroplasty: Numerical simulations and experimental validation".
The following data is included in the dataset:
Experimentally measured data:
“rheology_viscosity_time.csv”:
This file contains viscosity vs time characteristics of bone cement Vertecem V+ after 118 ± 10 s after start of bone cement preparation. The rheological measurements were performed on MCR302 rheometer from Anton Paar. Parallel plate geometry (PP) with a 25 mm diameter top plate and a 1.5 mm gap was used. Time sweep measurement using plate-plate with a total measurement time of 20 minutes was run in a rotational mode at a shear rate of 0.2 1/s. Each point was recorded every 5 seconds.
“baseline_slow.csv”:
This file contains force vs time data for injection (benchmark) experiment with and without aluminium foam at flow rate 0.1 ml/s.
“baseline_fast.csv”:
This file contains force vs time data for injection (benchmark) experiment with and without aluminium foam at flow rate 0.1 ml/s.
“CemFlow_CF_C06_CoarseFoam06_Cine-40mm-0.4s-AR50-A_302_<1-25>.tif” and “CemFlow_CF_C06_CoarseFoam06_Cine-40mm-0.4s-AR50-B_303_<1-31>.tif”:
These files contain TIF images from computed tomography (CT) scan of aluminium foam during injection with flow rate 0.1 ml/s. Images are in chronological order, i.e. the first 25 files have the suffix “A_302_<1-25>.tif” and the following 31 files have suffix “B_303_<1-31>.tif”. Image resolution: 200 x 200 x 625 microns, time resolution: 0.4 s. The CT scan was carried out on Revolution EVO, GE Medical Systems (Schweiz) AG, Glattbrugg, Switzerland) set at 120 kV and 150 mA, with 0.625 mm slice thickness, bone reconstruction kernel, and 0.4 s rotation time.
“CemFlow_CF_C04_CoarseFoam04_Cine-40mm-0.4s-AR50_301_<1-15>.tif”:
These files contain TIF images from computed tomography scan of aluminium foam during injection with flow rate 0.4 ml/s. Images are in chronological order. Image resolution: 200 x 200 x 625 microns, time resolution: 0.4 s. The CT scan was carried out on Revolution EVO, GE Medical Systems (Schweiz) AG, Glattbrugg, Switzerland) set at 120 kV and 150 mA, with 0.625 mm slice thickness, bone reconstruction kernel, and 0.4 s rotation time.
Data from simulations:
The files from the simulations are labelled with prefixes, the meanings of which are explained below, followed by numbers from “0000” to “0051”, each being a subsequent time step of 0.1 second, except for the ones simulated over 20 seconds (with prefix including ‘_slow’) which are in time steps of 0.4 seconds. The files can be opened and visualized in Paraview. Please use Paraview version 5.5.2, since newer versions have trouble loading these files.
“canella_slow”: Simulation of bone cement injection in empty aluminium foam at 0.1 ml/s using the Cannella model for upscaling the rheology.
“hirasaki_slow”: Simulation of bone cement injection in empty aluminium foam at 0.1 ml/s using the Hirasaki and Popre model for upscaling the rheology.
“eberhard_slow”: Simulation of bone cement injection in empty aluminium foam at 0.1 ml/s using the average viscosity model for upscaling the rheology.
“canella”: Simulation of bone cement injection in empty aluminium foam at 0.4 ml/s using the Cannella model for upscaling the rheology.
“hirasaki”: Simulation of bone cement injection in empty aluminium foam at 0.4 ml/s using the Hirasaki and Popre model for upscaling the rheology.
“eberhard”: Simulation of bone cement injection in empty aluminium foam at 0.4 ml/s using the average viscosity model for upscaling the rheology.
“vertebra_empty”: Simulation of bone cement injection in empty vertebra at 0.4 ml/s using the Cannella model for upscaling the rheology.
“marrow_n<1-6>”: Simulation of bone cement injection in vertebra filled with Newtonian marrow with viscosity 1000, 100, … , 0.01 Pa s (in the given sequence), at 0.4 ml/s using the Cannella model for upscaling the rheology.
“marrow_nn<1-5>”: Simulation of bone cement injection in vertebra filled with non-Newtonian marrow with viscosity limits 1000 to 100, 100 to 10, .. , 0.1 to 0.01 Pa s (in the given sequence), at 0.4 ml/s using the Cannella model for upscaling the rheology.
“perm_<x>em<y>”: Simulation of bone cement injection in an empty aluminium foam with permeability value “x times 10-y”, at 0.4 ml/s using the Cannella model for upscaling the rheology.
“poro_<x>”: Simulation of bone cement injection in an empty aluminium foam with porosity value “x times 10-2” at 0.4 ml/s using the Cannella model for upscaling the rheology.
“bc_1_<x>”: Simulation of bone cement injection in an empty aluminium foam with Brooks-Corey uniformity parameter value “x”, at 0.4 ml/s using the Cannella model for upscaling the rheology.
“bc_1_<x>em<y>”: Simulation of bone cement injection in an empty aluminium foam with Brooks-Corey uniformity parameter value “x times 10-y”, at 0.4 ml/s using the Cannella model for upscaling the rheology.
“bc_2_<x>”: Simulation of bone cement injection in an aluminium foam with Brooks-Corey uniformity parameter value “x” and filled with Newtonian marrow with viscosity 1000 Pa s at 0.4 ml/s using the Cannella model for upscaling the rheology.
“bc_2_<x>em<y>”: Simulation of bone cement injection in an aluminium foam with Brooks-Corey uniformity parameter value “x times 10-y” and filled with Newtonian marrow with viscosity 1000 Pa s, at 0.4 ml/s using the Cannella model for upscaling the rheology.
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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