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openother-atApr 2023View details →

Artefact for PhD thesis "Dynamic Fault Trees: Semantics, Analysis and Applications"

This artefact contains the example files, tools, and log files for the evaluation in Chapter 7 of the PhD thesis Matthias Volk - Dynamic Fault Trees: Semantics, Analysis and Applications   The tools are licensed according to the license provided with each tool.

opencc-by-4.0May 2023View details →

Telemedicine for cancer pain management: CTGAN application and ML classification

Background: The utilization of artificial intelligence (AI) in healthcare has significant potential to revolutionize the delivery of medical services, particularly in the field of telemedicine. In this article, we investigate the capabilities of a specific deep learning model, a Generative Adversarial Network (GAN), and explore its potential for enhancing the telemedicine approach to cancer pain management. Materials and Methods: We implemented a structured dataset comprising demographic and clinical variables from 226 patients and 489 telemedicine visits for cancer pain management. The deep learning model, specifically a conditional GAN, was employed to generate synthetic samples from our dataset that closely resemble real individuals in terms of their characteristics. Subsequently, four machine learning algorithms were implemented to assess the variables associated with a higher number of remote visits. Results: The generated dataset exhibits a distribution comparable to the reference dataset for all considered variables, including age, number of visits, tumor type, performance status, characteristics of metastasis, opioid dosage, and type of pain. Among the algorithms tested, Random Forest demonstrated the highest performance in predicting a higher number of remote visits, achieving an accuracy of 0.8 on the test data. Conclusion: As the advancement of healthcare processes relies on scientific evidence, AI techniques such as GANs can play a vital role in bridging knowledge gaps and accelerating the integration of telemedicine into clinical practice. Nonetheless, it is crucial to carefully address the limitations associated with these approaches.

opencc-by-4.0Jun 2022View details →

Preparation of Deuterium-Labeled Armodafinil by Hydrogen–Deuterium Exchange and Its Application in Quantitative Analysis by LC-MS

Armodafinil, the R enantiomer of modafinil, was approved in 2007 by the US Food and Drug Administration as a wake-promoting agent for excessive sleepiness treatment. Due to its abuse by students and athletes, there is a need of its quantification. Quantitative analysis by liquid chromatography-mass spectrometry, however, though very common and sensitive, frequently cannot be performed without isotopically labeled standards which usually have to be specially synthesized. Here we reported our investigation on the preparation of deuterated standard of armodafinil based on the simple and inexpensive hydrogen–deuterium exchange reaction at the carbon centers. The obtained results clearly indicate the possibility of introduction of three deuterons into the armodafinil molecule. The introduced deuterons do not undergo back exchange under neutral and acidic conditions. Moreover, the deuterated and non-deuterated armodafinil isotopologues revealed coelution during the chromatographic analysis. The ability to control the degree of deuteration using different reaction conditions was determined. The proposed method of deuterated armodafinil standard preparation is rapid, cost-efficient and may be successfully used in its quantitative analysis by LC-MS.

opencc-by-nc-4.0Jun 2023View details →

Qualitative Interview Study to Assess the Usefulness of the IaC-based Application Compliance Management Framework (IACMF)

This dataset contains anonymized answers from nine industry experts interviewed to assess the usefulness of the IaC-based Application Compliance Management Framework (IACMF). The interviews were held online after the participants <a href="https://clipchamp.com/watch/5tUiwnTqyXW">watched a video tutorial of the framework</a>. The dataset also contains the questions that were asked during the interviews, a fundamentals document sent to the participants before the interviews, and the results of structuring and summarizing the answers.

opencc-by-4.0Dec 2021View details →

Clinical applicability of COVID-19 detection in chest radiography with deep learning - radiologist annotations

Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. In order to evaluate the performance of radiologists in the detection of COVID-19 radiological features in CXR, manual annotation of a subset of random CXR images was performed by two radiologists using an in-house software. The software presented CXRs from a randomly selected subset and allowed for window center/width adjustment, zooming and panning. Radiologists were asked to label CXRs into one of 4 classes: Normal, Not indicative of COVID-19 (pathological), Indicative of COVID-19 and Undetermined. The Indicative of COVID-19 class was defined as CXRs where the patient presented findings indicative of COVID-19, namely bilateral pulmonary opacities of low/medium density. The Undetermined class was defined as CXRs where the patient presented findings that could be indicative of COVID-19 but which could also be indicative of another condition, namely unilateral lung opacities, diffuse bilateral opacities of ARDS pattern or diffuse reticular opacities. The Not indicative of COVID-19 (pathological) class was defined as CXRs where the patient presented findings indicative of any other pathology except for COVID-19. CXRs where the patient presented medical devices were classified as Normal if the underlying pathology was not visible. Additionally, CXRs without sufficient quality for visual assessment by the radiologists due to bad image quality, patient positioning or any other factors could be labelled as Compromised for exclusion. A total of 1,845 CXRs were selected for annotation by two radiologists. Of these, 1,256 belong to the Mixed dataset(a combination of multiple public sources), 289 belong to BIMCV and 300 belong to COVIDGR. Selection of CXRs for annotation was performed randomly: for the Mixed dataset, a balanced selection strategy was used during image selection, whereas for BIMCV and COVIDGR, the dataset class distribution was maintained in the subset selected for annotation. Manual labelling of CXRs was performed in two stages. First, both radiologists independently classified each CXR. CXRs where the two radiologists disagreed were then selected for the second stage where the two radiologists assessed the CXRs together to achieve consensus. At no point were radiologists given access to the ground truth label, RT-PCR results or any other information besides the CXR image. To ensure that written information present in the CXR image (such as hospital system, health service, laterality markers, patient positioning, etc.) did not bias the annotation, all written labels were blacked out during before annotation. This dataset contains the information regarding each annotated CXR and the resulting radiologist labels for each image before and after consensus. The public datasets used in this study are available in the following repositories: CheXpert: <a href="https://stanfordmlgroup.github.io/competitions/chexpert/">https://stanfordmlgroup.github.io/competitions/chexpert/</a> ChestXRay-8: <a href="https://nihcc.app.box.com/v/ChestXray-NIHCC">https://nihcc.app.box.com/v/ChestXray-NIHCC</a> COVID-19 IDC: <a href="https://github.com/ieee8023/covid-chestxray-dataset">https://github.com/ieee8023/covid-chestxray-dataset</a> COVIDx: <a href="https://github.com/lindawangg/COVID-Net">https://github.com/lindawangg/COVID-Net</a> RSNA-PDC: <a href="https://www.kaggle.com/c/rsna-pneumonia-detection-challenge">https://www.kaggle.com/c/rsna-pneumonia-detection-challenge</a> SAVE LIVES: <a href="https://www.hmhospitales.com/coronavirus/covid-data-save-lives">https://www.hmhospitales.com/coronavirus/covid-data-save-lives</a> SERAM: <a href="https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf">https://seram.es/images/site/TUTORIAL_CSI_RX_TORAX_COVID-19_vs_4.0.pdf</a> Twitter: <a href="https://twitter.com/ChestImaging">https://twitter.com/ChestImaging</a> BIMCV PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/padchest/">https://bimcv.cipf.es/bimcv-projects/padchest/</a> BIMCV COVID-19-PADCHEST: <a href="https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/">https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/</a> COVIDGR: <a href="https://dasci.es/transferencia/open-data/covidgr-2/">https://dasci.es/transferencia/open-data/covidgr-2/</a>

opencc-by-4.0Jul 2023View details →

Application of murexide as a capping agent for fabrication of magnetite anodes for supercapacitors: experimental and DFT studies (supporting files)

Supporting files.

openNov 2022View details →

Data Repository for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin"

Data for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin". This repository contains all the processed data used in the simulation (in "processed" folder), part of the raw data (in "raw" folder), and the SWAT simulation results (in "SWAT" folder). The code for processing the raw data, which are either provided here or publicly available online, is provided in the <a href="https://doi.org/10.5281/zenodo.8132662">code repository</a>. The links to the publicly available raw data are also provided in the code repository.

opencc-by-4.0Nov 2016View details →

Fig. 3 in A new R package and web application for detecting bilateral asymmetry in parasitic infections

Fig. 3. Volcano plot: statistical significance (-log (p-value)) of 10 G-tests for asymmetry of parasite distributions in individual hosts plotted against fold difference (log2 ratio) in parasite abundance between right and left eyes. Each point in the scatterplot represents the parasite distribution between eyes (excluding lenses) in an individual host. The G-test p-values were adjusted using the Benjamini and Hochberg (1995) procedure before -log10 transformation. The dashed horizontal line represents the p-value threshold for a 5% false discovery rate. Parasite distributions deviating significantly from symmetry are shown as red squares, whereas those not differing significantly from a 1: 1 ratio are represented by blue circles.

Fig. 8 in Application of cinnamic acid in the structural modification of natural products: A review

Fig. 8. CA derivatives 78–87 with anti-Antioxidant and anti-inflammatory activity.

Fig. 1 in Application of cinnamic acid in the structural modification of natural products: A review

Fig. 1. The structures of the most common cinnamic acids.

Fig. 5 in Application of cinnamic acid in the structural modification of natural products: A review

Fig. 5. CA derivatives 51–61 with anti-diabetes activity.

Fig. 7 in Application of cinnamic acid in the structural modification of natural products: A review

Fig. 7. CA derivatives 72–77 with anti-parasitic activity.

Fig. 6 in Application of cinnamic acid in the structural modification of natural products: A review

Fig. 6. CA derivatives 62–71 with anti-microbial activity.

Fig. 4 in Application of cinnamic acid in the structural modification of natural products: A review

Fig. 4. CA derivatives 41–50 with neuroprotective activities.

Fig. 9 in Application of cinnamic acid in the structural modification of natural products: A review

Fig. 9. CA derivatives 88–97 with Other activity.

Fig. 11 in Intramolecular transesterification of depsides yields fluorescent 1H-isochromen-1-ones: Application as a chemical probe for lichen determination

Fig. 11. Structural data of compound s8 (NMR in acetone-d6).

Fig. 4 in Intramolecular transesterification of depsides yields fluorescent 1H-isochromen-1-ones: Application as a chemical probe for lichen determination

Fig. 4. Reactivity of the methyl, 2′-O-methylmicrophyllinate 4′ with K.

Fig. 3 in Intramolecular transesterification of depsides yields fluorescent 1H-isochromen-1-ones: Application as a chemical probe for lichen determination

Fig. 3. Reactivity of depsides with K.

Fig. 7. A. Depsides rearrangement mechanisms with K in Intramolecular transesterification of depsides yields fluorescent 1H-isochromen-1-ones: Application as a chemical probe for lichen determination

Fig. 7. A. Depsides rearrangement mechanisms with K; B. MS fragmentation.

Fig. 6 in Intramolecular transesterification of depsides yields fluorescent 1H-isochromen-1-ones: Application as a chemical probe for lichen determination

Fig. 6. UV and fluorescence spectra of the isochromen-1-ones 9 and 11.