cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10048093
title: Supplemental Material for Context Switch Sensitive Fault Localization
DOI: 10.5281/zenodo.10059361
title: Supplemental Material for Context Switch Sensitive Fault Localization
DOI: 10.5281/zenodo.10532988
title: Supplemental Material for Context Switch Sensitive Fault Localization
DOI: 10.5281/zenodo.10532133
title: Molecular dynamics simulations of cubic LLZO at 700 K

description: Classical molecular dynamics were run using the metalwalls code [doi:10.21105/joss.02373]. We used the DIPPIM polarisable ion force field, as parameterised by Burbano et al. [doi:10.1103/physrevlett.116.135901], due to its proven accuracy in accounting for the effect of ion polarisability on diffusion [doi:10.1088/0953-8984/5/17/004, doi:10.1103/physrevlett.116.135901]. We simulated the cubic phase of LLZO in NVT ensemble at a temperature of 700 K. Simulations were run for 6 ns with a 2 fs timestep. To control temperature, we used a Nosé-Hoover thermostat, with a relaxation time of 121 fs (5000 $\hbar / E_{h}$) [doi:10.1063/1.447334, doi:10.1103/physreva.31.1695, doi:10.1063/1.463940]. Simulations were performed using 2 × 2 × 2 supercells with 1536 atoms following the same protocol as in [doi:10.1103/physrevlett.116.135901].
DOI: 10.6084/m9.figshare.25027874
title: <b>A Comprehensive Triangulation Study About The Impact of Duolingo on Language Skills</b>

description: Duolingo's impact on Language skills
DOI: 10.6084/m9.figshare.25027766
title: <b>Sexual pleasure or HIV prevention?: </b><b>A qualitative study of </b><b>male circumcision </b><b>in Zambia</b>

description: This study was conducted in Lusaka and Shibuyunji, Zambia. Lusaka is the Capital of Zambia while the latter is a rural district. The study employed a qualitative approach and participants were drawn from both urban and rural areas. This was to ensure a diverse range of opinions about VMMC. Men and women of reproductive age were purposively selected and recruited to participate in the study. They were approached at health facilities and in their communities. To identify and recruit circumcised men, snowball sampling was also used.In total, 30 in-depth interviews and two focus group interviews were conducted. Individual interviews were held with 10 women and 20 men. There was an equal distribution of each sex from the rural and urban areas. Two focus group interviews were conducted exclusively with men. In total, each focus group had 10 participants and only one focus group was conducted in the rural area and one in the urban area. The participants in the in-depth interviews did not participate in the group interviews.Ethical clearance was granted by the University of KwaZulu-Natal Humanities and Social Sciences Research Ethics Committee (HSSREC) and the University of Zambia Biomedical Research Ethics Committee (UNZA-BREC). Meanwhile the Zambia National Health Research Authority (NHRA) also authorised the study.The interview recordings were then transcribed verbatim by the first author, with translations effected where needed. Transcripts were imported into NVivo 12 Plus, and were then reviewed; themes were then identified after which codes were developed and the data was analyzed thematically.<br>
DOI: 10.6084/m9.figshare.25027766.v1
title: <b>Sexual pleasure or HIV prevention?: </b><b>A qualitative study of </b><b>male circumcision </b><b>in Zambia</b>

description: This study was conducted in Lusaka and Shibuyunji, Zambia. Lusaka is the Capital of Zambia while the latter is a rural district. The study employed a qualitative approach and participants were drawn from both urban and rural areas. This was to ensure a diverse range of opinions about VMMC. Men and women of reproductive age were purposively selected and recruited to participate in the study. They were approached at health facilities and in their communities. To identify and recruit circumcised men, snowball sampling was also used.In total, 30 in-depth interviews and two focus group interviews were conducted. Individual interviews were held with 10 women and 20 men. There was an equal distribution of each sex from the rural and urban areas. Two focus group interviews were conducted exclusively with men. In total, each focus group had 10 participants and only one focus group was conducted in the rural area and one in the urban area. The participants in the in-depth interviews did not participate in the group interviews.Ethical clearance was granted by the University of KwaZulu-Natal Humanities and Social Sciences Research Ethics Committee (HSSREC) and the University of Zambia Biomedical Research Ethics Committee (UNZA-BREC). Meanwhile the Zambia National Health Research Authority (NHRA) also authorised the study.The interview recordings were then transcribed verbatim by the first author, with translations effected where needed. Transcripts were imported into NVivo 12 Plus, and were then reviewed; themes were then identified after which codes were developed and the data was analyzed thematically.<br>
DOI: 10.5281/zenodo.7628650
title: Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1

description: Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387).

The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.

The dataset contains the following files:

1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of E. coli metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).

2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)

3. ODE_solutions.zip - self-explanatory (Figure 2c)

4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)

5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)

6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g

The remaining datasets are published in the following links

 - https://doi.org/10.5281/zenodo.7930084

 - https://doi.org/10.5281/zenodo.10391802
description: This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).
DOI: 10.5281/zenodo.7628651
title: Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states"

description: Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details. The dataset contains the following files: 1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of <em>E. coli </em>metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4). 2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b) 3. ODE_solutions.zip - self-explanatory (Figure 2c) 4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d) 5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a) 6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g 7. renaissance_parameter_fixing.zip - self-explanatory (Figure 4); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv). 8. scripts.zip - scripts used to create Figures 2-4.
description: This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).
DOI: 10.5281/zenodo.7930002
title: Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1

description: Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387).

The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.

The dataset contains the following files:

1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of E. coli metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).

2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)

3. ODE_solutions.zip - self-explanatory (Figure 2c)

4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)

5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)

6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g

The remaining datasets are published in the following links

 - https://doi.org/10.5281/zenodo.7930084

 - https://doi.org/10.5281/zenodo.10391802
description: This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).
DOI: 10.5281/zenodo.7930083
title: Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2

description: Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al.


The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.


The dataset contains the following files:


1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).


2. scripts.zip - scripts to generate figure 2-5 on toy data
description: This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).
DOI: 10.5281/zenodo.7930084
title: Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2

description: Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al.


The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.


The dataset contains the following files:


1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).


2. scripts.zip - scripts to generate figure 2-5 on toy data
description: This work was supported by funding from the Swiss National Science Foundation grant 315230_163423, the European Union's Horizon 2020 research and innovation programme under grant agreement 814408, Swedish Research Council Vetenskapsradet grant 2016-06160, and the Ecole Polytechnique Fédérale de Lausanne (EPFL).
DOI: 10.5281/zenodo.10391801
title: Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 3

description: Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387) - Part 3

The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.

The dataset contains the following files:



Distribution_comparison - contains 2 folders



No integration test - 10 repeats of RENAISSANCE generated models with 25 generations each with maximal eigenvalues

All integration test - 5 repeats of RENAISSANCE with 108 Kms integrate with 25 generations each with maximal eigenvalues



Shikki bioreactor -contains 350000 RENAISSANCe generated parameter sets with the corresponding bioreactor simulation solutions. 

Slow and steady - contains 30 sampled slow steady states and their RENAISSANCE achieved maximal eigenvalues (originally sampled from index 1586 to 1581 from 5000 smaples already provided)
DOI: 10.5281/zenodo.10391802
title: Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 3

description: Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387) - Part 3

The code to use with these data and reproduce the manuscript results is available at  https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.

The dataset contains the following files:



Distribution_comparison - contains 2 folders



No integration test - 10 repeats of RENAISSANCE generated models with 25 generations each with maximal eigenvalues

All integration test - 5 repeats of RENAISSANCE with 108 Kms integrate with 25 generations each with maximal eigenvalues



Shikki bioreactor -contains 350000 RENAISSANCe generated parameter sets with the corresponding bioreactor simulation solutions. 

Slow and steady - contains 30 sampled slow steady states and their RENAISSANCE achieved maximal eigenvalues (originally sampled from index 1586 to 1581 from 5000 smaples already provided)
DOI: 10.5281/zenodo.10532882
title: Dataset related to article "Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

description: This record contains raw data related to article “Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

Abstract



 Background and aims:  Autoimmune hepatitis (AIH) is a rare chronic liver disease of unknown aetiology; the risk of hepatocellular carcinoma (HCC) remains unclear and risk factors are not well-defined. We aimed to investigate the risk of HCC across a multicentre AIH cohort and to identify predictive factors.

 Methods:  We performed a retrospective, observational, multicentric study of patients included in the International Autoimmune Hepatitis Group Retrospective Registry. The assessed clinical outcomes were HCC development, liver transplantation, and death. Fine and Gray regression analysis stratified by centre was applied to determine the effects of individual covariates; the cumulative incidence of HCC was estimated using the competing risk method with death as a competing risk.

 Results:  A total of 1,428 patients diagnosed with AIH from 1980 to 2020 from 22 eligible centres across Europe and Canada were included, with a median follow-up of 11.1 years (interquartile range 5.2-15.9). Two hundred and ninety-three (20.5%) patients had cirrhosis at diagnosis. During follow-up, 24 patients developed HCC (1.7%), an incidence rate of 1.44 cases/1,000 patient-years; the cumulative incidence of HCC increased over time (0.6% at 5 years, 0.9% at 10 years, 2.7% at 20 years, and 6.6% at 30 years of follow-up). Patients who developed cirrhosis during follow-up had a significantly higher incidence of HCC. The cumulative incidence of HCC was 2.6%, 4.6%, 5.6% and 6.6% at 5, 10, 15, and 20 years after the development of cirrhosis, respectively. Obesity (hazard ratio [HR] 2.94, p = 0.04), cirrhosis (HR 3.17, p = 0.01), and AIH/PSC variant syndrome (HR 5.18, p = 0.007) at baseline were independent risk factors for HCC development.

 Conclusions:  HCC incidence in AIH is low even after cirrhosis development and is associated with risk factors including obesity, cirrhosis, and AIH/PSC variant syndrome.

 Impact and implications:  The risk of developing hepatocellular carcinoma (HCC) in individuals with autoimmune hepatitis (AIH) seems to be lower than for other aetiologies of chronic liver disease. Yet, solid data for this specific patient group remain elusive, given that most of the existing evidence comes from small, single-centre studies. In our study, we found that HCC incidence in patients with AIH is low even after the onset of cirrhosis. Additionally, factors such as advanced age, obesity, cirrhosis, alcohol consumption, and the presence of the AIH/PSC variant syndrome at the time of AIH diagnosis are linked to a higher risk of HCC. Based on these findings, there seems to be merit in adopting a specialized HCC monitoring programme for patients with AIH based on their individual risk factors.
DOI: 10.5281/zenodo.10532883
title: Dataset related to article "Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

description: This record contains raw data related to article “Incidence and predictors of hepatocellular carcinoma in patients with autoimmune hepatitis"

Abstract



 Background and aims:  Autoimmune hepatitis (AIH) is a rare chronic liver disease of unknown aetiology; the risk of hepatocellular carcinoma (HCC) remains unclear and risk factors are not well-defined. We aimed to investigate the risk of HCC across a multicentre AIH cohort and to identify predictive factors.

 Methods:  We performed a retrospective, observational, multicentric study of patients included in the International Autoimmune Hepatitis Group Retrospective Registry. The assessed clinical outcomes were HCC development, liver transplantation, and death. Fine and Gray regression analysis stratified by centre was applied to determine the effects of individual covariates; the cumulative incidence of HCC was estimated using the competing risk method with death as a competing risk.

 Results:  A total of 1,428 patients diagnosed with AIH from 1980 to 2020 from 22 eligible centres across Europe and Canada were included, with a median follow-up of 11.1 years (interquartile range 5.2-15.9). Two hundred and ninety-three (20.5%) patients had cirrhosis at diagnosis. During follow-up, 24 patients developed HCC (1.7%), an incidence rate of 1.44 cases/1,000 patient-years; the cumulative incidence of HCC increased over time (0.6% at 5 years, 0.9% at 10 years, 2.7% at 20 years, and 6.6% at 30 years of follow-up). Patients who developed cirrhosis during follow-up had a significantly higher incidence of HCC. The cumulative incidence of HCC was 2.6%, 4.6%, 5.6% and 6.6% at 5, 10, 15, and 20 years after the development of cirrhosis, respectively. Obesity (hazard ratio [HR] 2.94, p = 0.04), cirrhosis (HR 3.17, p = 0.01), and AIH/PSC variant syndrome (HR 5.18, p = 0.007) at baseline were independent risk factors for HCC development.

 Conclusions:  HCC incidence in AIH is low even after cirrhosis development and is associated with risk factors including obesity, cirrhosis, and AIH/PSC variant syndrome.

 Impact and implications:  The risk of developing hepatocellular carcinoma (HCC) in individuals with autoimmune hepatitis (AIH) seems to be lower than for other aetiologies of chronic liver disease. Yet, solid data for this specific patient group remain elusive, given that most of the existing evidence comes from small, single-centre studies. In our study, we found that HCC incidence in patients with AIH is low even after the onset of cirrhosis. Additionally, factors such as advanced age, obesity, cirrhosis, alcohol consumption, and the presence of the AIH/PSC variant syndrome at the time of AIH diagnosis are linked to a higher risk of HCC. Based on these findings, there seems to be merit in adopting a specialized HCC monitoring programme for patients with AIH based on their individual risk factors.