44 Works

Feasibility Study of Monitoring Delft Geothermal Project Using Land Controlled-Source Electromagnetic Method

Mahmoud Eltayieb , Dieter Werthmüller , Guy Drijkoningen & Evert Slob
Delft geothermal project (DAPwell) is a planned geothermal well doublet, where relatively cold water is going to be injected through one well into a low enthalpy geothermal reservoir to produce hot water from the other well. The volume of the cold water around the injection well will increase over time and, in the end, result in a thermal breakthrough. Thus, it is essential to trace the time-lapse change in the volume of the cold water...

Supplemental Information Appendix from A retrospective assessment of COVID-19 model performance in the USA

Kyle J. Colonna, Gabriela F. Nane, Ernani F. Choma, Roger M. Cooke & John S. Evans
Contains supplementary text, as well as supplemental figures and tables referenced in the main text.

Additional file 3 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 3: Table S2. List of variables and mouse genes correspondence. All mathematical variables and the corresponding node in the network have names written in upper cases and do not reflect the official human or mouse nomenclatures. To relate those variables to actual genes more easily, we provide this table of correspondence. A related mouse gene name and NCBI ID is indicated for each variable. Nevertheless, this is not exhaustive since some variables represent...

Additional file 12 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 12: Table S4. Targets and associated small molecules or growth factors for in vitro validation. The first row indicates the targets to be perturbed, [+] stands for activation while [-] stands for inhibition. The name (resp. cat number) of the small molecule or growth factor employed to achieve that effect is indicated in the row called ‘Molecule name’ (resp. ‘Cat n°’).

Additional file 14 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 14: Fig. S5. Illustration of the algorithm for the asynchronous updating of variables with a simplified (3 nodes) example network. The network represents interactions happening in one of the subnetworks (protein= fast reactions or genetic = slow reactions). Inhibitions are represented in red and activations are in black. The mathematical rules corresponding to the network are displayed. If the rules result in a value lower than 0 (resp. higher than 1), the value...

Additional file 1 of Implementation of a medicine management plan (MMP) to reduce medication-related harm (MRH) in older people post-hospital discharge: a randomised controlled trial

Khalid Ali, Ekow A. Mensah, Eugene Ace McDermott, Frances A. Kirkham, Jennifer Stevenson, Victoria Hamer, Nikesh Parekh, Rebekah Schiff, Tischa Van Der Cammen, Stephen Nyangoma, Sally Fowler-Davis, Graham Davies, Heather Gage & Chakravarthi Rajkumar
Additional file 1: Appendix 1. Formula for calculating patient risk of experiencing MRH within 8 weeks post hospital discharge. Appendix 2. Visual analogue of risk prediction tool. Appendix 3. Study participants flow chart.

Additional file 5 of An engineered non-oxidative glycolytic bypass based on Calvin-cycle enzymes enables anaerobic co-fermentation of glucose and sorbitol by Saccharomyces cerevisiae

Aafke C. A. van Aalst, Robert Mans & Jack T. Pronk
Additional file 5: Table S1. Predicted ethanol yields on substrate, biomass-specific substrate-uptake rates (qsubstrate) and biomass yields on substrate for wild-type S. cerevisiae (WT) and strains with an engineered PRK-RuBisCO bypass of the oxidative reaction in glycolysis on both glucose and on sorbitol. Rates and yields were predicted for cultures growing at different specific growth rates, using an extended stoichiometric model of the core metabolic network of S. cerevisiae (1, 2). A Cmol biomass (CH1.8O0.5N0.2,...

Characterization and assessment of the mechanical properties of spruce foundation piles retrieved from bridges in Amsterdam

Giorgio Pagella, Geert Ravenshorst, Wolfgang Gard & Jan Willem van de Kuilen

High temperature aquifer thermal energy storage (HT-ATES) in combination with geothermal heat production on the TU Delft campus: feasibility study and next steps

Stijn Beernink , Martin Bloemendal , Phil Vardon , Auke Barnhoorn & Niels Hartog
One of the most important actions to limit climate change is to decrease worldwide CO2 emissions. A large contributor to worldwide CO2 emissions is the production of heat. Therefore, the recently started transition from fossil based fuels to renewable heat sources is of great importance. Renewable heat sources like geothermal and solar energy often exhibit a temporal mismatch between the availability and demand of heat. Excess heat is available in summer while the heat demand...

Additional file 2 of Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques

Jasmijn A. Baaijens, Alessandro Zulli, Isabel M. Ott, Ioanna Nika, Mart J. van der Lugt, Mary E. Petrone, Tara Alpert, Joseph R. Fauver, Chaney C. Kalinich, Chantal B. F. Vogels, Mallery I. Breban, Claire Duvallet, Kyle A. McElroy, Newsha Ghaeli, Maxim Imakaev, Malaika F. Mckenzie-Bennett, Keith Robison, Alex Plocik, Rebecca Schilling, Martha Pierson, Rebecca Littlefield, Michelle L. Spencer, Birgitte B. Simen, William P. Hanage, Nathan D. Grubaugh … & Michael Baym
Additional file 2. Review history.

Additional file 2 of Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques

Jasmijn A. Baaijens, Alessandro Zulli, Isabel M. Ott, Ioanna Nika, Mart J. van der Lugt, Mary E. Petrone, Tara Alpert, Joseph R. Fauver, Chaney C. Kalinich, Chantal B. F. Vogels, Mallery I. Breban, Claire Duvallet, Kyle A. McElroy, Newsha Ghaeli, Maxim Imakaev, Malaika F. Mckenzie-Bennett, Keith Robison, Alex Plocik, Rebecca Schilling, Martha Pierson, Rebecca Littlefield, Michelle L. Spencer, Birgitte B. Simen, William P. Hanage, Nathan D. Grubaugh … & Michael Baym
Additional file 2. Review history.

Additional file 12 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 12: Table S4. Targets and associated small molecules or growth factors for in vitro validation. The first row indicates the targets to be perturbed, [+] stands for activation while [-] stands for inhibition. The name (resp. cat number) of the small molecule or growth factor employed to achieve that effect is indicated in the row called ‘Molecule name’ (resp. ‘Cat n°’).

Additional file 14 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 14: Fig. S5. Illustration of the algorithm for the asynchronous updating of variables with a simplified (3 nodes) example network. The network represents interactions happening in one of the subnetworks (protein= fast reactions or genetic = slow reactions). Inhibitions are represented in red and activations are in black. The mathematical rules corresponding to the network are displayed. If the rules result in a value lower than 0 (resp. higher than 1), the value...

Additional file 2 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 2. Supplementary computational method. Equations, mathematical framework and justification of deviation from the general rule in the equations.

Additional file 2 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 2. Supplementary computational method. Equations, mathematical framework and justification of deviation from the general rule in the equations.

Additional file 11 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 11: Fig. S3. Screenshot of the user-friendly interface for the virtual chondrocytes App. The standalone Matlab-based applications can be launched and used without Matlab license, provided that the compiler Matlab Runtime is installed ( https://nl.mathworks.com/products/compiler/matlab-runtime.html ). The virtual chondrocyte initial state can be set as healthy or hypertrophic, allowing the user to test any scenarios. All the 60 components may be perturbed alone or in any sort of combination by forcing the variables...

Additional file 3 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 3: Table S2. List of variables and mouse genes correspondence. All mathematical variables and the corresponding node in the network have names written in upper cases and do not reflect the official human or mouse nomenclatures. To relate those variables to actual genes more easily, we provide this table of correspondence. A related mouse gene name and NCBI ID is indicated for each variable. Nevertheless, this is not exhaustive since some variables represent...

sj-docx-1-pec-10.1177_03010066221122697 - Supplemental material for How do people distribute their attention while observing The Night Watch?

Joost C. F. de Winter, Dimitra Dodou & Wilbert Tabone
Supplemental material, sj-docx-1-pec-10.1177_03010066221122697 for How do people distribute their attention while observing The Night Watch? by Joost C. F. de Winter, Dimitra Dodou, and Wilbert Tabone in Perception

Additional file 13 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 13: Fig. S4. Decision tree summarizing the variable updating scheme employed in algorithm to simulate the in silico chondrocyte. Each biological component is represented by the gene expression level (slow variable) and the protein activity potential (fast variable). Variables are updated based on the rules stored in the model’s equations. First, fast variables are updated in random order, when a pseudo-stable state is reached and that all fast variables have been updated, the...

Additional file 7 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 7: Table S3. Validation of transcriptional data-inferred regulatory interactions. Transcriptional interactions were inferred from the merged OA dataset. Inference was run with three algorithms and only interactions that were present in the results of the three algorithms (GENIE3, ARACNE, TIGRESS) were considered as additions to the model. An interaction was considered present for one algorithm if it scored higher than a threshold defined as the difference between the mean and standard deviation of...

Additional file 7 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 7: Table S3. Validation of transcriptional data-inferred regulatory interactions. Transcriptional interactions were inferred from the merged OA dataset. Inference was run with three algorithms and only interactions that were present in the results of the three algorithms (GENIE3, ARACNE, TIGRESS) were considered as additions to the model. An interaction was considered present for one algorithm if it scored higher than a threshold defined as the difference between the mean and standard deviation of...

Additional file 15 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 15: Fig. S6. Sensitivity analysis: impact of the number of initialization on the canalization results during the Monte Carlo. The percentage of random initialization reaching each attractor is displayed with the Sox9 positive state (healthy) in orange, the Runx2 positive state in blue and the None in grey. Data labels indicate the absolute amount of state reaching the attractors. None of initializations reached an alternative attractor, even for higher amount of random initializations...

Additional file 16 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 16: Fig. S7. Pseudo-time evolution of variables during simulations. The sequence of variable updating over each time steps after introducing a perturbation from the healthy state was saved as a timeseries and plotted. It shows the discrete behavior of the simulation and that a steady state is reached way before the duration of the perturbation (1000 time steps) is reached in such a way that maintain the perturbation longer would not change the...

Additional file 1 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 1: Table S1. Definitions. Definitions of technical terms pertaining to biological signaling network modeling with a semi-quantitative additive formalism.

Additional file 10 of An integrated in silico-in vitro approach for identifying therapeutic targets against osteoarthritis

Raphaëlle Lesage, Mauricio N. Ferrao Blanco, Roberto Narcisi, Tim Welting, Gerjo J. V. M. van Osch & Liesbet Geris
Additional file 10: Fig. S2. Effect of ALKs ratio in the influence of inflammation and TGFβ signaling on chondrocyte hypertrophy. Percentage of perturbations remaining in the healthy state (right) or transitioning towards the hypertrophic one (left) during inflammatory pathway activation with TGF-B treatment while changing the ratio between ALK1 and ALK5. The inflammatory and TGF-B profiles that were imposed are the same as in Fig. 4A except that the value imposed for ALK1 and ALK5...

Registration Year

  • 2022
    44

Resource Types

  • Text
    44

Affiliations

  • Delft University of Technology
    44
  • Erasmus MC
    26
  • University of Liège
    24
  • KU Leuven
    24
  • University of Nebraska Medical Center
    4
  • Washington University in St. Louis
    4
  • Ginkgo BioWorks (United States)
    4
  • Colorado State University
    4
  • Yale University
    4
  • Harvard University
    4