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7,327 results for “pathways”
Preprocessing scripts and data for study: Pathway-Based Subnetworks Enable Cross-Disease Biomarker Discovery
<p>Supplementary File 1 containing preprocessing scripts and data for converting pathway databases into subnetworks.</p>
Overview of acute stroke and rehabilitation pathway.
<p><strong><em>This image is supplementary material for:</em></strong></p> <p>Currie, C. S.M. Monks, T. (2018) <em>Modelling Diseases: Prevention, Cure, and Management.</em> In M. Rabe, A. Juan, N. Mustafee, A. Skoogh, & B. Johansson (Eds.). Proceedings of the Winter Simulation Conference 2018. Gothenburg, Sweden.</p> <p><em>Relevant to:</em></p> <p><em>Section 5.1</em></p>
How small amounts of Ge modify the formation pathways and crystallization of kesterites
<p>The inclusion of Ge into the synthesis of Cu2ZnSn(S,Se)4 absorbers for kesterite solar cells has been proven to<br> be a very efficient way to boost the device efficiency in a couple of recent publications. This highlights the<br> importance to elucidate the mechanisms by which Ge improves the kesterite solar cells properties to such a<br> large extent. In this contribution, we first show how controlling the position and thickness of a very thin<br> (10–15 nm) layer of Ge greatly influences the crystallization of kesterite thin films prepared in a sequential<br> process. Typically, Cu2ZnSnSe4 (CZTSe) films form in a bi-layer structure with large grains in the upper region<br> and small grains at the back. By introducing Ge nanolayers below our precursors, we observe that large<br> CZTSe grains extending over the whole absorber thickness are formed. Additionally, we observe that Ge<br> induces fundamental changes in the formation mechanism of the kesterite absorber. In a detailed analysis of<br> the phase evolution with and without Ge, we combine the results of X-ray fluorescence, X-ray diffraction<br> and Raman spectroscopy to demonstrate how the Ge influences the preferred reaction scheme during the<br> selenization. We reveal that the presence of Ge causes a large change in the in-depth elemental distribution,<br> induces a stabilizing Cu–Sn intermixing, and thus prevents drastic compositional fluctuations during the<br> annealing process. This finally leads to a change from a tri-molecular towards, mainly, a bi-molecular CZTSe<br> formation mechanism. Kesterite thin films with surprisingly large crystals of several microns in diameter can<br> be fabricated using this approach. The results are related to the increase in device performance, where power<br> conversion efficiencies of up to 11.8% were obtained. Finally, the consequences of the disclosed crystallization<br> pathways and the extension to other chalcogenide technologies are discussed.</p>
Understanding the Role of Symmetric Functionalisation on a Photoisomerisation Pathway.
<p>This archive contains DFT geometry optimizations of potential sunscreen compounds as well as TD-DFT calculations on the optimized structures. The calculations were conducted using NWChem (PBE0/cc-pVTZ) with a COSMO implicit solvent model (acetonitrile).</p>
Systematic spatio-temporal mapping reveals divergent cell death pathways in three mouse models of hereditary retinal degeneration
<p>Values for immuohistochemical analysis or enzymatic analysis of various markers theorised to have roles in retinal dystrophies in three models of mouse retinal degeneration with a control. Analysis has been broken down by marker, mouseline, age and region of the retina recorded from</p>
Risks of Transition Pathways of the Steel and Electricity Sectors – The Case of Austria
<p>This dataset contains the underlying data for the following publication: <strong>Risks of Transition Pathways of the Steel and Electricity Sectors – The Case of Austria], [upcoming in TRANSrisk Special Issue in Environmental Innovation and Societal Transitions (EIST)], [https://www.journals.elsevier.com/environmental-innovation-and-societal-transitions/].</strong></p>
Formation pathway of Population III coalescing binary black holes through stable mass transfer
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2017MNRAS.468.5020I">Inayoshi et al. (2017)</a>. MESA version 8845.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx757">10.1093/mnras/stx757</a></p>
Raw data of metabolic pathways and candidate olfactory genes in Holotrichia parallela (Coleoptera: Scarabaeidae)
<p>Analysis of antennal transcriptome data caused the discovery of metabolic and signaling pathways and the identification of olfactory genes. According to KEGG pathway enrichment analysis, KEGG pathways and KEGG modules were annotated. Based on the FuncTree software analysis, the functional potential of<em> H. parallela</em> unigenes was visualized across different functional layers, including biological progress, KEGG module and KEGG pathway. We successfully identified a series of putative olfactory genes, including OBPs, CSPs SNMPs and ODEs. The relationship between <em>H. parallela</em> with other species was predicted and many olfactory genes with various functions were analyzed based on phylogenetic analysis. Heat maps and qRT-PCR experiments were performed to display and verify biased or specific expression profiles between sexes and between olfactory and non olfactory tissues.</p>
Pathways enriched in downstream target genes of miRNAs associated with NT-proBNP and OPN
<p>The full set of 30 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were significantly enriched (FDR-adjusted p<0.01) in the predicted mRNA targets of either the OPN- or NT-proBNP-associated miRNAs; 21 of these pathways were significantly enriched in both sets of targets.</p> <p>KEGG: Kyoto Encyclopedia of Genes and Genomes; miRNAs: microRNAs; NT-proBNP: N-terminal pro <a href="http://www.mayomedicallaboratories.com/test-catalog/Clinical+and+Interpretive/83873">B-type natriuretic peptide</a>; OPN: osteopontin</p>
Movie files: Actin dynamics and the Bmp pathway drive apical extrusion of proepicardial cells.
<p>The epicardium, the outer mesothelial layer enclosing the myocardium, plays key roles in heart development and regeneration. During embryogenesis it arises from the proepicardium (PE), a cell cluster that appears in the dorsal pericardium (DP) close to the venous pole of the heart. Little is known about how the PE emerges from the pericardial mesothelium. Using the zebrafish model and a combination of genetic tools, pharmacological agents and quantitative <em>in vivo</em> imaging, we reveal that a coordinated collective movement of DP cells drives PE formation. We found that BMP signaling and the actomyosin cytoskeleton promote constriction of the DP, which enabled PE cells to extrude apically. We provide evidence that cell extrusion, which has been described in the elimination of unfit cells from epithelia and the emergence of hematopoietic stem cells, is also a mechanism for PE cells to exit an organized mesothelium and fulfil the developmental fate to form a new tissue layer, the epicardium.</p>
An evolved artificial radical cyclase enables the construction of bicyclic terpenoid scaffolds via an H-atom transfer pathway
<p>Data underlying the figures/tables of the publication "An evolved artificial radical cyclase enables the construction of bicyclic terpenoid scaffolds via an H-atom transfer pathway" <em>Nat. Chem.</em> (2024). https://doi.org/10.1038/s41557-024-01562-5</p>
Study on the role of AIMP1 gene activating PINK1/Parkin pathway in mediating mitochondrial autophagy in the pathogenesis of ARHL
Open the record for dataset details and reuse information.
Primary in vivo screen for MAPK/ERK pathway inhibitors among 433 annotated oncology related compounds
<p>The aim was to test if a C. elegans model could identify known MEK inhibitors in a blinded screen of 433 annotated oncology related compounds. Primary hits were further validated for pathway specificity. Please see the paper cross-reference for more details.</p> <p>We bought a chemical library (SciLife lab, Stockholm, Sweden) with 433 oncology-related compounds (see table S1 for the full list), where compounds had been acoustically dispensed from 10 mM stocks into 96-well plates. DMSO concentrations were adjusted to 0.5 % in all wells, including negative controls. Trametinib was used as a positive control at 7 muM. All chemicals were kept at -20 degrees C until use. The C. elegans strain ST65 was used. We dispensed 50 muL of worm culture into each well and incubated animals for 4-5 days at 20 degrees C until DMSO control animals reached early adulthood. Animals were then sedated for 30 minutes by adding 4 muL 20 mM levamisole per well before scoring.<br><br>Primary hit selection: 1) Wells were excluded if they contained progeny or fluorescent specles from drugs or bacteria, indicated by too many objects classified as vulvae. The cutoff was set to vulvae >2x the number of adults in the positive control. 2) Wells were excluded they contained < 30 % adults compared to the positive control, indicative of severely reduced larval growth. 3) Remaining compounds with vulvae/adult scores < 0.4 were considered primary hits. Scoring of the Muv phenotype in adult lin-1(e1777) mutants was done manually from ImageXpress images. See the paper cross-reference for more details.</p>
Neural pathways and computations that achieve stable contrast processing tuned to natural scenes
<h1>Gur et al. 2024 database</h1> <p>Source data of the paper Gür et al. 2024, “Neural pathways and computations that achieve stable contrast processing tuned to natural scenes”, Nature Communications. This work contains an analysis of post-receptor luminance gain in the Drosophila visual system, focusing on the circuitry and algorithms for implementation of rapid luminance gain control.</p> <p>All data can be analyzed using the code provided in the Github repository: <a href="https://github.com/silieslab/Gur-etal-2024">https://github.com/silieslab/Gur-etal-2024</a>. Please go to the “Readme” file in the repository for how to use the code.</p> <h2>Raw data</h2> <p>All raw data is located in the folder “raw_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <h2>Processed data</h2> <p>All processed data is located in the folder “processed_data”. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <p>- 2p_imaging_python_pickle: Processed data stored as .pickle files. <br>- Dm12_Figure7_Mat_files: Processed data for Dm12 imaging and optogenetics experiments presented in Figure 7 stored as .mat files.<br>- EM_data: Processed data for EM analysis done in Figure 7.<br>- Figure 6 Tm9 flpSTOP: tdTomato expression data for Figure 6 Tm9 flpSTOP experiments.<br>- Figure S5 Tm1 flpSTOP: tdTomato expression data for FigureS5 Tm1 flpSTOP experiments.</p>
Pathway 2.0 Techno-economic data
<p>This databook contains all techno-economic data that have been used during the Pathway 2.0 modelling. Sources, wherever applicable, are listed within the databook itself. Version 11.0 is the latest update.</p>
ANTI IL-12 / IL-23 PATHWAY
<p>Interleukin-12 (IL-12) and Interleukin-23 (IL-23) are cytokines that play pivotal roles in the immune system by promoting inflammatory responses and influencing the differentiation and activation of immune cells. These cytokines are critical in the pathogenesis of several chronic inflammatory and autoimmune diseases, including psoriasis, Crohn's disease, and ankylosing spondylitis. Inhibition of IL-12 and IL-23 has emerged as a promising therapeutic strategy to modulate the immune response and reduce inflammation. By targeting these cytokines, IL-12 and IL-23 inhibitors can prevent the activation of specific T cells and the release of pro-inflammatory cytokines, thereby alleviating symptoms and improving patient outcomes. Understanding the mechanisms and benefits of IL-12 and IL-23 inhibition is crucial for advancing the treatment of immune-mediated diseases.</p>
Improved estimation of global soil moisture of different shared socioeconomic pathways for 2016-2099
<p>We design a novel Transformer SM Simulation Net (TSMSNet) to conduct global monthly 0.5°×0.5° SM simulation of SSP1-2.6, SSP2-4.5, and SSP5-8.5 from 2016 to 2099. Nine qualified future SM datasets, along with corresponding spatial distribution dataset of error parameters, and geographic background data are selected as model inputs. The learning target is calculated through merging the merits from Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM. The results indicate the TSMSNet simulated SM (R = 0.68, ubRMSE = 0.045 m<sup>3</sup>/m<sup>3</sup>) performs notable superiority in matching both temporal variation and absolute value of in-situ measurements across different landcover and climate regions. Besides, the TSMSNet simulated SM could favorably match the long-term trend of learning target. TSMSNet simulated SM has an overwhelming drying trend during 2016 to 2099. The decline magnitude rises accompanied by SSP changing from sustainable pathway to fossil-fueled development. The areas with significant drying trend mainly distributed in plateau and mid-latitude region. In terms of land cover types, evident drying trends are found in cropland and forest. SM shows faster descent rate in livable areas than unlivable areas. In summary, we develop a reliable future SM dataset, that is expected to act as a valuable reference for understanding future water cycle patterns.</p>
Data for 'Evaluating the role of enhanced weathering in marine carbon dioxide removal under high emission pathway'
<p><span>1. ALK.mat </span></p> <p><a name="OLE_LINK86"></a><a name="OLE_LINK87"></a><span>Description: This file includes three structs, ALK_CTL, ALK_OWE, ALK_NUT, representing alkalinity data under control run, OWE simulation, NUT simulation. Each struct includes 5 parameters, lat: latitude, lon: longitude, mean_sur: variation of average alkalinity for the upper 100 m, mean_total: variation of average alkalinity for the whole water column, para_a10: 10-years average alkalinity for the upper 100 m. </span></p> <p><span>Units: meq/m<sup>3</sup></span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1a, b and Fig.2 </span></p> <p><span> </span></p> <p><span>2. pH.mat</span></p> <p><span>Description: This file includes three structs, pH_CTL, pH_OWE, pH_NUT, representing surface pH data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pH, para_a10: 10-years average of surface pH. </span></p> <p><span>Units: unitless</span></p> <p><a name="OLE_LINK92"></a><a name="OLE_LINK93"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 1c and Fig.3 </span></p> <p><span> </span></p> <p><span>3. total_DIC_ALK.mat</span></p> <p><a name="OLE_LINK88"></a><a name="OLE_LINK89"></a><span>Description: This file includes six structs, CTLALKt, CTLDICt, OWEALKt, OWEDICt, NUTALKt, NUTDICt, representing integrated alkalinity and DIC data in control run, OWE simulation, NUT simulation. Each struct includes two parameters, para_a: decadal average of DIC inventory, para_int: the sum of global DIC inventory.</span></p> <p><span>Units: mmol (DIC)/ meq (ALK) (we plot the figure with the unit Tmol in Fig. 4 and Pmol in Fig. 1d)</span></p> <p><span>Data in this file is used in Fig. 1d and Fig. 4.</span></p> <p><span> </span></p> <p><span>4. remapped_DIC_CTL.nc, remapped_DIC_OWE.nc and DIC_remapped_NUT.nc</span></p> <p><span>Description: The three .nc files include remapped standard-grid (360*180) DIC inventory under control run, OWE simulation and NUT simulation. Each .nc file has 4 variables, lon: longitude, lat: latitude, z_t: depth, DIC: remapped DIC. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><span>Coordinates: DIC: longitude * latitude* depth* time</span></p> <p><span>Data in this file is used in Fig. 5</span></p> <p><span> </span></p> <p><span>5. pCO2.mat</span></p> <p><span>Description: This file includes three structs, pCO2_CTL, pCO2_OWE, pCO2_NUT, representing surface pCO2 data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pCO2, para_a10: 10-years average of surface pCO2.</span></p> <p><span>Unit: ppmv</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig.6</span></p> <p><span> </span></p> <p><span>6. FG_CO2.mat</span></p> <p><a name="OLE_LINK94"></a><a name="OLE_LINK95"></a><span>Description: This file includes three structs, </span><a name="OLE_LINK90"></a><a name="OLE_LINK91"></a><span>FG_</span><span>CO2_CTL, FG_CO2_OWE, FG_CO2_NUT, representing DIC surface gas flux data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of FG_CO2, para_a10: 10-years average of FG_CO2.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK96"></a><a name="OLE_LINK97"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 7</span></p> <p><span> </span></p> <p><span>7. NPP.mat</span></p> <p><a name="OLE_LINK98"></a><a name="OLE_LINK99"></a><span>Description: This file includes three structs, NPP_CTL, NPP_OWE, NPP_NUT, representing total C fixation vertical integral data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of NPP, para_a10: 10-years average of NPP.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><a name="OLE_LINK104"></a><a name="OLE_LINK105"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in this file is used in Fig. 8</span></p> <p><span> </span></p> <p><span>8. spC.mat, diatC.mat, diazC.mat</span></p> <p><span>Description: The three files include phytoplankton biomass of small phytoplankton (spC_CTL, spC_OWE, spC_NUT), diatom (<a name="OLE_LINK100"></a><a name="OLE_LINK101"></a>diatC_CTL, diatC_OWE, diatC_NUT), and diazotroph (diazC_CTL, diazC_OWE, diazC_NUT). All phytoplankton biomass is measured in the unit of carbon. We store data on each phytoplankton species in the form of a struct. Each struct has five parameters, lat: latitude, lon: longitude, mean_sur: variation of <a name="OLE_LINK102"></a><a name="OLE_LINK103"></a>average of upper 100 m phytoplankton biomass; mean_total: variation of average phytoplankton biomass in the upper ocean, para_a10: 10-years average of average phytoplankton biomass in the upper ocean. </span></p> <p><span>Unit: mmol/m<sup>3</sup></span></p> <p><a name="OLE_LINK108"></a><a name="OLE_LINK109"></a><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK110"></a><a name="OLE_LINK111"></a><span>Data in these files are used in Fig. 9</span></p> <p><span> </span></p> <p><span>9. <a name="OLE_LINK106"></a><a name="OLE_LINK107"></a>calcToSed.mat</span></p> <p><span>Description: This file includes three structs, calcToSed_CTL, calcToSed_OWE, calcToSed_NUT, representing CaCO3 flux to sediments flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of CaCO3 flux, para_a10: 10-years average of CaCO3 flux. </span></p> <p><span>Unit: nmol/cm<sup>2</sup>/s (need to convert unit to mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><a name="OLE_LINK112"></a><a name="OLE_LINK113"></a><span>Data in these files are used in Fig. 10</span></p> <p><span> </span></p> <p><span>10. POC.mat</span></p> <p><span>Description: This file includes three structs, POC_CTL, POC_OWE, POC_NUT, representing 100 m POC flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of flux, para_a10: 10-years average of flux.</span></p> <p><span>Unit: mmol/m<sup>3</sup> cm/s (Need to convert unit into mol/m<sup>2</sup>/yr)</span></p> <p><span>Coordinates: Para_a10: longitude * latitude * time</span></p> <p><span>Data in these files are used in Fig. 11</span></p> <p><span> </span></p> <p><span>11. atmoCO2.mat</span></p> <p><span>Description: This file includes three structs, CTLATM, OWEATM, NUTATM. In each struct, atm_co2_trend represent the atmospheric CO<sub>2</sub> variation with the unit ppm. </span></p> <p><span>Data in this file is used in Fig.1f</span></p> <p><span> </span></p> <p><span> </span></p> <p><span> </span></p>
Table 1 in Agistemus collyerae (Acari: Trombidiformes: Stigmaeidae) in South Africa: first record, introduction pathways and a re-description including additional life stages
<p><b>Table 1</b> Measurements of <i>Agistemus collyerae</i> collected from South Africa, and comparison with New Zealand female specimens. All measurements are lengths unless otherwise noted. Leg length was measured from coxa to tip of empodium. Measurements are given in μm. SA = South Africa; NZ = New Zealand.</p><table><tbody><tr><th></th><th>NZ female</th><th>SA female</th><th>SA male</th><th>SA deutonymphs</th></tr></tbody><tbody><tr><th></th><td>(n=6)</td><td>(n=8)</td><td>(n = 2)</td><td>(n=1)</td></tr><tr><th><b>BODY SIZE</b></th></tr><tr><th>Length (excl. gnathosoma)</th><td>224 – 298</td><td>234 – 262</td><td>193 – 201</td><td>188</td></tr><tr><th>Length (incl. gnathosoma)</th><td>323 – 416</td><td>335 – 379</td><td>308</td><td>277</td></tr><tr><th>Width (at level of c2)</th><td>171 – 242</td><td>172 – 207</td><td>156 – 160</td><td>137</td></tr><tr><th><b>GNATHOSOMA</b></th></tr><tr><th><i>m</i></th><td>21 – 25</td><td>18 – 23</td><td>20 – 23</td><td>18</td></tr><tr><th><i>n</i></th><td>23 – 27</td><td>18 – 27</td><td>18 – 19</td><td>19</td></tr><tr><th><i>or1</i></th><td>11 – 12</td><td>11 – 14</td><td>12 – 13</td><td>10</td></tr><tr><th><i>or2</i></th><td>11 – 12</td><td>10 – 14</td><td>10 – 14</td><td>9</td></tr><tr><th>Moveable cheliceral digit</th><td>31 – 36</td><td>36 – 39</td><td>35 – 40</td><td></td></tr><tr><th>Palp</th><td>72 – 80</td><td>71 – 85</td><td>86 – 89</td><td>63</td></tr><tr><th>Palp tibial claw</th><td>13 – 18</td><td>15 – 17</td><td>19 – 20</td><td>7</td></tr><tr><th><b>DORSUM</b></th></tr><tr><th>Eyes (diameter)</th><td></td><td>9 – 10</td><td></td><td>8</td></tr><tr><th>Post-ocular bodies (diameter)</th><td></td><td>6 – 8</td><td></td><td>7</td></tr><tr><th><i>ve</i></th><td>24 – 27</td><td>15 – 17</td><td>15 – 16</td><td>13</td></tr><tr><th><i>sci</i></th><td>30 – 39</td><td>21 – 28</td><td>23</td><td>19</td></tr><tr><th><i>sce</i></th><td>31 – 38</td><td>23 – 28</td><td>22 – 24</td><td>18</td></tr><tr><th><i>c1</i></th><td>24 – 31</td><td>20 – 26</td><td>15 – 17</td><td>15</td></tr><tr><th><i>c2</i></th><td>35 – 39</td><td>21 – 31</td><td>23 – 27</td><td>18</td></tr><tr><th><i>d1</i></th><td>30 – 37</td><td>20 – 25</td><td>17</td><td>17</td></tr><tr><th><i>d2</i></th><td>32 – 39</td><td>23 – 28</td><td>23 – 26</td><td>16</td></tr><tr><th><i>e1</i></th><td>42 – 46</td><td>29 – 34</td><td>12 – 13</td><td>22</td></tr><tr><th><i>e2</i></th><td>42 – 45</td><td>27 – 34</td><td>25 – 27</td><td>24</td></tr><tr><th><i>f</i></th><td>34 – 42</td><td>31 – 35</td><td>32 – 34</td><td>25</td></tr><tr><th><i>h1</i></th><td>38 – 43</td><td>34 – 39</td><td>13 – 14</td><td>26</td></tr><tr><th><i>h2</i></th><td>32 – 38</td><td>32 – 36</td><td>21 – 26</td><td>22</td></tr><tr><th><i>ve – ve</i></th><td>27 – 45</td><td>28 – 34</td><td>30 – 31</td><td>33</td></tr><tr><th><i>c1 – c1</i></th><td>36 – 43</td><td>26 – 36</td><td>34 – 40</td><td>26</td></tr><tr><th><i>d1 – d1</i></th><td>61 – 73</td><td>52 – 64</td><td>54 – 56</td><td>48</td></tr><tr><th><i>e1 – e1</i></th><td>33 – 34</td><td>24 – 34</td><td>27</td><td>22</td></tr><tr><th><i>f – f</i></th><td>65 – 80</td><td>58 – 65</td><td>38 – 42</td><td>44</td></tr><tr><th><i>h1 – h1</i></th><td>15 – 18</td><td>13 – 21</td><td>9</td><td>13</td></tr><tr><th><b>VENTER</b></th></tr><tr><th><i>g</i></th><td>35 – 39</td><td>27 – 37</td><td>-</td><td></td></tr><tr><th>Aedeagus</th><td>-</td><td>-</td><td>48 – 55</td><td>-</td></tr><tr><th><b>LEGS</b></th></tr><tr><th>Leg I</th><td>185 – 200</td><td>184 – 195</td><td>187 – 191</td><td>148</td></tr><tr><th>ωI</th><td>13 – 16</td><td>9 – 12</td><td>9 – 10</td><td>7</td></tr><tr><th>ωI♂</th><td>-</td><td>-</td><td>10 – 13</td><td>-</td></tr><tr><th>ϕI</th><td>10 – 14</td><td>8 – 11</td><td>9</td><td>5</td></tr><tr><th>leg II</th><td>166 – 189</td><td>168 – 188</td><td>169 – 174</td><td>138</td></tr><tr><th>ωII</th><td>10 – 13</td><td>6 – 10</td><td>9 – 11</td><td>5</td></tr><tr><th>ωII♂</th><td>-</td><td>-</td><td>11 – 13</td><td>-</td></tr><tr><th>ϕII</th><td>9 – 10</td><td>4 – 6</td><td>5 – 6</td><td>3</td></tr><tr><th>leg III</th><td>169 – 187</td><td>173 – 194</td><td>166 – 175</td><td>141</td></tr><tr><th>ωIII</th><td>8 – 10</td><td>5 – 6</td><td>5</td><td>3</td></tr><tr><th>ϕIII</th><td>7 – 10</td><td>3 – 5</td><td>4</td><td>2</td></tr><tr><th>leg IV</th><td>185 – 210</td><td>188 – 198</td><td>184 – 186</td><td>144</td></tr><tr><th>ωIV♂</th><td>-</td><td>-</td><td>5 – 6</td><td>-</td></tr></tbody></table>
Self-organization of conducting pathways explains complex wave trajectories in procedurally interpolated fibrotic cardiac tissue: a digital-twin study
<p><span>In precision cardiology, digital twinning technology (DT) holds promise for predicting arrhythmias by </span><span>leveraging patient data and biophysics knowledge. However, current DTs are designed to directly reproduce biopotential conduction in cardiac tissue, while only indirect non-invasive methods can be clinically implemented on real organs. This discrepancy challenges our understanding of DT applicability limits. This study aims to enhance DT by developing an in-vitro training complement. We conducted a frame-by-frame comparison of in-vitro optical mapping of biopotential conduction with machine learning (ML) optimized DT predictions. Patient-specific self-organized tissue samples of human induced pluripotent stem cells-derived cardiomyocytes (CMs) with diffuse fibrosis served as DT prototypes. High spatiotemporal resolution optical mapping recordings (</span><span>Δ</span><span>x=117 ± 4 </span><span>μ</span><span>m, </span><span>Δ</span><span>t=7.69 ms) and immunostainings were used to reproduce fibrotic samples with a linear size of 7.5 mm. Using data-driven ML-optimization of the Cellular Potts model, we examined wave propagation at the subcellular level. The modified Glazier-Graner-Hogeweg model accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs self-organization. The percolation threshold of virtual conductive pathways reached 26% (26.7 ± 2.9% of CMs in-vitro), resulting in a spatial correlation of amplitude maps between prototype samples and their DT with Pearson’s coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated the ability of ML-optimized DT to predict and interpolate wavefront trajectories in optical mapping recordings. We found that mathematical approximation of fibrosis distribution played a key role in DT prediction accuracy, potentially informing the implementation of LGE-MRI detection of fibrosis within cardiac DT frameworks.<br><br>Dataset A: <span>Immunostaining images were sorted based on the day of enzymatic disaggregation (before and after day 20). We collected and sorted </span><span>α</span><span>-actinin, Connexin43 and DAPI immunostainings </span><span>for Cellular Potts Model optimization. During data processing, cell shape<span>s (n=109 and n=69 for CM and BPs respectively after day 20, n=209 and n=90 for CM and BPs respectively before day 20) were formalized.<br>Dataset C corresponds to FluoVolt recordings <span>(3 samples). Dataset B corresponds to Fluo-4 AM recordings in iPSC-CMs samples with diffuse fibrosis imitation (4 samples)</span>. </span></span></span></p>
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