dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, we have calculated a two-tailed P-value per each gene Z-score and have visualized the Z-score distribution of the transcriptional profile in a plot. Lastly, all overexpressed (Z-score ≥ 2) and underexpressed genes (Z-score ≤ -2) with significant P-values (P < 0. 05) encompassed the WH genes from the patientderived xenograft approach. Wound healing protein-protein interactome network. In order to identify the most essential protein interactions, a WH protein-protein interactome (WH-PPi) network was created by using the human proteome of the Cytoscape StringApp, taking into account zero node addition and the highest confidence interactions (cutoff = 0. 9) related to experiments, databases, and co-expression  28, 29. The degree of centrality represents the number of edges the nodes have in a network  [30] [31] [32]  , and this centrality index was calculated using the CytoNCA app  33.
Nodes and edges were organized through the organic layout, and the WH-PPi network was visualized through the Cytoscape software v. 3. 7. 1  34. Lastly, the interactome network analysis considered all WH proteins with at least one high-confidence interaction (cutoff = 0. 9) in the human proteome. Regarding the cancer driver proteins encompassing the WH-PPi network, we compared the degree centrality between the cancer driver nodes and the wound healing nodes by using the Mann-Whitney U test (P < 0. 001) in order to determine a correlation between both groups of proteins. Shortest paths from wound healing proteins to cancer hallmark phenotypes. CancerGenNet (https:// signor. uniro ma2. it/ Cance rGene Net/) is a resource that links proteins that are frequently altered in all cancer types to cancer hallmark phenotypes  35.
3. FN1, PDGFB, HRAS, EGFR, PIK3CB, SYK, CXCR4, PRKCD, KDR, and PTPN6 were the top ten cancer driver proteins with the highest degree of centrality. The comparison of degree centralities between the cancer driver nodes and the wound healing nodes showed a correlation between both networks with a not significant Mann-Whitney U test (P > 0. 05). Lastly, the overall analysis revealed 233 (67%) WH proteins with at least one high-confidence interaction (cutoff = 0. 9) in the human proteome as detailed in the Supplementary Table  16. Shortest paths from wound healing proteins to cancer hallmark phenotypes. We analyzed the 347 WH proteins by using CancerGeneNet software to find the distance score of the shortest paths to cancer hallmark phenotypes according to Iannuccelli et al. 35. On the one hand, Fig.
Hallmarks of cancer and wound healing. The hallmarks of cancer constitute an organizing principle for rationalizing the complexities of neoplastic disease. Nowadays, there are ten biological capabilities acquired during the multistep development of human tumors: (1) sustaining proliferative signaling, (2) evading growth suppressors, (3) resisting cell death, (4) enabling replicative immortality, (5) inducing angiogenesis, (6) activating invasion and metastasis, (7) genome instability, (8) inflammation, (9) reprogramming of energy metabolism, and (10) evading immune destruction 2. On the one hand, six WH proteins of our study were already catalogued as key proteins in six hallmarks of cancer features according to the COSMIC and CGC databases  19. NFE2L2, MTOR, and ERBB2 promotes change of cellular energetics; NOTCH2, MTOR, GNAS, and ERBB2 promotes proliferative signaling; MTOR promotes angiogenesis and NF1 suppresses angiogenesis; NOTCH2, NF1, and GNAS promotes suppression of growth; MTOR promotes invasion and metastasis and NF1 suppresses invasion and metastasis; and, NOTCH2, NFE2L2, and MTOR promotes escaping programmed cell death and NF1 suppresses escaping programmed cell death (Fig.
4 × 10 -5 ; GO:0032943, 2. 3 × 10 -4 ; GO:0061351, 2. 8 × 10 -4 ; GO:0001935, 7. 2 × 10 -4 ; and GO:0042098, 7. 5 × 10 -4 ); ANXA1, CDKN1A, CRK, ERBB2, F2, GNAS, MTOR, NOTCH2, YAP1, and VEGFA were involved in 2 significant annotations Figure  4. Wound healing protein-protein interactome network. Network of 233 wound healing proteins with at least one high confidence interaction (cutoff = 0. 9). Darkest nodes represent proteins with the highest degrees of centrality (mean = 11. 2). The WH-PPi network includes 35 cancer driver proteins with a degree centrality mean of 11. 3. The Mann-Whitney U test showed a correlation of degrees of centrality between WH nodes and cancer driver nodes (P > 0. 05). Lastly, the WH-PPi network was visualized through the Cytoscape software v. 3. 7. 1  34. related to growth (GO:0040008, 1.
The trend observed by Shao et al. , through the analysis of the copy number and mRNA expression data of Broad-Novartis Cancer Cell Line Encyclopedia (CCLE), NCI-60 and TCGA, showed similarities with our study regarding the mRNA high and CNV amplification frequency in solid tumors. Similarly, CNVs change the expression levels of genes located in the concerned genomic region  52. However, a different set of genes were identified among studies due to the focus of our work in WH genes associated in cancer. Understanding the relationship between WH genes in cancer, their CNV (in germ cells), CNA (in somatic cells) and gene expression characteristics could help in the development of better diagnostic tools and therapeutics  52, 53. Recently, processes involved in cell survival and proliferation that might apply to both cancer and WH have been proposed as converging hallmarks, grouping common pathways of regulation and activation.
Acknowledgements This work was supported by the  Latin American Society of Pharmacogenomics and Personalized Medicine (SOLFAGEM). We thank the  School of Medicine at the Universidad San Francisco de Quito, USFQ  for their constant support of our work and initiatives. We are grateful to  Vice-Rectorate  for Research, the Directorate of the  Life Sciences Area  and the  Biotechnology Career of Universidad Politécnica Salesiana  for the support given in this investigation. We are thankful to the " Corporación Ecuatoriana para el Desarrollo de la Investigación y Académica ,  CEDIA " for the financial support provided to our project  CEPRA XIV-2020-04 , MITOCHONDRIAS, promoting the development of research and innovation in Ecuador.
Thus, with the explosion of big data, network embeddings have been used to study many different networks, such as social  [52] , neuronal  [57]  and molecular networks  [66]. So far, network embedding approaches have been mainly applied to monoplex networks (i. e. single networks composed of one type of nodes and edges)  [69, 35, 39]. Current technological advances however generate a large spectrum of data, which form large heterogeneous datasets. By design, monoplex networks are not suited to represent such diversity and complexity. Multi-layer networks, including multiplex  [46]  and multiplex-heterogeneous  [87]  networks have been proposed to handle these more complex but richer heterogeneous interaction datasets. Multiplex networks are composed of several layers, each layer being a monoplex network.
It is based on 7 datasets in 4 disciplines (biological, neuronal, co-authorship and social networks), 6 embedding methods (and 4 additional link prediction heuristics), and two tasks: link prediction and a new protocol approach based on network reconstruction. • We demonstrate the higher performance of MultiVERSE over state-of-the-art network embedding methods in the tasks of link prediction and network reconstruction for multiplex network embedding. • We propose, to our knowledge, the first multiplex-heterogeneous network embedding method (with an embedding of the different types of nodes). • We propose a method to evaluate multiplex-heterogeneous network embedding on link prediction. We demonstrate the efficiency of MultiVERSE on this task on two biological multiplex-heterogeneous networks.
Monoplex network embedding Many network embedding methods have been recently developed to study a large variety of networks, from biological to social ones. The classical method deepwalk  [69]  inspired a series of methods such as node2vec  [35]  and LINE (for Large-scale Information Network Embedding)  [83]. Deepwalk uses truncated random walks to compute the node similarity in the network. Then, a combination of the skip-gram learning algorithm  [60]  and hierarchical softmax  [62]  is used to learn the graph representations. Skip-gram is a model based on natural language processing. It intends to maximize the probability of co-occurrence of nodes within a walk, focusing on a window, i. e. a section of the path around the node. Node2vec  [35]  upgrades deepwalk by introducing negative sampling during the learning phase  [61].
MultiVERSE algorithm Algorithm 1 presents the pseudo-code of MultiVERSE based on RWR on multiplex and multiplexheterogeneous networks  [87]  and Kullback-Leibler optimization from the VERSE algorithm  [85]. Our implementation of VERSE with NCE is slightly different from the original. We perform first the RWR-M or RWR-MH for all the nodes of the network in order to obtain the similarity distribution sim G M. The output of this step is the probability matrix p, where p u is the probability vector representing the similarities between u and all the other nodes. The matrix of the embedded representation of the nodes, W , is randomly initialized. For each iteration, from one node u sampled randomly from a uniform distribution U, we filter the probability vector p u. We keep the N max highest probabilities because the shape of the distribution of probabilities falls very fast to very low probabilities.
Doing so, we can speed up the calculation by filtering out the lowest probabilities and by reducing the size of the similarity matrix. We normalize this resulting probability vector ṗu , and sample one node v according to its probability in p u. We set empirically the parameter N max = 300 for large networks (number of nodes exceeding 5000). For smaller networks, we set this parameter to 10% -20% of the number of nodes of the network, depending on the shape of the distribution. These two steps (lines 6 and 7) were not in the original VERSE. We parallelized the repeat loop (line 4) and added a parallelized for loop after line 5 in order to run the code from line 6 to 12 in parallel P times. In our simulations, we set P = 100.
Algorithm 2 Update 1: Input: u, v, D, bias, lr 2: g ← [D -σ(W u • W v -bias)] * lr 3: W u ← g • W v Then, s negative nodes are sampled from Q(u) and we update the corresponding embeddings by increasing their distances in the embedding space. The update can also be seen as the training part with lr as the learning rate of the binary classifier of the NCE estimation as described in equation 5. The whole process is repeated until the maximum steps are reached. MultiVERSE is freely available on github at https://github. com/Lpiol/MultiVERSE.
Multiplex network datasets We used 7 multiplex networks (2 molecular, 1 disease, 1 neuronal, 1 co-authorship and 2 social networks) to evaluate the different approaches of multiplex network embedding. The networks CKM, LAZEGA, C. ELE, ARXIV, and HOMO have been extracted from the CoMuNe lab database https://comunelab. fbk. eu/data. php. We constructed the other two networks, DIS and MOL. A description of each of these multiplex networks follows. The number of nodes and edges of the different layers are detailed in Table  1. • CKM physician innovation (CKM) is a multiplex network describing how physicians in four towns in Illinois used the new drug tetracycline  [13]. It is composed of 3 layers corresponding to three questions asked to the physicians: i) to whom do you usually turn when you need information or advice about questions of therapy? ii) who are the three or four physicians with whom you most often find yourself discussing cases or therapy in the course of an ordinary week -last week for instance? iii) would you tell me the first names of your three friends whom you see most often socially? • Lazega network (LAZEGA) is a multiplex social network composed of 3 layers based on co-working, friendship and advice between partners and associates of a corporate law partnership  [28].
• Caenorabidis Elegans connectome (C. ELE) is a neuronal multiplex network composed of 3 layers corresponding to different synaptic junctions  [11, 19] : electrical, chemical poladic and chemical monadic. • ArXiv network (ARXIV) is composed of 8 layers corresponding to different ArXiv categories. The dataset has been restricted to papers with 'networks' in the title or abstract, up to May 2014  [18]. The original data from the CoMuNe Lab database is divided in 13 layers. We extracted the 8 layers (1-2-3-5-6-8-11-12) containing more than 1000 edges. • Homo sapiens network (HOMO) is composed of 4 layers extracted from the original network on CoMuNe Lab  [19] , keeping physical association, direct interaction, association and co-localization layers. The data are initially extracted from BioGRID  [81]  • Disease multiplex network (DIS) has been constructed, composed of 3 layers: i) A disease-disease network based on a projection of a disease-drug network from the Comparative Toxicogenomics Database (CTD)  [15]  extracted from BioSNAP  [101].
In this network, an edge between two diseases is created if the Jaccard Index between the neighborhoods of the two nodes in the original bipartite network is superior to 0. 4. Two diseases are thereby linked if they share a similar set of drugs. This projection has been done using NetworkX  [38]. ii) A disease-disease network where the edges are based on shared symptoms. The network has been constructed from the bipartite disease-symptoms network from  [99]. Similarly to  [99] , we use the cosine distance to compute the symptom-based diseases similarity for this network. We kept for the disease-disease network all interactions with a cosine distance superior to 0. 5 iii) A comorbidity network from epidemiological data extracted from  [43]. • Human molecular multiplex network (MOL) is a molecular network, consisting of 3 layers: i) A protein-protein interaction (PPI) layer corresponding to the fusion of 3 datasets: APID (apid.
The final embedding is computed using a weighted sum of these two high-dimensional and low-dimensional embeddings. We used the default parameters (https://github. com/HKUST-KnowComp/MNE). • Multi-node2vec  [92] : This multiplex network embedding method is also based on node2vec. The random walks can jump to different layers and explore in this way the multiplex neighborhood. The length of the random walks is set to 100. We used OpenNE (https://github. com/thunlp/OpenNE) to implement deepwalk, node2vec and LINE. The other methods have been implemented from the source code associated to the different publications.
Multiplex-heterogeneous network datasets • Gene-disease multiplex-heterogeneous network: We use the two multiplex networks presented in the previous sections: the disease (DIS) and molecular multiplex networks (MOL) (Table  1 ). In addition, we extracted the curated gene-disease bipartite network from the DisGeNET database  [70]  in order to connect the two multiplex networks. This bipartite interaction network contains 75445 interactions between 5188 diseases and 9179 genes. We Figure  4 : General approach for network reconstruction on multiplex networks: (top) for monoplexbased methods, embed each layer with the given method, then average it ; (bottom) for multiplexbased methods, apply the specific embedding method to the network. Embedding operators are then applied to monoplex-and multiplex-based method embeddings.
Materials and methods In this study, we firstly evaluated the relaxation behavior of the sensory hair via sustained deflection tests. Next, we estimated the suitable viscoelastic parameters of the hair's tissues by calibrating the experimental data with numerical simulations using a FEM model of the sensory hair. Additionally, we implemented an intercellular fluid transport condition between the sensory cells in the model using membrane conductivity data taken from the literature. Finally, the cell wall stretch in the sensory cells was calculated for different stimulus loading rates.
2020) , no trap closure is initiated at this speed through a single deflection. Note that the hairs of the lobe were manually stimulated after each experiment to verify that the flytraps were in a healthy state and could be triggered successfully. Two USB microscopes (DigiMicro Profi and DigiMicro, DNT) were used for visual feedback during the manual positioning of the sensor as well as to observe the deflection procedure. From the captured images, we measured the contact height h and calculated the angular displacement = tan -1 ( x∕h). The equivalent values for , corre- sponding to x , were classified into three categories: small ( 0. 1 rad ≤ ≤ 0. 15 rad ), medium ( 0. 15 rad ≤ ≤ 0. 2 rad ), and large ( 0. 2 rad ≤ ≤ 0. 25 rad ). Using the force-relaxa- tion data obtained from the deflection tests, we calculated the variation in the reaction moment M = F × h during the 30-s time period.
Multi-scale FEM model of the sensory hair We built a multi-scale numerical model of the sensory hair in the commercial finite element method (FEM) software ABAQUS 2019 to simulate the mechanical stimulus and observe the resulting deformation of the sensory cells. The model comprises of the lever tissue, the podium tissue and, lastly, the constriction tissue, where 16 sensory cells are embedded radially (see Fig. 2a, b ). Two additional strategies were adopted for reducing computational costs: Firstly, a half-symmetric geometry of the sensory hair is considered and, secondly, the region of the lever tissue above the contact height h = 1000 μm was neglected. The morphology of the tissue and the cells in the sensory hair model were obtained from -CT scans (see Fig. 2c-e ) of three sensory hairs (Sai- kia et al.
Calculation of cell wall stretch at different stimulus loading rates Membrane stretch is an important mechanical parameter because it is responsible for the opening of the mechanosensitive ion channels (MSCs)  (Sachs 2010; Haswell et al. 2011)  present in the sensory cells of the trigger hair  (Suda et al. 2020; Procko et al. 2021). Therefore, we are interested in the stretch produced on the cell walls (see Fig. 2g ) of the sensory cell as well as in quantifying its variation during the deflection simulations. In order to calculate the cell wall stretch, the logarithmic strain values at the FEM mesh nodes, which lie on the inner surface of the cell wall, are monitored. These nodal strains are accessed from the output database by post-processing with the ABAQUS-Python interface. Then, for each element i ∈ [1, n] on the inner cell wall, a single strain tensor is calculated by averaging the contributions from its associated nodes.
Chemical study 2. 5. 1. Size exclusion chromatography (SEC) Experimental conditions for alginate and chitosan scaffolds dissolution and processing for SEC are resumed in Table  2. After scaffold dissolution, solutions were filtered through 0. 45 μm nylon filter membrane. The detection was operated by a differential refractometer (Shodex RI-101) and a 18 angles static light scattering detector (MALLS Wyatt Dawn Heleos, laser =658 nm; Wyatt Technology, USA) and a 254 nm UV detector (Varian, Australia). Data were analyzed with ASTRA VI software (Wyatt Technology, USA).
Statistical analysis Data in the figures are given as mean ± standard error of the mean (SEM). Statistical significances were determined using Graph Pad Prism software by unpaired t-tests if only two groups were in the study or by two-way analysis of variance (ANOVA) with Tukey post-tests for multiple comparisons with more than two groups (GraphPad Prism 6, version 6. 01). Differences between the groups were considered as statistically significant at the level of p < 0. 05 and marked with asterisks (*; **; *** = p < 0. 05; 0. 01; 0. 001).
Acknowledgments This work was supported by  Région Occitanie  and INSERM grants. We are particularly grateful for the help provided by  ITHPP Alcen (Thégra, France)  for pulsed-beam irradiation treatments and helpful scientific discussions. We wish to thank E-beam technologies ( COMET AG, Switzerland ) for continuous beam irradiation service,  Pascale Saint-Aguet  from the  "Plateforme Technologique pour la Caractérisation de Matériaux Polymères" (Technopolym platform, Toulouse, France ) for SEC measurements,  Olivier Marsan  for ATR FTIR technical support (CIRIMAT),  Lionel Rechignat  for EPR measurements ( LCC, Toulouse), the "Centre de Microscopie Electronique Appliquée à la Biologie" (CMEAB platform, Toulouse, France ) for SEM facility, the  Cellular Imaging Facility (T. R. I. Genotoul Plateform, Toulouse, France)  for confocal facility, the FERMAT federation for X-ray tomography facility, and the  FONDEREPHAR foundation for microbiological assay (Toulouse, France).
cgd. ucar. edu/tss/clm/pfts/index. html)); is the absorbed photosynthetically active radiation, which is converted to photosynthetic photon flux assuming 4. 6 μmol photonos per Joule; Photosynthesis is calculated for sunlit and shaded leaves using average absorbed photosynthetically active radiation for sunlit and shaded leaves ( and ) to give sunlit and shaded stomatal resistance ( and ) and photosynthesis ( and ): where: and are the sunlit and shaded leaf area indices. Implementation of the new photosyntesis algorithm demaned changes in the radiation module of COSMO-CLM. It was changed and the new algorithm for "two-big leaf" approach were added to COSMO-CLM insted of "one-big leaf" approach. https://agu2021fallmeeting-agu. ipostersessions. com/Default. aspx?s=42-59-33-3F-6E-DC-12-F8-6B-33-4B-F1-41-AA-8E-86&pdfprint=true&guestview… 6/13 The new algorithm for "two-big leaf" canopy (sunlit and shaded leaves): The "two-big leaf" approach (Thornton and Zimmermann, 2007 (https://journals.
Statistical analysis at sites: We compared the experimental results with the real data from the meteorological and eddy covariance stations. The model results and the data from the HYRAS and GLEAM datasets were averaged to the one point (station) based on the four closest to the station model grid points. We used the standard deviation (STD), the mean absolute error (MAE (https://medium. com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d)), the root mean square error (RMSE (https://medium. com/human-in-a-machine-world/mae-andrmse-which-metric-is-better-e60ac3bde13d)) and the Pearson correlation coefficient (PCC (https://www. investopedia. com/terms/c/correlationcoefficient. asp)). where: is the total number of data, is actual output value, is mean value where: is covariance of model and observational variables (subscripts -exp and obs); is standard deviation of model data; is standard deviation of in-situ data.
Statistical analysis at COSMO-CLM grid points: The model results are presented on the COSMO-CLM model grid (http://chromeextension://efaidnbmnnnibpcajpcglclefindmkaj/viewer. html? pdfurl=https%3A%2F%2Fwww. hereon. de%2Fimperia%2Fmd%2Fassets%2Fclm%2F2018_cosmo_tutorial_2018. pdf&clen=2167072&chunk=true (spatial resolution is equal to 2. 2 km), because of that we decided to apply additional datasets which are also presented on the personal dataset grids. For this purpose, we used the HYRAS (https://www. dwd. de/DE/leistungen/hyras/hyras. html)and GLEAM (https://www. gleam. eu/) datasets. At first, we converted the data to a standard COSMO-CLM format (rotated grid with 2. 2 km), after which we extracted data for the similar research domains and period  (2010 -2015). As a result, we prepared the six couples of model variables (four air temperature: two meter -T2m, surface -TS, maximum -Tmax and minimum -Tmin, the total evapotranspiration -ZVERBO and the amount of water evaporation -AEVAP) which were used for statistical analysis at COSMO-CLM grid points.