dataesr/scientific-paragraphs-categorization huggingface dataset:
KEGG provides a set of well-established pathways, while Pathway Commons allows a higher coverage by integrating pathways from multiple sources (notably Reactome, Panther, and PID). To select the most relevant pathways guaranteeing to cover most identified targets, we filtered the pathways based on their enrichment p-value and selected those presenting a p-value lower than 0. 1 (in this step we aim to be as complete as possible, selecting 83 pathways from KEGG and 419 from Pathway Commons). Furthermore, we included the pathways containing Syk targets not covered by significantly over-represented pathways from the same database. This allowed to integrate 41 additional pathways from KEGG and 9 from Pathway Commons (S5 and S6 Tables). In this merged network, each protein and interaction keeps track of the list of pathways in which it is involved.
We also used the GO annotation to identify the proteins involved in processes in which Syk is implicated (cell adhesion and motility, cell growth and death, immunity and inflammation, cell differentiation). The resulting oriented and partly signed network comprises 6438 proteins and 62322 interactions, from 552 pathways (124 from KEGG, 428 from Pathway Commons), covering 350 of the 687 identified targets (75 are only found in KEGG, 125 in Pathway Commons and 150 in both). Among these 350 targets, 245 are reachable from Syk (steps 1-2 in Fig  1 ). This network contains 979 interactions between two targets identified in the datasets. A closer scrutiny of these interactions further highlights connections between the two datasets: 146 and 260 interactions are associating targets specific to the MCF7 and MDA231 datasets respectively, 253 involve at least one target shared by the two datasets and 320 connect targets specific to different datasets.
We observed that the shortest paths between Syk and cortactin contain two intermediates, with three alternative proteins directly upstream of cortactin (Fig  2A ). Src is the only phosphotyrosine modifier (tyrosine kinases and phosphatases) amongst them, suggesting that the paths involving Src are more credible to explain the change in cortactin phosphorylation. The shortest paths network linking Syk to ezrin included more proteins and contained also two intermediate nodes  (Fig 2B). These paths were related with the classical regulation of ezrin by the membrane lipid PIP2 or its phosphorylation on serine/threonine residue(s)  [48, 49]. None of the nodes directly upstream of ezrin were phospho-tyrosine modifiers that could explain the effect of Syk on ezrin tyrosine phosphorylation. The sub-networks obtained for cortactin and ezrin illustrate the need for careful analysis of phospho-tyrosine modifiers to explain the phosphorylation-based modifications noticed in the MDA231 dataset.
As shown in the dedicated Results subsection, biological validation confirmed the role of Src in mediating Syk signal propagation to cortactin. However, it did not enable the identification of significantly better suggestions for ezrin, suggesting a "missing link" (paths via white nodes in Fig  2B ). To resolve this inconsistency, we decided to evaluate the possibility of a direct interaction between Syk and ezrin. These results are described below and prompted us to propose that Syk directly phosphorylates ezrin. Taking into account our biological validation, we added the new protein interaction from Syk to ezrin to the Syk network. We also extended the list of Syk direct substrates by integrating the results of a third dataset that identified the peptides phosphorylated on tyrosine by Syk The grey nodes are only involved in the unweighted shortest paths, while the white ones are also involved in the weighted ones.
The red borders denote phospho-tyrosine modifiers (tyrosine kinases and phosphatases). Note that some related proteins have been grouped in single nodes for clarity (denoted by curly brackets at the end of the name, and "PI3K class I"). Interactions annotated as "phosphorylation" (not particularly on tyrosine) appear in red, while "protein modifications" in general appear in blue. Line ends indicate the sign of the interaction: arrow for positive, T for negative, and circle for unknown. Finally, node shapes denote identified proteins: squares have identified differentially phosphorylated peptides (MDA231 dataset), circles correspond to differentially enriched proteins (MCF7 dataset), and diamonds are not part of the datasets. https://doi. org/10. 1371/journal. pcbi. 1005432. g002 after an in vitro kinase reaction  [18]  (S2 Table ; for details, see Materials and methods).
Reasonability of network reconstruction and shortest path analysis Using datasets extracted from two published complementary phospho-proteomic studies that identified Syk targets in breast cancer cells, we reconstructed a Syk-controled molecular network by integrating the components of signaling pathways enriched for Syk targets (S1 File). Two different breast cancer cell models were used: Syk-positive MCF7 cells treated or not with a pharmacological Syk inhibitor versus exogenous Syk expression in the Syk-negative MDA-MB-231 cells. This was expected to bring some heterogeneity in the molecular paths linking Syk to its targets. Nevertheless, lists of Syk targets emerging from the different datasets showed similarities in pathway enrichment. Moreover, in the Syk network subpart containing the reachable targets, we found that one third of the interactions involving two targets connect targets identified in different datasets, which justified combining them into a unique set despite the partial overlapping.
Interest for cancer research and extent of application of the methods By this study, we do provide to the cancer cell signaling community access to the Syk network and sub-networks of the paths from Syk to its targets involved in (i) cell adhesion and motility, (ii) cell growth and death, (iii) cell differentiation and (iv) immunity and inflammation (see Supporting Information). As Syk is involved in breast cancer suppression, it is not surprising that these cellular processes involved in cancer progression can be affected by Syk in breast cancer cells. Nonetheless, many of the Syk interactions described in pathway databases are extracted from molecular studies in cells of hematopoietic origin. More precisely, Syk signaling has been extensively studied in B lymphocytes where it is indispensable for immune cell differentiation.
Which part of Syk signaling is shared between epithelial and hematopoietic cell types could be determined by Syk related phospho-proteomic experiments with a more comprehensive collection of cell models. At this stage, our model suggests a number of plausible mechanisms linking Syk with cancer-related cellular processes. These can be used to generate more hypotheses, validation and provide valuable inputs for further developments. A crucial issue in Syk-related research is how to activate compensatory mechanisms maintaining tumor suppression signaling even when Syk is downregulated. The network we propose here is the first step towards addressing this question. To advance towards more refined mechanistic models the annotation of the interactions, for instance the sign, should be completed by integration of more data and by formal inference methods  [64, 65].
We consider that the combination of ad-hoc distances, random walk, and nearshortest paths provides good candidate mechanisms, by implementing the following requirements: (i) minimize the number of steps (shortest paths); (ii) fit with the data (ad-hoc distances); (iii) further favor intermediates which belong to multiple appropriate candidate paths (random walk); (iv) propose and rank multiple alternatives (near-shortest paths). This strategy can be generally applied to other studies of signaling networks using datasets based on distinct post-translational modifications, separately or combined. The open source code for the network reconstruction and extraction of relevant sub-networks has been made available for the computational biology community (S1 File and https://github. com/aurelien-naldi/ NetworkReconstruct).
Annotation of Syk network The network is annotated based on the dataset, the pathways, and the GO annotation. Nodes and interactions keep track of the list of pathways in which they appear. Interactions types (phosphorylation, modification, regulation) and signs (+ or -) are transferred from the pathways when available. When the same interaction is described in several pathways, all types and a combination of the signs are conserved (an interaction described as positive in a pathway and negative in another is considered as unclear). We selected some groups of GO terms representing relevant processes and functions in this dataset, in particular cell adhesion and motility (GO:0048870, GO:0007155, GO:0034330, GO:0022610, GO:0060352, GO:0030030), cell growth and death (GO:0008283, GO:0007049, GO:0008219, GO:0019835, GO:0000920, GO:0007569, GO:0051301, GO:0060242), immunity and inflammation (GO:0002376, GO:0001906), and cell differentiation (GO:0030154, GO:0036166).
Immunoblots were revealed using a standard chemoluminescent method (ECL, Ozyme) and a Multiapplication gel imaging system (PXi, Syngene). Membranes were optionally stripped with the Restore PLUS Western blot stripping buffer (Thermo Scientific) before a second immunoblotting. Immunoblot-derived signals were quantified using the ImageJ software (NIH) with three independent biological and technical replicates for each quantification. The signals were normalized on the lane corresponding to the total protein quantity loaded (immunoglobulin heavy chain in case of immunoprecipitation; Tubulin-α in case of whole cell lysate) and the unstimulated condition was arbitrarily set at 1.
Proteomic analyses Sample preparation. Proteins were separated by SDS-PAGE and the gel was stained with the Colloidal Brillant Blue G (Sigma). Protein bands were in-gel digested using trypsin (sequencing grade; Promega, Charbonnières, France)  [70]. Tandem mass spectrometry. Analysis of the samples was performed on a QSTAR pulsari quadripole-time-of-flight mass spectrometer (Applied Biosystems, Foster City, USA) coupled to an Ultimate 3000 (Dionex, Amsterdam, Netherland) nanoflow system driven by Chromeleon software. A gradient consisting of 0-40% B in A for 60 min, 80% B in A for 15 min (A = 0. 1% formic acid, 2% acetonitrile in water; B = 0. 1% formic acid in acetonitrile) flowing at 300 nl/min was used to elute peptides from the capillary (75 μm x 150 mm) reverse-phase column (Pepmap, Dionex).
Desalting and pre-concentration of samples were achieved on-line on a Pepmap precolumn (300 μm x 10 mm). All MS spectra were acquired in data-dependent mode using Analyst QS 1. 1 software. Briefly, the mass spectrometer was operated in the information-dependent acquisition mode to automatically switch between MS and MS/MS acquisition. Survey full-scan TOF-MS spectra (from m/z 350-1600) were acquired during 1 s. The two most intense ions were sequentially isolated and fragmented using collisional-induced dissociation. Each MS/MS spectrum was acquired during 3 s and the precursor was excluded during 45 s. Parameters were adjusted as follows: ion spray voltage (IS), 1800 V; curtain gas (CUR), 25; declustering potential (DP), 60 V; focusing potential (FP), 265 V; declustering potential 2 (DP2), 15 V.
Peptide fragmentation was performed in the collision cell using nitrogen gas on the doubly, triply, or quadruply charged ions detected, with a collision energy set "on-the-fly" using the rolling collision energy feature based on their charge and mass. Database analysis. MS/MS spectra were searched against the human entries of UniProt Knowledgebase Release 10. 2 database (http://www. expasy. ch) by using the Mascot v2. 1 algorithm (http://www. matrixscience. com). Search parameters were mass accuracy 0. 1 Da for MS and MS/MS data; 1 miscleavage; variable modifications: oxidized methionine, Phospho-Ser/ Thr, Phospho-Tyr, SILAC-labels: Lys-8 and Arg-10. All significant hits (p<0. 05) were manually inspected. Quantification. Quantification was done on at least two MS spectra per protein by using MSQuant v1.
4. 1 software developed by Mann and colleagues  [71]  (http://msquant. sourceforge. net). Data were manually inspected and corrected when necessary. Post-treatment of phospho-proteomic data. The first dataset was obtained after inhibition of Syk catalytic activity in Syk-positive MCF7 cells  [19]. Proteins were purified on an antiphospho-tyrosine affinity column, and enriched phospho-tyrosine-dependent complexes were identified by mass spectrometry. The SILAC (Stable Isotope Labeling with Amino acids in Cell culture) strategy allows relative quantification of protein ratio between control and Syk-inhibited cells. As the Syk inhibitor concentration only partly inhibits the Syk catalytic activity, we selected proteins with a SILAC ratio reflecting a variation of 10% as Syk targets. On 479 proteins identified, 240 proteins exhibited an increased phosphorylation or association with phosphoproteins in presence of full Syk activity, 25 proteins in presence of inhibited Syk activity (S1 Table ).
The second dataset was obtained after Syk expression in Syk-negative MDA-MB-231 cells  [17]. In this study, proteins were trypsin-digested prior to two steps purification on an anti-phospho-tyrosine affinity column and on polymer-based metal ion affinity column. We compared the tyrosine-phosphorylated peptides identified in Syk-positive and Syk-negative cells by Iliuk and colleagues (2010). Phospho-peptides identified in only one experimental condition were considered as differentially phosphorylated (461 in Syk-positive cells and 125 in Syk-negative cells). Phospho-peptides identified in Syk-positive cells match to 385 proteins, those identified in the Syk-negative cells to 117 proteins. Amongst them, 15 proteins displayed distinct phospho-peptides identified in both experimental conditions.
Due to amino acid sequence redundancy, 25 of the differentially phosphorylated peptides matched to several proteins that we all included in the differentially phosphorylated proteins. We considered all these proteins as Syk targets. 131 phospho-peptides were found in both experimental conditions, including 9 with the same phospho-tyrosine residue but with a differential methionine oxidation, and were excluded (S2 Table ). The third dataset identified the peptides phosphorylated on tyrosine by Syk after in vitro kinase reaction  [18]. Amongst them, we selected as Syk direct substrates those that were also identified in the MDA-MB-231 cells after Syk expression by  Iliuk and colleagues (2010). Contrary to the list published by Xue and colleagues (2012), we excluded the following proteins because their phospho-peptides identified in vitro were not retrieved exclusively in Syk expressing MDA-MB-231 cells: Four and a half LIM domains protein 2 (Uniprot #Q14192), Tyrosine-protein phosphatase non-receptor type 11 (Uniprot #Q06124), Tyrosine-protein phosphatase non-receptor type 1 (Uniprot #P18031), cortactin (Uniprot #Q14247), Vimentin (Uniprot #P08670) and Tyrosine-protein phosphatase nonreceptor type 12 (Uniprot #Q05209).
Fig). Mass spectrometric analysis demonstrated that ezrin is phosphorylated by Syk on a peptide containing the phosphorylated Tyr424 residue (S11 Fig). The same ezrin phospho-peptide was the only one identified in the Syk-positive breast cancer cells in the MDA231 dataset. Taken together, these results prompted us to propose the new protein interaction between Syk and ezrin, leading to ezrin phosphorylation on the Tyr424 residue (Fig 6D).
1-5 Hz  [8]. A different method to model CICCs coupling losses is using a numerical approach. The code JackPot-AC/DC is able to model a complete CICC as a network of superconducting and resistive elements. Its backbone is the cabling subroutine that calculates the trajectories of all the strands inside the cable. All involved model parameters are obtained from geometrical dimensions and experiments, thus there are no free parameters in the model. Starting from the cable design, JackPot generates a complete CICC geometry, with strand level detail, using experimentally determined properties of the strands with mutual inductive coupling between strands and coupling with the applied magnetic field. The numerical code is able to calculate critical current and current distribution in all individual strands, at any location in the cable and for any chosen scenario.
Based on the local power and heat generation, AC loss and stability can be evaluated in a chosen scenario. After the proper calibration finding the inter-strand contact resistivity, it is possible to successfully calculate the energy deposited inside the cable during the MQE tests and to look into the details of the electromagnetic performance of the CICC, such as current distribution and peak electric fields at strand level. In this study, the THEA code (CryoSoft  [9] ) is used in combination with MCM and JackPot respectively, to model the thermal behavior of two samples during their MQE tests. One conductor from the ITER Poloidal Field coil #1 called PFEU2  [10]  and one ITER CS conductor, the CSJA8 sample  [11]. The aim is to evaluate the agreement between THEA calculations and the recent stability experiments in view of a possible prediction of the margin with a different disturbance profile relevant for the ITER plasma-operating scenario.
JackPot-THEA simulations After the calibration of the inter-strand contact resistance, the MQE tests can be simulated by the JackPot-THEA combination. The output power calculated by JackPot is used as input in THEA, instead of using the analytical MCM equation. The temperature, background magnetic field and transport current are set to the same values in JackPot as used in the table  3. The sinusoidal magnetic field pulse used in JackPot has the same amplitude and shape as the pulsed field used in the SULTAN stability experiment. The power versus time deposited during a MQE test computed with JackPot is shown in figure  12 , and compared to the MCM * result for the CSJA8 sample. Although there is a difference between both model results, the profile generated with the MCM * code resembles closely the results of the more detailed JackPot approach.
The purpose of the linker is to deactivate the effector. The trigger group is the critical group that determines prodrug activation and hypoxia selectivity. Under low-oxygen conditions, the effector is selectively released after reduction  [9]. The five classes commonly described for HAP count a number of nitro (hetero) aromatic compounds, such as the 2-nitroimidazole HAP of bromo-isophosphoramide, TH-302, which is currently considered as the gold standard. This HAP is in clinical trials, with phase-I studies published from 2011 and phase III ongoing against soft tissue sarcoma (NCT01440088 study) and pancreatic cancer (NCT01746979, MAESTRO study)  [10, 11]. Drugs targeting the hypoxic microenvironment can only be effective if they physically reach their targets. Exploiting the physical and chemical properties of the extracellular matrix of chondrosarcoma, composed of fibrous structural and adhesive proteins as well as proteoglycans, may be a way to complement these HAP therapies.
The studied molecules (analytes) were diluted in running buffer HBS-P + 1X before application to the sensor chip at 30 μL/min. Regeneration of the sensor chips was achieved using 2 M NaCl solution during 150 seconds. Dissociation constants (K D ) were determined using Biacore T200 Evaluation software with a "steadystate affinity analysis" after injection of five different concentrations of analytes.
The cell cycle distribution and proportion of apoptotic cells were analyzed by flow cytometrey (LSR II flow cytometer, BD Biosciences). Experiments were performed in triplicates. Data are represented as means ± SD. For cell cycle analyses, statistical significance was determined using two-way ANOVA followed by Tukey's post-hoc multiple comparison test. For Annexin V test, significance was determined using two-way ANOVA followed by Bonferroni's post-hoc multiple comparison test. Results were considered significant at p < 0. 05.
Global tumor hypoxia was investigated by photoacoustic imaging in OxyHemo-Mode to determine the average values of oxygen perfusion (SO 2 ). For vascularization assessment, Doppler imaging was used to monitor the tumors. Tumor perfusion status was assessed by contrast-enhanced ultrasound (CEUS) imaging after an i. v. injection of 50 μL of Vevo MicroMarker™. Data were processed using the VevoCQ™ software.
ACKNOWLEDGMENTS The authors thank  Cecile Polge  ( INRA, UMR 1019, CNRH, Clermont Ferrrand ),  Romain Vivès  (  Institut de Biologie Structurale, Grenoble ),  Lucie Tixier  ( Service Anatomopathologie, Centre Jean Perrin, Clermont-Ferrand ), and  Fernand Léal  ( Université Clermont Auvergne ,  UMR 1240 INSERM ).
Study Characteristics The study was designed by referring to a survey paper  [5]  reviewing laparoscopic Simulation-Based Training studies, from 1997 to 2011. The hired population were nine engineering students (19 to 28 years old, including 3 men, all right-handed), with no prior knowledge in laparoscopy. Their skill improvement was assessed during a month on different LapSim tasks. Each participant had ten training sessions, with only four recorded sessions. Each session was conducted as follows: two warming exercises (Camera navigation, Instrument navigation) and three recorded exercises (Coordination, Grasping, Cutting). They are Basic Skills tasks of the LapSim software. The chosen parameters were the mainly found in the literature, namely task duration, path length (in rotation and translation) and number of movements.
IGlove Data Processing The selected parameters were extracted from the IMU's thanks to a MATLAB data processing. The mainly used sensor was the gyroscope. A sliding-hanning-window filter was used to smooth the signals (width: 2 s). The task duration (time between first and last movement) was deduced from the number of peaks as described in Figure  1a. The angular path (path taken by the sensor of the hand, in degrees) was calculated by estimating the integration of the gyroscope signal.
Validation of the IGlove The extracted data were compared with two parameters from the LapSim reference data: the task duration and the angular path length. Different analyses can be conducted: to compare the parameters' value, to compare the parameters' evolution and to compare the participants' ranking. Comparison of the parameters' value. The parameters' error  was defined as the average difference between the IGlove values and the LapSim values, for one parameter, such as: 𝑒 = 1 𝑁 × 𝑆 ∑ ∑ | 𝑥 𝐼𝐺𝑙𝑜𝑣𝑒 -𝑥 𝐿𝑎𝑝𝑆𝑖𝑚 𝑥 𝐿𝑎𝑝𝑆𝑖𝑚 | 𝑆 𝑗=1 𝑁 𝑖=1 × 100 (1) where  is the number of participants,  the number of sessions,   the value of the parameter recorded by the IGlove and   the value of the parameter recorded by the LapSim. was obtained using only sensor 1. The smaller  is, the closer to the LapSim is the IGlove.
Discussion The selected tasks were basic enough for every participant to achieve a quantifiable improvement. The selected parameters extracted from the IGlove sensors seemed all relevant to show how the participants went towards a more economical gesture. Their absence of knowledge or skills in laparoscopy made their progression clearly identifiable. The IMU sensors give a lot of information, their place on the IGlove has to be well chosen to balance redundant and additional information. Finally, the physical and psychological discomfort has to be taken into account as it appeared the participants felt much more at ease practicing without being observed and measured. The IGlove outcomes are always higher than the LapSim data. It is because of the movement detection method: it takes into account additional movements that are not parts of the exercise itself, while the LapSim software doesn't.