dataesr/scientific-paragraphs-categorization huggingface dataset:
Confocal microscopy of ChR2-Venus fluorescence After cell fixation with 4% paraformaldehyde (4% PFA) for 10 min, ChR2-Venus myotubes were stained with 4 ′ ,6-diamidino-2-phenylindole (DAPI, 1/200, Sigma-Aldrich, Lyon, France) for 5 min to identify nuclei and mounted in Mowiol (Sigma-Aldrich, Lyon, France). Confocal images and 3D-sections of endogenous fluorescence of Venus protein were then obtained at D3 and D7 by confocal scanning microscopy using a FV-1000 system mounted on an Olympus IX81 inverted microscope (Olympus, Tokyo, Japan). Cells were excited with a 488 nm laser at x40 objective and fluorescence images were acquired with Fluoview software (Olympus, Tokyo, Japan). Images were then analysed with Bitplane Imaris 9 software (Oxford instruments Company, Abingdon, UK).
Light-evoked calcium fluorescence recording For the identification of ChR2-positive myotubes, cells were rapidly excited with a 488 nm laser to reveal Venus fluorescence recorded through a GFP filter set. Fast spinning disk confocal images were acquired using the Olympus IX81 microscope coupled to an Andor Revolution Imaging System (Andor Technology, Oxford Instruments, Belfast, UK). IQ3 acquisition software used to acquire images of 512 × 512 pixels. For Ca 2+ imaging, cells loaded with Rhod-2 AM (5 µM, Invitrogen, ThermoFisher Scientific, Villebon sur Yvette, France) were illuminated at 561 nm and fluorescence emission was collected with a red filter set (dichroic mirror: 561 nm, emission filter: 607 nm). Optical stimulation at 488 nm was performed with the use of the FRAP (Fluorescence Recovery after photobleaching) module of the Andor Revolution Imaging system on a small area of 16 µm 2 delimited in the center of the myotube.
Light stimulation was produced by the homemade stimulator arranged below the culture dish at 470 nm with a light intensity of 3. 2 mW/mm 2. Fluorescence recordings were acquired at 20 frames/s using the CellSens software (Olympus, Tokyo, Japan) and each recording was analysis under ImageJ software (NIH, Bethesda, MD, USA). Fluorescence changes were expressed as the fluorescence normalized to basal values (ΔF/F0). Each culture dish was divided into 5-10 regions of interests randomly selected. These experiments were performed at 22 • C and in a standard Tyrode solution. From fluorescence image series, fluorescence measurements were extracted under ImageJ software (NIH, Bethesda, MD, USA) and amplitude peak was analyzed with the macro-software developed under IDL language.
Contractility The contractility of myotubes was monitored using the same MVX10 Olympus macroscopic system described in 2. 7. Myotubes expressing ChR2-Venus were identified with a GFP filter set and were photostimulated with blue LED flashes produced by the homemade stimulator (470 nm, 1 Hz, 20 ms) arranged below the culture dish. Myotubes were illuminated from above with a weak white light which was insufficient to induce stimulation of the channelrhodopsin. Image series of contractility were achieved with a bright field filter at 20 frames/s using the CellSens software (Olympus, Tokyo, Japan). Each culture dish was divided into 5-10 regions of interests randomly selected. Flash-saturated frames were deleted, and the difference extraction image was obtained after the standard deviation of the movie  [32].
We then determined other parameters such as calcium oscillation frequency, the time percent in activity as well as the inactivity duration between each oscillation. The calcium event frequency was around 1. 04 ± 0. 08 Hz (n = 95, Fig. 4E ), the percentage of time spent in activity was around 44. 1 ± 3. 6% (n = 73, Fig. 4F ) and the inactivity time was around 27 ± 1. 5 s (n = 91, Fig. 4G ) in myotubes in differentiation. However, no evolution of these parameters was observed during the differentiation (Fig. 4E-G ). All these data (summarized in Table  1 ) reveal first that, globally, calcium oscillations increased in amplitude with differentiation and second, that intermediate and longer events, becoming a major part of the signature in mature cells, displayed higher values in both calcium increase (TTP) and recovery (RT).
Data Collection Data were collected from the 3 groups of patients constituted, namely the cases (all the patients with a "negative" or a "weak" mitogen response) and the control patients (1 control patient/case with a "normal" mitogen response). Clinical and biological data at the time of the QFT test were collected retrospectively. Patients were categorized as immunocompromised if they were treated with an immunosuppressive or corticosteroid therapy (>0. 15 mg/kg/day prednisone equivalent during more than 2 weeks or more than to 2 mg/kg per day within 3 months before QFT), or if they had active cancer or hematologic disorder, HIV infection, or primary immune deficiency. Tuberculosis (TB) was recorded if there were associated clinical-radiological signs of TB and it was confirmed with laboratory testing (Mycobacterium tuberculosis identification with culture and/or polymerase chain reaction).
The presence of an active infection within the last 3 months or at the time of the QFT sampling were recorded if the infection was clinically/biologically suspected or confirmed by the clinician in charge of the patient. Sepsis was considered as severe if requiring ICU admission. Septic shock as sepsis with persisting hypotension requiring vasopressors to maintain mean arterial pressure ≥65 mm Hg and had a serum lactate level >2 mmol/L (18 mg/dL) despite adequate volume resuscitation  (19). Primary site and microbial origin of the infection at the time of the QFT were also recorded. Clinical outcomes (onset of an acute infection, sepsis or septic shock after QFT sampling, ICU admission, in-hospital mortality and hospital length of stay) were also recorded. Study protocol and data collection were registered with the CNIL (Commission nationale de l'informatique et des libertés) and are in accordance with French (Loi Informatique et Liberté n • 78-17 du 6 janvier 1978) and European (GRPD EU 2016/679) regulations on data protection and patient information (Commitment of compliance MR004 n • 2210228 of 3 December 2018) with a waiver of informed consent given the noninterventional study design.
ACKNOWLEDGMENTS We thank the patients,  Jean-Baptiste Bour  (for his invaluable help in conducting this study) and the technicians from the virology laboratory,  Maud Carpentier  from the  DRCI (Direction de la Recherche Clinique et de l'Innovation) ,  Emmanuel Florentin  for the extraction of biological data,  Aurélie Bertaut ,  Olivera Buirey  and  Julie Gentil  from the CGFL, and  Suzanne Rankin  for proofreading and editing the manuscript.
Patient characteristics and atrial tissue samples Tissue samples from the left atrial appendage (LAA) and right atrial appendage (RAA) were collected from 5 participating centers in the CATCH ME (Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly) consortium (www. catch-me. info). Samples were collected during surgery and flash-frozen in liquid nitrogen to prevent RNA degradation. The investigation complied with the principles outlined in the Declaration of Helsinki. Individual, written informed consent was obtained from each patient, and the Medical Ethics Committee of each center approved sample collection. Centralized processing and analysis were performed under CATCH ME consortium-wide Data Sharing and Material Transfer Agreements.
Statistical analysis Patient characteristics were compared using 1-way analysis of variance for continuous data (Kruskal-Wallis for nonnormally distributed data) and c 2 tests for categorical data. Spearman correlation matrices between expression levels of transcripts were calculated using the "Hmisc" (https:// hbiostat. org/R/Hmisc) package for R. 16  Differences were tested using the Steiger test (R-package "psych" (https:// personality-project. org/r/psych)  17  ).
Acknowledgments We thank  Dr Winnie Chua  for help with ethics for joint analysis of samples within the  CATCH ME consortium. We thank all surgical teams involved for supporting atrial sample collection and patients for participating in this study. Furthermore, we thank the  Core Facility Genomics of the Medical Faculty of the University of Muenster  and excellent technical assistance of  Dr Anika Witten ,  Elvira Barg , and  Marianne Jansen-Rust  in RNA preparation and RNA sequencing.
This work was supported by the  European Union  (EUTRAF Grant Number  261057 ;  CATCH ME Grant Number   633196 ; MAESTRIA Grant Number  965286  to  Dr Schotten ;  ITN Network AFibTrainNet Grant Number   675351  to  Dr Schotten ;  ERACoSysMED H2020 ERA-NET Cofund  Project SysAfib Grant Number  JTC1_82  to  Dr Schotten );  British Heart Foundation  (Grant Number  FS/13/43/30324  to  Dr Kirchhof  and  Dr Fabritz ;  PG/17/30/32961  to  Dr Kirchhof );  Leducq Foundation  (Grant Number  DFG 413/3-1  to  Dr Kirchhof  and  Dr Fabritz ); and  Dutch Heart Foundation  ( RACE V Grant Number   CVON2014-09  to  Dr Schotten  and  Dr Stoll ). Disclosures:  Dr Kirchhof  received research grants from  Menarini  and non-financial support from  Daiichi Sankyo ; support for research from the  German Centre for Heart Research  and from several drug and device companies active in atrial fibrillation; and honoraria from several such companies.
Data Management Data were retrieved from the vascular unit database, SecuTrial ® (interActive Systems GmbH, Berlin, Germany) and the CHUV electronic patient database. All data were anonymized. Data from CTAs were analyzed with Vue-PACS ® (Carestream Health, Ontario, ON, Canada). Preoperative CTAs were compared to postoperative CTAs, both with 1 mm slices and centerline measurements using outer-to-outer diameters. If more than one postoperative CTA was done, the second of the two scans was considered the index CTA compared to the preoperative CTA. Briefly, the following imaging data, taken from CTAs, were examined: neck diameter with four measurements starting from the level of the lowest renal artery with the calculation of the maximum diameter; neck length calculated as the distance between the lowest renal artery and the beginning of the aneurysm sac; degree of suprarenal neck angulation, calculated according to  van Keulen, et al.
According to Rouby, et al. [15] , any CIA of a maximum diameter ≥17 mm was classified as an aneurysm. Finally, endoleaks were classified as Type I, Type II, or Type III. Compliance with the instructions for use (IFU) for Endurant ® endoprosthesis (https://www. medtronic. com/us-en/ healthcare-professionals/products/cardiovascular/aortic-stent-grafts/endurantii/indicationssafety-warnings. html, accessed on 27 May 2022) was analyzed, including neck length ≥10 mm, neck angulation ≤60 degrees, neck diameter 19-32 mm, and iliac diameter 8-25 mm. IFU were coded as a single binary variable. Any EVAR procedure with a single unmet IFU instruction was classified as non-compliant. Patient demographics; clinical features; and preoperative cardiological  [16] , respiratory  [17]  and renal [18] assessments were evaluated with ASA scoring, and entered into the database.
Statistical Analysis Univariate analysis was performed using t-test or ANOVA for normally distributed continuous variables and Wilcoxon rank-sum test for non-normally distributed continuous variables. Fisher's test (two-sided) was used for categorical variables. Patient survival, shrinkage during follow-up and freedom from reintervention were calculated by the Kaplan-Meier method. The Logrank test was used to compare groups of interest. A p-value <0. 05 was used to assess statistical significance. All analyses were performed with SPSS V. 28 (IBM Corp) and R version 4. 1. 1 (The R Foundation for Statistical Computing).
S A M P L E C O L L E C T I O N A N D C L I N I C A L D A T A Tumour samples were provided by the consultation archive database (1982-2021) from the Department of Neuropathology at Sainte-Anne Hospital, NetSarc+ and Institut Bergonie. Fifteen mesenchymal tumours with a proven NTRK fusion were selected for this study. Additionally, one CNS case suspected to be SCN-NTRK harbouring a TFG::MET fusion was also included. Patient characteristics and clinical data were retrieved from hospital records and included sex, age at presentation and medical history. Parents/guardians gave written informed consent for the retrospective analysis of clinical data according to the Institutional Review Board and before inclusion into ongoing protocols. All soft tissue cases are recorded in the database of the French expert sarcoma network (RRePS/Netsarc+), which is approved by the National Committee for Protection of Personal Data (CNIL, no.
34 Gene expression was measured using counts of sequencing reads with the software HtSeq version 0. 6. 0. 35 Gene count data were normalised using the VOOM method. 36 nsupervised clustering was performed using agglomerative hierarchical clustering with distance criteria (1-Pearson_correlation) and linkage criteria using the function hclust available in R. 37  Consensus clustering was performed with the package ConsensusCluster-ingPlus from BioConductor. 38 The fusion and cluster analyses were performed as previously described. 34 To achieve clustering, we used a set of control samples that encompassed similar histotypes to those included in the methylation analysis: undifferentiated pleomorphic sarcomas (n = 12), MPNST (four), atypical neurofibromatous neoplasms of uncertain biological potential (two), meningiomas (five), dermatofibrosarcoma protuberans (nine), infantile hemispheric glioma with BRAF fusion (one), IFS (one), solitary fibrous tumour (SFT) (three), low-grade fibromyxoid sarcomas (four), fibromatosis with CTNNB1 mutations (seven) and angiomatoid fibrous histiocytomas (AFH) (four) (Supporting information, Table  S1 ).
Acknowledgements The authors are grateful to the laboratory technicians from  GHU Paris Neurosciences, Hospital Sainte-Anne and Institut Bergonie (Bordeaux) , for their assistance and the clinicians and pathologists who provided material and clinical follow-up. FLL's research is supported by grants from the  Fondation Bergonie , the  SIRIC Bordeaux Brio and the charity 'Au fil d'Oriane'. ATE 's research is supported by grants from the  Foundation 'Liv et Lumiere'. AT-E, MD JB , MLeQ, DB, PV and FLeL contributed equally to this work.
Patients and Samples We collected pre-therapeutic diagnostic formaldehyde-fixed, paraffin-embedded (FFPE) tumor samples (180 IBC and 355 nIBC) from patients treated at Salah Azaiez Institute (SAI) of Tunis (Tunisia), and Paoli-Calmettes Institute (IPC) of Marseille (France). The clinicopathological data were retrospectively collected from our institutional medical registries and pathology databases. The inclusion criteria included pathologically confirmed IBC (AJCC T4d), availability of pre-therapeutic diagnostic FFPE tumor samples, clinicopathological annotations including treatment and follow-up, and patient's written informed consent. The patients had received standard multidisciplinary treatment in both hospitals, including neoadjuvant and/or adjuvant chemotherapy in all cases, hormone therapy and anti-HER2 treatment when indicated, and surgery and adjuvant radiotherapy.
Statistical Analysis Cell proliferation, colony formation, migration, invasion and mammosphere formation rates were summarized with descriptive statistics and box plots for each treatment group. A two-tailed unpaired Student t-test was used for statistical analysis using GraphPad Prism software (version 9. 4. 0, Chicago, IL, USA). Regarding the analysis of clinicopathological variables, data were summarized by numbers and percentages for categorical variables and median and range for continuous variables. Mann-Whitney and Fisher's exact tests, when appropriate, were used to analyze correlations between patients' groups and clinicopathological variables. Uni-and multivariate analyses regarding the IBC/nIBC distinction were carried out using logistic regression analysis using the glm function and the significance was estimated by specifying a binomial family for model with a logit link.
Negative Correlation between MARCKS and PTEN Protein Expression in IBC Patients and Prognostic Value Given the upregulation of PTEN after MPS-based MARCKS inhibition in IBC and our prognostic results, we analyzed the correlation between MARCKS (discrete value) and PTEN protein expression in the informative samples (54 IBC and 231 nIBC). Analysis of PTEN as continuous value (Quick Score) found a negative correlation within IBC samples (p = 1. 18 × 10 -1 , Mann-Whitney test), but no correlation within nIBC samples (p = 0. 235; Figure  3F ). When PTEN was analyzed as discrete value (Quick Score), there was a trend for negative correlation among IBC samples (p = 9. 53 × 10 -1 ; logit link test), but no correlation amongn IBC samples (p = 0. 85; data not shown). Patients with MARCKS-positive IBC displayed smaller PTEN expression than patients with MARCKS-negative IBC.
Acknowledgments: We thank the  «Ministère d'Enseignement Supérieur et de la Recherche Scientifique de Tunisie». Wealsothank Salah AzaiezInstitute of Tunis, the «Ministère d'Enseignement Supérieur de la Recherche Scientifique de Tunisie», Pasteur Institute of Tunis, «Agence Nationale de la Promotion de la Recherche scientifique (ANPR)-Mobidoc-postdoc program» (Tunis, Tunisia ), and  ICEP Platform (Marseille, France). We thank  Jeannine Donahue  ( Breast Program Manager  at  Weill Cornell Medicine/New York-Presbyterian Hospital ) for the proofreading.
Changes in the cardio-metabolomics profile and hormonal status have been associated with long QT syndrome, sudden cardiac death and increased mortality. The mechanisms underlying QTc duration are not fully understood. Therefore, an identification of novel markers that complement the diagnosis in these patients is needed. In the present study, we performed untargeted metabolomics on the sera of diabetic patients at a high risk of cardiovascular disease, followed up for 2. 55 [2. 34-2. 88] years (NCT02431234), with the aim of identifying the metabolomic changes associated with QTc. We used independent weighted gene correlation network analysis (WGCNA) to explore the association between metabolites clusters and QTc at T1 (baseline) and T2 (follow up). The overlap of the highly correlated modules at T1 and T2 identified N-Acetyl asparagine as the only metabolite in common, which was involved with the urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine.
Study Design Our study is a sub-analysis of the Diabète et Calcification Arterielle (DIACART) study, a single-center prospective observational cohort study among French patients with T2DM (ClinicalTrials. gov: NCT02431234). The participants were prospectively enrolled at Pitié-Salpêtrière Hospital (Paris, France) and included type 2 diabetic patients (T2DM), at high risk for CVD  [13]. A total of 170 patients at baseline visit (T1) and 139 patients at T2 (follow-up for 2. 55 years [IQR 2. 34-2. 88 years]) were included in the study. Patients were excluded if they had severe chronic kidney disease or end-stage renal failure (estimated glomerular filtration rate < 30 mL/min using the Modification of Diet in Renal Disease equation). As QTc becomes unreliable in conditions with prolonged QRS (>130 ms), multiple premature ventricular contractions, ventricular pacing and supraventricular tachycardia, these conditions were excluded from this study  [13].
Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) Analyses of Serum Samples Metabolomic preparation of samples for analyses was detailed previously  [15]. In brief, LC-MS experiments were performed using PFPP, Discovery HS F5-PFPP column, 5 µm, 2. 1 × 150 mm (Sigma, Saint Quentin Fallavier, France) on a UPLC ® Waters Acquity (Waters Corp, Saint-Quentin-en-Yvelines, France) and Q-Exactive mass spectrometer (Thermo Scientific, San Jose, CA, USA). LC-MS raw data were first converted into mzXML format using MSconvert tool  [16]. Peak detection, correction, alignment and integration were processed using the XCMS R package with CentWave algorithm  [17]  and workflow4metabolomics platforms  [18]. The resulting dataset was Log-10 normalized, filtered and cleaned based on quality control (QC) samples  [19].
Weighted Gene Co-Expression Network Analysis and Visualization Standard WGCNA procedure was followed to create unsigned gene co-expression networks from the WGCNA R-package v1. 68  [21]. Gene cluster dendrogram was constructed with a power value = 3. A total of 132 metabolites were imported for the WGCNA analysis. The modules identified by WGCNA analysis were further associated, using Spearman correlation, with QTc independently at T1 and T2. The results identified at T1 and T2 were overlapped using Venn diagrams in R software and visualized using MetScape  [22]  in Cytoscape software  [23].
Electrocardiography Acquisition and QTc Analysis Electrocardiograms were recorded using a digital electrocardiograph by trained personnel, with a sampling rate of 1000 Hz and a filter of 150 Hz. The methodology for QTc assessment (CalECG software ® , New York City, NY, USA) on 10 s triplicated digitized ECG, Bazett's heart rate correction) has been extensively detailed elsewhere. Inter-and intra-observer variability assessment for QTc measurement was also performed as has been previously described  [4].
Untargeted Metabolomics Analysis Using Weighted Gene Correlation Network Analysis (WGCNA) To capture the full extent of the metabolomics expression profiles, we performed a weighted gene co-expression network analysis (WGCNA). WGCNA is a useful method to tightly link co-expressed gene modules to phenotypic traits (e. g. , QTc)  [21]. The full dataset of 170 patients at T1 and 139 patients at T2 was used for WGCNA analysis. No outliers were identified; therefore, all samples were included in the study analyses. At T1, there were a total of ten clusters of metabolites identified (Figure  1A , left panel). Correlations between clusters of metabolites and QTc were evaluated through Spearman's correlation analysis. QTc was significantly correlated with the dark grey and the pink clusters (r = 0.
N-Acetyl Asparagine Positively Correlates with QTc To evaluate the potential involvement of N-Acetyl asparagine with QTc, we performed Spearman correlation analysis to evaluate the actual relationship between QTc and N-Acetyl asparagine. We found that in both T1 and T2, N-Acetyl asparagine was positively correlated with QTc (r = 0. 19, p = 0. 01 and r = 0. 23, p = 0. 007, respectively) (Figure  3A ). In contrast, L-asparagine was negatively correlated with QTc at T2 (r = -0. 18, p = 0. 03) (Figure  3A ). We further confirmed the association of N-Acetyl asparagine with QTc in this cohort by applying mixed models (R-software, package 'nlme', random effect = subjects; fixed effects: sex and N-Acetyl asparagine) integrating the evolution over time of N-Acetyl asparagine circulating levels in the same subject (Figure  3B ).
These islands are home to a fairly diverse community of reptiles and amphibians that account for most of the islands' non-flying terrestrial biodiversity  [8]. The insular Caribbean has seen numerous zooarchaeological studies over the last few decades  [9, 10] , with many focusing on the lifeways and history of Indigenous groups that arrived in the archipelago some 5000 years ago until their partial demise following European colonization in the seventeenth century  [11, 12]. This work has resulted in a dataset including numerous archaeological deposits whose faunal component provides important information regarding the subsistence strategies and human-animal interactions across the different Amerindian periods. However, few regional studies focusing on specific taxa have been conducted  [13] [14] [15]  and none concern reptiles and amphibians despite their importance in the modern biodiversity of the area.