dataesr/scientific-paragraphs-categorization huggingface dataset:
67, i. e. a stoichiometric ratio characteristic of Ca-HA. The pH of the solution was monitored continuously via Mettler Toledo InLab Reach Pro electrodes with an acquisition every 5 s using Labview software. The mixture was stirred at a speed of 350 rpm for 48 h. The resulting suspension was filtered at the end of the synthesis (Millipore-MCE 0. 45 lm filter) and then rinsed with deionized water. The collected solid is dried in an oven at 105 °C for 24 h and then reduced to powder, by a mild grinding in a mortar. Fig. 1  presents schematically the synthesis and experimental modes used for the synthesis.
In these invasion assays, breast cancer cells invaded inserts precoated with Matrigel Matrix. Figure  1C  reported that 24 h of exposure to 1 µM Leb-C, significantly reduced the invasion of MDA-MB-231 and D3H2LN cells by 80 % and 65 %, respectively. The IC50 for this inhibition was about 0. 1 µM for both cell types (Figure  1C ). To examine whether the effects of Leb-C were due to its growth suppressing activity, a cell proliferation assay was carried out. It should be noted that up to a concentration of 2 µM, Leb-C did not affect the proliferation of MDA-MB-231 and D3H2LN cells after three days of exposure (data not shown). Thus, Leb-C dose-dependently inhibited breast cancer cell adhesion, migration, and invasion (p < 0. 05).
Leb-C reduced tumor angiogenesis in vivo To assess intratumoral angiogenesis in paraffin sections, endothelial cells were identified using GSL-1, a lectin that specifically binds to galactosyl residues present on the vascular endothelium. Histological analysis of the tumor at the end of the experiment revealed the presence of necrotic areas in the central regions of most tumors in the control group (data not shown). Additionally, the analysis revealed a reduction in the number of blood vessels within viable tumor fields in the Leb-C treated group compared to the control group (Figure  5 ). Notably, the vessel diameters were larger in the control group compared to the Leb-C treated group (Figure  5A ). Quantification of vessel density demonstrated that treatment with 2 µM Leb-C, administered twice a week, led to a significant 50% reduction in vessel density within viable tumor fields compared to the control group (p < 0.
Bioluminescent Imaging Bioluminescence images were obtained from the dorsal view using the In Vivo Imaging System (IVIS) (Xenogen). The images were acquired approximately 5 minutes after intraperitoneal injection of D-luciferin to anesthetized mice  [60]. To prevent saturation, the acquisition times were adjusted based on the signal strength. The bioluminescence signal was analyzed once a week using Living Image software (Xenogen). A region of interest (ROI) was drawn around the bioluminescence signal, and the photon flux (photon/s/cm 2 ) was measured within the ROI.
Acknowledgments: The authors are very grateful to  Mrs Nicole Peyri  from  INSERM Unité 553 (hôpital St Louis, Paris, France ) for her help in Immunohistochemical analysis. This work was supported by grants from  Tunisian Ministry of Higher Education  and the  INCA PL06_130 , for financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
1 µM] and oxaliplatin [0. 5 µM], red bars) in CRC 3D models, and activity in 3Dcc CCD841 (stripped black bars) used to generate the therapeutic window (TW). Activity is measured by ATP levels vs. CTRL (< 0. 15% DMSO). Data are presented as the mean of N = 2-3 independent experiments, error bars represent SD. Significance is determined by one-way ANOVA (regression models, left panel) and two-way ANOVA (activity graphs, right panel) with *p < 0. 05, **p < 0. 01 and ***p < 0. 001 the analysis, drugs with antagonistic interactions were eliminated, and the next screening round was performed (search 2), Fig. 1B. The regression models generated from each search are presented in Supplementary Figs. S3-4. The last search 3 served to optimize the drug doses within the selected drug combinations (Fig.
Whole exome sequencing (WES) and analysis For PCRC-1, mutation profile is routinely performed by clinical pathology (HUG). DNA was extracted from two FFPE tissue sections of about 10 μm with Promega Maxwell ® RSC DNA FFPE Kit (Promega AS1450) on the automated machine (Promega RSC). The concentration was measured using fluorometric Qubit reagents (Thermofisher Scientific, cat n° Q32851). 50 ng DNA was used to generate a sequencing library with a custom 100-gene panel using the SureSelect XT-HS Target Enrichement System from Agilent Technologies (Santa Clara, USA). The sequencing was performed on an Illumina NextSeq 500 sequencing system. The results were analysed using an in-house analysis software and variant calling performed with Oncobench ® (Swiss Institute of Bioinformatics). For PCR2-3, organoids were collected, trypsinized to single cells and resuspended in 200 μL of PBS.
vcf. gz as a reference), ApplyBQSR functions. GATK was used to call haplotypes and obtain corresponding vcf files using HaplotypeCaller function, as well as for consolidating corresponding vcf files, selecting SNP and INDEL variants and variant filtration. snpEff was used for variants annotation for human genome (GRCh38. 86). GATK VariantsToTable function was used to obtain the final table containing all SNPs and INDELs. ClinVar database of pathogenic variants and OncoKB database of known variants for 500 most mutated genes in CRC were used to select the corresponding relevant variants.
However, our FFT-based micromechanics simulations enable us to parametrically change the character of the dislocation and the magnitude of the step height. As discussed in the Results section, we therefore also conducted simulations with various hypothetical dislocation and step configurations to illustrate and isolate their role on the stress field near the disconnection. For all the configurations listed above (atomistic-based or hypothetical), we simulated the normalized hydrostatic stress profiles given by the numerical calculations using our FFT-based dislocation-disclination method  [59, 60]  and then visualized these profiles using the Paraview software  [62].
Acknowledgments The authors would like to thanks  B. L. Boyce  from  Sandia National Laboratories  for suggestions and comments on the manuscript. C. H. , D. M. , and R. D. are supported by the  United States (U. S. ) Department of Energy (DOE) Office of Basic Energy Sciences  (BES),  Department of Materials Science and Engineering. S. B. is supported by the  French State through the program  ''Investment in the future'' operated by the  National Research Agency (ANR)  and referenced by  ANR-11-LABX-0008-01  ( Laboratory of Excellence 'DAMAS': Design of Alloy Metals for low-mAss Structures ). Computational resources are supported by the  Center for Integrated Nanotechnologies , an  Office of Science User Facility  operated for the  U. S. Department of Energy, USA. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under Contract No.
Background Our aim was to explore the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy. Methods We retrospectively included 623 patients with advanced non-small cell lung cancer (NSCLC) (n=318) or melanoma (n=305) treated by an immune-checkpointinhibitor having a pretreatment (thorax-)abdomen-pelvis CT scan. An external validation cohort of 55 patients with NSCLC was used. Anthropometric parameters were measured three-dimensionally (3D) by a deep learning software (Anthropometer3DNet) allowing an automatic multislice measurement of lean body mass, fat body mass (FBM), muscle body mass (MBM), visceral fat mass (VFM) and sub-cutaneous fat mass (SFM). Body mass index (BMI) and weight loss (WL) were also retrieved. Receiver operator characteristic (ROC) curve analysis was performed and overall survival was calculated using Kaplan-Meier (KM) curve and Cox regression analysis.
18 22  One of these algorithms has been tested on a limited number of patients with stage IV NSCLC who received immunotherapy. The results showed that subcutaneous fat mass can be a prognostic factor for this patient population. 13  The main objective of this study was to evaluate the prognostic value of anthropometric parameters evaluated on CT scan using an automatic 3D deep learning software, on a large database of patients with NSCLC and melanoma treated with ICI.
MATERIALS AND METHODS Population The study included adult patients with advanced melanoma or NSCLC tumors who received ICI at Gustave Roussy cancer center in France between June 2014 and December 2018. Population characteristics are presented in table  1. Included patients were extracted from a previous database  23  with the condition of having a widefield CT scan (of PET/CT or thoraco-abdominopelvic or abdominopelvic diagnostic CT scan) within 90 days before immunotherapy. For the external validation, patients with stage IV NSCLC treated by nivolumab after at least one chemotherapy session in pulmonology, thoracic oncology, and respiratory intensive care department of Rouen University Hospital between February 2015 and October 2017 were included.
Body composition Height and weight measurements taken just before treatment and within 6 months prior to starting ICI treatment were collected from medical records and used to calculate BMI and weight loss (WL). Anthropometer3DNet, an updated version of Anthropometer3D,  13 24 25  was used to extract body composition parameters from CT images. This software, which is available for research purposes on www. oncometer3d. com, can automatically measure 3D fat body mass (FBM), subcutaneous fat mass (SFM), visceral fat mass (VFM), muscle body mass (MBM) and lean body mass (LBM) (in kilograms) on multislice CT scans in less than 5 min (see figure  1 ). This software performs a deep learning-based segmentation  26  of fat (visceral and subcutaneous) and muscle voxels. For parts outside the acquisition area, it uses adaptive extrapolation factors  24  for the tissues of interest to determine the whole-body mass.
Open access External Figure  5  shows a Kaplan-Meier survival analysis on the external database of 55 patients with NSCLC using parameters and their cut-offs (SFM<5. 22 kg/m² and MBM<6. 86 kg/m²) determined on the NSCLC population. A significant prognostic value was found for SFM alone (p=0042) and for the combination of SFM and MBM with a worse prognosis when both SFM and MBM were low with 63% of mortality at 1 year versus 25% (p=0. 0029).
DISCUSSION The aim of the present study was to investigate the prognostic value of anthropometric parameters in a large population of patients treated with immunotherapy for NSCLC and melanoma. In a population of 623 patients (n=318 with NSCLC and n=305 for melanoma), anthropometric parameters were measured on pretherapeutic 3D CT scan by an automatic 3D software (Anthropom-eter3DNet). In our study, only MBM was a significant predictor of survival in melanoma, while all of the body composition parameters were significant in NSCLC. In multivariate Cox analysis, the significant predictors of survival in NSCLC were MBM (HR=0. 81, p<0. 0001), SFM (HR=0. 93, p=0. 01), and WL (HR=1. 06, p=0. 004). Kaplan-Meier analysis combining SFM and MBM was able to stratify patients into three prognostic categories, with the worst prognosis observed in those with both low SFM (<3.
In the whole study population and in NSCLC specifically, Kaplan-Meier analysis combining SFM and MBM was able to stratify patients into three prognostic categories, with the worst prognosis observed in those with both low SFM and MBM. This suggests that a combination of these two body composition variables may provide more prognostic information than either alone. One of the advantages of the 3D analysis performed by Anthropometer3DNet is that it uses extrapolation factors to expand on the data beyond the scope of acquisition, resulting in a total mass measurement instead of just an area or index. This total mass measurement has potential therapeutic implications and is obtainable through automatic segmentation and a wide range of Hounsfield units. Furthermore, the software, compatible with both injected and non-injected scanners, is currently accessible for research purposes on the Oncometer3D.
We stopped the experiment at day 16. Tumors were carefully removed and weighed to evaluate the impact of drugs on tumor growth. Tumors were then fixed in formalin and embedded in paraffin according to standard protocols. Then, consecutive 3μm paraffin-sections were processed, stained with hematoxylin-eosin (H&E) or incubated with anti-Ki67 (as a marker of proliferation) or anti-cleaved (Cl. ) caspase 3 (as a marker of caspase-dependent apoptosis activation) Abs (Table  S1 ). Slides were examined under an Olympus BX53 microscope (Tokyo, Japan) and images were processed with the software platform Leica Application Suite v4. 9 (Weitzlar, Germany).
Docking of selinexor The crystal structure of XPO1 (CRM1)/snurportin 1 (SNP1) complex was retrieved from the pdb (Protein Data Bank, www. rcsb. org, pdbid: 4gmx and 3gb8). The conformation of the side chain of XPO1, using the 3gb8 for the backbone, was optimized with the software Scwrl4 (ref. 18). Scwrl4 was also used to introduce the E571K mutation. The resulting pdb files of the wt and the E571K variant were converted to pdbqt files with Open Babel v2. 4. 1 (ref. 19). The 3D-structure of selinexor was retrieved from Pubchem (71481097) as an sdf file and converted to a pdbqt file with Open babel v2. 4. 1. The docking of selinexor to both proteins was conducted with the software smina with default parameters except for the flexible residues (residues 537, 568 and 571) and exhaustiveness of 16.
Modeling of the XPO1/NES interaction The model was prepared by homology modeling using Modeler version 10. 2 (ref. 21 ) using the structure of XPO1 in complex with the NES of MEK1 as a template (PDB code 6X2X). Default settings were used and the fast protocol chosen. Ten models were prepared, and the best model according to the Discrete Optimized Protein Energy function (DOPE) was selected.
The copyright holder for this preprint this version posted July 20, 2022. ; https://doi. org/10. 1101/2022. 07. 19. 500622 doi: bioRxiv preprint XPO1 mutation alters NES recognition of cargos in a sequence-specific manner. Baumhardt and coworkers reported that almost all NESs bind mutant or wt XPO1 proteins with the same affinity, only a few bind the two forms of the protein very differently  [38]. According to the pCRM1exportome data base, the NESs of p52 and p65 belong to the class 1a (http://prodata. swmed. edu/pCRM1exportome/Human-NES. html). Both possess the sequence Φ0XXΦ1XXXΦ2XXΦ3XβΦ4 (with Φ: L, I, V, M or F and X: any amino acid). A positively or negatively charge of the Xβ residue affects the NES structure and its interactions with mutant or wt XPO1 protein  [39]. The NFκB2 and p65 Xβ residues are uncharged being either a glycine (G) or a glutamine (Q), respectively (Fig.
S9 ) and the levels of p-p65 and β-actin proteins estimated by densitometry C, SUPHD1 and UHO1 slides were incubated with primary Abs (Table  S1 ), except for the negative control (Ctrl-), and with the secondary Abs conjugated with the PLUS and MINUS probes. Positive slides are shown (x 540, magnification). Red dots (arrowed in white) were counted in 100 cells from each slide. For each experiment, three slides were set up. The means and s. d. of red dots for each analysis are presented in the histogram and in the Table  S12. D, NES sequences of NFκB2 (UniProt, ID: Q00653) and p65 (ID: Q04206) are presented with the five hydrophobic residues (Φ) in red, the Xβ residue located between Φ3 and Φ4 in bold, and the negatively charged amino acids beyond Φ4 in blue. They have been downloaded from the pCRM1exportome database.
Acknowledgements The authors thank  Fabrice Gouilleux  ( GICC, Université de Tours, France ) and  Frédéric Mazurier  ( IGDR, Université de Rennes 1, France ) for critical reading of the manuscript, and the  Structure Fédérative de Recherche ICORE  ( Université de Caen Normandie, France ) for the microscopy and immunohistochemistry technical facilities.
Patients and methods After institutional data protection and ethics committee approval, the LiThuHol Trial (NCT04871984) prospectively included all patients treated with URS and laser lithotripsy for renal and/or ureteral stones between February 2021 and February 2022 in a single academic center. The exclusion criteria were urological anatomic abnormalities, pregnancy, age <18 yr, and an untreated positive urine culture. For the first 5 mo, we included patients who had URS with Ho:YAG laser lithotripsy (Medilas H 20; Dornier MedTech). Then we used TFL for 2 mo without including patients treated via URS (training period). Finally, in the past 5 mo, we included all patients undergoing URS and TFL (Soltive Premium 60 W; Olympus, USA) lithotripsy. The anesthesia record was used to extract preoperative data (age, sex, body mass index, American Society of Anesthesiologists score, comorbidities, and anticoagulation).
035-inch Terumo guidewire (Nitinol stiff guidewire; Radiofocus). Surgeons were free to use a ureteral access sheath or not in flexible URS, according to their routine practice. A dedicated form for gathering intraoperative data was completed after the surgery by the surgeon or the resident. Type of procedure, surgeon experience, operative time, laser type, disposable equipment, fiber size, intraoperative complications (bleeding, ureteral injuries, contrast extravasation), postoperative JJ stent use, and length of stay were collected. Laser settings were left to the discretion of the surgeon for Ho:YAG and TFL lithotripsy. The gravity-based irrigation method was used in each URS procedure. Ambulatory cases involved patients who were admitted and discharged on the same day. Postoperative pain was evaluated by a nurse using a verbal rating scale.
0 cm optical path quartz cuvettes (Hellma). The absorption spectra thus obtained are reported in Figure  2. In some cases, absorption spectra were also measured of irradiated lake water samples, and the spectral slope S was calculated as a function of the irradiation time. To do so, the 300-345 nm spectral interval was considered for the absorption spectra, because the relevant data were well described by an exponential function (absorbance above 345 nm was often negligible, and absorbance below 300 nm was often non-exponential, see Figure  2 ):   S o e A A   ) ( (3) In Eq. (3), A() is the absorbance at the wavelength , Ao is the pre-exponential term, and S is the spectral slope  (Helms et al. , 2008; Loiselle et al. , 2009; Sharpless and Blough, 2014). The value of S was obtained by a linear least-square fit procedure of lnA(λ) vs.
PARAFAC Analysis The PARAFAC analysis of the EEM spectra was carried out with the Progmeef software (http://protee. univ-tln. fr/PROGMEEF. html), with preliminary elimination of the Rayleigh and Raman scattering signals, using Zepp's method  (Zepp et al. , 2004). The low absorbance values of the samples (see Figure  2 ) made inner-filter correction unnecessary. PARAFAC was applied both to all the initial EEM spectra of the lake water samples and, for every lake taken separately, to each series of irradiation times. A range of 2 to 4 components was initially set, and the maximum number of components ensuring a CONCORDIA score > 70% was chosen. In all the cases, a threecomponent model proved suitable to reproduce sample fluorescence.
Time evolution of lake-water samples upon irradiation: EEM spectra EEM spectra of the lake water samples were measured at different irradiation times. The fluorescence signals of the lake water evolved upon irradiation, coherently with the fact that the studied samples all showed non-negligible absorption in the UVA region (see Figure  2 ). In particular, in the case of the lakes Tre Becchi B (Figure  3 ) and Soprano (Figure  4 ), some evidence could be seen at first sight of a gradual shift in fluorescence from the protein/phenolic region (emission wavelength EM around 325-350 nm;  Stedmon et al. , 2003)  to the humic one (EM around 400-450 nm;  Stedmon et al. , 2003)  Control experiments carried out in the dark did not show significant changes in the fluorescence spectra (data not shown), which suggests that any residual biological activity in the filtered lake water samples was not able to affect fluorophores to a significant extent.
HFOT was delivered at 40 L/min and at an inspired oxygen fraction of 21%, dry air. We selected a 40 L/min flow rate for HFOT because this value falls between the lower and upper limits of flows commonly used in clinical practice (i. e. , 20 and 60 L/min)  (7). The flow, airway pressure (Paw), and muscular pressure (Pmus) were measured by the ASL5000 and were acquired at 512 Hz for 2 min, with each device attached to either a NC or TC without and then with HFOT. The data were stored and analyzed off-line. The total WOB per breath and its resistive and elastic components were automatically analyzed breath by breath over the last 20 breaths by using the Campbell's diagram in Matlab (MATLAB 2019b, MathWorks), i. e. , the volume-pressure curve, in which the volume was plotted on the y-axis and pressure on the x-axis.
The flow-resistance of the TC was also measured by recording the airway pressure and flow upstream of the TC, through which flows of 0. 5 and 1 L/s were actuated. The data are presented as medians (1st-3rd quartiles) and were compared using the non-parametric two-factor ANOVA Scheirer-RayHare test. GLM with quasi-Poisson regression, non-parametric Man-Whitney, or Wilcoxon tests were used to compare each device using R software (version 4. 2. 0. ), with the Optiflow device taken as a control.
(2004). The antibody directed against P5CS1 is also able to recognize recombinant P5CS2 (data not shown). The antibody against P5CR was kindly provided by Dr D. Funck. Antibodies against SAG12 were purchased from Agrisera. Primary antibody binding was detected either using StarBright fluorescent secondary antibodies (1:2500 dilution) with the ChemiDoc system (Bio-Rad) or a secondary antibody conjugated to horseradish peroxidase (1:8000 dilution) with the ECL prime system (GE Healthcare). ImageJ/Fiji software was used to quantify protein amounts.
The instrument was tuned with perfluorotributylamine with the 69 and 219 m/z peaks of equal intensities. The split mode conditions were 70°C for 2 min then 30°C per min to 330°C for 5 min. Helium flow was set constant at 1 ml min. -  Raw Agilent data files were converted into NetCDF format and analysed with AMDIS (http://chemdata. nist. gov/mass-spc/amdis/). A custom retention indices/mass spectra library built from the NIST, Golm (http://gmd. mpimp-golm. mpg. de/) and Fiehn databases and standard compounds was used for metabolite identification. Peak areas were also determined with the Targetlynx software (Waters) after conversion of the NetCDF file into masslynx format. Data from AMDIS and Target Lynx in splitless and split 30 mode were compiled in a single Excel file for comparison. After subtraction of the values of the blank mean, peak areas were normalized to the values for ribitol and fresh weight.
| Data analysis Statistics were performed using R3. 6. To assess the general dynamics among the 116 metabolites during DIS, we computed a principal component analysis (PCA) using the package ade4 in R, and obtained principal components (PCs), that is, a few summarizing variables. To interpret the PCs, we computed the Pearson's correlation coefficient R between each PC and the values of each metabolite. Often, a metabolite would be significantly correlated to only one PC, indicating it covaries with this PC (and other associated metabolites), but not with the other PCs. The R package caret (version 6. 0-92) and stats (version 4. 2. 1) were used to normalize metabolite abundance to their minimum value (set to 0) and maximum value (set to 1). The correlation heatmaps were made using the R package gplots (version 3.