dataesr/scientific-paragraphs-categorization huggingface dataset:
54. Figure  14  shows the tagging performance using the CHS    and PUPPI algorithms with the soft drop mass and τ 32 conditions applied separately, and with both of them together. Although the efficiency is slightly different between the application of PUPPI or CHS, the same stability is observed with respect to PU as for W tagging. The performance of the W boson tagger with the CHS and PUPPI algorithms is compared in data and simulation following the procedure described in Ref. [20]. The W boson identification efficiency is measured in a region enriched in tt events, where one top quark decays to a final state with a lepton, neutrino, and a bottom quark and is used to tag the event. The other top quark is required to decay to a bottom quark and a W boson that further decays to a quarkantiquark pair.
The recoil is defined as the vector sum of the momenta of all the objects (with the same PU mitigation applied as for p miss T ) in the event but the Z boson. The transverse momenta of the recoil and of the Z boson are balanced against each other, such that their difference allows the determination of the momentum resolution. The momentum of the Z boson decaying into charged leptons can be reconstructed with high resolution such that it can serve as a reference for the measurement of the energy resolution of the hadronic recoil. The momentum of the recoil is projected to axes parallel and perpendicular to the momentum of the reconstructed Z boson. The resolution of the former is sensitive to the energy resolution and the latter to the PU contribution. The pp collision data collected with a dielectron trigger are used to evaluate the performance.
The threshold of each isolation is set to yield a 12% misidentification rate for reconstructed muons in QCD multijet simulation. The error bars correspond to the statistical uncertainty in the simulation. Figure  18  shows a receiver operating characteristic (ROC) curve, i. e. , the efficiency as a function of the misidentification rate, when using different definitions of the isolation. The combined PUPPI isolation provides the best performance over the typical analysis working points. The PUPPI isolation is further investigated in collision data collected with a single-muon trigger path requiring an isolated muon with p T > 24 GeV. Two levels of muons are defined: loose muons are required to have p T > 15 GeV and |η| < 2. 4 with no isolation requirement and tight muons p T > 26 GeV and |η| < 2.
1 with a δβ-corrected isolation corresponding to an efficiency of 95% (threshold of 0. 15). One tight and one loose muon, with the invariant mass of the two The identification efficiency for prompt muons in simulated Z+jets events as a function of the misidentification rate for nonprompt muons in QCD multijet simulated events for the different definitions of the isolation: δβ-corrected isolation (black solid line), PUPPI-withlepton (blue dashed line), PUPPI-no-lepton (red mixed dashed), PUPPI-combined (green long mixed dashed). The average number of interactions is 27. muons within a 10 GeV window centered around the Z boson mass are selected. The performance is measured using a tag-and-probe method, with the tight muon as the tag muon and the loose muon as the probe muon. The behavior of the isolation variables in data are compared with Z+jets simulation.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT ,  PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
60 g, Potassium chloride 0. 30 g, Calcium chloride dihydrate 0. 33 g. ) at the natural pH (pH = 6. 5), then Ringer solution at basic pH (pH=8) and acidic pH (pH=4). HCl and NaOH were used to modify the pH of initial Ringer solution. To conduct the electrochemical tests, a glass electrochemical cell was used, with a simple wall and a potentiostatic assembly consisting of a working electrode, a reference electrode (SCE) and a platinum auxiliary electrode. The potentiostat used was PGZ-301. The experimental data was processed with the Voltamaster 4 software. EIS data processing (electrochemical impedance spectroscopy) was performed using the Zsimwiew program. The tests were performed by scanning the potential with scan rate of 10mV/s, the applied potential being between -500÷2000mV. Tafel analysis was applied to extract the Ecorr corrosion potential and the corrosion current density jcorr.
Colony Formation Assay A total of 1 × 10 3 cells/mL were grown in semi-solid methylcellulose medium (StemCell Technologies Inc. , Vancouver, Canada) supplemented with 10% FBS and indicated drug concentrations. After 10 days of culturing, 1 mg/mL of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) reagent (Sigma-Aldrich, St. Louis, MO, USA) was added to colonies. The colonies were detected by ImageQuant LAS 4000 mini system (GE Healthcare Life Science, Chicago, IL, USA) and quantified by ImageJ 1. 52a software (U. S. National Institute of Health, Bethesda, MD, USA). buffer twice and then further fixed in 2% osmium tetroxide for 2 h at room temperature. Samples were washed with distilled water and then stained with 0. 5% uranyl acetate at 4 • C overnight. After 24 h, samples were dehydrated through a graded series of ethanol solutions to water followed by propylene oxide, and then infiltrated in 1:1 propylene oxide/Spurr's resin.
Samples were kept overnight embedded in Spurr's resin, mounted in molds, and left to polymerize in an oven at 56 • C for 48 h. Ultrathin sections (70-90 nm) were obtained with ultramicrotome, EM UC7 (Leica, Wetzlar, Germany). Sections were stained with uranyl acetate and lead citrate and, subsequently, examined with a JEM1010 transmission electron microscope (JEOL, Tokyo, Japan). All auto-phagolysosmes identifiable at the selected magnifications (12,000× and 25,000×) were segmented (red overlay in Figures  5C  and 9C ) by Adobe Photoshop (San Jose, CA, USA) and measured by using FIJI software  [33]. An area bigger than 0. 2 µm 2 was chosen to distinguish "macro-auto-phagolysosomes" (Table  5 ).
Analysis of the Mitochondrial Membrane Potential Cells were incubated at 37 • C for 30 min with 50 nM Mito-Tracker Red CMXRosRed (Molecular Probes, Invitrogen, Grand Island, NY, USA) and then analyzed by flow cytometry. Data were recorded statistically (10,000 events/sample) using the CellQuest Pro software. Data were analyzed using the Flow-Jo 8. 8. 7 software and results were expressed as mean fluorescence intensity (MFI).
Determination of the Oxygen Consumption Rate The oxygen consumption rate (OCR) was measured using a Seahorse XFp cell mito stress assay (#103010-100, Agilent, Santa Clara, CA, USA) ran on a Seahorse XFp analyzer (Agilent, Santa Clara, CA, USA), according to the manufacturer's instructions. Cells were seeded at 100,000 cells per well and treated with petromurin C for 24 h in 175 µL medium. Before measurements, plates were equilibrated in a CO 2 -free incubator at 37 • C for 1 h. Analysis was performed using 1. 5 µM oligomycin, 0. 5 µM carbonyl cyanide-4-(trifluoromethoxy)phenylhydrazone (FCCP), and 1 µM rotenone/antimycin A, as indicated. Data were analyzed using the Seahorse XF Cell Mito stress rest report generator software (Agilent, Santa Clara, CA, USA).
In Silico Drug-Likeness Properties In silico drug-likeness properties according to Lipinski's 'rule-of-five' and other parameters for drug-likeness and oral bioavailability were evaluated by using the SCFBio website (www. scfbio-iitd. res. in/). 4. 2. 12. Statistical Analysis GraphPad Prism 8. 1. 2 software (La Jolla, CA, USA) was utilized to statistically analyze data by one-way or two-way ANOVA. Data with p-values < 0. 05 were considered as significant and represented by the following legend: * p ≤ 0. 05, ** p ≤ 0. 01, *** p ≤ 0. 001. Data were expressed as the mean ± SD of at least three independent experiments. Combination index (CI) was calculated by using Compusyn Software (ComboSyn, Inc. , Paramus, NJ, USA), following the instruction by Chou and Talalay. CI values < 1 and Fa values > 0.
Introduction Malignant diseases are one of the major threats to global public health. Based on the GLOBOCAN 2018 database, which gathered estimates of incidence and mortality for 36 types of cancer in 185 countries, the number of new cases of cancer in 2018 was estimated to be 18. 1 million and the number of deaths from cancer was 9. 6 million worldwide  [1]. In 2009, the medical and indirect costs of new cancer cases globally were estimated to be 286 billion $  [2]. Despite advanced work on new methods of cancer therapy such as immunotherapy, nanoscale and nanostructure-based therapeutics or gene therapy, the main methods of choice remain surgery, radiotherapy and chemotherapy  [3]. Therefore, the search for novel small molecule chemotherapeutic drugs is one of the major challenges of modern medicinal chemistry.
Compounds with optimal conformation were sent to the Molecular Operating Environment software (Chemical Computing Group) to calculate a set of 293 quantitative molecular descriptors. To check the internal structure of the obtained data, we used hierarchical cluster analysis to group compounds 7-36 based on the received descriptors. Cluster analysis (CA) was used for the natural grouping of samples in clusters that are not known beforehand, with a common property characterized by the values of a set of variables  [19]. In QSAR modelling, it is widely used to check out the homogeneity of data, identify some unusual data points, detect patterns, and represent potentially interesting relationships in the data  [20, 21]. The results are presented in the form of a dendrogram (Figure  4 ) which displays the distance level at which there was a combination of objects and clusters-the more on left the clusters merge, the more dissimilarity they show.
many descriptors strictly depend on the three-dimensional structure, we performed a two-step procedure to determine the lowest-energy conformation. First, it consists of searching the conformational space of compounds using molecular mechanics. Then, the lowest energy conformations were treated as input conformations for semi-empirical geometry optimization using the PM6 method, which ensures an optimal ratio of precision to computation time. Compounds with optimal conformation were sent to the Molecular Operating Environment software (Chemical Computing Group) to calculate a set of 293 quantitative molecular descriptors. To check the internal structure of the obtained data, we used hierarchical cluster analysis to group compounds 7-36 based on the received descriptors. Cluster analysis (CA) was used for the natural grouping of samples in clusters that are not known beforehand, with a common property characterized by the values of a set of variables  [19].
08% (v/v) trifluoroacetic acid, linear gradient 5-100% B in 60 min, flow rate: 1 mL/min. Elemental analyses were performed on PerkinElmer 2400 Series II CHN Elemental Analyzer (PerkinElmer, Inc. Waltham, MA USA). The 2,4-dichloro-5-(carbazoyl)benzenesulfonamide 1 was obtained according to the method described previously  [24]. Synthesis and spectral data for compounds 2, 3, 6, 8, 10, 12, 14 and 16 were reported in our previous work  [18]. Methyl esters 39, 40 hydrazides 41, 42 were prepared according to methods described in  [25]  and their melting points and IR spectra were in accordance with generally available literature data. 2-(Chloromethyl)-5-(2,4-dichlorophenyl)-1,3,4-oxadiazole 44 was prepared by the same method as 3, 4 and 43 and its melting point and spectra were in accordance with the literature  [26].
QSAR Studies Two dimensional structures of compounds were draw using MarvinSketch (MarvinSketch 18. 24. 0 2018, ChemAxon, Budapest, Hungary) and were exported to the database created in Molecular Operating Environment (MOE, 2019. 01) (Chemical Computing Group, Montreal, QC, Canada). Structures were pre-optimized to three-dimensions with the molecular mechanic method using the MMFF94 forcefield. Next, to search for the lowest energy conformation, pre-optimized structures were subjected to a conformational search using the LowModeMD method implemented in MOE. For every compound, 50 lowest energy conformations were treated as the starting point to perform geometry optimization with semi empirical PM6 method implemented in MOPAC2016 (MOPAC2016, Version 18. 305W, Stewart Computational Chemistry, Colorado Springs, CO, USA.
). If, after PM6 calculation, the total energy of two or more conformers was equal, the selection of the lowest energy one was based on a single-point energy calculated with Hartree-fock method and STO-3G* basis set using GAMESS software  [27]  (GAMESS version 14 FEB 2018 (R1), Iowa State University Quantum Chemistry Group, Ames, IA, USA). For structures optimized in this way, 293 two-and three-dimensional molecular descriptors were calculated using MOE. The descriptors matrix was then subjected to standardization procedure according to the formula: z = (xµ)/σ where x is the descriptor value; µ is the average; and σ is the standard deviation. Cluster analysis, as well as multiple linear regression, along with forward stepwise algorithm and model validation, was performed in Statistica (Statistica version 13, TIBCO Software Inc Palo Alto, CA, USA) software  [28]  on a personal computer.
Conclusions Hierarchical cluster analysis of compounds 7-36 with the use of 293 quantitative molecular descriptors obtained by Molecular Operating Environment (MOE) software. Analysis showed a reasonable division of compounds into two clusters (R 1 = Cl and R 1 = SBn), and showed that the most active compound 31 exhibits outlying molecular properties. Using the MLR method, QSAR equations E1-E6 with good predicting properties were obtained, useful for assessing the activity of new derivatives during further optimization steps. The molecular descriptors used in the QSAR Equations indicate the potentially beneficial effect of increasing the proportion of the surface of negatively charged atoms and increasing the number of atoms with greater polarity or molar refraction. Supplementary Materials: Supplementary materials can be found at http://www.
Genomic Studies and Gene Expression Analysis Genome studies for copy number alterations (CNA) and lossof-heterozygosity analysis were performed with OncoScan CNV Assay (Thermo Fisher) or SureSelect Clinical Research Exome version 2 (Agilent Technologies), followed by sequencing on an Illumina HiSeq 4000 instrument (Illumina Inc), depending on the availability of fresh or formalin-fixed, paraffinembedded samples. The analysis of CNAs and somatic and germline variants was performed as described in the eMethods in the Supplement. Two-color, microarray-based geneexpression analysis was performed according to manufacturer's recommendations (eMethods in the Supplement). Data were collected from August 2016 to October 2017.
Data Analysis Data were analyzed from November 2017 to January 2019. A detailed description is in the eMethods in the Supplement. Briefly, genomic variant analysis from whole exome sequencing was performed by IntegraGen according to their pipeline (http://www. integragen-genomics. com/bioinformatics-andbioanalysis) and manual review was done with Alamut Visual version 2. 10 (Interactive Biosoftware). Copy number alterations were evaluated by reprocessing sequencing files with the Burrows-Wheeler Aligner for alignment, Sambamba for duplicates removal, and the Genome Alteration Print method for ploidy and copy number profiles. The CNA analysis for DNA array files was performed with Chromosome Analysis Software (Thermo Fisher). Final processing in both cases was done in R/Bioconductor version 3.
5 (R Project for Statistical Computing). Expression data analysis was performed in R/Bioconductor version 3. 5 (R Project for Statistical Computing), the InnateDB database, Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM), and MultiExperiment Viewer software MeV version 4. 9 (MEV). Significance for differentially expressed genes was set at P <. 001 and fold change at 2 or greater.
Results Genomic differences between eyes were present in patient 1, while the orbital relapse resembled the homolateral eye. Patient 1 showed a previously reported germline heterozygous deletion of the retinoblastoma protein (RB1) gene (Leiden Open Variation Database; RB1_00509). A CNA analysis showed that only gains at chromosomes 1q and 6p were present in both ocular tumors, despite differences in the absolute copy numbers. Chromosome 13q showed a normal copy number in the right tumor, while in the left tumor it was in loss of heterozygosity,.
Peptides were labelled via isobaric mass tags (TMT10; Thermo Fisher Scientific). TMT labelling was performed using the 10-plex TMT reagents, enabling relative quantification of 10 conditions in a single experiment  [30]. Briefly, the labelling reaction was performed in 40 mM triethylammonium bicarbonate, pH 8. 53 at 22°C, and quenched with glycine. Labelled peptide extracts were combined to a single sample per experiment. Lyophilised samples were re-suspended in 1. 25% ammonia in water and subjected to LC-MS/MS  [30]. Peptide and protein identification Mascot 2. 4 (Matrix Science, Boston, MA, USA) was used for protein identification using a 10 parts per million mass tolerance for peptide precursors and 20 mD higher-energy collisional dissociation (HCD) mass tolerance for fragment ions. The search database consisted of a customised version of the International Protein Index protein sequence database combined with a decoy version of this database created using scripts supplied by MatrixScience.
settings: sheath gas, 35 a. u. ; auxiliary gas, 10 a. u. ; auxiliary gas heater, 200°C; sweep gas, 1 a. u. ; spray voltage, -3 kV; capillary temperature, 250°C; S-lens radio frequency level, 50 a. u. ; resolution, 70 k at 200 m•z -1 ; automatic gain control target, 3×10 6 ions, maximum injection time, 120 ms; acquisition duration, 60 s. Spectral data processing was performed using an automated pipeline in R (www. r-project. org) as described previously  [32]. Detected ions were tentatively annotated as metabolites based on matching accurate mass within a tolerance of 5 mDa using the Human Metabolome database  [33].
Data analysis Proteomics The R programming language (www. r-project. org) was employed to process the protein output files of IsobarQuant. Only proteins that were quantified with at least two unique peptides and that were quantified in all experiments were used for further analysis. The "sumionarea protein" columns were annotated to different experimental conditions. Then, potential batch effects were removed using the limma package  [34]  and the results were normalised using the vsn package  [35]. Limma was also used to test for differential expression of proteins. A protein was considered significant with a two-fold difference and a false discovery rate <5%. All significant proteins were clustered (hierarchical clustering using the ward. d2 method) based on their Euclidean distances of log2 ratios towards the respective control.
Lung Gene Expression Analysis-based analysis for cell types The list of genes for major cell types was downloaded from the "LungSortedCells" tool on the Lung Gene Expression Analysis (LGEA) database. Here, RNA-sequencing (RNA-seq) data defined as belonging to the category "adult" was used as a reference. These data were generated after RNA-seq of sorted cells as per the following markers: CD45 + immune cells, CD45 -/PECAM -/VECadherin -/EpCAM + mixed epithelial cells, CD45 -/PECAM + /VECadherin + mixed endothelial cells and CD45 -/PECAM -/VECadherin -/ EpCAM -mixed mesenchymal cells. The curation of commonly used lung cell-type markers was done using the LGEA Tool-Box feature. Here, LungGENS Human RNA-seq data classified under "Human-Dropseq-PND1" was used as reference.
Statistical analysis of ELISA and SHG imaging data In general, all the data were normalised to the mean value of all the hPCLS measurements ( per donor) in the respective control (defined as per the experiment). First, the raw values of SHGintensity (iSHG) from different stimulation or disease conditions were log2 transformed (Log2iSHG) and the mean iSHG of unstimulated hPCLS was calculated and log2 transformed (Log2iSHGc). Next, the log fold change was calculated as Log2SHGi=Log2iSHG-Log2iSHGc for each slice of the respective donor. Upon global normalisation, the Log2iSHG of each hPCLS was normalised to the Log2iSHGc of all unstimulated hPCLS across the respective stimulation or condition. Two-way ANOVA with Tukey's multiple comparisons test was performed to determine the significance of the differences between comparisons.
SHG image analysis Image analysis of z-stacks consisting of SHG channels (backwards and forwards) and 2PEA were analysed using a semi-automated Fiji  [37]  pipeline defined as Jython script (supplementary material). Briefly, all the channels were thresholded and the area or the number of pixels below the threshold (PBT) were counted for each channel. The integrated intensity of PBT was also calculated for each channel (Sumintensity). To measure the total fibrillar collagen content, Sumintensity of PBT of the backwards and forwards SHG signal were summed up and termed as the total fibrillar collagen content. A sum of the maximum projections of forwards and backwards SHG (two-dimensional) images was created and this image was divided into 8×8 equally sized regions of interest (ROIs). Each ROI was manually classified as interstitial collagen or non-interstitial collagen.
Approximately 5500 proteins were detected across the four donors tested, of which 4288 proteins were in common (supplementary table  S1 ). These proteins could be attributed to different cellular localisations bioinformatically using the Gene Ontology (GO) database  [38]. The percentage of proteins per GO term was calculated relative to the whole GO term independent of its expression in human lung tissue. Here, 3648 proteins (31. 75%, reference list of 11 488 proteins, GO: 0005737) were cytoplasmic, 2556 (25. 9%, reference list of 9856 proteins, GO: 0016020) were membrane related, 1149 (54. 2%, reference list of 2119 proteins, GO: 1903561) were extracellular vesicle related and 207 (38. 6%, reference list of 536 proteins, GO: 0031012) were ECM related. 187 proteins (45. 7%, reference list of 409 proteins, GO: 0062023) could be associated with the collagen-containing extracellular matrix GO term.