dataesr/scientific-paragraphs-categorization huggingface dataset:
Forward folding of model predictions The measured track multiplicity is distorted with respect to the true charged particle multiplicity by the effects of acceptance and efficiency of the CMS pixel tracker. Likewise, the energies observed in CASTOR are affected by the energy resolution and the response of the calorimeter. In the present paper, the data are not corrected for these effects, and should thus be compared to the results of a full Monte Carlo detector simulation to compare with other experimental data and to future model predictions. For this purpose, a "forward folding" approach is used here, in which all known detector effects are applied to a given model prediction or theoretical calculation. The forward folding approach is chosen since it yields better systematic uncertainties compared to an unfolding of these data.
Results Various measurements of the average energy reconstructed in the region -6. 6 < η < -5. 2 are presented as a function of the track multiplicity for |η| < 2 in Figs. 1 2 3. The statistical uncertainties of the data are small and therefore not visible. The systematic uncertainties are shown with a gray band. The data are not corrected for detector effects and are compared to the predictions of models commonly used to describe hadron interactions at the LHC and in high energy cosmic ray air showers. These models are grouped into two sets: The first contains PYTHIA 8 tune CUETP8M1 and tune 4C+MBR, EPOS LHC and SIBYLL 2. 1. All these have a full detector simulation. The error bands shown for these models reflect only the Monte Carlo statistical uncertainties. These become visible especially in the last bin.
Summary and discussion The average energy per event in the pseudorapidity region -6. 6 < η < -5. 2 was measured as a function of the observed central track multiplicity (|η| < 2) in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data are recorded during the first days of 13 TeV running with low beam intensities. The measurement is presented in terms of the total energy as well as its electromagnetic and hadronic components. The very forward region covered by the data contains the highest energy densities studied in proton-proton collisions at the LHC so far. This makes the present data relevant for improving the modelling of multiparticle production in event generators of ultra-high energy cosmic ray air showers. The measured average total energy as a function of the track multiplicity is described by all models reasonably well.
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 centres 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  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Analysis phase The questionnaires were digitalised and the interviews and focus groups were typed ad verbatim. All socio-demographic quantitative results were grouped in an SPSS database. The qualitative results were grouped in an NVivo database. Before coding started, a basic code tree based on the questions in the interview and focus group guide was constructed. During coding, this tree was adapted based on the results of the Descriptive Thematic Framework Analysis where similarities and differences were sought in the answers of the different participants in duo.
Structure Solution and Refinement The lower resolution crystal structure of oxidized CrTRXh1 reported by  [19]  was directly used for refinement in the case of crystals isomorphous with the chosen model (Table  1 ), i. e. , red-and ox-CrTRXh1, and dimeric C36S and C39S mutants. The same structure was used as a model to solve the structure of monomeric C36S mutant crystal (Table  1 ) by molecular replacement, using the software MOLREP  [38]. The structures were refined with REFMAC 5. 8. 0135  [39]  by selecting 5% of reflections for R free calculation, and manually rebuild with Coot  [40]. Water molecules automatically added with Coot  [40] , were visually checked and kept into the model if the relative electron density value in the (2F o -F c ) map was higher than 1. 0 σ, and if stabilized by hydrogen bonds with the protein.
Data Availability The coordinates of the structural models and the experimental data (structure factors) are deposited in the Protein Data Bank (PDB). The assigned accession codes are: 6Q46 for red-CrTRXh1, 6Q47 for ox-CrTRXh1, 6Q6U for C39S mutant, and 6Q6T and 6Q6V for monomeric and dimeric C36S mutant, respectively.
The conservation of α-helix 2 is sensibly higher than the rest of TRX sequences, being in the ~39-81% range. Four amino acids are fully conserved, whereas the other residues, though not conserved, share common physicochemical properties (Figure  1a ), suggesting their importance for proper folding of α-helix 2. 2VM1,  [28] ) and TRX h2 (PDB ID: 2IWT,  [28] ); Populus trichocarpa (Pt) TRX h1 (PDB ID: 1TI3,  [29] ) and TRX h4 (PDB ID: 3D21,  [30] ), and Spinacia oleracea (So) TRX f (PDB ID: 1F9M,  [31] ) and TRX m (PDB ID: 1FB0,  [31] ). Conserved residues are highlighted in white on red boxes, whereas residues with similar physicochemical properties are written in black on yellow boxes. The sequence identities were calculated with Clustal Omega  [43]. (b) Ribbon representation of the three-dimensional structures of CrTRXh1, CrTRXm, SoTRXm, and SoTRXf.
Four amino acids are fully conserved, whereas the other residues, though not conserved, share common physicochemical properties (Figure  1a ), suggesting their importance for proper folding of α-helix 2. ibcp. fr, [42] ) using Chlamydomonas reinhardtii (Cr) TRX h1 (Protein Data Bank (PDB) ID: 1EP7,  [19] ) and TRX m (PDB ID: 1DBY,  [25] ), Arabidopsis thaliana (At) TRX h1 (PDB ID: 1XFL,  [26] ), TRX o1 (PDB ID: 6G61,  [27] ), and TRX o2 (PDB ID: 6G62,  [27] , Hordeum vulgare (Hv) TRX h1 (PDB ID: 2VM1,  [28] ) and TRX h2 (PDB ID: 2IWT,  [28] ); Populus trichocarpa (Pt) TRX h1 (PDB ID: 1TI3,  [29] ) and TRX h4 (PDB ID: 3D21,  [30] ), and Spinacia oleracea (So) TRX f (PDB ID: 1F9M,  [31] ) and TRX m (PDB ID: 1FB0,  [31] ). Conserved residues are highlighted in white on red boxes, whereas residues with similar physicochemical properties are written in black on yellow boxes.
Structure Analysis of Reduced and Oxidized CrTRXh1 The structure of wild-type CrTRXh1 in its reduced and oxidized forms (red-and ox-CrTRXh1, respectively) were solved at a resolution of 1. 70 and 1. 57 Å, respectively. Crystals of red-and ox-CrTRXh1 are isomorphous to each other (Table  1 ), and to that previously reported for ox-CrTRXh1 structure (PDB ID: 1EP7,  [19] ). The asymmetric unit is composed by a non-covalent dimer, with the two monomers (chains A and B) related by a non-crystallographic two-fold axis. The total accessible surface area (ASA) is similar in the two enzyme forms (10,329 and 10,300 Å 2 in red-and ox-CrTRXh1, respectively), while the buried surface area (BSA) is slightly lower in the reduced form (1117 Å 2 ) with respect to the oxidized one (1169 Å 2 ). Similarly, the calculated interface area between chains A and B is 556 Å 2 in red-CrTRXh1 and 556 Å 2 in ox-CrTRXh1.
Conceptualization, C. H. M. , S. F. , and M. Z. ; Investigation, C. H. M. , S. F. , J. R. , L. G. , D. T. , and M. Z. ; Writing-Original Draft Preparation, C. H. M. , S. F. , J. R. , L. G. , and M. Z. ; Writing-Review & Editing, C. H. M. , S. F. , J. R. , L. G. , D. T. , J. H. , F. S. , P. T. , S. D. L. , and M. Z. Funding: This work was supported by the University of Bologna Grant FARB2012 (to S. F. , F. S. , and M. Z. ), by CNRS and Sorbonne Université, by Agence Nationale de la Recherche Grant 17-CE05-0001 CalvinDesign (to C. H. M. , J. H. , S. D. L. ), by LABEX DYNAMO ANR-LABX-011 and EQUIPEX CACSICE ANR-11-EQPX-0008, notably through funding of the mass spectrometry platform of Institut de Biologie Physico-Chimique.
Acknowledgments: The authors thank the IBPC proteomic platform as well as the  Necker Hospital Proteomic Platform  for providing access to MALDI-TOF mass spectrometers, and  European Synchrotron Radiation Facility (ESRF, Grenoble)  and  Elettra (Trieste)  for allocation of X-ray diffraction beam time. M. Z. and D. T. gratefully acknowledge supporting of their work by the  University of Bologna  ( Alma Idea 2017 Program ). S. F. thanks the  Consorzio Interuniversitario di Ricerca in Chimica dei Metalli nei Sistemi Biologici  (CIRCMSB). M. Z. is indebted to  Stephan Olomun  and  Carl Cox  for helpful suggestions and stimulating discussions.
Author Contributions: Conceptualization, C. H. M. , S. F. , and M. Z. ; Investigation, C. H. M. , S. F. , J. R. , L. G. , D. T. , and M. Z. ; Writing-Original Draft Preparation, C. H. M. , S. F. , J. R. , L. G. , and M. Z. ; Writing-Review & Editing, C. H. M. , S. F. , J. R. , L. G. , D. T. , J. H. , F. S. , P. T. , S. D. L. , and M. Z. Funding: This work was supported by the University of Bologna Grant FARB2012 (to S. F. , F. S. , and M. Z. ), by CNRS and Sorbonne Université, by Agence Nationale de la Recherche Grant 17-CE05-0001 CalvinDesign (to C. H. M. , J. H. , S. D. L. ), by LABEX DYNAMO ANR-LABX-011 and EQUIPEX CACSICE ANR-11-EQPX-0008, notably through funding of the mass spectrometry platform of Institut de Biologie Physico-Chimique.
The methodology is then illustrated with two examples in Greece and in France. The third section details the economic sensitivity analysis and shows the effects of price variations on the Pareto front, the plant performances and the sizing of the optimal solutions. Finally the fourth section presents four energy management strategies and how they can affect the results. A framework to optimize the size of each component for a given plant has been proposed in  [33]. The method aims to reduce the amount of energy not produced by renewable sources -refered as imported energy-and the total cost of the equipments. The algorithm presented can easily be adapted to a wide array of plant configurations. As it is coded in Matlab, it is more flexible than the commercial simulation tools  [37].
Optimization problem The plant components are modeled by their maximum power and their efficiency, that depends on the input power. The powers produced by the Renewable Energy Sources (RES) are obtained from meteorological data using numerical models. Those powers are then routed to the load or to the storage, taking into account the power limitations and the efficiencies of each component. If needed, energy can be imported to back up the RES. Those importations can come from an external grid or from controllable sources such as Fuel Generators. If the production is above the consumption, the algorithm keeps track of the exceeding power and labels it as power available for export. In the post processing, this energy will be discarded or sold to the grid if exportation is possible. In the same way, energy can be exported if the production exceeds the consumption.
Before reaching the batteries, the power is converted to DC and regulated by inverters. Those inverters convert the current back to AC when the batteries are discharged. Figure  1  presents the algorithm set up to model this configuration. The weather data comes from measurement stations installed on purpose in Tilos. The PV production is estimated from the global irradiance and the temperature and the wind power from the wind speed. Although the data is recorded every minute, the simulations are run with one hour resolution to reduce the computation time needed. For one year simulation, the difference in the model outputs induced by the new timestep is not significant. Furthermore, the plant modeling has been tested during the H2020 project and has proven to be representative of the real plant behavior.
To obtain the optimal size for the plant elements with confidence, it is mandatory to have trustworthy data on their buying cost, maintenance and life expectancy. In future works, we will focus on implementing new management strategies that complies with the grid code so that the modeling can more accurately represent a real life power plant. We will also include peak and off-peak energy pricing in the strategy and in the decision tool. As the data collected from the Tilos installation supports our results, it is also envisaged to use our algorithm in order to size a small scale PV power plant associated with a storage to supply a smart-village in Corsica.
Flow cytometry Fluorescence (FL) was quantified on a SORP FACSAria2 (BD Biosciences) as previously described  [13, 15]. Data were processed with FACSDiva 6. 3 software (BD Biosciences). Doublets were excluded by FSC-W x FSC-H and SSC-W x SSC-H analysis. eGFP fluorescence was acquired with 488 nm blue laser and 510/50 nm emission, EpCam APC conjugated (BD Biosciences) was acquired with 647 nm red laser and 670/14 nm emission, Pkh red fluorescence was acquired with 535 nm green laser and 582/15 nm emission. Charts display the median of fluorescence intensity (mfi) relative to control. Single stained channels were used for compensation and fluorophore minus one (FMO) controls were used for gating. 20,000 events were acquired per sample. Viability was assessed by flow cytometry evaluation of Calcein AM staining as described by the manufacturer (Live Dead Viability/ Cytotoxicity Kit, Molecular Probes, Invitrogen).
Western blots analysis Western blots were carried out as previously described  [16]. Immunostaining was carried out using a goat monoclonal antibodies against phospho-Akt (S473) (Cell Signaling #9271) and actin (1/1000, Cell signaling) and a secondary polyclonal mouse anti-goat antibody HRP conjugated (1/2000, cell signaling). Blots were developed using HRP and chemiluminescent peroxidase substrate (#CPS1120, Sigma). Data were collected using Geliance CCD camera (Perkin Elmer), and analyzed using ImageJ software (NIH).
RNA sequencing, gene expression and pathway analysis RNA was extracted using RNeasy mini kit Qiagen kit and all RNA quality was assessed using the Agilent RNA 6000 Nano kit on the Agilent 2100 Bioanalyzer. Pairedend sequencing was performed on Illumina Hiseq 2500 after library creation using the Nugen Ovation Single Cell kit. Raw reads were deduplicated and aligned with RNA STAR in 2-pass mode to HG19 using GENCODE annotations. Gene read counts were obtained with Rsubread using gencode v. 19 annotation and passed to EdgeR for differential expression analysis. Significant differentially expressed genes were those that with an FDR less than 0. 05. Significant differentially expressed genes were uploaded to Reactome (Oct. 16, 2018) using no interactors. Significant pathways were those with an FDR less than 0.
Methods The study comprised a 40-year-old male patient (84 kg, 1. 82 m tall) with a tibial plateau fracture from road accident. The fractures classified Schatzker type-II  (Kfuri & Schatzker 2018)  corresponded to the splitting and the depression of the lateral tibial plateau. We segmented the model geometry from 3 D images acquired from a per-operative O-arm CT-Scan with a semi-automatic method based on a fast growing algorithm, with 3 D Slicer software  (Fedorov et al. 2012). The model geometry was composed of two fragments, unbroken tibia, cement and screw (Figure  1 ). The bone material properties were assigned according to local Hounsfield units using Bonemat software. The relation used between Hounsfield unit and elastic material behavior was based on the work of  (Rho et al. 1995)  for the proximal tibia: q kg:m À3 Â Ã ¼ 114 þ 0:916 Â UH r 2 ¼ 0:8 ð Þ E ½MPa ¼ 0:51 Â q 1:37 ðr 2 ¼ 0:96Þ The screw and cement material proprieties were: titanium alloy E ¼ 110 GPa, ¼ 0:3 and PMMA E ¼ 2163 MPa, ¼ 0:375 respectively.
Results Structural and thermal Characterization. Yellow crystals of Li 2 Fe(C 2 O 4 ) 2 (LFOx) (Fig. 1a ) were synthesized via a hydrothermal process (see Methods). Scanning electron microscope images showed that these crystals have sizes of 20-50 μm and exhibit a typical polyhedral morphology. Energy dispersive spectroscopy revealed a uniform distribution of Fe, C, and O in the obtained crystallites (Fig. 1b ). We used low temperature (173 K) single crystal X-ray diffraction (XRD) to determine the precise crystal structure of LFOx. The detailed crystallographic data has been deposited in the Cambridge Crystallographic Data Centre (CCDC No. 1416422). Figure  1c  displays a unit cell with ellipsoidal thermal displacement. LFOx crystallizes in the monoclinic system, space group P2 1 /n, with a = 7.
Tests were carried out at Synchrotron Light Research Institute (SLRI, public organization), Thailand  42, 43. The beamline photon source covers an energy range of 40-1040 eV at the resolving power of 10,000. The synchrotron radiation source at the storage ring was generated using a beam energy of 1. 2 GeV. In-situ synchrotron-based X-ray absorption spectroscopy: X-ray absorption near-edge spectra (XANES) and extended X-ray absorption finestructure (EXAFS) of Fe K-edge data of the samples were collected at the SUT-NANOTEC-SLRI XAS beamline (BL5. 2). The in-situ coin cells were installed using CR2016 case with two-side kapton windows to allow X-ray transmission (Supplementary Fig. 20 ). Typical loading of LFOx is 3-5 mg cm -2. A detailed investigation of the fine structure at the adsorption edges of light elements on ex-situ samples, i.
Acknowledgements We acknowledge the financial support from the  National Natural Science Foundation of China  ( 51822210 ), the  Science and Technology Planning Project of Guangdong Province  ( 2018A050506066 ), the  Shenzhen Peacock Plan  ( KQJSCX20170331161244761 ), the  Australian Research Council (ARC)  for its support through  Discover Project  ( DP 140100193 ), the  Program for Guangdong Innovative and Entrepreneurial Teams  (No. 2017ZT07C341 ), and the  Development and Reform Commission of Shenzhen Municipality  for the development of the " Low-Dimensional Materials and Devices " discipline. We are indebted to  Dr Bela Bode ,  Dr Jianing Hui ,  Dr Khang Hoang ,  Dr Qirong Liu ,  Xiaohe Song ,  Lei Shi ,  Prof. Lin Gu  and  Prof. Qiaobao Zhang  for useful discussions and advices. We thank  Prof.
Whole Exome Sequencing and Bioinformatics Pipelines Exomes from selected tissues were captured and enriched using the SureSelect Human All Exon Kit (78 Mb) (Agilent Technologies, Santa Clara, CA). Enriched samples were paired-end sequenced on an Illumina Genome Analyzer II sequencing platform using 2 lanes per sample and generating 101 base-pair long reads. FASTQ files of short reads were aligned using the BWA algorithm to the GRCh37/hg19 reference genome  22    variants (<10% alternative variant allele frequency) and those with low coverage (<69) were discarded. Only quality-filtered variants affecting coding sequences of canonical transcripts (nonsynonymous, essential splice site, frame shift or gain/loss of stops) were taken into account. Variant type annotation, population statistics, disease-specific sequence databases, and in silico predictive algorithms were according to AMCG standards and guidelines.
23 Variants with a minor allele frequency of <0. 01 and <0. 05 (dbSNP130, HapMap, or 1000 Genomes) were considered for stringent and relaxed filtering, respectively. Their potential damaging effect was assessed using the VEP  24  script software package (including Sift, Polyphen and Condel damage predictors). Stringent filtering only considered the variants annotated as pathogenic by all damage predictors. Whole-exome sequencing data have been deposited in the ArrayExpress public database under accession number E-MTAB-7999 (available at https://www. ebi. ac. uk/fg/annotare/). Variants found in DNA blood samples and variants found in common between paired normal/tumor tissues were considered as constitutional. Variants found only in tumor tissue were considered somatic variants. Constitutional variants were validated in DNA from blood and normal tissue samples, and somatic variants were validated in DNA from tissue samples (tumor) by Sanger sequencing.
Pathway Enrichment Analyses Two different software packages were used for independent assessments of the gene set analyses. Data were analyzed with Qiagen's Ingenuity Pathway Analysis (IPA, Qiagen, Redwood City, CA www. qiagen. com/ingenuity) and Consen-susPathDB (available at http://cpdb. molgen. mpg. de/). 25 verrepresentation analysis of the gene set list was performed with a minimum overlap of 4 genes with the pathway database set size and a P-value cutoff of 0. 001. Induced network module analysis without intermediate nodes and considering high-confidence binary protein and genetic/gene regulatory interactions were also evaluated.
Trusight Tumor 170 Panel and IBM Watson Study Resequencing of tumor DNA from FFPE tissue samples was performed with TruSight Tumor 170 from Illumina (San Diego, CA), a novel sequencing platform that predicts the most probable variant causing the pathology and provides suggestions for translating the data to the clinic. The sequencing of the hybrid capture was run on the HiSeq 2500 System. The captured gene content in the TruSight Tumor 170 Assay, data sheet, and specifications are available at https://www. illumina. com. Low variant quality of <20 and low depth (<100 for variant calls and <250 for reference call filters) were considered. Variants were supported with >7 reads and filtered by frequency (minor allele frequency <0. 05). The copy number variation call was calculated for the fold-change results for each gene.
Sequencing Replication With the Truseq170 Panel and IBM Watson for Genomics Platform A sequencing replication was performed for 2 previously sequenced CAS patients without mutations in the POT1 gene. The Truseq170 panel was run for tumor tissue of the T1 and T4 individuals and analyzed with the IBM Watson for Genomics platform (version 33. 148), which is a novel sequencing platform that predicts the most probable variant causing the pathology and provides suggestions for translating the data to the clinic. The Watson for Genomics pipeline revealed 15 variants of unknown significance, 2 alterations without proposed therapies, and only 1 actionable alteration for patient T1 (Table  S4 ). Three variants of unknown significance, 3 alterations with no proposed therapies, and another 3 actionable alterations were described for patient T4 (Table  S5 ).