dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Descriptive statistics for a set of predefined variables of interest are provided (see study variables above). The degree of agreement between the manually determined presumed optimal and computed optimal device dimensions was evaluated with the Cohen. The Wilcoxon signed rank test was performed to analyze differences in nominal device length and nominal device diameter between sizing methods. Differences in baseline characteristics were evaluated with a Student t test, Fisher exact test, and Wilcoxon signed rank test, as applicable. Statistical analysis was performed with SPSS (Version 22. 0; IBM, Armonk, New York). Statistical significance was defined as P Ͻ. 05.
Software-Based Computation of PED Dimensions The commercially available Sim&Size software is a fast simulation tool that predicts intravascular behavior of flow diverters such as the PED (Fig  1 ). 17 On the basis of DSA DICOM data, the software reconstructs the 3D vessel geometry using volume-rendering. The accuracy of the reconstruction can be reviewed in real-time and, if necessary, optimized manually. Potential microcatheter trajectories in the target vessel are then computed on the basis of the vessel centerline. Once trajectory and device type have been selected, the desired distal and proximal landing zones are defined manually. A first proposal is provided instantly for device dimensions compatible with the chosen landing zones. Device deformation, porosity, and degree of wall apposition are simultaneously incorporated and displayed.
DISCUSSION We analyzed a multicenter cohort of 74 patients undergoing treatment with PEDs for intracranial aneurysms (Table  1 ) and found low agreement between the neurointerventionalists' manual device selection and the computed optimal PED dimensions determined by a team of Sim&Size experts blinded to the neurointerventionalist's manual-sizing decision. While we believe that this low agreement ultimately reflects the potential for optimization of the device-sizing process, it should not be interpreted per se as proof that virtual simulation performs better than conventional manual selection. We found that the computed optimal PEDs were significantly shorter (Table  2  and Fig     accurate landing zones, thereby allowing confident selection of the shortest possible device. Accordingly, the proportion of cases in which the Sim&Size software suggested a shorter device was higher for intradural aneurysms where side-branches and perforators come into play (Fig  4 ).
CONCLUSIONS Experience with PEDs remains characterized by heterogeneity in device sizing and deployment technique. Furthermore, the properties of the PED require not only appropriate but optimal device dimensions to achieve treatment success and minimize complications. It is therefore appealing to facilitate device sizing and positioning through preimplantation virtual simulation techniques. We found, in the present study, robust evidence that preimplantation simulation with the Sim&Size software results in selection of different PED dimensions compared with conventional manual device sizing, and preimplantation device simulation led, in particular, to a measurable decrease in median device length. We believe that the benefits of the Sim&Size software may, therefore, be the greatest for intradural aneurysms, where side branches and perforators are relevant.
ACKNOWLEDGMENTS We thank  Piergiorgio Canci , Application Engineer at Sim&Cure, and  Christophe Chnafa , Research and Development engineer at  Sim&Cure , who provided us with data;  Cindy Wehrli  and  Phil Ha  ¨fliger from  Medtronic, Switzerland , for providing us with Fig 1 and with their Sim&Size expertise; and  Dr Selina Ackermann  from the  University Hospital Basel  for editorial assistance. Disclosures: Vincent Costalat-RELATED: Stock/Stock Options: Sim&Cure.
Reagents and analytical methods All chemicals and solvents were of reagent grade and purchased from Sigma-Aldrich and used without further purification. Infrared spectra were recorded on a PerkinElmer Spectrum One FT-IR spectrometer using either a universal ATR sampling accessory or a diffuse reflectance sampling accessory. Data were collected and processed using Spectrum v5. 0. 1 (2002 PerkinElmer Instrument LLC) software. The scan rate was 16 scans per minute with a resolution of 4 scans in the range 4000-500 cm 1 (ATR measurements were performed on an ATR IRAffinity-1 instrument (Shimadzu Scientific Instruments). The analyzed samples were prepared by deposition of powder or crystals directly onto the sample plate surface). The standard abbreviations were used to describe the intensities: s, strong; m, medium; w, weak; br, broad.
Data acquisition was performed in the full-scan mode (50-500). Hydrogen (99. 999 % purity) was used as carrier gas at a constant flow rate of 1. 5 mL/min. Synthesis Synthesis of [Mn III 12Mn II 7(μ4-O)8(μ3-OCH3)2(μ3- Br)6(HL Me )12(MeOH)6]Br2 (2) MnCl2•4H2O (0. 2 g, 1 mmol, 1. 0 equiv), Et3N (0. 1 g, 1 mmol, 1. 0 equiv) and 2,6-bis(hydroxymethyl)-4-methylphenol (0. 17 g, 1 mmol, 1. 0 equiv) were respectively added, under stirring, to a MeCN/MeOH solution (25 mL/5 mL). The resulting slurry was stirred for 1 h at RT, before being heated at reflux for 2 h, to afford a dark brown solution, which was cooled and filtered. Dark brown crystals of 2 were obtained after 48 h slow evaporation at RT. The crystals were filtered, washed and dried. Yield: 30 % (based on Mn). Elemental analysis (%) calc. for C137H184Br8Mn19N10O53: C 36.
Single-crystal X-ray crystallographic measurements X-ray structural analyses for crystals of 2 (TCD304) and 3 (TCD 716) were performed on at 100K on a Bruker APEX DUO CCD and Bruker D8 Quest ECO diffractometer respectively (Mo-Kα λ = 0. 71073 Ǻ). The maximum resolution for data collection was 0. 77 Å with data collected using ω and φ scans for 2 and 0. 80 Å using ω scans only for 3. The data were reduced and processed using the Bruker APEX suite of programs. 38 Multi-scan absorption corrections were applied using SADABS. 39 Using Olex2,  40  the structure was solved with the XT 41 structure solution program using Intrinsic Phasing and refined with the XL 42 refinement package using Least Squares minimisation. All non-hydrogen atoms were refined anisotropically. Hydrogen atoms were placed in calculated positions and refined with a riding model, with isotropic displacement parameters equal to either 1.
Electrochemistry Electrochemical measurements were carried out in DMF containing 0. 1 M Bu4NPF6 in a classical three-electrode cell by cyclic voltammetry (CV) and rotating-disk voltammetry (RDV). The working electrode was a glassy carbon disk (GC, 3 mm in diameter), the auxiliary electrode a Pt wire, and the pseudo reference electrode a Pt wire. The cell was connected to an Autolab PGST, AT30 potentiostat (Eco Chemie, Holland) driven by a GPSE software running on a personal computer. All potentials are given vs. Fc + /Fc used as internal reference and are uncorrected from ohmic drop.
Electrochemistry Investigations of the species were carried out by cyclic voltammetry and rotating disk voltammetry (RDV) in DMF + 0. 1 M Bu4NPF6. The electrochemical data for compound 2 and 3 are shown in Table  1. Table  1  Electrochemical data for 2 and 3 in DMF (+ 0. 1 M Bu4NPF6 against Fc + /Fc). c Ep = irreversible peak potential. d Due to important adsorption at the electrode, the RDV in the cathodic domain is ill defined and is not exploitable. Species CV RDV E° a) V/Fc+/Fc DEp b) mV Ep c) V/Fc+/Fc E1/2 d) V/Fc+/Fc In oxidation, cyclic voltammetry (CV) of 2 showed two successive irreversible peaks measured at -0. 12 V and 0. 42 V vs. Fc + /Fc measured without Fc after the reduction of Mn19 (2) (Fig. 2 , red curve with initial scanning direction to negative potential, explicitly reduction followed by the oxidation) while RDV presented only one wave at 0.
Microarray Gene expression was profiled using a 4×44K human whole genome (G4112F) expression array (Agilent Technologies) with dual-color dye-swap competitive hybridization. Total RNA from untreated parental IGR-CaP1 cells was the RNA reference. Total RNA from IGR-CaP1 cells resistant to 5, 12, 25 50, 100 and 200nM of docetaxel were used as samples (two replicates/sample). Image (Feature Extraction software: Agilent Technologies) and gene expression (Bioconductor) analyses was performed. For resistant cell lines, Log10 ratios were computed against the relevant sensitive cell line. To select relevant genes we combined three strategies. Genes permanently over/under-expressed in all resistant cell lines were tested using multiple t-tests with bootstrap resampling-based analysis (10,000 samples with replacement).
1% formic acid (5% starting and 35% final acetonitrile concentrations). Mass resolution for the full scan was set at 70,000 at 400 m/Z. The 10 most intense precursor ions from a survey scan were selected for MS/MS fragmentation using high-energy collision dissociation fragmentation with 27% normalized collision energy (detected at mass resolution 17,500 at 400 m/z). Dynamic exclusion was set for 30 seconds with a 10 ppm mass window. Each sample was analyzed in triplicate. The acquired data were analyzed with Proteome Discoverer software using a Mascot search engine (Proteome ID UP000005640; 20253 sequences). MS/MS spectra were searched with a precursor mass tolerance of 10 ppm and fragment mass tolerance of 0. 05 Da. Trypsin was specified as protease with a maximum of two missed cleavages allowed.
The minimum peptide length was specified as 6 amino acids. Data were searched against a decoy database, and the false discovery rate was set at 1% of the peptide level. FKBP7 interactors were selected when proteins (compared to IgG control) were only identified in the FKBP7 immunoprecipitation or were enriched at least 3-fold in the specific immunoprecipitation. Specific protein interactors were processed [Ingenuity Pathway Analysis® (IPA)].
Proximity ligation assay FKBP7-eIF4G and eiF4E-eiF4G interactions were detected by in situ proximity ligation assay (PLA) (Duolink, Sigma-Aldrich). Cells were fixed with paraformaldehyde, permeabilized and the PLA protocol was performed (Olink Bioscience). After blocking, primary antibodies were incubated for 1h at 37°C. PLA ® probe secondary antibodies were incubated for 1h at 37°C. After ligation and DNA amplification, amplicons were detected using far red fluorescence, nuclei were stained with DAPI and slides were mounted with Olink Mounting Medium. Images were acquired with a Virtual Slides VS120-SL microscope [Olympus, Tokyo, Japan; magnification 20X, air objective (0. 75 NA), 10-ms exposure for the DAPI channel and 300-ms exposure for the Cy5 channel; 1pixel=0. 32µm] and the number of PLA signals/cell was counted using Image Analysis toolbox in Matlab (2011a).
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on October 15, 2018; DOI: 10. 1158/1078-0432. CCR-18-0704 Microarray experiments were submitted to the Array Express data base (EBI, UK) (www. ebi. ac. uk/arrayexpress/), accession number E-MTAB-4869. MS data were deposited in PRIDE (www. ebi. ac. uk/pride/).
ACKNOWLEDGMENTS We gratefully thank  A. Lescure  and  S. Tessier , who are Biophenics staff, the platform of preclinical evaluation AMMICA, the  bioinformatic Core Facility , the  Development in Pathology Group  ( UMR981 ), and  S. Shen  ( UMR981 ). We thank and pay tribute to  Vasily Ogryzko  ( UMR8126 ), who gave us precious help with proteomics but passed away before this publication. RWPE-1 was a kind gift from  G. Mouchiroud  ( University Claude Bernard, Lyon ), HK-2 was provided by  S. Gad-Lapiteau  ( UMR1186 , Villejuif), and HUVEC was provided by  S. Rodrigues-Ferreira  ( UMR981 , Villejuif). We also thanks  Joanna Moore ,  ELS (Citoxlab France ), for assistance in the preparation of the manuscript. MGa was supported by the  Idex Paris-Saclay  fellowship and the  Association pour la Recherche sur les tumeurs de la Prostate (ARTP).
NJ-PM, MB and NEK were supported by the  PARRAINAGE CHERCHEUR charity program of Gustave Roussy. This work was supported by grants from:  INSERM , the  Université Paris-Sud11 , the grant  PAIR PROSTATE program n°2010-1-PRO-03  from the  INCA , the  ARC Foundation , the Ligue contre le cancer, the  ECOS-Sud A10S03 program , AMGEN, the  Paris Alliance of Cancer Research Institutes program , " Investissements d'Avenir" an initiative  of the  French Government implemented by ANR  under reference  ANR-11-PHUC-002 , Taxe d'apprentissage  Gustave Roussy   P21NNGE  for genomic and 2014MG for proteomic analyses, and  Terry Fox New Frontiers Program Project  Grant  TFF116129.
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on October 15, 2018; DOI: 10. 1158/1078-0432. CCR-18-0704 Microarray experiments were submitted to the Array Express data base (EBI, UK) (www. ebi. ac. uk/arrayexpress/), accession number E-MTAB-4869. MS data were deposited in PRIDE (www. ebi. ac. uk/pride/). Accession number Research. Research. on October 16, 2018. © 2018 American Association for Cancer clincancerres. aacrjournals. org Downloaded from on October 16, 2018. © 2018 American Association for Cancer clincancerres. aacrjournals. org Downloaded from.
During the Final Bronze Age (around 3750/3700 BP), the proto-urban sedentary cultural entity in southern Central Asia-known as the Oxus civilization or Bactria-Margiana Archaeological Complex-underwent major social transformations in different field aspects leading to a deep cultural change in the middle of the 4th millennium BP. Among the different reasons suggested to explain these sociocultural changes, the hypothesis of global climate change in Central Asia at the beginning of the 4th millennium BP has been emphasized by different scholars. In this paper, I will examine current paleo-environmental data in relation with the climate evolution during the Mid-and Late Holocene. A critical assessment of the hypothesis of climatic change in Central Asia at the beginning of the 4th millennium BP allows to stimulate the discussion anew.
Models were adjusted for individual socioeconomic characteristics (age, education, number of years in the village, number of people in the household, relative wealth) and measures of the individuals' dependence on fishing (number of fishing gears, proportion of income from fishing, days of the week fish is eaten in the All analyses were conducted in SAS 9. 4 and R-3. 5. 1 (R Core Team 2018; SAS 2018).
ACKNOWLEDGMENTS This research was carried out in the framework of FishM-PABlue 2 project (https://fishmpablue-2. interreg-med. eu/)funded by  European Territorial Cooperation Programme MED  and co-financed by  European Regional Development Fund (ERDF). Authors are grateful to  Federic Bachet ,  Eric Charbonnel ,  Francesco de Franco ,  Victor Decugis ,  Charalampos Dimitriadis ,  Ilenia Domina ,  Ivoni Fournari-Konstantinidou ,  Luka Kastelic ,  Daniela Marzo ,  Lorenzo Merotto ,  Milena Ramov ,  Marie-Catherine Santoni ,  Leila Seddiki ,  Francisco Sobrado-Llompart , and  María.
, 2015). (D) RT-qPCR expression analysis of midgut-specific sugar genes in males (M) and females (F) with germline-specific sans fille (snf) mutation (snf 148 ), KD (nos > snf RNAi ) or germline-specific Sex lethal (Sxl) KD (nos > Sxl RNAi ) in relation to relevant controls. (E) RT-qPCR expression analysis of midgut-specific sugar genes in flies with Act5C-Gal4 driven tra KD (Act5C > flp,tra FRT ) in relation to controls. (F) RT-qPCR expression analysis of midgut-specific sugar genes in sterile male flies lacking hub and germline stem cells (esg SHOF ), and in sterile males lacking germline but with intact hub (zpg z-2533/z-5352 ). In all graphs, n = number of fly groups analyzed per genotype (each group, 20 flies), except in (B), where n = number of viscera analyzed. Asterisks highlighting significant comparisons across sexes are displayed in gray boxes at bottom of graphs; those highlighting significant comparisons within female and male datasets are displayed in red and blue boxes, respectively.
To test this idea, we used genetically encoded FRETbased metabolic sensors expressed specifically in the ECs of R2 and R4, together with a glucose sensor (UAS-FLII12Pglu-700md6)  (Takanaga et al. , 2008; Volkenhoff et al. , 2018)  and a lactate sensor (a UAS-based version of the laconic sensor) (San Martı ´n et al. , 2013) (see STAR Methods). The glucose sensor revealed higher glucose levels in male than in female ECs of the R4 (but not the R2) region (Figure  5A ), consistent n = number of fly groups analyzed per genotype in (A) and (B) (each group, 20 flies), or fly numbers monitored via FlyPAD in (I). Asterisks highlighting significant comparisons across sexes are displayed in gray boxes, those highlighting significant comparisons within same-sex datasets are displayed in blue boxes (males) and red boxes for females.
On the right is the quantification of FRET signal of CIT8 citrate sensor in R4 ECs of control midguts and midguts with R2 and R4 EC-specific KD of plasma membrane citrate transporter I'm not dead yet (Indy RNAi ). Representative FRET ratio images in R4 are shown. In all graphs, n = number of midguts analyzed per genotype/condition. Scale bars, 5 mm in all images. Asterisks highlighting significant comparisons across sexes are displayed in gray boxes at bottom of boxplots, those highlighting significant comparisons within male datasets are displayed in blue boxes. See Table  S4  for a list of full genotypes. (legend continued on next page) A third way in which pyruvate is utilized involves the anaplerotic pyruvate carboxylase (PCB)-mediated pathway leading to citrate production through the pyruvate/citrate cycle  (Iacobazzi and Infantino, 2014; Jensen et al.
(G) Food intake quantifications based on FlyPAD-monitored sips per male fly after R2 and R4 EC-specific KD of: Mpc1, PCB, kdn, ATPCL, Indy. n = fly numbers analyzed per genotype, except in (E), where n = midguts. Scale bars, 50 mm in all images. Asterisks highlighting significant comparisons across sexes are displayed in gray boxes at bottom of graphs, whereas those highlighting significant comparisons within male datasets are displayed in blue boxes. See Table  S4  for a list of full genotypes. See also Figures  S6  and S7. (legend continued on next page) although there were no obvious differences in mitoses in the tip region where spermatogonia are generated from stem cells  (Greenspan et al. , 2015) , pH3 numbers were substantially reduced in the region in which spermatids are produced from spermatogonia, consistent with a delay in gamete maturation (Figure  7B ).
(J) Food intake quantification based on FlyPAD-monitored sips per male fly after testis-specific Indy KD (tj-Gal4 line). n = testes number analyzed per genotype except for in  (J) , where n = fly number analyzed. Scale bars, 200 mm in all images. Asterisks highlighting significant comparisons across male datasets are displayed in a blue box. See Table  S2  for metabolites/ concentrations. See Table  S4  for a list of full genotypes. See also Figure  S7  and Table  S2. to sex differences in immunity and autoimmune disease  (Russi et al. , 2018; Voigt et al. , 2016; Xiong et al. , 2015). A possible contribution of cytokines such as leptin to sex differences in organ physiology deserves further investigation, particularly in light of leptin's known reproductive and gastrointestinal roles  (Sainz et al.
Metabolite measurements using FRET-based metabolite sensors All imaging experiments were performed on dissected midguts or testes expressing laconic, the glucose sensor or the citrate sensors. Adult midguts or testes of 5-day-old flies were dissected in HL3 buffer (70 mM NaCl, 5 mM KCl, 1. 5 mM CaCl 2 , 4 mM MgC l2 , 10 mM NaHCO 3 , 115 mM sucrose, 5 mM trehalose, 5 mM HEPES; pH 7. 1; around 350 mOsm). The dissected organs were placed into an open m-slide (chambered coverslip, ibidi #80826) and analyzed using a confocal microscope. Fluorescent images were acquired using a 20x objective, and the following filter sets: excitation 405 nm, emission 470-522 nm (CFP channel); excitation 405 nm, emission 532-627 nm (FRET channel). For data analysis, regions of interest (ROI) were delimited and the average intensity of both mTFP and Venus channels over each ROI were calculated.
After incubation, 1 mL of the prepared sample was injected into the gas chromatograph inlet in the split mode with the inlet temperature held at 250 C. A 10:1 split ratio was used for analysis. The gas chromatograph had an initial temperature of 60 C for 1 min, followed by a 10 C/min ramp to 325 C and a hold time of 2 min. A 30 m Agilent Zorbax DB-5MS with 10 m Duraguard capillary column was employed for chromatographic separation. Helium was used as the carrier gas at a rate of 1 mL/min. Data was collected using MassHunter software (Agilent). Metabolites were identified and their peak area was recorded using MassHunter Quant. This data was transferred to an Excel spread sheet (Microsoft, Redmond WA). Metabolite identity was established using a combination of an in-house metabolite library developed using pure purchased standards, the NIST library and the Fiehn library.
The compounds were measured in the Cation and Anion modes of CE-TOFMS based metabolome as previously described  (Soga et al. , 2003). All CEÀMS experiments were performed using an Agilent CE Capillary Electrophoresis System equipped with an air pressure pump, an Agilent 1100 series MSD mass spectrometer and an Agilent1100 series isocratic HPLC pump, a G1603A Agilent CEÀMS adaptor kit and a G1607A Agilent CEÀESIÀMS sprayer kit (Agilent Technologies). System control, data acquisition and MSD data evaluation were performed via a G2201AA Agilent ChemStation software for CEÀMSD. CEÀMS Conditions for Cationic Metabolites. Separations were carried out on a fused silica capillary (50 mm i. d. 3 100 cm total length) using 1M formic acid as the electrolyte. Sample was injected with a pressure injection of 50 mbar for 3 s (3 nL).
The electrolyte for the CE separation was 50 mM ammonium acetate solution, pH 8. 5. Sample was injected with a pressure injection of 50mbar for 30 s (30nL). The applied voltage was set at À30 kV. ESIÀMS was conducted in the negative ion mode and the capillary voltage was set at 3500 V. In MS with SIM, sets of 30 deprotonated [MÀH]-ions were analyzed successively to cover the whole range of m/z values from 70 through 1027. Other conditions were the same as in cationic metabolite analysis. Peaks detected in CE-TOFMS analysis were extracted using automatic integration software (MasterHands ver. 2. 17. 1. 11 developed at Keio University) in order to obtain peak information including m/z, migration time (MT), and peak area. Putative metabolites were then assigned from HMT's standard library and Known-Unknown peak library on the basis of m/z and MT.
QUANTIFICATIONS AND STATISTICAL ANALYSES GFP and pH3 quantifications Mitotic and meiotic indices were quantified by counting pH3-positive cells in > 40 testes or > 10 midguts per genotype and/or condition (e. g. , male or female). For quantification of intestinal GFP protein expression level, a midgut portion (corresponding to R2 or R4 regions) was imaged at 20x magnification. GFP level was quantified using ImageJ in areas of identical size across all genotypes. Threshold was adjusted for the GFP channel (ImageJ function: Image > Adjust > Threshold) to subtract background, then the size and the intensity mean of the area above the threshold was considered (ImageJ function: analyze particles). Data was collected from at least 10 midguts per genotype and/or sex, and is displayed as boxplots showing all data points.