dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The data collection and labeling of the cerebellum was supported in part by the  NIH/NINDS  grant  R01 NS056307  (PI: J. L. Prince) and  NIH/NIMH  grants  R01 MH078160 & R01 MH085328  (PI:  S. H. Mostofsky ). PMT is supported in part by the  NIH/NIBIB  grant  U54 EB020403. CERES2 development was supported by grant  UPV2016-0099  from the  Universitat Politécnica de Valencia  (PI:  J. V. Manjón ); the  French National Research Agency  through the  Investments for the future Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project; PI:  P. Coupé ) and  Cluster of excellence CPU  and TRAIL ( HR-DTI ANR-10-LABX-57 ; PI:  P. Coupé ). Support for the development of FCN was provided by the  National Science and Engineering Research Council of Canada (NSERC) , discovery grant program, and by the  ETS Research Chair on Artificial Intelligence in Medical Imaging.
Flow Cytometry Cells were washed, detached, labeled and analyzed by flow cytometry (BD LSR II), and the data assessed using FCS Express software (De Novo software). Antibodies were diluted in PBA (PBS, BSA 0. 5%, Azide 0. 1%): anti-C3c (Quidel, A205, 10 µg/ml), anti-FH (antiFH#1, Quidel, A229, 55 µg/ml), anti-MCP-PE (Bio Rad, MCA2113, 10 µg/ml), anti-DAF (Bio Rad, MCA1614, 10 µg/ml). Staining was revealed by goat anti-mouse IgG PE (Beckman Coulter, IM0551). Cell viability was followed by annexin V-APC/DAPI staining (BD Bioscience).
RTqPCR RNA extraction from cells was performed with a Qiagen kit. Quality and quantity of RNA were measured by an Agilent 2100 Bioanalyzer (Agilent Techonologies). RNA Integrity Number was considered acceptable if >9. After standard RT-PCR, amplification of cDNA was proceeded with following probes: actin-4332645, TM-hs00264920-s1, HO-1-hs01110250_m1, KLF2-Hs00360439_g1, KLF4-Hs00358836_m1, NRF2-Hs00975961_g1, BACH1-Hs00230917_m1. Data were analyzed by SDS2. 3 and RQ manager software. The mean cycle threshold (CT) values for both the target and internal control (β-actin) were determined for each sample. The fold change in the target gene, normalized to β-actin and relative to the expression of untreated HUVEC, was calculated as 2-CT  (19).
ACKNOWLEDGMENTS This work was supported by grants from  Agence Nationale de la Recherche   ANR JCJC-INFLACOMP 2015-2018 ANR-15-CE15-0001  to LR,  ANR JCJC-COBIG ANR-13-JSV1-0006  to JD,  Fondation du Rein  under the aegis of the  French Medical Research Foundation AMGEN 2014 FdR-SdN /FRM_FRIMAT  and  INSERM. The cytometric and microscopy analysis were performed at the  Centre d'Histologie, d'Imagerie et de Cytométrie, (CHIC), Centre de Recherche des Cordeliers UMRS1138, (Paris, France ) and we are grateful to the  CHIC  team for the excellent technical assistance. CHIC is a member of the  UPMC Flow Cytometry network (RECYF). We are grateful for excellent technical assistance of the  CEF team of the Centre de Recherche des Cordeliers  for their support with the animal experimentation. The authors thank  Dr.
In order to strengthen these observations, cell viability was assessed in vitro with XTT assays. The results showed that RANK over-expression in HOS cells did not modify cell viability compared to the control cells (Figure  2A ). However, while addition of soluble RANKL to native cells did not influence cell viability, RANKL seemed to induce a moderate (though not significant) decrease in the viability of RANK expressing HOS cell (Figure  2A ). This slight tendency was also observed for MOS-J PG1 cells (Figure  2A ). Overall, these data confirmed that RANK expression by osteosarcoma cells had no major influence on cell proliferation or tumor growth. A moderate decrease (tendency) in the cell viability in response to the addition of RANKL to the culture medium was observed for both RANK over-expressing HOS cells and PG1 cells (A).
Mouse Models of Osteosarcoma All procedures involving mice were conducted in accordance with the institutional guidelines of the French Ethical Committee (n • 1280. 01 and 1281. 01). Mice were housed under pathogen-free conditions at the Experimental Therapy Unit (Faculty of Medicine, Nantes, France). For injection and tumor monitoring, mice were anesthetized by inhalation of a combination of isoflurane/air (1. 5%, 1 L/min). Tumor volume (V), measured twice a week, was calculated using the formula: length × width × depth × 0. 5. Data points were expressed as average tumor-volume ± S. E. M. Mice were sacrificed as soon as the tumor volume reached 2500-3000 mm 3 (10% of body weight) for ethical reasons. The number of macroscopic lung metastases was counted manually for each mouse. The MOS-J models were induced in six different mouse strains (C57BL6/J immune-competent, NMRI-nude immune-deficient, Rankl Flox/Flox Lck-Cre (Rankl ∆T ), Rankl -/-, Opg -/-and Rank Tg ) by an intramuscular injection of 1 × 10 6 PG1 cells or RANK over-expressing PG1 cells in a paratibial site at the age of 5 weeks.
Micro-CT Analysis Analyses of bone microarchitecture were performed using a Skyscan 1076 in vivo micro-CT scanner (Skyscan, Kontich, Belgium). Tests were performed after sacrifice on tibias for each group. All tibias were scanned using the same parameters (pixel size 18 µm, 50 kV, 0. 5-mm Al filter, 10 min of scanning). The reconstruction was analyzed using NRecon and CTan software (Skyscan). The following bone parameters were measured: tissue volume (TV, mm 3 ), bone volume (BV, mm 3 ), percentage of bone volume (BV/TV, %), tissue surface (TS, mm 2 ), bone surface (BS, mm 2 ), bone surface/bone volume ratio (BS/BV, mm 3 ), bone surface density (BS/TV, mm 3 ), trabecular spaces (Tb. Sp, mm) and cortical or trabecular thickness (CTh, Tb. Th, mm).
Acknowledgments: The authors would like to thank  G. Hamery  and  J. Pajot  from the  Therapeutic Experimental Unit (Nantes, France)  for their technical assistance. The authors would also like to thank the virology laboratory at the  CHU Hotel-Dieu, University Hospital of Nantes , for the access they provided to their infrastructures that are adapted to the preparation of Lentivirus. The authors would also like to especially thank  C. O'Brien  ( Center for Osteoporosis and Metabolic Bone Diseases, Central Arkansas Veterans Healthcare System, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA ) for the unrestricted access to the RANKL floxed mouse model.
Consistent with these results, both Rab22A Q64L -and Rab22A S19N -expressing compared to Rab22A WT -or GFP-expressing cells showed a moderate reduction in melanocyte pigmentation without affecting the cargo (TYRP1) stability (Fig EV2B for BF images, EV2C for melanin content and EV2D for immunoblotting). Altogether, these findings indicate that RE localized Rab22A regulates cargo transport to melanosomes in melanocytes similar to cell surface trafficking of Tf/ MHC-1 in HeLa cells  [7, 22]. ◀ Figure  1. Selected endosomal Rab RNAi screen identified Rab22A as a regulator of RE dynamics. The degree of colocalization between the two markers measured as Pearson's coefficient value "r" (mean AE SEM) using cellSens Dimension software (Olympus). The depletion of BLOC-1 or KIF13A activity in HeLa cells has been shown to cause a defect in cargo recycling and accumulation of Tf in the vacuolar endosomes  [8, 13].
Similarly, TYRP1 trafficking to melanosomes has shown to be regulated by the retromer-VARP  [40]  and BLOC-1-BLOC-2  [14, 18]  complexes, suggesting that cargo might be shared between the different types of REs. Nevertheless, our results support that Rab22A-BLOC-1-BLOC-2-KIF13A complex controls the generation (number of tubules), stabilization (length of the tubule) and likely tethering  [18]  of REs, but not the cargo sorting on REs. Moreover, the data presented here demonstrate that defects in function of Rab22A/BLOC-1/BLOC-2 were not compensated by any other RE-associated complexes. This suggests that these molecules and their association are important for the formation and thus the function of REs in regulating cargo recycling and LRO biogenesis. In addition, we also observed reduced expression of Rab22A equivalent to the pallidin subunit of BLOC-1 in the hippocampus of schizophrenia patient data set GSE53987 (Fig  EV4H )  [41] , suggesting that Rab22A possibly mediates these processes in a variety of mechanisms, and it will be interesting to connect these processes to REs in future.
Transcript analysis (semiquantitative PCR or schizophrenia dataset) RNA was isolated from HeLa cells or melan-Ink melanocytes using TRIzol (Sigma-Aldrich) method. Briefly, cells were treated with TRIzol reagent and then extracted with chloroform at room temperature. The aqueous layer containing RNA was precipitated with isopropanol followed by a wash with 70% ethanol. Finally, the RNA pellet was suspended in 0. 01% DEPC-treated water (Sigma-Aldrich) and then estimated the concentration using NanoDrop 2000C spectrophotometer (Thermo Fisher Scientific). Further, cDNA was prepared by using cDNA synthesis kit (Fermentas). The transcripts were amplified by Bio-Rad S1000 Thermal Cycler using gene-specific primers (listed in the Appendix Table  S2 ), and the GAPDH was used as a loading control. Band intensities were measured, normalized with GAPDH, quantified fold change with respect to the control and then listed in the figure.
Immunofluorescence microscopy and image analysis For steady-state localization studies, cells on coverslips were fixed with methanol (KIF13A-YFP-transfected cells) or 3% formaldehyde (in PBS) and then stained with primary antibodies followed by the respective secondary antibodies as described previously  [8, 14]. In some experiments, cells on coverslips were subjected to internalization of Tf-Alexa Fluor 594 or fluoresceinconjugated dextran, chased for different time points, fixed with 3% formaldehyde and then imaged. Bright-field (BF) and immunofluorescence (IF) microscopy of cells was performed on an Olympus IX81 motorized inverted fluorescence microscope equipped with a CoolSNAP HQ2 (Photometrics) CCD camera using 60× (oil) U Plan super apochromat objective. Acquired images were deconvolved and analysed using cellSens Dimension software (Olympus).
The colocalization between two colours was measured by selecting the entire cell excluding the perinuclear area and then estimated the Pearson's correlation coefficient (r) value using cellSens Dimension software. The average r value from 10 to 20 cells was calculated and then represented as mean AE SEM. Note that the maximum intensity projection of undeconvolved Z-stack images was used during the measurement of r values. The analysed images were assembled using Adobe Photoshop. Corrected total cell fluorescence (CTCF) of mCherry-Rab22A and KIF13A-YFP was calculated using below formula, and the mean fluorescence intensity was measured using Image J software. CTCF (in arbitrary units, A. U. ) = area of the cell (mean cell fluorescence intensity-mean background fluorescence intensity). Individual and the averaged CTCF values from 6 to 9 cells were plotted separately.
Length and number of KIF13A-YFP-positive tubules were quantified (listed in Table  1 ) in unbiased way by using below Macro programme plugged into Fiji software (ImageJ). In this analysis, images (~15 or more cells/condition) were captured randomly and converted their maximum intensity projections into binary and then skeletonized (2D/3D) using Fiji. Note, the tubule length was considered as 1. 3-20 lm with the assumption that the size of SEs possibly ranges in the order of ≤ 1. 3 lm diameter. Macro programme: run("8-bit"); run("Tubeness", "sigma=. 1935 use"); run("8-bit"); setAutoThreshold("Default dark"); //run ("Threshold. "); //setThreshold  (40, 255) ; setOption("BlackBackground", false); run("Convert to Mask"); and run("Skeletonize").
Live cell imaging Cells were plated on 35-mm glass-bottomed dishes (MatTek Corporation) and then transfected with respective constructs. Post 24 h, cells were visualized under Olympus IX81 fluorescence microscope equipped with an environmental chamber maintained at 37°C with 5% CO 2 and analysed by cellSens Dimension software. Time-lapse microscopy of both GFP and RFP/mCherry was performed by capturing image streams over 3-5 min using a CoolSNAP HQ2 (Photometrics) CCD camera. Images were analysed and converted into avi format for visualization.
Acknowledgements We thank  G Raposo ,  G van Niel, MS Marks ,  E Dell'Angelica ,  A Peden  and  P Cresswell  for generous gifts of reagents; EV Sviderskaya and  DC Bennett  for mouse melanocytes. We also thank  D Majumder ,  D Kumari  and  A Ghosh  for their technical help. We thank  MS Reddy ,  DK Saini ,  P Rajyaguru  and  S Mahanty  for critical reading of the manuscript. We acknowledge the  PICT-IbiSA , shRNA resource center and the Bioimaging and flow cytometry facility. This work was supported by a  Wellcome Trust-DBT India Alliance  ( 500122/Z/09/Z  to SRGS); CEFIPRA (4903-1 to SRGS and  G Raposo );  DBT-RNAi  ( BT/PR4982/AGR/36/718/2012  to SRGS); IISc-DBT partnership programme (to SRGS);  Fondation pour la Recherche Médicale  (Equipe FRM DEQ20140329491 Team label to  G Raposo );  Fondation ARC pour la Recherche sur le Cancer  ( PJA20161204965  to CD); and  Institut Curie.
We also developed a paired-end processing program tailored for the analysis of phage-displayed libraries. NT-TriNuc and NT-SX4 libraries were characterized by paired-end deep sequencing at every step of library production. Although paired-end processing pipelines are common in the literature, we sought to build on published concepts  22, 46  and re-implement the processing as an open-source MATLAB code. To track mutations caused by PCR, we used the Primer ID technology on the synthetic NT-TriNuc library. In addition, we also characterized biases arising from chemical post-translational modification (cPTM) through N-terminal oxime ligation  12, 47  and dichloro-oxime-mediated cyclization  11, 12.
Illumina introduced new, improved chemistry for paired-end sequencing at the time of ). (e) Schematic comparison of NT-SX4 and ID-SX4 library production. In the NT-SX4 library (left), the SX4 library is expressed at the N-terminus of the N1 domain of pIII, while in the ID-SX4 library (right), the same library is expressed between N1 and N2 domains of pIII. preparation of this publication. We found that the new reagents led to a significant improvement in the accuracy of sequencing. We envision that further improvements in sequencing protocols and the development of PCR-free sequencing pipelines will further boost the quality of the analysis of naïve libraries. While short, tetrapeptide libraries are rarely used for ligand discovery, the maximum diversity of such libraries (10 5 -10 6 ) is easily accessible to routine Illumina sequencing and data analysis, which makes them model libraries for investigation of library composition.
We used both the NextSeq ® 500 Mid Output Kit (150 cycles) (cat FC-404-1001) and the NextSeq ® 500/550 Mid Output Kit v2 (150 cycles) (cat FC-404-2001) to sequence the reads in both the forward (F) and reverse (R) directions. FASTQ files were processed using paired-end analysis in MATLAB (See MatLab. ZIP in Supplementary Information). Raw FASTQ files (>10 Gb of data) are not included in this manuscript, but are available on request. Analysis of Illumina data. Supplementary information (SI) contains a detailed description of this pipeline. All MATLAB codes (available in MatLab. ZIP in SI) with model FASTQ files are available at http://www. chem. ualberta. ca/~derda/trinucpaper/rawfiles/data/. Briefly, the pipeline starts from alignment of sequences from F and R FASTQ files, identification of barcode sequences that correspond to individual experiments and separation of aligned reads to individual files that contain sequences that map to specific sequencing barcodes.
Within these files, we aligned the F and R strands and identified FA and RA sequences, allowing no more than one point mutation per adapter region. FA and RA sequences were trimmed, leaving behind library regions flanked by FA/RA. Library regions that contained identical nucleotide sequences were combined, counted and translated into peptide sequences. The final output of the pipeline is plain-text-based lists of peptide sequences and their abundances. Intermediate data files that contain F and R aligned sequences with untrimmed adapters were used to evaluate the fidelity of sequencing and processing. The newly-developed paired-end processing software supersedes our standard single-end published routine  22, 46  (Table  S2  compares our single-end and paired-end processing pipelines). It allows for analysis of longer sequences and for additional quality control: a significant fraction of point-mutants emanating from a single-end pipeline can be resolved as FR-mismatches.
Differential enrichment analysis. In the differential enrichment analysis, three factors were considered: (i) appropriate modeling of the counts using a negative binomial model; (ii) Benjamini-Hochberg (BH) correction to control the false discovery rate (FDR) at α = 0. 05 52 ; (iii) normalization of data across multiple replicates using the Trimmed Mean of M-values (TMM) normalization  66. The differential enrichment analysis was performed using Bioconductor package edgeR  67, 68  (see R. zip file in the Supplementary Information for *. RMD R-code).
Acknowledgements This work was supported by research grants from the  National Science and Engineering Research Council of Canada (NSERC , # 492943 ),  NSERC Accelerator Supplement , and  Alberta Glycomics Centre. Infrastructure support was provided by  Canadian Foundation for Innovation (CFI) New Leaders Opportunity. K. T. acknowledges  Alberta Innovates Health Solutions  and  NSERC USRA  for Summer Fellowship support. B. H. acknowledges  China Scholarship Council  Fellowship. We thank  J. Maxwell Douglas  for help with testing and annotation of MATLAB scripts. We are grateful to  Ferring Research Institute  for providing the synthetic DNA of TriNuc libraries.
This forced the teams to collect specific data from their patient electronic records. The teams were offered Action Guide tools (satisfaction surveys, activity analysis grids, communication tools, etc. ) and took advantage of the opportunity to adapt them to their setting. International experiences published in the literature were presented  [15, 16]  and the teams were reminded of CF care guidelines  [17]. Each team identified actions to redesign its processes, in line with its theme for improvement, to be tested according to successive PDSA cycles. The teams' satisfaction and suggestions were recorded at each meeting and an overall score was displayed on the PHARE-M website. Close collaboration with the TDIMA and the CFF was sustained over the course of LLC1 through: the participation of members of the national team, as well as physicians at several pilot centers, in the adult LLC session at the North American conference in Anaheim (October 2011); the participation of the Director of TDIMA Clinical Microsystem Group in the third face-to-face meeting to supervise the poster session meeting (PHARE-M LLC1, Marseille, March 2012); the trainings for the physiotherapist and the parent on the national team in the TDIMA's "eCoach the Coaches" course at the same time as the PHARE-M LLC1.
All interviews and focus groups were recorded and fully transcribed. The data was exploited (coding, categorization), processed (analysis, validity) and interpreted according to the standard thematic content analysis protocol  (Miles & Huberman [18] ). This was followed by manual grouping and counting within an analysis framework with the following dimensions: process applicability (terminology, formalization, tools, distance web meetings); incorporation of patients and parents (roles, time spent, barriers); national/regional coordination (roles, nature of support, incorporation mechanisms); process adoption (perceived benefits and costs, working atmosphere, engagement, acquisitions); and impact (operation, working practices, cooperation with the stakeholders). The report was submitted in July 2012 for consideration to adjust the PHARE-M LLC2.
Companion articles in this supplement present the changes in processes and clinical outcomes achieved in some centers between 2012 and 2015 and the links developed between the program and the general quality process at the hospital  [25] [26] [27]. They show that working in QI has allowed these teams to achieve their goals and even exceed them on various themes of improvement such as FEV1 for adolescents, BMI for children 2 to 12 y. o. or time on the lung transplant waiting list. The statistical analysis of the PHARE-M Performance research project, which will assess the effectiveness of the program to change patient outcomes at centers involved in LLC1 & 2, will be performed on the Registry data from 2011 to year 2015 and results will be available by the end of 2017. The assessment of the teams' satisfaction showed an increase between LLC1 and LLC2, as expressed at each training meeting and for the LLC overall, reflected in the median of all the participants' scores on a scale from 0 to 10, where 10 represented maximum satisfaction (median score = 7.
Improvement of the patient registry The French Registry contains one value in a given year for patient health outcomes and long-term treatments, while patient data are recorded at each clinic visit in the electronic patient record within the hospital information system. The Registry Committee establishes rules to select the clinic visit in a given year from which the FEV1, height and weight values are taken to be transmitted to the Registry. In 2011-2012, the histograms presenting the median values of the centers remained anonymous in the Patient Registry report by center. The transparency brought in the PHARE-M meetings opened up discussions between the teams, leading them either to focus on the themes of improvement when the centers presented unsatisfactory results compared to national median values, or to question the measurement processes at the center.
An onsite quality audit of the data transmitted to the Registry was organized in 2014-2015 pointed to variability in the measurement processes and in the application of the selection rule  [28]. Avenues for improvement have been identified to support quality improvement of the data transmitted to the Registry by the centers. To respond to the requests were made to the Registry team, the body mass index (BMI) for children was presented in Z-score value for LLC2. The lag between the year to which the data refer and the time of publication of the report (approximately 2 years in 2011) led the teams to supplement the Registry data with more recent data pulled directly from their patient records. The 2015 Patient Registry report has been issued by the end of 2016 and then provide more actual data for the PHARE-M LLC5.
The spatial resolution of these simulations is 0. 11 • in latitude and longitude, i. e. about 12 km over the European domain. For wind and solar PV analyses we retrieved three-hourly data of the relevant variables (described in the here below subsections) from the climate simulations while daily time-series were used for hydropower and thermoelectric power. The recent past climatology of these simulated climate variables relevant for the four sub-sectors have already been evaluated against observational data in previous studies (Tobin et al 2016 for wind speed, Jerez et al 2015a for solar radiation, temperature, wind speed, Casanueva et al 2015, and Prein et al 2016 for precipitation, and Vautard et al 2013 for summer temperatures and heat waves critical to thermoelectric power). Daily model outputs of temperature, precipitation, radiation and wind speed were bias-corrected to be used as input for the hydrological and water temperature models.