dataesr/scientific-paragraphs-categorization huggingface dataset:
The DES data management system is supported by the  National Science Foundation  under Grant Number  AST-1138766. The Collaborating Institutions are  Argonne National Laboratory , the  University of California at Santa Cruz , the  University of Cambridge, Centro de Investigaciones Enérgeticas ,  Medioambientales y Tecnológicas-Madrid , the  University of Chicago ,  University College London , the  DES-Brazil Consortium , the  University of Edinburgh , the  Eidgenössische Technische Hochschule (ETH) Zürich ,  Fermi National Accelerator Laboratory , the  University of Illinois at Urbana-Champaign , the  Institut de Ciències de l'Espai (IEEC/CSIC) , the  Institut de Física d'Altes Energies ,  Lawrence Berkeley National Laboratory , the  Ludwig-Maximilians Universität München  and the associated  Excellence Cluster Universe , the  University of Michigan , the  National Optical Astronomy Observatory , the  University of Nottingham , The  Ohio State University , the  University of Pennsylvania , the  University of Portsmouth ,  SLAC National Accelerator Laboratory ,  Stanford University , the  University of Sussex , and  Texas A&M University.
The DES participants from Spanish institutions are partially supported by  MINECO  under grants  AYA2012-39559 ,  ESP2013-48274 ,  FPA2013-47986 , and  Centro de Excelencia Severo Ochoa   SEV-2012-0234. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
Tumors which had been subjected to Foslip-PDT were completely eradicated just leaving fibrotic scar tissue behind. Tumors after Foscan-PDT were not fully destroyed leaving some tumor tissue intact while the tumors after Lipidot-PDT were only partly destroyed with some cells positive for ki-67, thus still proliferating (data not shown). Possible side effects of the treatments were investigated by analyzing livers, kidneys and spleens 48 h and 14 days after PDT. Liver damage was recognizable by deformed Fig. 3  Caliper measurements of tumor volume changes after PDT (day 0) with 10 J/cm 2 (a) and 20 J/cm 2 (b) Fig. 4  Images of tumors after PDT with 10 J/cm 2. The irradiation area had a diameter of 1. 5 cm (circle) Fig. 5  Images of tumors after PDT with 20 J/cm 2. The irradiation area had a diameter of 1.
Patients across the treatment arms were pooled and divided into groups according to the GOLD 2011 guideline classification of A-D, 2 according to baseline mMRC dyspnea score (while on baseline therapy), exacerbations in year prior to the trial (based on antibiotic/systemic steroid use), and post-bronchodilator FEV 1. 1  Patients were included in the analysis if all these data were available.
Statistical analysis For patients classified into the GOLD Groups A-D, we examined patient baseline characteristics and all-cause mortality, including respiratory and CV deaths, major adverse CV events (MACE; defined as stroke, myocardial infarction [MI], sudden death, cardiac death, sudden cardiac death, or fatal event in system organ classes for cardiac and vascular disorders), fatal MACE, MI events (including fatal events), and hospitalized exacerbations occurring during the trial. Data by GOLD Stages I/II-IV were obtained for comparison. Data are presented as mean (standard deviation [SD]) unless otherwise stated. Hazard ratios (HRs) and P-values were calculated based on Cox regression by GOLD 2011 groups (or GOLD Stage). No other covariates were included in the model. Only patients who had both GOLD 2011 group and GOLD Stage assigned were included, and patients with post-bronchodilator FEV 1 /FVC 70% were excluded.
Methods General. Epitaxial Fe 3 O 4 (111) thin films were grown by molecular beam epitaxy on a-Al 2 O 3 (0001) according to literature procedures  50. Unless otherwise stated, all reagents were purchased from Aldrich or TCI and used without further purification. Electrospray ionization mass spectrometry (ESI-MS) spectra were recorded on a Thermo Scientific 2009 mass spectrometer. IR spectra were recorded on a Bruker TENSOR-27 Fourier transform infrared (FT-IR) spectrometer equipped with an attenuated total reflectance (ATR crystal diamond/ZnSe) sample holder in the 4000-500 cm À 1 range. Elemental analysis was taken on a Thermo Scientific Flash analyzer. Single crystal X-ray diffraction studies. X-ray diffraction data were collected by using a Kappa X8 APPEX II Bruker diffractometer with graphite-monochromated Mo Ka radiation (l ¼ 0.
71073 Å). Crystals were mounted on a CryoLoop (Hampton Research) with Paratone-N (Hampton Research) as cryoprotectant and then flashfrozen in a nitrogen-gas stream at 100 K. The temperature of the crystal was maintained at the selected value (100 K) by means of a 700 series Cryostream cooling device to within an accuracy of ± 1 K. The data were corrected for Lorentz polarization, and absorption effects. The structures were solved by direct methods using SHELXS-97 (ref. 51) and refined against F 2 by full-matrix least-squares techniques using SHELXL-97 (ref. 52) with anisotropic displacement parameters for all non-hydrogen atoms. Hydrogen atoms were located on a difference Fourier map and introduced into the calculations as a riding model with isotropic thermal parameters. All calculations were performed by using the Crystal Structure crystallographic software package WINGX  53.
The data for Co-and Ni(Pyipa) 2 were fitted by a full diagonalization of the energy matrices considering many orientations of the magnetic field. The best fits lead to the following parameters: Monolayer preparation. The monolayers were prepared by immersion of the Fe 3 O 4 substrates into 10 ml of a freshly filtered 0. 5 mM solution of either Co(Pyipa) 2 or Ni(Pyipa) 2 dissolved in MeOH. After 3 days of immersion the substrates were removed from the solution and rinsed thoroughly with neat solvent. All the monolayer preparation experiments were carried out at room temperature. D Ni ¼ -5. 2 cm À 1 , E Ni ¼ 0, g Ni ¼ 2. 22, R (agreement factor) ¼ 2 Â 10 -5 , D Co ¼ þ 30. 2 cm À 1 , E Co ¼ 5. 0 cm À 1 , g Co ¼ 2. Geometrical optimization of the geometry of the molecule on the surface. The FIREBALL package uses a localized optimized minimal basis set  29  and the self-consistency is achieved over the occupation numbers through the Harris functional  30.
Time-of-flight secondary ion mass spectrometry. Time-of-flight secondary ion mass spectrometry data were acquired using a TOF. SIMS V spectrometer (ION-TOF GmbH, Muenster, Germany). The analysis chamber was maintained at less than 5 Â 10 À 7 Pa under operational conditions. The total primary ion flux was less than 10 12 ions cm -2 ensuring static conditions. A pulsed 25 keV Bi þ primary ion source (Liquid Metal Ion Gun, LMIG) at a current of about 1 pA (high current bunched mode), rastered over a scan area of 500 Â 500 mm was used as the analysis beam. Data acquisition and processing analyses were performed using the commercial IonSpec program. The exact mass values of at least seven known species, from H À , C À , C À 2 , C À 3 , PO 2 À , PO 3 À , FeO À , FeO 2 À , Fe 2 O À , FeOPO 3 À , and H þ , CH 3 þ , Na þ , Ca þ , Fe 2 þ , C 5 H 5 Fe þ , C 6 H 6 Fe þ , were used for calibration of the data, acquired in the negative and positive ion mode, respectively.
Acknowledgements This work is supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  'Investissements d'Avenir' program 'Labex NanoSaclay ' (reference:  ANR-10-LABX-0035 ). We acknowledge SOLEIL for provision of synchrotron radiation facilities. This work was performed using HPC resources from  CALMIP  (Grant  2015-1517 ).
Data availability. The X-ray crystallographic coordinates for structures reported in this Article have been deposited at the Cambridge Crystallographic Data Centre (CCDC), under deposition numbers CCDC-1049643 and CCDC-1049644. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif. The remaining data that support the findings of this study are available from the corresponding authors upon reasonable request.
Footprint Analysis Due to variations in the meteorological conditions, air masses arriving at the stations can be affected by 222 Rn sources of different areas: local and remote. Local and regional footprints influencing 222 Rn concentrations measured at each station have been evaluated by analyzing both observational data and model results. Local and regional footprints have been analyzed on a seasonal basis grouped as December, January, and February as winter; March, April, and May as spring; June, July, and August as summer; and September, October, and November as autumn. 1. The local footprint at each site has been analyzed using the wind data measured at each station (windrose analysis) on seasonal and on diurnal bases. 2. The regional footprint analysis offers information about the synoptic atmospheric circulation and the influence of 222 Rn remote source regions on 222 Rn concentrations variability.
The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (version 4) [e. g. ,  Draxler and Hess, 1998; Stein et al. , 2015]  was used to study the main long-range air masses transport patterns for each station during different seasons. HYSPLIT was run with meteorological data from the Global Data Assimilation System reanalysis archive maintained by the Air Resources Laboratory (ARL) available at http://ready. arl. noaa. gov/. A subset of this data is available from ARL in a format suitable for transport and dispersion calculations using HYSPLIT with 2. 5°global latitude-longitude projection and a temporal resolution of 6 h. Kinematic 3-D back trajectories in time (72 h) were computed for a period of 4 years (2012-2015) twice per day (at 12:00 UTC and 00:00 UTC), in order to include in the analysis diurnal and nocturnal atmospheric dynamics, and at 500 m above ground level (agl) following  Jorba et al.
Conclusions and Outlooks The objective to expand the existing European network of radon observations and move toward harmonized atmospheric 222 Rn data was carried out by the installation of recently improved direct 222 Rn monitors (ARMONs) at three contrasting locations in Spain. Atmospheric radon concentrations were monitored at each of the three stations using electrostatic deposition detectors that had been calibrated with a 222 Rn chamber as described by  Grossi et al. [2012]. The response of the ARMON monitor, in uncontrolled environmental conditions, was tested by a comparison study with an established one-filter radon progeny monitor, used at several European stations. This new high-quality data will provide a much needed enhancement of the European network of 222 Rn monitoring stations in the hitherto underrepresented area of southwestern Europe.
222 Rn data from DEC3 and UHU stations will be useful to improve mesoscale models of sea-land and oceanland breeze phenomena, which strongly influence the atmospheric composition and air quality at coastal cities. In the same way, observational data of GIC3 station will help our understanding of mountain-valley breeze phenomena important for the improvement of high-resolution transport models. The significant increase in 222 Rn concentrations observed at UHU station under calm conditions confirms results of past studies about the presence of a strong localized artificial radon source close to the station probably associated to phosphogypsum piles (PG). This last result will be analyzed more deeply in our future investigations.
Study design The REFRACT-LYMA Study is an observational cohort study that aims at including all patients diagnosed with MCL followed in specialized oncology centers within three public hospitals in Northwestern France (Nantes University Hospital and the General Hospitals in La Roche sur Yon and in Vannes) over at least a 10-year period. All newly diagnosed patients are followed prospectively. Patients already dealt with in study sites at study start are followed prospectively, from their inclusion and onwards. For these patients, previous clinical data is recorded retrospectively from medical files. The cohort is registered by the French Data Protection Authority in clinical research (Commission Nationale de l'Informatique et des Libertés or CNIL). The study was approved by an ethics committee (Groupe Nantais d'Ethique dans le Domaine de la Santé, RC14_0358).
Data collection The REFRACT-LYMA study is based on a continuous and comprehensive record of pre-specified and standardized information on all included patients. Specific data is compiled at baseline plus at each follow-up visit, according to the reason for follow-up. In the REFRACT-LYMA Cohort Study, the reason for follow-up is categorized as follows: disease diagnostic, disease progression, disease relapse, treatment initiation, treatment monitoring, treatment toxicity, end of treatment and monitoring in absence of treatment. As the study is non-interventional, each study physician follows included patients as well as possible according to their clinical, biological, psychological and social situation. Subjects are followed indefinitely until secondary refusal to take part in the study, emigration or death.
Save for a significant event (therapeutic failure, adverse events…), the three study hospitals will usually follow up patients every 6 months. Follow-up is continuous. It includes collecting data on socioeconomic and medical status plus on MCL therapies and associated events (resistance and side effects). The participants also complete standardized quality of life questionnaires. The participants are additionally asked to give a blood sample at baseline and at specific follow-up stages. For any diagnostic biopsy performed during the course of the disease (e. g. bone marrow, lymph node, pleural effusion, cerebrospinal fluid), extracted biological samples will also be kept in a dedicated biobank. Table  1  shows the collected data according to reason for follow-up throughout the study.
Sociodemographic and epidemiological data Sociodemographic and epidemiological characteristics are assessed by means of standardized questionnaires managed by the physicians. They cover demographics (birth year, sex, education, profession, living situation, place of residence, ethnicity, number of children and, when applicable, date and associated cause of death), body weight, height, physical activities, smoking and alcohol habits, family and personal history of lymphoma and others types of cancer and of constitutive or acquired immunodeficiency. This questionnaire is completed at baseline and follow-up. Time-varying sociodemographic and epidemiological data is updated every year.
Medical and therapeutic data The Eastern Cooperative Oncology Group (ECOG) Performance Status  [22]  and disease status (complete remission/stable disease/partial response) are systematically assessed at follow-up. The International Prognostic Index (IPI)  [23]  and MIPI  [11]  calculation results are calculated. The treatment initiation date, treatment type, response to treatment and date of response are recorded for each administrated treatment. The REFRACT-LYMA Cohort also collects the Ann Arbor staging (I, II, III, IV), the presence/absence of splenomegaly, a digestive expression of lymphoma and circulating tumor cells, the number of extranodal territories, the serum lactate dehydrogenase and creatinine levels, the hemoglobin concentration and the white blood cell count, as needed for the IPI  [23]  and the MIPI  [11]  calculation.
Anatomological and genetic characteristics are assessed with data from a bone marrow biopsy with associated results (absence/presence of bone marrow lesions). Tumor cells are classified into classic or variant types (blastoid variant and polymorphic variant) according to cytologic morphology. Information on specific molecular markers and on t  (11, 14)  genetic translocation or del17 genetic deletion is recorded. Quantitative data on expressions of B-cell and MCL markers and on the Minimal Residual Disease (MRD) is collected. If positron emission topography (PET) is used during follow-up, the date, place and associated standardized uptake values are registered.
Adverse event data All adverse events with date of occurrence are recorded in the database. The classification used to describe the severity of organ toxicity for patients receiving cancer therapy  [24]  is based on the Common Terminology Criteria for Adverse Events (CTCAE) V3. 0 from the National Cancer Institute (NCI) of the National Institutes of Health (NIH). Toxicity is graded as mild (Grade 1), moderate (Grade 2), severe (Grade 3) or life-threatening (Grade 4), with specific parameters according to the organ system involved. Death (Grade 5) is used for some criteria to point out fatality. In this shared terminology, adverse event means any abnormal clinical finding temporally associated with the use of a therapy. The causality is not required.
Data management This multicenter study relies on a web-based system with data being entered into a central database. The system provides security with a protected access and complies with French safety policy. For each new patient included and associated follow-up, data is collected on paper forms and entered into the web-based data entry portal by the project workers and the medical team. The data management team performs a data quality control every year. The local medical team is notified in case of discrepancy or incomplete data.
CD36 is a multifunctional membrane-type receptor glycoprotein that reacts with oxidized low-density lipoprotein and long-chain fatty acid (LCFA). However, much remains to be understood about the molecular mechanism of the cardio-myopathy observed in CD36-KO mice. In this study, we identify different genes pathways involved in response to CD36 cardio-myopathy phenotype by identifying the differences among biological processes, molecular pathways and networks of interactions that emerge from knocking CD3 and using different bioinformatics tools such as STRING, GeneMANIA and Cytoscape. We were able list all the CD36-regulated genes, their related function and their specific networks. Data analysis showed that CD36-regulated genes differentially expressed are involved in biological processes such as FA metabolism, angiogenesis/apoptosis and cell structure.
We present here a patient with hereditary hypertrophic cardiomyopathy (HCM) and type I CD36 deficiency that showed no myocardial LCFA accumulation. CD36-KO mice exhibit between 60 and 80% reduction in betamethyl-p-[123I]-iodophenyl-pentadecanoic acid (BMIPP) uptake by heart tissue  [14]  which is paralleled by an increase in the heart/body index and the left ventricular size  [13]. We propose in this study to define the molecular defects that lead to cardiac hypertrophy and consequent of the fatty acid transporter CD36 deficiency. To define this molecular defect, we used two microarrays technologies (Affymetrix, Agilent) in order to identify genetic alterations related to the myocardial phenotype of CD36-Ko mice. In a previous study  [15] , heterogeneity of the arrays data was analyzed by splitting them on three levels: genes, genes set, and network/pathway.
Materials and Methods: Dataset: To identify a comprehensive set of genes that are differentially regulated by CD36 expression in the heart, we used two microarray technologies (Affymetrix and Agilent) to compare gene expression in heart tissues from CD36 KnocK-Out (KO-CD36) versus wild type (WT-CD36) mice. The obtained results using the two technologies were similar with around 35 genes differentially expressed using both technologies  [15]. Absence of CD36 led to down-regulation of the expression of three groups of genes involved in pathways of FA metabolism, angiogenesis/apoptosis and structure. These data are consistent with the fact that the CD36 protein binds FA and thrombospondin 1 involved respectively in lipid metabolism and anti-angiogenic activities  [15]. Summary of dataset analysis methodology used in this study is shown in Figure  1.
Gene Set Enrichment Analysis and Enrichment Map Gene set enrichment analyses (GSEA)  [16, 17]  are commonly used to determine the biological characterization, statistical significance, and concordant differences between an experimental gene set and a selected gene list from annotated gene sets knowledge bases to red on Molecular Signatures Database (MSigDB). GSEA can be downloaded from http://www. broadinstitute. org/gsea/downloads. jsp. The Jaccard coefficient is used to compare the similarity between two sample gene sets A and B and defined as the intersection between group A and B divided by their union. The results from GSEA are then visualized through the enrichment map [18], a Cytoscape plugin for network visualization. The ranked experimental gene list along with the enriched gene sets from GSEA is used to build the network of gene sets (nodes) where edges represent their similarity.
Networks and Pathway Analysis Analysis of Network Invoked by CD36-Regulated Genes The biological knowledge of gene and protein interactions is growing rapidly and there are many tools and curated databases available on a large scale. Insightful knowledge gained from studying gene sets rather than individual genes using networkbased approaches can reveal network patterns and relevant molecular pathways from the experiment gene sets. In this study, we utilized two different freely accessible and user-friendly web tools as follows. Gene Multiple Association Network Integration Algorithm (GeneMANIA) [19, 20] (http://www. genemania. org/) is a web-based tool for prediction of gene function or implemented as a Cytoscape plugin tool. Based on single gene or gene set query from 7 organisms, it shows results for interactive functional associative network according to their co-expression data from Gene Expression Omnibus (GEO), physical and genetic interaction data derived from BioGRID, predicted protein interaction data based on orthology from I2D, co-localization, shared protein domain, and GO function.
Results and Discussion: Identification of CD36-Regulated Genes through a Refined Analysis of Data Our initial analysis of this time series gene expression data for differentially expressed gene identification followed the same method used in a previous paper  [15]. All files were processed and normalized by Robust Multiarray Average (RMA) in R as in the original study. The selected normalization method may have an effect on downstream analysis, for example, reverse engineering analyses [15]; however, investigating this effect is beyond the scope of this study. We found that combining CD36+/+ and CD36-/-data compromises the accuracy of selection of differentially expressed. Table  1  summarizes the 30 differentially expressed genes from the combined dataset.
©2016 We selected two different web-based network tools: GeneMANIA and STRING using the differentially expressed genes as a query gene sets. These toolsets integrates computational methods to predict the gene functions based on a collection of interaction networks. GeneMANIA is a large collection of interaction networks from several data sources which identify genes and networks that are functionally associated (protein and genetic interactions, pathways, coexpression, colocalization, and protein domain similarity) with the query gene sets. Another advantage is that the user can run this as a plugin with the Cytoscape tool allowing the user to apply other tools to analyze the networks (Figure  2 ). STRING relies on the phylogeny to infer the functional interaction (protein networks) with direct interaction to score nodes, while GeneMANIA uses functional genomic data with label propagation to score nodes and generate gene networks (Figure  3 ).