dataesr/scientific-paragraphs-categorization huggingface dataset:
This ambitious exercise, involving 28 models used in 11 countries, seeks to quantify prediction accuracy among models for estimates of crop yield, grassland dry-matter production, N 2 O emission, net CO 2 exchange and soil C stocks. In other work, the Global Research Alliance Modeling Platform (GRAMP) was created to facilitate development, evaluation and adaptation of ecosystem models for estimating GHG emissions from agroecosystems  [12]. GRAMP currently includes selected metadata derived from MAGGnet through an interactive map (  [13]  GRAMP, 2016). Finally, the MAGGnet spreadsheet was adapted by members of the GRA Paddy Rice Research Group in August 2014 to gather metadata from experiments focused on rice production. As of August 2015, metadata from 13 sites across five countries were included in the adapted spreadsheet, which included additional input metadata related to weather variables, field type, and water and rice management.
Open invitation MAGGnet was initiated in the spirit of advancing GHG mitigation science through a multi-national research effort facilitated by the GRA. The value of MAGGnet to the scientific community will be directly proportional to its capacity to provide useful information for the analysis of GHG mitigation data. As currently organized, MAGGnet serves as a conduit for that purpose, with an explicit focus on metadata. We believe the focus on metadata was an appropriate "first step" to illustrate potential value. We envision the voluntary addition of key response data will provide even greater value to the scientific community as the "next step. " Information from additional studies continues to be entered into MAGGnet. Researchers interested in contributing or accessing metadata from experimental sites with published outcomes are encouraged to contact the corresponding author for the most recent versions of the MAGGnet spreadsheet and metadata sharing agreement.
Acknowledgments We acknowledge  Nicanor Saliendra  for creating Figure 1, and  Eva Magnuson  for compiling author contact information. MAGGnet activities are partially supported by a  Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI)  with funding from the  Italian Ministry of Agricultural, Food, and Forestry Policies ,  Swiss National Science Foundation , and  USDA National Institute of Food and Agriculture  (award numbers documented below). The  US Department of Agriculture (USDA)  prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability and, where applicable, sex, marital status, family status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because all or part of an individual's income is derived from any public assistance program (not all prohibited bases apply to all programs).
Spectroscopic characterization of samples Pt@MCM-22 sample has been measured by X-ray adsorption spectroscopy (XAS) to study the local environment of Pt and to estimate the coordination numbers of Pt species. The Fourier transform of EXAFS spectra of Pt@MCM-22 and the Pt and PtO2 reference are shown in Figure  3a  and Figure  S12. Considering the first shell, Pt@MCM-22 spectrum is dominated by a peak centered around 2. 65 Å with a smaller contribution at 2. 08 Å (both distances not phase corrected). Comparing with reference samples, the stronger contribution can be assigned to Pt-Pt bonds in Pt clusters. The assignation of weaker peak is too long to be ascribed Pt-O bonds from PtO2 (1. 65 Å, see Figure  S12 ), but according to literature data it is compatible with Pt-O bonds between Pt and the zeolite frameworks.
The typical probe current was set to 2 pA and the total dose on the sample varied between ~ 0. 2-3 C/cm 2 under the HRSTEM imaging conditions, which corresponds to low-dose conditions. [1] Multi-slice HRSTEM-HAADF image simulations have been carried out using QSTEM software [2], with the experimental settings of the FEI Titan Low Base as inputs. For this purpose, the thermal diffuse scattering parameter was fixed at 30 for a temperature of 300 K. Hydrogen/deuterium (H2-D2) exchange experiments were carried out in a flow reactor at room temperature (25 o C). The formation rate of HD species can be measured by the enhancement of the mass signal intensity (ion current). The feed gas consisted of 4 mL/min H2, 4 mL/min D2 and 18 mL/min argon, and the total weight of catalyst was 115 mg. The sample has been diluted with 230 mg of SiC.
Acknowledgement This work was funded by the  Spanish Government  ( Consolider Ingenio 2010-MULTICAT  ( CSD2009-00050 ) and  MAT2014-52085-C2-1-P ) and by the  Generalitat Valenciana (Prometeo). The  Severo Ochoa program  ( SEV-2012-0267 ) is thankfully acknowledged. L. L. thanks  ITQ  for a contract. The authors also thank  Microscopy Service  of  UVP  for the TEM and STEM measurements. The HAADF-HRSTEM works have been conducted in the  Laboratorio de Microscopias Avanzadas (LMA)  at the  Instituto de Nanociencia de Aragon (INA)-Universidad de Zaragoza (Spain) ,  Spanish ICTS National facility. Some of the research leading to these results has received funding from the  European Union   Seventh Framework Program  under Grant Agreement  312483-ESTEEM2  ( Integrated Infrastructure.
1 ). The blood sample from each parent was placed in two capillaries (up to 75 μl per capillary, one for telomere and one for oxidative stress measurements) after a brachial vein puncture. Body mass of adults (Fig. 1 ) and nestlings (Fig. S1 ) was measured at five different time points to the nearest 0. 01 g. The right tarsus of all birds was measured when nestlings were 30 days old to the nearest 0. 01 mm. At the end of the experiment (65 days after hatching), nestlings and parents were separated and placed in outdoor aviaries. It was not possible to record data blind because our study involved focal animals in the experimental groups.
Acknowledgments We thank two anonymous reviewers for providing valuable comments that improved our manuscript and  Marzena Majgier  and  Agata Rozik  for helping with bird husbandry and sample collection. Authors' contribution statement JS and MC designed the study; JS, MC and JR collected samples; JS and GC analysed samples and data; and JS wrote the manuscript with contributions from MC, GC and JR. Compliance with ethical standards All applicable institutional and/or national guidelines for the care and use of animals were followed. This article does not contain any studies with human participants performed by any of the authors.
PWV was measured by the foot-to-foot wave velocity method using the SphygmoCor system (SphygmoCor Mx, AtCor Medical, version 6. 31), in which a high-fidelity micromanometer (SPC-301, Millar Instruments) was used to determine carotid-femoral PWV  (16). Data storage and statistical analysis. This study was powered on the basis of a healthy subfoveal choroidal thickness of 235 ± 53 μm (47) and a healthy temporal RNFL thickness of 79 ± 16 μm  (19). To detect a 15% difference in choroidal thickness from normal in those with hypertension or CKD with 90% power and at significance level P = 0. 05, we needed to include ~22 subjects; for RNFL thickness the corresponding figure was ~30 subjects. Thus, we aimed for 35 subjects in each group. Data were stored and analyzed in Prism, version 6. 0 (GraphPad Software Inc.
). Data are presented as mean ± standard deviation (SD). Thicknesses were examined by 2-way ANOVA, comparing thicknesses between all 3 patient groups at each macular location using Tukey correction for multiple comparisons. For blood measurements, differences between groups were assessed using parametric or nonparametric analyses as appropriate and similarly, correlation coefficients calculated using the Pearson or Spearman method as appropriate. Stepwise linear regression was used for multivariable analysis using variables showing linear relationship with choroidal thickness (age, CRP, urine P:Cr, IL-6, ET-1, and ADMA). Significance was set at a P value of less than 0. 05. To measure the sensitivity and specificity of macular volume and subfoveal choroidal thickness at different values, a conventional ROC curve was generated using healthy subjects as controls.
The SEM was performed under vacuum using an 8 kV accelerating voltage and a spot size of 4. 0 nm, at a working distance (WD) of approximately 15 mm. Statistical analysis of microporosity was performed on ca. 1000 pores (per specimen) located in random locations on polished sections of 316L. Surfaces were repolished several times to reveal different analysis planes. SEM images of pores were analyzed using ImageJ particle analysis software, and the gray scale threshold adjusted such that particle areas could be measured, from which area fractions were calculated.
Statistical analysis was performed on the pores, as detailed in Section II, and the results presented in Figure  6. Figure  6 (a) shows the mean pore radii (lm) vs oxygen content and the average pore radius is consistently less than 0. 3 lm for the oxygen contents studied. The data indicate that the average pore radius is not affected by oxygen content. Herein, measured pore sizes are expressed as pore radii, in lm, determined from sectioned metallographic planes. It is important to note that this is not a true representation of particle size, since it is a 2-dimensional simplification of a 3-dimensional feature, and the probability of sectioning a pore directly along the equator is highly unlikely. As a result, the measured radii of the pores are a slight underestimation of the actual 3-dimensional radii of the inclusions that previously accommodated these pores.
The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) involving a total of 26 studies: 25 included men with European ancestry (22 301 cases and 22 320 controls) and 3 included African-American men (623 cases and 569 controls). The majority of studies were population-based or hospital-based case-control studies (see online supplementary table  S2 ). The Ovarian Cancer Association Consortium (OCAC), involving a total of 46 studies. Some studies were case-only and their data were combined with case-control studies from the same geographical region (leaving 36 study groupings). Of these groupings, 33 included women from populations with predominantly European ancestry (16 287 cases (14 542 with invasive disease) and 23 491 controls), 25 included Asian women (813 cases (720 with invasive disease) and 1574 controls), 17 included African-American women (186 cases (150 with invasive disease) and 200 controls) and 29 included women of other ethnic origin (893 cases (709 with invasive disease) and 864 controls).
Genotyping Three PALB2 variants c. 2323C>T ( p. Gln775*), c. 3116delA ( p. Asn1039IIefs) and c. 3549C>G ( p. Tyr1183*) were unable to be designed for measurement on the custom Illumina iSelect genotyping array and were not considered further (table  1 ). Genotyping was conducted using a custom Illumina Infinium array (iCOGS) in four centres, as part of a multiconsortia collaboration as described previously. 22 Genotypes were called using Illumina's proprietary GenCall algorithm and then, for the data generated from the rare variant probes, manually confirmed with reference to the positive control sample. Two per cent of samples were provided in duplicate by all studies and 270 HapMap2 samples were genotyped in all four genotyping centres. Subjects with an overall call rate <95% were excluded.
Addition of further principal components did not reduce inflation further. Data from all breast cancer studies were included to assess statistical significance. Data from cases selected for inclusion based on personal or family history of breast cancer were excluded in order to obtain unbiased OR estimates for the general population of white European women (leaving 37 039 cases and 38 260 controls from 32 studies). Multiple testing was adjusted for using the Benjamini-Hochberg procedure to control the false discovery rate, with a significance threshold of 0. 05. 25 eported p values are unadjusted unless otherwise stated. Reported CIs are all nominal. We included two race-specific principal components in each of the main breast cancer analyses of Asian and African-American women. Similar analyses were conducted using the data from PRACTICAL and OCAC, consistent with those used previously.
Acknowledgements The authors thank the following for their contributions to this study:  Qin Wang  (BCAC),  Lesley McGuffog , and  Ken Offit  (CIMBA),  Andrew Lee , and  Ed Dicks  and the staff of the  Centre for Genetic Epidemiology Laboratory , staff of the CNIO genotyping unit,  Sylvie LaBoissière  and  Frederic Robidoux  and the staff of the  McGill University and Génome Québec Innovation Centre , staff of the  Copenhagen DNA laboratory , and  Sharon A Windebank ,  Christopher A Hilker ,  Jeffrey Meyer  and the staff of  Mayo Clinic Genotyping Core Facility. The authors also thank  UM1 CA167552  (Willett) Cancer Epidemiology Cohort in Male Health Professionals,  P01 CA87969  (Stampfer) Dietary and  Hormonal Determinants of Cancer in Women ,  UM1 CA186107  (Stampfer) Long term multidisciplinary study of cancer in women: The Nurses'  Health Study ,  R01CA141298  (Stampfer) Growth Factors and Lethal Prostate Cancer Signature,  NCI :  K07-CA80668 DAMD17-02-1-0669   NIH/National Center for Research Resources/General Clinical Research Center  grant  MO1-RR000056 ,  R01CA095023 ,  P50-CA159981 ,  NCI CCSG  award  P30-CA008748  ( Memorial Sloan Kettering Cancer Center ),  Department of Defense  ( W81XWH-07-0449 ),  National Health and Medical Research Council of Australia   APP1029974  and  APP1061177 ,  Deutsche Krebshilfe (Maier) ,  Wellcome Trust   Centre for Human Genetics  from the  Wellcome Trust  ( 090532/Z/09/Z ).
Funding Funding for the iCOGS infrastructure came from: the  European Community 's  Seventh Framework Programme  under grant agreement  n°223175  ( HEALTH-F2-2009-223175 ) (COGS),  Cancer Research UK  ( C1287/A10118 ,  C1287/A 10710 ,  C12292/A11174 ,  C5047/A8384 ,  C5047/A15007 ,  C5047/A10692 ,  CRUK C8197/A10123 ), the  National Institutes of Health  ( CA128978 ) and  Post-Cancer GWAS initiative  (No. 1 U19 CA 148537 -the  GAME-ON initiative ), the  Department of Defense  ( W81XWH-10-1-0341 ), the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer, Komen Foundation  for the Cure, the  Breast Cancer Research Foundation , the  Ovarian Cancer Research Fund and Susan G Komen (WF). Further information about the financial report received is outlined in the supplementary file online.
Sample Size The minimum sample size required to obtain a significant result has been calculated with the SPSS version 21. 0 Sample Power program (IBM Corp). The study will include at least 563 patients in each group for a total of 1126 patients, assuming a 20% attrition or dropout rate. With this sample size, the result will be statistically significant (85% statistical power) when comparing both groups, even if they differ by one point only on the subscales of the PHQ measures, with a standard deviation of 5. This will enable us to conclude that the result is different for each group with a 95% confidence level. As previous studies have reported  [11] , we expect that the rate of loss to follow-up will be considerable, despite the strong study design, which includes telephone follow-up to reduce attrition.
Randomization Participants are randomly assigned after informed consent or assent is obtained by a blinded researcher using a computer-generated allocation sequence, assuring that the groups are comparable (ie, without differences in key baseline measures). Each group will include 8-10 patients randomly allocated either to the experimental group (TD-CBT) or to the control group (TAU). They receive this allocation information via email from a graduate student trainee affiliated with the project. The email also provides login and website information for the allocated intervention. One clinical psychologist is assigned to the TD-CBT group; the clinical psychologist involved in the pre-and posttreatment assessment phases will not participate in the TD-CBT therapy. Data managers and statisticians are blinded to the treatment allocation.
Data Collection After providing written informed consent, the participants are registered in the treating center. Pre-and posttreatment assessments are carried out using computerized self-reported screening tests. All pretreatment assessments are performed at the treating PC center after scheduling an appointment with the clinical psychologist. A computer with Internet access is used to collect data. All data are stored on a general virtual website (surveymonkey. com). At all posttreatment follow-up assessments, the same instruments will be completed in person at the treating center. However, if necessary, we will send the participant a link by email to enable the patient to complete the computerized measures at home. Patients are contacted by phone to encourage completion of the questionnaires.
Data Analysis Analysis will be carried out using SPSS version 21. 0 (IBM Corp). Intention-to-treat (ITT) analysis will be performed. The ITT analysis will include all randomized patients in the groups to which they were randomly assigned. Analysis will take into account noncompliance, protocol deviations, dropouts, and anything else that happens after randomization. Using the ITT approach will enable us to include situations likely to occur in actual clinical practice. This "real-life" analytical approach allows us to assess the prognostic balance resulting from the original random treatment allocation, thus providing a more accurate estimation of treatment effect. Missing-data analysis will be computed using Student's t test and chi-square tests. Variables included in the analysis will be severity level, gender, and age; this will allow us to ascertain whether unexpected missing data due to participant dropout are related to chance or not The two randomized groups will be compared in the treatment period; posttreatment; and at 3, 6, and 12 months after treatment finalization.
Acknowledgments We thank the  Ministerio de Economía y Competitividad, Psicofundación , and  Fundación Mutua Madrileña , who kindly helped this project with support funding. We thank all members of the  PsicAP Research Group  who kindly participated in this large project:  María Dolores Gómez Castillo ,  Patricia Tomás Tomás ,  Gonzalo Jiménez Cabré ,  Juan Antonio Moriana ,  Antonio Capafons Bonet ,  María Rosa Pizà ,  Ignasi Ramírez-Manent ,  Laura Agüero ,  Mónica Rodríguez-Enríquez ,  Estefanía Salgado Kvedaras ,  Jorge Perpiñá González ,  Florian Schmitz ,  Carmen Abellán Maeso ,  Ana María Agudo Rodrigo ,  María del Mar Aguilar Uceda ,  Manuel Aires López ,  Ana María Alayeto Sánchez ,  Dolores Alfonso Doménech ,  Vicenta Almonacid Guinot ,  Luciana Moretti ,  Sinuhe Alvarado Torres ,  Sonia Álvarez Gómez ,  Luisa María Andrés Arreaza ,  Ana María Arnaiz Kompanietz ,  María Pilar Arranz García ,  Eugenia Avelino Hidalgo ,  María José Ávila Sánchez ,  Josune Barbero Goicoetxea ,  Manuel Barragán Solís ,  Pilar Barroso López ,  Sofía Bauer Izquierdo ,  Carmen Benavente Torres ,  Pilar Bermejo Ortega ,  Greta Borrás Moreno ,  Alejandro Buendía Romero ,  Carlos Buiza Aguado ,  José Caballero Moral ,  Nieves Caparrós Ezpeleta ,  Alejandro Casado Martínez ,  Cristina Casado Rodríguez ,  Consuelo Castiblanque Ballesteros ,  Francisca Ceinós Vicente ,  Patricia Cordero Andrés ,  Ana Costa Alcaraz ,  Isabel de Andrés Cara ,  Victoria de la Riva Casares ,  Bárbara Díaz Gómez ,  María Dolores Domínguez Manrique ,  Soledad Escolar Llamazares , Encarnación.
Sealing the samples prevented additional exposure to light and moisture. STXM analysis was performed continuously from 2010 to 2015 at Lawrence Berkeley National Laboratory's (LBNL) Advanced Light Source (ALS) at beamline 5. 3. 2. 2 described in detail elsewhere  (Kilcoyne et al. , 2003). The STXM instrument provides raw images with photon counts representing pixel intensities. The pixel intensities are converted to optical density (OD) by the relation ln I I 0 = -µρt where I 0 and I are the photon counts for particle-free and particlecontaining regions of the image, respectively, µ is the mass absorption coefficient, ρ is the material density, and t is the particle thickness. MATLAB algorithms described originally in an earlier publication  (Moffet et al. , 2010)  were used to identify the regions of an aerosol containing BC, organic carbon, and inorganic species.
, 2011). Areas of each of the maps with fewer than 7 conjoined pixels were excluded. Thresholds for maps in Fig. 1e -g were applied to produce binary images. In Fig. 1h , to highlight the BC mixing state, these three binary images are overlaid in the following order: organic, inorganic, sp 2. Morphological information for the compositional regions of interest (inclusion center of mass, convexity, area) was obtained using the MATLAB image processing toolbox and other custom-written algorithms. Particles not completely captured by the image were not included in the analysis. In- dividual particle maps were cropped and stored for interactive and query based plotting. Interactive single particle maps were utilized for quality control of the data and to exclude particles that were misidentified as BC.
For the interactive visualization, all of the particles within a user-defined subset are displayed. Individual particles are selected via a graphical user interface. Upon selection, the raw data for that particular data set are activated to allow further scrutiny of the data. Occasionally, visual inspection indicated particles may have been misclassified. These particles were omitted from the analysis. Specific biological particle types were identified erroneously as BC and were excluded from the data set based on their morphology and/or spectral characteristics. Additionally, small nominally pure BC particles were occasionally unidentified due to the specifics of the initial particle detection methods. Another problem area for the identification of BC particles is regions close to inorganic inclusions due to high background levels caused by non-carbonaceous species.
The organic map shown in Fig. 1e  demonstrates that there are organic coatings surrounding most of the particles. Furthermore, the branched BC particle shows considerable intensity in the organic (C*OOH). The magnitude of the ratio of the pre-edge to the post-edge is proportional to the total mass of non-carbonaceous inorganic species. Inorganic inclusions appear as regions of high intensity in Fig. 1f  or blue areas in Fig. 1h. Sea salt particles are common in the region and, based on their cubic morphology, the larger particles containing inorganic regions are likely sea salt. Smaller particles having inorganic dominant regions likely contain sulfate  (Moffet et al. , 2013). Based on these maps, the mixing state of the particles was stored in a database for the subsequent analysis of BC morphology.
Acknowledgements. Funding for sample collection during the CARES study was provided by the  Atmospheric Radiation Measurement Program  sponsored by the  US Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (OBER) ,  Climate and Environmental Sciences Division (CESD). Funding for the data analysis was provided by the  US DOE 's  Atmospheric System Research Program ,  BER  under grants  DE-SC0008643  and  DE-SC0008613. The STXM/NEXAFS particle analysis was performed at beamlines 11. 0. 2 and 5. 3. 2 at the  Advanced Light Source (ALS)  at  Lawrence Berkeley National Laboratory. The work at the ALS was supported by the Director,  Office of Science, Office of Basic Energy Sciences , of the  US DOE  under contract  DE-AC02-05CH11231. We thank  A. L. D.