dataesr/scientific-paragraphs-categorization huggingface dataset:
Unfortunately, details of hormone receptor scoring for all mutation carriers were not available to standardise definitions across centres. However, data collected by CIMBA are more representative of typical assessment of pathology conducted in routine practice, and the distributions of hormone receptors' status across different study centres and countries in CIMBA were generally consistent. There was some variation in the distribution of some variables, including ER status, probably due to changing assay thresholds and detection methods over time and from country to country. Therefore, adjustments based on calendar year of diagnosis and country of origin were included for all analyses. Missing data for some variables, including HER2 status, and the very small number of male BRCA1 mutation carriers in the study may have impacted the statistical power to detect associations.
CIMBA collects data only on BRCA1 and BRCA2 mutation carriers. Therefore, to compare the tumour characteristics of MBC from the general population, we took advantage of the publicly available SEER data  [20]. Although the U. S. SEER program is the largest source of epidemiologic information on the incidence and survival rates of cancer, it includes data from a single country, and this represents a limitation when attempting to generalise our findings to what one would expect in a collaborative international consortium. However, results from this study, based on a large, multicentre series, replicated previous findings of much smaller studies carried out in single populations  [14] [15] [16] [17] , providing some reassurance that our results were not biased by the different selection of cases in SEER and in CIMBA.
Acknowledgements BCFR: This work was supported by grant  UM1 CA164920  from the  National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the  National Cancer Institute  or any of the collaborating centres in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organizations imply endorsement by the U. S. government or the BCFR. BCFR-AU:  Acknowledge Maggie Angelakos ,  Judi Maskiell ,  Gillian Dite  and  Helen Tsimiklis. BCFR-NY: We thank members and participants in the New York site of the  Breast Cancer Family Registry  for their contributions to the study. BCFR-ON: We thank members and participants in the  Ontario Familial Breast Cancer Registry  for their contributions to the study. BFBOCC-LT is partly supported by  Research Council of Lithuania  grant  LIG-07/2012.
The investigators at  The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust  are supported by an  NIHR  grant to the  Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. RE and Elizabeth Bancroft are supported by  Cancer Research UK  grant  C5047/A8385. RE is also supported by  NIHR support of the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. RE is supported by  NIHR  support to the  Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. FCCC: The authors acknowledge support from  The University of Kansas Cancer Center  ( P30 CA168524 ) and the  Kansas Bioscience Authority   Eminent Scholar Program. AKG was funded by  5U01CA113916 ,  R01CA140323 , and by the  Chancellors Distinguished Chair in Biomedical Sciences Professorship.
HRBCP is supported by  The Hong Kong Hereditary Breast Cancer Family Registry  and the  Dr. Ellen Li Charitable Foundation ,  Hong Kong. We the thank  Hong Kong Sanatorium and Hospital  for their continual support. HUNBOCS:  Hungarian Breast  and  Ovarian Cancer Study  was supported by  Hungarian  research grants  KTIA-OTKA CK-80745 ,  OTKA K-112228  and the  Norwegian EEA Financial Mechanism   Hu0115/NA/2008-3/OP-9. We thank the  Hungarian Breast and Ovarian Cancer Study Group members  ( Janos Papp ,  Tibor Vaszko ,  Aniko Bozsik ,  Timea Pocza ,  Judit Franko ,  Maria Balogh ,  Gabriella Domokos ,  Judit Ferenczi ,  Department of Molecular Genetics, National Institute of Oncology, Budapest, Hungary ) and the clinicians and patients for their contributions to this study. HVH: We thank the  Oncogenetics Group (VHIO) , and the  High Risk and Cancer Prevention Unit  of the  University Hospital Vall d'Hebron.
OCGN: We thank members and participants in the  Ontario Cancer Genetics Network  for their contributions to the study. OSUCCG is supported by The  Ohio State University Comprehensive Cancer Center. Leigha Senter ,  Kevin Sweet ,  Caroline Craven  and  Michelle O'Conor  were instrumental in accrual of study participants, ascertainment of medical records and database management. Samples were processed by the  OSU Human Genetics Sample Bank. PBCS: This work was supported by the  ITT (Istituto Toscano Tumori ) grants 2011-2013. SEABASS:  Ministry of Science, Technology and Innovation ,  Ministry of Higher Education  ( UM. C/HlR/MOHE/06 ) and  Cancer Research Initiatives Foundation. We thank  Yip Cheng Har ,  Nur Aishah Mohd Taib ,  Phuah Sze Yee ,  Norhashimah Hassan  and all the research nurses, research assistants and doctors involved in the MyBrCa Study for assistance in patient recruitment, data collection and sample preparation.
In addition, we thank  Philip Iau ,  Sng Jen-Hwei  and  Sharifah Nor Akmal  for contributing samples from the  Singapore Breast Cancer Study  and the  HUKM-HKL Study , respectively. The  Malaysian Breast Cancer Genetic Study  is funded by research grants from the  Malaysian Ministry of Science, Technology and Innovation, Ministry of Higher Education  ( UM. C/HIR/MOHE/06 ) and charitable funding from  Cancer Research Initiatives Foundation. SWE-BRCA collaborators are supported by the  Swedish Cancer Society. Swedish scientists participating as SWE-BRCA collaborators are as follows: from  Lund University  and  University Hospital :  Åke Borg ,  Håkan Olsson ,  Helena Jernström ,  Karin Henriksson ,  Katja Harbst ,  Maria Soller ,  Ulf Kristoffersson ; from  Gothenburg Sahlgrenska University Hospital :  Anna Öfverholm ,  Margareta Nordling ,  Per Karlsson ,  Zakaria Einbeigi ; from  Stockholm and Karolinska University Hospital: Anna von Wachenfeldt ,  Annelie Liljegren ,  Annika Lindblom ,  Brita Arver ,  Gisela Barbany Bustinza ,  Johanna Rantala ; from  Umeå University Hospital :  Beatrice Melin ,  Christina Edwinsdotter Ardnor ,  Monica Emanuelsson ; from  Uppsala University :  Hans Ehrencrona ,  Maritta Hellström Pigg ,  Richard Rosenquist ; from  Linköping University Hospital :  Marie Stenmark-Askmalm ,  Sigrun Liedgren.
We thank  Sarah Sawyer  and  Rebecca Driessen  for assembling this data and  Ella Thompson  for performing all DNA amplification. WCP: BYK is funded by the  American Cancer Society Early Detection Professorship  ( SIOP-06-258-01-COUN ) and  National Center for Advancing Translational Sciences (NCATS)  grant  UL1TR000124. Funding for the iCOGS infrastructure came from: the  European Community 's  Seventh Framework Programme  under grant agreement number  223175  ( HEALTH-F2-2009-223175 ) (COGS), Cancer Research UK  ( C1287/A10118 ,  C1287/A 10710 ,  C12292/A11174 ,  C1281/ A12014 ,  C5047/A8384 ,  C5047/A15007 ,  C5047/A10692 ,  C8197/A16565 ), the  National Institutes of Health  ( CA128978 ) and  Post-Cancer GWAS initiative  ( 1U19 CA148537 ,  1U19 CA148065  and  1U19 CA148112  -the  GAME-ON Initiative ), the  U.
This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512 × 512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.
Then, fractal geometry was used to determine benign and malignant microcalcification clusters, and in particular, a Bfractal zone^and BEuclidean zones^(nonfractal) were defined. The authors analyzed 118 images of 59 patients. According to their results, the probability that fractal breast lesions are malignant is between 74 and 98% and the probability that Euclidean breast lesions are benign is between 76 and 96%. Arikidis et al. [8]  presented multiscale active contours method (MAC) which enable single microcalcifications to be segmented. This method requires the seed contour to be initiated manually. In  [8] , rectangular ROIs 81 × 81 pixels in size were analyzed and experiments were carried out for the DDSM database, with the reported mean value of the area overlap measure of 0. 61 ± 0.
15. Duarte et al. [9]  presented a geometric active contour method (GAC) for segmenting single microcalcifications. In every instance, the active contour is initiated for a single microcalcification. In  [9] , researches used 1000 rectangular ROIs taken from mammograms from the DDSM database, sized from 20 × 20 pixels to 41 × 41 pixels. Duarte et al. [9]  report that they obtained a mean value of the area overlap measure of 0. 52 ± 0. 20. The purpose of this publication was to propose solutions for the following: & Obtain the best possible segmentation results. & Achieving a fast operation of all the methods used. This project uses morphological image transformations  [23, 26]  to detect microcalcifications, and then watershed segmentation  [23, 26]  which makes it possible to extract the shape of microcalcifications just as in  [22, 27].
Image Dataset Used The research project used 220 ROIs with the constant dimensions of 512 × 512 pixels, in an 8-bit format, obtained from mammograms with the original high resolution (43. 5 and 50 μm/pixel, digitized using the following scanners-H o w t e k 9 6 0 , L u m i s y s 2 0 0 L a s e r, a n d H o w t e k MultiRad850), which came from the publicly accessible DDSM database  [31, 32]. Of that number, 110 ROIs contain benign lesions, and the remaining 110 ROIs show malignant cases. The images were selected by a breast radiologist with 10 years of experience and are mainly fatty breast cases from different patients. Each ROI corresponds to a different patient. It should be noted that 20 ROIs, or more exactly 10 benign and 10 malignant ones, were used to determine the necessary parameters allowing the segmentation process to be controlled.
This is illustrated in Fig. 1. For every case in the DDSM database, there is a radiological diagnosis available. In addition, for images with microcalcifications, a coded contour identifying the area in which microcalcifications occur called a ground truth area (GTA) is available. Each case has four images acquired in the CC and MLO projections for the left and the right breast. CC is the cranio-caudal projection showing that central and medial part of the mamma. MLO is the medio-lateral oblique projection. In the experiments, a single view was taken, namely the CC or MLO view for each patient.
In  [8, 9] , only the OV index was analyzed. During research work, ROIs with the constant dimensions of 512 × 512 pixels were analyzed, while for mammograms from the DDSM database, there are GTA contours identifying the areas in which microcalcifications occur. Therefore, a radiologist participated in the experiments carried out and made the appropriate assessments of the detected or undetected actual and assumed microcalcification signals, namely & If the computer method identified a microcalcification in the GTA area correctly, it was classified as true positive (TP).
Selecting Parameters in Computer Method Table  1  presents the values of parameters established for the morphological detection of microcalcifications using 20 preselected ROIs from mammograms from the DDSM database, namely 10 benign and 10 malignant cases The 20-element training set included ROIs containing microcalcifications of various shapes, sizes, numbers, and distribution as well as brightness levels. The watershed segmentation carried out after the microcalcifications are detected is automated and requires no parameters to be used. The parameters presented in Table  1  are selected so that the SI, OF, and OV indices are the highest possible, while the EF index is as low as possible.
This study produced higher average of the OV index than in  [9]  (52%) and in  [8]  (61%). However, it is worth noting that Duarte et al. [9]  researched 1000 ROIs from mammograms from the DDSM database, so significantly more experiments were carried out than in this publication (altogether 220 ROIs, with 20 used to determine the parameters of the method, and tests carried out on the remaining 200). What is more, in  [9] , the researchers analyzed various types of microcalcifications and for different types of breast tissues according to their classification to four tissue density categories  [37]. In this study, two types of microcalcifications were analyzed, namely those which are symptoms of malignant cases and those which represent benign cases, and they are generally fatty breast cases.
, 2011; Merz et al. , 2014b). As an example, if we use surface temperature and precipitation rate at a monthly resolution from the Norwegian Earth System Model (NorESM) at the NEEM LIG deposition site, we observe a simulated increase in summer temperature (accumulation rate) of 3. 5 • C (7 mm month -1 ) and a decrease in winter temperature (accumulation rate) of 2 • C (3 mm month -1 ). This seasonality effect indeed leads to a factor of 2 difference between the precipitation-weighted temperature change and the annual mean temperature change between the preindustrial period and LIG. Recently, new information on climatic controls on NEEM δ 18 O ice has emerged from present-day water isotope monitoring and multi-decadal trends from shallow ice cores  (Steen-Larsen et al. , 2011 , 2014). All these datasets coherently document a surprisingly large present-day response of NEEM δ 18 O ice to temperature, with a slope of [0.
Because of stratigraphic disruptions, no continuous record is available at Summit. We thus cannot exclude that low δ 15 N levels observed at GRIP and GISP2 on 122-126 ka ice sections reflect a temporary δ 15 N decrease caused by thermal fractionation or firn shrinking as for the NEEM δ 15 N value at 2384 m (121 ka). We therefore note regional differences for the different available datasets but stress their heterogeneities (time span, discontinuity, and lack of present-day reference) preventing any robust conclusion.
5 ‰ • C -1 based on the calibration with borehole temperature data at other Greenland ice core sites  (Vinther et al. , 2009 (Vinther et al. , , 2010) )  may not be valid for NEEM. Differences between Greenland locations are expected due to changes in the seasonality of precipitation (summer bias at NEEM but not in central or south Greenland) and moisture origin as well as possible changes in boundary layer stability and relationships between the surface and temperature. Applying the multi-decadal temporal slope given above, the LIG δ 18 O ice anomaly at the NEEM deposition site translates into a warming of 2. 9-4. 2 • C, twice smaller than the initial estimate based on Holocene calibrations for other sites. Still, it is difficult to assess whether the present-day calibration can apply to the LIG, marked by a different orbital forcing than today, likely with a reduced sea ice extent and different moisture transport pathways  (Sime et al.
ii. Empirical relationships between accumulation rate and Greenland temperature have been provided by different methods. The dataset obtained in 1952-1955 by  Benson (1962)  remains a reference today for evaluating surface accumulation rate reconstructions above the Greenland ice sheet (e. g. ,  Hawley et al. , 2014; Munk et al. , 2003). Surface accumulation rate and temperature data from 146 sites show an exponential increase in accumulation rate versus temperature. Within the associated 1σ envelope, the increase encompasses the accumulation rate versus temperature increase deduced from the P sat approach. This spatial relationship between accumulation rate and temperature is however associated with a very large uncertainty envelope challenging the validity of any relationship between temperature and accumulation in Greenland.
Conclusions and perspectives In this study, we have compiled the Greenland ice core data and methods available to quantify LIG temperature change. New estimates of temperature change were provided based on δ 15 N and firn densification modeling, largely independent of water stable isotope data. They imply that the mean annual firn temperature at the LIG deposition site, upstream of the current NEEM site, experienced 6-11 • C warming, without correcting for changes in elevation related to ice thickness change. As a comparison, the initial estimate of NEEM community members (2013) based on δ 18 O-temperature relationships calibrated against Holocene data led to a LIG surface air temperature at the upstream NEEM deposition site 5. 7-9. 3 • C warmer than in the preindustrial period  (NEEM community members, 2013).
Acknowledgements. NEEM is directed and organized by the  Centre of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST) ,  France (IPEV, CNRS/INSU, CEA and ANR), Germany (AWI), Iceland (RannIs), Japan (NIPR) ,  South Korea (KOPRI) , the  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland , the  United Kingdom (NERC)  and the  USA (USNSF, Office of Polar Programs)  and the  EU Seventh Framework programs. The Division for Climate and Environmental Physics, Physics Institute, University of Bern , acknowledges continuing support by the  Swiss National Science Foundation  as well as access to the infrastructure at the  Swiss National Supercomputing Centre.
The HadCM3 climate model simulations were carried out using the computational facilities of the  Advanced Computing Research Centre, University of Bristol  -http://www. bris. ac. uk/acrc/. This project benefitted from support from  Past4Future "Climate change -Learning  from the past climate", a collaborative project under the 7th  Framework Programme  of the  European Commission  (grant agreement no. 243908 ). Petra M. Langebroek  is funded by the  Research Council of Norway  through the  IceBed  project ( 221598 ). This work was supported by  European Research Council  Advanced Grant no. 246815  WATERundertheICE. The research leading to these results has received funding from the  European Research Council  under the.
European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 306045  (COMBINISO) and from  Agence Nationale de la Recherche  ( ANR-07-VULN-0009  and  ANR-10-CEPL-0008 ). We thank the two reviewers for their helpful comments that permitted us to significantly improve the first version of this manuscript.
Subjects Data used in the preparation of this article were obtained from the ADNI database (adni. loni. usc. edu) and the AddNeuroMed study. In total, 1008 subjects were included, consisting of 301 controls, 425 MCI, and 282 AD patients. Regarding MCI patients, 87 converted to AD after 1 year (eMCIc), 71 converted to AD after 3 years (lMCIc), and 110 remained stable after 3 years (sMCI). In addition, 157 MCI patients remained stable after 1 year but had no additional follow-ups after that period. We classified these subjects as sMCI-1y and compared them with the other groups in a supplementary analysis. Eight subjects were excluded from the previous groups due to uncertain diagnosis. The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD.
MRI Acquisition Data acquisition for the AddNeuroMed study was designed to be compatible with  ADNI (Jack et al. 2008; Simmons et al. 2009 Simmons et al. , 2011)). In particular, all participants, both from ADNI and AddNeur-oMed, were scanned on a 1. 5 Tesla MRI system using a sagittal 3D T 1 -weighted MPRAGE sequence: repetition time (TR) = 9-13 ms; echo time (TE) = 3. 0-4. 1 ms; inversion time (IT) = 1000 ms; flip angle (FA) = 8°; voxel size = 1. 1 × 1. 1 × 1. 2 mm 3. Images from ADNI were acquired in 58 sites, while images from AddNeuroMed were acquired in 5 sites or centers. We have combined these 2 cohorts in several previous studies  (Spulber et al. 2013; Falahati et al. 2016) , showing that they present similar patterns of atrophy and predictive power in discriminating patients with AD or MCI from controls  (Westman et al.
Image Preprocessing All T 1 -weighted images were preprocessed using the FreeSurfer software, version 5. 1. Briefly, preprocessing included: correction of motion artifacts and spatial distortions due to gradient nonlinearity and B1 field inhomogeneity; removal of nonbrain tissue using a hybrid watershed/surface deformation procedure  (Segonne et al. 2004) ; automated transformation into the Talairach standard space; intensity normalization  (Sled et al. 1998) ; tessellation of the gray/white matter boundary; automated topology correction  (Segonne et al. 2007) ; and surface deformation following intensity gradients to optimally place the gray/white and gray/CSF borders at the location where the greatest shift in intensity defines the transition to the other tissue class  (Fischl and Dale 2000).
Once the cortical models were complete, registration to a spherical atlas took place, which utilizes individual cortical folding patterns to match cortical geometry across subjects  (Fischl et al. 1999). This was followed by parcellation of the cerebral cortex into 68 cortical regions using the atlas by  Desikan et al. (2006)  (Fig. 1 ). In addition to these 68 regions, 7 subcortical structures were also included: hippocampus, amygdala, thalamus, caudate, putamen, accumbens, and pallidum (Fig. 1 ). All data was preprocessed through the HiveDB database system  (Muehlboeck et al. 2014). We excluded 8 subjects that were outliers in cortical thickness and subcortical volume measures.
The within-module degree measures the connectivity of the node within the module compared with the other nodes in the same module. The participation coefficient expresses how strongly a node is connected to other modules and tends to 1 if a node has a homogeneous connection distribution with all the modules and 0 if it does not have any intermodule connections  (Guimera and Amaral 2005; Guimera et al. 2005). The formulas that were used to calculate the global and nodal graph theory measures are provided by  Rubinov and Sporns (2010). We used BrainNet Viewer (http://www. nitrc. org/projects/ bnv/) for network visualization  (Xia et al. 2013).
Differences in the within-module degree and participation coefficient between groups : CTR, controls; sMCI, stable mild cognitive impairment; lMCIc, late mild cognitive impairment converters; eMCIc, early mild cognitive impairment converters; AD, Alzheimer's disease; Lh, left hemisphere; Rh, right hemisphere; G, gyrus. Differences between controls and AD patients survived corrections for multiple comparisons with FDR, while the other differences between groups were significant at an uncorrected level (P < 0. 05). This study was supported by InnoMed, (Innovative Medicines in Europe) an Integrated Project funded by the European Union of the Sixth Framework program priority FP6-2004-LIFESCIHEALTH-5, Life Sciences, Genomics and Biotechnology for Health. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012).