dataesr/scientific-paragraphs-categorization huggingface dataset:
Brentuximab vedotin, an anti-CD30 monomethyl auristatin E (MMAE) conjugate approved for the treatment of relapsed Hodgkin's lymphoma and relapsed systemic anaplastic large cell lymphoma,  226, 227  is being investigated, either as a single therapeutic agent or combined with (often cyclophosphamide-based) chemotherapy, in patients affected by acute myeloid leukemia (NCT01830777), mast cell leukemia or systemic mastocytosis (NCT01807598); DLBCL or other forms of lymphoma (NCT01777152; NCT01805037; NCT01841021; NCT01925612), and CD30 + germ cell tumors (NCT01851200). The clinical profile of cetuximab, invariably in combination with chemotherapy or multimodal therapy, is being evaluated in subjects bearing esophageal or gastric carcinoma (NCT01787006; NCT01904435), brain neoplasms (NCT01884740) or other advanced solid tumors (NCT01727869; NCT01787500).
Rituximab, given as a standalone therapeutic regimen or combined with brentuximab vedotin, dexamethasone or INCB040093 (an orally available inhibitor of the δ isoform of the 110 kDa catalytic subunit of class I phosphoinositide-3-kinases)  228, 229  is under investigation for its therapeutic potential in cohorts of individuals with various B-cell malignancies (NCT01905813), Hodgkin's lymphoma (NCT01900496), neuroblastoma-associated opsoclonus myoclonus syndrome (a rare neurological disorder of unclear origin)  230  (NCT01868269), and prostate carcinoma (NCT01804712). The clinical profile of trastuzumab, in combination with either conventional chemotherapy or lapatinib (a tyrosine kinase inhibitor currently approved for use in HER2 + breast carcinoma patients),  231  is being assessed in patients bearing bladder neoplasms (NCT01828736) or other solid tumors (NCT01771458).
Pertuzumab is being tested in combination with trastuzumab as a first-line therapeutic intervention in patients with gastric or gastresophageal carcinoma (NCT01774786). Catumaxomab is now being evaluated as a standalone therapeutic agent in patients with gastric peritoneal carcinomatosis (NCT01784900) or ovarian carcinoma (NCT01815528). Denosumab plus standard chemotherapy is under investigation as a first-line intervention against metastatic NSCLC (NCT01951586). Ofatumumab, a human IgG1 targeting CD20 that is approved by FDA for the treatment of CLL,  232, 233  is currently being tested, in combination with cyclophosphamide-based chemotherapy or human recombinant IL-18,  234  in patients with other forms of leukemia (NCT01762202) or NHL (NCT01768338). Alemtuzumab, a CD52-specific humanized IgG1 that is licensed for use in CLL patients,  235, 236  is being evaluated as a consolidation regimen upon cyclophosphamide-based chemotherapy in patients with peripheral T-cell lymphoma (NCT01806337) or in combination with donor lymphocyte infusions in subjects with multiple hematological malignancies (NCT01875237).
Finally, panitumumab, an EGFR-specific humanized IgG2 currently licensed for use in CRC patients,  237, 238  is under investigation as a therapeutic measure against anal cancer (NCT01843452) and bladder carcinoma (NCT01916109) (Table  4 ). As for the clinical trials listed in our previous Trial Watches dealing with this topic,  5, 6  the following studies have changed status: NCT00560794, NCT00848926, NCT00866047, and NCT00986674, now listed as "Active, not recruiting"; NCT00563680, NCT00947856, NCT00778167, and NCT00838201, now listed as "Completed"; NCT01614795, now listed as "Temporarily closed to accrual"; NCT00385827, NCT01335204, and NCT01513317, now listed as "Terminated"; and NCT01034787, whose status is now "Unknown. " NCT01513317, comparing siltuximab (a chimeric mAb that neutralizes IL-6, also known as CNTO 328)  239, 240  plus best supportive care to placebo plus best supportive care in anemic patients with low/intermediate-risk myelodysplastic syndrome, has been stopped after the interim analysis, based on lack of efficacy (although there were no safety concerns).
Conversely, the reasons underlying the suspension of NCT01614795 and the termination of both NCT00385827 and NCT01335204 are not available. Among "Active, not recruiting" and "Completed" studies, (preliminary or definitive) results appear to be available for NCT00560794; 241 NCT00778167; NCT00838201; NCT00848926; NCT00866047; NCT00947856;  242  and NCT00986674 (source http://www. clinicaltrials. gov).
18 GD2 Neuroblastoma II recruiting Combined with irinotecan and temozolomide NCT01767194 Conatumumab TraILr2 reproductive tract cancers I Not yet recruiting Combined with birinapant NCT01940172 Denosumab raNKL NSCLC II Not yet recruiting as single agent NCT01951586 Lintuzumab CD33 Leukemia I/II recruiting Combined with cytarabine NCT01756677 I/II recruiting Combined with cisplatin and gemcitabine NCT01763788 Necitumumab eGFr NSCLC II recruiting Combined with carboplatin and paclitaxel NCT01769391 II recruiting Combined with cisplatin and gemcitabine NCT01788566 Breast carcinoma II Not yet recruiting Combined with capecitabine and docetaxel NCT01939054 Cervical cancer II recruiting Combined with chemoradiotherapy NCT01938105 Nimotuzumab eGFr Gastric cancer III recruiting Combined with irinotecan NCT01813253 NSCLC I/II Not yet recruiting Combined with afatinib NCT01861223 rectal cancer II recruiting Combined with radiotherapy, capecitabine and oxaliplatin NCT01899118 Leukemia II Not yet recruiting Combined with cyclophosphamide and fludarabine NCT01762202 Ofatumumab CD20 NHL I recruiting Combined with rIL-18 NCT01768338 Panitumumab eGFr anal cancer II recruiting Combined with capecitabine, mitomycin and radiotherapy NCT01843452 Bladder cancer II recruiting Combined with carboplatin and gemcitabine NCT01916109 Pertuzumab Her2 Gastric cancer Gastresophageal cancer III recruiting Combined with capecitabine, cisplatin, 5-FU and trastuzumab NCT01774786 B-cell malignancies I recruiting Combined with a PI3K inhibitor NCT01905813 rituximab CD20 Hodgkin's lymphoma 0 Not yet recruiting Combined with brentuximab vedotin NCT01900496 Neuroblastoma III recruiting Combined with dexamethasone NCT01868269 Prostate cancer 0 recruiting as single agent NCT01804712 (source http://www.
*between 2012, October 1st and the day of submission. **refers to hitherto investigational tumor-targeting mabs as well as to FDa-approved tumor-targeting mabs employed as off-label interventions. mAb Sar650984 TF2 Trastuzumab Abbreviations: 5-(continued) Target(s) Indication(s) Phase Status Note Ref. CD38 MM I recruiting Combined with lenalidomide and dexamethasone NCT01749969 Cea Breast cancer I/II recruiting as single agent NCT01730612 Medullary thyroid carcinoma I/II recruiting as single agent NCT01730638 Her2 Bladder cancer II active, not recruiting Combined with carboplatin, cisplatin and gemcitabine NCT01828736 recurrent or metastatic tumors II recruiting Combined with lapatinib NCT01771458.
(1996). However, our formulation does not include information about tourist affluence due to the lack of appropriate data in the region. The value of K in Eq. (  3 ) is chosen so that the maximum benefit obtained from RLM is of the same order of magnitude as the one obtained from HEP, if they were considered separately. This makes it possible to analyse a double-objective configuration with objectives of equivalent economic value, a situation that could occur in the future. The inflows to the reservoir are modelled with CEQUEAU  (Morin et al. , 1975) , a semi-distributed hydrological model already applied by EDF for previous climate change impact studies on different mesoscale French basins. Snow accumulation and melt, effective rainfall, infiltration and evapotranspiration fluxes are estimated for each of the 99 subbasins from daily series of mean areal precipitation and surface air temperature.
Acknowledgements. This work was initiated within the  RI-WER2030   research project on Regional Climate, Water, Energy Resources and Uncertainties  from  1960  to 2030 (grant number  ANR-08-VULN-014-01  funded by the  VMCS Program  of the  French National Research Agency ; http://www. lthe. fr/RIWER2030/). It also contributes to the  COMPLEX Project on Knowledge Based Climate Mitigation Systems for a Low Carbon Economy (European Collaborative  Project  FP7-ENV-2012  number: 308601; http://www. complex. ac. uk/). Edited by: N. Ursino The publication of this article is financed by  CNRS-INSU.
However, the roundoff error bounds in their methodologies are computed a posteriori by simulating the fixedpoint design and comparing its output to that of a floating-point design. Also when determining fixedpoint formats of intermediate variables, most of these works use a technique introduced by Sung et al. (Kim et al. , 1998). This technique consists in using floatingpoint simulation to estimate the range of intermediate computations and convert the program to fixed-point arithmetic. In  (Nikolic et al. , 2007) , the Sung technique is used to suggest a number of linear algebra routines. This simulation based technique has two drawbacks: 1. Its duration is exponentially proportional to the number of input variables. It is therefore impractical for large problems. 2. It provides no strict guaranties that intermediate computations will 1 See http://gappa.
Accurate and Compact Approaches Following Equation (2), a first straightforward approach to write code for matrix multiplication consists in synthesizing a program for each dot-product A i,: • B :, j. Algorithm 1 below implements this approach, where DPSynthesis(A i,: , B :, j ) produces a fast and numerically certified code for the computation of A i,: • B :, j. Remark that Algorithm 1 issues m × p requests to the DPSynthesis routine. At runtime, only one call to each generated code will be issued, for a total of m × p calls. To significantly reduce the size of the whole program, the computed codes could be refactored to evaluate more than one dot-product. Algorithm 2 below, Algorithm 1 Accurate algorithm.
Input: Two matrices A ∈ Fix m×n and B ∈ Fix n×p Output: Code to compute the product A • B Algorithm: 1: for 1 ≤ i ≤ m do 2: for 1 ≤ j ≤ p do 3: DPSynthesis(A i,: , B :, j ) 4: end for 5: end for Algorithm 2 Compact algorithm. Algorithm: 1: U ← A 1,: ∪ A 2,: ∪ • • • ∪ A m,: , with U ∈ Fix 1×n 2: V ← B :,1 ∪ B :,2 ∪ • • • ∪ B :,p , with V ∈ Fix n×1 3: DPSynthesis(U, V ) whose input and output are the same as Algorithm 1, pushes this idea to the limits by merging element by element the matrices A and B into a unique row U and column V , respectively. Merging two fixed-point intervals means computing their union. Using this approach, it issues a unique call to DPSynthesis at synthesis. But, at runtime, m × p calls to the synthesized code are still needed to evaluate the matrix product. Let us now illustrate the differences between these two algorithms by considering the problem of generating code for the product of the following two fixedpoint interval matrices: A = [-1000, 1000] [-3000, 3000] [-1, 1] [-1, 1] and B = [-2000, 2000] [-2, 2] [-4000, 4000] [-10, 10] , where A 1,1 and Then, DPCode U,V is generated that evaluates the dotproduct of U and V.
How to Achieve Tradeoffs On the first hand, when developing a numerical application for embedded systems, the amount of program memory available imposes an upper bound on the code size. On the other hand, the nature of the application and its environment help in deciding on the required degree of accuracy. Once these parameters set, the programmer tries Algorithms 1 and 2. Either the accurate algorithm (Algorithm 1) is not accurate enough: a solution we do not discuss here consists in adapting the fixedpoint computation wordlengths to reach the required accuracy, as in  (Lee and Villasenor, 2009). Or the compact algorithm (Algorithm 2) does not satisfy the code size constraint: other solutions must be considered such as adding more hardware resources. Finally, the only uncertainty that remains is when Algorithm 1 satisfies the accuracy constraint but has a large code size while Algorithm 2 satisfies the code size bound but is not accurate enough.
Experimental Environment Experiments have been carried out on an Intel Xeon Quad Core 3. 1 GHz running the GNU/Linux environment. In our experiments, we used 3 structured and 1 unstructured benchmarks. For structured benchmarks, the large coefficient distributions throughout the matrices follow different patterns. This is achieved through weight matrices, as shown in Table 2 where W i, j corresponds to the element of row i and column j of the considered weight matrix. Notice, that the dynamic range defined as max(W i, j )/min(W i, j ) is the same for all benchmarks, and is equal to 2 n/2. The reason we did not directly use these matrices in our experiments is that the 3 first patterns correspond to structured matrices in the usual sense and that better algorithms to multiply structured matrices exist  (Mouilleron, 2011).
To obtain random matrices where the large coefficients are still distributed according to the pattern described by the weight matrices, we computed the Hadamard product of Table  2  matrices with normally distributed matrices generated using Matlab R 's randn function. Finally, notice that the matrices obtained this way have floating-point coefficients. In order to get fixedpoint matrices, we first converted them to interval matrices by considering the radius 1 intervals centered at each coefficient. Next, the floating-point intervals are converted into fixed-point variables by considering the smallest fixed-point format that holds all the interval's values.
This allows them to relate the observed radiative biases in the cloud regimes with the meteorological conditions in which they occur. They focus their analysis on cyclonic conditions, but they do not study the role of noncyclonic conditions. We extend that methodology here to provide insights in to the biases in solar radiation over the Southern Ocean in the latest generation of climate models. In particular, we calculate the contribution of cyclonic and noncyclonic conditions to the climatological RSR, allowing us to assess their relative importance in the climatological biases observed in models. Section 2 describes the methodology and the data used. Section 3 presents a climatological overview of the RSR biases in the CMIP5 ensemble of atmosphere-only models. Then, section 4 presents the results of the cyclone composite for a subset of models and looks at the role of cyclones in the RSR climatological biases.
Then, the relative frequency of occurrence (RFO) of each regime at each grid box around the cyclone center is found. This allows us to identify the typical synoptic conditions in which the cloud regime biases occur. This could be potentially used to develop hypotheses of model changes that target the meteorological conditions that prevail in those regions of the cyclones that are mostly responsible for the radiation bias, as shown by  Bodas-Salcedo et al. (2012). As the focus of this paper is to study the solar radiation biases over the Southern Ocean in climate models, we restrict our analysis to the summer months of December, January, and February. We use daily-mean cloud retrievals (a, CTP, and CF) from the D1 series of the ISCCP database  (Rossow and Schiffer 1999)  and daily-mean radiative fluxes from Clouds and the Earth's Radiant Energy System (CERES) SYN1deg-Day_Ed2.
6 dataset  (Wielicki et al. 1996). Daily-mean TOA radiative fluxes from the ISCCP flux data (FD) dataset  (Zhang et al. 2004 ) are used as a second data source, to provide an estimate of the observational uncertainty. We use CERES as preferred dataset because it is a more direct measurement of the TOA fluxes, and it also uses a better scene identification from the Moderate Resolution Imaging Spectroradiometer, better calibration and stability of the satellite instruments over time, and other advantages  (Wielicki et al. 1996; Loeb et al. 2009). We also use the 10-yr monthly climatology of TOA radiative fluxes from the CERES Energy Balanced and Filled (EBAF) Ed2. 6r dataset  (Loeb et al. 2009 ). We use daily-mean sea level pressure from ERA-Interim  (Dee et al. 2011)  as the observational dataset for the identification of cyclone centers.
We also use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard CALIPSO  (Winker et al. 2009). In particular, we use the GCM-Oriented CALIPSO Cloud Product (GOCCP;  Chepfer et al. 2010). Model data are obtained from the Atmospheric Model Intercomparison Project (AMIP) experiment of the CMIP5 archive. Table  1  shows the list of models that had submitted data from the AMIP experiment to the CMIP5 archive at the time of conducting this study (including expanded names for these models). These models are used to provide an overview of the climatological biases in a relatively large ensemble of models in section 3. The Cloud Feedback Model Intercomparison Project phase 2 (CFMIP2), which is part of CMIP5, requests additional diagnostics to be produced from a subset of the CMIP5 experiments.
Radiative impact of cloud regimes in cyclonic conditions Having established that the climatological biases are mostly attributable to cyclonic grid points, we classify the cyclonic grid points into seven cloud regimes, using the methodology of  Williams and Webb (2009). Data from IPSL-CM5A-LR and IPSL-CM5A-MR are not used because of problems in the implementation of the ISCCP simulator in these models. As in the previous section, we use daily data from the months of December, January, and February from 2002 to 2007, for both the models and the observations. To relate the radiative biases to errors in the representation of the different cloud regimes, we compute the mean contribution of each cloud regime to the total error in RSR (model minus CERES) in the cyclone composite domain, as shown by the diamonds in Fig.
ISCCP-FD (leftmost bar in each regime in Fig. 6 ) shows very small and generally positive errors with respect to CERES in all clusters. The consistency in the RSR between these datasets is quite remarkable. Not only do the climatological values agree quite well (with ISCCP-FD slightly biased high), but there are no large regime-dependent biases either. Figure  6  also includes clear sky as an additional regime. The clear-sky contribution to the error is negligible in all models but MRI-CGCM3. MRI-CGCM3 is the only model that produces a significant amount of clear-sky grid boxes (daily means at 2. 58 resolution) in the cyclone composite domain. To check whether the mean cluster contributions shown in Fig. 6  are representative of the entire composite domain, we obtain the cloud regime with the largest contribution to the total RSR error for each grid box in the composite domain (Fig.
As the previous analysis shows that this regime is the regime responsible for most of the radiative biases in models over the Southern Ocean, it is relevant to understand the types of clouds that make this regime. This information can then be used by modelers to direct development efforts to those cloud types that are responsible for the errors in the radiation fields. We now use CALIPSO-GOCCP data to understand the cloud scenes within the midlevel ISCCP regime. GOCCP produces instantaneous profiles of the lidar scattering ratio (SR) at 480-m vertical resolution and 330-m horizontal resolution. The SR is the ratio of the attenuated total backscatter measured by CALIOP to the attenuated molecular backscatter that would be measured in the presence of a pristine atmosphere, without clouds or aerosols.
Similarly, CALIPSO-GOCCP TABLE 2. Contributions to the DJF-mean RSR biases over the Southern Ocean (408-708S) from cyclonic (cy) and noncyclonic (nc) grid boxes. Angle brackets denote area-weighted means, so hRFO cy i Á hRSR cy i 6 ¼ hRFO cy Á RSR cy i. Values for the hRFOi (%), hRSRi (W m 22 ), and hRFO Á RSRi (W m 22 ) are shown. The first row shows the absolute values for CERES, and the rows below show the differences of ISCCP, ERA-Interim, and the models with respect to CERES. As we use the ERA-Interim mean sea level pressure for CERES and ISCCP, the RFOs of these three datasets are identical by construction. The last row is the average of the model differences. The fact that CALIPSO-GOCCP also shows a substantial amount of cloud at lower levels is not necessarily indicative of misclassification by ISCCP, as CALIPSO is able to penetrate through ice clouds.
Atmosphere and  Ocean Research Institute (AORI) ,  The University of Tokyo , Chiba;  National Institute for Environmental Studies (NIES) ,  Ibaraki ; and  Japan Agency for Marine-Earth Science and Technology, (JAMSTEC) , Kanagawa,  Japan Watanabe  et al. 2010  MPI-ESM-LR Max Planck Institute (MPI) Earth System Model , low resolution Acknowledgments. We thank two anonymous reviewers for providing constructive comments that improved the quality of the paper. We thank  H. Douville  for providing comments and  S. Tyteca  for producing the data from the CNRM-CM5 model. Special thanks to  W. Ingram  for helping to make the manuscript clearer. This work was supported by the  Joint DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). This work was funded by the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement 244067 via the  EU Cloud Intercomparison and Process Study Evaluation  project (EUCLIPSE).
Public-sector cancer patients are predominantly those without medical scheme coverage and comprise approximately 90% of black, 80% of mixed-ancestry, 50% of Indian/Asian, and 30% of the white population  (24). At breast cancer diagnosis, the NHLS routinely performs histology and immunohistochemistry (IHC) for ER, PR, and HER2. For the present study, we included all publicsector female breast cancer cases diagnosed at the age of 20 years or older between 2009 and 2011 inclusive (n ¼ 11,921). Data on date of birth, date of breast cancer diagnosis, hospital, province, laboratory name, laboratory reports (approximately 2 per patient), and race were obtained from NCR. NCR predicts race using a surname analysis hot-deck imputation algorithm and provides plausible race-specific cancer distributions (Supplementary Methods).
As the primary information was extracted from pathology reports, data on tumor staging was missing for over 80% of women and so was not included in the analysis. Our analyses focused principally on ER, which was the receptor most frequently determined. PR and HER2 statuses were only included for those women for whom ER status was known [i. e. , almost all of those with PR/HER2 status known (>99%)]. ER and PR were considered positive if >1% staining was present, as per international guidelines  (19)  and HER2 as positive if FISH was positive or HER2 score was 3.
2 ). At ages 50 years and older, ER þ incidence rates accelerated at a slower rate (1% to 3% per year), whereas ER À rates plateaued, or, in white women, declined slightly. Visual examination shows some convexity in the ER þ curves. When compared with data from the SEER database, ER À percentages in black women ages 40 to 70 years from this study overlapped with those for recently diagnosed U. S. black women (Fig. 3 ). For younger patients, proportions were lower than in the United States and over age 70 they were higher. In contrast, white women generally had a greater proportion of ER À disease than their U. S. counterparts, except for women diagnosed at extremely young or old ages.
Grant Support This work was supported by a  Postdoctoral Fellowship  from the  International Agency for Research on Cancer  (to C. Dickens) and partially supported by the  European Commission FP7 Marie Curie Actions-People-Co -funding of regional, national, and international programmes (COFUND). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U. S. C. Section 1734 solely to indicate this fact. Received May 27, 2014; revised July 2, 2014; accepted July 29, 2014; published OnlineFirst August 20, 2014.