dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Collection and Statistical Methods Relevant demographic, treatment and outcome variables of the Spanish and KU cohorts were combined for analysis. An independent statistical analysis of the combined data set was performed by both groups and any discrepancies between the two independent analyses were resolved by review of source documents. Statistical section-Primary objective was to determine the rate of pathological complete response (pCR) and near pCR as defined above. All analyses were conducted using SPSS statistics version 22 (IBM corporation). All reported p values were two-sided without corrections for multiple comparisons. p values less than 0. 05 were considered to indicate statistically significant results. Overall frequencies and percentages were summarized for race, ethnicity, menopausal status, T-stage, lymph node status, TNM stage, hormone receptor status, germline BRCA mutation status, surgery type, and family history of breast/ovarian cancer  (36).
Treatment Response Pathological complete response and RCB 0+1 for the per protocol study population was 55% (95% CI: 48-62%) and 68% (95% CI: 61%, 75%), respectively. Both pCR and RCB 0+1 rates were higher in the KU cohort compared to the Spanish trial (Table  2 ). pCR and RCB 0+1 rates were lower in stage III disease compared to stage I-II disease (pCR: 37% vs. 63% p=. 002; RCB 0+1: 49% vs. 77%, p=<. 001). When assessed by individual TNM stage, the pCR rates were similar in the KU and Spanish cohorts (Table  2 ). When assessed by germline BRCA status, pCR rate was 56% in BRCA wild type and 59% in BRCA mutationassociated TNBC (p=0. 83) (Figure  2 ). Family history of breast and ovarian cancer did not impact pCR or RCB 0+1 rates using individual cohort positive family history criteria (Supplemental Table  1 ) or using Spanish trial positive family history criteria (data not shown).
Several ongoing randomized phase III trials are evaluating various schedules and combinations of platinum salts in early stage TNBC (NCT02488967, NCT02455141, NCT02441933, NCT02445391, NCT02125344, NCT02032277). Pathological complete response in our study with the CbD regimen was 55% which is similar to pCR attained with addition of carboplatin to anthracycline/taxane chemotherapy. However, the addition of carboplatin to anthracycline-taxane regimens is associated with a significant increase in acute toxicity with only 50-76% of patients completing all study treatment  (14, 16, 25). In contrast, chemotherapy regimen of CbD has a much favorable toxicity profile with 83% of patients completing all 6 cycles of therapy. There were some differences between the KU and Spanish cohorts. Compared to the KU cohort, patients in the Spanish cohort had higher disease burden (larger tumors, more node positive and stage III disease).
Although presenting very robust pathological response data our study does have several limitations. The decision to combine the two series with the purpose of reporting pathological response was made post hoc and was not preplanned. However, this report includes almost 200 TNBC patients treated with the same regimen in three different continents. In this combined analysis more than half of the patients had node positive disease, a third had stage III disease and 16% harbored deleterious BRCA mutation. These patient characteristics are very similar to contemporary randomized neoadjuvant clinical trials in TNBC. We also acknowledge the lack of a control arm in both series; a deficit which can only be addressed in setting of a randomized trial. In fact, an ongoing randomized phase II study is currently comparing neoadjuvant CbD regimen to the anthracycline-taxanecarboplatin regimen used in CALGB 40603 (NCT02413320).
Funding for  M Martin  was supported by research grant from  Instituto de Salud Carlos III (ISCIII ,  PI 12/02684 ) and  FEDER  ( RETICC-RD12/0036/0076 ). "Ayuda cofinanciada por el  Fondo Europeo de Desarrollo Regional (FEDER). Una manera de hacer Europa"). CM Perou was supported by funds from the  NCI Breast SPORE  program  P50-CA58223.
Throughout the patch's life time, its surface area increased linearly at an average rate of 2,400±370 km 2 d À 1 (28,000±4,300 m 2 s À 1 ). The linear increase in A p is typical for dispersion occurring on a scale that is larger than the size of the local eddies  22, 23. As the plankton patch is dispersed by the turbulent oceanic flow, the rate of its expansion is a measure of k e. Following Garret  22  , we formulate the linear relationship between k e and patch expansion rate as follows: k e ¼ 1 8p dA p dt ð1Þ Using this formulation, we estimate k e to be 1,100 ± 150 m 2 s À 1 , which is consistent with previous estimates that are based on in-situ drifter data  24. Satellite observations of time-varying A p were used in conjunction with mixed layer depth (MLD) from ARGO floats to quantify the mixed layer chlorophyll biomass associated with the bloom evolution.
Methods Satellite data. Quantification of bloom dynamics is based primarily on a detailed analysis of ocean colour satellite data. To reduce the surface area masked by clouds we integrate observations from three independent sensors: MODIS-Aqua, MODIS-Terra and MERIS. This approach allows continuous tracking of phytoplankton blooms as they are advected and deformed by the currents and provides detailed quantification ) of small-scale spatio-temporal changes in bloom properties. The data set comprises 8day composites of 4 km Level 3 data obtained from the ocean colour data distribution site (https://oceancolor. gsfc. nasa. gov/). From the ocean-colour satellite data, we extract sea surface concentrations of chlorophyll and POC. Both chlorophyll and POC are calculated using band-ratio algorithms, which are based on blue and green regions of the electromagnetic radiation spectrum.
The band-ratio algorithms were tested against field measurements of chlorophyll  38  and POC  39  , showing good agreement between satellite-derived and in-situ measured values. Lagrangian time series of chlorophyll or POC are extracted by averaging the satellite-derived concentrations over the time-varying patch surface area. Total biomass accumulation is derived by integrating concentrations spatially over the patch surface area and vertically over mixed-layer depth extracted from ARGO float temperature profiles (see below). Lagrangian time series of spatially and vertically integrated POC are used as a measure to the amount of organic carbon accumulated in the course of the observed blooms. (d -1 ) b G =0. 5 G =0. 2 G =0. 1 G =0. 05 G =0. 02 Geostrophic surface currents were obtained from the AVISO database (http://www.
Argo floats. MLD was diagnosed from Argo floats temperature profiles over a 17 0 Â 9 0 area (197-214°E/27-36°N) overlapping the 2007 bloom. MLD is defined as the depth at which temperature differs from a surface value by 0. 2 °C. Argo floats data were collected and have been made freely available by the International Argo Program and the national programmes that contribute to it (http://www. argo. ucsd. edu and http://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System. Ecosystem model for a dispersed nutrient-enriched water patch. We simulate the effect of patch dispersion and consequent dilution on plankton biomass accumulation, by implementing a simple nutrient-phytoplankton-zooplankton ecosystem model in a variable-volume reactor model  26. At time t ¼ 0, the patch receives a pulse of nutrients.
Phytoplankton mortality is neglected under the assumption that zooplankton do not discriminate between live and dead cells. The model does not include losses due to viral and bacterial lysis, and there is no explicit representation of the effect of temperature and light. As a first approximation, we assume uniform fields averaging of temporal and spatial variations in radiation and temperature. The model was run using MATLAB and its built-in ode45 solver. Parameter values were similar to those used in recent ocean ecosystem model studies  40, 41  (Supplementary Table  1 ). Sensitivity tests showed that the presented results are only weakly affected by the choice of parameters (Supplementary Fig. 3 ) or whether or not grazers are recycled as nutrients.
Acknowledgements We would like to thank  O. Aharony ,  N. Arkani-Hamed ,  A. Armoni ,  O. Ben-Ami ,  M. Bochicchio ,  P. Di Vecchia ,  Z. Komargodski ,  A. Sever  and  M. Strikman  for interesting discussions. One of us (E. O. ) would like to thank  Prof. N. Hale  for useful correspondence about leg2cheb and  P. Holoborodko  for useful suggestions on our Advanpix/Matlab code. The work of S. Y. is supported in part by the  Israel Science Foundation (ISF) Center of Excellence  (grant  1989/14 ) ; by the  US-Israel bi-national science foundation (BSF)  grant  2012383  and by the  German-Israel bi-national science foundation (GIF)  grant  I-244-303. 7-2013.
The PF candidates are used to reconstruct jets using the anti-k T algorithm  [38]  implemented using the FASTJET package  [39, 40]. Charged particles not originating from the primary vertex are omitted in the jet clustering. The jet momentum is the vector sum of the momenta of all particles clustered in the jet. The jet energy scale is determined from a detailed simulation of the CMS detector. The estimated pileup contribution to the jet energy is subtracted using an event-by-event jet area based correction  [41, 42]. Further corrections are applied to account for the detector response to hadrons as a function of the jet p T and η. Additional corrections are then applied to the data to account for any remaining differences with the simulations in the jet energy measurement. From simulations, the average jet momentum is found to be within 5% of the true momentum over the whole range of detector acceptance.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR  and  RAEP (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
In addition, individual groups and members have received support from  BCKDF , the  Canada Council, CANARIE ,  CRC ,  Compute Canada, FQRNT , and the  Ontario Innovation Trust ,  Canada ; EPLANET,  ERC ,  ERDF ,  FP7 ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex and Idex ,  ANR ,  Région Auvergne  and  Fondation Partager le Savoir, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF ;.
Data collection Data were collected using in-depth interviews, documentary analysis, and concept mapping. The co-author (MM) and five research assistants conducted 163 in-depth interviews with policy-makers/ MOH officials (n = 15), health professionals (n = 102), and local health managers (n = 46) (Table  1 ). An interview guide was developed and pre-tested, which covered the conceptual framework dimensions and sociodemographic data. Most interviews were recorded, with the participants' consent; 29 (out of 163) were not recorded due to refusals-were mainly MOH senior managers. Systematic notes were taken during the interviews. Documentary analysis focused on texts, policies, national strategies, legislation, and regulatory texts concerning human resources management in Niger (Appendix 1). To understand the conditions under which midwifery students would be willing to practise in rural settings, we used concept mapping, a research method for obtaining group consensus on a given problem  [29].
Data analysis The interviews were fully transcribed and their content analysed  [30]. Data were organized and analysed based on the conceptual framework dimensions. The documents were synthesized and analysed based on the research objectives. The concept mapping was analysed with statistical methods (hierarchical cluster analysis, then multidimensional scaling) carried out using an analysis module developed by PROVALIS (http://provalisresearch. com/). The preliminary map and results were presented to the participants, who then named each cluster produced by the statistical analysis. Diversifying the data collection methods and data sources enhanced the study's internal validity through triangulation  [31]. The qualitative and quantitative data are presented here in an integrated manner, as recommended by the MMAT tool  [27].
Study strengths and limitations This study's strength resides in its mixed-method design (qualitative and quantitative) and in the diversity of data sources (diversification of actors) and data collection methods (concept mapping, interviews, documentary analysis). The data were collected by a research team familiar with qualitative research methods and with both the context and the local language. However, this study has limitations. Social desirability bias could have been introduced during the concept mapping exercise with the midwifery students. To reduce this bias, participants were assured of the anonymity of their responses. They were told that the aim was to understand the conditions under which future midwives would be willing to work in rural areas and that their views were important for influencing policies on human resources management.
(1987). On scales of 10 pc, molecular line observations probe the substructure of molecular clouds, producing a "Type-4" (single-cloud, single-tracer) R-σ v relation in the terminology of  Goodman et al. (1998). With the resolution provided by the Atacama Large Millimeter/submillimeter Array (ALMA), it is now possible to conduct such observations in the LMC. Here we focus on a particularly quiescent cloud in the LMC which has been designated as Planck Galactic Cold Clump (PGCC) G282. 98-32. 40 (Planck Collaboration et al. 2016). Based on estimated dust temperatures inferred from Planck data at 4 resolution, it is very cold (T d 15 K) and lacking in active massive star formation. Although the cloud falls outside the region surveyed by NANTEN, its size and mass are consistent with GMCs detected by NANTEN, and it was also detected in the Planck integrated CO(1-0) map.
Mopra CO(1-0) Data The MAGMA survey presented by  Wong et al. (2011)  includes observations made in 2005-2010 of a subset of CO clouds identified by the NANTEN survey  (Fukui et al. 2008). Several additional projects undertaken with Mopra in 2012-3 (M624, PI: Roman-Duval; M625, PI: Bernard; M633, PI: Wong; M1022, PI: Hughes) expanded the MAGMA areal coverage by ∼20% to include additional fainter clouds, as well as clouds falling outside the areal coverage of the NANTEN survey. The region of particular interest for this paper was observed as part of project M625 in 2013. All of the additional regions observed in 2012-3 have been incorporated into the Third MAGMA Data Release 1 (DR3), which we document for completeness here. We employed the same mapping approach as with MAGMA, scanning 5 × 5 square regions to reach a map sensitivity of σ(T mb ) ≈ 0.
7 × 10 4 M using a standard α  latto et al. 2013). We note that values of α CO roughly a factor of 2 larger than the standard value have been derived for the LMC using virial or dust-based mass estimators  (Hughes et al. 2010; Leroy et al. 2011; Jameson et al. 2016) , which would require that the mass quoted here be scaled up by a similar factor. The observations cover a region of 220 × 80 elongated in the north-south direction, covering the northern half of the PCC cloud. [Figure  1  (right), Table  1 ]. The correlator was set to simultaneously observe the 12 CO(2-1), 13 CO(2-1), and C 18 O(2-1) lines with a velocity resolution of 0. 1 km s -1 , along with two continuum bands at 218 and 232 GHz. Observations were performed separately using the 12m, 7m (ACA), and total power (TP) arrays, and calibrated data sets were delivered via the ALMA pipeline.
CO = 4. 3 M pc -2 (K km s -1 ) -1 [equivalent to X CO = 2×10 20 cm -2 (K km s -1 ) -1 ] (Bo- Because the pipeline-reduced 12m data showed obvious negative artifacts, we re-imaged the calibrated visibilities in CASA. This was performed initially using automatically generated CLEAN boxes (as described in the M100 Band 3 tutorial of the online CASA Guides) followed by a final interactive CLEAN. The resulting 12m images were combined with the corresponding TP images using the CASA task feather. In this study we have not incorporated the ACA data, given that the ACA cubes have limited sensitivity (1σ noise of 200 mJy beam -1 in a 0. 2 km s -1 channel, twice larger than the 12m data convolved to the same resolution). We compared the flux distribution of the 12m+TP cube with two methods of incorporating the ACA data (in the visibility plane or by feathering) and found the fluxes to differ by 10%.
Integrated intensity images of the CO(2-1) and 13 CO(2-1) lines are shown in Figure  2. As with the MAGMA data, a signal mask was applied to the cube prior to summing in velocity. The mask was obtained by starting at the 3. 5σ contour and expanding to the 2σ contour, then padding the mask by 1 pixel in all directions. The 1mm continuum emission was not detected at an RMS noise level of 0. 21 mJy beam -1 for a 1. 58 × 1. 02 beam, while the C 18 O(2-1) line was only marginally detected at the location of the southern peak. For the remainder of the paper we focus on the CO and 13 CO data. For comparison purposes, we have also analyzed ALMA data for an additional cloud, 30Dor-10 (Indebetouw et al. 2013), observed in Cycle 0 under project code 2011. 0. 00471. S (PI: Indebetouw). Prior to further analysis, we convolve the data for both clouds to achieve a circular Gaussian beam of FWHM 2.
Fluxes and Line Ratios From Total Power Data Since ALMA provides total power (TP) data for the CO(2-1) and 13 CO(2-1) lines (at 28 resolution), which can be compared with the MAGMA CO(1-0) map at 45 resolution, we have derived integrated line ratios for the PCC cloud for comparison with previously published work. Integrating over the observed ALMA field, we measure a CO(2-1) flux of 1691±10 Jy km s -1 and a 13 CO(2-1) flux of 125±15 Jy km s -1 , implying a CO(2-1)/ 13 CO(2-1) flux ratio of 13. 5±1. 6. The quoted uncertainties reflect half the difference in flux measured with and without a signal mask; the formal uncertainties due to map noise are much smaller (∼1%) because of the very high signal-to-noise ratio (up to 30-70 in each 0. 2 km s -1 channel) of the TP data. Uncertainties in the absolute flux scale are expected to be <10% 2  and are not included in these uncertainties-since the lines are observed simultaneously, errors in the flux scale should not affect the line ratio.
Dust Temperature Validation The PCC cloud was originally identified as a very cold region in a map of the LMC's dust temperature that was constructed from Planck data with a resolution of ∼ 5. This is considerably larger than both the angular resolution and the field-of-view (FoV) of the ALMA mosaic presented here. To cross-check our assumption that the PCC cloud is one of the coldest molecular gas regions in the LMC, we re-estimated the average dust temperature of the PCC, and compared it to the 30 Doradus molecular cloud and other CO-emitting regions identified in the MAGMA survey. For each region, we estimated the dust temperature using a simple modified blackbody (MBB) model for the spectral energy distribution (SED) of the thermal dust emission at wavelengths between 100 and 500 µm. The input SED was constructed using the MIPS, PACS and SPIRE data delivered by the Spitzer SAGE and Herschel HERITAGE programs  (Meixner et al.
2006 (Meixner et al. , 2013)). All bands were convolved to a common resolution of 1. For each region, the MBB fit is performed using an iterative procedure based on the Levenberg-Marquardt method, as implemented in the DUSTEM software package (Compiègne et al. 2011). 3  The fitted parameters of the MBB model are the dust temperature T d , the spectral index β and the normalization of the dust SED. We constrain T d and β to lie in the intervals  [5, 55] K and [0. 8, 2. 8]  respectively. We assume a 10% total uncertainty in the average flux densities at all bands; for simplicity, we do not impose any correlation between bands in these uncertainties. Although neglecting correlated uncertainties can lead to significant mis-estimates of dust parameters  (Gordon et al. 2014) , we stress that we are primarily interested here in the relative temperatures of GMCs.
We note that some difference in the dust temperatures estimated using the different masks is to be expected since the ALMA observations focus on a subregion of the molecular cloud identified by MAGMA. For all clouds, the background regions that we use have roughly twice the projected spatial extent as the target molecular regions. Sightlines with a significant detection of emission in the MAGMA 12 CO(1-0) cube are excluded from our defined background regions. Figure  3  presents the distribution of dust temperatures that we obtain for the CO-emitting 'islands' identified in the MAGMA survey. The dust temperatures that we infer for the PCC and 30 Doradus molecular clouds are overplotted as blue and red lines, with shaded regions used to indicate the uncertainty. For consistency with the data points in the histogram, we overplot the average dust temperatures of PCC and 30 Doradus within mask 2.
Structural Decomposition To identify structures within the ALMA spectral line cubes, we used the Python package astrodendro, which decomposes emission into a hierarchy of structures  (Rosolowsky et al. 2008; Shetty et al. 2012; Colombo et al. 2015). Parameters were chosen so that the algorithm identified local maxima in the cube above the 3σ rms level that were also at least 2. 5σ rms above the merge level with adjacent structures. Each local maximum was required to span at least two synthesized beams in area. Isosurfaces surrounding the local maxima were categorized as trunks, branches, or leaves according to whether they were the largest contiguous structures (trunks), were intermediate in scale (branches), or had no resolved substructure (leaves). The resulting dendrograms for CO in PCC and the 30 Dor cloud are shown in the right panels of Figure  4.