dataesr/scientific-paragraphs-categorization huggingface dataset:
These data, as well as the range of secondary outcomes, will inform modern multidisciplinary team discussions and provide benchmark data for both treatment approaches. patients with primary tumours with a length of 8 cm or less, or with a combined length (tumour and nodes) for radiotherapy planning of 10 cm or less, were included. Patients were also required to have an Eastern Cooperative Oncology Group performance status of 0-2, a cardiac ejection fraction of greater than 50%, a forced expiratory volume in 1 s of 1⋅5 L or more on pulmonary function testing, absolute neutrophil count of more than 1•5 × 10⁹ cells per L, white blood cell count of more than 3 × 10⁹ cells per L, platelet count of more than 100 × 10⁹ per L, haemoglobin of more than 9 g/dL, glomerular filtration rate of more than 60 mL per min, serum bilirubin at the upper limit of normal (ULN) or below, aspartate aminotransferase of less than 2•5 times ULN, and alkaline phosphatase of less than 3 times the ULN.
All efficacy analyses were done in the intention-to-treat population, defined as all randomly assigned patients who received at least one dose of study drug. The intention-to-treat analyses were performed using treatment allocated regardless of dosing errors. Toxicity and safety data were analysed in the safety population, defined as patients who took at least one dose of study drug, according to treatment actually received. Overall survival was calculated from randomisation to death from any cause. Disease-free survival was calculated from randomisation to the first event (ie, local recurrence or progression, distant recurrence, or death from any cause). We compared survival times between the two groups using the log-rank test and a Cox regression analysis to obtain a HR with a 95% CI. A multivariate analysis was used to calculate HRs and 95% CIs adjusted for baseline age, sex, smoking status, and tumour staging.
The χ² test (or Fisher's exact test for small counts) was used for categorical data, and appropriate parametric t tests or non-parametric Mann-Whitney tests were used otherwise. All tests are two sided unless stated otherwise. Interim futility analyses applied the rule of Freidlin and colleagues with a linear 20% inefficacy boundary and were planned a priori for 25% and 50% predicted deaths. 22  The primary endpoint was checked for non-inferiority; all other p values reported are for superiority. Analysis performed using SAS (version 9. 4) and R (version 4. 1. 2). This study is registered with ClinicalTrials. gov, NCT01726452.
Minimum follow-up was 18 months after the last recruited patient. The final data analysis was done on March 22, 2023. Median follow-up was 38⋅8 months (IQR 16⋅3-55⋅1). In the intention-to-treat population, in the perioperative chemotherapy group, 95 (52%) of 184 patients died: 82 (45%) from recurrent or progressive cancer, seven (4%) from non-cancer-related deaths, five (3%) from postoperative mortality, and one (1%) from a serious adverse event. In the trimodality therapy group, 91 (51%) of 178 patients died: 73 (41%) from cancer recurrence or progression, 11 (6%) from non-cancerrelated deaths, four (2%) from postoperative complications, and three (2%) from serious adverse events. Median overall survival was 48•0 months (95% CI 33⋅6-64⋅8) in the perioperative chemotherapy group versus 49•2 months (34⋅8-74⋅4) in the trimodality therapy group (figure  2 ; HR 1⋅03 [95% CI 0⋅77-1⋅38], p=0⋅82).
At the 1-year follow-up, emotional functioning, pain, and coughing were significantly (p<0⋅05) worse in the trimodality therapy group than in the perioperative chemotherapy group, with trouble with coughing persisting at 3 years, whereas diarrhoea was significantly (p<0⋅05) more marked at 1 year and 3 years in the perioperative chemotherapy group. The treatment effect on overall survival according to baseline characteristics and postoperative pathology shows no significant differences between treatment groups (figure  3 ). Data are n (%). Serious adverse events are reported for all patients in the safety population and include postoperative mortality events. *One patient in the intention-to-treat population had surgery but not chemotherapy and was therefore excluded from the safety population.
A markedly lower pathological complete response rate was, however, observed, at 12% versus 23%. With radiotherapy qualityassured in Neo-AEGIS, this finding cannot be explained but most plausibly suggests differences in primary tumour extent. 10-year outcome data from the CROSS trial show a 36% survival rate and highlight reduced locoregional recurrence; however, the absence of an effect on systemic relapse revealed the limitations of this approach. 28  In this context, adjuvant immunotherapy holds promise. The CheckMate 577 trial evaluated nivolumab as adjuvant therapy after trimodality therapy in patients with residual pathological disease and an R0 resection after chemoradiotherapy and reported a median disease-free survival of 22 months versus 11 months with placebo; the overall survival data are eagerly awaited.
10, 23  The results of ESOPEC (NCT02509286) will be informative. This trial directly compares trimodality therapy with the FLOT regimen in patients with adenocarcinoma of the oesophagus and oesophagogastric junction, with similar inclusion criteria to Neo-AEGIS. 30  It is powered on FLOT being 13% superior to the CROSS trimodality regimen in 3-year survival (HR 0⋅65) and will be of major interest alone and as a companion and comparison to Neo-AEGIS. 30  In addition, the narrative of the debate on the optimum approach to treat locally advanced adenocarcinoma of the oesophagus and oesophagogastric junction will evolve in the years ahead, informed by studies of neoadjuvant and adjuvant immunotherapy combined with chemo therapy, including MATTERHORN (NCT04592913), Keynote-585 (NCT03221426), and DANTE (NCT03421288).
Acknowledgments The study is funded by  Health Research Board Ireland, Cancer Research UK  ( C49462/A18483 ),  Irish Cancer Society, Oesophageal Cancer Fund Ireland , and the  French National Cancer Institute  ( 2015-096  and  2017-046 ). We thank the  Southampton Clinical Trials Unit  for setting up the trial in the UK. Special appreciation for  Anna Shevlin  and  Aoife Shannon  ( Cancer Trials Ireland ) and  Kelly Cozens  and  Daniel Griffiths  ( Southampton Clinical Trials Unit ). We thank our patients, the investigators, and the research support staff at all participating centres. We also thank the independent data monitoring committee and trial steering committee.
Data sharing Cancer Trials Ireland supports the wider dissemination of information from the research it conducts and increased cooperation between investigators. Trial data are collected, managed, stored, shared, and archived according to Cancer Trials Ireland and Health Research Board Clinical Research Facility Standard Operating Procedures to ensure the enduring quality, integrity, and utility of the data. Formal requests for data sharing are considered in line with Cancer Trials Ireland procedures with due regard given to patient consent. Data requests can be submitted to Cancer Trials Ireland via info@cancertrials. ie and must include a description of the research proposal. Data sharing requests are reviewed and approved by the chief investigator, the trial steering committee, and Cancer Trials Ireland in terms of scientific merit and ethical considerations including patient consent.
The upgrade or extension possibility is important for three reasons. The first is that the top quark is another important object that needs precision study. This includes the measurement of the top quark mass but, more importantly, the measurement of the top quark electroweak couplings, which give some of the best tests of models with Higgs boson compositeness or new strong interactions. This is an essential part of the program of precision Standard Model tests. The second is that above 350 GeV, the primary Higgs boson production mechanism changes from e + e -→ ZH to WW fusion production of the Higgs. This means that, in the second stage, one can acquire a new Higgs boson data set, comparable to the earlier one, with different characteristics. Thus it is possible to discover an effect in the 240-250 GeV stage and confirm it using measurements with different initial conditions at the higher-energy stage.
Location and sampling of the farms Studies were carried out in the northwest region of France, a bastion of dairy farming featuring a wide diversity of dairy farms in terms of production systems and grazing practices  (Chatellier 2011). These farms provide an opportunity to study different practices, work arrangements and production systems over a small geographic area. The goal was to have a diverse range of dairy farms based on several criteria: feed system and especially fodder system (either relying heavily on grazing or based on maize throughout the year), number of animals per work unit, workforce composition (from one permanent farmer alone to several partners, with or without employees), and use of robots and sensors. We started out with 150 farm contacts from different databases (producer associations such as Contrôle Laitier (farmers' association in charge of milk analysis for animal performance monitoring) and Civam (farmers' associations in charge of rural development and promotion of grassland systems) and pre-selected 30 farms based on our sampling criteria.
Collection of farm data The interviews took place between November 2021 and March 2022 and lasted 90 min on average. All interviews were recorded and all the participants gave written consent to share the data collected from their interviews anonymously within the framework of the thesis being studied. Three farmers had to be interviewed by telephone due to lockdown measures in place, and the remaining seven were interviewed in-person. The farmers who gave interviews by telephone were met at a later date in order to fill in any missing information. In each case, the self-identified head farmer was interviewed to get a better understanding of the coherence of the system as a whole. The interviews were semi-directive, i. e. the farmers were able to express themselves freely for the entire time  (Darré et al.
Characterization of work-health management linkages based on farmers' patterns of association between variables The final analysis is an exploration of potential linkages between variables related to work and animal health. The data coming from the 10 farms using Bertin's graphing technique gathers the previous aggregated variables, built with the farmers discourses, and two other indicators: "Work pressure" (P) and "Work productivity" (PT), to better understand the coherence of our groups. Work pressure is calculated as number of dairy cows per WU on the dairy unit. It is considered as an indicator of the work demands directly related to herd size. Work productivity is calculated as number of liters of milk/dairy cows multiplied by WU on dairy unit. It expresses the farmers ambitions related to a major proxy of the dairy income.
The all-jets event category trigger efficiency is measured in the data requiring a single AK4 jet with p T > 260 GeV by applying the above offline selection, and counting the number of events passing the trigger selection. It is found to be between 94 and 100%. Simulated events are weighted by this efficiency as a function of the invariant mass of the two leading-p T AK8 jets in the event, M JJ. A graph convolutional neural network algorithm, ParticleNet  [67] , is employed to discriminate the decays of a boosted massive particle R, which could be an H boson or a Y resonance, to a pair of b quarks against a background of other jets, using the properties of the jet PF constituents as features. As with all heavy-flavour jet classifiers, displaced tracks and vertices are the most important features.
As a consequence, background enriched regions can be defined using low ParticleNet scores on jets that have the same mass spectra as that of the background in the signal region. An accurate background model can therefore be developed. The ParticleNet scores used for selecting the H → bb and the Y → bb candidates ("signal jets") are either >0. 98 (tight requirement) or >0. 94 (loose requirement). Depending on the jet p T , the former has an efficiency of 62-72% and a misidentification rate of 0. 45%, while the latter has an efficiency of 80-85% and a misidentification rate of 1%. The efficiency of the ParticleNet classifier is calibrated in data using a sample of jets originating from fragmentation of a gluon to bb (g → bb), which are similar to H → bb and Y → bb jets. Such jets are selected from the data using a boosted decision tree (BDT) classifier, such that their ensemble ParticleNet score resembles that of Y and H jets.
Background estimation The analysis searches for a narrow signal in the 2-dimensional plane spanned by M JJ and M Y J. The two-dimensional (M JJ , M Y J ) distributions of the multijet events are estimated using a passto-fail ratio method, described in the following paragraphs. The simulated tt+jets event distributions are corrected by fitting the top quark jet mass M t J distributions to the data in the jets+lepton regions. The multijet background is estimated for the three data-taking years combined. First, transfer functions, R P/F , are defined as the ratio of event distributions in the (M JJ , M Y J ) plane in the signal regions to those in the sidebands, SR1-to-SB1 and SR2-to-SB2. These are a priori unknown, and are determined from the fit of signal and background distributions to the data.
An initial estimate R init P/F is made using the first set of validation regions, using the data and correcting for the simulated tt+jets component: VS1-to-VB1 and VS2-to-VB1. With the definition R P/F ≡ R init P/F R ratio , only the correction function R ratio needs to be determined directly from the fit to the data. The validation regions provide a good estimate of R P/F , because the pass-to-fail event ratios SR1-to-SB1 and SR2-to-SB2 are close to VS1-to-VB1 and VS2-to-VB1, as borne out in simulations. The values of R ratio are therefore of order unity and lead to stability of the fit of signal and background models to the data. The values of R init P/F , closely related to the loose and tight misidentification rates of the Parti-cleNet tagger, are determined as functions of M Y J only.
Summary A search for massive scalar resonances X and Y, where X decays to Y and the standard model Higgs boson H, has been performed using proton-proton collision data collected at the LHC by the CMS detector between 2016 and 2018, and corresponding to an integrated luminosity of 138 fb -1. Events are selected using jet substructure and neural network based boosted H/Y → bb identification algorithms. Upper limits at 95% confidence level are set on the cross section of the process pp → X → YH → bbbb for assumed masses of X in the range 0. 9-4 TeV and Y between 60-600 GeV. The expected and observed cross section limits for the considered process, set between 0. 1 and 150 fb, are the most stringent to date over much of the explored mass range. These limits are interpreted as exclusion of possible M X and M Y within the frameworks of the next-to-minimal supersymmetric model and the two-real-scalar-singlet extension of the standard model.
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 centres and personnel of the Worldwide LHC Computing Grid and other centres 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, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NKFIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MES  and  NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI  and  PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI  and  NSTDA (Thailand) ;  TUBITAK  and  TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Results. The models can explain the main features of SN 2021aefx over the full wavelength range. However, no single model, or mechanism, emerges as a preferred match, and the predicted spectra are similar to each other despite the very different explosion mechanisms. We discuss possible causes for the mismatch of the models, including ejecta asymmetries and ionisation effects. Our new calculations of the collisional strengths for Ni iii have a major impact on the two prominent lines at 7. 35 µm and 11. 00 µm, and highlight the need for more accurate collisional data for forbidden transitions. Using updated atomic data, we identify a strong feature due to [Ca iv] 3. 21 µm, attributed to [Ni i] in previous studies. We also provide a tentative identification of a forbidden line due to [Ne ii] 12.
(2023a)  covered the full 0. 35-14 µm range and revealed the presence of numerous forbidden lines of IGEs and a distinct line due to argon ([Ar iii] 8. 99 µm), whose flat-topped profile was interpreted as indicative of a chemically stratified ejecta. Subsequent modelling by  DerKacy et al. (2023)  of a MIR spectrum taken ∼2 months later showed satisfactory agreement with a near-M Ch delayed-detonation model with ∼0. 6 M of 56 Ni. However, their study did not confront these data to alternative SN Ia explosion models. Here we compare a diverse set of state-of-the-art, public SN Ia explosion models with the full optical-MIR nebular spectrum of SN 2021aefx, and evaluate which one, if any, fares better. In the next section, we present our numerical approach. We then present our theoretical spectra for each SN Ia explosion model in Sect.
Numerical approach We select previously-published models from the Heidelberg Supernova Model Archive (HESMA;  Kromer et al. 2017 ) with a 56 Ni yield in the range 0. 5-0. 8 M (see Table  1 ), compatible with the amount inferred for SN 2021aefx 2. These include 17 models but for only four explosion scenarios, namely: a M Ch 1 https://zenodo. org/record/8290155 2 The estimated peak B-band magnitudes for SN 2021aefx range from M B = -19. 28  (Ashall et al. 2022) , typical for normal SNe Ia, to M B = -19. 62  (Hosseinzadeh et al. 2022) , similar to the most luminous events. The difference is largely due to the assumed host-galaxy extinction. Given the uncertainty on the estimated distance to the host galaxy NGC 1566 (18 ± 2 Mpc;  Sabbi et al. 2018) , both peak M B values are statistically consistent with one another.
Model spectra: Overall comparison to SN 2021aefx Figure  4  shows the synthetic spectra for models DDT, GCD, DBLEDET, and MERGER compared to SN 2021aefx at 270 d post explosion in the wavelength range 0. 35-14 µm 3. These data have been corrected for redshift and reddening  (Kwok et al. 2023a) , while the models have been scaled to a distance of 18 Mpc. We report line identifications in Table  2 , based on their Sobolev equivalent width (EW)  4. The main lesson from this figure is that the models predict all the observed spectral features of SN 2021aefx from the blue end of the optical until the red end of the JWST range. Unfortunately, no single model produces a perfect match. The modest difference between the spectral properties of these four distinct explosion models reflects the similarity in composition for the dominant coolants in the ejecta.
Conclusions We have compared four classes of public state-of-the-art SN Ia explosion models to nebular observations of SN 2021aefx covering the full 0. 35-14 µm range  (Kwok et al. 2023a ). The input models include M Ch delayed detonations, pulsationally assisted gravitationally-confined detonations, sub-M Ch double detonations and a violent WD-WD merger. They were selected from the public HESMA archive to match the 56 Ni yields expected for normal SNe Ia (∼0. 5-0. 8 M ). The spherically-averaged density and abundance profiles served as initial conditions to 1D non-LTE radiative-transfer simulations with CMFGEN at 270 d post explosion. A170, page 15 of 30 Our main result is that no single model emerges as an obvious candidate for SN 2021aefx based on these data alone. All models predict the same set of spectroscopic features, all of which have an observed counterpart.
g. Bravo 2020). Inferring reliable constraints on the progenitor mass and explosion mechanism of SNe Ia remains a formidable challenge, even with the extended wavelength coverage and exquisite data quality offered by JWST. Combined with early-time and optical observations, nebular infrared spectra of SNe Ia provide an additional validation criterion when comparing a diverse set of SN Ia models to observations. The 'Score' estimator is defined in  Omand & Jerkstrand (2023). Here we used its mean value: Score = 1 N λ N λ i=1 log 10 F mod,i F obs,i.
Acknowledgements. This paper is dedicated to  Prof. Tom Marsh  , who was S. B. 's lecturer and Bachelor thesis supervisor at the University of Southampton during 1998-2002. S. B. acknowledges support from the  Alexander von Humboldt foundation  and thanks  Sherry Suyu  and her group at the  Technische Universität München   (TUM)  for their hospitality. We acknowledge useful discussions with  Chris Ashall ,  Andreas Floers ,  Peter Hoeflich ,  Griffin Hosseinzadeh ,  Saurabh Jha ,  Anders Jerkstrand ,  Lindsey Kwok , and  Ruediger Pakmor. Many thanks to:  Manuel Bautista  for providing unpublished collisional strengths for [Ni iii] based on Bautista (2001);  Lindsey Kwok  for sending us the combined spectrum of SN 2021aefx ahead of publication;  Elena Sabbi  and  Brent Tully  for confirming the error on the estimated distance to NGC 1566;  Ivo Seitenzahl  for extracting radial profiles from the 3D delayed-detonation model N100 of Seitenzahl et al.
(2013);  Steve Shore , the referee of this paper, for his detailed review and valuable comments that led to the detailed comparison of the 3D vs. spherically-averaged yields in Appendix A. This work was supported by the ' Programme National de Physique Stellaire' (PNPS) of CNRS/INSU  cofunded by  CEA  and  CNES. This research has made use of computing facilities operated by CeSAM data centre at  LAM, Marseille, France. D. J. H. acknowledges partial support for this work through the  NASA  astrophysical theory grant  80NSSC20K0524  and the  STScI  theory grant  HST-AR-16131. 001-A. STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-26555. This research was supported by the  Munich Institute for Astro-, Particle and BioPhysics (MIAPbP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy  - EXC-2094 -390783311.