dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the performance of this new NDoE process is computed. The kriging surrogate model is as accurate as predicted by the bayesian network. The observed accuracy was 0. 011, while the predicted accuracy was between 0. 01 and 0. 05. But the predictivity of the surrogate model is better than expected. The probability that the value of predictivity was between 0. 90 and 0. 99 was equal to 0. 75 (Table  3 ). This wrong classification could have been expected, and it will not cause any loss for designers in this case. This new NDoE process, linked to these new observed results, is capitalized in the knowledge base. Thus, the dataset can continuously grow. The use of this proposed KBS shorten the configuration step of the NDoE process. From thousands possible configurations, the set of relevant configurations was reduced to ten in a very short time.
Knowledge is continuously updated and capitalized. With the ontological structure, designers can learn from the KBS, and enrich it with their own personal reasoning. The application of this KBS may lead to more efficient design processes of complex products. It also leads to better product quality by controlling the effects of uncertainties, with minimal computational cost, earlier in the design process. To improve this inference engine, learning process could be enhanced, for instance by applying an hybrid system, or by applying other learning approach, such as unsupervised and reinforcement learning, to be more efficient with a minimal amount of capitalized data. The effect of the number of data (NDoE processes capitalized in the knowledge base) should be studied to evaluate its ability to be use on small knowledge bases.
Open access If the patient experiences an obstructed labour, the midwife must call the physician on duty. FHR abnormalities are interpreted according to revised FIGO classifications. 21 The final delivery method (vaginal delivery or caesarean extraction) is decided by the physician on duty. After delivery, the midwife takes samples from the placenta and newborn (gastric fluid, umbilicus, anus and ear canal). After 2 hours of postdelivery supervision, the patient is taken back to her room. Data regarding potential maternofetal infections are recorded and used in the analysis. No additional visits after discharge are scheduled in the protocol.
Patient and public involvement This study investigates patients' interests. The regional ethics committee is composed by patients' representatives who gave their agreement for the design of the protocol. By means of the committee, data about pain during placement of device and the ripening are recorded. After the ripening, safety profiles will be recorded by the following question: "for next pregnancy, if a ripening will be indicated, will you agree to have the same device of ripening?" Patients are clearly informed of the objectives, course, benefits and risks of the study and can ask all their questions to the investigator before giving their consent. An informative flyer presenting the results will be sent to them at the end of the study at their request.
sample size calculation We determined sample size with the aim of highlighting a significant difference between randomised groups in the time between induction (the hour of placement of induction device) and delivery (hour of birth). According to data from our centre and a study by Mackeen et al,  10  the SD of the primary outcome will be around 10 hours. To achieve a two-sided type I error of 5% with a statistical power of 90% and to highlight a clinical and relevant absolute difference of 9 hours, 26 patients per group are needed. However, to guarantee satisfactory statistical power for secondary outcomes, we propose to include 40 patients per group. statistical analysis All analysis will be conducted with Stata (V. 13, StataCorp, College Station, Texas, USA) and R (http:// cran. rproject.
Missing values will be noted and analysed (this is particularly relevant to the fact that performing a caesarean may hasten delivery without overall clinical benefit). More precisely, a sensitivity analysis will be performed and the nature of missing data will be evaluated to determine the role of randomness. If the frequency is >5%, additional studies will be performed using the multiple imputation methods. data Monitoring safety Committee A Data Monitoring Safety Committee (independent from the laboratory) will discuss the results of intermediate analysis. Committee meetings will cover blocks of 20 patients. The committee consists of two independent members who work in the University Hospital of Clermont-Ferrand (one a gynaecologist who does not work in obstetrics unit, one paediatrician), and two doctors of public health from Grenoble University Hospital (France).
The committee will examine all records concerning the transfer of participating patients or their newborn infants to intensive care or reanimation units. Intermediate safety analysis will be conducted concerning the rate of caesarean and chorioamnionitis. The analysis will be performed both with and without knowledge of what data correspond to which study group, in order to determine which is the exposed group. The study will be halted if the exposed group is the double balloon catheter group. data storage and management Data storage and management are performed according to international guidelines relevant in French institutions. All data are recorded by a resident and a clinical research associate. The data are entered into the randomisation software REDCap  22  and anonymised. Data quality control measures will include queries to identify outliers and missing data.
Only the two above-mentioned staff members and the study principal investigator (PI) will have access to protected personal health information. After inclusion, a unique identifier will be stored in a locked cabinet. The PI will ensure that anonymity is preserved. The PI will have access to the final trial data set, as will a biostatistician. An adverse event (AE) is defined as any untoward medical occurrence in a patient taking part in the study. A serious AE is defined as any untoward occurrence that requires hospitalisation or prolongation of existing hospitalisation; results in persistent or significant disability; is life-threatening or results in death or is otherwise considered medically significant by the investigator. AE will be recorded on an AE form. An assessment of the gravity, possible causality and expectedness event/reaction will be undertaken.
Ethics and dissemination The first patient inclusion was on 12 February 2018, and we hope to recruit a total of 80 patients within 18 months of the first recruitment. The protocol respects the Declaration of Helsinki. This monocentric, randomised, parallel-group trial is registered at http://www. clinicaltrials. gov under the registration number NCT03310333. We will publish findings from this study in a peer-reviewed scientific journal and present results at national and international conferences, regardless of whether they are statistically significant. The study is funded from the research budget of the University Hospital of Clermont-Ferrand, and receives no Open access funding from double balloon catheter or vaginal prostaglandin insert manufacturers. dIsCussIon RUBAPRO is the first monocentric, open-label, parallel-group, randomised controlled trial to compare treatment using a double balloon catheter combined with oxytocin with treatment with vaginal prostaglandin in case of PROM at term with an unfavourable cervix.
Electrophysiological Recordings EEG signals were recorded through the OpenViBE software platform  (Renard et al. , 2010)  with a Biosemi Active Two 32channel EEG system. The EEG was recorded from 32 sites in accordance with the international 10-20 system. The selected electrodes are FC  and O 2. These sites were localized around the primary motor cortex, the motor cortex, the somatosensory cortex and the occipital cortex, which allowed us to observe the physiological changes due to the kinesthetic motor imagery  (Guillot et al. , 2009; Filgueiras et al. , 2017). Two additional electrodes are used in the Biosemi system: the Common Mode Sense (CMS) active electrode as reference and the Driven Right Leg (DRL) passive electrode, located over the parietal lobe and used to inject the current until the potential of the system and body are the same  (Winter and Webster, 1983; Maby, 2016).
Very recently, when the work presented here was nearing completion, ATLAS reported on a first search in this channel  [41]. The analysis described in this Letter is performed with data corresponding to an integrated luminosity of 35. 9 fb -1 recorded with the CMS detector at the LHC in proton-proton (pp) collisions at a center-of-mass energy of 13 TeV in 2016. We focus on Z masses above ≈700 GeV where the Higgs boson is produced with a sufficient boost to be efficiently reconstructed as a single, large-radius jet. The analysis uses control samples in data to predict the shape of the background and to optimize the selection. The background is obtained via a fit to data with a functional form determined using these control samples and simulation, and a signal is searched for as a narrow resonance on top of a smoothly falling background.
Muons are detected in gas-ionization chambers embedded in the steel flux-return yoke outside the solenoid. Events of interest are selected using a two-tiered trigger system  [43]. The first level, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a time interval of less than 4 µs. The second level, referred to as the high-level trigger, consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to less than 1 kHz before data storage. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [44].
The jet momentum is determined as the vectorial sum of all particle momenta in the jet, and is found from simulation to be within 5 to 10% of the true momentum over the whole p T spectrum and detector acceptance. Additional pp interactions within the same or nearby bunch crossings (pileup) can contribute additional tracks and calorimetric energy depositions to the jet momentum. To mitigate this effect, the pileup-per-particle identification (PUPPI) algorithm  [48]  is applied to PF candidates prior to jet clustering. Jet energy corrections are derived from both simulation and data to make the measured response of jets equal to that of particle-level jets on average  [49]. The jet energy resolution amounts typically to 8% at 100 GeV, and 4% at 1000 GeV. Photons are identified with a multivariate analysis (MVA) classifier  [50]  based on shower shape variables, isolation sums computed from PF candidates in a cone of radius ∆R = 0.
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:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , 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) ;  NKFIA (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) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  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).
All our papers are written by, or in conjunction with, those who carried out the conservation work and include some monitoring of its effects. Alongside this project, the Centre for Evidence-Based Conservation (www. cebc. bangor. ac. uk) and the Collaboration for Environmental Evidence (www. environmentalevidence. org) carry out and compile systematic reviews of evidence on the effectiveness of particular conservation interventions. We recommend carrying out a systematic review, which is more comprehensive than our summaries of evidence, when decisions have to be made with particularly important consequences. Systematic reviews are included in the Conservation Evidence database.
How we review the literature We gather evidence by searching relevant scientific journals from volume one through to the most recent volume. Thirty general conservation journals are regularly searched by Conservation Evidence. Specialist journals are also searched for each synopsis (156 have been searched so far). We also search reports, unpublished literature and evidence provided by our advisory boards. Two of the synopses used systematic mapping exercises undertaken by, or in partnership with, other institutions. Systematic mapping uses a rigorous search protocol (involving an array of specified search terms) to retrieve studies from several scientific databases. Evidence published in languages other than English is included when it is identified. Evidence from all around the world is included in synopses.
One exception is farmland conservation, which only covers northern Europe (all European countries west of Russia, but not those south of France, Switzerland, Austria, Hungary and Romania). Any apparent bias towards evidence from some regions in a particular synopsis reflects the current biases in published research papers available to Conservation Evidence. The criteria for inclusion of studies in the Conservation Evidence database are as follows: • A conservation intervention must have been carried out. • The effects of the intervention must have been monitored quantitatively. These criteria exclude studies examining the effects of specific interventions without actually doing them. For example, predictive modelling studies and studies looking at species distributions in areas with long-standing management histories (correlative studies) are excluded.
Retrospective and prospective cohort studies (crosssectional, case-control and longitudinal) reporting the association between AAC, BMD and fracture at any site will be included. At least two investigators will independently: (A) evaluate study eligibility and extract data, with a third investigator to adjudicate when discrepancies occur, (B) assess study quality by the Newcastle-Ottawa Scale for each cohort/study. The meta-analysis will be reported in adherence to the Meta-analysis of Observational Studies in Epidemiology criteria. AAC will be grouped as either: (1) AAC present or absent, (2) AAC categorised as 'low' (referentlowest reported group) versus 'high' (all other groups) or (3) dose-response when AAC was assessed in ≥3 groups. Where primary event data were reported in individual studies, pooled risk differences and risk ratios with 95% CI will be calculated, from which, a summary estimate will be determined using DerSimonian-Laird random effects models.
Eligibility criteria for studies included in this review ► Modality of assessing AAC (DXA, standard radiograph or CT). ► Cut-points chosen for comparison (low vs high, tertiles etc). ► Diabetes (% of cohort). ► Current smoker (% of cohort). ► History of smoking (% of cohort). ► Body mass index (cohort mean). ► Chronic kidney disease (% of cohort). ► History of CVD (% of cohort). ► Location of study (Europe, Asia-Pacific, North America), that is, are association consistent across ethnicities and nation wealth. ► Prevalence of CVD medication use (% of cohort). ► History of fracture (% of cohort). study design Search strategies A comprehensive literature search within MEDLINE, Web of Science core collection and EMBASE databases will be conducted to source all possibly relevant studies for review, without language restriction.
Google Scholar will be searched for the top 200 articles sorted by relevance. The search terms will be combined with the boolean 'AND' to find all potentially relevant studies. Conference proceedings and abstracts will also be evaluated. A hand Open access search of reference lists of eligible studies and previous meta-analyses will also be undertaken. Non-English papers will be translated and evaluated for eligibility. If more than one publication of a study is retrieved, articles with the most up to date and complete information will be included, although additional unique data from all sources will be considered and included when relevant. Examples of the search strategy are shown in table  1.
16 atistical analysis and data synthesis Analysis of outcome variables will be presented according to either: (1) AAC present or absent (2) AAC categorised as 'low' (referent-lowest reported group) versus 'high' (all other groups) or (3) dose-response when AAC was assessed in three or more groups. For the dose-response analysis, the lowest reported group (low AAC group) will be compared with the middle group(s) versus the highest reported AAC group (high AAC). Where data on more than three groups of AAC were presented the middle groups were combined as 'moderate AAC'. This approach was selected due to many studies reporting on variable number of AAC groups with the majority of studies using different cut-points for these groupings. Data on the severity of AAC quantification presented as a continuous measure or in three or more groupings of AAC will be used to determine the impact of increased abdominal aortic calcium load on outcomes.
Most remarkably, non-steady state diagenesis at this location is induced by methane consumption without contact to sulfate in the upper parts of the sediment succession. In addition, there is a significant and unusual occurrence of pore-water sulfate below the methanogenic zone, disconnected from the overlying seawater. Jaeger et al. (2014) postulated the existence of a deep inverse SMTZ at Site U1417, but both the source of the deep sulfate and its potential impact on diagenetic pathways are unconstrained. Detailed downhole geochemical analyses at Site U1417, coupled with regional geophysical imaging of the sub-seafloor, offer the possibility to explore these two aspects of diagenesis in a deep-sea setting. Here, we present bulk sediment, pore-water and gas geochemical data from IODP Site U1417.
Materials and Methods In   (Kulm and Von Huene, 1973) , of which Site U1417 is a re-drill, and seismic reflection data  (Reece et al. , 2011)  revealed that the sediment-basement boundary lies at 794 ± 74 m depth, and coring at Site U1417 was terminated ~50-100 m above basement (Figure  2 ). The age model for Site U1417  (Gulick et al. , 2015)  is based on paleomagnetics (14 datums), biostratigraphy (18 datums), and extrapolated to the basement. For this study, three basalt basal core samples from DSDP Site 178 were dated using the whole rock 40 Ar/ 39 Ar methods outlined in  Benowitz et al. (2014) , to attain a more robust age of the onset of sediment deposition at Site U1417. See supplementary text, figures and tables for more details on the 40 Ar/ 39 Ar methods and results. All dating points were included to construct minimum and maximum age models.
Immediately after sectioning the core, samples for pore-gas analyses were taken, sealed, and heated to 70˚C. Headspace gas was captured and analyzed on board for methane, ethane, ethene, propane and propene using a Hewlett Packard 6890 gas chromatograph equipped with a 2. 4 m × 3. 2 mm stainless steel column packed with 100/120 mesh HayeSep R and a flame ionization detector. For methane, the coefficient of variation was 1 to 5%  (Jaeger et al. , 2014). Pore-water from 72 whole-round core samples of 5 to 15 cm thickness was taken with variable depth resolution. The samples were squeezed at 5,000 to 30,000 psi (~ Coefficient of variation for all cations and anions was 3 to 5%  (Jaeger et al. , 2014). Pore-water data are presented in micromolar ( M) or millimolar (mM), pore-gas in milimolar (mM) concentrations.
XRF, C and S data are presented as weight percent (wt%) or mg/kg of dry sediment. To account for depositional changes of the sedimentary matrix and dilution effects, chemical data are also reported as element to Al ratios. A sequential Fe and S extraction scheme following the methods of  Canfield et al. (1986) ,  Poulton and Canfield (2005) , and  März et al. (2012)   (Poulton et al. , 2004). The reproducibility of the Fe extraction was within 5% for all four fractions, as determined from triplicate analyses of five samples. The standard error of the S extraction was within 20%, as determined by triplicate analysis of three randomly selected samples, mainly due to low total S contents (0. 13 -0. 2 wt%) in these samples. Following the S extraction, the isotopic composition of pyrite-bound S was determined on the precipitated silver sulfide.
A C C E P T E D M A N U S C R I P T Another dataset of 7 samples below 600 m core depth (CCSF-B) was used to assess organic matter reactivity  (Childress, 2016). These samples were taken independently from the above described sampling strategy and analyses. Subsamples for O-and H-index measurements were collected at ~15 m resolution and samples were stored frozen. Sediment samples were freeze-dried, after which a subsample of each was decarbonated via acidification with 4N HCl for 48 -72 hours  (Leithold and Blair, 2001). Subsamples of dried and ground marine sediments were analyzed by GeoMark Research using Rock-Eval 6 pyrolysis to determine the type and maturity of organic matter. The method consists of a programmed temperature heating to sequentially release free hydrocarbons, hydrocarbons generated by thermal cracking of nonvolatile organic matter, and CO2 from kerogen.
The resulting data are used to calculate H/C (hydrogen index) and O/C (oxygen index) ratios. All pore-water and solid phase constituents are displayed against composite coring depth in meters below the sea floor on the CCSF-B scale (core composite depth below seafloor, a depth scale that is corrected for sediment expansion;  Jaeger et al. , 2014). In the following, all depth information refers to sediment depth unless stated otherwise.
Extension due to plate bending generates an array of sub-parallel normal faults that are observed to offset from seafloor through the sediment sequence and the top of the oceanic crust  (Reece et al. , 2013). Such faults have previously been reported as an important process in hydrating subducted tectonic plates and exporting water into the mantle (e. g. Emry and Wiens, 2015; Naif et al. , 2015; Cai et al. , 2018; Shillington, 2018). Probably, in the GOA they could act as pathways for seawater supplying the observed deep sulfate pool at U1417. Based on existing seismic data within the GOA, plate bending faults are present within ~250 km of the Aleutian Trench (Figure  2 ). These faults occur near Site U1417 and for up to ~176 km to the southeast parallel to the direction of the Pacific Plate motion.