dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical treatment of data The AE signals were then analysed using an Unsupervised Pattern Recognition (UPR) technique with the NOESIS software (Enviroacoustics SA) to group signals with a similar acoustic signature. This type of statistical analysis is widely described in the literature  [28] ,  [29]. In this study we kept the maximum of AE features in order to obtain the most robust signal classification possible. These parameters were: rise time, counts, counts to peak, absolute energy, duration, amplitude, average frequency, frequency centroid, initiation frequency, reverberation frequency, and peak frequency. Naturally, in order to limit the weight of certain parameters in the analysis, a normalization of the data between 0 and 1 was carried out. After a comparative study of the various classification algorithms at our disposal, the k-means algorithm was used to classify the signals into different groups.
Database The local research coordinator and/or the staff from participating hospitals will identify eligible women and after counseling and reading the information form, patients will be asked for written consent. At study recruitment, demographic, obstetric and medical history data will be recorded into a web-based Case Report Form (CRF) that will be accessible through a restricted website. Details on delivery, maternal and neonatal assessments during pregnancy or post-partum data will be recorded in the CRF. The collected data will be coded and processed with adequate precaution to ensure patient confidentiality with the following measures: Initials of participants as well as a local patient number will be recorded in the electronic database. Linking names with patients' numbers will only be available in the local clinics.
Data collection Patients will be followed up at the obstetrics and fetal cardiology unit every 4 weeks with a multidisciplinary approach. At inclusion, a blood sample from both parents will be taken. During pregnancy, visits will be scheduled every 4 weeks, including fetal biometries and Doppler. At 28-32 weeks of pregnancy, a functional echocardiography will be performed and finally, at 35-37 weeks, a MRI for evaluating fetal brain will be done. At delivery, a cord blood and a maternal blood sample will be taken. Mode of delivery, perinatal complications, biometric data such birth weight and head circumference, need for intubation or need for drugs after birth will also be recorded. A FCUS will be performed during the ICU stay. Blood samples will be taken and continuous EEG for 2 h will be performed before and after surgery.
Neonatal evaluation and neurodevelopment During hospital stage, data on intensive care unit and hospital length stay, need for mechanic ventilation, need for vasoactive drugs and mortality will be recorded. Surgical duration, time on extracorporeal support, cardiac ischemia time and the need and duration of circulatory arrest will be also recorded. Need for renal replacement therapies, extracorporeal life support and cardiac arrest before or after surgery will be also recorded. Monitoring for oximetry and EEG will be recorded before and after surgery. Neurosonography will be used to evaluate brain anatomy and findings such as: ventriculomegaly (VMG) and / or increased subarachnoid space; signs of periventricular leukomalacia (PVL) signs of intraventricular hemorrhage (IVH); presence of calcifications and alterations in cortical development.
Cognitive neurodevelopment will be assessed at 1 year of age using the ASQ. This test is validated and extensively used as a screening method for detecting impaired neurodevelopment in high-risk children. Research nurses will be notified when a patient will approach the corrected age of 1 year by an email generated from our database. After this alert, children's parents will be called to complete again the questionnaire. In case the parents do not return the questionnaire, a reminder will be sent. We will be using the validated Spanish translation of the ASQ, covering the agerange 4-60 months. In the ASQ 3rd edition surveys, parents are asked to answer "YES" (10 points), "SOMETIMES" (5 points) or "NOT YET" (0 points) to a series of items of the following categories of global neurodevelopment: communication, large movements, fine movements, problem solving and socio-individual competence.
Patients selection All children who were admitted in the"Necker-Enfants Malades" hospital PICU in Paris, France, between January 1 st 2014 and December 31 st 2014 were screened through the PICU database. This is the medical PICU of a tertiary medical center providing medical care for any critical organ failure and post-operative transplantations, for patients aged from 28 days to 18 years. We retrospectively reviewed electronic and paper notes from both hospitalization reports and medical observations. Patients whose hospitalization reports and/or medical observations contained specific keywords ("cardiorespiratory arrest", "death", "withdrawal of treatment", "palliative care", "acute brain failure", or "chronic neurological disease") or a report of an examination conducted by a NP during the period of hospitalization, were selected.
Patient variables For selected patients we collected data about medical history (age, sex, reason for admission in PICU, preexisting pathologic conditions, previous PICU hospitalizations, preexisting withdrawn/withheld treatment, preexisting neurological status including developmental delay, and previous neuropediatric follow-up), conditions of hospitalization (duration, modalities and duration of ventilatory support, type of department at the end of the PICU hospitalization, death), the NP's conclusions (neurological examination, diagnosis, treatment's proposal, anticipated functional prognosis, participation to CMD). Finally we collected data about any possible collaborative multidisciplinary deliberation (CMD) and the final decision that emerged from that CMD, with specific concerns about any potential medication received after a LLST decision (amines, sedative drugs, ventilation, sedation, analgesia).
Furthermore, Castelli et al  [10]  have recently determined a total of 73 new energy levels, enriching the already long list of Mn II lines available in the NIST database. Our fine structure analysis has a double goal: First, to compare existing experimental hfs and oscillator strength data with our computed ones, and second to predict those which were not yet measured. To achieve this we have to determine beforehand level eigenvectors to express the hfs constants A and B and weighted oscillator gf in intermediate coupling, as for instance in  [11] [12]. We have already studied Mn II fine structure of even-parity levels  [13]  and the present work is devoted to fs oddparity ones. Our experience  [14] [15]  lets us expect that this latter analysis should be more difficult than the previous one.
Methods In this single-center study, we retrospectively analyzed data on all consecutive adult patients diagnosed with hematologic malignancies who underwent FMT before or after allo-HSCT due to MDRB colonization. In our center, microbiological screening is performed weekly in all inpatients, with consequent preventive measures in positive patients in order to limit MDRB spread, according to national guidelines. 7 (See Online Supplementary Appendix for details). This study was approved by the institutional ethics committees. The treatment plan was discussed in advance by a multidisciplinary team (hematologist, gastroenterologist, pharmacist) who approved the procedure. The decision was made on a patient-topatient basis. All patients signed informed consent explaining the theoretical risks of the procedure because of the current investigational use of FMT in the field of MDRB and in patients with hematologic malignancies.
In patients concomitantly colonized by ESBL-producing Enterobacteriaceae, "concomitant decolonization" was defined as negative results on at least three consecutive rectal swabs after FMT. The safety of the procedure was also registered. For all patients, data on significant infections (defined as bacteriemia or sepsis occurring during the first 90 days after FMT) were also collected. Although febrile neutropenia or fever of unknown origin was not considered a significant infectious episode, such events were also recorded. A second attempt could be proposed in those patients presenting either a relapse of MDRB colonization or experiencing FMT failure.
The ALICE detector is especially suited for the study of the Quark-Gluon Plasma (QGP) via relativistic heavyion collisions thanks to its exceptional particle identification capabilities. Recently, a systematic study was performed with a wide range of strange particles and hadronic resonances being measured in different colliding systems and at various energies, including the new Xe-Xe data sample collected in late 2017. The full description of the ALICE detector can be found in  [9, 10]  and for further information regarding the techniques used for the measurements reported here see  [11, 12, 13, 14, 15].
The paper is organized as follows. In Section 2, details about the ALICE detector setup and the dataset are given. Section 3 discusses the event and track selection criteria, particle identification procedure, and the analysis method. Estimates of statistical and systematic uncertainties are given in Section 4. Results on ν dyn [π, K], ν dyn [π, p] and ν dyn [p, K] in Pb-Pb collisions at √ s NN = 2. 76 TeV are presented in Section 5, and finally Section 6 summarizes the measurements presented in this paper. a central barrel detection system and forward/backward detectors. The main tracking and PID devices in the central barrel of the experiment are the Inner Tracking System (ITS) and the Time Projection Chamber (TPC), which are operated inside a large solenoidal magnet with B = 0. 5 T. Two forward scintillator arrays V0-A and V0-C are located on either side of the interaction point and cover the pseudorapidity (η) intervals 2.
Identity Method The standard approach of finding the moments N A , N B , N A (N A -1) and N B (N B -1) is to count the number of particles N A and N B event-by-event and calculate averages over the dataset. However, this approach suffers from incomplete particle identification due to overlapping dE/dx distribution functions, which could be circumvented by either selecting suitable phase-space regions or by using additional detector information such as time-of-flight measurements. These procedures reduce the overall phase-space coverage and detection efficiencies. The present study is based on the Identity Method  [19] [20] [21]  which overcomes the misidentification problem. The Identity Method was proposed in Ref. [19]  as a solution to the misidentification problem for the analysis of events with two different particle species.
Statistical and systematic uncertainties The statistical uncertainties were determined by the number of events in this analysis and the finite number of tracks in each event. The number of events also affects the uncertainty of the shape of the inclusive dE/dx spectra, which is determined by a fit. This uncertainty enters the calculation of ω and W , and finally the computation of the moments of multiplicity distributions with the Identity Method. Since standard error propagation is impractical given the rather complicated numerical derivation of the final result, the subsample method was chosen to evaluate the statistical uncertainties. To this end, the data set was subdivided into n = 25 random subsamples i. The ν dyn values were reconstructed for each subsample and the statistical uncertainty was obtained according to σ ν dyn = σ √ n , (5) where σ = ∑ i (ν dyn,i -ν dyn ) 2 n -1 , ν dyn = 1 n ∑ i ν dyn,i.
Acknowledgements  INFN, Sezione di Torino, Turin, Italy INFN, Sezione di Trieste, Trieste, Italy Inha University, Incheon, Republic  of  Korea Institut de Physique Nucléaire d'Orsay (IPNO) ,  Université Paris-Sud ,  CNRS-IN2P3 ,  Orsay, France Institute for Nuclear Research, Academy of Sciences, Moscow, Russia Institute for Subatomic Physics of Utrecht University, Utrecht ,  Netherlands Institute for Theoretical and Experimental Physics, Moscow, Russia Institute of Experimental Physics, Slovak Academy of Sciences, Košice, Slovakia Institute of Physics, Academy of Sciences of the Czech Republic, Prague ,  Czech Republic Institute of Physics, Bhubaneswar, India Institute of Space Science (ISS), Bucharest, Romania Institut für Informatik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt, Frankfurt, Germany Institut für Kernphysik, Westfälische Wilhelms-Universität Münster, Münster, Germany Instituto de Ciencias Nucleares ,  Universidad Nacional Autónoma de México, Mexico City ,  Mexico Instituto de Física ,  Universidade Federal do Rio Grande do Sul (UFRGS) ,  Porto Alegre ,  Brazil Instituto de Física ,  Universidad Nacional Autónoma de México, Mexico City, Mexico IRFU ,  CEA ,  Université Paris-Saclay , Saclay, France.
The impact of the presence of significant oxide layer (pre-oxidized specimen) stands outside the limit of the present paper and will need further dedicated works. To reach this goal, the Thermal Desorption Spectrometry (TDS) technique was extensively used to study hydrogen kinetics desorption  [20, 21]  in association with X-ray Photoelectron Spectroscopy (XPS) analyses. XPS analyzes enable to follow the evolution of the oxide layer during the desorption experiment. The TDS results were analysed thanks to simulations by finite elements (FE) using the Cast3M code  [22, 23]  and the optimization of the kinetic constants has been performed with URANIE tool developed at CEA. This tool is based on the "root" platform developed in the European Organization for Nuclear Research Center  [24]. 3 Experimental Procedure 3.
ppm (i. e. 156 mol m -3 ). Both experimental and simulated data of hydrogen desorption flux as function of temperature are shown in Figure  8. According to findings mentioned in section 3. 1 to 3. 3, the first experimental peak is not to be reproduced by the simulation since it was not assigned to desorption from the alloy. We always refer to the high temperature peak when comparing experimental data to simulation ones. The simulated hydrogen desorption flux (red circles in Figure  8 ) is undoubtedly far away from the experimental second peak of the TDS spectrum (blue dots in Figure  8 ). Clearly, the TDS thermogram cannot be correctly simulated by assuming only a diffusion-controlled desorption of hydrogen from the Zy4 specimens. It can be noted that the diffusion coefficient of hydrogen through the α-Zr matrix  [8] [9] [10] [11] [12] [13] [14] [15]  is independent of the microstructure, the alloying elements (pure zirconium, Zircaloys, Zr-Nb) and the precipitates (Zr(Fe,Cr)2, β-Nb).
Seeking for the surface recombination kinetic constants The "Uncertainty and Sensitivity" platform URANIE developed by the CEA aims to regroup methods and algorithms about Uncertainty and Sensitivity Analysis, Verification and Validation codes in the same framework  [24]. URANIE is based on the data analysis framework ROOT, an object-oriented and petaflopic computing system developed by CERN. URANIE was used to assess the activation energy (Eades) and the pre-exponential factor (k°des) of the surface recombination rate constant. Two URANIE tools were used in this study: the sampler and the optimizer libraries. The sampler library is developed to generate a design of experiment (deterministic/statistical) from Eades and k°des variables. In this study, the methodology LHS (Latin Hypercube Sampling) was employed.
This tool generates a dot matrix with random paired values {ln(k°des), Eades} as illustrated in Figure  12. Where RRi is called a partial "error" criterion for experiment "i", Fsimu(xj) and Fexp(xl) are the simulated flux at the abscissa xj and the experimental one at abscissa xl, respectively. Subscripts "i" (i ∈ {1;2;3;4;5}) refer to following experiments: {1} for the 10 K min -1 TDS, {2} for the first 7 K min -1 TDS, {3} for the second 7 K min -1 TDS, {4} for the first 3 K min -1 TDS and {5} for the second 3 K min -1 TDS. A global error criterion called RESULTAT was defined and corresponds to the RRi sum (Eq. 16): RESULTAT = ∑ --\ \˜ \˜G (Eq. 16) The second tool, optimizer library, performs multi-criteria optimization with Genetic Algorithms allowing finding a local minimum of RESULTAT from a given initial value of the recombination rate constant pair (see the blue empty circles in Figure  12 ).
It is also possible using the URANIE tools to determine the global minimum of the error criterion for each experiment. Table  3  presents the paired values of Eades and ln(k°des) corresponding to the global minimum for each experiment. It appears that there is a large difference of optimized values for the 10 K min -1 ramp experiment and the first 3 K min -1 ramp one. This final set of optimized values (Eades = 290 kJ mol -1 and k°des = 9. 7 x 10 9 m s -1 ) was then used to simulate the five TDS experiments and injected in the FE code. The good agreement, in terms of shape, peak position and peak FWHM, between experimental data and simulation using this set of recombination kinetic constants, whatever the experiment or the temperature ramp chosen, shows the reliability of the determined {Eades; ln(k°des)} pair values and the robustness of the method used.
Conclusion The hydrogen desorption process from an unoxidized Zircaloy-4 specimen has been evaluated through thermal desorption spectrometry tests and by using modelling programed with Cast3M code and URANIE optimization tool. The results of these investigations are consistent with the following conclusions: 1. The hydrogen desorption kinetics from the metal is limited by the surface recombination 2Hads H2. The surface reaction is a second-order equation (Eq. 10). 2. By coupling the Cast3M code with URANIE optimization platform, it is possible to optimize the error between the experimental spectra and the simulated ones and finally get the bestfitted setting of the kinetic constant k'des, of the surface recombination step, which is expressed as follows (Eq. 17 (m 4 mol -1 s -1 ) (Eq. 17) The impact of the presence of a significant oxide scale (pre-oxidized specimen) on desorption kinetics (barrier effect, recombination kinetics, rate-limiting step, etc.
Lawrence  (Mansfield 1966). Legal protection was key in gray seal recovery in the United States. The state of Massachusetts enacted a law to protect gray seals in 1965, and the federal government passed the U. S. Marine Mammal Protection Act (MMPA) in 1972. Under the MMPA, it is illegal to harass, hunt, capture, collect, or kill any marine mammal, or attempt any of these actions (50 CFR 216. 3). The goal of the MMPA is to maintain marine mammal populations at a level between the population's maximum net productivity and carrying capacity (otherwise referred to as the "optimal sustainable population"). Determining whether the size of a population is within this range requires substantial data not available for most species  (Gerrodette and DeMaster 1990) ; however, increasing trends in abundance in a population have been used as an indicator of recovery  (Magera et al.
2011; Cammen et al. 2018a Cammen et al. , 2018b)). This capacity for gray seals to disperse widely has contributed to their recovery in the Northwest Atlantic. The pupping season is an ideal time for surveying seals as their locations are predictable in time and space, and hauledout individuals are visibly available to be counted. Given pupping sites are often offshore islands or distributed along remote coastlines, aerial surveys are an efficient way to survey them. The data collected via aerial surveys of gray seal pupping sites can be used to simply document location and raw numbers or can be combined with other information (e. g. , ground surveys to collect pup age and mortality data) to model pup production or total population size. Multiple surveys are required over the length of the pupping season to model total pup production  (Bowen et al.
Finding overlap among transects was especially challenging for surveys with many transects or when the island was approached from multiple directions. Prior to the shift to digital images, slide images were projected onto a white board for counting. For the digital images, when enough image overlap existed, stitching software (Microsoft Image Composite Editor v. 1. 4. 4-64 bit; Microsoft Corporation, Redmond, Washington) was used to create composite digital images that were used to visualize island coverage and overlap in the aerial images. Seals in the images were counted using a paint-dot technique with standard image processing software (either Adobe Photoshop v. 2015. 5; Adobe, San Jose, California; or Paint Shop Pro Photo X2; Corel Corporation, Ottawa, Ontario, Canada; or GIMP; https://www.
g. , Muskeget), the creation and management of lands as wildlife refuges (Nomans Land, Monomoy Island, Seal Island, Great Point, and Matinicus Rock), and the cessation of U. S. Naval bombing practice (Nomans Land and Seal islands). Seal and Nomans Land islands are closed to the public due to unexploded ordnance. Wooden Ball and Green islands are privately owned and uninhabited. The recovering gray seal population has benefited from the availability of these relatively uninhabited areas. The gray seal pupping season occurs over a 6-week period so there is not a single day during the season when all pups are available for observation. The data presented here and used in the trend analysis are the minimum number of pups born and not total pup production, which would be higher. We restricted our dataset of single-day counts to include only those surveys that were conducted during the presumed peak pupping period and had sufficient coverage of pupping sites.
Acknowledgments This work would not have been possible without the crucial foundation built by  Ms. Valerie Rough  who documented gray seal recolonization and pupping site establishment in the northeastern United States. We also thank  A. Beet  and  T. MacDonald  for guidance on the analysis,  G. Waring  and  R. Merrick  for providing logistical and aerial survey support,  J. Bates ,  J. Ambroult ,  Telford Aviation  ( Rockland, Maine ), the  NOAA RW flight  team (especially  C. Christman ,  T. Cole ,  P. Duley ,  A. Henry ,  C. Khan , and  M. Niemeyer ), and the MML flight team (especially  L. Fritz  and  K. Sweeney ) for executing these flights. University of Massachusetts Boston  students  J. Crouse ,  P. Nelson ,  O. Taha ,  A. Ayaz ,  H. Henderson ,  S. Memnon ,  A. Keleher ,  M. Ciannevei , and  L.
Data management and monitoring study All findings including clinical, DXA and laboratory data will be documented by the investigator or an authorized member of the study team in the subject's medical record and in the eCRF (Software Clinsight® edited by Ennov Society) managed by the Data Management cell (DMC Claudius Regaud institute). This DMC is an independent organization and has no competing interests. Data will be stored and encrypted for 15 years by an online secure site (https://ec. claudiusregaud. fr/CSOnline) accessible only with individual username and password. All data entry, modification or deletion will be recorded automatically in an electronic audit trail. Investigators are responsible for ensuring that all sections of the eCRF are completed correctly and that entries can be verified against source data Investigators guarantee the privacy of patients and personal data are treated according to French laws (article L.