dataesr/scientific-paragraphs-categorization huggingface dataset:
The definition of Health activity has been created explicitly to take into account activities which would not improve someone's health status, for example COVID-19 exposure as part of a trial drug administration outside a prescribed setting. Drug misuse is a major public health concern. In response to the opioid crisis, electronic prescription drug monitoring programs (PDMPs) were set up in multiple jurisdictions (ref. ). The agglomerated data is a means to curb doctor and pharmacy shopping  [37]. They generally rely on drug-dispensing data and algorithms to identify patients at risk. Unfortunately, the limited information available can lead to false positives, especially in the case of cancer patients  [38]. Another important caveat to PDMPs is the duplication of information. As a matter of fact, their functioning relies on existing databases (from pharmacies) sending relevant dispensing data to a central database.
Criteria for result evaluation Main assessment criterion The main assessment criterion will be the number of apnoeas or breathing pauses per 24 hours. This number will be obtained by continuous recording of the cardiorespiratory parameters via RECAN software: raw ECG waveforms, plethysmography (SpO 2 -saturation of peripheral oxygen-sensor) and respiratory rate (impedancemetry with ECG electrodes). The averages of heart rate, SpO 2 and respiratory rate will be computed each second by the monitor. A specific graphical interface will detect events with a posteriori changeable threshold followed by manual validation. A pathological apnoea episode is defined either as complete cessation of breathing for more than 20 s (simple apnoea) or apnoea of any duration associated with bradycardia (heart rate <80 beats/ min).
Retention conditions Participation will be proposed to all parents or legal guardians of eligible neonates. The participants will be followed-up as long as their stay in the neonatal intensive care unit (NICU). The unit staff will be called to solicit participation before any transfer of eligible newborn from the unit and prevent unnecessary transfers before completion of the protocol. In case of enrolment problems, amendments to the protocol will be introduced to widen the age range. In case of intervention discontinuation, no further data for the study will be collected, except the reason for discontinuation. Whenever necessary, the participant's health status at NICU or hospital discharge might be later retrieved.
Clinical investigations During each experiment, cardiorespiratory parameters will be continuously recorded over 8 days; for four consecutive days from D4 to D1 (pre-randomisation) and then for five consecutive days from D0 to D4 included (postrandomisation) of which 3 days of administration (S0, S1, S2 in random order) and 2 days of washout (WO1, WO2) (figure  2 ). Cardiorespiratory parameters and stimulation times will be recorded over 24 hours a day and stored via RECAN software. This software is a data logger designed to acquire analogue and RS232 signals from different devices and collect continuous cardiorespiratory parameters over the 8 days. An RS232 link connects it to the olfactometer that sends a signal indicating the time, the duration and the nature of the stimulation in relation with the temporal courses of the cardiorespiratory events.
This ensures an exact timing of each cardiorespiratory event relative to the test modalities. The programme is also able to record information from the newborn monitor alarms (via USB). An interface dedicated to the nursing staff allows them to write, in real time, comments concerning the alarms as well as the events likely to disturb the protocol such as gastric feeding, handling the newborn, changing the diaper, etc. The reading and analysis of the recordings will be carried out by an independent evaluator who is an expert in the software and signal analysis. RECAN includes processing tools that allow defining multiparameter searches (threshold, frequency, co-occurrence of events, temporal and frequency analyses of the activity of the autonomic nervous system, etc). The analyses will determine changes in the occurrence of apnoeas under different experimental conditions (no, sham and real olfactory stimulation).
Data and study management Observation notebooks All data required by the protocol will be collected with explanations for missing data. All clinical or paraclinical data will be transferred to electronic case report forms (CRFs) as soon as they are obtained. The statistical analysis will apply methods to handle missing data according to their nature and number. The data will be available to the investigators, the trial physicians, the trial assistants, the biostatisticians and the members of the Data Safety Monitoring Board. The data confidentiality is governed by an official MR001 declaration and the European Union (EU) General Data Protection Regulation.
Source documents Source documents will be the original data and records from which patient data will be transferred to the case report book. These will include (but not be limited to) test result reports, hospital temperature curves and/or medical notes, dispensing notes and medical correspondence. The investigator will allow direct access to study source data during monitoring, audit or inspection visits. A copy of the CRF will be kept by the investigator for his/her own records. The investigator will keep all observations and the signed consent of the parents/legal guardians for a minimum of 25 years. The data of the study will be computerised in a coded way and in accordance with the Data Protection Act 2018 (EU General Data Protection Regulation). The CIC data management department (Centre d'Investigation Clinique, Lyon, France) will be responsible for the computer management of the data.
, 2019). In addition, during the lockdown the public (including the investigators of this study) could only make short trips of up to 1 h per day and within a maximum radius of 1 km of their home. Visual counting of boats was therefore impossible. Alternatively, the spectrograms (Fast Fourier Transform FFT, 128 points, Hamming window, 75 % overlap) of each 1 min recording were visually and aurally inspected and cross-checked by two investigators using the Avisoft-SASLab Pro software (version 5. 2. 10). The number of recordings during which at least one sound produced by boats was recorded were counted. Boat noises are long lasting sounds that cover both low and high frequency ranges with a broadband energy dominating at frequencies above several hundred Hertz  (Barlett and Wilson, 2002). Visual and aural inspections of the spectrograms were also performed to detect biotic sounds.
Boat noise here refers to all motor sounds that could be detected, from small crafts to large vessels. Acoustic analysis was conducted with Avisoft-SASLab Pro software on N = 8-12 sounds from the two most prominent sound types that showed a good signal-to-noise ratio allowing them to be analysed. The following acoustic characteristics were measured from oscillograms: the total duration of sounds (ms), the number of pulses within a sound, the period of pulses (peak-to-peak interval, ms) and the pulse duration (ms). The dominant frequency (Hz) was measured from logarithmic power spectra. The number of biotic sounds did not follow a Normal distribution (Shapiro-Wilk tests, W = 0. 52-0. 58, all P < 10 -3 ). Non-parametric Kruskal-Wallis tests were then performed to compare the number of sounds detected between the recording sessions and between the 3h-periods.
Dunn's multiple pairwise comparisons post-hoc tests were subsequently used to identify significant differences. All statistical analysis were performed with RStudio (R version 4. 0. 3) at the significance level α = 0. 05. In addition to probabilities (P-values), effect sizes of statistical measures made to compare PSD levels, and number of biotic sounds were estimated as the difference between medians of data obtained during and after the lockdown using the R package "dabestr"  (Ho et al. , 2019)  before inferences and conclusions were drawn.
Funding This work was supported by different sources: a  LabEx CORAIL postdoctoral grant  (project  EMuL ), and several research grants from  Fondation de France  ( 2019-08602 ), the  Agence Nationale pour la Recherche -ANR  ( ANR-19-CE34-0006 - Manini ,  ANR-19-CE14-0010-SENSO ) and the  Observatoire Homme Milieu Littoral Caraîbe (LabEx DRIIHM ,  French programme "Investissements d'Avenir ",  ANR-11-LABX-0010 , which is managed by the  ANR ).
Study setting and participants A prospective cohort study was conducted at the Toulouse University Hospital, France, from June 2017 to December 2018. Patients consecutively enrolled in this study were aged above 65 years old and hospitalised in an acute geriatric ward after visit in the ED. This geriatric ward has a maximum capacity of 20 beds, and an average of 4. 5 days of hospitalisation. A written informed consent was obtained from the patient or their family, those who could not provide one or were opposed to the use of their medical data and follow-up were excluded.
Data collection Medical data of patients were obtained from electronic medical records using a standardised data collection form and were collected within 48 hours after admission in the acute geriatric ward and at 3 months by telephone after discharge by a clinical research assistant. Patients were recruited over 9 months and overall study duration was 18 months. As part of usual care, the information included demographic data, medical history and underlying comorbidities. From the CriSTAL tool criteria, rapid response systems criteria for deteriorated inpatient on admission in the emergency room (ER) were not collected due to imprecise and tedious data collection in an emergency context. 22  total of eight items were understudy including: ► Cause for admission in ER. ► Hospitalisation history within the previous year (including intensive care admissions).
The pyrolyzer used is a Programmable CDS 5200 vertical pyrolyzer built by CDS analytical (Oxford, PA, USA). The latter comprises a heating probe equipped with a platinum filament which can be heated up to 1400 • C at a heating rate of 10 • C to 20,000 • C/s. This pyrolyzer allows multi-step temperature pyrolysis with the use or not of a reactant gas via the CDS5000 Pyroprobe software. In addition, this pyrolyzer is equipped with a cryotrap permitting it to trap volatiles before being injected. The GC-MS equipment is constituted of a CP-3800 gas chromatograph (Varian, Walnut Creek, CA, USA) equipped with a high temperature Zebron ZB-1HT capillary column (-60 • C to 400-430 • C, Phenomenex, Torrance, CA, USA) coated with 100% dimethyl polysiloxane stationary phase. The column dimensions are 30 m length × 0.
If so, the predictive models of processed puree quality traits (physical and chemical) using the reconstructed spectra dataset could open the possibility to provide a multicriteria optimization of puree formulation based on the prior information of single cultivar purees. Partial least squares (PLS), a typical linear algorithm, has been used to successfully determine the global quality parameters of apple purees using NIRS information, such as titratable acidity, dry matter and soluble solids  (Lan, Jaillais, Leca, Renard, & Bureau, 2020). However, the overlapping of absorption bands linked to non-linear rheological variations gave poor prediction of puree's texture by PLS regression. Machine learning approaches, such as random forest (RF) and Cubist, have been specially constructed to address large and complex nonlinear systems.
Spectrum acquisition The Vis-NIR spectral data of purees was acquired with a multi-purpose analyzer spectrometer (Bruker Optics®, Wissembourg, France) at 23°C, which provides diffuse reflectance measurements with a spectral resolution of 8 cm -1 from 12500 to 4000 cm -1 (wavelength from 400 to 2500 nm). For each spectrum, 32 scans were recorded and averaged. The spectral acquisition and instrument adjustments were controlled by OPUS software Version 5. 0 (Bruker Optics®). Puree were transferred into 10 mL glass vials (5 cm height x 18 mm diameter) which were placed on the automated sample wheel of the spectrophotometer. Each puree sample was measured three times on different aliquots. A reference background measurement was automatically activated before each data set acquisition using an internal Spectralon reference.
The MIR spectra of purees was collected at 23°C using a Tensor 27 FTIR spectrometer (Bruker Optics®, Wissembourg, France) equipped with a horizontal attenuated total reflectance (ATR) sampling accessory and a deuterated triglycine sulphate (DTGS) detector. Three replications of spectral measurement were performed on different aliquots. The purees were placed at the surface of a zinc selenide (ATR-ZnSe) crystal with six internal reflections. Spectra with 32 scans for ATR-ZnSe were collected from 4000 cm -1 to 650 cm -1 with a 4 cm -1 resolution and were corrected against the background spectrum of air. The whole spectral dataset of Vis-NIR or MIR included 36 spectra (3 replicates × 3 processing weeks × 4 varieties) of single-cultivar purees, 324 spectra of formulated purees spectra for the modelling set (3 replicates × 3 processing weeks × 6 formulated puree groups × 6 proportions) and 162 spectra for the external prediction set (3 replicates × 3 processing weeks × 6 formulated puree groups × 3 proportions) described in 2.
Statistical analyses of reference data After ensuring normal distribution with a Shapiro-Wilk test (α=0. 05), the reference data of processed purees were presented as mean values and the data dispersion within our experimental dataset expressed as standard deviation values (SD). Analysis of variance (ANOVA) was carried out to determine the significant differences due to the different single apple varieties (Table  S -1) or formulated puree groups (Table  S-2)  using XLSTAT (version 2018. 5. 52037, Addinsoft SARL, Paris, France) data analysis toolbox. And the pairwise comparison between means was performed using Tukey's test. Principal component analysis (PCA) was carried out on all reference data of single-cultivar purees or of formulated purees to evaluate their discriminant contributions using Matlab 7.
Spectral multivariate regression Spectral pre-processing and multivariate regression were performed with several packages ('prospectr'  (Stevens & Ramirez-Lopez, 2013) , 'pls'  (Mevik, Wehrens, & Liland, 2019) , 'Cubist'  (Kuhn, Weston, Keefer, Coulter, & Quinlan, 2014)  and 'caret'  (Kuhn, 2015) ) of the R software (version 2. 6. 2) (R Core Team, 2019). As demonstrated in previous works  (Bureau et al. , 2013; Ncama, Opara, Tesfay, Fawole, & Magwaza, 2017) , the wavelengths from 400 to 2500 nm of Vis-NIR and the wavenumbers from 900 to 1800 cm -1 in MIR were selected (Figure  3 ). For all spectral datasets, standard normal variate (SNV), resampling (intervals= 5, 10, 15), and derivative transform calculation (Savitzky-Golay method, gap size = 11, 21, 31, 41) of first or second order were compared before multivariate regression.
SNV pre-processing applied on the Vis-NIR and MIR data had the best performances to predict puree quality and was then systematically used. The partial least square (PLS), Cubist and RF regression models were developed to i) detect the proportions of each apple varieties in puree samples (Table  2 ) and predict the quality characteristics of formulated purees based on ii) the acquired Vis-NIR, MIR and their combined infrared spectra (CB) (Table  3 ) or iii) the reconstructed Vis-NIR, MIR and CB spectra (Table  4 ). All aforementioned spectral matrices (Vis-NIRS, MIRS and CB) corresponded to the same reference dataset. The set of all modelling spectra (324 spectra) was randomly split, with two-thirds of the dataset (216 spectra) used for calibration and a third (108 spectra) for internal validation.
Acknowledgements The authors thank  Patrice Reling ,  Xuwei Liu ,  Barbara Gouble ,  Marielle Boge ,  Caroline Garcia  and  Gisèle Riqueau  ( INRAE, SQPOV unit ) for their technical help. The ' Interfaces ' project is an  Agropolis Fondation Flashship project publicly funded  through the  ANR (French Research Agency)  under " Investissements d'Avenir" programme  ( ANR-10-LABX-01-001   Labex Agro , coordinated by  Agropolis Fondation). Weijie Lan  was supported by a doctoral grant from  Chinese Scholarship Council.
2020). The remaining details of the observational parameters are presented in  Dutta et al. (2020). The calibrated visibility data were obtained using CASA 5. 4 (Common Astronomy Software Applications package;  McMullin et al. 2007 ) pipeline script as delivered by the observatory. The visibility data for different configurations and executions corresponding to the 72 sources were then separated into continuum and spectral data, and imaged jointly. The 1. 3 mm continuum images of the sources are generated through CASA's tclean task with the 'automask' on, the hogbom deconvolver, and a robust weighting of 0. 5.
247 km s -1 , using the data from N 2 D +. The fate of the substructures can be assessed from their virial parameters. The parameter α vir = 2aE k /|E g |. where E k is the kinetic energy and E g is the gravitational potential energy, and a = 2±1 for a wide range of geometries and density structures  (Kauffmann et al. 2013 ). The virial parameter can be written as α vir = 5. 6 × 10 -3 ac 2 s,eff R(au)M(M ) -1. Taking the sizes and masses and the effective sound speed of 0. 247 km s -1 , we obtain α vir = 0. 36a to 0. 79a for B1 and α vir = 0. 74a to 1. 76a for B2. This calculation suggests that the substructures are close to being gravitationally bound, even if all the non-thermal component is entirely due to turbulence. Other motions, such as infall, may be present. It is at least plausible that the sub-structures are collapsing to form separate objects.
ACKNOWLEDGMENTS This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2018. 1. 00302. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. We thank the reviewer for his helpful comments and suggestions. DS and SYL acknowledge support from the  Ministry of Science and Technology (MoST)  with grants  108-2112-M-001-048-and 108-2112-M-001-052-. Tie Liu  acknowledges the supports from the international partnership program of Chinese academy of sciences through grant No. 114231KYSB20200009 ,  National Natural Science Foundation of China (NSFC)  through grant  NSFC  No.
12073061 , and  Shanghai Pujiang  Program  20PJ1415500. N. H. acknowledges MoST  108-2112-M-001-017 ,  MoST 109-2112-M-001-023  grant. G. G. acknowledges support from  ANID  project  AFB 170002. L. B. acknowledges support from  CON-ICYT  project  Basal   AFB-170002. S. L. Qin is supported by  National Natural Science Foundation of China  under grant No. U1631237. DJ is supported by  NRC Canada  and by an  NSERC   Discovery Grant. VMP acknowledges support by the  Spanish MINECO  under project  AYA2017-88754-P , and financial support from the  State Agency for Research of the Spanish Ministry of Science and Innovation through the "Unit of Excellence María de Maeztu   2020-2023 " award to the  Institute of Cosmos Sciences  ( CEX2019-000918-M ). C. W. L. is supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education, Science and Technology  ( NRF-2019R1A2C1010851 ).
Magnetic characterization Magnetization data of 4 were measured, in sintered powder form as well as on single crystals, with a commercial Magnetic Properties Measurement System (MPMS-Quantum Design), a magnetometer based on a SQUID sensor, between 1. 8 K and 300 K, magnetic fields up to 5 T and for different crystal orientations. The magnetic ac susceptibility of 1 (in powder form) and 4 (on a single crystal and on a powdered sample obtained by crushing the same crystal) was measured using the AC measuring options of the same SQUID magnetometer (for frequencies between 1 Hz and 1. 4 kHz) and of the PPMS system (for frequencies between 100 Hz and 10 kHz).
137 Å, see Table  S2 ) than the axial ones (2. 060 Å). The axial bond distances are identical within experimental errors while those in the equatorial plane differ by less than 0. 087 Å (Table  S2 ). Magnetic anisotropy determination. The magnetic data were first collected using a powder sample. The χT product (Figure  S10 ) remains approximately constant between room temperature and 20 K, with a value of 1. 19 cm 3 mol -1 K in line with a S = 1 state with a g-factor of 2. 16. Below T = 20 K, χT slightly decreases and reaches a value of 0. 99 cm 3 mol -1 K at T = 2 K. Because the structure does not show any intermolecular path for strong antiferromagnetic couplings, this small decrease can tentatively be attributed to the ZFS of the S = 1 state. Magnetization isotherms were measured at T = 2, 4 and 6 K.
The crystal data collection and refinement parameters are given in Table  S1. The asymmetric unit of 4 consists of two independent molecules of [Ni(2-Imdipa)](NCS), two (NCS) and only one methanol. CCDC 2000847 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from the Cambridge Crystallographic Data Centre via http://www. ccdc. cam. ac. uk/Community/Requestastructure.
Acknowledgements This work was supported by funds from the  EU  ( COST Action 15128 MOLSPIN ,  QUANTERA  project  SUMO ,  FET-OPEN  grant  862893 FATMOLS ), the  Spanish MICINN  (grants  RTI2018-096075-B-C21  and  PCI2018-093116 ) and the  Gobierno de Aragón  (grant  E09-17R-Q-MAD ). TM thanks the  IUF (Institut Universitaire de France)  for financial support.
z À Da xx 2hc E x 2 (1) Fig. 3  reports the origin electronic transitions of conformers B along the alkali cation series, plotted as a function of E x 2 for either ap or ac. Both sets (black and grey solid squares respectively) show a linear behaviour as expected from eqn (1), and demonstrate that the experimental shi observed for conformers B along the (M + , BA À ) series of systems can indeed be interpreted as a consistent series of quadratic Stark shis (Fig. 1 ). Although it is not possible to assign B to ap or ac from these datasets yet, the comparison of these systems with n-propylbenzene and benzylacetic acid is expected to be helpful. Indeed, n-propylbenzene and benzylacetic acid are made of a -CH 2 -CH 2 -CH 3 and -CH 2 -CH 2 -COOH moiety respectively, which are analogous to the -CH 2 -CH 2 -CO 2 À M + moiety of (M + , BA À ), where the ionic group -CO 2 À M + is replaced by either a methyl or carboxylic group.
Models and methods 4] [35] [36]  The calculations are done separately for the open-shell multi-radical and closed-shell quinoidal electronic solutions. The multi-radical solution is obtained from spin-unrestricted DFT calculations setting an anti-parallel spin initial guess on neighbouring aC centres. The closed-shell quinoidal solution is obtained from spin-restricted DFT calculations. The atomic structure of the 1D polymers and the a unit cell parameter (x-direction) are pre-optimized using the PBE 37 functional and a Tier-1 light numerical atom-centered orbital (NAO) basis set,  38  as implemented in the Fritz Haber Institute ab initio molecular simulations package (FHI-AIMS). 39, 40 These pre-optimizations are followed by full optimizations (atomic coordinates and a unit cell parameter) using the PBE0 hybrid functional  32  and the same light NAO basis set.
Acknowledgments F i n a n c i a l s u p p o r t f r o m t h e C o o r d e n a ç ã o de  Aperfeiçoamento de Pessoal de Nível Superior Brazil (CAPES)  for process:  88881. 337360/2019-01 , CNPq/FAPEMIG  (agreement recorded in SICONV:  793988/2013 ), the  University of Bordeaux  and the  Centre National de la Recherche Scientifique (CNRS)  are gratefully acknowledged. The authors thank the physics department of the  Universidade Federal de Viçosa  for the characterization analyzes.