dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Financial support for this study was provided by the  Académie Hassan II des Sciences et Techniques, Morocco , and the region  Nouvelle Aquitaine. The authors are grateful to  Lhoussain Ablouh  ( Centre des Analyses Chimique, UCA, France ) and  Emile Béré  ( UFR SFA, Pole IMAGE UP, University of Poitiers, France ) for technical support with SEM and EDX analyses. Mouad Akboub ,  Idir Elhabib , and  Mouhssin Elhalim  are thanked for their assistance during the field work. Leho Ainsaar  and an anonymous reviewer are acknowledged for their constructive comments. The analyses at the  University of Tartu, Estonia , were supported by the  Estonian Centre of Analytical Chemistry. The pub lication costs of this article were covered by the  Estonian Academy of Sciences.
The reads were analyzed using the TypeStream Visual Software version 2. 1 (One Lambda). This patient was found to have a new C*05 allele and was consequently typed A*02:01, 29:02; B*40:01, 57:01; C*05:01:01:81Q, 16:01:01:01; DRB1*07:01, 07:01; DRB4*01:01, 01:03:01:02N; DQA1*02:01, 02:01; DQB1*02:02, 03:03; DPA1*02:01, 02:02; DPB1*01:01, 11:01. Using the IPD-IMGT/HLA Database, 2 nucleotide sequence alignment with HLA-C alleles shows that this new allele has one nucleotide change from C*05:01:01:02 in acceptor splice site of intron 1 (c. 74-2A>G) (Figure 1). Three softwares predicted a change at acceptor site 2 bps downstream (MaxEnt, NNSPLICE and SSF) with a score of 1 and SpliceAI software with a score of 0. 57, resulting in a high probability of abolition of the splice site. This nucleotide change was confirmed using other NGS reagents provided by Immucor (Mia Fora NGS Flex, Norcross, GA) run on the Illumina MiSeq system (San Diego, CA) and analyzed with the Mia Fora Flex software (version 5.
The coding nucleotide sequence of the new allele has been submitted to the GenBank database (Accession No. OM913145) and to the IPD-IMGT/HLA Database (Submission No. HWS10060752). The name C*05:01:01:81Q has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System in March 2022. This follows the agreed policy that, subject to the conditions F I G U R E 1 Alignment of the sequence of exon 1, intron 1, exon 2 and partial intron 2 of HLA-C*05:01:01:81Q with the sequence of HLA-C*05:01:01:02. Dashes indicate nucleotide identity with the HLA-C*05:01:01:02 allele. Numbers above the sequence indicate nucleotide position. Pipes (j) are used to indicate the exon, intron boundary.
Samples and data Our data was collected through 12 online, cross-sectional surveys conducted from May to November 2020 among large representative samples of adults residing in France. 3  Therefore, the period studied does not cover the strict French lockdown which was implemented from 17 March to 10 May. Only the last two surveys took place during a less strict lockdown, in which schools were open and face-to-face work was possible. A stratified sampling method was adopted to recruit participants so as to represent the distribution of the French population, based on sex, age, occupation, community size and region recorded during the 2016 national general census conducted by the National Institute of Statistics and Economic Studies (INSEE). Samples consisted of N = 24,003 responses. Missing data were replaced by a multivariate imputation procedure  (van Buuren and Groothuis-Oudshoorn, 2011).
We proceeded in the same way for higher front values until all parameter sets were assigned. Finally, to determine the best fit parameter set, we needed to distinguish between Pareto efficient sets. To this end, we selected the combination that minimised the average of MSE and ΔMax, i. e. individual and total estimation errors. Thus, the selected parameter set places the estimated behaviour curve close to the empirical one, while ensuring a small individual estimation error. Simulation and dominance analyses were performed with Matlab.
Study design and data source The study used a cross-sectional design. All data were collected in January 2022 from the official results section. Despite the race being first held in 1924, available results comprised the period between 2007 and 2021 and included information up to the first 100 finalists in each age group in each year. Downloaded information included event year, date of birth, sex, and race time. The runners' age was computed using the date of birth and the date of the competition. Age categories were considered (18-20 years [y]; 21-30 y; 31-40 y; 41-50 y; 51-60 y; >60 y), and the running pace was estimated by the ratio "time conclusion/running distance (15 km)". For the analysis, the pace was used as "s/km".
To test that the data met the model assumptions, we performed goodness of fit (GOF) tests for single state models using the software U-Care (v3. 3,  Choquet et al. , 2009a). The overall GOF test of the CJS models indicated a clear lack of fit for all species, mostly caused by significant transience and trap dependence (significant test components 3. SR and 2. CT; Supplementary Table  A1 ). Test 2. CT tests the hypothesis that there is no difference in the probability of being recaptured at occasion t + 1 between those who were captured at occasion t and those who were not. Test 3. SR tests the hypothesis that there is no difference between previously and newly tagged individuals caught at time t in their probability of being recaptured later. To deal with trap dependence, we used the Pradel and Sanz Aguilar approach, which considers immediate trapdependence based on a multi-event framework (see details in  Pradel and Sanz-Aguilar, 2012).
Acknowledgments The authors also thank  K. Sagerup ,  S. Nilsen ,  A. Ask ,  T. Nordstad ,  A. Goutte ,  C. Clément-Chastel , and the  AWIPEV  staff for their help in the fieldwork. The authors thank  H. Steen  for initiating some of the monitoring of Svalbard seabirds in 2005 and all the field assistants who helped collecting these data every summer.
Funding This study was funded by the project  FACE-IT (The Future of Arctic Coastal Ecosystems-Identifying Transitions in Fjord Systems and Adjacent Coastal Areas ) and the programs SEAPOP (www. seapop. no) and MOSJ (www. mosj. no). FACE-IT has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  869154. The long-term study of kittiwakes in the Kongsfjord was supported by  Institut Polaire Français  ( IPEV  project no. 330 ) and the  BNP Paribas Foundation  (project  SENSEI ).
The final step in the analysis will be the K-Means clustering (which could be interpreted as an unsupervised learning approach). (2) Machine learning: Given a database of signals and the outcome of the questions, we can build a map to learn a relationship between the data and the outcome. We will use artificial neural networks, which are typical machine learning algorithms. They were used for instance in ECG analysis  [8] , cardiac arrhythmia prediction  [9, 10] , and drug safety studies  [11, 12]. (3) Mathematical modelling: By exploiting a priori information about the system, we build a set of equations to simulate the phenomenon under investigation. These equations provide a way to link observable quantities to the outcome. We will use a parametric 0-d model to simulate the global circulation (in the spirit of  [13] [14] [15] [16] [17] [18] [19] ).
Experimental Data Sets In this project, we have used cardiovascular data gathered from 4 dogs: two Beagles (Hexe and Happy) and two Labrador-mix mongrel dogs (Simba and Roxy), involved in safety pharmacology studies for several years. The data are acquired by telemetry from awake and non-anaesthetized animals for many hours (for more details, the reader can refer to  [20] ). This data set includes values of the Electrocardiogram (ECG), Arterial Pressure (AP), and Left Ventricular Pressure (LVP) signals, recorded every 2 milliseconds. For each dog, we had data corresponding to two different periods of the dog's life: the first one when the dog was 6-7 years old ("Historical data"), the second one when it was 8-9 years old ("New data"). We have used the cardiovascular data that has been recorded when the animal was younger and "healthy" state (we also call it "Historical" data) to compare with the recent recorded data, helping to decide whether or not this particular animal is "healthy".
Each data file includes a seven hours continue recording of a placebo cardiovascular safety pharmacology study (pharmacologically inactive substances  [21] ). Concerning the measurement of the arterial pressure, according to  [22] , the signal was sampled either by a catheter in the abdominal aorta or in the femoral artery. The LVP is measured by inserting a catheter connected to a fluid-filled transducer into the left ventricle  [23]. From each complete cardiac beat (which in dogs at rest is around 0. 8 s), we have extracted 9 quantities (those choices of 9 quantities have been decided by the data provider). All these features have been done by Notocord-Hem™ software. Human supervision and some manual corrections were needed to ensure the features are correctly extracted (especially for ECG).
We have trained a MLP to discriminate between the "Historical" and "New" state of 4 dogs to answer the first question. "Historical" and "New" data correspond the two classes in the binary classification task. "Historical" and "New" data are mixed in the model training phase. We have used the data set consisting of around 10000 samples of cardiovascular features for each state of each dog to train the MLP model. Those features are normalized using M inM axScaler, explained in  [33]. We have used 80% of the samples to train and validate (with cross validation), and 20% of samples to test the MLP model. Our MLP model consists of 3 fully connected hidden layers, which have 9, 6, and, 3 hidden units. The first hidden layer receives 9 cardiovascular features (LVP Systolic, LVP Diastolic, LVP dpdt(max), RR interval, PR duration, QT interval, QRS duration, APR Systolic, APR Diastolic).
Results from Mathematical Modelling To solve the equations in each case according to time we have used a backward differentiation formula (BDF) solver that is implemented in the Python built-in solver odeint (see  [39]  and  [40]  for more details). In order to solve the model, we need to give as input the parameters (R s , R mv , R av , C a , C s , L, P 0 , a, h) and the peaks of the ECG. Then we get as output the LVP, the AP, the LAP and the aortic flow. To obtain the optimized parameters for each dog we have used the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm that is implemented in Python in the pycma module  [41]. We need to give as input an initial estimation of the parameters and the experimental data for the AP, LVP and ECG. The initial standard deviation was chosen as σ 0 = 0.
Acknowledgements  Elham ,  Sara  and  Haibo  are ESR-fellows of the  INSPIRE European Training Network. INSPIRE  receives funding from the  EU Horizon 2020 Research and Innovation programme , under the  Marie Skłodowska-Curie   GA 858070  [1]. We appreciate all support from  Pieter-Jan Guns  as the scientific coordinator of  INSPIRE European Training Network ,  Michael Markert ,  Georg Rast ,  Karin Graf ,  Jessica Schiwon ,  Thomas Trautmann  and  Florian Krause  from  Boehringer Ingelheim Pharma GmbH & Co KG ,  Damiano Lombardi  from  Inria ,  Muriel Boulakia  from  Université Paris-Saclay, UVSQ, CNRS, Laboratoire de Mathématiques de Versailles  and  Sylvain Bernasconi  from  NOTOCORD  and Instem companies.
to the remote background. In a second step, the slant columns are converted to tropospheric vertical columns using a lookup table of vertically-resolved air mass factors calculated at 340 nm using the VLIDORT v2. 6 radiative transfer model  (Spurr, 2008). Surface albedo is obtained from the monthly OMI climatology at 0. 5 • resolution  (Kleipool et al. , 2008). Daily a priori vertical profiles are obtained from the TM5 analysis, at 1 • spatial resolution  (Williams et al. , 2017). For this work, the cloud correction is switched off, except for a strict filtering (CF<0. 2, CF being the cloud fraction) based on the Fresco v7 cloud product  (Veefkind et al. , 2016) , and clear-sky air mass factors (AMF) are used in lieu of the cloud-corrected AMFs of the standard QA4ECV product. This choice ensures an optimal consistency with the TROPOMI HCHO dataset  (De Smedt et al.
Harmonized retrieval settings have been set up and used within the whole IRWG in order to build a consistent groundbased FTIR HCHO data set for robust interpretation of satellite and model validation  (Vigouroux et al. , 2018). This data set has proven its value for the validation of TROPOMI HCHO  (Vigouroux et al. , 2020 , see also the quarterly reports https://mpc-vdaf. tropomi. eu), GOME-2 (https://acsaf. org/valreps. php) or more recently of OMPS  (Kwon et al. , 2023). We refer to  Vigouroux et al. (2018)  for details about the HCHO retrieval settings. The most critical aspects with regard to harmonization within the network are the spectral signatures and the spectroscopic parameters. The HCHO fitted spectral signatures belong to the ν 1 and ν 5 vibrational bands, around 3. 6 µm. The spectroscopic database is the atm16 line list from G.
Airborne HCHO data Table  1  lists the aircraft campaign data used in this study. Among those, four HCHO datasets from three campaigns conducted in 2012-2013 over the United States are used to evaluate OMI HCHO columns, as described in Sect. 3. 3. All campaigns are used to evaluate the inverse modelling results. More specifically, • the DC3 (Deep Convective Clouds and Chemistry) NASA mission sampled the atmospheric composition over the Central U. S. in May-June 2012  (Barth et al. , 2015). Formaldehyde mixing ratios were acquired from two aircraft platforms, the NASA DC-8 and the NSF/NCAR Gulfstream V (GV), equipped with similar infrared absorption spectrometers employing difference frequency generation (DFG) laser sources: the DFGAS (Difference Frequency Generation Absorption Spectrometer) instrument on the DC-8  (Weibring et al.
, 2015). The ISAF data were found to be about 10% higher than the CAMS values  (Zhu et al. , 2016). Only the CAMS data are used here. • the DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) was a multi-year aircraft mission led by NASA  (Crawford and Pickering, 2014). The HCHO measurements were acquired using the DFGAS technique  (Weibring et al. , 2007). The campaign over California    S1. Note that the stations Garmisch and St. Denis are not shown, given their close proximity to the stations Zugspitze (ZUG) and Maïdo (MAI), respectively. Research Observations)  (Molina et al. , 2010). The NASA DC8 measured chemical composition over Mexico, Texas and the Gulf of Mexico below ca. 10 km altitude. HCHO was measured using Tunable Diode Laser Spectroscopy (TDLAS)  (Fried et al.
, 2008). The INTEX-B campaign of April-May 2006 is not used here, as it was conducted primarily over the  • the ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) campaigns took place in 2008  (Jacob et al. , 2010). Whereas the ARCTAS-A campaign (not used here) targeted the springtime atmospheric composition over the Arctic, the ARCTAS-CARB (June) and ARCTAS-B (July) campaigns sampled the tropospheric composition during the summer between 34 and >80 • N. Only data below 60 • N are used here. The TDLAS technique (see above) was employed to measure HCHO during both missions. • the Southeast Nexus (SENEX) campaign  (Warneke et al. , 2016)  used the NOAA WP-3D aircraft to sample the lower troposphere (below ca. 6 km altitude) over the Southeast U. S. in June 2013.
HCHO was measured using the ISAF instrument (see above). • the KORUS-AQ (Korea-United States Air quality) campaign investigated air composition over Korea and surrounding areas in May-June 2016. Formaldehyde was measured throughout the troposphere by the NASA DC8 using CAMS (see above). For all campaigns, we exclude HCHO measurements from urban plumes (identified as [NO 2 ]> 4 ppbv or [NO x ]/[NO y ]> 0. 4) and biomass burning plumes ([CH 3 CN]> 225 pptv), as well as nighttime data (before 9 AM or after 18 PM local time) and measurements over oceans. Data above 4 km altitude are also excluded. Furthermore, as the average HCHO mixing ratio from the CAMS instrument were about 13% lower than those of the ISAF instrument during SEAC 4 RS, we increase the CAMS values by 6. 5% in order to bring the SEAC 4 RS data midway between CAMS and ISAF, while also reducing the bias between the GV data (CAMS) and DC8 data (DFGAS) from the DC3 mission.
This agreement is achieved through a substantial increase of anthropogenic emissions throughout the U. S. , whereas biogenic emissions are decreased over much of Southeast U. S. and biomass burning emissions undergo a moderate increase (see Table  S2  and Fig. S1  in the Supplement). Since the emission parameters are severely underconstrained by the inversion due to the poor coverage of the observations over the model domain, the solution found by the inversion has limited reliability and is strongly dependent on the a priori emission distribution and inversion setup, in particular the covariance matrix of the emission parameters. Despite this caveat, the optimization reproduces very well both the HCHO horizontal distribution (Fig. 3(g-i )) and the vertical profile shape of HCHO mixing ratios for all campaign datasets, as seen on Fig.
4. While panels (a-d) in Fig. 4  display the campaign-averaged profiles for the datasets used in the emission inversion, Fig. 4 (e) shows the profiles for the SENEX campaign which took place in 2013 but was not used as constraint in the inversion. Although the observed profile shape is correctly simulated by the model, the overall agreement is significantly lower for this campaign (-22% bias) than for the campaign datasets used in the inversion. Given the significant overlap of the SENEX and SEAC 4 RS spatial coverages (see Fig. 1 ), the underestimation of SENEX HCHO by the model suggests a shift of the summertime biogenic VOC emission peak towards spring, since SENEX and SEAC 4 RS were conducted (primarily) in June and August, respectively. This finding, which is in agreement with the previous determination of isoprene emissions based on satellite (GOME) data by  Palmer et al.
This suggests a trend in the emissions of isoprene, as its oxidation is believed to be the dominant source of HCHO over the Southeastern U. S. (Palmer et al. , 2006). Possible drivers of such trend (besides temperature and visible radiation fluxes, already considered in the MEGAN model) include soil moisture stress, CO 2 inhibition and land-use change. The effect of soil moisture stress is ignored in this study, given the difficulties associated to its parameterization  (Bauwens et al. , 2016; Opacka et al. , 2021) , but it could be significant in this area. The effect of CO 2 is considered in our a priori emissions using the parameterization of  Possell and Hewitt (2011) , which induces a emission decline of about ∼ 0. 45% yr -1 over 2005-2016, but this effect is highly uncertain. Using high-resolution tree cover information from the Global Forest Watch database  (Hansen et al.
Conclusions The HCHO vertical columns of the QA4ECV OMI dataset are evaluated using two qualitatively very different measurement types: ground-based vertical columns of formaldehyde measured at 26 FTIR stations worldwide, and in situ HCHO measurements from aircraft campaigns conducted over the U. S. in 2012-2013. A regional atmospheric chemistry-transport model is used to assimilate the airborne measurements and to derive HCHO distributions that can be compared to the satellite data while closely approximating the vertical and horizontal distribution of the measurements. The two datasets are complementary: whereas the FTIR dataset covers a wide range of conditions and is geographically more diverse, the aircraft campaigns used in this work cover a known major VOC source region and are particularly relevant to the evaluation of BVOC emissions using satellite data.
The evaluation of OMI columns against FTIR column data and airborne in situ measurements leads to similar conclusions, in spite of the large qualitative difference between the datasets. The regression parameters are remarkably similar, with nearly identical slopes (0. 66) and similar intercepts. On one hand, the large OMI columns (especially above ∼ 12×10 15 molec. cm -2 ) are generally underestimated, by a factor of 1. 29 (against aircraft data) to 1. 36 (against FTIR) for an OMI column of 20 × 10 15 molec. cm -2. On the other hand, low OMI columns (especially below ∼ 6×10 15 molec. cm -2 ) are often underestimated, by -10% and -37% against FTIR-and aircraft-based regressions, respectively, for an OMI column of 5 × 10 15 molec. cm -2. The correlation of FTIR vs. co-located OMI data is relatively poor (Pearson's coefficient of 0.
Acknowledgements. HCHO satellite data from OMI were produced in the scope of the European FP7 project  QA4ECV  (grant no. 6007405 ). We thank  Dan Smale  and the  National Institute of Water and Atmospheric Research   (NIWA)  for the provision of the Lauder FTIR data, and  Nicholas Jones  of the  Centre for Atmospheric Chemistry, University of Wollongong, Wollongong, Australia , for provision of Wollongong FTIR data. Thanks to  Thomas Hanisco  and the  NASA Goddard Space Flight Center (GSFC)  for provision of ISAF data (SENEX). B. O. was supported by the  EQUATOR  ( Emission Quantification of Atmospheric tracers in the Tropics using ObseRvations from satellites , contract no. B2/202/P1/EQUATOR ,  2021-2025 ) project of the  BRAIN-be 2. 0 programme  from the  Belgian Science Policy Office (Belspo).
G. -M. O. was supported by the  SEEDS  project funded by the  European Commission  under the  H2020 programme  (grant agreement no. 101004318 ). C. V. was supported by  Belspo  through the  ProDEx  project  TROVA-E2  ( TROPOMI Validation -Phase E2 ) of the  European Space Agency  ( 2020-2023 ). E. M. is a senior research associate with the  F. R. S. -FNRS. The Paris site has received funding from  Sorbonne Université , the  French research center CNRS , the  French space agency CNES , and  Région Île-de-France. FTIR operations of  Rikubetsu and Tsukuba  are supported in part by the  GOSAT  series project. The  Rikubetsu NDACC  site is funded by the  joint research program  of the  Institute for Space-Earth Environmental Research (ISEE) ,  Nagoya University. This work has been supported by the  BMBF (German Ministry of Research and Education)  in the project  ROMIC-II , subproject  TroStra  ( 01LG1904A ).
Data availability. The QA4ECV dataset can be found at http://doi. org/10. 18758/71021031 (De Smedt et al. , 2017). The NASA aircraft campaign datasets are available from the Langley Research Center at https://www-air. larc. nasa. gov/missions/merges. The HCHO FTIR data can be requested from the PIs of each station. The MEGAN-MOHYCAN isoprene inventory is available at https://emissions. aeronomie. be.
HCHO columns are used as constraints to optimize the emissions of VOCs in the MAGRITTE global model over2005-2017. Evaluation of the modelled distributions of HCHO against 11 aircraft datasets spanning 2006-2016 show that the bias correction leads to a considerable reduction of the average bias against aircraft observations, from -27% in the prior simulation and -28% in an optimization without bias correction to -9% when using bias-corrected data. The root mean square deviation (RMSD) is also decreased. Agreement with FTIR data is also improved, especially at South American sites, but the improve-https://doi. org/10. 5194/egusphere-2023-2456 Preprint. Discussion started: 6 November 2023 c Author(s) 2023. CC BY 4. 0 License.