dataesr/scientific-paragraphs-categorization huggingface dataset:
Data processing In situ microphysical measurements and radar reflectivity data used in this study were provided by three types of instruments, that were mounted on-board the F-20 for the HAIC-HIWC campaign flown out of Darwin: -The 94 GHz research cloud radar RASTA  (Protat et al. , 2009; Delanoe et al. 2013 ) measuring both cloud radar reflectivity factors and 3D cloud dynamics below and above the flight level. The uncertainty on the measured reflectivity is about ± 1 dBZ (e. g. ,  Protat et al. 2009 ). -Two optical array probes (OAP), the 2D-Stereo probe from SPEC (Stratton Park Engineering Company Inc. ) and the Precipitation Imaging Probe (PIP) from Droplet Measurement Technologies (DMT). -The Isokinetic Evaporator Probe (IKP-2) providing direct CWC measurements. The IKP-2 is a second-generation version of the prototype IKP  (Davison et al.
The ratio of counted particles (1D information) to recorded particle images (2D information) is used to correct for the concentration. During an OAP overload, 1D counted particles can reach 15000 while only about 10000 have a recorded images, which results an uncertainty by about 50% on the concentration of hydrometeors  (Fontaine, 2014). Further details on post-processing of 2D-S and PIP data are given in  Leroy et al. (2016). The individual 2D-S and PIP PSDs were merged into a composite PSD using the algorithm described in Eq. (  1 ). The resolution of the composite PSD is 10 microns (by interpolating the PIP raw PSDs at the 2D-S resolution), and PSDs are averaged over 5s time intervals for improved large particle statistics. The transition zone for changing from 2D-S to PIP data ∑ 𝑁(𝐷 𝑚𝑎𝑥 ) • ∆𝐷 𝑚𝑎𝑥 𝐷 𝑚𝑎𝑥 =12845 𝐷 𝑚𝑎𝑥 =15 = ∑ 𝑁 2𝐷-𝑆 (𝐷 𝑚𝑎𝑥 ) • ∆𝐷 𝑚𝑎𝑥 𝐷 𝑚𝑎𝑥 <805 𝐷 𝑚𝑎𝑥 =15 + 𝐶 1 (𝐷 𝑚𝑎𝑥 ) • ∑ 𝑁 2𝐷-𝑆 (𝐷 𝑚𝑎𝑥 ) • ∆𝐷 𝑚𝑎𝑥 𝐷 𝑚𝑎𝑥 <1205 𝐷 𝑚𝑎𝑥 =805 + 𝐶 2 (𝐷 𝑚𝑎𝑥 ) ∑ 𝑁 𝑃𝐼𝑃 (𝐷 𝑚𝑎𝑥 ) • ∆𝐷 𝑚𝑎𝑥 𝐷 𝑚𝑎𝑥 <1205 𝐷 𝑚𝑎𝑥 =805 + ∑ 𝑁 𝑃𝐼𝑃 (𝐷 𝑚𝑎𝑥 ) • ∆𝐷 𝑚𝑎𝑥 𝐷 𝑚𝑎𝑥 =12845 𝐷 𝑚𝑎𝑥 =1205 (1) where C1(Dmax) + C2(Dmax) =1, with: 𝐶 2 (𝐷 𝑚𝑎𝑥 ) = D 𝑚𝑎𝑥 -805 1205-805.
6 used in  Hogan et al. (2011)  is rather close to the peak of the  ̅̅̅ frequency distribution presented for African Monsoon MCS and also oceanic MCS over the Indian Ocean  (Fontaine et al. (2014) ). The average aspect ratio calculated for the HAIC-HIWC dataset is 0. 55, which is then very similar to values for various dataset sampled over African continent, United Kingdom, Indian Ocean and North of Australia. In  Fontaine et al. (2014)  the exponent β of the m(D) power law relationship has been constrained as a function of time from the ice particle images. For this study, no a-priori assumptions on the mass-size relationship of hydrometeors have been chosen and as a consequence a variational approach has been applied in order to calculate CWC from Ze reflectivity factor simulations. For a given but variable exponent βi the corresponding pre-factor αi is calculated in order to match the measured reflectivity Z with the simulated Ze.
βi is varying stepwise between 1 to 3 (by increments of 0. 01). Thereby, the range of potential solutions are explored using the oblate spheroids approximation with the T-matrix method  (Mishchenko et al. , 1996)  calculating Qback of each spheroid. Hence, for a given 5s data point, 201 calculations of αi and 201 calculations of corresponding CWC(βi) are performed (see Eq. (  10 )): 𝐶𝑊𝐶(𝛽 𝑖 ) = 10 3 • ∑ 𝑁(𝐷 𝑚𝑎𝑥 ) • 𝛼 • 𝐷 𝑚𝑎𝑥 𝛽 • ∆𝐷 𝑚𝑎𝑥 𝐷 𝑚𝑎𝑥 =12845 𝐷 𝑚𝑎𝑥 =15 (7) For each 5s data point, from the 201 possible CWC(βi) values, an average value  ̅̅̅̅̅̅̅ is deduced (Eq. (  11 )): where Ntot ≤ 201, since the minimum value allowed for αi is the mass of an empty sphere (air density). accounts for 61% of  ̅̅̅̅̅̅̅ (median relative error is 64%) for the entire dataset. Finally, the calculations reveal that 80% of CWC(βi) satisfy the condition CWCIKP =  ̅̅̅̅̅̅̅ ±32%, where no a priori assumptions on mass-size relationships were applied and βi linearly varies between 1 and 3, thereby producing equally eligible solutions CWC(βi).
Correction functions for CWC retrievals from T-matrix simulations of reflectivity As a consequence of the above findings, three different types of corrections are studied, in order to first quantify the limitations of the oblate spheroid approximation and second to suggest suitable correction functions that use in-situ measured quantities over the entire dataset with CWCIKP larger than 0. 1g/m 3. The impact of these corrections on relative errors as a function of NT, Without the above correction functions the retrieved initial  ̅̅̅̅̅̅̅ 5s averages are larger than CWCIKP by about 19% on average (with a median value of +16% (Table  1 , first row)). Therefore, all three correction functions (Eq. (  9 ) to (  11  correction functions NT, T, and max(Dmax) (Eq. (  9 ), (  10 ) and 11)) on the redistribution of the relative error also as a function of CWCIKP and Z.
5*N(Dmax)). Note that 50% is approximatively the missed number of ice crystal images by the PIP probe, due to the overload in high concentration of ice crystals, though data have been corrected for overload times (see section 2 and Fontaine, (2014)). Again, simulations of the reflectivity factor with modified N'(Dmax) size were performed with resulting prefactor αi,50% and CWCi,50%. Results of the comparison of αi,50% and CWCi,50% with αi and CWCi, respectively, are also presented in Table  2  and Table 3. Globally, this second scenario of concentration enhancement of original PSD has a larger impact on retrieved prefactor (αi) and CWCi, than has been the case for the first scenario. Indeed, adding 50% to the concentrations of hydrometeors results in a median decrease of prefactor αi,50% with respect to αi of -18% for β=1 and -20% for β=2 and 3.
Acknowledgments The research leading to these results has received funding from (i) the  European Union 's  Seventh Framework Program  in research, technological development and demonstration under grant agreement  n°ACP2-GA-2012-314314 , (ii) the  European Aviation Safety Agency (EASA)   Research Program  under service contract n° EASA. 2013. FC27, and (iii) the  Federal Aviation Administration (FAA) ,  Aviation Research Division, and Aviation Weather Division , under agreement  CON-I-1301  with the  Centre National de la Recherche Scientifique. Funding to support the  Darwin  flight project was also provided by the  NASA   Aviation Safety Program , the  Boeing Co. , and  Transport Canada. Additional support was also provided by  Airbus SAS Operations, Science Engineering Associates , the  Bureau of Meteorology, Environment Canada , the  National Research Council Atmos.
Complete anthropometric formulas are provided within the Additional file 1: online repository. They were chosen either because of their standard use within different domains such as geriatrics, anthropometry, and/ or forensic science or because of pragmatic issues. Only a few of them are specifically dedicated to a European Caucasian population. The simplified Chumlea method was proposed by our team after several preliminary tests (data not shown), using the same equation but performing different measurements to better fit to ICU requirements (Fig. 1 ). The four most accurate estimation indices were subsequently chosen (two for each limb segment), taking into account their accuracy (a correlation >0. 75 and bias level <5 % as compared with actual height were considered), but also practical issues such as the measurements ease in the recumbent position; all chosen measurements had to be performed in a "one shot, " using a single 1-m ruler, as a comparison with the actual height measurement that requires several steps to be performed.
Statistical analysis Anthropometric formulas have already been validated on various cohorts; however, few of them have been validated for a clinical use in our population of interest, except for the original Chumlea index  [29]. For such a preliminary evaluation, a number of 60 healthy volunteers and 60 mechanically ventilated patients for more than 48 h were determined a priori. All results are provided as mean ± SD, unless specified otherwise. Categorical variables are presented as counts. Relationship between variables was assessed using a Pearson's correlation coefficient (r) test with p value, and data were represented graphically by a scatter diagram depicting the identity line and the 95 % confidence interval (CI) for r. Method comparison and evaluation was performed using a Bland-Altman plot, taking into account the difference between the two methods on the X-axis, because all comparisons were performed.
1 ). All formulas are provided within the Additional file 1: Online Repository Italic value indicates the reference value for height, that was measured in the erect position considering the reference method  [32] ; bias (∂) plot either was reported using quantitative differences or expressed as % of difference, depending on the value type. Quantitative parameter comparisons were made using paired t test. A p value equal or below 0. 05 was considered statistically significant. All statistical analyses were performed using MedCalc ® 12. 4. 0 software for Windows (MedCalc Inc. , Ostend, Belgium).
Faecal Microbiota Analysis. Faecal samples were collected from mice with different genotype, diet and time points. DNA from B20 mg faecal pellets was isolated with the QIAamp DNA Stool Mini Kit (QIAGEN) as instructed by the manufacturer with additional bead beating steps. For in-depth microbiota profiling, 16S rRNA gene V4 region amplicons from faecal DNA isolates were amplified by the 515F/806R primers and sequenced on an Illumina MiSeq platform using 2 Â 300 bp according to the established protocols  57. Data were analysed using the QIIME software pipeline (v1. 9. 1)  58. The UCLUST algorithm was used with default variables to cluster the sequences into operational taxonomic units based on 97% identity against the Greengenes reference database (version 13_8)  59. The diversity between samples was measured by the weighted UniFrac distance, and a principal coordinate analysis of the weighted UniFracs was calculated and constructed with QIIME.
Bio-Plex Pro Mouse Cytokine GrpI panel 8-plex kit was used to measure TNF, IL-1b, IL-2, IL-5, IL-10, GM-CSF and interferon (IFN)-g in the serum of mice. The data were analysed with Bio-Plex Manager Instrument Control Version 6 (Bio-Rad). Measurement of body composition, metabolic parameters and adipose tissuerelated parameters from human patients was performed as described previously  56. BM transplantation. BM from WT and RIPK3 À / À donors (n ¼ 4 mice per group) was transplanted into 6-week-old RIPK3 À / À and WT recipient mice, respectively. After ablative g-irradiation of the recipient mice, 2. 5 Â 10 6 of the BM cells from donors were injected via the tail vein. After BM transfer, mice were treated with antibiotics (0. 02% Borgal) for 2 weeks before the feeding experiments were started.
All real-time PCR reactions were performed in duplicates. Data were generated and analysed using the SDS 2. 3 and RQ manager 1. 2 software. Primer sequences are available on request. All values were normalized to the level of beta-actin mRNA. The expression of TNF, MCP-1, IL-6, IL-1a, IL-1b, CycD1, IFN-g, GM-CSF and col1a1 was tested using the primers as follows: TNF: 5 0 -ACCACGCTCTTCTGTCTACTGA-3 0 (for), 5 0 -TCC ACTTGGTGGTTTGCTACG-3 0 (rev); MCP-1: 5 0 -GTGTTGGCTCAGCCAGAT GC-3 0 (for), 5 0 -GACACCTGCTGCTGGTGATCC-3 0 (rev); IL-6: 5 0 -GCTACC AAACTGGATATAATCAGGA-3 0 (for), 5 0 -CCAGGTAGCTATGGTACTCCAG AA-3 0 (rev); IL-1a: 5 0 -GCACCTTACACCTACCAGAGT-3 0 (for), 5 0 -AAACTT CTGCCTGACGAGCTT-3 0 (rev); IL-1b: 5 0 -GCAACTGTTCCTGAACTCAACT-3 0 (for), 5 0 -ATCTTTTGGGGTCCGTCAACT-3 0 (rev); CycD1: 5 0 -GCGTACCCTG ACACCAATCTC-3 0 (for), 5 0 -CTCCTCTTCGCACTTCTGCTC-3 0 (rev); IFN-g: 5 0 -ATGAACGCTACACACTGCATC-3 0 (for), 5 0 -CCATCCTTTTGCCAGT TCCTC-3 0 (rev); GM-CSF: 5 0 -GGCCTTGGAAGCATGTAGAGG-3 0 (for), 5 0 -G GAGAACTCGTTAGAGACGACTT-3 0 (rev); col1a1: 5 0 -GCTCCTCTTAGG GGCCACT-3 0 (for), 5 0 -CCACGTCTCACCATTGGGG-3 0 (rev).
Flow cytometric data are given as percentage of cells related to the number of CD45 þ cells. Data were analysed with the FlowJo software (TreeStar Inc. ). Cell culture. L929 (ATCC CCL-1) cells were cultured in DMEM supplemented with 10% fetal calf serum (FCS), penicillin (100 IU ml À 1 ), streptomycin (0. 1 mg ml À 1 ) and L-glutamine (0. 03%). 3T3-L1 CARD1 cells  64  were maintained at a non-differentiated state in DMEM supplemented with 10% bovine calf serum, and the medium was changed every 2 days without ever reaching confluence. Adipocyte conversion was induced by treating 2-day post-confluent cultures with DMEM supplemented with 10% FCS and dexamethasone, isobutylmethylxanthine and insulin according to the manufacturer's specifications (Differentiation Kit DIF001 Sigma). Cells were treated by zVAD (Merck Millipore-20 mM), Nec-1s (Santa Cruz-10 mM) and GSK-872 (Merck Millipore-3 mM).
0). All data are presented as mean ± s. e. m. and were analysed by analysis of variance (ANOVA) with Bonferroni's post hoc multiple comparison test. Analysis of two groups of samples was performed using Student's t-test. Correlations were assessed by non-parametric Spearman's test. Statistical significance was indicated as follows: ***Po0. 001; **Po0. 01; *Po0. 05; n. s. , not significant. Data availability. The authors declare that the data supporting the findings of this study are available within the article and its Supplementary Information files.
Acknowledgements We thank  R. Hillermann ,  D. Kull ,  V. Mangels ,  K. Kreggenwinkel ,  J. Ruske ,  N. Koch ,  C. Glantschnig  and  M. Scheideler  for their excellent technical and experimental support and  Professor T. Pufe  ( Institute of Anatomy and Cell Biology-RWTH Aachen ) for technical assistance. We thank  C. Gautheron  for expert artwork. Research in the laboratory of T. L. was supported by a  Mildred-Scheel Endowed Professorship  and a single project grant ( 110043 ) from the  German Cancer Aid (Deutsche Krebshilfe) , the  German-Research-Foundation  ( SFB-TRR57/P06  and  LU 1360/3-1 ), an  ERC Starting Grant  ( ERC-2007-Stg/ 208237-Luedde-Med3-Aachen ), the  EMBO   Young Investigator Program , the  Interdisciplinary-Centre-for-Clinical-Research (IZKF) Aachen-Germany  and the  Ernst-Jung-Foundation Hamburg.
Materials and Methods We established a database based on four Danish nationwide registers: the Medical Birth Registry (1997 to 2010),  [16]  the Fertility Database (1997 to 2010),  [17]  the National Patient Registry (1977 to 2011),  [18]  the Register of Medicinal Product Statistics (1997 to 2012)  [19]  and additional information from Statistics Denmark (1997 to 2012). By a unique personal identification number, linkage between registers and between children and their parents was possible. [20]  The Danish Data Protection Agency approved the study (Journal number: 2012-58-0004). After linkage data were de-identified to ensure data-safety. The Medical Birth Registry provided data on date of birth, vital status at birth, mode of delivery, parity, multiple births, birth weight and gestational age at birth.
The Fertility Database linked children to their parents and provided data on offspring sex, and date of birth for the parents. The National Patient Registry, delivered hospital admission dates, discharge diagnoses for diabetes in the child, mother and father, and codes for diagnoses and surgical procedures related to mode of delivery. The Register of Medicinal Product Statistics, valid on the individual level since 1997, captured redeemed prescriptions on insulin/insulin analogues and oral antidiabetics for the child, mother and father and on antibiotics for the child and the mother. The relevant codes and changes over time for diagnoses and procedures are listed in S1 Table. Statistics Denmark provided information on vital status, emigration and parental educational status.
Data analysis We estimated childhood type 1 diabetes hazard ratios (HRs) for redemption of antibiotics using a Cox proportional hazards model, using the survival package (version 2. 37-7) in R (version 3. 0. 2). The proportional hazards assumption of the Cox regression model implies that the association with antibiotics does not change with age. To examine the validity of this assumption, we fitted models where we allowed the association to change in three-year intervals. These models validated that the proportional hazards assumption was met. Robust (sandwich) standard errors were used to adjust for multiple births from the same woman. To examine the impact of different confounders we conducted partially adjusted models as well as fully adjusted models including all confounders (year of birth, mode of delivery, maternal-and paternal type 1 diabetes status at childbirth, maternal-and paternal age at childbirth, maternal-and paternal educational level at childbirth, offspring sex, parity).
Data analysis Force and EMG signals were assessed offline using MatLab software (The MathWorks, USA). The MVC force was calculated from the resultant force vector (Fx: medial-lateral, Fy: proximal-distal, Fz: extension) as the average force over 100 ms before the electrical stimulus (superimposed twitch), similar to previous studies  [13, 16]. Average force angles in the sagittal and coronal planes were calculated as atan(Fx/Fz) and atan(Fy/Fz), respectively. The EMG signals were digitally band-pass filtered between 20 and 400 Hz. The root mean square (RMS) of each EMG signal was calculated over a time window of 250 ms, from 300 ms to 50 ms before the stimulus, which avoided stimulus artifacts. The ratio between the RMS EMG of agonist (average of quadriceps muscles) and antagonist (biceps femoris) muscles was calculated to assess potential changes in co-contraction between knee extensor and flexor muscles.
Thus the role of fear and anxiety in task performance is less well-defined than in clinical settings. Participants reported anxiety levels from zero to 7. 9 cm on the 10-cm VAS, which would imply mild anxiety for most, but high levels for some. However, the broad differences in the reduction of MVC force during pain could not be explained by variations in pain or anxiety levels. It cannot be excluded that participants with extreme pain beliefs might show marked differences in performance during acute experimental pain, but this hypothesis remains to be tested. The positive correlation between pain and anxiety scores concurs with previous studies on experimental muscle pain  [47]  and data for patients with rheumatoid arthritis  [48]  and chronic back and neck pain  [49]. Taken together these results demonstrate that unhealthy pain beliefs negatively affect pain perception (e.
Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e. g. , ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels.
The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).
INTRODUCTION Fostering information extraction in forested areas, in particular at the stand level, is driven by two main goals : statistical inventory and mapping. Forest stands are the basic units and can be defined in terms of tree species or tree maturity. From a remote sensing point of view, the delineation of the stands is a segmentation problem. In statistical forest inventory, segmentation is helpful for extracting statistically meaningful sample points of field surveys and reliable features (basal area, dominant tree height, etc. )  (Means et al. , 2000, Kangas and Maltamo, 2006). In landcover mapping, this is highly helpful for forest database updating  (Kim et al. , 2009). Most of the time, for reliability purposes, each area is manually interpreted by human operators with very high resolution geospatial images.
Then, a supervised discrimination of the segmented image is performed using a Battacharya classifier, in order to define the maturity of the stands (i. e. , the labels correspond to the maturity of the stands). A forest stand delineation based on high density airborne lidar data is also proposed in  (Wu et al. , 2014). Three features are first extracted from the point cloud ; the tree size indicator (TSI), the forest density index (FDI), and the tree species indicator (SPI). The TSI is 85% of the canopy height model values with a spatial resolution of 4 m × 4 m per pixel. The FDI is the percentage of non-ground points over all points, at each pixel of 4 m×4 m size. The SPI is obtained from the point cloud using 3D alpha shape techniques and principal component analysis. A coarse forest stand delineation is then performed on the 3-band feature image using the Mean-Shift algorithm, with high value of the parameters in order to obtain under-segmented raw forest stands.
METHOD The proposed method is composed of three main steps. First, 12 features are derived from the ALS and multispectral data (Section 2. 1). They are computed at the pixel level as it is needed for the energy minimisation. They are also computed at the object level (trees); the features are more consistent at the tree level and significantly improve the accuracy of the classifiers. Then, the classification of tree species is performed using standard classifiers (Section 2. 2). Finally, a smooth regularisation based on an energy minimisation framework is carried out in order to obtain the final segments (Section 2. 3). A summary of the method is presented in Figure  1.
Tree extraction. Recently, tree delineation has been extensively investigated in the literature, see for instance  (Dalponte et al. , 2015b)  and  (Tochon et al. , 2015)  for the latest existing methods. Many methods exist and it is well known that there is no golden standard for individual tree crown delineation  (Kaartinen et al. , 2012). Here, accurate tree delineation is not the aim of the proposal but a geometrically meaningful over-segmentation technique for object-based image analysis and feature computation. Consequently and for scalability purposes, a simple method with few parameters is sufficient. A coarse method is adopted: the tree tops are first extracted using a local maxima filter (from experiments, a 5 meter radius filter appears to be the best choice on the considered dataset).