dataesr/scientific-paragraphs-categorization huggingface dataset:
LiDAR data capture the vertical structure of trees and allow the estimation of tree height up to 40 m with good precision  [18] [19] [20]. The tree height derived from LiDAR is strongly correlated with the AGB of the trees, with no saturation at higher AGB values  [19, 21, 22]. LiDAR data can be acquired from an aircraft and from space. Airborne and spaceborne LiDAR sensors record waveforms from small (<1 m) and large footprints (up to 60 m), respectively. Several studies have shown that the estimation of AGB from airborne LiDAR data is more accurate than that from spaceborne LiDAR  [23, 24]. However, the acquisition of airborne LiDAR data is costly, and the spatial coverage is limited to small areas. On the other hand, the available space-borne LiDAR data acquired by the Ice Cloud and Land Elevation Satellite (ICESat) are free, but do not provide continuous coverage of the earth.
To overcome the limitation of spatial cover of LiDAR data and the saturation of optical and SAR amplitude data at medium AGB values, several studies tend to combined LiDAR with optical or SAR data for continuous AGB mapping at regional and global scales. At the regional scale, Mitchard et al. [21]  estimated the AGB in Lopé National Park in central Gabon by coupling GLAS, PALSAR (L-band), and SRTM data. Lorey's height was first derived from GLAS data and then converted to AGB through a simple equation. This equation was fitted using plot field measurements of Lorey's height and AGB. Furthermore, a classification (40 classes) was performed using radar and SRTM data to determine regions with homogeneous vegetation. Finally, GLAS AGB estimates located within each region were averaged, enabling the spatial extrapolation of AGB estimates.
At the global scale, Saatchi et al. [22]  mapped the AGB of world tropical forests using a combination of data from 4,079 in situ inventory plots (across the three tropical continents) and GLAS samples of forest structure, plus optical and microwave imagery with 1-km spatial resolution. In this study, a power-law functional relationship (R 2 = 0. 85) between the in situ Lorey's height and in situ AGB was first performed. This relationship was then applied to tree height derived from GLAS to estimate AGB at each GLAS footprint location. Finally, a fusion model based on the maximum entropy (MaxEnt) approach was performed using spatial imagery to extrapolate AGB measurements from inventory plots and GLAS footprints to the entire landscape. Baccini et al. [26]  derived a carbon density map of pan-tropical forests using GLAS data together with MODIS images (Bidirectional Reflectance Distribution Function and land surface temperature) and SRTM data.
In this this study, in situ AGB was first derived from plots within GLAS footprints using trees characteristics. Then, a statistical relationship between the in situ AGB estimates and GLAS waveform metrics was established, allowing the estimation of AGB for all GLAS footprints located across the tropics. Finally, a model relating GLAS-based AGB estimates and MODIS and SRTM data was calibrated and applied to derive the AGB map. Mitchard et al. [27]  assessed the reliability of a global AGB map produced by Saatchi et al. [22]  and Baccini et al. [26]  by using an accurate AGB map of Amazonian Columbia as a reference dataset. Mitchard et al. [27]  observed substantial discrepancies between the maps of Saatchi et al. [22]  and Baccini et al. [26]  over tropical forest areas (up to ±150 t/ha), even though both maps give similar means and total AGB values on the continent scale.
Then, this random forest model was applied to map the AGB of forested areas in Madagascar. To date, the Vieilledent AGB map is the most recent and accurate map with medium resolution (250 m) for forested areas in Madagascar. The inconvenience of the Vieilledent AGB map is the inability to measure high AGB values, and therefore, a new method is required that incorporates LiDAR remote sensing to overcome such inconvenience. This inconvenience is mainly due to the use of the EVI and the percent tree cover derived from optical images at medium resolution as predictive variables for AGB estimates. The optical data at medium resolution saturates at high AGB values and induces an underestimation of high AGB  [6, 7]. An improvement in Vieilledent's AGB map will allow a more accurate estimation of carbon stocks and mapping in forested areas in Madagascar.
In our study, The AGB was first estimated from GLAS and DEM metrics, providing a spatially distributed (GLAS footprints geolocation) AGB estimation (GLAS AGB). Second, the spatial dependency of the additional correction factors (Vieilledent's AGB map-GLAS AGB at each GLAS footprint location) was modeled, and an ordinary kriging interpolation of additional correction factors was performed to provide a correction factor map. Finally, the correction factor map and Vieilledent's AGB map were summed to improve Vieilledent's AGB map, taking into account the addition of GLAS data in AGB estimation. A description of the study area and the different datasets used in this study is provided in Section 2. Section 3 presents the methodology. The results and discussions are shown in Sections 4 and 5, respectively.
Vieilledent's Aboveground Biomass Map Vieilledent's AGB map  [28]  provides the AGB for forests in Madagascar in 2010, with a spatial resolution of 250 m. Vieilledent's AGB map was derived from three types of explicative variables (vegetation indices, topography, and climatic data) using the Random Forest (RF) regression technique. Vegetation indices are the EVI (Enhanced Vegetation Index) and the percent tree cover (%VCF), both derived from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite remote sensing imagery acquired between 2000 and 2010. A topographic variable (elevation) was obtained from the 30-m resolution SRTM (Shuttle Radar Topography Mission) global elevation dataset. Climatic data  are the cumulative annual precipitation, mean annual temperature, and temperature seasonality (standard deviation of monthly temperature × 100), all of which are extracted from the MadaClim website (http://madaclim.
cirad. fr). To derive Vieilledent's AGB map, in situ AGB were first related to explicative variables by means of the RF regression technique. Then, the resulting model was applied to map AGB in forested areas in Madagascar with spatial resolution of 250 m. Climatic variables were the most important, compared with EVI, %VCF, and elevation. In the study of Vieilledent et al. [28] , a cross-validation procedure was used to validate the AGB map using field inventories as the reference dataset. LiDAR data were acquired between 2003 and 2009 by the Geoscience Laser Altimeter System (GLAS) sensor on board the Ice, Cloud, and Land Elevation (ICESat) Satellite. The GLAS sensor operates in the near-infrared (1064 nm) wavelength and illuminates footprints with a nearly circular shape that are approximately 70 m in diameter.
LiDAR Data To eliminate unreliable GLAS data (i. e. , data affected by atmospheric conditions), several filters were applied: (1) footprints with associated centroid elevations significantly different than the corresponding SRTM elevations were excluded (|GLAS -SRTM| > 100 m); (2) footprints corresponding to waveforms with a low signal to noise ratio (SNR) were also removed (SNR < 15)  [33] ; (3) saturated waveforms were eliminated (saturation index satNdx # 0); and (4) only the cloudfree footprints were conserved (cloud flag FRir_qaFlag = 15). In addition, GLAS footprints located inside forest stands (selected using the existing AGB map) were conserved. From the original database of 1,772,000 footprints, 48,247 footprints that respect all criteria mentioned above were kept (Figure  1 ). The density of GLAS footprints in forested areas is 0.
A brief scheme explaining the procedures for the improvement of Vieilledent's AGB map is shown in Figure  3. To assess the relevance of our approach, first, the improved AGB map was compared to the in situ AGB to determine the gain in precision brought to Vieilledent's AGB map. Then, the accuracy of the improved AGB map was compared to the accuracy of (1) the most recent pan-tropical AGB map produced by Avitabile et al. [39]  (Avitabile's AGB map); and (2) another AGB map computed in this present study using our database (48,247 GLAS-derived AGB, DEM metrics, and field inventories) following the method proposed by Baccini et al. [26] , called "Baccini's approach AGB map".
Comparison between the Improved AGB Map and the AGB Map from Baccini's Approach To assess the relevance of our approach it was important to compare it to Baccini's approach, since the latter is the most commonly used approach for AGB mapping  [22, 25, 41]. First, we used our database (48,247 GLAS derived AGB, DEM metrics, and field inventories) and the auxiliary variables from the study of Vieilledent et al. [28] , to produce a AGB map following the method proposed by Baccini et al. [26]. Then, we compared between precision of the improved AGB map and the precision of the AGB map from Baccini's approach. The precision of the AGB map from Baccini's approach was calculated in the same manner used to validate the improved AGB map. To produce the AGB map using Baccini's approach, first, the established relationships between in situ AGB and both GLAS and DEM metrics (cf.
Conclusions This study analyzed the potential of LiDAR sensor data and ICESat/GLAS data to improve a AGB map recently established in Madagascar (Vieilledent's AGB map) using optical and digital elevation model spatial imagery and climatic variables. First, GLAS data were used to provide AGB estimates at 48,247 footprint locations covering the forested areas in Madagascar between 2003 and 2009 with a density of 0. 5 points/km 2 of forest (Figure  1 ). Second, the additional correction factors, which are the difference between Vieilledent's AGB map and GLAS AGB, were calculated. Third, an ordinary kriging interpolation was performed using these additional correction factors to provide a correction factor map. Finally, Vieilledent's AGB map and the correction factor map were summed to improve Vieilledent's AGB map.
[26]  and Avitabile et al. [39]. This is because the in situ AGB measurements used to calibrate the GLAS data, i. e. , derive AGB from GLAS metrics, cover the range of all in situ AGB values. In addition, optical data that saturate at higher AGB values were not used in the procedure, which leads to the improvement in Vieilledent's AGB map. A limitation of our method is the unavailability of new GLAS data because ICESat ceased operations in 2009. However, this method could be applied to improve existing AGB maps constructed for forests before 2010 because data acquired by ICESat between 2003 and 2009 are free. The Global Ecosystem Dynamics Investigation LiDAR (GEDI) mission (launch date in 2019) will ensure LiDAR data with a smaller footprint (25 m) for better mapping of pan-tropical forests.
Acknowledgments: This research was supported by  IRSTEA (National Research Institute of Science and Technology for Environment and Agriculture)  and the  French Space Study Center (CNES ,  TOSCA 2016 ). G. V. was supported by the  JRC  through the  ReCaREDD  project funded by the  European Commission  and by the  Cirad  through the  BioSceneMada  project funded by  FRB/FFEM  (project agreement AAP-SCEN-2013 I). The authors wish to thank the institutes in Madagascar ( DGF, ONE, WWF, CI, WCS, Goodplanet/ETC Terra and Cirad ) for providing free access to the large AGB dataset derived from various forest inventories in Madagascar. The authors also acknowledge the  National Snow and Ice Data Center (NSDIC)  for the distribution of the ICESat/GLAS data.
Results ZNRF1 deficiency attenuates LPS-induced inflammation. Ubiquitination/deubiquitination is a crucial mechanism in the regulation of TLR-mediated immune responses. Several E3 ligases with roles in the control of TLR signalling pathways have been identified, such as TRAF3/6 and cIAP1/2 (ref. 15). We explored the GDS2047 microarray data set at the publicly accessible microarray database of the National Center for Biotechnology Information and identified an E3 ubiquitin ligase, ZNRF1, that was upregulated in macrophages upon LPS stimulation. To further explore the function of ZNRF1 in macrophages during inflammation, we knocked down the expression of the Znrf1 gene in murine RAW264. 7 macrophages using lentivirus-mediated shRNA transduction and stimulated these cells with a TLR4 agonist, LPS.
1d ). Taken together, these results indicate that ZNRF1 is involved in the regulation of cytokine production after TLR4 activation and the depletion of ZNRF1 impedes the coordination of cytokine synthesis in macrophages. These data confirm an important role for ZNRF1 in regulation of TLR4-induced pro-inflammatory cytokine production in macrophages, and this function requires the ubiquitin ligase activity ZNRF1, suggesting a novel mechanism for regulation of TLR4 signalling. BMDM #2 ZNRF1 GAPDH p-IRF3 IRF3 Znrf1 F/F Znrf1 Δ Znrf1 F/F Znrf1 Δ Znrf1 Δ Znrf1 F/F Ccl5 Znrf1 F/F Znrf1 Δ c Znrf1 F/F Znrf1 Δ 0 Znrf1 D mice are more resistant to septic shock. Sepsis is attributed to an exacerbated release of pro-inflammatory cytokines and results in high morbidity and mortality  17. We assessed  the physiological role of ZNRF1 in modulating inflammatory responses and septic shock in vivo by intraperitoneally injecting Znrf1 D and control mice with a lethal dose of LPS.
Acknowledgements We thank  Drs Tzong-Shyuan Lee  and  Song-Kun Shyue  for the Cav1 À / À mice and the 3rd Core Facility at  National Taiwan University Hospital ; the  Transgenic Mouse Model Core Facility of the National Core Facility Program for Biotechnology ; the  Ministry of Science and Technology, Taiwan ; and the  Gene Knockout Mouse Core Laboratory  of  National Taiwan University Center of Genomic Medicine  for providing technical services. We are grateful to  Drs Tsung-Hsien Chuang ,  Jerry Cao  and  Betty An-Ye Wu  for invaluable discussions and critical reading of the manuscript. This work was supported by  National Taiwan University  ( 102R7600  to L. -C. H. ), the  Ministry of Science and Technology (MOST) of Taiwan  ( 100-2314-B-002-021  to C. -Y. L. ;  100-2320-B-002-096-MY3 ,  103-2321-B-002-085 , and  105-2320-B-002-060-MY3  to L.
Identification of the model parameters Identification of the model parameters has been done for two di↵erent non-oriented (NO) Si-Fe electrical steel sheets from di↵erent suppliers. The grades of Material I and II are M330-50A and M400-50A, respectively. The uniaxial experimental data from  [5]  is used to characterize the magnetomechanical behavior of Material I. In the experiment process a cross-shaped sample which allows multiaxial magneto-mechanical loadings is used. The sample was loaded by stresses varying from 100 MPa compression (-) to 100 MPa tension (+) including biaxial ones. The surface magnetic field strength and the magnetic flux density were measured at 50 Hz using H -coils and needle probes, respectively. Only stress dependent magnetization curves were measured for Material I, not the magnetostriction.
Acknowledgements We acknowledge the support of  Dr. T C Tanwar , the District Tuberculosis Officer of District Rewari, Haryana, India;  Dr David Ford , Consultant & Clinical Lead,  Department of Respiratory Medicine, Scarborough District Hospital, Scarborough, UK ; and  Dr. Naresh Trehan , Chairman & Managing Director, Chief of  CTVS, Medanta the Medicity, Gurugram, India  for their support during the implementation of the pilot project. We thank the  Department for International Development (DFID), UK , for funding the  Global Operational Research Fellowship Programme  at the  International Union Against Tuberculosis and Lung Disease (The Union ), Paris, France in which Hemant Deepak Shewade works as an operational research fellow.
Acknowledgments. We thank  F. Bezrukov ,  A. G. Dias ,  J. R. Espinosa ,  D. Figueroa ,  F. Finelli ,  J. Garcia-Bellido ,  J. Jaeckel ,  F. Kahlhöfer ,  B. Kniehl ,  J. Lesgourgues ,  K. Saikawa ,  M. Shaposhnikov ,  B. Shuve ,  S. Sibiryakov  and  A. Westphal  for discussions. The work of G. B. is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement number  656794  and was partially supported by the  German Science Foundation (DFG)  within the  Collaborative Research Center SFB 676 Particles, Strings  and the  Early Universe. G. B. thanks the  DESY Theory Group  and the  CERN Theory Department  for hospitality. J. R. is supported by the  Ramon y Cajal Fellowship   2012-10597  and  FPA2015-65745-P (MINECO/FEDER ).
Study design and population EPIPAGE-2 is a prospective national population based cohort scheduled to follow preterm children up to the age of 12 years. Infants born at 22-34 completed weeks' gestation in all maternity units in 25 French regions (21 of the 22 metropolitan regions and all four overseas regions) were eligible for inclusion. The only region that did not participate accounted for 2% of births in France in 2011. 28 Recruitment took place at birth and children were included in the study and data collected only after families had received information and agreed to participate in the study. Hence, for children whose parents declined participation, only status at birth, mortality, and limited perinatal data were available. The study began on 28 March 2011. Infants born at 22-26 weeks' gestation were recruited during an eight month period, those born at 27-31 weeks' gestation during a six month period, and those born at 32-34 weeks' gestation during a five week period.
Cerebral palsy and sensory deficits We extracted data for cerebral palsy from the medical questionnaire, including information on major developmental steps (head control, sitting, standing, walking, and quality of gait), trunk and limb tone (low, normal, increased), and other abnormal neurological signs (see web appendix 1). Questions were pre-coded to minimise the risk of ambiguous answers. Questionnaires were similar in 1997 and 2011 to facilitate comparisons. Cerebral palsy was defined according to the diagnostic criteria of the Surveillance of Cerebral Palsy in Europe (SCPE) network,  30  and motor ability was graded by using the five level Gross Motor Function Classification System (GMFCS). 31 We also report the proportion of children with ambulatory cerebral palsy, defined as those with cerebral palsy who were walking at 2 years of age, with or without aids.
Paediatricians in charge of routine follow-up performed the examinations. If a routine follow-up was unavailable, parents were asked to get their practitioner of choice to complete the medical questionnaire to ensure higher follow-up rates. Three of the authors (VP, FBL, and SM) separately reviewed ambiguous cases, with agreement on discrepancies reached in a consensus meeting. Data on vision and hearing were obtained from medical reports available during the medical examination. In cases of blindness, visual impairment was classified as severe (bilateral) or moderate (unilateral). Squinting or the need for glasses was also recorded. In cases of deafness, auditory impairment was classified as severe (bilateral) or moderate (unilateral). Severe neuromotor or sensory disabilities included any of GMFCS level 3-5 cerebral palsy or severe visual or auditory impairment; moderate disability included GMFCS level 2 cerebral palsy with or without moderate visual or auditory impairment.
Overall neurodevelopment The child's development was assessed with the second version of the 24 month Ages and Stages Questionnaire (ASQ) validated in France  26  and completed by parents; data were analysed if completed between 22 and 26 months corrected age in children without cerebral palsy, deafness, blindness, or severe congenital brain malformations. Each questionnaire includes 30 items covering five developmental domains: communication abilities, gross motor skills, fine motor skills, problem solving abilities, and personal-social skills. Items are scored on a three point scale depending on whether the child performs the task: yes (10 points), sometimes (5 points), or not yet (0 points). Responses are summed to give a score of 0 to 60 for each domain and an overall maximum ASQ score of 300 points.
Data management and statistics Results among live births and at 2 years are presented by gestational age grouping (22-26, 27-31, combined 22-31, and 32-34 weeks' gestation for survival analyses; 24-26, 27-31, combined 24-31, and 32-34 weeks' gestation for disability analyses), and by week for those born at 22-26 weeks' gestation. Firstly, summary data on maternal characteristics (maternal age, birth outside of France, and parents' socioeconomic status), obstetric (multiple pregnancy, antenatal steroids, and caesarean section), and neonatal factors (sex, small for gestational age, inborn status, surfactant, postnatal steroids, severe neonatal morbidities, and breast milk at discharge) are presented for children whose parents gave consent at birth and for survivors included in the study at 2 years corrected age; data were compared with those who did not participate in the initial study or were lost to follow-up.
Multiple imputation was performed as a sensitivity analysis. Missing data were imputed by chained equations using the SAS "MI" procedure. 34 mputation model variables included both those potentially predicting non-response and/or outcomes (gestational age, maternal age and country of birth, parity, parental socioeconomic status, antenatal steroids, caesarean section, multiple pregnancy, sex, small for gestational age, severe congenital brain malformation, inborn status, surfactant, postnatal steroids, severe neonatal morbidities, use of breast milk at discharge), and outcomes (cerebral palsy, neuromotor or sensory disabilities, and ASQ). For doi: 10. 1136/bmj. j3448 | BMJ 2017;358:j3448 | the bmj ASQ, missing domains (defined as binary variables (yes/no) for a score below threshold in each of the five domains) were separately imputed to account for partially completed questionnaires.
Overall ASQ score was then estimated using the domain specific imputed datasets for infants without cerebral palsy, deafness, blindness, or severe congenital anomalies. Socioeconomic data were imputed using multinomial models. Other variables with missing data, all binary, were imputed using logistic regression. We generated 50 independent imputed datasets with 30 iterations each. Estimates were pooled according to Rubin's rule. 35 Further details are available in web appendix 2. Fourthly, to document trends over time, we compared 1997 and 2011 rates of survival, survival without severe or moderate neuromotor or sensory disabilities among live births, and cerebral palsy among survivors and calculated the difference between 1997 and 2011 adjusted for baseline factors that may influence outcomes (gestational age, small for gestational age, sex, and multiple pregnancy status).
Risk differences were estimated using a binomial model with identity link. Cohort data were obtained from the entire year in 1997 and from 28 March 2011 to 31 December 2011. Analyses for 1997 were run for the entire year and then separately for April to December. Results did not differ, and thus 1997 data are presented for the whole year. To better understand the link between the evolution of outcomes and variations in practice, we compared the maternal characteristics and obstetric and neonatal factors of the 1997 and 2011 populations. Finally, factors associated with cerebral palsy or an ASQ score below threshold were studied for complete cases by using multiple logistic regression models. Variables entered into the models were gestational age, sex, single or multiple pregnancy status, small for gestational age, and parental socioeconomic status.