dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. This work was supported by funding from the  Ice2sea  programme from the  European Union 7th Framework Programme  (grant number  226375 , Ice2sea contribution number  115 ). Work at the  University of Bristol  was supported by the  NERC  iSTAR and iGlass consortium projects, the  UK National Centre for Earth Observation  and the  Joint Climate and Weather Research Programme. Work at  Lawrence Berkeley National Laboratory  was supported by the  Scientific Discovery through Advanced Computing (SciDAC)  project funded by the  US Department of Energy, Office of Science, Advanced Scientific Computing Research and Biological and Environmental Research. Simulations were carried out using the computational facilities of the  Advanced Computing Research Centre, University of Bristol , and resources of the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the US Department of Energy  under Contract No.
Study design Four different methods were used in this study to estimate the total amount of food ingested by subjects at meal time: weighed food records completed by study staff; diet diaries completed by subjects; photographic food records taken by study staff; and mathematical models based on CCS. Data from weighed records, diet diaries, and photographic records were entered into the food analysis program, Nutrient Data System for Research (NDS-R; University of Minnesota, Minneapolis, MN) to derive total energy intake. Data entry was performed by a single, trained operator at the Colorado Clinical and Translational Sciences Institute's (CCTSI) Nutrition Core. Total energy intake for the CCS model was estimated by combining mass estimations and caloric densities of each food eaten. Individual caloric densities were extracted from the nutritional analysis performed by NDS-R.
The reporting error in estimated energy intake (EI) in a meal was calculated for each method with respect to weighed records (EI WR ) as follows: Means and SDs of the reporting errors were calculated for subjects who completed the 4 visits and had weighed food record, diet diary, photographic food record, and CCS data available for all meals. For the training meals, the average reporting error was evaluated using the 3-fold cross-validation technique  (Kohavi, 1995). Two of the training meals were used to train a CCS model and the third training meal was used to evaluate accuracy. This process was repeated 3 times, with each meal used once to evaluate the model. The results were averaged to produce a single estimation of the reporting error. For the validation meal, the CCS model was trained on the data from the three training meals and evaluated on the validation meal.
Reporting errors for CCS models, diet diaries, and photographic records were compared using a one-sided Wilcoxon-Mann-Whitney two-sample rank-sum test, which is preferred over Student's t-test due to the potential presence of outliers in the reporting errors. Additionally, Bland-Altman analysis  (Bland & Altman, 1999)  accounting for the clustering effect of repeated measures was performed to assess the accuracy of the estimation methods for the training meals. To evaluate the impact of physical properties of different foods on estimates of energy intake, means and SDs of MPChew and MPSw S values were calculated for 7 representative food items and compared using two-sample t-tests. In all cases, statistical differences were considered significant at p < 0. 05. R software (Version 2. 15. 1, The R Foundation for Statistical Computing, Vienna, Austria) was used to perform Wilcoxon-Mann-Whitney tests, whereas Matlab (R2011b, MathWorks Inc, Natick, MA) was used to perform all t-test analyses.
Acknowledgement This work was partially supported by Grant Number  R21DK085462  from the  National Institute of Diabetes and Digestive and Kidney Diseases  and also partially supported by  NIH/NCRR Colorado CTSI  Grant Number  UL1 RR025780. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of Diabetes and Digestive and Kidney Diseases  or the National Institutes of Health.
TnrA-binding sites overlap previous in silico predicted TnrA box To investigate the presence of TnrA boxes within the newly identified 20 promoter-proximal TnrA-binding sites, we compared our data with previous in silico studies available in the RegTransBase database (Fig. 3A )  (Cipriano et al. 2013 )  (Leyn et al. 2013). Nine of these enriched regions exhibited TnrA box motifs (Table  S1 ) (Fig. 2 ). These in vivo binding regions were detected less than 100 bp away from putative TnrA boxes, upstream of appD, dtpT, ydaB, yrbD, ysnD, yuiA, pucH, yvgT, and pgsB translational start sites. It is interesting to notice that two TnrA box motifs have been detected in silico in front of the dtpT, glnR, ycsF, and ysnD genes correlating with the detection of two TnrA-enriched regions by ChIP-on-chip (Table  S1 ).
Altogether, our study illustrates the power of ChIP-on-chip approaches, generating pertinent data to finely delineate a transcriptional regulator in vivo binding sites. ChIP-on-chip data combined with real-time in vivo transcriptional profiling enabled us to validate the functionality of seven in silico predicted TnrA boxes (Fig. 2 ). Moreover, we identified three TnrA-dependent promoter regions, which match with the newly defined TnrA box Positions of TnrA boxes are indicated relative to the translational start site (+1) according to  (Yoshida et al. 2003)  and the RegTransBase database  (Cipriano et al. 2013). Asterisks indicate TnrA boxes predicted in this work from the newly defined TnrA box consensus. The ratio of expression for each gene (DtnrA mutant/wild-type) was calculated as the average of three independent experiments obtained with the luc transcriptional fusions.
Statistical analysis Results were expressed as mean ± standard deviation (SD) or median and interquartile ranges (IQR). Quantitative data of the different solutions were compared by the Man-Withney U test, and anti Xa activity and ETP inhibition by Spearman correlation. Statistical analyses were carried out with Statview 5. 0 software (SAS Institute, Cary, NC, USA). A p value ≤0. 05 was considered statistically significant.
Results The mean basal value of ETP in the controls was 1730 ± 325 nmol*min. TG measured in PRP from whole blood samples at ethanol contents of 0. 05, 0. 1, 0. 15 and 0. 2 %v/v was similar to TG in controls, demonstrating that ethanol alone at these low concentrations has no impact on the antithrombotic activity (data not shown). Enox/Eth at Enox concentrations between 0. 5 IU/ mL and 2. 0 IU/mL was associated with a 15-54 % ETP inhibition (Table  1 , Fig. 1 ). For each Enox concentration, ETP inhibition of Enox/Eth was lower than that observed with Enox (Fig. 2 ). IC50 ETP median and IQR values were 1. 8 [1. 1-2. 4] IU/mL for Enox/Eth and 0. 7 [0. 3-0. 9] IU/mL for Enox, P = 0. 03. Lower ETP inhibition values and therefore higher IC 50 ETP values were observed in females than in males for both Enox/Eth and Enox (Fig.
Introduction It was a dark and stormy night … That is the scenario under which we commonly perceive precipitation to be produced outside the tropics: in storm clouds where ice-or mixed-phase processes dominate condensate particle growth. Spaceborne precipitation and cloud radars aboard the Tropical Rainfall Measurement Mission (TRMM) and CloudSat satellites show that rain over the tropical oceans outside the Intertropical Convergence Zone (ITCZ) is predominantly from warm clouds, while rain over the midlatitude oceans is predominantly from ice clouds  [Lau and Wu, 2003, 2011; Lebsock and L'Ecuyer, 2011]. However, to date, there is no global climatology of the thermodynamic phase of precipitating cloud over land and ocean. In this paper, we use data from several satellites in the A-Train constellation to investigate what fraction of rain forms in liquid-phase (warm), mixed-phase, and ice-phase (cold) clouds.
Data and Methods Two pieces of information about clouds are required for this analysis. First, we need to know whether a cloud is raining. Rain identification is provided by the Cloud-Profiling Radar (CPR) aboard CloudSat  [Stephens et al. , 2002]. Second, we need to know the thermodynamic phase at the height where the precipitation originates. We assume that if ice is present anywhere within the precipitating cloud column, the precipitation is due to the more efficient ice-phase processes rather than the less efficient liquid-phase process (collision-coalescence). Therefore, if the thermodynamic phase at cloud top-where it can be retrieved by satellite-borne instruments-is ice or mixed, we classify the precipitation as cold rain; if the thermodynamic phase at cloud top is liquid and no ice is detected below, we classify the precipitation as warm rain.
To understand this lower warm-rain probability, we next investigate the differences in liquid water content (LWC), effective radius (r e ), and cloud top height (z top ) between continental and marine raining and nonraining CloudSat-detected warm clouds. For the daytime portions of 1 year of data (  2008 ), the MODIS Collection 6 cloud product cloud top effective radius, and cloud liquid water content were collocated to the CloudSat track. Only profiles identified as single-layer by DARDAR were used so as to avoid artifacts from multilayer MODIS retrievals; approximately 40% of profiles satisfy this requirement. Due to the reduced number of observations, the cloud variables are aggregated to 5 ∘ latitude zonal bands over land and ocean; latitude bands with fewer than 100 observations (near the poleward edge of rain occurrence) are not used.
Assessment of Uncertainties Uncertainties in the underlying satellite retrievals result in uncertainties and biases in the phase fractions. Warm rain is often drizzle, which is less likely to be detected; as a cloud radar, CPR is more sensitive to drizzle than precipitation radars, but a residual effect still exists. Temperature, taken from the interpolated meteorological analysis of the European Centre for Medium-Range Weather Prediction (ECMWF), the ECMWF-AUX data set, is mostly excluded as an error source by choosing algorithms that derive the cloud top phase from radiative signatures of liquid and ice-phase condensate. A residual dependence remains because no ice detection is attempted in clouds with an ECMWF-AUX cloud top wet-bulb temperature above freezing; we show below that this error is small.
Acknowledgments This study was supported by the  European Union  in an  ERC starting grant  ( "QUAERERE," grant agreement   306284 ) and a collaborative project ( MACC-II , grant agreement  218793 ,  FP7 , and its successor project,  MACC-III , grant agreement  633080 ,  Horizon2020 ). We thank the  NASA CloudSat  project and the CloudSat.
Trial design We aimed to associate modifications of ANS (variations of the HR and SC time-series) with external events. As previously stated, individuals with ASD encounter difficulties in social interactions and would also experience challenges following any laboratory-based protocol. Subsequently, we decided to conduct a study in real life situations, outside the hospital or laboratory, in a so-called ecological situation. Statistical calculations are based on time-series analysis. First, we detect times of change for the mean of HR series and high-frequency (HF) as well as low-frequency (LF) energies. By doing so, we obtain segments where the different physiological series stay in a steady state. Next, we compute the mean HR, HF and LF energies of HRV on each segment. Then, these data are used for classifications to qualify the thresholds of stress for individuals (see details in section on statistics below).
23 he study design permits the synchronous recording of data from HR, SC, respiratory rate, wrist motion and the contextual events. Responses to four phases will be conducted during this research on ASD participants and aged-matched controls: (1) 24 h pre-experimental recording for baseline, (2) a 2 h experimental recording in real life situations, (3) a 30 min recording in a quiet environment, interrupted by a few seconds of stressful sound, (4) within the realms of possibility, an interview will be achieved with the participant to capture comments and feelings about events triggering anxiety. Each participant (ASD and control) will undergo phases 1 to 4, with the recording of data from HR, SC and contextual events during phases 1 to 3. ASD participants will be paired with their matched control during phases 2 and 3.
The HR data will first be examined according to the recommendations of the European Society of Cardiology and the North American Society (Task Force). 29  30 HR data will be explored simultaneously in time and frequency domains. 31 The methodology developed by our team will also be applied. 23  and motion series SC will be measured in micro Siemens with sampling rates at 2, 4, 8, 16 and 32 Hz during phases 1 to 3. The SC sensor (Q-Sensor-Affectiva, Massachusetts Institute of Technology, USA) is set on a wristband and has a 24 h battery life when logging. In addition, it will measure wrist movements with a built in triaxial accelerometer.
Recording contextual events During phase 2, a set of 'spy glasses' (Active Media Concept, Paris, France) will be used to record simultaneously the physical and social environment from the participant's point of view. A camera and a microphone compose the 'spy glasses'. The photodetector is a Complementary Metal Oxide Semiconductor (CMOS) sensor 1/18 with 720×480 resolution, a view angle of 60°, and records at 30 frames per second. Data are stored on a 32 GB MicroSDHC card allowing for 2 h15 min of recording time. For participants who already wear corrective glasses, the camera and microphone will be placed on their own glasses.
Number of participants needed Stress effects on HR or HRV are documented in the literature. 9 For instance, stress resulting from computer work induces a bilateral variation of HR of a magnitude greater than 5 bpm, without a significant change on LF energy, but a significant variation of HF energy. Specifically, Hjortskov et al 41 demonstrated that, during stress periods, the log(HF) was 7. 03, while at rest, the log(HF) was 7. 49. This variation is plainly confirmed by others,  29  who have shown that during night time, the log(HF) was 5. 8±0. 1 (average value±SD), or HR was 60. 8 ±1. 3 bpm, and during daytime the log(HF) was 5. 2±0. 2, or HR was 78. 3±2. 5 bpm. These data allowed us to compute the sample size for a unilateral test at level α=0. 05 for a change of log(HF) of approximately 0.
Method taking into account missing, unused or invalid data On each physiological series (HR, SC, respiratory rate, motion), missing or aberrant data are common and will be removed with the use of the preprocessing method. 23 hen, data sets with more than 10% of aberrant or missing data will be rejected. 29 30 asures to reduce or avoid bias Participant de-identification will blind assessors of participant status when processing data. Participants with autism will be paired with an age-matched control. The ANS will be measured through different devices. Ethics and dissemination 'Do Well B. ' is a multidisciplinary project. It involves eminent specialists in each field: probabilists and statisticians, autism scientists and neuroscientists, physiologists and cardiologists, and audio and audio-visual signal processing specialists.
Calls to establish a framework for using 'diversity statistics' grew signifi cantly and came from many different sectors of society. In turn, the French High Authority against Discrimination and for Equality ( Haute Autorité de Lutte contre les Discriminations et pour l'Egalité , or HALDE) declared its opposition to any 'ethnoracial nomenclature,' arguing that it was suffi cient in fi ghting discrimination to have data on the country of birth of individuals and their parents and to carry out audit studies. 12 he hostility of the HALDE towards any 'ethnic monitoring' carried out by companies for antidiscrimination purposes led the CNIL to reconvene a task force on the issue of measuring diversity. After several months of conducting hearings of researchers, representatives of anti-racist associations and human rights advocates, members of institutions, ministers and members of Parliament -some of which were held in front of the press, the CNIL issued a new report on May 16, 2007.
This report contains ten recommendations that open the door to a well thought-out collection of 'ethnic and racial' data as part of carefully supervised surveys, as well as to the collection of data that might be useful in analyzing how people 'experience discrimination. ' 13 The CNIL also conveyed that it was open to introducing the country of birth and nationality of parents into the census. To widespread surprise, these recommendations inspired the fi ling of an amendment on 'studies on the measurement of the diversity of origins' in a legislative bill on immigration control, which Parliament examined in September 2007. Several anti-racist associations and the socialist party criticized the amendment, either for its content or for its insertion in a bill on immigration which was acutely discriminatory in itself.
The HALDE and CNIL, however, supported it. 14 After having been slightly modifi ed by the Senate, the bill was offi cially approved on 23 October 2007. On this occasion, the HALDE issued a press release reconfi rming its opposition to the creation of offi cial 'ethnic statistics' while at the same time accepting the possibility of using 'ethnic' categories in scientifi c studies. 15 Yet, ultimately, the Constitutional Council accepted the claim put forward by a large number of leftwing members of the parliament that the provision in the law authorizing the collection of data on race and ethnicity was unconstitutional. That provision, whose raison d'être was paradoxically to strengthen the power of control of the data protection agency, was nullifi ed as a result, primarily on the ground that it was a rider devoid of any connection with the object and purpose of the law into which it had been inserted (regulating immigration and redefi ning the conditions under which foreigners could reside in France).
And even though these individual data are protected by the CNIL, which theoretically limits their use for statistical purposes, this approach is at the heart of a growing number of studies on segregation or discrimination. Applied to French National Education fi les  (Felouzis 2003 ; ORES 2007 ) , to corporate fi les  (Cédiey and Foroni 2005 )  or to judiciary data  (Jobard and Névanen 2007 ) , the 'patronymic method' undeniably yields results. To be sure, we have no precise measurement of the magnitude of the observational biases involved in relying on fi rst name choices. But if we assume that in most cases fi rst names given by parents are chosen from a culturally limited list, they can be considered as markers of 'cultural origin'  (Felouzis 2003 : 420)  and can therefore be used to track and measure the segregation or discrimination of the people with the relevant names.
Occupational categories have problems, too, then, but this does not mean we have to challenge their existence. Developments in the fi ght against discrimination have gradually led to calling into question the 'choice of ignorance'. This is no longer only a debate among specialists. The diversity charter, 22 the promotion of diversity in the public service, 23 diversity in the media: the calls and recommendations to introduce visible difference in the social makeup of organizations and their hierarchies are growing in numbers. Companies that have taken the initiative to ask the CNIL about the authorized methods for collecting data 'relating to diversity' are breaking new ground. The debate is far from theoretical. Directors of human resources are asking a pragmatic question: How to measure discrimination without identifying the groups likely to have suffered it? The HALDE in turn has taken a position against the establishment of 'ethnic' statistics while at the same time being open to targeted surveys.
Unfortunately, to date, a comprehensive and mapping of the availability and potential use of bioenergy feedstock for second and third generation biorefineries in Australia was missing. The aim of this paper is to explore the sustainable energy feedstocks landscape in order to determine how land may be best used for renewable energy production. There is no single database comparing the potential amount of all lignocelluloses and microalgae resources available for biofuel utilisation and so data was collected from government and academic sources for this purpose. Many previous studies have provided analyses on the suitability of land for a single technological pathway without a detailed regional survey of existing land use. This is particularly the case in the microalgae literature. A raster graphic program based on accurate map references provides a better illustration of collated values from the available literature and statistic institutions.
Estimates of the seasonal and interannual exchanges of carbon dioxide (CO 2 ) and methane (CH 4 ) between land ecosystems north of 45°N and the atmosphere are poorly constrained, in part, because of uncertainty in the temporal variability of water-inundated land area. Here we apply a process-based biogeochemistry model to evaluate how interannual changes in wetland inundation extent might have influenced the overall carbon dynamics of the region during the time period 1993-2004. We find that consideration by our model of these interannual variations between 1993 and 2004, on average, results in regional estimates of net methane sources of 67. 8±6. 2 Tg CH 4 yr -1 , which is intermediate to model estimates that use two static inundation extent datasets (51. 3±2. 6 and 73. 0±3. 6 Tg CH 4 yr -1 ).
In contrast, consideration of interannual changes of wetland inundation extent result in regional estimates of the net CO 2 sink of -1. 28±0. 03 Pg C yr -1 with a persistent wetland carbon sink from -0. 38 to -0. 41 Pg C yr -1 and a upland sink from -0. 82 to -0. 98 Pg C yr -1. Taken together, despite the large methane emissions from wetlands, the region is a consistent greenhouse gas sink per global warming potential (GWP) calculations irrespective of the type of wetland datasets being used. However, the use of satellite-detected wetland inundation extent estimates a smaller regional GWP sink than that estimated using static wetland datasets. Our sensitivity analysis indicates that if wetland inundation extent increases or decreases by 10% in each wetland grid cell, the regional source of methane increases 13% or decreases 12%, respectively.
Here, we conduct such a study with a processbased biogeochemistry model, the Terrestrial Ecosystem Model (TEM,  Zhuang et al 2004 Zhuang et al , 2006)). Our study focuses on analyzing how inter-annual variations in wetland inundation extent from 1993 to 2004 influence net land fluxes of CO 2 and CH 4 to the atmosphere. Specifically, we compare model simulations driven with three wetland distribution datasets including two synthetic, static wetland distribution products that were developed using various approaches including ground survey and satellite information (Matthews and Fung 1987,  Lehner and Döll 2004 ) and a satellite-based dynamic inundation product with a monthly time step  (Prigent et al 2007). The comparisons evaluate the impacts of wetland and upland area changes on the net ecosystem carbon exchange, net methane emissions, and their global warming potentials (GWPs) in northern high latitudes.