dataesr/scientific-paragraphs-categorization huggingface dataset:
Brief review of signal filtering theory In this section we briefly review the mathematical background that allows us to link vertical resolution to the lidar signal (or profile) filtering process. Signal filtering for lidar data processing consists of either smoothing, differentiating or smoothing, and differentiating at the same time. To describe the filtering process, a signal S is defined in its general sense; i. e. , it can be either a raw lidar signal from a single detection channel, the ratio of the corrected signals from two detection channels, or an unsmoothed ozone profile, temperature profile, calibrated or uncalibrated water vapor profile, etc. The only common requirement is that the signal is formed of a finite number of equally spaced samples in the vertical dimension S(k) with k = [1, nk].
Review of vertical resolution definitions used by NDACC lidar investigators The filtering schemes or methods of several NDACC lidar investigators have been reviewed and compared in previous works, e. g. ,  Beyerle and McDermid (1999)  and  Godin et al. (1999). These studies concluded that vertical resolution was not consistently reported between the various investigators. Here we briefly review the filtering schemes or methods used by various NDACC lidar investigators, and how vertical resolution is reported in their data files, as of 2011. This review provided critical input to the ISSI team to determine which definitions of vertical resolution are appropriate for use in a standardized way across the entire network (see Sect. 4). -Observatoire de Haute-Provence (OHP, France), stratospheric ozone differential absorption lidar: A seconddegree polynomial derivative filter (Savitsky-Golay derivative filter) is used  (Godin-Beekmann et al.
The definition of vertical resolution in the NDACC-archived data files is based on the impulse response of a delta function, by measuring the FWHM of the filter's response. For the temperature retrieval  (Gross et al. , 1997) , the profiles are smoothed using a low-pass filter  (Kaiser and Reed, 1977) , and a simple ad hoc step function is used to define the values of the vertical resolution. -Lauder (New Zealand) ozone lidar operated by RIVM (Netherlands): The definition of vertical resolution is based on the width of the fitting window used for the ozone derivation  (Swart et al. , 1994). -OHP and Réunion Island (France) tropospheric ozone DIAL: A second-degree polynomial least-squares fit (Savitsky-Golay derivative filter) is used to filter the ozone measurements. The vertical resolution is reported as the cut-off frequency of the corresponding digital filter.
In the case of the ozone DIAL, the vertical resolution of both the Blackmantype filter used and the combined least-squares derivative plus Blackman filter. A vertical resolution of 19. 2 % of the filtering interval was determined. For small intervals the latter value may change; i. e. , the least-squares fit for determining the derivative is executed over just a few data points. For comparison, an arithmetic average yields a vertical resolution of 50 % of the filtering interval. Having reviewed the vertical resolution definitions and schemes used across NDACC and elsewhere, three definitions or approaches can be clearly identified. The first definition is the number of filter coefficients used, the second definition is based on the cut-off frequency of the filter used, and the third definition is based on the width of the impulse response of the filter used.
Practical implementation within NDACC Numerical tools were developed and provided to the NDACC principal investigators (PIs) in order to facilitate the implementation of the network-wide use of the proposed standardized definitions. These tools consist of easy-to-use plug-in routines written in IDL, MATLAB and FORTRAN, which convert a set of filter coefficients into the needed standardized values of vertical resolution following one or the other proposed definitions. The tools are written in such a way that they can be called in a lidar data processing algorithm each time a smoothing and/or differentiating operation occurs. The routines can handle multiple smoothing and/or differentiating operations applied successively throughout the lidar data processing chain, as described in Sects. 4. 1 and 4.
2. The routines are available on the NDACC lidar working group website (http://ndacc-lidar. org/), or upon request from the first author (thierry. leblanc@jpl. nasa. gov). The routine "NDACC_ResolIR" computes vertical resolution values with a definition based on the FWHM of the filter's impulse response. When the routine is called for the first time in the data processing chain, the sampling resolution and the coefficients of the filter are the only input parameters of the routine. The routine convolves the coefficients with an impulse (delta function for smoothing filters and Heaviside function for derivative filters) to obtain the filter's impulse response, and then identifies the full width at half maximum (FWHM) of this response. The response and the value of vertical resolution are the output parameters of the routine.
The standardization tools became available in summer 2011. They were distributed to several members of the ISSI team for testing and validation. Using simulated lidar signals and a series of Monte Carlo experiments, their implementation was validated for several NDACC ozone and temperature lidar algorithms. Several examples of this validation are provided in the ISSI team report  (Leblanc et al. , 2016a). Ideally, an NDACC-wide implementation should follow. The implementation will not be considered complete until the vertical resolution outputs of all contributing data processing software have been quantified and validated following the same procedure as that described in  Leblanc et al. (2016a).
This result maps to the frequency domain as twice the Nyquist frequency. Like in the impulse response case, the values of vertical resolution computed for multiple, successive smoothing operations are conceptually, theoretically, and numerically exact. The ISSI team developed numerical tools to support the implementation of these definitions across the NDACC lidar groups. The tools consist of ready-to-use "plug-in" routines written in IDL, FORTRAN, MATLAB, C++, and PYTHON that can be inserted into any lidar data processing software each time a smoothing operation occurs in their data processing chain. The routine's input parameters are the lidar sampling resolution and the coefficients of the smoothing filter locally applied, and the output parameter is the vertical resolution following the impulse-response-based standardized definition or the cut-off frequency-based standardized definition.
When multiple smoothing operations occur within the same data processing chain, the plug-in routines must be called each time smoothing occurs, but the final vertical resolution to be reported is computed only at the final occurrence. The values output by the routines after the last call are reported in the lidar data files together with the ozone or temperature profiles. These standardized values of vertical resolution are theoretically and numerically exact, even after multiple filtering occurrences. Using simulated lidar signals and a series of Monte Carlo experiments, their implementation was validated for several NDACC ozone and temperature lidar algorithms. Examples of this validation are provided in the ISSI team report  (Leblanc et al. , 2016a).
Acknowledgements. This work was initiated in response to the 2010 call for international teams of experts in earth and space science by the  International Space Science Institute (ISSI) in Bern, Switzerland. It could not have been performed without the travel and logistical support of  ISSI. Part of the work described in this report was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under agreements with the  National Aeronautics and Space Administration. Part of this work was carried out in support of the  VALID  project. Robert J. Sica  would like to acknowledge the support of the  Canadian National Sciences and Engineering Research Council for support of the University of Western Ontario  lidar work. The team would also like to acknowledge  J. Bandoro  for his help in the design of the MATLAB filtering tools.
Post hoc comparisons were conducted with the Games-Howell test as the data did not meet the homogeneity of variances assumption. We excluded from analysis the score of a domain if the subject had more than 12% missing values in the given domain. That is a maximum of 1 missing value in energy subdomains and 3 missing values in the other subdomains. Analyses were carried out using SPSS version 22. 0 (SPSS Inc. Chicago, IL, USA).
Section 1: Do RCTs give good estimates of Average Treatment Effects In this section, we explore how to estimate average treatment effects (ATEs) and the role of randomization. We note first that estimating ATEs is only one of many uses for the data generated by an RCT. We start from a trial sample, a collection of subjects that will be allocated randomly to either the treatment or control arm of the trial. This "sample" might be, but rarely is, a random sample from some population of interest. More frequently, it is selected in some way, for example to those willing to participate, or is simply a convenience sample that is available to the trialists. Given random allocation to treatments and controls, the data from the trial allow the identification of two distributions,  " ( " ) and  & ( & ), of outcomes  " and  & in the treated and untreated cases within the trial sample.
Second, the ideal rules by which units are allocated to treatment or control depend on the covariates and on the investigators' priors about how the covariates affect the outcomes. This opens up all sorts of methods of inference that are long familiar to economists but that are excluded by pure randomization. For example, what philosophers call the hypothetico-deductive method works by using theory to make a prediction that can be taken to the data for potential falsification (as in the school example above). This is the way that physicists learn, as do economists when they use theory to derive predictions that can be tested against the data, perhaps in an RCT, but more frequently not. Some of the most fruitful research programs in economics have been generated by the puzzles that result when the data fail to match such theoretical predictions, such as the equity premium puzzle, various purchasing power parity puzzles, the Feldstein-Horioka puzzle, the consumption smoothness puzzle, the puzzle of calorie decline in the face of malnourishment and income growth, and many others.
This is one of the arguments for the importance of internal validity. Nor is transportation called for when an RCT is used for evaluation, for example to satisfy donors that the project they funded achieved its aims in the population in which it was conducted. Even so, for such evaluations, say by the World Bank, to be global public goods requires arguments and guidelines that justify using the results in some way elsewhere; the global public good is not an automatic by-product of the Bank fulfilling its fiduciary responsibility. When the components of treatments change across studies, evaluations need not lead to cumulative knowledge. Or as  Heckman et al (1999 Heckman et al ( , 1934) )  note, "the data produced from them [social experiments] are far from ideal for estimating the structural parameters of behavioral models.
Following these early trials, there has been a continuing tradition of using trial results, together with the baseline data collected for the trial, to fit structural models that are to be used more generally. Early examples include  Moffitt (1979)  on labor supply and  Wise (1985)  on housing; a more recent example is  Heckman, Pinto and Savelyev (2013)  for the Perry pre-school program. Development economics examples include  Attanasio, Meghir, and Santiago (2012) ,  Attanasio et al (2015) ,  Todd and Wolpin (2006) ,  Wolpin (2013), and Duflo, Hanna, and Ryan (2012). These structural models sometimes require formidable auxiliary assumptions on functional forms or the distributions of unobservables, but they have compensating advantages, including the ability to integrate theory and evidence, to make out-of-sample predictions, and to analyze welfare, and the use of RCT evidence allows the relaxation of at least some of the assumptions that are needed for identification.
Cotton GHB119 was developed by Agrobacterium tumefaciens-mediated transformation. It expresses the Cry2Ae and phosphinothricin acetyltransferase (PAT) proteins which, respectively, confer resistance to certain lepidopteran species, and tolerance to glufosinate ammonium-based herbicides. The molecular characterisation data establish that cotton GHB119 contains a single insert consisting of a single copy of the T-DNA containing the cry2Ae and the bar expression cassettes. No other parts of the plasmid used for transformation are present in cotton GHB119. Bioinformatic analyses and genetic stability studies were performed and the results did not give rise to safety issues. The levels of the newly expressed proteins present in cotton GHB119 were obtained and reported adequately. The agronomic, phenotypic and compositional characteristics of cotton GHB119 were compared with those of the conventional counterpart Coker 312 under field conditions.
Conclusion on molecular characterisation The molecular characterisation data establish that cotton GHB119 contains a single insert consisting of one copy of the cry2Ae and bar expression cassettes. Bioinformatic analyses of the sequences encoding the newly expressed proteins and of other ORFs present within the insert or spanning the junction sites between the insert and genomic DNA did not give rise to safety issues. The stability of the inserted DNA and of the herbicide tolerance traits was confirmed over several generations. The levels of the Cry2Ae and PAT proteins were obtained and reported adequately.
3. 2. Comparative analysis 3. 2. 1. Evaluation of relevant scientific data 3. 2. 1. 1. Choice of comparator and production of material for the comparative analysis 14 Application EFSA-GMO-NL-2011-96 presents data on cottonseed composition of cotton GHB119 derived from field trials performed at six sites in the USA 15 in the 2006 growing season and in Spain during two growing seasons, 2007 and 2008 (Table  2 ). The Spanish field trials were also used for the agronomic and phenotypic characterisation (Table  2 ). As indicated in Section 3. 1. 1. 1, GM cotton GHB119 was obtained by transforming cotton variety Coker 312. In these field trials, Coker 312 was used as a comparator. The GMO Panel considers that this non-transgenic line is the appropriate conventional counterpart, as it has the same genetic background as the cotton line of cotton GHB119.
Agronomic and phenotypic characteristics tested under controlled conditions 25 Pollen germinability and viability from cotton GHB119 and its conventional counterpart (Coker 312) were measured according to Barrow (1981). Pollen was obtained from plants grown under greenhouse conditions in 2007. The pollen germinability was tested between 0 and 78 h after its collection. The applicant observed no statistically significant differences between cotton GHB119 and its conventional counterpart for pollen germinability and viability. While Barrow's test provides an indication of the germination capacity, it does not measure pollen viability directly as germination is forcefully induced. Therefore, the data on pollen viability supplied by the applicant in support of the comparative assessment of cotton GHB119 are not considered suitable  16  In the Catalonian field trials, two commercial non-GM varieties were included at each location; in the Andalusian field trials, four commercial non-GM varieties were included at each location.
Compositional analysis 26 Fuzzy cottonseeds from the USA field trials in 2006 (Table  2 ) were analysed for proximates, fibre fractions, amino acids, fatty acids, vitamin E, minerals and anti-nutrients. 27 The choice of the compounds followed the recommendations of the OECD consensus document (OECD, 2009). The data for compositional analysis from the field trials in the USA in 2006 were analysed with ANOVA, 28 in order to test for differences between cotton GHB119 and its conventional counterpart Coker 312. Proximates and fibre compounds included moisture, crude protein, crude fat, ash, total carbohydrates, acid detergent fibre (ADF) and neutral detergent fibre (NDF); minerals included calcium, phosphorus, potassium, magnesium, iron and zinc; vitamin E compounds included a-tocopherol, b-tocopherol, c-tocopherol, d-tocopherol and total tocopherols; amino acids included alanine, arginine, aspartic acid, cystine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine; fatty acids included myristic (C14:0), palmitic (C16:0), margaric (C17:0), stearic (C18:0), arachidic (C20:0), behenic (C22:0), lignoceric (C24:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2) and linolenic (C18:3); antinutrients included free and total gossypol, phytic acid and the cyclopropenoid fatty acids sterculic, malvalic and dihydrosterculic.
Processed products Based on the outcome of the comparative assessment, processing of the cotton GHB119 into food and feed products is not expected to result in products being different from those of commercial non-GM cotton varieties. Compositional data were obtained on processed products of ginned cottonseeds from an additional field trial carried out at one site in Argentina in 2008. 32 In this field trial, cottonseed samples of cotton GHB119 and Coker 315 33 treated with glufosinate ammonium-based herbicides were harvested and processed, at a pilot processing plant simulating industrial practice, into cotton products, including delinted seeds, hulls, meal (heated, flaked and extruded, solvent extraction), toasted meal, crude oil and refined bleached deodorised (RBD) oil. Delinted seeds, meal and toasted meal were analysed for proximates, fibre, amino acids, minerals, a-tocopherol, free and total gossypol, cyclopropenoid fatty acids and phytic acid.
At the end of the treatment period, all animals were sacrificed and underwent a detailed necropsy examination with selected organs weighed. Organs and tissues from all animals from the Cry2Ae-treated and control groups were subjected to a comprehensive histological examination. Descriptive statistics were calculated for each group and per time period for body weight change parameters, and for average food consumption per day parameters. All the endpoints were tested for homogeneity of variance with an F-test (a = 0. 05). The group means were then compared either with a t-test (if the F-test was not significant) or with a modified t-test (if the F-test was significant). For body weight and average food consumption per day parameters, in case the initial F-test was significant, the analysis was repeated on transformed data (log or square root transformation).
All three of these STs share identical ace, lipA, narK, and ndh alleles but differ at the remaining three loci (gltB, gmhD, and lepA). The two isolates from Burkina Faso belong to STs 1122 and 1121 and are triple-locus variants of one another. STs 1122 and 1043 are double-locus variants, although one strain originated in Madagascar and the other originated in Burkina Faso, a relationship that is potentially misleading and not reflected on a whole-genome scale (Fig. 1A ). All five STs are unique, with no other matches in the B. pseudomallei MLST database, including other characterized strains from Africa and South America (see Table  S3  in the supplemental material). Although MLST shows some utility for determining strain origin, it can be unreliable in highly recombinogenic organisms like B.
ITS typing shows unexpected diversity in African, but not American, isolates. Previously, ITS typing of 1,191 B. pseudomallei isolates revealed that strains from Africa and the Americas have restricted diversity at the 16S-23S rDNA ITS locus, with a dominance of the ITS type G allele among these isolates  (36, 37)   (37). Using WGS data, we determined the ITS genotypes of all of the strains in our data set, including the five African strains. Australasian isolates (n ϭ 115; MSHR6137 could not be typed because of a lack of sequence information covering the 16S-23S spacer region) were predominantly ITS type E (53%), followed by ITS type C (33%) and ITS type CE (13%), with a very small percentage of ITS type G isolates (Ͻ1%). Asian isolates (n ϭ 19; excluding PB08298010 due to uncertain provenance) were mostly ITS type CE (42%), followed by ITS type E (37%) and ITS type C (21%).
Core genome, orthologous, biallelic SNPs were identified by GATK v3. 1 (63) within the comparative genomics pipeline SPANDx v2. 7 (75) by using default parameters. The B. pseudomallei K96243 reference genome  (67)  was used for read mapping by BWA v0. 6. 2 (68) (GenBank accession numbers BX571965 and BX971966). Maximum-parsimony analyses were performed by a heuristic search with PAUP v4. 0b10  (69). Trees were bootstrapped in PAUP using 200 replicates. 16S-23S rDNA ITS typing. The ITS types of the five African isolates were determined from whole-genome sequences. Briefly, genomes were assembled with an in-house assembly pipeline  (38)  or retrieved from the NCBI genome database. BLAST  (70)  was used to align the B. pseudomallei ITS types (E, C, and G) against the assembled genomes. All of the alignments were manually inspected to determine the ITS types.
Where BLAST results were ambiguous (because multiple ITS types were present in a single genome), short-read data were aligned against ITS types E, C, and G using SPANDx and alignments were visually inspected with Tablet v1. 15. 08. 25  (71)  to determine the ITS type(s) and their allelic abundance. Virulence gene and accessory gene screening. To assess the virulence potential of the five African strains, we developed an in silico virulence gene panel comprising all known B. pseudomallei virulence determinants (see reference 52 for a review). All of the genes in this panel are described in Table  S2  in the supplemental material. The tBLASTn  (72)  algorithm was used to assess amino acid identity. BLAST matches were normalized by calculating the BSR  (73)  with B. pseudomallei K96243 as the control.
Strain name Other name(s) ST b (allelic profile) ITS type Geographic origin, reference (GenBank accession no. ) 11-1617 MSHR7969 1054 (4, 12, CE Madagascar, 16 (SRR3145392) 34, 1, 5, 2, 1) 11-1696 MSHR7968 1053 (4, 12, C Madagascar, 16 (SRR3145393) 3, 2, 5, 2, 1) BEL2013 MSHR7966 1043 (4, 1, G Madagascar, 62 (SRR3145396) 3, 2, 5, 2, 1) BF103 ITM BF103, 1121 (1, 18, G Burkina Faso (SRR3145394) MSHR7964 13, 2, 5, 6, 1) BF111 ITM BF111, 1122 (1, 1, C Burkina Faso (SRR3145395) MSHR7965 19, 2, 5, 2, 1) 4900CFPatient1 NA a 92 (1, 1, G Brazil 2, 1, 5, 1, 1) (ARZE00000000) 7894 7894/300 11 (1, 1, G Ecuador (CP009535, 13, 1, 6, 1, 1) CP009536) BCC215 NA 1355 (1, 156, G Ceará, Brazil 2, 2, 5, 1, 1) (ABBR00000000) a NA, not applicable. b Per the B. pseudomallei MLST database (http://pubmlst. org/bpseudomallei).
The main goal of the present work is to analyze the need for microscale nesting to prevent atmospheric pollution risks over a complex terrain. The case of the densely populated area of a small mountainous island, the Guadeloupe archipelago, is examined. This study showing the first evaluation of the FLEXPART-WRF model sensitivity at subkilometer resolutions has also of significant interest to near-source air quality research. During a real case meteorological situation of weak trade winds, the transport and dispersion of nitrogen oxides (NO x ) emitted by the main power plant of Guadeloupe, is analyzed for three resolutions. Model outputs are evaluated with data recorded by two air quality stations. In the focused area, the main sources contributing to NO x are the main power plant and vehicles on the primary road network.