dataesr/scientific-paragraphs-categorization huggingface dataset:
5 and 2 mW. Spectra were acquired in 30 s with a spectrometer grating of 600 gr. mm 21 centered at 1600 cm 21. Quantification of the amount of DNA target SERRS spectra are processed with the Peakfit software. R6G is easily identified thanks to a series of intense Raman peaks  [34, 42, 43]. The parameter chosen for quantification is the area of the most intense peak centered at 1650 cm 21 , noted A 1650. The SERRS-hybridization assay allows quantifying the amount of probe in the elution solution, as previously showed  [36]. With a non-degraded target DNA, this parameter has a positive correlation with the amount of target in the initial solution.
PCR were also conducted without DNA template as a control to monitor DNA contaminations and/or primer dimers formation. PCR products were first separated on 2% highresolution agarose gel electrophoresis stained with ethidium bromide and then visualised under UV illumination. Positive PCR products were subsequently cloned using the Topo TA cloning kit (Invitrogen) following the manufacturer instructions and sequenced by Beckman Genomics (see Suppl. Info. for details). Sequences were aligned manually using the Seaview software  [45].
Over the water, at a 2 m height, a steel cable was extended with a pulley solution to fix the EMG cables. The active electrodes used a pre-amplifier (AD621BN), with a 100 gain and a CMRR of 110 dB  [6]. The pre-amplified signals were transmitted through 25m cables to the main amplifier, where the signals were conditioned and further amplified 11x, in a total amplification of 1100  [7]. The signals were acquired by an A/D converter (BIOPAC Systems, Inc. ) with a sampling frequency of 1000Hz. The numerical treatment of the signals was conducted using MATLAB software. The steps for the treatment of the EMG signal were: (i) digital filtering, of 35-500Hz; (ii) removal of the common component (DC offset); (iii) full-wave rectification; (iv) linear envelope; (v) normalization of the signal for the maximum value of maximal isometric voluntary contraction and (vi) integral of treated signal (iEMG).
Recommendation process In this section we detail the framework for recommending actions. First, we restrain our workspace by hypothesizing some assumptions: • The city, which wants to improve its smartness, chooses, at the beginning, only one category of smart cities (among the six) it wants to join. Future work will automatically select the efficient category by searching into the log 3 similar cities to the one looking for recommendations. • Indicator values are numerical. Future work will include semantic similarity between indicator values. 2 Ratings / scores are given by a person authorized to make the decision of implementing different actions and through this score indicates whether the action is (or was) relevant. 3 The log may be a database or other data structure. It will be powered by the recommender system as and when the use of the system by cities.
Toy example In this section, we illustrate our proposition with a toy example (data are synthetic). Suppose there is a log containing some informations about two cities: Smallville and Metropolis. An extract of this log could be: Smallville = small city of USA, { smart environment, { sustainable resource management, { water consumption, 300 (liter per year), { after midnight shut down the public fountains, do not water plants during summer } , electricity consumption, 3000 (kw per year), { after midnight shut down the public lights, solar street lamps } }, pollution, { air quality, 10  10 , / 0 } } } Metropolis = big city of USA, { smart environment, { sustainable resource management, { water consumption, 100 000 (liter per year), { after midnight shut down the public fountains, do not wash cars during summer } , electricity consumption, 200 000 (kw per year), { after midnight shut down the public lights } }, pollution, { air quality, 8  10 , { restrict vehicles access into town center, lower vehicle speed on big roads } } } } Now suppose that the city named Gotham (C c in algorithm 1) wants to improve its smartness for the category smart environment (G in algorithm 1) where: Gotham = city of Batman, { smart environment, { sustainable resource management, { water consumption, 150 000 (liter per year), / 0 , electricity consumption, 100 000 (kw per year), { after midnight shut down the public lights } }, pollution, { air quality, 8  10 , / 0 } } } where the action A 1 is after midnight shut down the public fountains, A 2 is do not water plants during summer, A 3 is do not wash cars during summer, A 4 is after midnight shut down the public lights, A 5 is solar street lamps, A 6 is restrict vehicles access into town center and A 7 is lower vehicles speed on big roads.
OBSERVATIONS & DATA REDUCTION 2. 1. NuSTAR NuSTAR consists of two independent grazing incidence telescopes focusing X-rays between 3-79 keV onto two Focal Plane Modules, FPMA and FPMB. We used the standard NUSTARDAS software v1. 2. 0 as distributed with HEA-SOFT 6. 14 to extract spectra and lightcurves. NuSTAR spectra were used between 3-60 keV. Above 60 keV the calibration at the time of writing shows increased systematic uncertainties, and we therefore do not use those data. A detailed analysis of the high energy calibration will be presented in a forthcoming publication. The source data were extracted from a 130 ′′ radius circular region centered at α J2000 = 19 h 49 m 36 s and δ J2000 = +30 • 12 ′ 22 ′′. Background spectra were extracted from a circular region with 105 ′′ radius as far away from the source as possible.
This formally introduces systematic uncertainties in the background estimation, but since KS 1947+300 is at least a factor of 40 brighter than the background at all energies, the influence on the source flux is negligible. Light-curves were extracted with a resolution of 1 s, the resolution corresponding to dead-time measurements in the standard operating mode. 2. 2. Swift/XRT Data from the Swift/XRT  (Burrows et al. 2005)  were extracted following the standard guidelines 14 , using XSELECT to extract spectra and lightcurves and xrtmkarf to create the response files. All data were obtained in window timing mode. The source data were extracted from a circular region with a radius of 20 sky pixels (≈ 47 ′′ ). Background spectra were extracted from the wings of the PSF using an annular region between 90 and 110 pixels radius (212 ′′ and 259 ′′ , respectively).
Time-resolved spectral analysis To study the evolution of the spectrum over the outburst we use all available Swift/XRT data between MJD 56581-56639, and describe them with the same cutoffpl plus bbody model as the time-averaged spectra. We fix the cutoffenergy at 23. 2 keV, the average value in the NuSTAR data and the absorption column to 8. 45 × 10 21 cm -2 , since the Galactic absorption column should not vary. We find a strong degeneracy between the power-law slope and the black-body temperature due to the limited energy range covered by Swift/XRT. From the simultaneous NuS-TAR and Swift/XRT spectra it becomes clear, however, that an almost linear correlation between the black-body temperature and the unabsorbed 3-10 keV flux is present, kT bbody = m × F 3-10 keV , (2) where kT bbody is measured in keV and F 3-10 keV in keV s -1 cm -2.
PHASE-RESOLVED SPECTROSCOPY To investigate changes with viewing angle onto the neutron star we split each NuSTAR data set into 20 phase bins. For this analysis we did not use the Swift/XRT data, as their short exposure does not allow us to split them up further. We define the phase bins to stretch over intervals of similar flux and hardness ratio, see Figure  3. The pulse profile changes drastically with energy, developing from one broad pulse to a double-peaked profile, with a narrow primary and broader secondary peak above ∼25 keV (see also  Naik et al. 2006). To define the phase bins individually for each observation, we first measure the local pulse period by folding the cleaned NuSTAR event list on trial periods around the expected period of 18. 8 s, following the description given by  Leahy & Scott (1998).
An extensive coxL database was assembled to optimize a PCR detection assay targeting sequences belonging to functional type I-CODH and hypothetical type II-CODH. Fully replicated coxL gene libraries unveiled a unique molecular signature in deciduous forest soil, with enrichment of type I sequences. Genetic profiles of larch and maize monocultures were not statistically different and showed higher level of coxL gene richness than deciduous forest. Soil water content and CO uptake activity explained 38% of the variation of coxL gene profiles in a canonical ordination analysis, leading to the identification of sequences belonging to the δ-Proteobacteria cluster as indicator for high affinity CO uptake activity. Enrichment of type I and δ-Proteobacteria coxL sequences in deciduous forest were confirmed by qPCR in an independent soil survey.
Apparent first order CO uptake rate constants were obtained by integrating the logarithmic decrease of headspace CO mixing ratio. Measurements were performed using biologically independent triplicates and at least five CO concentration points were used for data integration. Experiments involving soil samples were accomplished over 20-60 min, depending on microbial activity. CO uptake activity in bacterial cultures was measured over a 7-day period. Cell-specific CO oxidation rate (pmol cfu -1 h -1 ) was calculated by normalizing the activity to biomass as determined by agar plate enumeration. Reproducibility of the CO analyses was assessed before each set of measurements by repeated analysis of certified CO standard gas (2. 05 ± 0. 10 ppmv, GST-Welco, PA, USA), and standard deviations were lower than 5%.
No significant CO uptake was observed for blank experiments involving sterile media or empty glass bottles. Considering the occurrence of simultaneous CO production and consumption activities in nature and their dependence on temperature, moisture and solar radiations, rates of CO oxidation in soil presented in this study must be considered as potential CO uptake activities. (Altschul et al. , 1990). Nucleic acid sequences were imported in the software Mega  (Tamura et al. , 2007) , translated in silico, and the amino acid sequences were aligned using Muscle  (Edgar, 2004). Alignments were manually refined and functional amino acid sequence motifs of the active site distinguishing coxL sequences belonging to type I-CODH (AYXCSFR) from type II-CODH (AYRGAGR) were examined in order to validate specificity of the retrieved sequences  (King and Weber, 2007).
Phylogenetic tree of amino acid sequences translated from coxL gene sequences was constructed with maximum-likelihood algorithm. The alignment was used to identify consensus regions to design coxL-specific oligonucleotides. Three sets of degenerated primers were developed to detect and quantify presumptive CO-oxidizers in soil, so-called universal-coxL, type I-coxL, and δ-Proteobacteria-coxL assays (Table  2 ). 16S rRNA gene sequences were retrieved from the genome of 102 presumptive CO-oxidizing bacteria identified in the coxL database (Table  S1 ). 16S sequences were classified into two databases: type I-coxL and type II-coxL groups, as a function of coxL gene harbored by the bacteria, and then aligned. Pairwise difference (D) matrices were computed to obtain the similarity scores S (S = 1 -D) of all possible combinations of 16S rRNA and coxL gene sequences for both databases.
Comparisons between the percentages of similarity of all coxL pairs and the sequence similarities of the 16S rRNA genes of the same bacteria were performed by regression analysis (n = 820 and n = 1830 for type I and type II databases, respectively). This pairwise similarity score analysis has recently been utilized to establish a similarity score threshold value for nitrate/nitrous oxide reductases  (Palmer et al. , 2009) , particulate methane monooxygenase  (Degelmann et al. , 2010) , and hydrogenase  (Constant et al. , 2011b ) gene sequences at the species level.
coxL GENE LIBRARIES One coxL gene library was derived from each sampling station, resulting in nine fully replicated libraries. Partial coxL gene sequences were PCR-amplified using the universal-coxL assay and cloned in pGEM-T® Easy Vector cloning Kit (Promega, WI, USA). Recombinant colonies were selected, plasmid DNA extracted following standard procedure  (Sambrook and Russell, 2001)  and coxL inserts were PCR-amplified and sequenced using the Sanger's Method (Génome Québec Innovation Centre, McGill University, QC, Canada). In total, 279 clones were obtained. Clone sequences were aligned and in silico translated to verify the canonical signature of the active site characterizing type I and type II coxL sequences. The OTU representative sequences (0. 90 similarity cut-off) obtained using the universal-coxL assay were deposited in the GenBank database with accession numbers KJ395119 to KJ395310.
TYPE I-AND δ-PROTEOBACTERIA-coxL qPCR ASSAYS Type I-and δ-Proteobacteria-coxL genes were PCR-amplified using the universal-coxL assay and plasmid DNA of clone 55M3 (accession number KJ395179) and genomic DNA of Haliangium ochraceum DSM 14365 as matrices, respectively. PCR products were concentrated and purified with standard commercial kits (E. Z. N. A. Cycle Pure Kit, Omega Bio-Tek®, GA, USA). Purified DNA was quantified with fluorescent DNA-binding dye (Quantifluor dsDNA, Promega, WI, USA). Standard curves for type I-and δ-Proteobacteria-coxL qPCR assays were obtained using serial dilutions of quantified DNA (10 1 -10 9 copies µl -1 ). Reactions contained 1× Perfecta SYBR Green Fast Mix reaction buffer (Quanta Biosciences®, MD, USA), 15 µM of each primer (Table  2 ), 0. 3 mg ml -1 bovine serum albumin, 5 µL diluted DNA (1:500) and nuclease-free water to obtain a final volume of 20 µL.
STATISTICAL ANALYSIS Gene libraries were normalized to the sequencing effort of the smallest coxL library to avoid biases in comparative analyses introduced by the sampling depth. Using the software Mothur  (Schloss et al. , 2009) , 24 coxL sequences were randomly selected from the nine libraries. The resulting sequences were grouped into operational taxonomic units (OTU) defined by a similarity level of 0. 90. These files were used for diversity index calculation and statistical analysis. Redundancy analysis (RDA) was computed using the Vegan package  (Dixon, 2003)  implemented in R (R Development Core Team, 2008) according to the comprehensive procedure described by  Borcard et al. (2011). RDA is a constrained analysis, used to extract structures of an observational dataset related to explanatory variables.
In this study, RDA was considered to identify environmental variables influencing the structure of coxL gene profile in soil, in addition to highlight coxL sequences whose presence is related to elevated CO soil uptake activity. This test was preferred from canonical analysis due to the occurrence of several null values in the coxL data matrix. Soil variables (e. g. , pH, carbon, nitrogen, water content, CO uptake activity) were standardized by subtracting individual values by the average and dividing them by the standard deviation. This transformation procedure resulted in centered data or z-scores, generating variables characterized by an average of zero and a standard deviation of 1. The Hellinger transformation was applied to coxL OTU frequency distribution before computing the distance matrix to avoid unduly relationships between explanatory variables and coxL composition supported by the high weight of rare species  (Legendre and Gallagher, 2001).
The most parsimonious constrained model to explain coxL composition was obtained by forward selection of the environmental variables  (Blanchet et al. , 2008)  and permutation tests (n = 1000) were performed to assess the significance of the RDA. Pearson correlation analyses were conducted to identify environmental variables related to soil CO uptake activity. Analysis of variance with Bonferroni post-hoc statistical test was performed to compare CO uptake activity and abundance of coxL genes between the three land-use types (SigmaPlot 12®, Systat Software Inc. , CA, USA).
Evidence of lateral transfer was noticed for type II-CODH. For instance, type II-coxL sequence detected in the aerobic hyperthermophilic Crenarchaeota Aeropyrum pernix was affiliated with that of a member of the Chloroflexi phylum (Sphaerobacter thermophilus), supporting potential lateral gene transfer event in the Archaea (Figure  1A ). The extensive coxL database was utilized to optimize previous universal coxL PCR detection assay (Table  S2 ). Genomic DNA was extracted from nine composite soil samples and coxL genes were PCR-amplified, cloned and sequenced. In total, 279 clones were derived from the maize (73), larch (93), and deciduous forest (113) samples. Sequences were classified into 192 different OTU using an arbitrary cut-off of 10% difference to accommodate both type I-and type II-coxL sequences.
RELATIONSHIP BETWEEN coxL GENE SEQUENCES AND ENVIRONMENTAL VARIABLES A RDA was performed to infer the relationship of coxL gene sequences with environmental variables (Figure  5 ). The most parsimonious model to explain variation of coxL sequences included soil CO uptake activity and water content. The other variables being redundant to CO uptake and soil moisture, their addition in the analysis increased the variance inflation factor unduly and they were therefore ignored in the analysis. The first two canonical axes explained 38% of the total variance of coxL OTU frequency distribution. Significance of the RDA was confirmed with 1000 permutations of coxL data matrix (P = 0. 003). Soil water content played an important role for the dispersion of the samples along the first axis, while CO uptake activity discriminated the samples along the second.
Optimization of a broad assay, specific type I-coxL sequences derived from public database and clone sequences obtained in this study was unsuccessful due to no or unspecific amplification signals induced by consensus degenerated primers (data not shown). As an alternative, oligonucleotides were designed based on the clone sequences only. Specificity of the assays was confirmed by coxL gene libraries (23 clones per assay) with 13 and 0% unspecific sequences for type I and δ-Proteobacteria, respectively. The abundance of type I-coxL varied between 10 9 and 10 10 genes g -1 (dw) in maize and larch monocultures and 10 10 -10 11 genes g -1 (dw) in deciduous forest (Figure  6A ). A similar trend was observed for the δ-Proteobacteria subgroup with an average of 10 9 and 10 11 genes g -1 (dw) for monocultures and deciduous forest, respectively, (Figure  6A ).
coxL PHYLOGENETIC ANALYSIS AND PCR DETECTION ASSAYS Sequences similar to coxL in Mycobacterium smegmatis (type I-CODH) and Burkholderia xenovorans (type I-and type II-CODH) were retrieved from the National Center for Biotechnology Information (NCBI) database (http://www. ncbi. nlm. nih. gov/) using the protein Basic Local Alignment Search Tool.
7 mM KCl, 4. 3 mM Na 2 HPO4, 1. 4 mM KH 2 PO4, pH 7. 3, 0. 05% (w/v) Tween-20) at 4uC. Membranes were incubated with primary antibodies anti-PGP 1:200 (Signet Laboratories, Dedham, MA, USA) and anti-Actin 1:100 (Sigma-Aldrich) for 1 h in 2% milk in PBST at room temperature (RT) and washed three times for 10 min in PBS-T. Secondary antibodies anti-mouse-HRP 1:1000 and anti-rabbit-HRP 1:1000 (Dako, Hamburg, Germany) were incubated for 1 h in 2% milk in PBST at RT and washed three times for 10 min in PBST. Proteins were detected by enhanced chemiluminescence using SuperSignal West Femto Chemiluminescent Substrate (Thermo Scientific) and the ChemiDoc system (Bio-Rad, Munich, Germany) with QuantityOne software (Bio-Rad) according to the manufacturer's protocol. Relative protein expressions were quantified densitometrically with QuantityOne (Bio-Rad) software and calculated by normalization to the reference signals of actin with GraphPad Prism software (GraphPad, San Diego, CA, USA).
G VSV-G and pCMVDR8. 2. These lentiviral particles were then used to infect hCMEC/D3cells. Doxycycline-dependent expression of MDR1-EGFP was mediated by coinfection of hCMEC/D3 cells with MIK-lentivirus encoding the tet-transactivator. The successful infection was detected by EGFP fluorescence and Western blot analysis of the Pgp-EGFP fusion protein (data not shown). Subsequently, EGFP positive hCMEC/D3 cells (6. 4%) were isolated by fluorescenceactivated cell sorting.
Tariquidar counteracted the enhanced functionality of Pgp in response to MMC exposure. However, the tariquidarinduced increase in Rho123 accumulation in MMC-exposed cells remained significantly below the increase seen in the absence of MMC, which was seen both under doxycycline-on and -off conditions. (C) shows data from intracellular accumulation of eFluxx-ID Gold in transfected hCMEC/D3-MDR1-EGFP cells in the presence of doxycycline. In this assay, by using scatter parameters and FL1 (FITC) channel, only viable and EGFP-positive cells were analyzed for intracellular accumulation of the fluorescent Pgp probe. Following MMC exposure, eFluxx-ID Gold accumulation was significantly reduced by 46% under doxycycline-on conditions. Tariquidar completely counteracted the enhanced functionality of Pgp in response to MMC exposure.
When experiments were performed 20 h after a 4-h exposure to 1 mM MMC, Rho123 accumulation was significantly reduced by 15% under doxycycline-off and 22% under doxycycline-on conditions. Tariquidar counteracted the enhanced functionality of Pgp in response to MMC exposure. However, the tariquidarinduced increase in Rho123 accumulation in MMC-exposed cells remained significantly below the increase seen in the absence of MMC, which was seen both under doxycycline-on and -off conditions. (C) shows data from intracellular accumulation of eFluxx-ID Gold in transfected hCMEC/D3-MDR1-EGFP cells in the presence of doxycycline. In this assay, by using scatter parameters and FL1 (FITC) channel, only viable and EGFP-positive cells were analyzed for intracellular accumulation of the fluorescent Pgp probe.
Asthma-related outcomes in childhood We used preschool wheezing and school-age asthma as the main outcomes. These data were mainly obtained by using questionnaires adapted from the International Study on Asthma and Allergy in Childhood (ISAAC). 22 Cohortspecific information is shown in Table  E1. We defined preschool wheezing as ''ever reported wheezing during the first 4 years of life (no, yes)'' and school-age asthma as ''asthma diagnosis reported between 5 and 10 years (no, yes),'' preferably physician diagnosed. If cohorts had repeatedly collected data on ever wheezing in the first 4 years or asthma diagnosis between 5 and 10 years of life, we used data collected at the oldest age.
Statistical analysis First, we performed a 1-stage random-effect meta-analysis on an individual participant's data to examine the separate and combined associations of gestational age, birth weight, and infant weight gain with preschool wheezing and school-age asthma. For this analysis, individual participants' data from all cohorts were included in 1 multilevel analysis and were analyzed, simultaneously taking into account clustering of participants within studies. 28 ecause we used a Northern European reference curve for birth weight for gestational age (birth weight SDS), we performed a sensitivity analysis to explore whether the association was different in Northwest European subjects only (Denmark, France, Germany, Ireland, The Netherlands, Norway, Sweden, Switzerland, and United Kingdom).