dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectrum analysis Analyses and graphical outputs were performed using the free and open source statistical analysis software R (version 2. 13), along with specific analysis libraries (MALDIquant) as previously described  [20]. The gel view representation indicates the reproducibility of the spectra. A hierarchical clustering with a ward algorithm for agglomeration and a dissimilarity matrix based on the Jaccard distance were used to classify the spectra. The MALDI Biotyper 3 software (Bruker Daltonics) was used to create an average reference spectrum for each PBMC sample, corresponding to at least 10 individual spectra. The Biotyper software realigns acquired spectra and automatically creates an average spectrum using default Biotyper software settings provided by the manufacturer, and we created a database as previously described  [20].
The Biotyper software also allows the identification of unknown spectra as shown in clinical samples by comparison with reference spectra, for the identification and classification of microorganisms  [14]. The score values proposed by the manufacturer have been used for microorganism identification. The score values between 0. 000 and 1. 699 do not allow reliable microbe identification; the values between 1. 700 and 1. 999 allow probable cell identification and score values higher than 2. 0 are considered statistically significant; they allow the confident identification of different microbe species. We extended this method to assess PBMC activation status in septic patients. As the score values provided by Bruker Daltonics ranged from 0. 000 to 2. 000 when we used control samples and reference spectra, we considered that medians of matching scores higher than 1.
Finally, we used matching scores to compare the different modes of PBMC activation after having created averaged spectra of stimulated PBMCs. The MALDI-TOF MS profiles of PBMCs from septic patients were then compared with the database. None of the septic patients matched with the averaged spectrum of healthy donors, while the spectra of septic patients significantly matched with IFN-γ and IL-10 spectra regardless of whether the infection was documented (n = 6) or not (n = 6), confirming that sepsis is characterized by both inflammatory and immunoregulatory features (Fig. 5  and Additional file 1: Figure  S1 ). It is noteworthy that the scores obtained by comparing the spectra of E. coliand S. aureus-infected patients with the reference spectrum of E. coliand S. aureus-stimulated PBMCs were significantly lower (p < 0.
The Biotyper (Bruker Daltonics) software was used to create an averaged spectrum for each patient, corresponding to at least 10 individual spectra. The averaged spectra were added to the database using the Biotyper software and the dendrogram creation method. Peripheral blood mononuclear cells (PBMCs); polymorphonuclear cells (PMNs); dendritic cells (DCs); monocytederived macrophages (MDMs); bone marrow-derived macrophages (BMDMs); red blood cells (RBCs) the diagnostic process. Many biomarkers have been proposed over the years, but the diagnostic value of these molecules remains uncertain  [24, 25]. We reasoned that a more integrated approach such as MALDI-TOF MS may be used to detect sepsis specifically in patients without microbiological documentation. As expected, we found that the signature of PBMCs from different healthy subjects was highly reproducible.
[9]. Information on the shotgun sequencing, assembly, and binning processes  [19]  for the genome of R. cohabitans and the metagenome of enrichment culture Sob are described in the Supporting Information. The draft genomes of R. cohabitans, uncultured Spirochete bacterium bdmA 4, and uncultured Spirochete bacterium SA-8 were loaded into the MicroScope genome annotation platform and automatically annotated  [20]. The annotations were manually curated and edited using the Magnifying Genome (MaGe) environment  [21] , and metabolic pathways were predicted using the integrated pathway tools of MaGe that are based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) and MicroCyc databases  [22, 23]. The presence of glycoside hydrolases and extracellular peptidases was evaluated using dbCAN (E < 1e -10 and cover fraction >0.
Phylogenetic analysis Potential genes encoding hydrogenase catalytic subunits were identified in the genomes of R. cohabitans and Desulfobacterium N47 by screening translated sequences through the web database HydDB  [26]. Sequences were validated as hydrogenases if they encoded cysteinecontaining motifs characteristic of hydrogenases  [27]  and branched with hydrogenase reference sequences on phylogenetic trees. To infer evolutionary relationships, the source and reference sequences were aligned using the MUSCLE algorithm  [28]  and visualized on neighbor-joining phylogenetic trees  [29]  constructed with MEGA7  [30]  and bootstrapped with 500 replicates. For genetic organization maps, protein/domain function was predicted using the CDD (Conserved Domain Database)  [31]  and iron-sulfur clusters were predicted by identifying conserved cysteine residues as previously described  [27].
The phylogeny of the Spirochaetes was inferred using 16S rRNA gene sequences retrieved from the NCBI (National Center for Biotechnology Information) Genome Database. Sequences were aligned using the MUSCLE algorithm included in MEGA7 and trimmed to uniform length  [30]. A maximum likelihood phylogenetic tree was constructed in MEGA7 using a general time reversible substitution model and uniform rates among sites. The tree was bootstrapped with 500 replicates.
The turbid solution was cleared by centrifugation (12,000 × g, 4 °C, 5 min) and the supernatant was transferred to a fresh 1. 5 mL Eppendorf tube. The protein concentration was determined by using a modified Bradford assay (RotiNanoquant; Roth) following the manufacturer's instructions. A detailed protocol for the subsequent sample preparation for Liquid chromatography tandem-mass spectrometry, as well as the settings during data acquisition and data analysis, can be found in the Supporting Information. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  [32]  partner repository (https://www. ebi. ac. uk/pride/archive/) with the dataset identifier PXD005624.
Acknowledgements We thank  Masaru K. Nobu  for supplying genome sequences of uncultured Spirochete SA-8,  Melanie Klose  for hydrogen measurement,  Karine Labadie  and  Eric Mahieu  for Illumina sequencing, and the  Genoscope's LABGeM team  for providing the Micro-Scope platform. We acknowledge partial funding from the  FP7-KBBE MAGIC-PAH  (project  245226 ) for (meta)genome sequencing. Xiyang Dong  acknowledges a fellowship from the  China Scholarship Council  (File No. 201306060075 ). Chris Greening acknowledges funding from the  ARC   DECRA Fellowship  ( DE170100310 ) and  ARC Discovery Project  ( DP180101762 ). Markus Kaiser  acknowledges partial funding from the  ERC  starting grant No. 258413  and from the  DFG  grant No. INST 20876/127-1 FUGG. Rainer U. Meckenstock  acknowledges funding from the  ERC advanced grant EcOILogy  (No.
In this paper, we put firm level export prices explicitly at the center of the analysis of the international elasticity puzzle. An obvious difficulty to estimate the export price elasticity is that export prices and export quantities are endogenous at the firm level. This problem does not occur for exchange rates and tariffs shocks that could be considered exogenous to a single firm. To overcome this difficulty we use a firm level time varying instrumental variable for export prices. 2 To this end, we use an original dataset providing information on a firm specific cost shock, namely firm level electricity prices 3. We argue below that these firm level electricity cost shocks are related to factors exogenous to its export performance (regulation changes, year and length of beginning of contracts, national and local tax changes, location, changes in both market and regulated prices and local weather) and are likely to affect its export performance only through the firm export price.
We match this dataset to a data set on French export volumes and values to estimate the firm level export price elasticity. We do this by using French exporters data on the period 1996-2010 and we focus on the intensive margin of 1 Arkolakis et al. (2012) show that for a large class of trade models, the welfare gain from trade (as change in real income) can be expressed as Ŵ = λ1/ε where λ is the change in the share of domestic expenditure and ε is the trade elasticity to variable trade costs. 2 By instrumenting the export price we also improve on measurement errors in trade unit values. 3 An alternative for marginal cost shocks would be to use exchange rate shocks for intermediate imported inputs such as  Piveteau & Smagghue (2015)  and  Loecker & Biesebroeck (2016). Ganapati, Shapiro & Walker (2016)  use energy cost shocks as instruments for marginal cost shocks.
Data In this paper we use three confidential firm level datasets: (i) Douanes database on French firms exports, (ii) F icus/F are on French firms balance sheet information and (iii) EACEI data on energy consumption and purchase of French firms. 6 Then macro level control variables come from standard sources (World Bank, CEPII and Penn World Table ). The Douanes database is provided by French customs for the period 1995-2010 and gives us information on import and export flows of French firms by destination country, product (HS 6-digit classification) and year. This database contains all trade flows by firm-product-destination that are above 1,000 euros for extra EU trade and 200 euros for intra-EU trade, so it can be considered an exhaustive sample of all French exporting firms. Based on export values and quantity we computed the Trade Unit Values (TUV) for a specific firm-product (HS 6-digit)-destination-year cell (here used as proxy for the export price).
The potential amount of observations is thus very large: there are almost 100,000 exporting firms per year and 200 destination markets. For this reason (and also because our main instrumental variable does not vary with product dimension -see section 2), we collapse the French customs data at firm-destination-year level. So the resulting TUV is the weighted average across exported products of a given firm-destination-year cell. 7 Doing so, we lose the HS exported product dimension; but when needed, we still have the information of the main industry (NAF700 classification) in which the firm operates (as coded by the INSEE). 8 Indeed, the weighted average of TUVs can suffer from a composition bias (due to the aggregation of several products exported within a firm-destination-year cell). 9 Hence, in an alternative dataset, we retain the export product dimension of the dataset by restraining the analysis to the core product exported by the firm in a given market.
For each firm-destination we keep the HS-6 code that represents the maximum (average across years) exported value for the firm-destination. For the core product of the firm, TUVs do not suffer from a composition bias. Thus, in all the core product estimations we refer to a specific sector rather than to a more general industry dimension (as done in the baseline dataset described above). The second firm level database is F icus/F are which contains balance sheet information for all French firms. From this database we retain the turnover and the employment level of each French manufacturing firm. We use these as control variables in our main regressions. From F icus/F are we also keep the labor cost and the purchase of intermediate inputs and raw materials used to compute the share of electricity over the total cost reported below.
The information on firm level electricity price (used as instrumental variable for the export price, see section 2) is provided by the EACEI survey on energy purchase and consumption by around 11. 000 French firms in the period 1996-2010. 10 For each plant-year combination we have information about the use of different types of energy such as electricity, steam, coke and gas. For consistency with the French custom data, the EACEI database has been aggregated at firm level by summing electricity bill and consumption across plants within the same firm. 11 The price of electricity has been computed as the ratio between electricity bill (in e) and purchased quantity of electricity (in kWh). The final electricity price for the firm is thus expressed in e/kWh. When we merge the three firm level databases we are left with around 8,500 exporters per year.
Finally we merge firm level data with other macro datasets: (i) OECD. stat for the GDP of destination 7 We used the exported quantities as weights. 8 Notice that each firm is assigned to a unique industry of activity by the INSEE. In the NAF700 classification there are 615 industries. 9 This is not a big problem in our case since the majority of firm-destination cells (the 60%) involve export shipments within a unique HS 4-digit heading. Since products within a HS 4-digit heading are mostly homogeneous, the composition bias concern here is reduced. 10 The survey has been conducted on firms with more than 20 employees. 11 We use the French firm identifier siren to merge with the Custom database. countries, (ii) CEPII MacMap HS-6 data for tariffs and (iii) Penn World Table for nominal exchange rates and consumer price indexes (used to calculate the real exchange rate).
The MacMap database on tariffs records ad-valorem applied tariff for each country pair-sector (HS-6 digit) observed in four years  : 2001 , 2004 , 2007 and 2010 (see Bouet, Decreux, Fontagné, Jean & Laborde (2008)  and  Guimbard, Jean, Mimouni & Pichot (2012)  for more details on MacMap). 12 Since French exporters do not face tariff in EU, we simply set to zero intra-EU tariffs. As described above, for our baseline regressions we use a firm-destination-year specific dataset. So we follow  Fitzgerald & Haller (2014)  and use the weighted average tariff faced by a firm into a given destination-year (average across exported products). 13 In the core product estimations, since we keep the core exported product of each firm, we can use the (core) product level tariff.
Firm level electricity prices as instruments for export prices In our empirical strategy, we use the firm specific electricity price as an instrumental variable for the export price. The average electricity price in our dataset (reported in table 1) is in line with the publicly available average prices for the manufacturing sector. Importantly, our dataset exhibits variance across firms and within a firm over time. We also observe annual variations in prices that are not synchronized across firms. In figure  1  the dotted line is the average price of electricity paid by French firms between 1996 and 2010. We also show the price paid by two anonymous firms chosen here to have a mean price and a standard deviation similar to the mean and the standard deviation of the overall sample. Although the overall trend is similar (first downward then upward), we see that these firms experience yearly shocks that are very different.
The share of electricity over the total cost (as reported in table  1 ) is computed as the ratio between the electricity bill and the total production costs of the firms available in the Ficus/Fare dataset (i. e. labor cost, purchase of intermediate inputs, raw materials and electricity). Table  2  reports the summary statistics for the sample of firms we use in our baseline regressions, so the number of firms and the other statistics reported in the table refer to a sample of exporting firms for which we also have balance sheet and electricity bill data. The average size of the firm over the period 1996-2010 is large but this is not surprising since these are exporting firms only. 14 There is also some variation in the electricity cost share over time: from 1. 9% in 2005 up to 3. 6% in 2002 and back to 2.
The dependent variable is the log of the exported volume by firm i in a specific country j and year t. The main explanatory variable here is the log of the export price (i. e. trade unit value) -ln(T U V i,j,t ) -instrumented as in equation (  4 ), (  5 ) and (  6 ), so we expect a negative coefficient for σ. As explained in the data section we use two main regression samples: (i) exported volumes and average TUV across products within firm-destination-year (baseline full dataset), (ii) exported volumes and TUV of the HS-6 specific core product of the firm for a given destination (core product dataset). The subscript s refers to industry (NAF700 classification) and sector (HS classification) respectively for baseline and core product dataset (see section 1 for further details). We want to compare our estimates of the price elasticity with various fixed effect combinations.
All in all, we can conclude that the export price elasticity is strongly robust across different specifications (i. e. fixed effects) and always around 5 in our preferred specifications (columns 4-9). Table  3 : Baseline 2SLS regressions on full dataset. (1) (2) More details on the first stage results are reported in table A3 *** p < 0, 01; * * p < 0, 05; * p < 0, 1. (3) (4) (5) (6) (7) (8) 2. 1 Robustness checks using core product dataset. In table 4, we perform several robustness checks on the sample. First, we restrict the sample to the core product of the firm (for each firm we keep the product line having the maximum average exports over the period  [1996] [1997] [1998] [1999] [2000] [2001] [2002] [2003] [2004] [2005] [2006] [2007] [2008] [2009] [2010]. This solves the potential aggregation bias concern when firms export more than one product to a given destination.
Robustness checks controlling for strategic complementarity. A final issue we want to address is strategic complementarity that has been emphasized recently by ? in international pricing. The concern is that in the first stage regression, the electricity cost shock that generates the export price increase could also lead close competitors to increase their own price. In turn, this may alter the estimate of the impact of the export price increase on its export sales. If such a strategic complementarity exists, for example of the kind analyzed by ?, the perceived elasticity of demand is different (smaller) from the elasticity of substitution across products. A complete analysis of this issue is beyond the scope of our paper but we can take advantage of our dataset to check whether our estimates are robust to a crude measure of these strategic complementarities.
However, one could argue that the NAF4 digit sectors that we use for these fixed effects are not necessarily the valid ones to capture these strategic complementarities. However, destination-HS4-year fixed effects as in table A4 are a more compelling way of controlling for strategic complementarity and results hold. Moreover, as a further robustness test, we use the core product dataset and control for the prices of other French exporters to the same destination and in the same HS6 sector. We follow a similar empirical strategy as in ? although we depart from them because we use a different instrumental variable and we analyze the strategic complementarity on export prices while they analyze it on domestic prices. We proceed in two steps. First we control for strategic complementarity of French exporters only and then we control for strategic complementarity of non French exporters to the destination.
In order to define the relevant set of competitors, we need the specific HS 6-digit in which the firm operates. So, for this estimations we rely on the core product based sample of firms (as in table  4 ). For firm i exporting to a given HS6-Destination combination, we define the French competitors trade unit value (TUV) as the average TUV of French firms exporting to that HS6-Destination combination. We exclude from this average the TUV of firm i. We also exclude from the sample HS6-destination combinations with less than two competitors. Finally, we define foreign competitors TUV as the average import price (TUV) of non-French exporters to a given HS6destination where the French firm i is exporting (using BACI dataset). In table  5  we show results based on core product dataset (firms exporting more than 5 years) controlling for the average price of French competitors -columns 1 to 3 -and for both domestic and foreign competitors TUV -columns 5 to 7.
19 The results are intuitive as firm export prices increase with both domestic and foreign competitors prices (in the first stage) 19 In appendix table A6 we show the same type of estimations on the balanced core product dataset. suggesting the presence of strategic complementarity. The price of competitors also have a positive impact on export volumes. However, the main result is that the estimated elasticity is not much affected. As a robustness check in columns 4 of table 5 we replace the French competitors prices by an exogenous shock to these prices, i. e. the average electricity cost for these French competitors. Its effect on export volumes is positive and significant in column 4 but again the estimate of the export price elasticity is not much affected. All in all, from this first set of evidence we conclude that our estimate of the firm level export price elasticity is precisely estimated and relatively high at around 5.
We follow the same logic as in  Fitzgerald & Haller (2014)  and aggregate our dataset at industry-destination-year to estimate the effect of tariff and real exchange rate on both export volumes and revenues. 20 All variables are taken in log and we include destination and industry-year fixed effects in all the estimations. Results, reported in table 6 strongly confirm the presence of the international elasticity puzzle. The estimated coefficients on tariff range between 1 and 1. 23, while coefficients on real exchange rate are between 0. 57 and 0. 72. French exporters react more to tariffs than to real exchange movements. Then in a last specification (see columns 3 and 6), with a pure illustrative purpose, we include the (log of) export price. Coefficients on TUV have the expected sign with an elasticity of 0.
However, tariff movements seem less volatile than export prices so this may not be enough to explain the higher elasticity of exports to export prices than to tariffs. Another interpretation could also be linked to the literature on rational inattention. One result of this literature (see  Maćkowiak & Wiederholt (2009) ) is that firms pay more attention to idiosyncratic conditions than to aggregate conditions which would be consistent with retailers and importers reacting more to exporters changing the price of their goods, than to tariff changes or exchange rate movements. We leave for future research to test for these alternative explanations. Table  A3 : First stage regression results on full baseline dataset. Dep Var: TUV (ln) Dep Var: TUV (ln) Dep Var: TUV (ln) (2) (3) (5)  *** p < 0, 01; * * p < 0, 05; * p < 0, 1.
* This work benefited from financial assistance by the  French National Research Agency , through the  program Investissements d'Avenir  with the following reference:  ANR-10-EQPX-17  ( Remote Access  to data CASD). Philippe Martin  is grateful to the  Banque de France-Sciences Po partnership  for its financial support. We thank  Thierry Mayer  and  Thomas Chaney  for helpful discussions, in addition to participants at seminars at Sciences Po, PSE and the  IMF.
Our approach differs from Fitzgerald & Haller (2014)  in the sectorial aggregation. While Fitzgerald & Haller (2014)  aggregate at HS 6-digit level, we do it at industry NAF700 level to be coherent with the classification used in the baseline dataset described above. For these estimations, tariff corresponds to the average tariff faced by French firms in a given NAF700 when exporting into a given destination. Interestingly, we confirm the elasticity puzzle highlighted by Fitzgerald & Haller (2014)  also by using different sectorial aggregation.