dataesr/scientific-paragraphs-categorization huggingface dataset:
24  and Zak et al. 32  ), and given that for the fifth dataset, the module did not reach significance when compared to control). These genes were then reduced using the Boruta 41 package in R. Boruta is a feature selection wrapper algorithm based on random forest and is particularly useful in biomedical applications as it captures the features by incorporating the outcome variable. Next, the features identified as predictive using Boruta were ranked using the GINI score in random forest and the top 20 genes were selected. For classifying patients as active TB or latent TB, the random forest algorithm was used in caret  68  package in R using LOOCV over 1000 iterations. Each of the TB datasets was randomly split into training (70%) and test (30%) sets to classify patients as active TB or latent TB.
For analysis in the Zhai et al. 36  dataset, the Influenza A group at Day 0 and healthy controls were randomly split into training (70%) and test (30%) sets to classify patients as infected with Influenza A or healthy controls. Modified disease risk score. To test the TB-specific 20-gene signature, a modified version of the disease risk score (DRS) established by Kaforou et al. 24  was used. Briefly, the DRS is obtained from the normalised data in a non-log space by adding the total intensity of the upregulated transcripts and subtracting the total intensity of downregulated transcripts from the gene signature. In this study, the normalised CPM values were used for the RNA-seq data and non-log normalised expression values were used for the microarray data. As part of the modification of the DRS, the absolute values of the total intensity of upregulated transcripts and total intensity of downregulated transcripts were added to obtain a composite score.
Deconvolution analysis. Deconvolution analysis for quantification of relative levels of distinct cell types on a per sample basis was carried out using CIBER-SORT  69. CIBERSORT estimates the relative subsets of RNA transcripts using linear support vector regression. Cell signatures for 22 cell types were obtained using the LM22 database from CIBERSORT and grouped into 11 representative cell types. The fractions of cell types were compared across different groups using one-way ANOVA, and p-value < 0. 05 was considered significant.
Acknowledgements We acknowledge the  Francis Crick Advanced Sequencing Facility, and Bioinformatics and Biostatistics Science Technology Platforms  for their contribution to our sequencing processing. We acknowledge the  NIHR Leicester Biomedical Research Centre  for their support of the study at Leicester. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. We thank the patients for their participation. We thank  Asmaà Fritah-Lafont  for help in co-ordinating the meetings regarding the study. We thank  Dr. Lúcia Moreira-Teixeira  for reviewing the manuscript and for the valuable discussion. A. O. G. , C. M. G. , and A. S. were funded by  The Francis Crick Institute  ( Crick 10126; Crick 10468 ), which receives its core funding from  Cancer Research UK , the  UK Medical Research Council  and the  Wellcome Trust ; and the sequencing project by the  BIOASTER Microbiology Technology Institute, Lyon, France ;  Medical Diagnostic Discovery Department, bioMérieux SA, Marcy l'Etoile, France ; and funded in part by  Illumina Inc.
, San Diego, CA, USA. R. V. and J. L. , University of Leicester, were funded by  BIOASTER Microbiology Technology Institute, Lyon, France. This work has received, through  BIOASTER investment , the funding from the  French Government  through the  Investissement d'Avenir program  (Grant No. ANR-10-AIRT-03 ). R. J. W. was supported by  The Francis Crick Institute  ( Crick 10128 ), which receives its core funding from  Cancer Research UK , the  UK Medical Research Council and Wellcome ; by  Wellcome  (104803; 203135);  MRC South Africa  under strategic health innovation partnerships; and  NIH   019 AI 111276.
Data availability. Sequence data that support the findings of this study has been deposited in NCBI GEO database with the primary accession code GSE107995 and in BioProject with the primary accession code PRJNA422124. TB datasets referenced in this study as comparators are available in GEO with the primary accession codes GSE37250 and GSE79362, in BioProject with the primary accession code PRJNA315611 and in SRA with the primary accession codes SRP071965, GSE20346, GSE68310, GSE42026, GSE60244 and GSE42834.
CO fire emissions based on either MOPITT or IASI have a similar spatial pattern and evolution in time, and a 10% uncertainty based on a set of sensitivity tests we performed. Thus, CO satellite data have a high potential to complement existing operational fire emission estimates based on satellite observations of fire counts, fire radiative power and burned area, in better constraining fire occurrence and the associated conversion of peat carbon to atmospheric CO 2. A total carbon release to the atmosphere of 0. 35-0. 60 Pg C can be estimated based on our results. This article is part of a discussion meeting issue 'The impact of the 2015/ 2016 El Nin ˜o on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.
Indonesian fires often emit large quantities of CO by incomplete combustion associated with the occurrence of peat. CO is a major air pollutant that affects the oxidation capacity of the atmosphere through its reaction with the hydroxyl radical (OH) and simultaneously acts as a precursor for ozone formation, an important short-lived greenhouse gas. CO is converted to CO 2 with a lifetime of one to three months in the atmosphere, which makes it very suitable for detecting emissions from widespread fires. According to the Global Fire Emission Database  [12] , the 2015 Indonesian fires produced the largest CO emissions from biomass burning since 1997-1998, when another strong El Nin ˜o triggered the most severe Indonesian fire event to be recorded since 1990  [4]. Emissions from fires to the atmosphere are typically derived using space-based detection of fire counts (FC), fire radiative power (FRP) or burned area (BA).
Both studies used MOPITT data, which were shown to have a poorer performance at detecting extreme events compared to IASI  [17]. Huijnen et al. [15]  also used measured emission factors during this event to convert their estimates into CO 2 and CH 4 emission estimates. Other estimates for CO 2 emissions for this event using satellite data were made by Heymann et al. [18]  and Lohberger et al. [19]. We focus here on comparing IASI and MOPITT, which both measure CO in the thermal infrared (TIR), and have relatively high spatial resolution and global coverage. By contrast, TES has a sparser spatial coverage, while AIRS has a larger footprint and measurement error  [20, 21]. MOPITT differs from IASI in terms of measurement technique, vertical sensitivity and retrieval strategy, having an additional measurement band for CO in the near infrared (NIR).
Typical differences up to 20% in the retrieved total columns of the two products have been reported  [17]. The widespread 2015 Indonesian fires give us the opportunity to compare the estimated CO emissions of inventories with/against satellite products. To realize an atmosphericbased system for monitoring fire emissions, CO emissions need to be tracked in the atmosphere, where they mix with CO sources from industry and atmospheric oxidation of hydrocarbons, and in which CO is oxidized by atmospheric hydroxyl (OH). The TM5-4DVAR inverse modelling system  [22 -24]  is used in this paper to optimize CO emissions from the Indonesian fires by assimilating satellite CO data from MOPITT or IASI. In the following sections, we evaluate the performance of emission inventories in capturing the spatial and temporal evolution of CO emissions from the 2015 fire event in Indonesia and compare, for the first time, CO emission estimates for a large biomass burning event, optimized using IASI and MOPITT satellite data.
(b) MOPITT data MOPITT is a nadir-viewing instrument that uses gas filter correlation radiometry to measure CO at 4. 7 mm (TIR) and 2. 3 (NIR) mm. The 2. 3 mm band provides data only over land during day-time and typically has a higher sensitivity to the lower troposphere. In this paper, we use MOPITT version 7 Level 2 data from both the joint NIR-TIR product, which combines information from both wavelength regions, and the TIRonly product. MOPITT flies on the sun-synchronous orbiting Terra satellite launched in 1999, crossing the equator at approximately 10. 30 local time each morning and evening. It has a swath of 22 Â 650 km, with 116 cross-track pixels. The MOPITT retrieval algorithm assumes lognormal statistics for CO variability and uses a variable a priori based on a 1-degree monthly climatology from CAM-chem model simulations  [26].
(c) TM5-4DVAR The TM5-4DVAR model is employed in this study to estimate CO emissions from the 2015 fire event over Indonesia. The model consists of the Tracer Transport Model version 5 (TM5  [24] ) and the four-dimensional variational optimization shell. TM5-4DVAR was previously used successfully to optimize CO emissions on global and regional scales using MOPITT  [24]  and IASI data  [23]. The TM5 model is driven by offline meteorological fields from the ERA-Interim reanalysis of the European Centre for Mediumrange Weather Forecasts (ECMWF)  [28]. We run TM5 on a 68 Â 48 horizontal resolution and on 25 vertical hybrid sigma-pressure levels. We make use of the TM5 zoom capabilities to increase the horizontal resolution over southeast Asia to 18 Â 18, with an intermediate zoom region of 38 Â 28, as shown in figure  1.
(d) Simulation set-up We use the MACCity anthropogenic emissions and CO oxidation based on OH climatological fields from Spivakovsky et al. [31]  scaled with a global factor of 0. 92, as recommended in  [32]  based on methyl chloroform observations. CO chemical production from oxidation of methane (CH 4 ) and from non-methane volatile organic compounds (NMVOCs) is based on a 2010 simulation of the TM5 full chemistry version  [32]. The main results described in this paper are from the 'IASI' and 'MOPITT' simulations, where IASI and, respectively, MOPITT level 2 data are used together with NOAA surface stations, to optimize CO emissions. The IASI day-time only data are used because the day-time FORLI retrievals are more sensitive to lower altitudes than the night-time data  [33]. The observation variance is enhanced by a factor of 50 to account for observation error correlation, as suggested in  [29].
For MOPITT, we use both day-time and night-time data from the version 7 NIR -TIR product. No error inflation is typically applied for MOPITT. A set of tests was performed to evaluate the sensitivity of the resulting emissions with respect to the data used and the model set-up (electronic supplementary material, table  S1 ). In the 'MOPITT J day' and 'MOPITT T day', only day-time data were used from MOPITT from the NIR -TIR and TIR-only products, respectively. In 'MOPITT infl', we tested the impact of applying an error inflation factor of ffiffiffiffiffi 50 p for MOPITT, as done for IASI. IASI has a wider swath and a better spatial coverage than MOPITT, which can be seen also in the animations in the electronic supplementary material. We tested whether the difference in spatial coverage between IASI and MOPITT could impact the resulting emissions in the 'IASI filt' simulation, where we left out IASI data from locations more than 18 latitude or 18 longitude away from MOPITT observations from the same day.
To test the potential magnitude of the effect that changes in OH due to the Indonesian fires could have on estimated emissions  [34] , we performed two sensitivity tests, 'IASI OH' and 'IASI OH GFED,' using modified OH fields. In these simulations, standard OH fields were scaled with the daily three-dimensional ratio between two global chemistry simulations with the Composition Integrated Forecasting System (C-IFS) model  [15, 35] , with and without peat emissions of CO, NOx and NMVOC. Most simulations presented use GFAS v. 1. 3 [13]  as prior biomass burning emissions, except for 'IASI OH GFED', which uses GFED4s  [12]  as prior. 1 ) and the modelled columns using GFAS prior and optimized emission over the same areas based on each of the two datasets. The IASI CO columns (black line in figure  2b ) tripled from a background average of 1.
Somewhat larger differences are found when accounting for the effect of CO on the regional OH abundance and for the different daily coverage of the IASI and MOPITT data ('IASI OH' and 'IASI filt'). Total emissions are about 10 Tg lower for these simulations than the estimate with default OH and using the full IASI dataset. Looking at the sub-regions of Indonesia and Papua, the emissions for Sumatra are estimated in the range of 29 -38 Tg, in agreement with GFED4s estimates but significantly lower than GFAS, which estimates total Sumatra emissions of 55 Tg CO in this period. For the Central Indonesian region, which mainly includes Kalimantan and Sulawesi islands, the emissions increase from the prior estimates of 37 Tg (GFED4s) and 30 Tg (GFAS1. 3) to 63 -67 Tg for the range of simulations using MOPITT and to 74 -79 Tg for the simulations using IASI data.
Note that Papua New Guinea and small Indonesian islands were not included in their analysis; therefore, we do not show their results for the other two regions. Figure  5  shows the emission evolution in time over Indonesia and Papua, and their sub-regions. Most emissions are found in October in all regions, with particularly large emissions in the second half of October and a smaller emission peak occurring at the end of September and beginning of October. The results obtained with the IASI and MOPITT datasets are similar in terms of both time evolution and the split between regions. These features are robust with respect to the satellite dataset used and model settings (see electronic supplementary material).
Satellite data of CO can be used towards monitoring the effectiveness of measures taken in reducing fire occurrence and spread. CO satellite data products from IASI and MOPITT can complement existing fire indicators such as FRP, FCs and BA as part of operational systems such as CAMS (https:// atmosphere. copernicus. eu/). Leip et al. [45]  recommend monitoring greenhouse gas emissions by a combination of top-down and bottom-up methods. Similarly, we recommend combining current fire emission inventory techniques with top-down estimates based on atmospheric constraints. Integrating multiple data streams would improve emission estimates, fire monitoring and our general understanding on fire processes. Data accessibility. Model output is available at https://trng-b2share. eudat. eu/records/01649b2e67b242d3a69d356f9b976789, and model code is available upon request from Narcisa Nechita-Banda (n.
FUNDING  Sidaction  and  ANRS (Agence Nationale de Recherches sur le SIDA et les Hépatites Virales)  to R. M. ;  ANRS  [ ECTZ35556  to R. P. S. ] and IDEX: Par delà les frontières, l' Université de Strasbourg  to R. P. S. ;  German Ministry for Education & Science (BMBF)  [ 031A307  to M. S. and M. v. K. ];  Partenariat Hubert Curien (PHC) programme franco-allemand PROCOPE  [ 37709NJ  to R. P. S. and M. v. K. ]; IGBMC Microarray and Sequencing platform, a member of the 'France Génomique' consortium e57 Nucleic Acids Research, 2018, Vol. 46, No. 9 PAGE 14 OF 16  [ ANR-10-INBS-0009 ]. Funding for open access charge: CNRS. Author contributions: R. P. S. designed the study. R. P. S. performed the MIME experiments including libraries for sequencing. R. P. S. , M. R. S. , L. D, F. J. , P. C. and M.
Specifically, according to the RELATIVE model, the choices in the transfer test are not affected by the outcome valence or by the outcome magnitude, but dominated by options' context-dependent favorableness factor. Comparison between model simulations and experimental data falsified both models  31  , since in both the learning sessions and in the transfer test, subjects performance lied in between the predictions of the ABSOLUTE and RELATIVE models. To account for this pattern we designed a HYBRID model. The HYBRID model implements a trade-off between the absolute and relative learning modules, which is governed by an additional free parameter ('partial adaptation'  29  ). Owing to this partial adaptation, the HYBRID model accurately accounts for the performance in the learning sessions and for the preferences expressed in the transfer test, including the preference inversion patterns.
absolute valuelearning modules. A series of subsequent analyses revealed that several relevant factors affect this trade-off. First, we showed using an original trial-by-trial model comparison that the tradeoff between absolute value-learning and normalized value learning implemented by the HYBRID model is progressive and gradual. This is an important novelty compared to previous work which only suggested such progressivity by showing that value rescaling was dependent of progressively acquired feedback information  19. Note that learning normalized value ultimately converges to learning which option of a context is best, regardless of its valence or relative value compared to the alternative option. Second, and in line with the idea that information concerning the forgone outcome promotes state-dependent valuation  18, 32  , we also found that the relative weight of the normalized-value learning module (ω) increased when more information was available (counterfactual feedback).
Acknowledgements Emmanuel Noblins  and  Alexander Salvador  provided help for data collection. S. P. is supported by an  ATIP-Avenir grant  ( R16069JS )  Collaborative Research in Computational Neuroscience ANR-NSF  grant ( ANR-16-NEUC-0004 ), the  Programme Emergence (s) de la Ville de Paris , and the  Fondation Fyssen. S. B. is supported by  MILDECA  ( Mission Interministérielle de Lutte contre les Drogues et les Conduites Addictives ) and the  EHESS (Ecole des Hautes Etudes en Sciences Sociales ). M. L. is supported by an  NWO   Veni Fellowship (Grant   451-15-015 ) and a  Swiss National Fund Ambizione grant  ( PZ00P3_174127 ). The  Institut d'Etudes de la Cognition  is supported financially by the  LabEx IEC  ( ANR-10-LABX-0087 IEC ) and the  IDEX PSL*  ( ANR-10-IDEX-0001-02   PSL*). The funding agencies did not influence the content of the manuscript.
We performed electrostatic 2D simulations using COMSOL FEM simulation software to calculate the electric potential for material relative permittivity in an ideal environment. Detailed assumptions and the simulation process are described in Supplementary Fig. 2. We define four layers comprising the electric/electronic system and the ELRG: the electric system (V 1 ), the first dielectric layer (dielectric loss layer), the second dielectric layer (liquid dielectric layer), and the ELRG electrode (V 2 ). We assume a hypothetical layer called an "energy loss layer," which has an identical amount of surface charge density with different signs ð σ þ h ¼ σ À h Þ, to focus on the effect of the energy loss transfer process. Through these logical assumptions, we can compare the effects of the ELRG system with and without the liquid dielectric layer.
c Peak power performance of TEG under load resistances. Inset pictures show that TEG power with ELRG lighten an LED more brightly. d Capacitor charging performances of TEG liquid dielectric material, which has high relative permittivity and other significant advantages, to transfer the electrostatic potential energy. The mechanism was demonstrated using a TEG, and electrostatic simulations were implemented using COMSOL software. We further demonstrated the use of the ELRG system in electronics, with everyday electronic devices, to confirm the practical feasibility of the approach. The results show the innovation of the method, and indicate the potential for improving overall energy systems by means of the following three salient features. First, if the ELRG is integrated with energy-generating methods, the returned energy loss can amplify the energyharvesting efficiency under the same input conditions.
Acknowledgements This work was supported by the  National Research Foundation of Korea (NRF)  grant funded by the  Korean government (MSIP ; No. 2016R1A4A1012950 ) and the  Nano-Material Technology Development Program  through the  NRF  funded by the  Ministry of Science, ICT and Future Planning  ( NRF-2016M3A7B4910532 ) and the  Korea Institute of Industrial Technology (KITECH)  as  432  " Research source technique project  ( EO-17-0020  and  EO-17-0027 )".
Environmental data are currently gaining more and more interest as they are required to understand global changes. In this context, sensor data are collected and stored in dedicated databases. Frameworks have been developed for this purpose and rely on standards, as for instance the Sensor Observation Service (SOS) provided by the Open GeoSpatial Consortium (OGC), where all measurements are bound to a so-called Feature of Interest (FoI). These databases are used to validate and test scientific hypotheses often formulated as correlations and causality between variables, as for instance the study of the correlations between environmental factors and chlorophyll levels in the global ocean. However, the hypotheses of the correlations to be tested are often difficult to formulate as the number of variables that the user can navigate through can be huge.
Environmental Data Environmental data are crucial to understand phenomena like the global change and impacts on our planet for many aspects of life, such as droughts, hurricanes, etc. Such environmental data are often collected by sensors. They are mainly represented by the following essential information: "Feature of Interest" (FoI), "date", "location", "phenomena" (observed properties), "procedure" (sensor), "measured values" and "units of measurements". This information is collected and stored in the so-called "Sensor Observation Service (SOS) model", as presented by Figure  1  and detailed later on in this article. Many actors intervene in the collection and the transition of the produced data to its use: researcher producers, data users and contracted producers. Note here that the users of the data can be non-human, for example external information systems via web services.
How does one make the data produced by another intelligible for a purpose (possibly, but not necessarily scientific) that is very precise that has sometimes conditioned all the harvesting protocols and the format of the data? To be able to preserve and effectively use environmental datasets, we must thus take into account the mandatory integration of standards that require the adoption of common implementation rules for metadata, data specifications and data sharing. It is then a question of relying on the currently-referenced standards such as the Open Geospatial Consortium (http://www. opengeospatial. org) (OGC). These standards are intended to facilitate the transfer of data between itself and other organizations and cover a wide range of data types. By enforcing standard terminology and the inclusion of minimal metadata, they contribute to the interoperability of systems.
Points of View When dealing with environmental real data, the data producers often work on their model with an oriented vision that corresponds to their point of view. This point of view is determined by both their background and goals. For instance, geographers and biologists do not have the same interests when they explore parts of the world, and their interests and points of view guide the decisions on the data they collect or not and on the way they organize the data, for instance in several files or databases, with some focus or some other ones. If we consider the SOS model presented above, even if the data collected are the same, the so-called "Features of Interest" (FoI) and observed properties may vary depending on the fact that the scientist is observing the "geographic region" or "the population of animals" living in the area.