dataesr/scientific-paragraphs-categorization huggingface dataset:
Instrument description On 1 March 2002, the Environmental Satellite (ENVISAT) was launched into a Sun-synchronous polar orbit at about 800 km altitude. Among various other experiments the satellite contained the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). MIPAS is a limb-viewing FTIR emission spectrometer covering the mid-infrared spectral region between 685 and 2410 cm -1 (4. 1-14. 6 µm), which enables simultaneous observation of numerous trace gases (European Space Agency (ESA), 2000;  Fischer et al. , 2008). MIPAS data are available from June 2002 until April 2012, when the communication to ENVISAT was lost. MIPAS has been operated in two different measurement modes, the original high-resolution (HR) mode from June 2002 to April 2004 and in the reduced resolution (RR) mode since January 2005.
5 km for the upper part of the scan. MIPAS could be operated during day-time and night-time, and nominally produced 1000 and 1400 scans per day in HR and RR nominal mode, respectively. The level-1B radiance spectra used for retrieval are data version 5. 02/5. 06 (reprocessed data) provided by the ESA  (Nett et al. , 2002).
Retrieval method and error estimation Retrievals were performed with the common processor of the Institut für Meteorologie und Klimaforschung and the Instituto de Astrofísica de Andalucía (IMK/IAA). This processor uses the Karlsruhe Optimized and Precise Radiative Algorithm (KOPRA)  (Stiller, 2000)  for radiative transfer calculations and the Retrieval Control Program (RCP) of IMK/IAA for inverse modelling. Processing of MIPAS data at IMK has been described in, e. g. , von  Clarmann et al. (2003)  and  Höpfner et al. (2004). The presented OCS distributions are the most recent data versions, i. e. V5H_OCS_20 of the MIPAS HR mode and V5R_OCS_221/222 as well as V5R_OCS_120 of the MIPAS RR mode. The OCS retrieval at IMK is performed using 7 microwindows, which cover the spectral range 839-876 cm -1 of the fundamental ν 1 band.
To model the spectroscopic signature of OCS, line data of the 2009 update for OCS of the HI-TRAN 2008 database  (Rothman et al. , 2009)  were applied. This update is consistent with the OCS spectroscopy in the HITRAN 2012 release  (Rothman et al. , 2013). The spectral line shape of the interfering gases H 2 O, CO 2 , O 3 and NO 2 was modelled by means of the spectroscopy of the MI-PAS database, version 3. 2  (Flaud et al. , 2003). For simulation of the signature of the remaining interfering gases, the spectroscopy of the HITRAN database was used. Figure  1  shows the OCS signatures in the spectral range 839-876 cm -1 for a MIPAS scan obtained on 12 July 2009 at southern mid-latitudes (42. 8 • S, 18. 7 • E). The displayed radiances are the differences between model spectra resulting from the OCS retrieval and model spectra for the same, but OCS-free, atmosphere at tangent heights of 9.
The contribution of interfering gases, which had not been prefitted, was modelled using the profiles of the climatology by  Remedios et al. (2007). MIPAS data provide information on atmospheric OCS from the lower end of the profiles in the free troposphere up to about 40 km altitude. To illustrate the performance of a single scan retrieval, Fig. 2  (top left) shows an OCS profile obtained on 12 July 2009 at the same geolocation as in Fig. 1 , along with the measurement noise error. The profile exhibits OCS volume mixing ratios (VMRs) of 425-450 pptv in the troposphere and a strong decline in the stratosphere to values below 100 pptv above 30 km altitude. For altitudes between 10 and 19 km the rows of the respective averaging kernels (AKs) are symmetrical to their middle value (Fig. 2 , top right), which means that in this height region the OCS signal results from the nominal altitudes.
Comparison with ACE-FTS Another global OCS data set was derived from spaceborne measurements of the ACE-FTS experiment on SCISAT  (Bernath et al. , 2005; Boone et al. , 2005). Different to the MIPAS retrieval setup, the region 2039-2057. 5 cm -1 is used for ACE-FTS retrievals of OCS  (Hughes et al. , 2016) , and the line list of the HITRAN 2004 database  (Rothman et al. , 2005)  is applied. In Fig. 5  we show an intercomparison of mean OCS profiles of coincident MIPAS and ACE-FTS observations. The ACE-FTS profiles taken into account are data version v3. 5  (Boone et al. , 2013). The comparison is based on 9452 ACE-FTS and 19990 MIPAS profiles of the time period February 2004 to April 2012. For each ACE-FTS profile all MIPAS profiles within a maximum temporal offset of 5 h and a maximum spatial distance of 500 km were taken into account.
Caused by contamination by polar stratospheric clouds, the number of data points can also be considerably reduced inside the polar vortices. The data gap during most of the year 2004 results from the operational shutdown between MIPAS HR and RR measurement periods. At most altitudes there is no distinct bias between the OCS amounts of the HR and of the RR period, indicating consistent OCS amounts over the whole operational period. However, at 14 km the OCS amounts of the RR period are significantly higher (by ∼ 25 pptv in the tropics, by ∼ 50 pptv at mid-and high latitudes) than those of the HR period. A closer look shows that the period of higher OCS amounts does not coincide with the beginning of the RR mode immediately after the data gap, but starts some months later in June 2005. Another slight increase occurs in 2006, just as at 18 km altitude.
The profiles are seasonal averages of the whole OCS data set. Up to 13-15 km the mixing ratios of the tropical and mid-latitude profiles vary only a little between about 490 and 530 pptv. The respective variation at high latitudes is somewhat larger. In the stratosphere the profiles strongly decrease to below 100 pptv around 20 km at high latitudes and at 30 km in the tropics. The largest seasonal variations occur in the Arctic and Antarctic lower stratosphere, reflecting subsidence of OCS-poor air masses in the polar vortices during winter and spring and the recovery during summer and fall. The subsidence is stronger and continues until austral spring in the Antarctic vortex. The seasonal variations in the troposphere are generally larger in the Northern Hemisphere. In the northern tropics and subtropics the OCS amounts between 7 and 12 km are 510-530 pptv in boreal spring and summer and about 20-30 pptv lower during boreal fall and winter.
Annual means Since OCS is assumed to be relatively well mixed in the troposphere, we compared the latitudinal variation of MIPAS OCS amounts at 8 km altitude with ground-based flask data from the NOAA ESRL network, taken from the website at http://www. esrl. noaa. gov/gmd/hats/gases/OCS. html. The result of the comparison is shown in Fig. 8 , where both data sets are averaged over the whole measurement period of MI-PAS. This kind of temporal averaging eliminates a potential bias due to the time required for vertical transport of seasonal variations to the upper troposphere. Between southern mid-latitudes and northern subtropics there are differences of 10 pptv or less. The largest deviations occur at two groundbased stations at northern mid-latitudes, where the OCS amounts are lower by 50 pptv.
Seasonal variations Figure  9  (left column) shows latitude-height cross sections of MIPAS OCS VMRs measured in winter, spring, summer and fall. The seasonal data sets are averaged over the whole measurement period of  MIPAS (2002 MIPAS ( -2012)). Averaging was performed for 7. 5 • × 1 km latitude-altitude bins at the poles and 5 • ×1 km bins elsewhere. Above 10 km altitude the averaged MIPAS data are generally based on 10 000-15 000 values. Due to cloud contamination and to the upward shift of the MIPAS RR mode scans towards low latitudes, increasingly fewer data points could be binned at 10 km and below, e. g. only some dozens or even less than 10 values at 7 km altitude in the tropics. Because of the long lifetime of OCS, the latitude-height cross sections of measured OCS appear to be rather similar through all seasons.
During boreal summer, signatures of biomass burning are generally detectable inside the Asian monsoon anticyclone (AMA), over central and western Africa and the north-western subtropical Pacific. Again, this is reflected in enhanced HCN amounts detected by MIPAS in these regions during June to  August 2002 -2011 (Fig. 11, bottom left). However, the distribution of enhanced OCS amounts observed by MIPAS during boreal summer appears rather different (Fig. 11 , top left). It is much more widespread over the tropics and subtropics and apparently has strong sources around Indonesia and in the western Pacific. For a more quantitative estimation we performed correlation analyses between upper tropospheric MIPAS OCS and MIPAS HCN for different years, seasons and latitude bands. We checked the whole OCS data set measured by MIPAS for potential signatures of biomass burning, but found neither strong correlations with, e.
Meridional transport around the AMA is corroborated by, e. g. , the ozone distribution measured by MIPAS (bottom row), which in the UTLS region exhibits a tongue of enhanced values reaching from the western Pacific to South Asia and northern Africa. A similar structure has also been found in ozone data of the Atmospheric Infrared Sounder (AIRS) averaged over July and August 2003  (Randel and Park, 2006, Fig. 1d ). Further, this transport process was modelled by, e. g. ,  Ploeger et al. (2012)  by back trajectory calculations. The major part of modelled southward transport into the tropical latitude band occurred during August to September at potential temperature levels of 420-450 K (∼ 16. 5-18 km), which is in good agreement with our observations. A weaker, but similar feature of meridional OCS transport is also visible over Central America.
Conclusions We have presented a long-term spaceborne global data set of MIPAS OCS covering the period from June 2002 to April 2012, providing information on one of the major sources of atmospheric sulfur. This data set can be used to constrain future model runs and thus can help to improve the understanding of the global sulfur cycle. In fact, a paper on this topic, also using MIPAS OCS and SO 2 data, is in preparation  (Deshler, personal communication, 2014). The measurements were compared with balloon profiles, ACE-FTS data and with ground-based measurements of the NOAA ESRL network. No systematic bias was detected with respect to balloon profiles, but MIPAS OCS amounts at 13-16 km altitude are up to 100 pptv higher than coincident ACE-FTS values. The reason for this discrepancy is an open issue.
Time series of MIPAS OCS data show annual variations in the troposphere and a rather biennial cycle in the tropical stratosphere. Interannual variations are only moderate, indicating a closed budget of tropospheric OCS sources and sinks. This is confirmed by a dedicated trend analysis, which resulted in no significant trends in the upper troposphere. In the stratosphere, a large area of strong negative trends was detected in the Northern Hemisphere, accompanied by an area of positive trends in the Southern Hemisphere. This pattern indicates a change in the Brewer-Dobson circulation. Zonal averages of tropospheric OCS amounts observed by MIPAS range from below 470 pptv at high latitudes to 540 pptv in the tropics and at northern mid-latitudes, but show only moderate seasonal variations. These features are in reasonably good agreement with EMAC model simulations.
This depletion obviously is caused by vegetative uptake in the tropical forests. Typical features of biomass burning like the southern hemispheric plume are not visible in MIPAS data, indicating that biomass burning is only a minor source of atmo-spheric OCS. The enhanced amounts of OCS observed in the AMA instead result from oceanic and industrial emissions. The OCS distributions at 80 hPa altitude reflect equatorward transport of mid-latitude air masses around summer anticyclones prevailing in the upper troposphere. This process is most distinct at the western and southern edge of the AMA, but is also observable in the western hemisphere, where air masses deleted in OCS are carried towards Central America. 6 Data availability MIPAS data are available from http://www. imk-asf. kit. edu/english/308.
php (upon registration) and from the corresponding author (Norbert. Glatthor@kit. edu). The ACE-FTS data used in this study can be obtained upon registration via the website https://databace. scisat. ca or from Kaley A. Walker (kaley. walker@utoronto. ca. ). MkIV balloon data are available at the website http: //mark4sun. jpl. nasa. gov/m4data. html. SPIRALE data are available from Gisèle Krysztofiak (gisele. krysztofiak@cnrsorleans. fr). EMAC OCS-data are available from Gisèle Krysztofiak (gisele. krysztofiak@cnrs-orleans. fr) or from the corresponding author (Norbert. Glatthor@kit. edu).
Acknowledgements. Part of this work has been funded by the  Federal Ministry for Economic Affairs and Energy  under contract number  50EE1547. The authors would like to thank the  European Space Agency  for giving access to MIPAS level-1 data. Meteorological analysis data have been provided by  ECMWF. We acknowledge support by the  Deutsche Forschungsgemeinschaft and Open-Access Publishing Fund of the Karlsruhe Institute of Technology. The  Atmospheric Chemistry Experiment (ACE) , also known as SCISAT, is a Canadian-led mission mainly supported by the  Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. We acknowledge the provision of MkIV and SPIRALE balloon profiles. OCS flask data were provided from the  Global Monitoring Division of the Nationals Oceanic and Atmospheric Administration's Earth System Research Laboratory (NOAA ESRL/GMD ).
Results The phenotypic landscape of the E. coli collection We have assembled a large genetic reference panel of 894 E. coli strains able to capture the genetic and phenotypic diversity of the species. These are broadly divided into natural isolates (N = 527) and strains derived from evolution experiments (N = 367). Out of these, 321 had available genome sequences, and we obtained the genomes of additional 375, reaching a total of 696 strains. The full list of strains, including name, collection of origin and links to relevant references is provided in Supplementary file 1 and online at https://evocellnet. github. io/ecoref. To test our capacity to develop genotype-to-phenotype predictive models we measured the fitness of this strain collection on a large variety of conditions (N = 214). We used high-density colony arrays and measured colony size as a proxy for fitness, similar to what has been applied before for the E.
Phylogenetics Strains phylogenetic tree was computed using a single ParSNP  (Treangen et al. , 2014)  analysis, which generates a whole genome nucleotide alignment across all strains, which is then used as an input for FastTree  (Price et al. , 2010)  version 2. 1. 7. The tree was visualized using the ete3 library (Huerta-Cepas et al. , 2016a) version 3. 0. 0. Phylogenetic distances between pairs of strains has been computed using phylogenetic independent contrasts, in order to account for phylogenetic interdependencies between strains  (Felsenstein, 1985). In short, we have first restricted the phenotypic data to those strains and conditions with less than 10% missing data; we then imputed the rest using the average S-score for each strain, using the scikit-learn library  (Pedregosa et al.
Computation of all possible mutations and their effect The impact of all possible nonsynonymous substitutions on the reference individual has been precomputed to speed up the lookup process. Functional impact of nonsynonymous substitutions has been computed using SIFT  (Ng and Henikoff, 2001)  version 5. 1. 1, using uniref50 as a sequence search database. Structural impact of nonsynonymous substitutions has been computed using FoldX  (Guerois et al. , 2002)  version 4; both 3-D structures present in the PDB database and homology models were used. Homology models were created using ModPipe  (Pieper et al. , 2014)  version 2. 2. 0. Conversion from PDB to Uniprot residues coordinates was derived from the SIFTS  (Velankar et al. , 2013)  database. PDB structures were 'repaired' to fix residues' torsion angles and Van der Waals clashes before computing the impact of non-synonymous variants on structural stability.
Computation of the disruption score For each strain and each protein coding genes we have computed the overall impact of all nonsynonymous and nonsense substitutions, in a similar approach as the one used for Saccharomyces cerevisiae  (Jelier et al. , 2011). The output of each predictor (a deleterious probability for SIFT and a DDG value for FoldX) has been converted to the probability of the substitution being neutral pðneutralÞ. Mutations with known impact on the reference individual have been downloaded from Uniprot (UniProt Consortium, 2015, N = 3673) and used to derive such conversion; since only 580 mutations are reported to have a neutral impact, we added all observed nonsynonymous variants affecting known essential genes (as reported in the OGEE database,  Chen et al. , 2017)  to the list of tolerated mutations.
Use of the disruption score as a functional association predictor We used the proteins pairwise Pearson's correlation of disruption scores as a predictor of genes functional associations. We used four benchmarking sets: operons, as derived from the DOOR database  (Mao et al. , 2014) , protein complexes and pathways derived from the EcoCyc database  (Keseler et al. , 2013) , and protein-protein interactions derived from a recent yeast two-hybrid experiment  (Rajagopala et al. , 2014). The distribution of the disruption score correlation for each pair of related genes was compared against the same number of gene pairs randomly drawn from all reference genes. We also assessed the predictive power of the disruption score correlations by drawing a receiving operator characteristics curve (ROC) across each correlation threshold, using the scikit-learn library  (Pedregosa et al.
Code, data and strains collection availability New genomic sequences have been deposited at the European Nucleotide Archive (ENA) with accession number PRJEB20550. The source code used to perform the analysis reported here and generate the figures is available as Source code 1 and at the following URLs: https://github. com/mgalardini/screenings, https:// github. com/mgalardini/pangenome_variation, https://github. com/mgalardini/ecopredict (copies archived at https://github. com/elifesciences-publications/screenings, https://github. com/elifesciences-publications/pangenome_variation and https://github. com/elifesciences-publications/ecopredict). Code is mostly based on the Python programming language, and using the following libraries: Numpy  (Vanvan Derder Walt et al. , 2011)  version 1. 10.
4, SciPy version 0. 17. 0, Pandas (McKinney, 2010) version 0. 18. 0, Biopython  (Cock et al. , 2009)  version 1. 68, scikit-learn  (Pedregosa et al. , 2011 ) version 0. 17. 1, fastcluster (Mu ¨llner, 2013 ) version 1. 1. 20, statsmodels version 0. 6. 1, PyVCF version 0. 6. 8, ete3 (Huerta-Cepas et al. , 2016a)  version 3. 0. 0, Matplotlib  (Hunter, 2007)  version 1. 5. 1, Seaborn  (Waskom et al. , 2016)  version 0. 7. 1 and svgutils version 0. 2. 0. Genomic and phenotypic data, as well as relevant information on how to obtain the members of the strain collection is available at the following URL: https://evocellnet. github. io/ecoref. Vladimir Benes for the support in genome sequencing. We thank Ewan Birney, Oliver Stegle, KC Huang and Zam Iqbal for critical reading of the manuscript. This work was partially supported by the Sofja Kovaleskaja Award of the Alexander von Humboldt Foundation to ATy and a grant from the Fondation pour la Recherche Medicale (Equipe FRM 2016, DEQ20161136698) to ED.
'Other complementation' reports strains expressing a different gene than the focal one, indicated between parenthesis. (G) Cartoon representation of AcrB 3D structure (PDB entry: 2dr6). Only one of the three monomers is highlighted in blue; colored spheres represent known non-synonymous variants in the reported strains. DOI: https://doi. org/10. 7554/eLife. 31035. 010 The following figure supplement is available for figure 5: Figure supplement 1. Detailed view of the experimental confirmation of the predicted phenotype-causing genotypes. DOI: https://doi. org/10. 7554/eLife. 31035. 011.
Acknowledgements We are particularly grateful to the various people providing us with many of the strains of this E. coli genetic reference panel, specifically (in alphabetical order):  Alfredo G Torres ,  Catharina Svanborg ,  David Clarke ,  Erick Denamur ,  Ewa Bok  and  Pawel Pusz ,  Isabel Gordo  and  Lilia Perfeito ,  Jorg Weinreich  and  Peter Schierack ,  KC Huang ,  Lisa Nolan ,  Mark Goulian ,  Mathew Upton ,  Olin Silander ,  Richard Lenski ,  Scott Hultgren  and  Wanderley Dias da Silveira. We thank  Amanda Miguel  for helping in the phenotypic screen. We also thank the  EMBL Gene Core , and especially  Rajna Hercog  and.
Methodology To analyze the temporal and spatial evolution of the Mexico City subsidence phenomena, a set of 38 SAR images from the ENVISAT satellite, provided by the European Space Agency and covering the 20021122 to 20070316 time span, were acquired and processed. A 3-arc second sampled, void filled, Digital Elevation Model from the Shuttle Radar Topography Mission  (Farr and Kobrick, 2000) , was used to remove topography from the interferograms. We performed an SBAS processing chain, selecting interferograms having less than 9 months of temporal baseline and less than 500 m of perpendicular baseline. The total number of calculated interferograms was 71. All of them were processed using the JPL-CalTech Repeat Orbit Interferometry Package (ROI_PAC)  (Rosen et al. , 2004). A dedicated algorithm was then applied to solve the unwrapping problems of some of the interferograms; including the use of a stack of interferograms and the atmospheric correction of the vertically stratified tropospheric phase  (Cavalié et al.
Software and map design Information technologies enable the development of online, real-time, multi-scale, interoperable, open and collaborative monitoring systems for different geospatial products. The dynamic and animated visualization of subsidence and similar information from the geography and geosciences areas, allows to visualize its evolution over time, in order to determine historical trends and possible scenarios of vulnerability and risk, revealing the value of this kind of tools for the scientific knowledge production at international scale. Open source Geographic Resources Analysis Support System (GRASS GIS), was used to format the 38 cumulated subsidence rasters. ArcGIS by ESRI, was also used to create the project containing the corresponding 38 rasters layers and its related color scale.
Geoserver and Geonode were used for the publication of online geospatial data. The geo-visualizer was developed with OpenLayers library and PHP programming Language. All this software is open source. Maximum cumulated subsidence occurs in the Nezahualcoyotl (1. 66 m) and Tlahuac (1. 33 m) zones, represented with red triangles N and T, respectively. Lámina 1. Mapa de la subsidencia acumulada para la fecha 20070316, esto es, subsidencia total acumulada en el periodo comprendido entre 20021122 y 20070316 (4 años con 4 meses). Las áreas con subsidencia acumulada negativa o igual a cero están representadas con el color gris. La escala de color se repite cada 0. 5 m. Cada curva o contorno de color, representa un área con la misma subsidencia acumulada (curva de isodeformación). La máxima subsidencia acumulada ocurre en las zonas de Nezahualcóyotl (1.