dataesr/scientific-paragraphs-categorization huggingface dataset:
The logic trigger signal is also sent to a channel of TPC acquisition monitor. For every muon veto PMT, the digitized waveform has a length of 5. 12 µs around the trigger signal. The data are written to the central MongoDB database and stored in files in the same way as the TPC data. Raw data from the DAQ system are temporarily moved to a buffer-storage at LNGS by an undergroundto-aboveground connection using two 10 Gbps fibers. Subsequently, the data are automatically transferred  [68]  to dedicated storage points on the U. S. Open Science Grid (OSG)  [69]  and the European Grid Infrastructure (EGI)  [70]. The data are backed up in Stockholm. Data processing follows a similar philosophy and leverages the CI Connect service  [71]. It allows for a unique submission portal while granting access to shared resources on OSG and EGI (using HTCondor  [72]  and glideinWMS services  [73] ) as well as dedicated allocations on local clusters at the member institutions.
Slow Control System The various XENON1T subsystems and their instruments are operated, controlled and their status are monitored and recorded by a slow control system which is based on industry-standard process control hardware and software from General Electric (GE): Programmable Automation Controllers (PACs) for hardware and Cimplicity SCADA (Supervisory Control And Data Acquisition) for software. Alarm conditions (e. g. , parameter out of range, equipment failure, connection loss, etc. ) are notified by email, cellular phone SMS [75] and pre-recorded voice messages via a landline. The values of nearly 2500 parameters are stored in a GE Proficy Historian database, which offline analysis programs may query via a custom-developed Web API. The alarm notification, slow control viewer and offline analysis tool were custom-developed to complement the GE functionality.
Two redundant SCADA servers in active-passive failover mode connect to the PACs and OPC servers on the private front-end network. All supervisory and data storage elements, such as the Historian database, the alarm system, the slow control viewer as well as the central XENON1T control room in an aboveground building are connected to the private back-end network. Two dedicated, redundant fiber links connect the experiment underground with the aboveground laboratory. In case of failure of the laboratory network, the slow control system is directly connected to a backup network at a point outside of LNGS. For safety reasons, the entire slow control system is powered by a dedicated uninterruptable power supply with extended on-battery runtime and generator backup. The system is protected by a firewall and only authorized users have the possibility to perform operations beyond data access, according to their pre-defined role.
Acknowledgements We would like to thank  D. Bar ,  R. Berendes ,  G. Bourichter ,  G. Bucciarelli ,  S. Felzer ,  S. Form ,  B. Gramlich ,  M. Guerzoni ,  R. Hänni ,  R. Hofacker ,  G. Korga ,  A. Korporaal ,  P. Martella ,  R. Michinelli ,  G. Panella ,  M. Reissfelder ,  D. Stefanik ,  G. Tajiri ,  M. Tobia  and  J. Westermann  for their contributions to our experiment. We gratefully acknowledge support from the  National Science Foundation ,  Swiss National Science Foundation ,  Deutsche Forschungsgemeinschaft ,  Max Planck Gesellschaft ,  German Ministry for Education and Research, Netherlands Organisation for Scientific Research ,  Weizmann Institute of Science, I-CORE ,  Pazy-VATAT, Initial Training Network Invisibles (Marie Curie Actions ,  PITNGA-2011-289442 ),  Fundacao para a Ciencia e a Tecnologia ,  Region des Pays de la Loire, Knut and Alice Wallenberg Foundation ,  Kavli Foundation , and  Istituto Nazionale di Fisica Nucleare.
However, Axton's experimental database (Table  1  of  [8] ) contains many data not available in the EXFOR library  [9]. We have reviewed Axton's experimental database to identify the data missing in EXFOR and possible experimental corrections. The results are summarized in Table  1 , where The table shows that the same quantity is often not reported by the experimentalist (e. g. , absolute cross section in the literature converted to the cross section ratio by Axton, 2200 m/s cross section in the literature converted to the Maxwellian spectrum averaged cross section by Axton, prompt fission neutron multiplicity in the literature converted to the total fission neutron multiplicity by Axton). There are also many cases where the original value is corrected with a newer reference value by Axton (e.
g. , half-life adopted in sample mass determination, average fission neutron energy for fission neutron detector efficiency calibration). Such values corrected or derived from the literature values are often taken by Axton from Sjöstrand and Story  [10] , Lemmel  [3]  and Divadeenam  [11]  as noted by Axton in Appendix 4 of Ref. [7]. They are not for EXFOR compilation, but we plan to keep them in the "EXFOR Data Correction System" (a complement to the EXFOR database maintained by IAEA NDS) as they could be useful to trace corrections done by the experts.
000 M. J. Cabell,R,AERE-R-5874,1968 155 (σ a,1 g a,1 -σ a,9 g a,9 )/F1HLF 21410 (quantity not for EXFOR) 156 σ a,1 g a,1 /σ γ,1 g γ,1 F2HLF 21410 (quantity not for EXFOR) 157 σ γ,0 (quantity not for EXFOR) 158 σ γ,2 (quantity not for EXFOR) 159 g γ,9 (quantity not for EXFOR) 160 g γ,0 (quantity not for EXFOR) 161 g γ,1 (quantity not for EXFOR) 162 g γ,2 (quantity not for EXFOR) 163 g a,9 (quantity not for EXFOR) 164 g a,1 (quantity not for EXFOR) 165 η 9 /σ f,9 12793 (quantity not reported in the literature) 166 σ a,5 +σ sm,5 1. 000 12941. 004 R. R. Spencer,J,NSE,96,318,1987 167 σ a,9 +σ sm,9 0. 999 12941. 005 R. R. Spencer,J,NSE,96,318,1987 ID: the data point identification number in Axton's database. Quantity: the quantity in Axton's database. See text for details. X4/Axton: the ratio of the EXFOR value to Axton's value.
Methodology To monitor the effects of cyclone Nargis on the Ayeyarwady delta, we: collated information on the cyclone track; collected offshore wave and tidal data; downloaded data on surface-suspended particulate matter (SPM) concentrations offshore of the delta; mapped flooding of the coastal fringe and changes in the position of the high-water line alongshore. The cyclone track was determined from the UNOSAT cyclone database. Offshore wave data covering the landfall of the cyclone (2-4 May, 2008) were analysed from the ERA-Interim hindcast wave database generated by the European Centre for Medium-Range Weather Forecasting (ECMWF) Wave Atmospheric Model. Mean wave directions (8), significant wave heights (m), and periods (s) were obtained for the period 27 April to 1 June 2008. The astronomical tides off the delta for the period 1-8 May 2008 were downloaded from the tide and current prediction programme WXTide32 C for Yangon.
, 2006 ): E ¼ p ðd 1 2 þ d 2 2 Þ where d 1 and d 2 are the uncertainty estimates, respectively of resolution and georeferencing, for the successive sets of images. Finally, recently collated MERIS satellite data on suspended particulate matter (SPM) offshore of the Ayeyarwady delta (GlobCoast project, http://sextant. ifremer. fr/en/geoportail/sextant) were used to determine the impact of cyclone Nargis on nearshore fine-grained sediment spread in 2008 by comparing monthly values prior to, during, and after Nargis with averages over a 10year period  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012).
5a ). The data of 25 April 2010 showed even more significant landward translation (average: À148 m) of the high-water line 20 months after the passage of Nargis (Fig. 5b ). In order to determine to which extent flooding of the coast occurred in response to the increase in sea level caused jointly by the cyclone surge, the large waves and the spring tide range (Fig. 3 ), the18 April and 4 May highwater lines were compared (Fig. 6 ), the latter representing the situation barely 36 h after the cyclone landfall on 2 May 2008. The results show a landward translation of the highwater line that attained 600 m in places. The offshore concentrations in SPM in 2008 show a significant increase during the passage of Nargis, compared to the monthly averages based on the 10-year SPM dataset (Fig. 7a ). From January to April, the monthly values followed fairly closely the 10-year average trend.
Acknowledgements We thank  WWF Asia  for funding of the Project '' Recent shoreline changes and morpho-sedimentary dynamics of the Ayeyarwady River delta: assessing the impact of anthropogenic activities on delta shoreline stability '' via the  Helmsley Foundation. We thank  Associate Editors Isabelle Manighetti  and  Rutger de Wit , and two reviewers, among whom  Toru Tamura , for their salient suggestions for improvement of the manuscript. We are especially grateful to  Rutger de Wit  and an anonymous reviewer for their insightful comments on the analysis of the shoreline data. Appendix A. Supplementary material.
Geophysical data acquisition and processing High-resolution marine geophysical data were acquired during the BOBGEO2 expedition in 2010 and more significantly during the GAZCOGNE1 survey in 2013 covering 3200 km² of the seafloor at water depths ranging from 130 m to 1600 m (Fig. 2 ). During the GAZCOGNE1 survey, multibeam bathymetry, water column and seafloor backscatter and seismic reflection (sub-bottom profiler) data were acquired simultaneously. Multibeam data were collected onboard the R/V Le Suroît with a Kongsberg EM302 ship-borne multibeam echosounder operated at a frequency of 30 kHz with the celerity profile calibrated with ©Sippican shots. Seafloor multibeam data were processed through CARAIBES software (©IFREMER) with application of bathymetric filters and correction of position, pitch, roll and tide effects for raw bathymetric data and with the generation of a compensation curve to harmonize values along the survey lines for seafloor backscatter data.
A c c e p t e d v e r s i o n C R G during the GAZCOGNE1 marine expedition were processed in SonarScope software (©IFREMER) and then interpreted in GLOBE/3DViewer (©IFREMER)  (Dupré et al. , 2015). The sub-bottom profiles were recorded with the ship-borne sub-bottom profiler ECHOES 3500 ©T7iXblue emitting a linear frequency modulated signal, ranging from 1. 8 to 5. 3 kHz, with a vertical resolution of 10 cm and a maximum vertical penetration of 100 m. A 2D sub-bottom profiler insonifies a surface at the seafloor defined by the Fresnel equation and may record lateral reflexions from close-by 3D features, as well as artefacts. These artefacts may be displayed as diffraction hyperbola  (Dupré et al. , 2014b)  and triplication points, socalled "bow ties"  (Moss et al. , 2012). Raw data were processed with QC-SUBOP software (©IFREMER) before being exported in SEG-Y and then interpreted in ©Kingdom software (Fig.
3 ). The water current data were acquired during the ASPEX2010A mooring survey (Le  Boyer et al. , 2013)  with an Acoustic Doppler Current Profiler (ADCP) operated at a frequency of 75 kHz and recording every 2 minutes. The data discussed in this paper come from mooring 10 located at 44°00. 069'N -02°08. 644'W at 450 m water depth in the sediment wave field (Figs. 2a  and c ). Water current data were recorded over more than 6 months (18 th July 2009 -30 th January 2010). Current velocities were integrated between 17 m and 33 m above the seafloor and averaged every 20 minutes. Classic harmonic tide analyses were conducted on ASPEX current data to extract tide-related signals from the raw signal  (Lazure et al. , 2009).
A c c e p t e d v e r s i o n C R G Eleven morphological attributes were extracted from GIS for each pockmark: its area, perimeter, area/perimeter ratio, internal depth (from the rim down to the apex of the pockmark), minor and major axis lengths, major axis direction, elongation (major/minor axis length ratio), bathymetry, slope within the pockmark and morphological domain. The morphological attributes of the Aquitaine slope pockmarks are available online as a SEANOE public database with information on pockmark location and seafloor backscatter data  (Michel et al. , 2017).
A c c e p t e d v e r s i o n C R G column acoustic anomalies corresponding to gas bubbles were detected in the whole slope area. Although the temporal variability of seepage activity may be invoked, the 6 days of the acoustic survey are sufficient to cover the time window for the tidal cycle which could be a possible triggering mechanism  (Baltzer et al. , 2014). Thus, pockmarks along the Aquitaine slope are interpreted as currently inactive in terms of free gas seepage. Considering the sediment cover, methane-derived authigenic carbonates are considered as confident indicators of long-term gas circulation  (Bayon et al. , 2009). Outcrops and sub-outcrops of carbonate structures are easily detected on seafloor backscatter data as occurrence of high amplitude anomaly patches  (Dupré et al.
, 2010). Based on sub-bottom profiler data displayed in  Gonthier et al. (2006)  and in accordance with the seismic signature of pockmarks from our dataset, we suspected the occurrence of pockmarks within the recent sedimentary cover, which corresponds in the sediment wave field mainly to the so-called U4 unit  (Faugères et al. , 2002). This view is strengthened by the fact that above the pinch out of this unit U4 on the upper slope, roughly at water depth of 350 m, pockmarks are absent. This reinforces the shallow character (a few tens of metres maximum) of the Aquitaine slope pockmarks. The formation of the pockmarks appears therefore to postdate the sediment wave formation (U3 unit). Based on the age of the base of the 12-15 m thick U4 unit, which depends on the sediment rates, 10 cm/ky  (Winnock, 1973)  or 100 cm/ky  (Schmidt et al.
, 2014 (Schmidt et al. , , 2009)) , the pockmarks along the Aquitaine slope may have been initiated after 120-150 ky BP or 12-15 ky BP, respectively. Within this context, sea level falls may have triggered fluid escapes and initiation of pockmarks in the Aquitaine Basin as evidenced e. g. in the Gulf of Lions  (Riboulot et al. , 2014)  and offshore West Africa  (Plaza-Faverola et al. , 2011). But without any detailed seismic data and dating of long cores through the Aquitaine slope, it is impossible to conclude.
Thus, irrespective of the strain, ethanol homofermentation is the main (only) metabolic route when PFL is blocked, which is likely to proceed via PDC and ADHE (Fig. 1 ). The HP-inhibited cells showed the same fermentation trend during extended anoxia as compared with non-inhibited cells, i. e. fermentation by strain CC-124 stopped after 6 h but continued in strain 10-6C. The ethanol production by the latter strain almost doubled between 6 and 24 h, reaching concentrations of 1. 75 mM (Fig. 6 ). Altogether, our data indicated that the ADHEoveraccumulating strain exhibits an extended fermentative capacity compared with the wild-type strain, whether PFL is inactivated or not. Protein Levels in Dark Anoxia-Protein extracts from CC-124 and 10-6C cells exposed to dark anoxia were analyzed by SDS-PAGE.
1 ). We therefore examined the starch content in the cells analyzed above. As shown in Fig. 9A , the amounts of starch in strain CC-124 increased 3-4-fold when zinc was removed from medium, reaching up to 5-6 g starch/10 6 cells. The analysis of cell sections by electron microscopy confirmed the higher starch content in zinc-deficient cells relative to zinc-replete cells (Fig. 9B ). Of note, starch content in TAP-grown 10-6C cells was also found to be higher than in the reference CC-124 cells, with ϳ6 -7 g/10 6 cells (not shown). These data thus suggest that the higher fermentation capacity of zinc-deficient CC-124 cells   and strain 10-6C, relative to standard CC-124 cells, is a direct result of a higher content in C-reserves. The associated increase in ADHE levels may be then projected to facilitate the ethanol fermentation of starch over longer periods.
The nanoLC methods consisted of 25-and 120-min acetronitrile gradients in 0. 1% formic acid at a flow rate of 300 nl/min for, respectively, gel band and stacking analyses. MS and MS/MS (Top20) data were acquired using Xcalibur (Thermo Scientific) and processed automatically using Mascot Daemon software (version 2. 5; Matrix Science). Searches against the Phytozome database (version 9. 0, C. reinhardtii taxonomy) (61), a homemade classical contaminants database, and the corresponding reversed databases were performed using Mascot (version 2. 5. 1). ESI-TRAP was chosen as the instrument, trypsin/P was chosen as the enzyme, and two missed cleavages allowed. Precursor and fragment mass error tolerances were set, respectively, at 10 ppm and at 0. 6 Da. Peptide modifications allowed during the search were carbamidomethyl (C, fixed) acetyl (N-terminal, variable), and oxidation (M, variable).
The IRMa software (62) (version 1. 31. 1) was used to filter the results: conservation of rank 1 peptides, peptide identification false discovery rate Ͻ 1% (as calculated on peptide scores by employing the reverse database strategy), and minimum of 1 specific peptide/identified protein group. Dark Fermentation-500 -700 ml of cultures in their midexponential phase were harvested, washed in 10 -20 ml of filtered-sterilized anaerobic induction buffer (AIB) medium, and finally resuspended in sterile AIB medium to a cell concentration of ϳ1 ϫ 10 7 cells ml Ϫ1. 30 ml of cell suspension was transferred to a 60-ml glass serum bottle. Each bottle was closed with a butyl rubber stopper, wrapped in aluminum foil, degassed for 10 min, and then purged with argon for 15 min. The bottles were placed in an incubator at 24 °C and agitated at 125 rpm.
Digital acquisitions were made with a numeric camera (Eagle, FEI). For morphology, in preparation for high-pressure freezing, aliquots of the liquid cultures were transferred to 50-ml conical centrifuge tubes, spun at 500 ϫ g for 5 min, and then gently resuspended in the growth medium containing 150 mM mannitol  (66). Subsequently the cells were pelleted, high pressure frozen, freeze substituted, and embedded in LR White resin (hard grade), according to Ref. 67. Ultrathin sections were prepared with an ultracryomicrotome (EM UC7 Leica). Bioinformatic and Phylogenetic Analyses-Sequence alignments were done with the Muscle module as part of the MEGA 6 program (68) and manually adjusted to optimize alignments. The maximum likelihood phylogenetic tree was also produced using the MEGA 6 program.
Acknowledgments-We thank are due to  Dr. Göran Samuelsson  ( Umeå University ) for providing strain cia3,  Dr. Joanna Porankiewicz-Asplund  ( Agrisera ) for several useful insights regarding the antibodies,  Sabeeha Merchant  ( UCLA ) for the antibodies to ZCP2 and discussions on zinc deficiency at the early stage of this work,  Sandrine Bujaldon  ( IBPC ) for various discussions around the strains and for fluorescence measurements, and  Jean-François Sassi  ( Cité des Energies, CEA-Cadarache ) for the cultures in  2% CO 2.
* This work was supported by the  Centre National de la Recherche Scientifique  and partly supported by  Agence Nationale pour la Recherche  Grant  ANR-10-BIOE-0004  (to A. A. , R. V. L. , and M. P. ), and  ProFi Infrastructure  Grant  ANR-10-INBS-08-01  (to Y. C. ). The authors declare that they have no conflicts of interest with the contents of this article.
Acknowledgments The authors acknowledge all the partners who collaborated during this work. We thank the technical teams of SIAAP; those of the  Val-de-Marne General Council ;  Anne Chabas  of the  Interuniversity Laboratory of Atmospheric Systems (LISA) ;  Emmanuel Rinner  and  Kada Boukerma  of the  IFREMER Detection, Sensors , and  Measurements Laboratory ; and the  Bruker demonstrating laboratory in Champs-sur-Marne  and its staff. This work is part of a thesis funded by the  Paris research network on sustainable development (R2DS Ile-de-France ).
suggest using a simple wind correction to make to a wind U a to mimic the coupled response in an uncoupled oceanic model. Such a parameterization is based on the current-wind coupling coefficient s w estimated from a coupled simulation. A different parameterization could be based on a stress correction to make a τ that mimics the coupled surface stress response (i. e. , that includes the wind adjustment) in an uncoupled oceanic model, viz. , τ τ = +. τ s U (10) a o When forcing an ocean model, this correction could be applied on a prescribed surface stress or to a surface stress estimated using a bulk formulae and the absolute wind. We intend to investigate this further. Both datasets used in this study have limitations mainly due to their effective spatial resolution. There are eddies in the ocean scales smaller than can be resolved by the AVISO dataset (i.
| Paleoclimatic data We compiled paleoclimate data including SST and SL from ocean sediment cores archived in the National Climatic Data Center database for paleoclimatology of the National Oceanic and Atmospheric Administration. Many sediment cores have been extracted in the context of climate research program and record SST or SL at multiple locations (e. g. ,  Miller et al. , 2005). We selected sediment cores in the tropical to subtropical Indo-Pacific Ocean situated between latitudes of  35°N and 35°S (Figs S1 and S2). We retained data sets starting before the Holocene (i. e. , 15 ka before present, BP) and covering at least partially the Quaternary. We assembled 25 time series at a temporal resolution of 1,000 years. We performed Pearson correlations between time series to check for consistency.
We calculated the cumulative distance between a given cell and the closest cell suitable for coral reefs over the three million years divided by the number of times the cell was suitable over the same period  (Pellissier et al. , 2014). The lowest values represent area in proximity to stable areas while the highest represent locations with frequent local habitat collapse during the glacial periods. To compute geographic distances, we used the "costdistance" function in the package gdistance in R, to account for land masses as barriers to dispersal.
| Fish geographic distribution data Fish distribution information was extracted from about 500 references, covering 169 different locations, and then completed by the construction of the extent of occurrence map for each species  (Parravicini et al. , 2013; Pellissier et al. , 2014). We defined the extent of occurrence as the convex hull polygon of locations where each species was present, where each polygon was individually inspected in comparison with knowledge of reef fish species distribution. In order to avoid discontinuities to be merged, polygons were separated in multiple polygons when necessary. Cells containing at least 90% of land, those without coral reef habitat, and those with an original gap of information (few cells in northwestern Australia) were excluded. The data were set at 5° resolution (i.
| Biological traits data We investigated the relationships between the variance purely explained by isolation from stable areas in the model of each species and the corresponding life-history traits. We investigated body size, mobility, and habitat specialization. Maximum size of each species and home range size were extracted from the FishBase database  (Froese & Pauly, 2016). The home range size trait was coded using three ordered categories: sedentary (including territorial species), mobile within a reef, and mobile between reefs. We compiled a large literature on the interaction of fish species with the coral reef to produce a trait of habitat specialization. This trait is classified into three categories, if a fish species is specialized on coral reefs, or not, or show a specialized relationship with another organism living in or in proximity to the coral reef habitat (e.