dataesr/scientific-paragraphs-categorization huggingface dataset:
ncbi. nlm. nih. gov/BLAST/Blast. cgi). Only very similar sequences from Genbank were retained (98% or more). In total, twenty-two COI sequences were added to our dataset. Intra and inter-lineage genetic distance (COI and ITS2) calculations were based on K2P model, as implemented in MEGA 5. 1 software  [47]. On the illustrated COI tree, the sequences that were identical or diverged by less than 1% were combined, but all sequences of the dataset were taken into account for the genetic distance calculations. A median joining network of the ITS2 sequences was drawn using the software Network  [48]. Both site mutations and indels were equally weighted and all the structural mutations (insertions/deletions of motifs of more than 1 bp and the inversion) were treated as singlestep events. The Automatic Barcode Gap Discovery (ABGD) method for species delimitation  [49]  was applied in order to validate species delimitations based on COI distances.
The defaults values were used as given at ABGD website http://wwwabi. snv. jussieu. fr/public/abgd/abgdweb. html, except for the relative gap width, which was set at 1. 0. The sequences are accessible in the European Nucleotide Archive (ENA) at http://www. ebi. ac. uk/ena/data/view/LN810242-LN810426 for COI and at http://www. ebi. ac. uk/ena/data/ view/LN810169-LN810241 for ITS2. The accession numbers are provided as S2 Table.
Forty-one lineages are distinguished when considering the 10% threshold of genetic divergence. The application of the ABGD method confirms all these lineage delimitations, except for LC2 and LC3 that are regrouped. Among the 41 lineages, 26 can be associated to known species or genera. For seven out of these 26 lineages, no sequences existed in Genbank. These newly sequenced species are: Tubifex montanus Kowalewski, 1919, Limnodrilus claparedianus Ratzel, 1868, Limnodrilus udekemianus Claparède, 1862, Piguetiella blanci (Piguet, 1906), Eiseniella tetraedra (Savigny, 1826), Bothrioneurum vejdovskyanum Stolc, 1886 and Fridericia sp. The sequences of Fridericia spp. , Aulodrilus pluriseta (Piguet, 1906), Marionina argentea (Michaelsen, 1889), Lumbricillus rivalis Levinsen, 1884 and Enchytraeus buchholzi Vejdovsky, 1878 present in Genbank are different from the sequences obtained in our study.
The Genbank sequences of E. bucholzi and L. rivalis branch with our sequences of these species but the Genbank sequences of A. pluriseta and M. argentea do not branch with our sequences. In fact, Genbank sequence of M. argentea (GU 902092. 1) branches with Lumbriculidae, so can not correspond to M. argentea. The lineages T1, T2 and T3 seem to belong to the genus Potamothrix as they all branch with Potamothrix bavaricus. The sequences of these lineages branch with the common species of Potamothrix (P. hammoniensis, P. vejdovski, P. bedoti, P. moldaviensis and P. heuscheri) present in Genbank but are different (more than 10% of divergence). Four lineages were attributed to Tubifex tubifex (Müller, 1774) (lineage T10), Stylodrilus heringianus (lineage LL3), Helodrilus oculatus Hoffmeisteri, 1845 and Dendrodrilus rubidus (Savigny, 1826) on the basis of GenBank data but their identification could not be verified, either because of their immature state or due to lack of expertise for terrestrial species.
Lineages T9 and T10 (T. tubifex), lineages T18 and T16/T17 (L. hoffmeisteri) and lineages LC2 and LC3 (E. tetraedra) differ by only 1. 67, 2. 11 and 1. 76%, respectively. The highest intra-lineage variation of lineage T22 (L. claparedeanus) is 2. 12%. The network analysis (S1 Fig) presents the patterns of variations in lineages T9-T10, T14-T15-T16-T17-T18, LC2-LC3 and T19-T20-T22. It shows that a high number of mutations (more than 10) separate lineage T9 from lineage T10, lineage T18 from lineage T16/T17 and lineage LC2 from lineage LC3. These lineages can therefore be considered as distinct. In the    1. The numbers above the internal nodes correspond to bootstrap values of ML and NJ analyses; only those higher than 80% are indicated. The names of taxa comprise the number of isolate, the morphological identification and the accession number in the case of sequences from GenBank.
[21]  revealed the existence of at least six lineages within Nais communis / variabilis complex based on the analysis of COI and 16S genes and ITS region. Our study confirms the presence of cryptic species in Tubifex tubifex and Limnodrilus hoffmeisteri, as shown previously by the analysis of mitochondrial 16S rRNA  [22, 23, 53]. However, our data do not confirm the existence of cryptic lineages in Lumbriculus variegatus (Müller, 1774)  [24]  because only two specimens were examined. In Eiseniella tetraedra, several subspecies have been described based on morphological characters  [39] , but no molecular data were available. The two different lineages of this taxon reported in the present study could correspond to these subspecies. Differences between Genbank sequences provided for Enchytraeus buchholzi and our sequence of this species is not unexpected as E.
buchholzi is known to be a species complex  [54]. The other differences between Genbank sequences and our sequences could be explained by the existence of cryptic species within Lumbricillus rivalis, Aulodrilus pluriseta and the genus Potamothrix. As cryptic species can show differences in resistance to pollution, the distinction of cryptic species is particularly important for ecological studies and may help interpreting environmental conditions. Sturmbauer et al. [22]  showed that the cryptic species of Tubifex tubifex did not have all the same resistance to Cd. Tubifex tubifex and Lumbriculus variegatus are commonly used as test organisms in ecotoxicological studies. Due to the existence of cryptic diversity within these two species, Gustafsson et al. [24]  and Hallett et al. [55]  recommend to systematically identify these test organisms genetically before their use in ecotoxicological studies.
Our comparison of COI and ITS2 shows that both molecular markers provide a good taxonomic resolution of oligochaete diversity. However, the use of COI is much more convenient from a practical point of view. PCR amplifications of COI are easy and give positive products in nearly 95% of examined specimens. Although we used the universal COI primers  [42] , we do not detect any contamination and direct COI sequences were always of very good quality. The ITS primers used for oligochaete identification by Envall et al. [21]  and Achurra & Erséus  [27]  did not work for all species and the ITS2 primers we used amplified only some specimens. Moreover, the PCR products have to be cloned because of high polymorphism of ITS2 copies. The sampling was restricted to the Geneva area as our first aim was to create a COI barcode database for aquatic oligochaetes originated from Switzerland.
We observed that, for several species, our COI barcode sequences were similar to those obtained in other European countries (Sweden, Italy, Denmark) and in the North America. The scope of our work is probably not only regional but also international. To conclude, we confirm the usefulness of COI barcode for aquatic oligochaetes and the pertinence of the 10% threshold of divergence for delimiting species. We propose to use this threshold as a standard for biomonitoring studies, as at this level of divergence the number of conflicts with other molecular markers and morphological characters is relatively limited. The creation of a comprehensive reference database is the most important challenge for more general use of oligochaetes as bioindicators of aquatic ecosystems. With further development of high-throughput sequencing technologies applied to biomonitoring  [26, 56, 57] , the COIbased assessment of aquatic oligochaetes diversity may become a very useful tool for ecological diagnostics.
The routines are deployed on the new highly automated ESRF beamline MASSIF-1 and are reliant on well established technology to deliver a stable beam and new developments in sample handling (the RoboDiff sample changer and goniometer) developed at the ESRF (Nurizzo et al. , in preparation). In combination with the ESRF MX beamline environment, which includes automatic beam delivery  (Gabadinho et al. , 2010) , data processing and analysis  (Brockhauser et al. , 2012; Incardona et al. , 2009; Monaco et al. , 2013) , and a database (ISPyB) for sample tracking and data display  (Delageniere et al. , 2011) , the prospect of analysing many more samples than can be performed manually becomes possible, as the system runs unsupervised and presents data in an intuitive manner. With over 8000 user samples now Figure  1  Graphical overview of the automated characterization and data-collection process running on MASSIF-1.
All steps in the process are shown. The beamline-control GUI MXCuBE takes care of sample mounting/ unmounting and optical centring of the sample mount. Information about the samples, dewar tracking and the presentation of results are via the beamline LIMS system ISPyB. The software described here starts once a sample has been mounted and optically centred to the X-ray beam. processed, the system has been thoroughly tested and presents additional data from the consistent collection of information from multiple crystals from the same project. This allows the comparison of many parameters that are not usually recorded during experiments, such as exact crystal dimensions and diffraction variation within crystals  (Bowler & Bowler, 2014).
MASSIF-1 The automatic routine described here, and the modules such as X-ray centring, have been designed to run on any of the ESRF MX beamlines. However, it is only run as a dedicated service on the new highly automated beamline MASSIF-1. MASSIF-1 is a ESRF undulator beamline dedicated to the fully automatic characterization of and data collection from crystals of macromolecules. The beamline uses an artificial asymmetric Laue [110] diamond as a monochromator and a compound refractive lens (CRL) as the only focusing element. This simple optical setup produces a highly stable beam of 100 Â 65 mm (H Â V, FWHM) with a typical flux of 2 Â 10 12 photons s À1 at a fixed energy of 12. 8 keV, allowing structure solution by singlewavelength anomalous diffraction (SAD). The beamline is equipped with a sample changer that also acts as a diffractometer (RoboDiff), a high-capacity dewar able to store 240 SPINE standard samples and a PILATUS3 2M detector.
5). As the mesh is typically larger horizontally, and a delay is associated between measuring horizontal lines, the mesh is oversampled in the horizontal direction only. For each position, an image is acquired and processed using the program Dozor. The result of Dozor is a scalar value which estimates the diffraction signal of each image by combining an analysis of Bragg peaks with an intensity distribution as a function of resolution. Once all images have been processed by Dozor, the two-dimensional array containing the measured diffraction signal at each point is analysed (Fig. 4c ). The position of the best point is determined by first applying a threshold (50% of the maximum) to the signal and then performing a centre-of-mass calculation over any connected regions containing the strongest diffraction signal.
If this is the case, the characterization process is repeated with the new detector distance. In this way, optimal data are always collected. All data are subsequently processed automatically  (Monaco et al. , 2013)  using pipelines based on XDS  (Kabsch, 2010)  and, if a significant anomalous signal is detected, are also fed into SAD pipelines for structure determination. The results from all steps in the process are displayed in ISPyB. After collection of data is completed the workflow stops and the next sample can be mounted.
Pipelines and the diffraction plan The automation of processes necessarily involves standardization, such as the requirement for SPINE standard sample mounts. However, in order for an automatic data-collection service to be successful for a range of projects, considerable flexibility in data-collection parameters must be allowed. This has been implemented by combining different workflows with user input via the diffraction plan in the beamline database ISPyB. Here, information and specific data-collection requirements for each sample can be entered and pipelines for differing data-collection strategies can be selected 1). Where no entry is given, a default value is used (Table  1 ). In this way, the characterization and data collection can be tailored to each individual sample. Essential entries in the diffraction plan are the sample acronym (the protein being studied that has been approved by the ESRF safety group) and a unique sample name.
Acknowledgements The automatic processing of crystals presented here represents the culmination of decades of work both at the  ESRF  and many other sites and we are standing on the shoulders of giants. We thank the  ESRF-EMBL Joint Structural Biology Group (JSBG)  and the  ESRF Automation Task Force (ATF)  for supporting the developments. We also thank  Sandor Brockhauser  for initial contributions to the X-ray centring routine.
Due to the low level of light involved, the detector was a photon multiplier tube (PMT; Hamamatsu). The spot interrogation system used a field-programmable gate array (Xilinx Spartan-3 XC3S400-5PQ208C FPGA) to control the PMT and LED. Custom-made electronics (Sensoptics Ltd 1  , SGS 42000) were used with dedicated software (Sensoptics Photon Counter V1. 2) to record the fluorescence decay curve. A diagram of the hardware setup is shown in Fig. 1. The sensor spot (PreSens, non-invasive pH spot, SP-HP5-D7) was glued to a clear poly (methyl methacrylate) (PMMA) disc using silicone rubber glue (RS Silicone rubber glue, 692-542). Tests showed a negligible autofluorescence effect from the PMMA and glue. The set-up was left to dry for 2e3 days in the dark before being fastened using epoxy glue (Intertronic, OPT500149) to a polyether ether ketone (PEEK) head attached to the fibre optic cable (600 mm diameter multimode optical fibre, Thorlabs, 3.
Measurements of pH tot from the DIC/TA CRMs were undertaken at the halfway of each spot deployment on the cruise; two spots were used across the cruise. The spot was changed on 23/01/13. pH was calculated from certified values of DIC and TA using CO 2 SYS  [69]  (Matlab v2. 1) with carbon dissociation constants from  Roy (1993)   [70] , sulphate dissociation constants from  Dickson (1990)   [71]  and borate dissociation constants from Lee (2010)  [72]. The calculated pH from the DIC and TA CRM measurements was compared to the sensor-measured pH and any drift from the certified value was corrected using the ratio between the measured and the calculated value. This calculation has an estimated error of ±0. 0062, which is comparable to the current precision of the pH optode.
Multi-linear regression for fluorescent signal conversion to pH In order to convert the fluorescent signals obtained by the optode sensor to pH, a stepwise multi-linear regression was performed on data from the temperature and salinity investigations (n ¼ 120). The regression yielded Equation (  2 ), where T is temperature in C and S is salinity, and R is the ratio as determined from the optode output with the coefficients specified below. R ¼ 0:00034À0:17$pHþ0:15$S 2 þ0:0067$TÀ0:0084$S $1:075 The equation was derived for the pH range 7. 6e8. 3, a temperature range of 5e25 C, and a salinity range of 5e35. It has an adjusted r 2 of 0. 973 (n ¼ 120), a standard error of pH is 0. 005. The temperature and salinity data were obtained using a single spot. Differences between spots may occur during manufacturing process, which might create an offset when used with the above equation, but should not affect the temperature and salinity dependence determined here.
Cruise data Over 3000 data points were collected on a research cruise in the Southern Ocean, involving the use of two spots (~1500 data points each). The first spot was replaced during the cruise due to concerns of light exposure and consequent photo bleaching during  maintenance of another system coupled to the same underway seawater supply. Certified reference material was analysed at the halfway point of the run of each spot to correct for manufacturing offsets and drift as discussed above. The total drift of the spots measurements over the course of the cruise for both spots combined was 0. 06 pH units before correction using the CRM measurement. This drift exceeds the error in the calculated pH (±0. 0062) from the CRM values. The shipboard precision (0. 0074 pH, n ¼ 10) was comparable to ISFET sensors  [27].
The third, an advanced mission and operation research (AMOR) quadcopter, has never before been used under such conditions. In this paper, we first evaluate the RPAS data against the above-mentioned measurements. Second, we compare the SUMO to the new AMOR quadcopter for measuring temperature profiles up to 100 m above the sea ice. Our motivation for using the quadcopter is a more accurate sampling of fine-scale ABL structures, such as sharp temperature gradients, especially close to the surface in terms of surface-based inversions, since these are notoriously difficult to capture using fast ascending RPAS platforms like the SUMO, as will be demonstrated herein. This study should be regarded as proof-of-concept and the emphasis will be on evaluating the RPAS performance and describing challenges with operations in harsh, polar conditions.
Afterwards, a horizontal leg in the form of a rounded square was performed to calibrate the wind components before the slow descent began including several wind calibration legs at different altitudes. Note the safety altitude at 100 m in the flight pattern for SUMO and the corresponding one at 20 m for the quadcopter. These flight patterns were designed to obtain as much information as possible on the structure of the ABL, not particularly for comparisons of SUMO, MMAV and the quadcopter. An AMOR quadcopter was used in order to get more information from the lowest 100-m layer of the atmosphere. It is developed at the University of Applied Sciences Ostwestfalen-Lippe and utilizes a multi-level structure, enabling enhanced accuracy of the position and the sensor data (Du ¨nnermann & Wrenger 2011).
All meteorological parameters from the quadcopter were sampled at 10 Hz. The data acquisition system enables onboard data storage as well as continuous data monitoring on the ground control station during the flight. Carrying out flight missions with the quadcopter is possible in manual (stabilized), loiter and autonomous mode. During this campaign, the fully autonomous mode was used in all our flight missions, including a predefined flight scenario with automatic take-off and landing procedures. Supplementary Fig. S1  shows all three RPAS during operation.
Additional meteorological measurements Routine meteorological observations are performed by the Meteorological Observatory of RV Polarstern during all cruises (Ko ¨nig-  Langlo et al. 2006 ) and these data are also available for the present cruise (Ko ¨nig-Langlo 2013a). Standard synoptic observations were carried out every three hours (Ko ¨nig-Langlo 2013b). In addition, air temperature measurements were made at 29-m height and wind measurements at 39-m height, both with a oneminute interval. Upper air soundings with Vaisala radiosondes were carried out on a daily basis at around 11 UTC, providing profile measurements of pressure, temperature, relative humidity and the wind vector (Ko ¨nig-Langlo 2013c). All these data are available at www. pangaea. de/ search?q0Campaign:ANT-XXIX/6. Data from the airborne instruments and routine measurements were supplemented by meteorological observations from a small portable weather mast, deployed on the ice during the ice stations.
Air temperature was measured at the heights of 0. 1, 0. 5 and 2 m. Wind speed and direction were measured at the height of 2 m. The measurements were obtained using Aanderaa (Bergen, Norway) and MSR145 (MSR Electronics, Seuzach, Switzerland) sensors/data logging systems. Details about the airborne flight missions and weather mast measurements are given in Supplementary Table  S1.
A corresponding, but smaller warm bias is found in the SUMO descent profile. These systematic warm and cold biases are caused by the high ascent and descent rates of the SUMO aircraft (typically 2Á5 m s (1 ), combined with a relatively slow sensor response. Like in the ''raw'' SUMO profiles from 11 July, on this day we can see some irregularities. To compare the RPAS profiles against the ship-based measurements, all RPAS GPS height data points within 2995 m a. s. l. were identified and an average temperature was calculated for these data points. This was done separately for the RPAS ascent and descent profile parts. The comparison was made for simultaneous (92 min) ship and RPAS data. The near-surface estimates for the RPAS were calculated over a height interval of 0 m a. s. l. 95 m. Negative altitudes were included to account for smaller GPS height inaccuracies that were observed to occur occasionally in both RPAS.
Discussion The RPAS add a detailed picture of the vertical temperature profile of the ABL to the rare radiosonde data. The data reveal highly resolved structures, which cannot be captured with the rather coarse resolution of the soundings. A further advantage of RPAS is the flexibility to probe the ABL consecutively at short intervals, which allows retrieving the temporal development on the local scale. In summary, we find that the added value of the AMOR quadcopter in profiling the lower ABL depends on the vertical temperature gradient. Under neutral or close-to-neutral, near-surface temperature stratifications, SUMO and the quadcopter perform about equally well by judging how well their pairs of ascent and descent profiles match. Under strong temperature stratifications, however, the quadcopter reproduces more realistic nearsurface temperature profiles, with a generally closer match between ascent and descent profiles and with fewer profile irregularities (as described in the Results section) than SUMO.
This indicates the ongoing necessity for small and fast sensors, particularly when humidity measurements are considered. Also the wind measuring capabilities for AMOR and SUMO should be improved. Tests are currently being conducted in which both systems are fitted with probes for direct flow measurements and the preliminary results are promising. During the expedition, a unique and extensive data set on the vertical structure of the ABL was obtained, providing excellent opportunity for analyses of ABL processes as well as for validation and further improvement of weather and climate models.
Acknowledgements The work has been part of the  Antarctic Meteorology and its Interaction  with the Cryosphere and the  Ocean  project, funded by the  Academy of Finland  (contract no. 263918 ). The authors thank  Martin Mu  ¨ller and  Christian Lindenberg  for development and technical support on the SUMO system. The authors are grateful to  Burkhard Wrenger  and  Jens Du  ¨nnermann for providing the AMOR quadcopter and the training necessary to operate the system. The authors also thank  Mario Hoppmann  for providing the photographs. The authors acknowledge the  Alfred Wegener Institute  for the opportunity to participate in the ANT-XXIX/6 winter expedition and the RV Polarstern crew for assistance during the research cruise.
A search for the lepton flavour violating decay τ -→ µ -µ + µ -is performed with the LHCb experiment. The data sample corresponds to an integrated luminosity of 1. 0 fb -1 of proton-proton collisions at a centre-of-mass energy of 7 TeV and 2. 0 fb -1 at 8 TeV. No evidence is found for a signal, and a limit is set at 90% confidence level on the branching fraction, B(τ -→ µ -µ + µ -) < 4. 6 × 10 -8.