dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We wish to thank  Clinical Malaria Research Unit, Faculty of Tropical Medicine, Mahidol University  for providing stored blood samples. We thank  Somporn Krasaesub ,  Department of Administration, Armed Forces Research Institute of Medical Sciences (AFRIMS) ,  Bangkok ,  Thailand  for providing suggestions for data analysis. This study is supported by the  Royal Golden Jubilee Ph. D. Program (RGJ) of the Thailand Research Fund , Grant No. PHD/0095/2554 ; the  Office of Higher Education Commission, Ministry of Education, Thailand ,  Mahidol University  under the National Research Universities Initiative; and the  Faculty of Tropical Medicine, Mahidol University.
Spontaneous (8 s period before the stimulus) and sensory-evoked activity was recorded for 1 h before and 2-3 h after the injection of 50-μl fluoxetine solution in normal saline prepared from a 1 % stock solution of fluoxetine hydrochloride (Sigma) at a dose of 10-120 mg/kg. Sensoryevoked responses within 15 min recording periods before and 2 h after fluoxetine administration were analyzed in each animal. The signals were amplified and filtered (×10,000; 0. 1 Hz-10 kHz) using a Digital Lynx (Neuralynx, USA) amplifier, digitized at 32 kHz and saved on a PC for a post hoc analysis using customwritten functions in Matlab (MathWorks, USA) as described previously  [4, 5]. The statistical analysis was performed using the Matlab Statistics toolbox. The statistical comparisons between the groups were performed using the paired sample one tailed Wilcoxon signedrank test.
Preceding Diet at Baseline The NB study will be preceded by an outpatient diet stabilization period lasting 7 days. During this time, a meal plan will be tailored to each participant and PS will be provided, based on the individual's preferences, to promote adherence. Participants will be asked to complete a 7-day food record during this outpatient period, and will be contacted by phone one time during this period in order to evaluate adherence to, and tolerance of, the prescribed diet. This diet stabilization period is designed to allow each participant to adapt to the required level of PS  [55]. Energy and protein requirements will be based on 35 kilocalorie (kcal)/IBW per day and 1. 2 g protein/IBW per day, respectively  [56] , and will be calculated using the Nutrition Data System for Research (NDSR), 2011  [57].
Statistical Analyses Data will be analyzed using the SPSS statistical program (IBM Software; Armonk, NY). For most study aims, a repeated measures design will be used to examine the trajectory of changes in subjects between HPS-G and SPS-G groups regarding BC, EE, NB, satiety, and circulating BCAAs, from presurgery to 3-month and 12-month postsurgery levels. Nonlinear mixed model regression (SAS PROC NLMIXED) will be used for the actual analyses since these outcomes are not likely to be normally distributed. In addition to overall tests for differences between groups (GBP and SG, and HPS-G vs SPS-G), differences over time (presurgery, 3 months, and 12 months postsurgery), and group per time interactions. Other factors will be explored as covariates to determine possible explanatory factors for significant differences between presurgery and 3-month and 12-month postsurgery levels.
Acknowledgments This project is supported by the  National Center for Advancing Translational Sciences , and the  National Institutes of Health  (Grant Number  UL1 TR000040 ), formerly the  National Center for Research Resources  (Grant Number  UL1 RR024156 ). VM is funded by the  Marie Curie International Outgoing Fellowships for Career Development  ( FP7-PEOPLE-2012-IOF , Proposal # 326342 ).
This patient with a focal injury (labelled VSF) actually suffered from a limited brainstem lesion, like the LIS patients, although his diagnosis was definitely VS with no sign of consciousness. Because of his cortex was preserved, this patient was considered separately. Therefore, 12 patients in VS with diffuse lesions (mean age, 54. 2 years; range, 21-87 years; seven females), 1 VSF patient (age, 40 years; male), 5 patients in LIS (mean age, 49. 0 years; range, 37-70; one female, and 19 healthy control (CTRL) participants (mean age, 30. 9 years; range, 19-51; five females) were included in the study. Controls had no history of neurological or psychiatric disorders. Demographic and clinical data are presented in Table  1. A LIS diagnosis required concordant assessment of preserved consciousness between the rehabilitation unit staff and the patient's relatives.
Data acquisition Four hundred and five whole-brain T2*-weighted echo planar images were acquired interleaved on a Siemens Magnetom® Avanto 1. 5T (Siemens, Erlangen, Germany) with the following session parameters: TR = 3 s; flip angle = 90°; TE = 43 ms; FOV = 256 mm × 256 mm × 128 mm; Imaging matrix = 64 × 64 × 32; 4mm 3 isotropic voxels, with fat saturation preparation, leading to a total acquisition time lasting about 20 min. A 3D MPRAGE T1-weighted image was also acquired at the same session (1-mm 3 isotropic voxels).
Data preprocessing After conversion to Nifti format, the images were preprocessed using Statistical Parametric Mapping software v8 (Welcome Department of Cognitive Neurology, London, UK) working on Matlab R2012b (The MathWorks, Inc. , Sherborn, MA, USA). For each participant, the first five images were removed to account for T1 partial saturation and the 400 remaining images were then motion corrected. One participant had translation or rotation N1. 5 mm or 1. 5°and was consequently removed from the analysis.
Connectivity analysis For each participant, a single-subject ICA was performed using FMRLAB software 2. 3 (Swartz Center for Computational Neuroscience, University of San Diego, San Diego, CA, USA), modified to work on Nifti format, with an implementation of the INFOMAX algorithm  (Bell and Sejnowski, 1995). Dimensions were reduced from 400 to 250 by principal component analysis before running ICA. From the whole set of 250 independent components (each one is a z-score 3D map, thresholded at ±1. 5 for display purposes), the SAMs were manually selected by an expert (DR) according to validated operationalised criteria  (Roquet et al. , 2014). All SAMs were further classified according to a simplified version of the  Kalcher et al. proposal (Kalcher et al. , 2012)  based on the individual ICAs of 1000 healthy controls.
Data Recording and Statistical Analysis Gait speed (m. s -1 ) was collected on a 4. 5 m distance according to  Fried et al. (2001). All EMG signals were preamplified at the source (VICON R , Oxford Metrics Group) and were recorded at a frequency of 2000 Hz. Raw EMG signals were first bandpass filtered between 5 and 400 Hz and then full-wave rectified and filtered using a no-lag averaging moving-window algorithm (window size: 10 ms). As previously defined by  Fautrelle et al. (2010) , in this study, we determined the initiation time as the delay between illumination of the diode (the "go-signal") and the beginning of significant muscle activation  (Fautrelle et al. , 2010). To do this for the 11 recorded muscles, the EMG values after the illumination of the diode and the EMG baselines were compared for each value using t-tests for each muscle in each group.
Muscle Activation Timing between Groups To clarify the results and highlight the potential differences between groups, muscle synergies were studied, with data for YA subjects, considered optimal, as the reference. Figure  2  shows the raw EMG data obtained for 20 trials for a typical subject of each group. Figure  3  shows the scatter of the overall dataset of the timing of muscle activations (all the trials of all participants in the three experimental groups). In the YA group, 3 muscles participated in the APA in the following order: lESl3, lESd7, and rBF. The ANOVA showed a main Group effect for several muscles: Comparing the timing of muscle activation between YA and OA groups, we found 1 significant difference: the activation timing of the rBF (p = 0. 008), was delayed for the OA group compared with the YA group.
Muscle Activation Timing and Clinical Data in the MCI Group The ADS was calculated to highlight potential links between muscle activation timings and clinical data in MCI patients, in milliseconds (ms), between MCI and OA subjects, for each of the 2 muscles showing significant differences in their activation timing (see Section Muscle Activation Timing between Groups and Figure  4A ): the lESl3 and the lOI. Please note here that the decision was made to exclude the lRF muscle from this analysis, as its timing was also modified in the MCI group compared to the OA group, because this muscle activation presented an important variability (see on Figure  3  lRF' graph, and Figure  4A ) and a poor activation rate (see on Figure  4B ). A cumulative score of these differences (Total ADS) was calculated by summing the 2 ADS scores in absolute value.
Measuring temperature and salinity profiles in the world's oceans is crucial to understanding ocean dynamics and its influence on the heat budget, the water cycle, the marine environment and on our climate. Since 1983 the German research vessel and icebreaker Polarstern has been the platform of numerous CTD (conductivity, temperature, depth instrument) deployments in the Arctic and the Antarctic. We report on a unique data collection spanning 33 years of polar CTD data. In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts are now freely available at doi:10. 1594/PANGAEA. 860066. During this long period five CTD types with different characteristics and accuracies have been used. Therefore the instruments and processing procedures (sensor calibration, data validation, etc.
For example, in 1974 Neil Brown formed Neil Brown Instrument Systems, Inc. and manufactured the Mark III CTD 1   (Brown, 1991). R/V Polarstern is a research icebreaker operated by the Alfred Wegener Institute (AWI) in Bremerhaven (Germany), which has operated since 1982 in Antarctica (austral summer) and the Arctic (northern summer)  2. The first CTD used on Polarstern was the aforementioned Neil Brown, Mark IIIB CTD. It was deployed for the first time on cruise leg ANT-II/3, during the ship's second trip to Antarctica in November/December 1983. This giant step for AWI oceanographers, which was supervised by Gerd  Rohardt (Rohardt, 2010a) , ended abruptly when that same probe accidentally "flew" overboard a month later during ANT-II/4. Despite many efforts to regain it, the probe was lost, which is why the respective data set of this leg  (Rohardt, 2010b)  contains CTD as well as Nansen-bottle-derived data.
The latter was only possible due to the fact that guest researcher Manfred Stein (Institut für Seefischerei, Hamburg) had brought Nansen bottles and reversing thermometers as a backup for his ME-OTS-CTD on board during leg ANT-II/2 (no data). This anecdote clearly demonstrates that 1983 was still a transition period for hydrographic observations to electronic devices. Despite this rather unfortunate start, a Neil Brown, Mark IIIB CTD was successfully used on Polarstern until 1996. Starting in 1992, a Sea-Bird SBE 911plus CTD was in use by Kees Veth (Royal Netherlands Institute for Sea Research, data included here). A year later, Gereon Budéus was the first AWI researcher to use the SBE 911plus on Polarstern, testing the behavior of the probe in cold conditions. The instrument has been used routinely on Polarstern since then, in parallel with Neil Brown equipment.
On four cruise legs  (1995) (1996) (1997) (1998) (1999)  Polarstern was equipped with the direct successor of the Mark IIIB, called the ICTD and manufactured by Falmouth Scientific. Additionally, during two legs  (1986) (1987) , guest researchers deployed a ME-OTS-CTD. The SEA-BIRD SBE 911plus is probably the most widely used CTD type currently, and has been the only type used on Polarstern since 1999 (see also Fig. 1 ). In the following, we describe a data compilation of 33 years  of CTD measurements from R/V Polarstern. In Sect. 2 we provide details on the CTD types used, the parameters measured, and on data processing. A focus is set on the improvement of the salinity measurements over time and the reasons thereof. In Sect. 3 we describe the data sets in respect to composition, extent, access, and quality.
In situ calibration Laboratory calibration of instruments (see Sect. 2. 2) is crucial to maintain the sensors and obtain comparable results. It is not sufficient, however to anticipate how a sensor behaves at sea under tough environmental conditions, especially dur-ing deep casts. Also sensor drift is not necessarily a continuous process. For this purpose in situ calibrations are essential. Temperature: the Mark IIIB CTD was equipped with one temperature (and one conductivity) sensor only. Therefore reversing thermometers attached to the bottles of the water sampler had to be used to verify the quality of the temperature data. The Triton ICTD was equipped with a redundant temperature sensor which allowed for much better control of temperature data than the reversing thermometers. Lastly, the SBE911plus features double sensors, both for temperature and conductivity measurements, allowing the plotting of the difference between both sensors versus depth, which eases identification of individual sensor problems and pressure effects.
Data processing The data processing procedures were substantially dependent on the development of the CTD and the computer generation. In 1983, CTD data were recorded on nine-track magnetic tape. The station data (location, water depth, date, and time) were noted on a sheet of paper. An HP 9825B computer was used to visualize the temperature and salinity profile on a connected plotter. The data processing was performed at the institute. Due to the fact that, for safety reasons, the magnetic tapes always came back to Bremerhaven with Polarstern, the data processing often only started several months after the end of the cruise leg. Later (around 1986), EG&G -who took over the production of the Mark IIIB in 1984 -transferred the FORTRAN code of the data acquisition and processing routines of the Woods Hole Oceanographic Institution (WHOI)  (Millard and Yang, 1993)  for use on PCs.
A similar software package was also provided for the ICTD from Falmouth Scientific. This made the data acquisition and visualization as well as the transfer of raw data to AWI much easier. The substructure of the software for applying the SIO calibration came from R. Williams and F. Delahoyde (personal communication, 1990). Sea-Bird Electronics provided the data acquisition software SEASAVE and developed a package especially for their pumped CTD SBE911plus, SBE DataProcessing. This software became the primary tool for CTD data processing at the AWI. Also, the raw data were routinely stored on the onboard computer and transferred to the AWI in an automatic workflow. The data processing workflow can be divided into four parts, as explained in the following subsections:.
Data cropping and handling Data recording started before the actual profile began (starting point at the lowering of the CTD/rosette to the water surface). Thus, one of the first tasks was the truncation of the unused beginning (the depth of the first "used" data point depends on the wave height). Converting the raw file into readable engineering units was the next step as well as the separation between the down-and up-cast, if both had been saved in one file. Afterwards, the station information was added to the data file. In the past this information was manually edited from handwritten station protocols. With the inauguration of the DSHIP electronic station book (http://www. werum. de/ en/platforms/DSHIP. jsp) station details were directly merged with the CTD data.
Correction of measurement errors Physical properties of the sensors and environmental influences on them, as well as disturbances of the data transmission between sensors and recording units on deck, can create measurement errors. These were reduced using suitable software in the following ways: -Spikes: spikes in the pressure measurements resulting, for example, from winch cable or slip ring problems, were removed. The procedure is called "par" in Sea-Bird's SBEDataProcessing software package. -Response time/time lag correction: salinity was computed from conductivity, temperature, and pressure. The response time of a temperature sensor, however, is higher than the response time of the pressure and conductivity sensors. If left uncorrected, this would result in salinity spikes in layers with strong gradients.
A precise correction for this time lag would require a constant lowering speed of the CTD, which is not possible on a moving ship. Sea-Bird solved this problem by pumping water with a constant speed through the temperature and conductivity sensors. For Mark IIIB and ICTD the time lag was adjusted/corrected by minimizing the salinity spikes and evaluated visually based on profile plots. -Pressure hysteresis: Mark IIIB strain gauge pressure sensors did not respond linearly to increasing pressure and additionally exhibited a lagged response during decreasing pressure. This behavior also depended on the maximum pressure. A laboratory calibration (see. Driemel et al. :  33 years of Polarstern CTD data Sect. 2. 2) revealed this behavior and provided the coefficients for the software to apply the correction.
Creation of a uniform profile -Monotonic increasing pressure: as a ship is always pitching and rolling, the constant lowering speed of the winch is superimposed by the ships motion. Rejecting all records with pressure reversals is thus one of the standard procedures in CTD data processing, and was also applied on Polarstern data. -Averaging: the SBE911plus CTD sampled with a frequency of 24 Hz. A typical lowering speed of 0. 8 m s -1 resulted in a vertical resolution of around 3 cm. This sample rate was needed to apply the time lag correction reliably and also to guarantee that, although lots of records were rejected, a monotonic increasing pressure record could be created. In the end, the profile was smoothed by averaging on 1 dbar levels (i. e. , P , T and C were averaged between ≥ 1. 5 and < 2.
The station log sheets (which essentially contain descriptions of special occurrences), the pre-, post-, and in situ calibration helped to reconstruct the history of a sensor during a cruise and to identify which T -C sensor pair should be used. General plotting software can be used to visualize the in situ calibrations versus pressure or versus time to investigate the dependency (drift), and to then apply and verify the corrections. -Validation: all profiles were imported into Ocean Data View  (Schlitzer, 2015)  which provides various plots (profiles, scatter, and sections) for a visual inspection. When a suspicious profile was found, the processing steps mentioned above were repeated from the necessary level onwards. Additionally, these profiles were compared to profiles from previous cruises.
A Guildline Autosal 8400a/b salinometer was in use until 2010. Since then, it has been replaced by a new laboratory salinometer, the OPS developed by AWI scientists and en- gineers and manufactured by OPTIMARE 4  (Budéus, 2011 (Budéus, , 2015)). The highly accurate OPS lab measurements have been in use since June 2010 to cross-calibrate in situ salinity data measured by the CTD. As a result, beginning with campaign ANT-XXV/1 (2010/06) the accuracy of the salinity measurements improved tremendously and the resulting data sets are of the highest quality possible for these kinds of measurements.
Resulting data sets In total 131 data sets (1 data set per cruise leg) containing data from 10 063 CTD casts have been produced on Polarstern in the course of 33 years (22 November 1983 to 14 February 2016, Fig. 3 ) and are archived in the PAN-GAEA (Data Publisher for Earth and Environmental Science, www. pangaea. de) database. The data sets can be accessed at http://doi. pangaea. de/10. 1594/PANGAEA. 860066 (Rohardt et al. , 2016). This link leads to the central page which contains all meta-information of the respective cruise legs (name of leg, start/end, area, link to cruise report), the number of CTD casts, the CTD type used, the overall quality 4 Optimare Sensorsysteme GmbH & Co. KG of the data, the link to a map displaying all CTD stations, and the link to the data set of the specific leg.
When clicking on the link to a data set of one cruise leg (see, e. g. ,  Rohardt, 2010c , doi:10. 1594/PANGAEA. 733664)  the data set page contains metadata, a Google map of all sample sites, and on the bottom the actual data. On the top of the page the citation of the data set is given, followed by the citation of the respective cruise report (if available). The CTD type used is indicated in the "Method" column of the Parameter(s) overview table of the page. The data table opens by clicking on "View dataset as html". Here, the position, date/time (at maximum depth) of sampling, and the water depth precede the actual data. The "Elevation" is the bathymetric depth relative to sea level and is therefore negative. It can be used, for example, to extract information on how close to the seafloor the CTD measurements ended (comparing water depth of the last measurement with the elevation).
The "Number of observations" is the number of measurements included in one averaging step (see Sect. 2. 5. 3). With programs like Ocean Data View  (Schlitzer, 2015)  and Pan2Applic  (Sieger and Grobe, 2005)  the data can be visualized easily (for more information, see https://wiki. pangaea. de/wiki/ODV). With respect to CTD type, the 131 data sets are composed of 27 legs with Mark IIIB CTD data, 4 legs with ICTD data, 2 legs with ME-OTS data, 5 legs with data -We defined a column on the overall quality of the data of each leg in  Rohardt et al. (2016)  called "Quality code". Here we use flags "A", "B", and "C" to classify the data with A being high quality data (see Table  2  for details). -In general, the number of decimals in the data sets is at least n + 1, with n being the last significant decimal.
This was done deliberately, as we experienced that for calculations (in models), the actual (unrounded) number of the last significant decimal can be essential. -You can search for specific parameters, regions, etc. , in the data sets described here using the www. pangaea. de search engine and adding "PSctd". You can then define a geographic bounding box in the map (right side of search page) to search for specific regions (e. g. , Arctic data) and press "apply". Or you can try "PSctd + parameter:oxygen" to get all data sets with oxygen measurements. We also added an overview in. xls format of these additional measurements at http://doi. pangaea. de/10. 1594/PANGAEA. 860066 (under "Further details") which contains information (where available) on whether or not these measurements were calibrated, and during which campaign which additional measurements were taken.
- Please be aware that downloading all files requires over 1. 5 GB which might take some time to download. How to download files with PanGet is described at https: //wiki. pangaea. de/wiki/PanGet. -A CTD file downloaded from PANGAEA can easily be imported into Ocean Data View  (Schlitzer, 2015)  using Pan2Applic  (Sieger and Grobe, 2005). Open the downloaded file in Pan2Applic, click on "Convert ⇒ Ocean Data View" and click "OK", then choose "Select data (2:)" and "Select geocode (3:)" and press "OK". The data are now loaded into ODV and you can, for example, visualize it with different modes at "View ⇒ Layout templates". -For a detailed geographical search of the available data we created a Google. kmz file containing all CTD casts. When clicking on a single cast, a small window opens up displaying metadata details and a link to the data in PANGAEA.