dataesr/scientific-paragraphs-categorization huggingface dataset:
To obtain fast transfers from server-side to client-side, first spectral data are not stored as text format but only as binary format, because text to binary conversion is time consuming. Then, the open-source software Gnuplot (http://gnuplot. info) was chosen to generate images in PNG format directly from the binary data. Thus, data reading/writing and data transfers are minimized. The NMRProcFlow tool is available online as a web tool to provide advanced processing tools for 1D NMR spectral data for anyone who needs to process NMR spectra sets, expert or not, in order to save users the trouble of installing the software. In addition, virtual appliances of NMRProcFlow compliant with the two major virtualization platforms (VMware http://www. vmware. com/ and Oracle VirtualBox https://www. virtualbox.
Results and Discussion One of the strongest point of NMRProcFlow is the possibility of visualizing the experimental factor levels within the NMR spectra set through the spectral viewer making the tool valuable to create links between the experimental design and subsequent statistical analyses, and thus facilitate interactions between biologists and NMR spectroscopists. The NMR spectra viewer is the central tool of NMRProcFlow and the core of the application. It allows users to visually explore the spectra overlaid or stacked, to zoom in on intensity scale, to group sets of spectra by coloring them based on their factor levels (Supplementary Information S1). Compared with the existing software based on a GUI dedicated to 1D NMR spectra processing (https://omictools. com/dataprocessing-category, Alonso et al.
2015) , besides that some are commercial software (or implying to buy a software's license), neither of them allows specifying the factor levels within the spectra viewer, nor capturing areas of ppm to be treated either on a full set of spectra, or on a subset belonging to the same factorial group as readily as NMRProcFlow can do. Moreover, another key point linked to the ability to proceed interactively with the NMR spectra viewer within NMRProcFlow, is to allow users choosing the processing method (Fig. 1 ) that seems most relevant to them depending on the ppm region. For example, to align a spectral region with intertwined peaks, a Parametric Time Warping  (Bloemberg et al. 2010 ) method will produce a better result provided that all spectra have the same amount of peaks within this region.
In addition, software provided by NMR manufacturers, such as TopSpin (Bruker GmbH) for example, are designed to accomplish this task, knowing that it is often performed by NMR spectroscopists as a first level of quality control. Moreover, it implies that all NMR spectra were acquired and pre-proceeded with the same number of points. However, it is possible for example to mix spectra acquired with different pulse sequences in order to compare them. Beyond its capabilities of spectra viewing and processing, NMRProcFlow is especially dedicated to metabolomics. The two major metabolomics approaches, namely metabolic fingerprinting (MF) and targeted metabolomics (TM) are taken into account. The workflow covers all steps from the spectral data up to the output data matrix (Fig. 1 ). Regarding the TM approach, the identity of the metabolites of interest is established before statistical data analysis, and this involves to be able to: i) identify the spectral regions for which the quantification will be performed based on both knowledge and well-established metabolomic profiles, ii) ensure that each of these regions is not polluted by peak contribution of neighbor regions.
AcknowledgmentThe authors are grateful to the NMR users of  Bordeaux Metabolome Facility  for assistance in beta-testing, troubleshooting and fruitful discussions, especially  E Martineau  and  P Giraudeau  (Ceisam, Nantes), as well as the NMR members of the  French-speaking Metabolomic and Fluxomic Network, especially F Fauvelle (IRMaGE, Grenoble ).
, 2013). So far most studies that include size distribution measurements in the summertime Arctic were conducted from ground stations or ship cruises. To date there are only two studies that assess the altitude dependence of the size distribution, i. e. one in the area of Svalbard  (Engvall et al. , 2008b)  and one from the Oden performing vertical profiles with a helicopter  (Kupiszewski et al. , 2013). Although no size distribution measurements were performed,  Heintzen-berg et al. (1991)  measured vertical profiles of the total particle number concentration greater than 10 nm during June and July 1984 over the Fram Strait-Spitsbergen area, and found a "rather uniform distribution" with altitude. Their measurements, however, were confined to 500 m a. s. l. and above. In this study we present data from aerosol size distribution measurements taken from an aircraft during a 3 week period in July 2014 in the high Arctic area of Resolute Bay, Nunavut, Canada.
Meteorological parameters and state parameters Aircraft state parameters and meteorological measurements were performed with an AIMMS-20 manufactured by Aventech Research Inc. at a very high sampling frequency (> 40 Hz). The AIMMS-20 consists of three modules: (1) the air data probe that measures the three-dimensional (3-D) aircraft-relative flow vector (true air speed, angle-of-attack and sideslip) and turbulence with a 3-D accelerometer; furthermore, temperature and humidity sensors are contained within this unit and provide an accuracy and resolution of 0. 30 and 0. 01 • C for temperature and 2. 0 and 0. 1 % for relative humidity measurements; (2) an inertial measurement unit that consists of three gyros and three accelerometers providing the aircraft angular rate and acceleration; (3) A Global Positioning System for aircraft 3-D position and inertial velocity.
, 2016)  showed that a nominal supersaturation of 1 % at the reduced pressure translated into 0. 6 % effective supersaturation. Cloud droplet sizes from 2 to 45 µm were measured using a wing mounted particle measuring system FSSP 100. In this study these data are only used to identify periods when the aircraft was flying in clouds. To avoid possible artefacts produced from shattering of cloud droplets at the aerosol inlet, data from in-cloud times are discarded for the purposes of this study. A DMT single-particle soot photometer (SP2) was deployed to measure refractory black carbon (rBC) number and mass concentrations. We refer to rBC mass concentrations as an indication of pollution influence. Calibrations with Aquadag soot were performed prior to and during the campaign. The lower size limit of detection of rBC particles by the SP2 was approximately 80 nm.
Data analysis and nomenclature of particle size data All particle data were averaged to 1 min intervals to match the time resolution of the BMI SMS. Particle concentrations within different size intervals were calculated. The notation N a-b is used; "a" gives the lower limit and "b" the upper limit of the calculated size interval. The BMI SMS was used to determine concentrations of particles from 20 to 90 nm in diameter, and concentrations of particles larger than 90 nm in diameter were determined by the UHSAS. If the size interval is expressed as N >a the upper limit is given by the detection limit of the UHSAS (1 µm). Additionally, particle concentrations from 5 to 20 nm (short: N 5-20 ) were obtained by subtracting particle concentrations measured by the BMI SMS and by the UHSAS from the N tot as determined by the CPC.
The N 5-20 are also referred to as ultrafine particles (UFP) in this study and may be indicative of newly formed particles. Willis et al. (2016)  showed that for this environment particles were able to grow to 50 nm low above the open water over approximately 1 h, which means that the growth from 1 to 10 nm can occur over approximately 1 min or less, justifying our use of instruments that are sensitive to 5 nm particles and larger. For the concentration range of N 5-20 particles (less than 2000 cm -3 ), it requires more than 30 h for the concentration of 10 nm particles to be reduced by coagulation to a concentration of 500 cm -3 (Agranovski 2010). In order to obtain vertical profiles the data were averaged within altitude intervals. An average profile for a single flight was obtained by binning all data from the respective flight into altitude intervals of 100 m starting at the lowest flight altitude.
In addition to data obtained during vertical profile flights, data acquired while flying at a constant level were also included. Average profiles containing data from more than one flight were calculated by averaging the respective single flight profiles. Average size distributions were obtained by simply averaging each bin for the desired time and altitude range. The size distributions measured by the BMI SMS were used for particle sizes from 20 to 90 nm, and the distributions at larger sizes are taken from the UHSAS. All particle concentrations are expressed for ambient pressure conditions, i. e. they have not been adjusted to standard temperature and pressure con- ditions. The N 5-20 referred to as UFP are added to the size distributions as additional bin assuming a bin width of 15 nm (from 5 to 20 nm) with a mid-diameter of 12 nm.
Acknowledgements. The authors would like to thank a large number of people for their contributions to this work. We thank  Kenn Borek Air , in particular the pilots  Kevin Elke  and  John Bayes  and the aircraft engineer  Kevin Riehl. We are grateful to  John Ford ,  David Heath  and the  University of Toronto  machine shop for safely mounting our instruments on racks for aircraft deployment. We thank  Jim Hodgson  and Lake Central Air Services in Muskoka,  Jim Watson  ( Scale Modelbuilders, Inc. ),  Julia Binder  and  Martin Gerhmann  ( Alfred Wegener Institute, Helmholtz Center for Polar Marine Research, AWI ), and  Mike Harwood  and  Andrew Elford  ( Environment and Climate Change Canada, ECCC ) for their support of the integration of the instrumentation and aircraft. We gratefully acknowledge  Carrie Taylor (ECCC) ,  Bob Christensen  ( U of T ),  Lukas Kandora ,  Manuel Sellmann  and  Jens Herrmann  (AWI),  Desiree Toom ,  Sangeeta Sharma ,  Dan Veber ,  Andrew Platt ,  Anne Marie Macdonald ,  Ralf Staebler  and  Maurice Watt  ( ECCC ) for their support of the study.
We thank the  Biogeochemistry department of MPIC  for providing the CO instrument and  Dieter Scharffe  for his support during the preparation phase of the campaign. The authors  J. L. Thomas  and  K. S. Law  acknowledge funding support from the  European Union  under Grant Agreement no. 5265863 -ACCESS  ( Arctic Climate Change, Economy and Society ) project ( 2012-2015 ) and TOTAL SA. Computer simulations were performed on the IPSL mesoscale computer centre (Mésocentre IPSL), which includes support for calculations and data storage facilities. We thank the  Nunavut Research Institute  and the  Nunavut Impact Review Board  for licensing the study. Logistical support in  Resolute Bay  was provided by the  Polar Continental Shelf Project (PCSP) of Natural Resources Canada  under PCSP Field Project no.
A Third Major Group of PolBs Position-specific iterative (PSI)-BLAST searches against the RefSeq bacterial genome database at NCBI seeded with the sequence of experimentally characterized pPolB from bacteriophage Bam35 (NP_943751) retrieved numerous hits to highly divergent PolBs (16%-20% sequence identity to the pPolB of Bam35) encoded within chromosomes and several plasmids from widely diverse bacteria, such as Firmicutes, Actinobacteria, and Proteobacteria. Nevertheless, analysis of multiple sequence alignments of these divergent DNAPs showed that all of them contained the TPR1 and TPR2 subdomains, a hallmark of pPolBs (Figure  1A ), and the active site residues of the exonuclease and DNAP domains of PolBs  (Braithwaite and Ito, 1993)  were conserved, albeit with notable variations within the KxY and PolC motifs (Figure  1B , see also below).
Statistical Methods Data analysis and representation was performed using R and R-Studio (Studio, Boston, MA; http://www. rstudio. com), using packages Dplyr, Stats, and Ggplot2, available from CRAN (the comprehensive R archive network; https:// cran. rstudio. com). Based on Shapiro-Wilk normality tests, results were analyzed by either paired t test or Dependent 2-group Wilcoxon signed rank test.
ACKNOWLEDGMENTS We thank  Laurentino Villar  for protein purifications,  Dr. Miguel de Vega  for valuable suggestions and critical reading of the manuscript, and  Professor Luis Blanco  for helpful discussions and the [g-32 P]ATP-(dGMP)n ladder. M. S. 's lab is funded by the  Spanish Ministry of Economy and Competitiveness  ( BFU2014-52656P ). M. R. -R. was supported by a  ComFuturo Grant  ( NewPols4Biotech ) from  Fundacion General CSIC. C. D. O. was holder of a '' Plan de Empleo Juvenil '' contract from  Madrid Regional Government  (funded by  YEI program  from  European Social Fund, EC). An institutional grant from  Fundacion Ramon Areces  to the  Centro de Biologı ´a Molecular Severo Ochoa  is also acknowledged. P. F. was funded by the  European Research Council  under the European Union's  Seventh Framework Program  (FP/2007-2013)/ Project  EVOMOBIL -ERC Grant Agreement 340440.
The analysis of multi-unit extracellular recordings of brain activity has led to the development of numerous tools, ranging from signal processing algorithms to electronic devices and applications. Currently, the evaluation and optimisation of these tools are hampered by the lack of ground-truth databases of neural signals. These databases must be parameterisable, easy to generate and bioinspired, i. e. containing features encountered in real electrophysiological recording sessions. Towards that end, this article introduces an original computational approach to create fully annotated and parameterised benchmark datasets, generated from the summation of three components: neural signals from compartmental models and recorded extracellular spikes, non-stationary slow oscillations, and a variety of different types of artefacts.
We present three application examples. (1) We reproduced in-vivo extracellular hippocampal multi-unit recordings from either tetrode or polytrode designs. (2) We simulated recordings in two different experimental conditions: anaesthetised and awake subjects. (3) Last, we also conducted a series of simulations to study the impact of different level of artefacts on extracellular recordings and their influence in the frequency domain. Beyond the results presented here, such a benchmark dataset generator has many applications such as calibration, evaluation and development of both hardware and software architectures. Electrical recording of extracellular action potentials is the "gold standard" technique widely used in electrophysiology 1 , where the signals are exploited to correlate neural activity with a behavioural output and/or the electrophysiological consequences of brain lesions or drug infusion, etc.
The emergence of novel methods for neural analysis together with high-throughput data acquisition technologies 2 provide new possibilities for the exploitation of brain activity at the single unit level, for example, giving instantaneous feedback for closed-loop interactions with brain circuits when abnormal neural signals are detected 3. This approach has proven effective for several pathological conditions such as Epilepsy, Parkinson's disease, or Essential Tremor 4-7. From a more fundamental perspective, novel algorithms have been recently proposed to process these large amounts of neural data, such as semi-automatic and automatic clustering techniques, to distinguish different neural sources in multi-unit extracellular recordings  [8] [9] [10] [11] [12]. In order to validate the performance and accuracy of these different algorithms or devices, reliable datasets, where the majority of the signal content is known, are essential.
Ideally, this ground-truth reference should be a completely annotated and parameterised dataset, in which three levels of information should be modifiable and known in detail: the recording environment (e. g. density of active population of neurons or distance from neurons to recording sites), the population dynamics (e. g. firing rate, spike timing of each neuron and spike waveforms) and the noise content (e. g. background noise level contribution and number of artefacts). There are several applications (Fig. 1 ) where using a parameterised dataset can be advantageous, ranging from algorithm design to development and evaluation of electronic devices. Moreover, parameterised datasets are needed to evaluate the efficiency of unsupervised classification algorithms. In recent years, several spike sorting algorithms have been proposed  [8] [9] [10] [11] [12]  , however, it is difficult to assess their sorting efficiency since the datasets used to evaluate their performance were heterogeneous.
performance of these different spike sorting algorithms (Fig. 1a ). In the same manner, fully annotated datasets could also be used to challenge event detectors or noise reduction algorithms (Fig. 1b  and c ). In addition, these benchmarks could be very useful for brain-computer interfaces and neural prosthetic devices (Fig. 1d  and e ). The common approach to assess the performance of such electronic devices is to use a large number of neural signal datasets that include a range of various features (e. g. different noise levels, a degree of meaningful information load, signal resolution etc. ). For this purpose, parameterised datasets with independently modifiable features would allow the generation of a large variety of neural signal profiles in a controlled manner. This approach could also enable the simulation of experiments for calibration purposes instead of performing labour-and cost-intensive experiments with real subjects.
In the present study, we propose a computational procedure to generate realistic neural signals based on a hybrid model approach, in which both real and simulated signal features are combined with a relatively low computational requirement. The generated datasets are fully parameterisable and include all the original features found in real recordings such as a variety of different types of artefact and background noise. The validation stage of our procedure explores the similarity between real recordings and our model-generated signals. We show that our model is easily modifiable and generates synthetic signals similar to those obtained in distinct experimental conditions. We also illustrate the flexibility of our simulator by modelling different types of recording configuration (tetrodes and microelectrode arrays), brain tissue (such as juxtaposed layers) and experimental conditions (awake or anaesthetised animals).
Results Creation of a three module simulator of extracellular multi-unit signals. Our work proposes a computational procedure to generate datasets that will provide neuroscientists with a ground-truth reference for algorithm and tool evaluation of single and multi-unit signal processing. In our approach, ground-truth from real and simulated signals is obtained by adding spike activity, that is, action potentials from nearby neurons and background noise from distant neurons (x (n)), slow oscillations (< 300 Hz) from synaptic current inputs (w (n)) and artefacts (a (n)) that can be expressed as: In equation (  1 ) s 1 (n) refers to bio-inspired simulation of electrode number 1, with n as discrete time variable and suffix e as the total number of simulated electrodes. = + + = + +  s n x n w n a n s n x n w n a n ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) (1) Figure  2  summarises the general approach and highlights the modifiable parameters in each individual computing module.
Comparison of simulated and real extracellular hippocampal recordings. As a starting point, we created the contribution of the local spike activity to the signal. For this, we modified an existing simulation platform  25  designed for a single multi-electrode (i. e. tetrode) to include multiple spatially distinct recording sites. The existing simulator implements a hybrid model that combines detailed compartment models of pyramidal cells and interneurons  18, [27] [28] [29] [30] [31]  (available via the NEURON 32 project) for the closest neurons to the recording sites, coupled with spike templates for the distant neurons, all in a 3D volume of "virtual tissue" (Fig. 3a ). The initial hybrid model  25  that generated the spiking activity and background noise also gave the user, via a graphical interface, the option to modify various parameters to generate the datasets.
We extracted slow oscillations (< 300 Hz) from real datasets containing extracellular multichannel recordings made from the CA1 hippocampal region of rats  14, 26, 34, 35  (and added them linearly to the neural simulations as the non-stationary low-frequency components  36. Given that local field potential (LFP) < 300 Hz can be contaminated by action potentials, we were aware that the extraction of low frequency components require a preceding exploration  37  on the original data to reveal the degree of spike contamination in LFP. In our case, we verified that the contribution of the spectral density of the mean spike waveform was negligible at low frequencies (≲ 300 Hz). One element that is often omitted while developing realistic neural simulators is the inclusion of artefacts that contaminate real microelectrode recordings.
In any extracellular data analysis, these undesired features should be considered, especially when it comes to unsupervised methods. Many algorithms of detection and artefact suppression have been previously reported, and they all require ground-truth data for evaluation and optimisation purposes. Hence, the inclusion of identified artefacts plays an important role in our neural database creation procedure. For the artefact component of the benchmark generator, an artefact library was created by extracting artefact events from real data recordings  26, 38, 39. The library contains spike-like sharp artefacts, grooming artefacts and mastication artefacts identified from different in-vivo extracellular recording experiments. The library was organised as indicated in Supplementary Fig. S1. The identification was made following multiple validation criteria stages that include: a test for simultaneous cross-channel appearance within an artefact gap of 300 μ s in at least 80% of the total number of channels, a visual waveform inspection and a time-coincident comparison with simultaneous video recording (Fig.
Identified grooming artefacts appear across channels with large amplitudes and a heterogeneous duration range of ~0. 4-28 s. The grooming events identified (phases 1 to 4) constitute a flexible grooming chain. The beginning of each phase of stereotyped movements was annotated within the artefact sequences (Fig. 4c  and Supplementary Table  S3 ). The complete process of database generation is summarised in Fig. 5  and consists of the summing of the following three components: (1) non-stationary low frequencies, (2) annotated and parameterised action potential simulations and (3) the addition of identified artefacts.
Generation of bio-realistic hippocampal benchmark databases in different experimental conditions. To challenge the accuracy and the performance of our model, we aimed to reproduce two types of real hippocampal extracellular multi-unit recording: in awake and in anaesthetised rodents. To mimic the macroscopic population activity of hippocampal neurons used in the real recordings, we set common parameters for both experimental conditions (i. e. those related to the recording environment such as the selected array of electrodes and those related to the simulation environment such as population density) but we differentiated two input parameters for the simulator that best approximated the dynamics of neural populations in our two distinct cases: firing rate and percentage of active neurons (Table  2  summarises the parameters chosen).