dataesr/scientific-paragraphs-categorization huggingface dataset:
Expert annotations are continuously gathered in the MicroScope database. Indeed, $35 000 annotations are made in a year (Figure  1 ), and >370 000 genes have been curated so far. A third of these annotations correspond to the description of precise molecular functions supported by direct or indirect (i. e. from homology relationships) experimental evidences. Biologists generally focused their annotations on proteins/functions of interest; however, it is interesting to note that about 50 genomes integrated in MicroScope are near completely curated (!80% of the genes were expertly annotated), and 124 additional genomes got >300 curated genes. MicroScope annotations are submitted to INSDC databanks when the genomes get published and can be easily downloaded via the Web interface ('Search/Export->Download Data' functionality).
Process management system The annotation pipelines are organized in a robust automated workflow management system using the jBPM framework (java Business Process Management; http://jbpm. org), which allows us to handle simultaneously millions of tasks for the analysis of several new microbial genomes. These tasks are parallelized on hundreds of CPU cores using Pegasus MPI cluster module (https://pegasus. isi. edu). The pipelines for the structural, functional and relational annotation orchestrate >50 external/internal bioinformatics software (see section 'Running the MicroScope pipelines'). A large part of these analyses are updated at regular intervals to take into account primary databases growth and new expert annotations.
Data management system The results of these analysis tools, together with the primary data used as inputs, are stored in a relational database named PkGDB and based on the open-source MySQL relational database management system and the InnoDB (for continuous data integration and incremental updates) and MyISAM (for large bulk inserts) table engines. The PkGDB architecture supports integration of automatic and human-curated functional annotations and records a history of all the modifications. Finally, for metabolic comparative analyses purposes (see the 'Metabolic profiles' functionality in the 'Exploration of metabolic data' section), relational tables have been designed in PkGDB to store information of the MicroCyc PGDBs, together with the KEGG metabolic pathways and modules. The size of PkGDB is today 1 TB for databanks and genome data, and 30 TB for the computational results (Figure  8 ).
The Web visualization component The MicroScope Web interface (http://www. genoscope. cns. fr/ agc/microscope) is developed using the Apache/PHP serverbased language and consists of numerous dynamic Web pages containing textual and graphical representations for accessing and querying data. Several useful graphical applications, such as Artemis  [38] , MeV  [39]  and IGV  [40] , are also available in the MicroScope interface through plugged Java applications. As shown in this article, the tools are organized in a menu bar to facilitate the exploration and the curation process. At any level of the interface, a 'Help' functionality is available, and a complete tutorial can be found in the 'About' menu.
ac. uk/sci to detect HGT regions ence/tools/alien-hunter SIGI-HMM x http://www. brinkman. mbb. sfu. ca/$mlangill/sigi- hmm GenProtFeat x Gene/protein features x Taxonomy x NCBI taxonomy database https://www. ncbi. nlm. nih. gov/taxonomy Functional BLASTþ x DNA/protein sequence https://blast. ncbi. nlm. nih. annotation alignment gov Diamond x https://github. com/bbuch fink/diamond UniProtKB x Protein sequence and func- http://www. uniprot. org tion database InterPro x x Protein signature and fam- https://www. ebi. ac. uk/ ily prediction interpro COG x x Protein family annotation https://www. ncbi. nlm. nih. and prediction gov/COG FigFam x x http://www. nmpdr. org/FIG/ wiki/view. cgi/FIG/FigFam MICFAM x Protein sequence family x classification with SiliX SiliX x Clustering of protein https://lbbe.
univ-lyon1. fr/- sequences SiLiX-. html ENZYME x Enzymatic activity database http://enzyme. expasy. org PRIAM x Enzymatic activity http://priam. prabi. fr prediction dbCAN x Carbohydrate-active en- http://csbl. bmb. uga. edu/ zyme prediction dbCAN/ SignalP x Signal peptide cleavage site http://www. cbs. dtu. dk/ser prediction vices/SignalP TMHMM x Transmembrane helix http://www. cbs. dtu. dk/ser prediction vices/TMHMM LipoP x Lipoprotein prediction http://www. cbs. dtu. dk/ser vices/LipoP PSORTb x Subcellular localization http://www. psort. org prediction VFDB x Virulence factor database http://www. mgc. ac. cn/VFs VirulenceFinder x https://cge. cbs. dtu. dk/ser vices/VirulenceFinder CARD/RGI x x Antibiotic resistance data- https://card. mcmaster. ca base and prediction AutoFassign x Automatic functional anno- x tation of proteins Relational Syntonizer x Synteny conservation annotation detection x http://www.
inrialpes. fr/ helix/people/viari/ cccpart/ (continued) MicroScope annotation platform | 1075 overpredicted or missed enzymatic reactions [20]. The collection of MicroScope PGDBs is made available at the MicroCyc Web site (http://www. genoscope. cns. fr/agc/microcyc) and in the MicroScope database (see 'Exploration of metabolic data' section). Moreover, these metabolic networks are synchronized each night with new MicroScope genomes and expert annotations.
Specimens preparation Three different types of specimen were included in this study. Porcine whole aortae (n = 9) were collected at a local slaughterhouse from domestic pigs aged 6-12 months. They were extracted carefully, removing surrounding connective tissue around the segments, within 12 h after the death. Before dissecting, the external diameter of the aorta was measured in different zones using a digital caliper. Aortae were cut open along the longitudinal direction, following the line of the intercostal arteries. 45-mm-square specimens were cut. The edges were parallel to the longitudinal and circumferential direction of the aortic tube. Fresh non-aneurysmal human aortae (n = 2) were collected through the French voluntary body donation program from the Department of Anatomy of the University of Saint-Etienne.
Bulge inflation testing protocol Clamping was achieved by aligning and gluing the intimal side of the specimen to a second PVC support and assembling and tightening the device. In this way the adventitial surface was exposed and a hermetic closure was ensured on the intimal surface (Fig. 2 ). The circular exposed area of the adventitial surface was covered with PBS and remained immersed throughout the test duration. Inflation of the specimen was performed with an automatic water pumping system (WPI®, NE-501 Multi-Phaser) and a program developed in LabVIEW which controlled the injected volume at a constant rate. Pressure values were recorded simultaneously by a pressure transducer (Omega®). The inflation was performed injecting water at a constant rate of 2 mL/min. Then it was stopped, and the volume was kept constant, when specific levels of pressure showed in Table  1  were reached.
The goal of these QI scans was to provide data to determine the z-correction balancing possible z-motion during the acquisition of the normal scan. A custom MATLAB program based on image correlation was developed for this purpose (see Section 2. 5. 1). Then inflation was resumed up to the next threshold. A continuous time-imaging was adopted during the time intervals in which the inflated volume changed and during the stabilization periods. A group of fibers lying on the same focal plane was followed during the entire acquisition, thereby allowing to double check that the same ROI was tracked under the microscope.
Planar orientation assessment of collagen fibers In order to assess structural evolutions of the fibrous structure of adventitia under bulge inflation, it was decided to focus on the orientation distribution of the collagen fibrous network in the axialcircumferential plane. Taking into account previous observations of our stacks in the radial direction (results not shown here), fiber exhibited practically planar orientation in the axialcircumferential plane, in good agreement with other studies  (Rezakhaniha et al. , 2012) , Thus, the planar orientation distribution function of the collagen fibers was estimated using the OrientationJ plugin for ImageJ software. This plugin is based on the computation of grey scale gradients and their related weighted 2D structure tensors at each pixel of a given slice, as described in  Rezakhaniha et al.
3D visualization in aortic adventitia Following the previous results, the stabilization period imposed between the inflation and the 3D scans with the MCM was set at 15-20 min. The post processing slice matching program through 2D inter-correlation (see section 2. 5. 1) permitted then to quantify the motion along the radial direction (or z direction) of each 3D stack, and correct the interslice distances. The average percentage change between the estimated interslice distance and the z-step of the 3D acquisition for all specimens independently of the category were: 16. 4 ± 26. 5 % at 0 mmHg (25 stacks, 14 specimens), -12. 6 ± 19. 3 % at 200 mmHg (21 stacks, 13 specimens), and -1. 5 ± 18. 9 % at 450 mmHg (12 stacks, 7 specimens). A positive value indicates an increase of the interslice distance after the correction.
Bigger pores were associated with more zippered SNAREs at the waist (Figure  7 -figure supplement 1). With 15 SNAREs per face, the entropic expansion force of 17 pN was within 5 pN of the 22 pN resistance. Table  1. Parameters used in the analytical model of fusion pores. (A) Measured by fitting a cylinder to the part of the fully zippered SNARE protein structure without the TMDs, produced using PyMOL software with PDB code 3HD7 (http://www. pymol. org). (B) Estimated in this study as a fitting parameter. (C) Measured by  Mitra et al. (2004). (D) Measured in this study (Figure  1 ). (E) Calculated using a weighed average of the P 0 of palmitoyl-2-oleoyl phosphatidylcholine (POPC) and 1,2-dioleoyl phosphatidylserine (DOPS) from  Rand and Parsegian (1989). The weighed average of the two pressure parameters, according to the 85:15 molar ratio of POPC:DOPS present in the NLPs in this study, is used to obtain P 0.
Stable flipped SNARE and wild-type HeLa cell culture HeLa cell lines stably co-expressing flipped v-SNAREs (flipped VAMP2 and cytosolic DsRed2-nes, 'vCells') and t-SNAREs (flipped Syntaxin-1 and flipped SNAP-25 and the nuclear fluorescent marker CFP-nls, 'tCells') were generated in the Rothman laboratory as described  (Giraudo et al. , 2006). The cells were a generous gift from the Rothman laboratory. The cells were maintained in DMEM (4500 mg/L glucose, L-glutamine, sodium pyruvate and sodium bicarbonate) and 10% (v/v) fetal calf serum at 37 ˚C. A new aliquot of cryopreserved cells was thawed after at most three weeks of cell culture and cultured at least five days before data acquisition. The flipped t-SNARE HeLa cells were tested by PCR Mycoplasma Test Kit I/C (cat. No. PK-CA91-1048, Promo Kine, Heidelberg, Germany), which showed contamination (Figure  4 -figure supplement 3A).
2 using NaOH) for the cell-attached recordings. 10 mM glucose was added to the medium before use. All voltage-and current-clamp recordings were made using a HEKA EPC10 Double USB amplifier (HEKA Elektronik Dr. Schulze GmbH, Lambrecht/Pfalz, Germany), controlled by Patchmaster software (HEKA). Currents were digitized at 20 kHz and filtered at 3 kHz. To measure SNARE-mediated single fusion pore currents in the cell-attached mode  (Yang and Sigworth, 1998) , electrodes were filled with the pipette solution composed of 125 mM NaCl, 4 mM KCl, 10mM HEPES, 13 mM or 26 mM tetraethylammonium-Cl (TEA-Cl, K + -channel antagonist), adjusted to pH 7. 2 using NaOH. This solution had resistivity of 0:60 Ohm. m, measured using a conductivity cell  (DuraProbe, Orion Versa Star, Thermo Scientific). For experiments designed to test the presence of multiple pores connecting large copy number vNLPs to tCells (Figure  5 -figure supplement 4), electrodes were filled with a solution containing: 129 mM N-methyl-d-glucamine (NMDG), 10 mM HEPES, 26 mM TEA-Cl, pH adjusted to 7.
Analysis of fusion pore data The analysis of fusion pores is described in detail in  Wu et al. (2016). Briefly, we developed an interactive graphical user interface in Matlab to help to identify, crop and process single fusion pore currents. Traces were exported from Patchmaster (HEKA Electronik) to Matlab (Mathworks) and lowpass filtered (280 Hz cutoff); frequencies that were the result of line voltage were removed using notch filtering. Zero phase shift digital filtering algorithms (Matlab Signal Processing Toolbox function filtfilt) were employed to prevent signal distortion. Filtered traces were averaged in blocks of 80 points (125 Hz final bandwidth) to achieve rms baseline noise < ~0:2 pA. Currents I for which jIj>2:0 pA for at least 250 ms were accepted as fusion pore current bursts.
Many recordings ended with what seemed to be currents from overlapping fusion pores. Such end-of-record currents were also excluded because they could also be attributed to a loose seal. Thus, the fusion rates that we report may underestimate the true rates, especially for conditions in which fusion activity was high. For distributions of conductances and radii, we used pore open-state values, denoted by the subscript 'po'. For Figure  4e ,f, we first computed the probability density functions (PDFs) for individual pores using a fixed bin width for all, then averaged these to give equal weight to all pores. All distribution fitting was performed using Matlab Statistics Toolbox functions fitdist or mle, using maximum likelihood estimation. Open-pore conductance values were used point-by-point to estimate the open-pore radii, by approximating the pore as a cylinder and using the expression  (Hille, 2001) r po ¼ lG po =p À Á 1=2 , where is the resistivity of the solution, l ¼ 15 nm is the length of the cylinder, and G po is the open-pore conductance.
For assessing statistical significance when comparing sample means, we used the two-sample t-test or the nonparametric twosample Kolmogorov-Smirnov test (ttest2 or kstest2, Matlab Statistics Toolbox), as indicated in the figure legends. We considered each single-pore measurement to be a biological replicate. For clustering average single-pore conductances G po for vNLP30 measurements, we used a two-component Gaussian mixture model (Figure  6b ) that indicated a boundary between the two components at ~1 nS. Applying this cutoff to all vNLP samples, we produced the boxplot in Figure  6c , where the central red line on each box marks the median, the edges of the box are the 25th and 75th percentiles, and the whiskers extend from q3 +1. 5(q3 -q1) to q1 -1. 5(q3 -q1), where q1 and q3 are the 25th and 75th percentiles, respectively.
Calculation of the total free energy U as a function of pore size and number of SNAREs The probability that a fusion pore accesses a radius r po in the presence of N SNAREs is proportional to exp ÀU r po ; N À Á =kT À Á in equilibrium, as this is the Boltzmann distribution where U is the total free energy. We assumed that the bilayer-SNARE system is equilibrated as the current bursts measured experimentally are long-lived. To calculate this free energy, we summed the Boltzmann factor of all states that comprise such a system: exp À U r po ; N À Á kT ¼ X N Nz¼0 Z ¥ b exp À U tot r po ; h; N; N z À Á kT dh (11) Here, U tot is the total free energy of one fusion pore state with N z zippered SNAREs and of membrane separation h, given by U tot ¼ U mb þ U z þ U uz þ U hyd þ U ApoE (12) We performed the integration and the sum over all states in Equation 11 numerically in MATLAB.
Acknowledgements We thank  F Sigworth  and  D Zenisek  for expert advice and discussions, and  J E Rothman ,  M Caplan , and members of the  Karatekin and Rothman laboratories  for helpful discussions. We are grateful to  James E Rothman ,  Frederick Sigworth ,  David Zenisek ,  Yongli Zhang , and  Thomas Melia  for critically reading the manuscript. Jing Wang  kindly provided C45 lipids. This work was supported by  NIH  grant  R01GM108954  and a  Kavli Neuroscience Scholar Award  to EK. SMA, OB, and SK are members of the  Rothman laboratory , supported by  NIH   R01DK027044. WV was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ), fellowship  VE760/1-1.
SPIDER vacuum vessel The SPIDER Vacuum Vessel (VV) is an AISI 304 L stainless steel cylindrical vessel having diameter of 4 m and an overall length of about 6 m (see figure  3 ). It is made of two separate and demountable modules and two end lids, each of them installed on rails to facilitate the assembly and the opening/closing movements for future maintenance. Manufacture of the VV required optimized welding processes, very accurate machining, surface preparation and intermediate tests at the factory to fulfil the requirements for vacuum tightness, vacuum compatibility, precise positioning and opening/closing operations  [11]. The VV is provided with three 100 kV ceramic bushings for service line feedthroughs: one for electrical connections (power and signal) and two for hydraulic and gas injection pipes.
Other diagnostics that are required at the beginning of SPIDER operation (thermocouples, emission and laser spectroscopy, electrostatic probes, visible and infrared (IR) imaging) are almost ready including vacuum windows and the integration with the data acquisition system, while other ones still need R&D to be completed  [28]. Prototypes, like tomography cameras, and full setups from sensor to acquisition system, basic analysis and visualization software codes, like those for thermocouples and IR cameras, are being tested at the NIO1 RF source test facility in operation at Consorzio RFX in Padova  [29].
SPIDER caesium ovens SPIDER will be equipped with three caesium (Cs) ovens to inject fresh caesium into the beam source in a controlled way. A dedicated R&D program  [30]  has been planned since the design is complicated by the vacuum environment, the high reactivity of caesium, the difficulty of controlling the temperature distribution among the parts and maintaining its stability, the need to develop a special customized solenoid valve in vacuum. The first oven prototype is ready to be tested on a caesium test facility at the PRIMA site; the three ovens are expected to be installed on the SPIDER beam source by the end of 2017. In parallel, the caesium oven test facility being established at the PRIMA site (section 4. 6) will allow development and optimization of this component, crucial for the source performance.
SPIDER instrumentation and control The SPIDER Instrumentation and Control (I&C) system is implemented in three vertical tiers: conventional control (CODAS, Control and Data Acquisition System)  [35] , plant-wide investment protection (Central Interlock)  [36]  and Safety systems. These three tiers are not required to comply with the ITER Plant Control Design Handbook as SPIDER is not an ITER plant system. Nevertheless, most hardware/software technologies prescribed by ITER for the implementation of plant system I&C have been prototyped, tested and finally used  [37]. SPIDER CODAS integrates three software frameworks: EPICS  [38] , MDSplus  [39] , and MARTe  [40] , and much effort has been devoted to encompassing them into a single, flexible and reliable software environment  [41]. The scopes of these three software frameworks do not overlap significantly: EPICS is aimed at system supervision (about 20 plant systems are monitored), MDSplus at data management (130 Mbyte s -1 data throughput, 50 TByte/year estimated annual data storage in normal operation), and MARTe at fast, real-time control (500 μs fast control cycle time).
Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets. While there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data are often require subject-specific decisions which are hard to document. This work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space.
, 2011; Litvak et al. , 2011) , each of which is different. Even though these packages provide tutorials for single subject data analysis, it is typically left up to the investigator to coordinate and implement multi-subject analyses. Here, we try to address this gap by demonstrating a principled approach to the assembly of group analysis pipelines with publicly available code 1  and extensive documentation. As members and maintainers within the MNE community, we will present analyses that make use of the MNE software suite  (Gramfort et al. , 2014). Historically, MNE was designed to calculate minimum-norm estimates from M/EEG data, and consisted in a collection of C-routines interfaced through bash shell scripts. Today, the MNE software has been reimplemented in  (Gramfort et al. , 2013a)  and transformed into a general purpose toolbox for processing electrophysiology data.
Here, we demonstrate how to use MNE to reanalyze the OpenfMRI dataset ds000117 by  Wakeman and Henson (2015). This requires setting the objectives for the data analysis, breaking them down into separate steps and taking a series of decisions on how to handle the data at each of those steps. While there may be several interesting scientific questions that have not yet been addressed on this dataset, here we confine ourselves to the analysis of well-studied time-locked event-related M/EEG components, i. e, event-related fields (ERF) and event-related potentials (ERP). This is motivated by educational purposes to help facilitate comparisons between software packages and address reproducibility concerns. To this end, we will lay out all essential steps from single subject raw M/EEG recordings to group level statistics.
Preliminaries In this work, we describe a full pipeline using MNE to analyze the OpenfMRI dataset ds000117 by  Wakeman and Henson (2015). The data consist of simultaneous M/EEG recordings from 19 healthy participants performing a visual recognition task. Subjects were presented images of famous, unfamiliar and scrambled faces. The dataset provides a rich context to study different neuroscientific and cognitive questions, such as: Which brain dynamics are characteristic of recognizing familiar as compared to unfamiliar faces? How do commonly studied face-responsive brain regions such as the Superior Temporal Sulcus (STS), the Fusiform Face Area (FFA) and the Occipital Face Area (OFA) interact when processing the familiarity of the face? At the same time, it presents a well-studied paradigm which can be particularly beneficial for the development of methods related to connectivity and source localization.
Data description The subjects participated in 6 runs, each 7. 5 minutes in duration. In the original study, three subjects were discarded due to excessive artifacts in the data. To produce comparable results, the same subjects are also discarded from the group results in this study. The data were acquired with an Elekta Neuromag Vectorview 306 system consisting of 102 magnetometers and 204 planar gradiometers. In addition, a 70 channel Easycap EEG system was used for recording EEG data simultaneously.