dataesr/scientific-paragraphs-categorization huggingface dataset:
Cryo-EM image processing The 16,000 movies collected were aligned using MotionCor2, with dose weighting (6 e-/A ˚2/frame) and with 5 × 5 patches applied  (Zheng et al, 2017). gCTF was used to estimate the CTF parameters  (Zhang, 2016)  and low-quality images were discarded. Relion 2. 1  (Scheres, 2012)  autopicked 227,527 particles and after several rounds of 2D classification in cryosparc  (Punjani et al, 2017)  and a heterogeneous ab initio reconstruction (two classes), 37,435 particles were converted using the script csparc2star. py (Asarnow, 2016)  and selected for a final 2D classification in relion 2. 1 (Appendix Fig S2C ), of which 36,828 particles were selected. An initial unmasked refinement using the ab initio model from crysoparc gave us a resolution of 7. 6 A ˚with 5-fold applied symmetry and a soft mask of 450 A ˚.
1  (Fig EV2C). For figures and to build de novo pseudoatomic models in Coot  (Emsley et al, 2010) , the cryo-EM density was initially sharpened using phenix. autosharpen (Terwilliger et al, 2018)  and later with LocalDeblur (preprint: Ramı ´rez-Aportela et al, 2018). Fitting of density, correlation calculations, molecular graphics and analyses were performed on UCSF Chimera  (Pettersen et al, 2004). For the amphipoles dataset, 2D classes were calculated from a total of 8,637 particles  (Appendix Fig S10A).
Model building Model building proceeded by fitting the PDB 4Y7O  (Durand et al, 2015)  into the density 2 times for each pillar, with the cross-correlation being calculated using Chimera  (Pettersen et al, 2004; Fig EV2D). To complete the structure of TssM beyond the known region, which spanned aa. 869-1129, we used the de novo tracing strategy that we introduced in an earlier work  (Cherrak et al, 2018). Briefly, we iterated between manual model building and structural refinement on Coot  (Emsley et al, 2010)  using bulky sidechains and secondary structure predictions obtained by Phyre2  (Kelley et al, 2015)  as guides, and sequence-structure registration based on contact prediction obtained by RaptorX  (Ka ¨llberg et al, 2012). The map of the predicted contacts was aligned with those of the built PDB, the algorithm introduced by the MapAlign software  (Ovchinnikov et al, 2017).
Electron cryomicroscopy and electron cryotomography Escherichia coli BL21 and EAEC cells (overexpressing TssJLM where indicated), cryo-FIB-processed E. coli BL21 cells overexpressing TssJLM, and purified TssJLM samples were examined by electron cryotomography (cryo-ET). Images were recorded on a Tecnai Polara TEM (Thermo Fisher Scientific) equipped with post-column GIF 2002 imaging filter and K2 Summit direct electron detector (Gatan), or on a Titan Krios TEM (Thermo Fisher Scientific) equipped with a Quantum LS imaging filter and K2 Summit (Gatan). Both microscopes were operated at 300 kV and the imaging filters with a 20 eV slit width. The pixel size at the specimen level ranged from 4. 93 to 4. 05 A ˚. The latter pixel-sized was used for the subtomogram average. Tilt series covered an angular range from À60°to +60°with 2°(lamellae, sheath preparations) increments and À10 to À6 lm defocus, or in focus (0 lm defocus) when the data were collected on the Titan Krios with a Volta phase plate (Thermo Fisher Scientific;  Danev & Baumeister, 2016).
The total dose of a tilt series was 60-100 e À /A ˚2. Tilt series and 2D projection images were acquired automatically using UCSF Tomo  (Zheng et al, 2007)  on the Tecnai Polara and SerialEM  (Mastronarde, 2005)  on the Titan Krios. Three-dimensional reconstructions and segmentations were generated using the IMOD program suite  (Kremer et al, 1996). Table  3  summarizes the data collection, refinement and validation statistics.
Subtomogram averaging Tomograms used for subtomogram averaging were not CTFcorrected, as most of the particles were extracted from tomograms collected in focus with the Volta phase plate. Individual particles were identified visually in tomograms as 5-branched stars shapes in top and bottom views and as inverted Y shapes in side views and their longitudinal axes were manually modelled with open contours in 3dmod  (Kremer et al, 1996). The manual particle picking and first round of subtomogram averaging were performed with the PEET software package on tomograms that were binned by 4 (1k reconstructions). Model points, the initial motive list and the particle rotation axes were generated using the stalkInit program from the PEET package  (Nicastro, 2006). This approach allowed the definition of each structure's longitudinal axis as the particle y-axis.
Asarnow D (2016) Pyem. GitHub Available at: https://github. com/asarnow/ pyem Aschtgen M-S, Bernard CS, De Bentzmann S, Lloubes R, Cascales E (2008) SciN is an outer membrane lipoprotein required for type VI secretion in enteroaggregative Escherichia coli. J Bacteriol 190: 7523 -7531 Aschtgen MS, Zoued A, Lloubès R, Journet L, Cascales E (2012) The C-tail anchored TssL subunit, an essential protein of the enteroaggregative Escherichia coli Sci-1 Type VI secretion system, is inserted by YidC. Microbiologyopen 1: 71 -82 Basler M, Pilhofer M, Henderson GP, Jensen GJ, Mekalanos JJ (2012) Type VI secretion requires a dynamic contractile phage tail-like structure. Nature 483: 182 -186 Boyer F, Fichant G, Berthod J, Vandenbrouck Y, Attree I (2009) Dissecting the bacterial type VI secretion system by a genome wide in silico analysis: what can be learned from available microbial genomic resources? BMC Genom 10: 104 Brunet YR, Bernard CS, Gavioli M, Lloubès R, Cascales E (2011) An epigenetic switch involving overlapping fur and DNA methylation optimizes expression of a type VI secretion gene cluster.
Acknowledgements We would like to thank  Yoann Santin  for advice on fluorescent microscopy data recording, treatment and analysis,  Erney Ramírez-Aportela  for help with map sharpening with LocalDeblur,  Marion Decossas-Mendoza  and  Marie Glavier  for help with graphene grids preparation,  Laetitia Daury-Joucla  and  Olivier Lambert  for help with nanodiscs reconstitution, and  Tobias Zachs  for manual particle picking of subvolumes and for the design of a mask for subtomogram averaging. This work has benefitted from the facilities and expertise of the  Biophysical and Structural Chemistry platform (BPCS)  at  IECB ,  CNRS UMS3033 , Inserm  US001 ,  Bordeaux University , in particular we would like to thank  Armel Bezault. The authors acknowledge the support and the use of resources of Instruct-ERIC, Diamond light source and  Kyle Dent  in particular, for the collect of the amphipols-containing sample.
ScopeM is acknowledged for instrument access at  ETH Zürich , and  Ohad Medalia  is acknowledged for instrument access at the  University of Zürich. This work was funded by the  Centre National de la Recherche Scientifique , the  Aix-Marseille Université , and grants from the  Agence Nationale de la Recherche  ( ANR-14-CE14-0006-02 ,  ANR-17-CE11-0039-01 ) and the  Fondation pour la Recherche Médicale  ( DEQ20180339165 ) to EC. ED was supported by the  INSERM  and an  EMBO  short-term fellowship ( ASTF 417 -2015 ). YC is supported by a  Doctoral school PhD fellowship  from the  FRM  ( ECO20160736014 ). VS is supported by a post-doctoral fellowship from the association Espoir contre la Mucoviscidose. LL was supported by a fourth year PhD fellowship from the  FRM  ( FDT20160435498 ). MP is funded by the  European Research Council , the  Swiss National Science Foundation  and the  Helmut Horten Foundation.
Data availability The cryo-EM structure of the TssJLM core, full complex (C5 and C1 symmetry) and base were deposited in the EMDB under ID codes EMD-0264, EMD-0265, EMD-0266 and EMD-0267, respectively. The TssJLM core atomic model was deposited in the PDB under ID code 6HS7. The cryotomography maps were deposited under ID codes EMD-4561 and EMD-4562. Raw cryo-EM data are available on request. Expanded View for this article is available online.
6 × 10 -04 ). No differential response to the overfeeding was observed in the 2 groups. Conclusions: The distinct molecular signature of the adipose tissue in CT individuals suggests the presence of augm ented futile lipid cycling, rather than mitochondrial uncoupling, as a way to increase energy expenditure in CT individuals. We propose that increased mitochondrial function in adipose tissue is an important mediator in sustaining the low body weight in CT individuals. This knowledge could ultimately allow more targeted approaches for weight management treatment strategies. This trial was registered at clinicaltrials. gov as NCT02004821.
Plasma proteomics Proteomic analysis was performed on 203 EDTA-plasma samples in a fasted state before (visit 1), during (visit 6), and 11 d after the overfeeding intervention (visit 9) (7) using LC-MS/MS. Full details characterizing the workflow and describing the laboratory method were previously reported  (13, 14). LC-MS/MS was performed with an Orbitrap Fusion Lumos Tribrid mass spectrometer and an Ultimate 3000 RSLC nano system (Thermo Scientific). Protein identification was performed against the human UniProtKB/Swiss-Prot database (26/10/2015 release) as previously described  (14). A total of 999 proteins were measured. Biological references (identical pools of all individual plasma samples) were used to obtain relative protein quantification in each sample. A maximum of 30% missing quantitative values per protein was accepted.
Staining and imaging of adipose tissue Tissues were collected, fixed, and stabilized using Paxgene tissue containers (Qiagen) and paraffin embedded and sectioned (4 μm). Hematoxylin and eosin (H&E) staining was performed to determine adipocyte area. Slides were scanned with the Aperio AT2 (Leica; 20×amplification). Ten to 12 images (containing at least 80 adipocytes per image) were evaluated per sample using the Adiposoft software  (18)  in a semiautomated fashion with manual removal of false positives and negatives (including removal of areas <350 μm 2 ). To evaluate the size distribution frequency, histograms were made by grouping areas from 350 to 15,000 in 500-μm 2 increments. Immunohistochemistry for uncoupling protein 1 (UCP1) was performed with primary polyclonal rabbit UCP1 antibody (dilution 1:20, 1-UN009-07; Quartett) on paraffin-embedded tissue sections using an automated immunostainer (BOND-III; Leica Biosystems).
Ten flow cells were necessary to obtain the necessary read depth. Sequencing was performed for 2 × 125 cycles on a HiSeq 2500 strictly following Illumina's recommendations. The demultiplexing and the quality control steps were performed as described in Armenise et al. (21). Briefly, sequencing data were demultiplexed with the Bcl2FastQ Demux Pipeline (UBP_ bcl2fastq v2. 19. 1). The resulting FASTQ files were mapped onto the human genome (GRCh37. 75 assembly) with STAR2. 5. 3a using default parameters. Multimapper reads, PCR duplicates, and reads with more than 5 mismatches were filtered out. For each sample, the number of reads mapping onto genes was retrieved using HTSEq software. Only reads with both ends mapping onto 1 single gene were considered. Low expressed genes were excluded by using the threshold of at least 1 count per million in a third of the samples.
Validation of gene expression using Nanostring Gene expression was validated using Nanostring technology. The RNA samples derived from adipose tissue at baseline were used. Probes were designed for the top 62 genes differentially expressed in baseline adipose tissue samples between CT and control individuals from RNA sequencing (RNA-seq) analysis, as well as 9 normalization genes (manually selected based on stable expression in RNA-seq analysis and covering a range of expression levels). Normalization was performed by the Nanostring software. Principal component analysis of the expression FC on the quality-controlled data set did not identify any outliers. For all genes, a correlation test between RNAseq and Nanostring gene quantification was performed using a nonparametric Spearman correlation test.
Differential expression was tested by using a logistic regression model correcting for age and sex followed by a Wald test to assess the statistical significance of the coefficient of interest. As no sex interaction was found, association was evaluated without interaction term. Benjamini-Hochberg multiple testing correction was applied within the fitted model on the derived P values (FDR). (B) Correlation is shown between RNA sequencing (RNA-seq) and Nanostring gene expression analysis of top 65 differentially expressed genes, tested using a nonparametric Spearman correlation (Spearman's r = 0. 8926; 95% CI: 0. 82, 0. 94; P < 0. 0001). (C) Ingenuity Pathway Analysis showing the top 5 significantly affected pathways. Genes that passed the FDR-adjusted P value < 10% were used as input data (orange and dark blue dots in the volcano plot, corresponding to 753 genes).
The Kinetic Energy Operator For the MCTDH algorithm to be efficient, the Hamiltonian operator must be written as a sum of products (SOP) of single-particle operators. The Kinetic Energy Operator (KEO) has always the required form when using polyspherical coordinates, such as the Jacobi coordinates used in this work where we followed the approach of Ref. 35 the subsystem KEO derivation presented by Gatti and Iung  [36]  which was used recently to describe an asymmetric rotor -atom collision  [26]. As was the case with previous MCTDH calculations involving fragments, we do not work in the Body-Fixed frame but in the E 2 frame  [36]  which is obtained by rotation of the two first Euler angles of the SF frame. A choice of the vectors for this calculation is displayed in Figure  1. The KEO in the E 2 frame is generally expressed as 2 T = - 1 µ R ∂ 2 ∂R 2 + 2 TA + 2 TB + 1 µ R R 2 J † J + ( L A + L B ) 2 -2( L A + L B ) J E2 , (3) where µ R is the reduced mass of the H 2 O-H 2 cluster, the A and B subscripts refer respectively to the H 2 O and H 2 fragments.
J, L A and L B are respectively the total angular momentum of the system and the angular momenta of the fragments A (H 2 O) and B (H 2 ). The rigid rotor Hamiltonian of the H 2 O molecule is expressed as  [37] TA = A 2 L 2 A,+ + L 2 A,-+ L A,+ L A,-+ L A,-L A,+ - C 2 L 2 A,+ + L 2 A,--L A,+ L A,--L A,-L A,+ +BL 2 z BF A , (4) where the rotational constants have values A = 27. 8572 cm -1 , B = 14. 5145 cm -1 and C = 9. 2799 cm -1. The rigid rotor kinetic energy of the H 2 fragment is written simply as TB = B H2 L 2 B where the rotational constant B H2 = 59. 2434 cm -1. The final form of the KEO translated and implemented in the MCTDH code is then 2 T = - 1 µ R ∂ 2 ∂R 2 + 2 TA + 2 TB + 1 µ R R 2 J(J + 1) + L 2 A + L 2 B -2L 2 A,z -2L 2 B,z + 1 µ R R 2 (L A,+ L B,-+ L A,-L B,+ -2L A,z L B,z ) + 1 µ R R 2 (C + (J, K)(L A,+ + L B,+ )) + 1 µ R R 2 (C -(J, K)(L A,-+ L B,-)) , (5) with L A(B),± = L A(B),x ± iL A(B),y , (6) and C ± (J, K) = (J(J + 1) -K(K ± 1).
The Potential Energy Surface The potential energy operator just as the KEO needs to be expressed in the product form. For systems of low dimensionality which are not already in the product form, there exists an efficient fitting procedure (Potfit)  [38, 39]  implemented in the MCTDH package  [40]  to obtain the desired representation. For high dimensionality potentials MultiGrid  [41] , Multi-Layer  [42]  and Monte Carlo  [43]  versions exist to transform a general potential to the product form. However those implementations often lead to a large number of potential terms which will slow down the computations, for example in scattering calculations where several long propagations are necessary to obtain the cross-sections. In this work, we are able to go around this difficulty since the potential  [25]  is already provided in a multipolar form and we only had to make a transformation to express it in the coordinates representation used for the MCTDH calculations.
Rovibrational states calculations with MCTDH The rovibrational bound states of the H 2 O-H 2 complex are obtained with the block improved relaxation method  [44, 33] , implemented in the MCTDH package  [40]. The block improved relaxation is derived from the improved relaxation method  [45] , an MCSCF approach where the SPFs are optimized by relaxation  [46]  (propagation in imaginary time) and the coefficients vector (A-vector) is determined by diagonalization of the Hamiltonian matrix evaluated in the set of SPFs. The working equations of the improved relaxation and block improved relaxation have already been presented elsewhere  [47, 33]  and thus aren't given here. The primitive basis, its range and the number of the SPFs used for the calculations of the rovibrational states are summarized in Table  1.
We see that not more than 48,000 Single Particle Functions are used to run the calculations. As the calculation method is the Block Improved Relaxation, we have to perform several of those to obtain the complete set of expected rovibrational levels. Each calculation for a block of 6 functions with 48,000 SPFs and using 32 processors on a Linux based cluster takes approximately 48 hours. While the time for each calculation is not very long, the blocks are typically done sequentially, waiting for each block to complete (or be near completed) before starting the next block of higher states: this procedure adds to the overall duration of the calculation which therefore could even take longer than traditional time-independent calculations, depending on the number of desired states. However, quite often in this type of application, one is interested only in a limited number of low lying states making this procedure advantageous.
A primitive basis composed of Fast Fourier Transform (FFT) functions for the intermolecular distance R was coupled with a Wigner-DVR  [37]  basis where α A , γ A are replaced by their momentum representation k α , k γ and a two dimensional extended Legendre-K DVR  [48]  (replacing φ B by k φ ) to describe respectively the orientation of the H 2 O and the H 2 fragments in the E 2 frame. The Wigner and Legendre-K DVRs switch between their respective FBR (Wigner and Spherical harmonics) and DVR to calculate the action of the derivatives of operators (and the singularities) and to evaluate the potential respectively. Leforestier  [37]  defined the unitary transformation that goes from the 3D DVR to the 3D FBR: this transformaton is what is implemented in the MCTDH code for the Wigner-DVR, and a 2D version for the Legendre-K DVR.
For testing purposes, we also ran (J = 0) calculations with the angular primitive basis composed of the "usual" Wigner-DVR basis and a two dimensional Legendre DVR. The (Wigner,K,K) and (KLeg,K) DVRs are then replaced by their counterparts the (Wigner,Exp,Exp) and (PLeg,Exp) DVRs, where KLeg and PLeg are respectively the extended Legendre DVR and two dimensional Legendre DVR. Results of the two sets of calculations were confirmed to be identical, with the second set running faster than the first one. However that approach is not yet implemented in the MCTDH package for J > 0: thus, in the rest of this work we used only the first method for our calculations. After transforming the PES from Equation  (8)  to the E 2 frame we can write V (R, β A , γ A , α A , θ B , φ B ) = r β ,rγ rα,r θ Ṽr β ,rγ rα,r θ (R)frβ,rγ rα,r θ (ω A , ω B ) (12) where frβ,rγ rα,r θ (ω A , ω B ) = D (r β ) rαrγ (α A , β A , γ A ) C r θ ,-rα (θ B , φ B ) (13) with D and C being respectively the Wigner D-matrix and the Racah normalized spherical harmonics.
We show in Table  2  the convergence of the first few states for J = 0, an output from the MCTDH calculations. We use for these calculations a primitive basis which is similar in its range and size to the results reported by Wang and Carrington  [23] , but where we varied the number of SPFs. We can see from the Table the convergence of the calculation, but also the dummy (or spurious) states in italic, as mentioned earlier. The results of the rovibrational calculations from the Heidelberg MCTDH program provide relevant information that can be used to assign the various states. First, it is possible to remove spurious or non-physical rovibrational states (as we mentioned before) with m A + m B > J from the calculations by individually analysing each state, doing for example a short time single-state calculation.
Conclusion We report calculations of rovibrational energies on the H 2 O-H 2 system in the rigid rotor approximation, obtained with the MCTDH algorithm on the Valiron PES  [25]. Our results are found to be in very close agreement with the values previously reported. The calculations performed here with the MCTDH program are relatively fast, computationally cheaper and relatively easier to implement than P Λ(j kakc ) Ref. [23]  Ref. [24]   standard calculations and therefore appear as a bright prospect for the investigation of similar or other even more complex systems. Additionally, the extension to full dimensionality (9D) calculations on this system can be easily implemented and performed at a lower cost than other traditional methods, even though it may become more difficult to implement the symmetry in higher dimensionality.
The performance of this sampling is approved by simulation and practice, using respectively Matlab and Arduino microcontroller, respectively, for acquiring signals at a randomly spaced clock. A database was created in order to observe the impact of statistical parameters such as the mean and the standard deviation for Gaussian distribution, and the interval endpoints of uniform distribution. In conclusion, the ARS with uniform distribution has been proved to have less limitations  [4] , whereas the spectrum of sampled signal is free of alias and the sampling frequency is reduced (a much smaller than Nyquist-Shannon sampling rate). In order to take advantage of this mode, it is applied on vibration signals to enhance remoted machine monitoring.
Due to hardware limitations the ratio R couldn't have an exact value of 1, it is slightly greater than 0. 8. The samples of the signal with their instants of sampling are saved, so the DFT of the randomly sampled signal can be calculated, in Matlab, as mentioned in  (17). The offset of the signal is eliminated before calculating the DFT. A uniformly sampled triangular waveform, with a frequency of 1 kHz, an amplitude equal to 2. 5 and an offset of 2. 5, is simulated on Matlab without noise. It has a spectrum with peaks on 1 kHz and its odd multiples as in Figure  9 , and the fundamental has the highest amplitude while the other harmonics have reduced amplitudes. To observe the two harmonics at 3 kHz and 5 kHz, the uniform sampling frequency is taken 10. 2 kHz, the length of the signal is 2040 points.
The signal (of 100 000 pts), was processed in Matlab. In diagnostic, many tools of bearing signals' processing are based on the signal envelope's extraction to detect the important frequencies that give information about the bearing state: rotation frequency, defection characteristic frequency and others  [17]. In this study, all the vibration signals, randomly or uniformly sampled, were explored without any pre-processing or filtering. The start is with the case of normal bearing, where the frequency to detect is the rotation frequency. In uniform sampling, only one segment of 1000 points of the signal and its envelop are shown in Figure  13. First, the start will be with the case of normal bearing, where the frequency to detect is the rotation frequency. In uniform sampling, the signal in time domain is shown in Figure  13 (a).