dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatic analysis. The prevalence and sequence conservation of yeeJ was examined using the FASTA36 software package  34  to probe 96 complete E. coli genomes on the National Centre for Biotechnology Information (NCBI) database (Figure  S1 ). The prevalence of genes was determined using a cut-off of >75% over a 75% amino acid sequence alignment. The E. coli strains were classified into major phylogroups (A, B1, B2, D, E and F) based on an in silico analysis of the arpA, chuA, yjaA and TSPE4. C2 loci  35. Amino acid alignments were performed using ClustalW  36. The Conserved Domain Database (CDD)  37  , Phyre2  38  and InterPro  39  were used to analyze protein structures, and SignalP4. 1  40  was used to predict the presence of signal sequences. The genomic context of genes was analysed with Easyfig.
RNA extraction and qRT-PCR. 500 µl of exponentially growing cells (OD 600 = 0. 6) were stabilized in 1 ml of RNAprotect Bacteria Reagent (Qiagen), and subsequent RNA extraction was performed using the RNeasy Mini Kit (Qiagen). After treatment with rDNase I (Ambion) to remove contaminating gDNA, the RNA was re-purified using the RNeasy Mini Kit RNA cleanup protocol (Qiagen). First-strand cDNA synthesis was performed using the SuperScript ® III First-Strand Synthesis System (Invitrogen) as per manufacturer's recommendation. Real-time PCR was performed using SYBR ® Green PCR Master Mix (Applied Biosystems) on the ViiA TM 7 Real-Time PCR System (Applied Biosystems) using primers 6965 (5′-tttgcgactgaccaactg) and 6966 (5′-agaccagtttaccgccaa) for yeeJ. Transcript levels of each gene were normalized to gapA as the endogenous gene control using primers 820 (5′-ggtgcgaagaaagtggttatgac) and 821 (5′-ggccagcatatttgtcgaagttag).
Bioinformatic analysis of the YeeJ AT protein. Bioinformatic analysis of YeeJ protein from the strain MG1655 revealed a 2340 amino acid protein (instead of the 2358 amino acid protein indicated in the automated annotation). YeeJ MG1655 is predicted to possess a multi-domain structure: (i) an N-terminal signal sequence, (ii) a periplasmic LysM (Lysin Motif) domain (Prosite PS51782), (iii) an inverse autotransporter translocator domain (Pfam PF11924), (iv) a passenger domain containing an invasin domain 3 (Pfam PF09134) and 13 repeated bacterial immunoglobulin-like 1 domains (Pfam PF02369) and (v) a C-type lectin terminal domain (Pfam PF07979) (Fig. 1B ). The LysM domain is found in many bacterial membrane-associated and secreted proteins, and mediates direct interaction with the peptidoglycan layer to stabilize cell-surface associated proteins.
It may even contribute to the formation of pores in the peptidoglycan layer that assist protein translocation  18, 54. Modeling of the 49 amino acid YeeJ LysM domain using the PHYRE2 program  38  indicated that it strongly resembles the domain found in other adhesins including intimin, FsaP of Francisella tularensis and TspA of Neisseria meningitidis (Figure  S2 )  55, 56. The translocator domain of YeeJ revealed a predicted 12-stranded β-barrel structure (Figure  S3 ). This domain also shares 59% amino acid sequence identity with the β-barrel domain of the intimin-like protein FdeC, and is likely to be involved in the insertion of YeeJ into the outer membrane  57. The passenger domain is predicted to be extracellular, as described for the intimin and invasin proteins  17  and contains 13 bacterial immunoglobulin-like domain (Big) repeats.
These repeats are typical of type Ve autotransporter proteins such as intimin and FdeC  18, 57. The last 103 amino acids of YeeJ correspond to a C-type lectin domain, a structural domain also found in intimin and some other invasins that bind to carbohydrates  58  (Figure  S4 ). Analysis of the YeeJ variant from strain UMN026 revealed a similar overall predicted structure, with the additional 906 bp in the passenger domain. The 906 bp fragment is predicted to encode for three additional Big domains. BLAST analysis of the 906 bp fragment revealed that it is made up of a repeated section of sequence also found in YeeJ MG1655. Cloning and expression of the yeeJ gene from MG1655 and UMN026. In order to examine the function of YeeJ and the impact of the additional 906 bp region in yeeJ UMN026 , the yeeJ MG1655 and yeeJ UMN026 genes were PCR amplified and cloned into the low copy IPTG inducible pSU2718 expression vector to generate the plasmids pYeeJ MG1655 and pYeeJ UMN026 , respectively.
All three mutants contained independent insertions within the pnp gene (Accession Number: ECK3152). The pnp gene encodes for a multi-enzyme complex polynucleotide phosphorylase (PNPase), which is involved in RNA metabolism and controls numerous phenotypes such as biofilm formation, motility and bacterial survival  [62] [63] [64]. In order to confirm the activity of the yeeJ promoter in the pnp mutant, we generated a specific pnp mutant in the MG1655 ΔlacIZ ∆yeeJ::lacZ strain and performed a ß-galactosidase assay (Fig. 6A ). To complement pnp mutation, the pnp gene from strain MG1655 was amplified and cloned into the pZE12CFP plasmid, under the control of an IPTG inducible promoter, to generate plasmid pPNP2. Our results demonstrated that the activity of the yeeJ promoter is induced in the pnp mutant (MG1655 Δpnp ΔlacIZ ΔyeeJ::lacZ), and complementation of the pnp mutation with pPNP2 restores promoter activity to wild type level.
To further confirm the effect of PNPase on yeeJ transcription, we deleted pnp in MG1655Δpnp and we evaluated levels of these transcripts by qRT-PCR. The strain MG1655 PcLyeeJ, that constitutively expresses yeeJ was used as a positive control. The level of yeeJ transcript was examined in wild type MG1655, MG1655 Δpnp (pZE12CFP), complemented MG1655 Δpnp (pPNP2) and MG1655 PcLyeeJ strains by qRT-PCR (Fig. 6B ). Consistent with our ß-galactosidase assay, inactivation of the pnp gene led to an ~9-fold increase in relative yeeJ transcript level, and complementation of the mutant with pPNP2 plasmid restored relative yeeJ transcripts to wild type level. In contrast to MG1655 Δpnp, the level of yeeJ transcription in MG1655 PcLyeeJ was 900-fold higher than in MG1655. Deletion of the pnp gene in MG1655 did not lead to increased biofilm formation (data not shown), suggesting that a threshold level of YeeJ is required to translate into increased adhesion.
Our in silico screen of complete E. coli genomes publicly available on the NCBI database revealed that 38 strains possess an intact yeeJ gene encoding for the full length protein. These strains belong to different pathotypes and phylogroups, suggesting that the yeeJ gene is conserved across a diverse range of strains. An additional 15 strains possess the yeeJ gene, but have frame-shift mutations that result in a truncated YeeJ protein. However, it is possible that these mutations are artifacts of sequencing or post-sequencing genome assembly errors. Indeed, this type of miss-assembly has previously been observed in the highly repetitive upaH AT gene from CFT073, which was initially reported to contain a frame-shift resulting in a truncated protein. Subsequent work determined that the upaH gene was misassembled during genome closure, and in fact it encodes for a full-length surface-expressed protein that mediates biofilm formation  73.
Acknowledgements We thank  L. Férrières  and  Laura Grange  for the construction of the yeeJ-lacZ reporter fusion. This work was supported by the  Institut Pasteur , the  French Government Investissement d'Avenir Program Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant no. ANR-10-LABX-62-IBEID ),  Fondation pour la Recherche Médicale  grant "Equipe  FRM DEQ. 20140329508 " and from the  Région Ile-de-France (DIM Malinf) , and grants from the  Australian National Health and Medical Research Council (NHMRC). MAS is supported by an  NHMRC   Senior Research Fellowship  ( GNT1106930 ). MT is supported by a  Queensland University of Technology Vice-Chancellor's Senior Research Fellowship.
Since we set low i max , the evaluation procedure is quite fast, and adds only (g -1)i max iterations compared to the case where only one pattern is considered. For the chosen Γ , we can continue iterating from i = i max + 1 (using ( X(k) , µ ) as the initial point) until reaching β-defined convergence. The overall functioning of the AUDASCITY algorithm is described by the pseudo code in Algorithm 2 for a given block of adjacent frames Z ∈ R L×(2b+1).
G. Backward-caching scheme To speed up computations, and possibly to account for previously processed information, we incorporate a "backwardcaching scheme". This simply means that instead of processing data blocks indexed by [n -b, n + b], we consider only the ones linked to indexes in [n, n + b], assuming that the frames in the interval [n-b, n-1] have been already recovered. These blocks, denoted by Xbw , are then given to the algorithm, but considered as fixed: the PEW shrinkage in the Z-update step is applied to the augmented matrix [ A Xbw AX (i-1) +U (i-1) ]. Also, when computing the empirical entropy, we account for the Xbw blocks. We have seen that, in practice, this has a negligible effect on the recovery performance, while indeed reducing the computational cost (see III-B for details).
III. EXPERIMENTS We conduct experiments on the MPEG items and the RWC Music Database  [12]. On the latter, we use the "Pop" and "Jazz" genres as is, and subcategorize the "Classic" genre (Vocals, Chamber, Symphonies), leading to 6 subsets. All the tracks are sufficiently diverse to reflect the robustness of the approach on different audio content. We contaminate the audio tracks with additive white gaussian noise at five SNR levels. The AUDASCITY algorithm is confronted to the state-of-theart time-frequency block thresholding (BT). We note that BT is parameterized with true variance σ 2. AUDASCITY parameters are as listed in table  I. In this study, we choose a non redundant DFT transform for A i. e. P = L. We conduct three phases in testings: one to analyze SNR numerical results, a second to look at performance on sound classification.
B. Computational Aspects To quantify the speed performance of AUDASCITY we perform run time tests on a laptop computer with 2. 8 GHz Intel Core i7 processor and 16 GB ram memory. All tests are run on 30 second audio excerpts sampled at 16 kHz with Matlab in single thread mode. Table III displays all the results. The computational cost is, as the improvement, strongly linked to the input SNR. Indeed, as the SNR decreases, the inner loop takes more iterations to converge. On the other hand, runtime benefits clearly from the backward-caching scheme, and it appears that we can reach a 30% speed-up without performance loss. These encouraging results make it possible to envision real-time applications even for moderate SNR.
C. Classification Results One example for usefulness of a performant denoising is classification of audio signals, where the noise pollution, generally, has adversarial effect on performance. The extent to which the performance is affected depends on several factors, among them the type of extracted features being an important one. For this reason, we conduct a small scale classification benchmark, involving two types of features: i) widely used Mel Frequency Cepstral Coefficients (MFCC), and ii) the features based on the (second order) scattering transform, recently proposed by Mallat et al. [15]. The latter has been shown to outperform MFCC on several occasions, owing to its ability to exploit information from a wider frequency range  [16]. We use the training database provided by DCASE 2016 sound event recognition challenge  [17].
The database split in 15 classes, is randomly partitioned (balanced classes), such that 70% of audio files are used for training, and the remaining 30% are used as test data. The chosen database enables us to directly evaluate the effect of SNR and denoising on classification performance. The following experiments are performed using a kernel-based SVM classifier  [18] , and the features are extracted from overlapping audio segments of duration T ≈ 0. 18s. For scattering, we use Scatnet toolbox provided by the authors  [19]. Table  IV  presents the classification accuracy for the noiseless, noisy (SNR = 0dB) and denoised audio, with the training and test sets equally treated. The negative impact of low SNR on accuracy can be clearly seen, particularly for the MFCC features. Likewise, denoising leads to an improvement in performance, with the proposed algorithm outperforming state-of-the-art BT, for both types of features.
D. Perceptual Results While the objective assessment is certainly an important quality metric, the usefulness of a performant denoising may also include the auditory appearance. In this way, in this part, we are interested in the perceptual quality of the denoising procedure. For that purpose, we use a listening test available online through the application framework presented in  [20]. This procedure is based on the MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) evaluation framework  [21]. We evaluate a subset of the conditions presented for the numerical experiments in Section III-A. For the test not to last longer than 40 minutes, we choose 3 input SNR {0, 5, 10 dB}. As for the sound material, we pick a 4 seconds excerpt randomly chosen from each genre of the RWC database and the all MPEG database leading to a total For quite adversarial conditions (0 and 5dB) we can see a slight advantage for BT while as the SNR increases, the trend tends to reverse.
An experimental program, called AMSTRAMGRAM, was recently conducted in the Minerve low power reactor operated by CEA Cadarache within the frame of the CHANDA initiative (Solving CHAllenges in Nuclear Data). Its aim was to measure the integral capture cross section of 241 Am in the thermal domain. Motivation of this work is driven by large differences in this actinide thermal point reported by major nuclear data libraries. The AMSTRAMGRAM experiment, that made use of well characterized EC-JRC americium samples, was based on the oscillation technique commonly implemented in the Minerve reactor. First results are presented and discussed in this article. A preliminary calculation scheme was used to compare measured and calculated results. It is shown that this work confirms a bias previously observed with JEFF-3.
Introduction For nearly 10 years, several experiments have been conducted to improve 241 Am radiative capture cross section. Indeed, evaluated cross sections from major libraries differ from 10% to 15%, especially in the thermal domain  [1] [2] [3]. For instance, a difference of 9% on the thermal point can be observed between JEFF-3. 2 and ENDF/B-VII. 1 nuclear data libraries. An international collaborative working group organized by NEA-OECD started in 2015 with the aim of explaining such discrepancies  [4]. In this framework, an experimental program was recently conducted by CEA Cadarache in the Minerve reactor. Its main objective was to measure 241 Am integral capture cross section in a very thermal neutron spectrum, providing valuable information on the 241 Am thermal point, with a target accuracy of 3% (1σ ).
Measurement setup An americium sample has a reactivity worth of -0. 2 pcm roughly, which is quite low compared to the measurement range (∼10 pcm). In order to increase the signal-to-noise ratio of the measurement, several samples were oscillated at the same time. For that purpose, up to 5 americium samples (and up to 7 gold samples) were loaded columnwise in a sample container. The container was made of aluminum alloy 5754 and sealed by screw-in plugs. It was placed in an oscillator tube along with aluminum rods (al 5754 as well) so that its mid-plane was aligned with the core mid-plane when the oscillator was in its lower position. Measurements were composed of sets of 5 oscillation cycles with 120 s period. Acquisition was trigged by the oscillator control system and recorded data included 3 analog signals: oscillator position, current of the boron chamber and angle of the cadmium rod versus time.
Conclusions This paper presents preliminary experimental results from the AMSTRAMGRAM program conducted by CEA in the Minerve reactor, in collaboration with EC-JRC. A very thermal neutron spectrum was designed for the experiment that aimed at providing a new integral measurement of 241 Am radiative capture cross section with a target uncertainty of less than 3%. The experiment, based on the pile oscillator technique, was only possible thanks to EC-JRC that provided several well characterized americium samples. Preliminary analysis shows a clear trend when comparing measurements to calculations associated to JEFF-3. 1. 1 and JEFF-3. 2 libraries. First results are in very good agreement with JEFF-3. 2 thermal point with a preliminary discrepancy of 0. 5 ± 2%. This work was conducted in the framework of the CHANDA project, which is part of the European Commission 7 th Framework Programme.
Training Setup Training Databases. We train our system using the 3DDFA data of  [55]. The 3DDFA data provides projection and 3DMM model parameters for the Basel  [35]  + Face-Warehouse  [7]  model for each image of the 300W database. We use the topology defined by this model to define our UV space and rasterize the images to obtain per-pixel ground truth UV coordinates. Our training set consists of the LFPW trainset, Helen trainset and AFW, thus 3148 images that are captured under completely unconstrained conditions and exhibit large variations in pose, expression, illumination, age, etc. Many of these images contain multiple faces, some of which are not annotated. We deal with this issue by employing the out-of-the-box DPM face detector of Mathias et al. [32]  to obtain the regions that contain a face for all of the images.
We present experimental results using the very challenging 300W benchmark. This is the testing database that was used in the 300W competition  [41, 40]  -the most important facial landmark localization challenge. The error is measured using the point-to-point RMS error normalized with the interocular distance and reported in the form of Cumulative Error Distribution (CED). Figure  7  (bottom) presents some self-evaluations in which we compare the quality of initialization for deformable modelling between DenseReg and two other standard face detection techniques (HOG-SVM  [23] , DPM  [32] ). The employed deformable models are the popular generative approach of patch-based Active Appearance Models (AAM)  [34, 46, 1] , as well as Input Image the current state-of-the-art approach of Mnemonic Descent Method (MDM)  [44].
It is interesting to notice that the performance of DenseReg without any additional deformable model on top, already outperforms even HOG-SVM detection combined with MDM. Especially when DenseReg is combined with MDM, it greatly outperforms all other combinations. Groundtruth U (u h ) Estimated U (û h ) Groundtruth V (u v ) Estimated V (û v ) DenseReg DenseReg+MDM Method AUC Failure Rate (%) DenseReg + MDM 0. 5219 3. 67 DenseReg 0. 3605 10. 83 Fan et al. [15]  0. 4802 14. 83 Deng et al. [14]  0. 4752 5. 5 Martinez et al. [30]  0. 3779 16. 0 Cech et al. [8]  0. 2218 33. 83 Uricar et al. [48]  0. 2109 32. 17 Table  2 : Landmark localization results on the 300W testing dataset using 68 points. Accuracy is reported as the AUC and the Failure Rate. Figure  7  (top) compares DenseReg+MDM with the results of the latest 300W competition  [40].
Deformable Tracking Method AUC Failure Rate (%) DenseReg + MDM 0. 5937 4. 57 DenseReg 0. 4320 8. 1 Yang et al. [52]  0. 5832 4. 66 Xiao et al. [51]  0. 5800 9. 1 Rajamanoharan et al. [38]  0. 5154 9. 68 Wu et al. [50]  0. 4887 15. 39 Unicar et al. [47]  0. 4059 16. 7 Table  3 : Deformable tracking results against the state-ofthe-art on the 300VW testing dataset using 68 points. Accuracy is reported as AUC and the Failure Rate. For the challenging task of deformable face tracking on lengthy videos, we employ the testing database of the 300VW challenge  [42, 12]  -the only existing benchmark for deformable tracking "in-the-wild". The benchmark consists of 114 videos (∼ 218k frames in total) and includes videos captured in totally arbitrary conditions (severe occlusions and extreme illuminations).
Dense Correspondence for the Human Body To portray that the DenseReg system can be used for articulated shapes of complex topology, we present results on the human shape. We use the recently proposed "Unite the People" (UP) dataset  [25] , which provides a 3D deformable human shape model  [28]  in correspondence with images from several publicly available datasets. We handle the complex geometry of the human shape by manually partitioning the surface into patches. We unwrap each patch using multidimensional scaling. The partitioning replaces the quantization and the rest of the system remains the same. Since there are no dense correspondence results between a 3D human model and image pixels in literature, we demonstrate the performance of our system through visual results from our test-set partition of the UP dataset in Fig.
JANIS (JAva-based Nuclear Data Information System) software is developed by the OECD Nuclear Energy Agency (NEA) Data Bank to facilitate the visualization and manipulation of nuclear data, giving access to evaluated nuclear data libraries, such as ENDF, JEFF, JENDL, TENDL etc. , and also to experimental nuclear data (EXFOR) and bibliographical references (CINDA). It is available as a standalone Java program, downloadable and distributed on DVD and also a web application available on the NEA website. One of the main new features in JANIS is the scripting capability via command line, which notably automatizes plots generation and permits automatically extracting data from the JANIS database. Recent NEA software developments rely on these JANIS features to access nuclear data, for example the Nuclear Data Sensitivity Tool (NDaST) makes use of covariance data in BOXER and COVERX formats, which are retrieved from the JANIS database.
Introduction JANIS  [1]  software is developed by the NEA Data Bank to facilitate the visualization and manipulation of nuclear data, giving access to evaluated data libraries, and to the EXFOR and CINDA databases  [2]. It is available in several ways: as a standalone Java software, downloadable from the web and distributed on DVD and also a web application available on the NEA website. JANIS 4. 0 was delivered in September 2013  [1]  and new features described below will be available in JANIS 4. 1. A summary of JANIS databases and JANIS handbooks is presented in the paper. The procedure used to process evaluated data files into JANIS database is also described. Recent NEA software developments rely on JANIS to access nuclear data such as covariances for the Nuclear Data Sensitivity Tool (NDaST)  [3]  and automated plots generation for the Nuclear Data Evaluation Cycle (NDEC) system  [4].
JANIS databases Neutron evaluated libraries (e. g. TENDL-2015  [5] ) are processed for being uploaded in JANIS Database (see Fig. 1 ) by merging ENDF-6 (original files) and PENDF (reconstructed-pointwise ENDF) files to produce HENDF (Hybrid ENDF) files, an acronym used at NEA, which includes reconstructed cross section from MF3, in consistency with MF33 covariance data when necessary, plus all the other MF data included in the original ENDF-6 format file. Figure  2  presents the procedure to generate HENDF files. The processing code system used is essentially a e-mail: Nicolas. SOPPERA@oecd. org NJOY2012. 50 [6]  together with routines from the PREPRO and ENDF Checking & Utility packages. The NJOY modules used in this processing are: MODER, RECONR, BROADR, HEATR, GASPR, ERRORR and COVR. For HENDF files, PENDF data are generated with general tolerance criteria for the reconstruction, thinning and broadening of 0.