dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion and Discussion We first compared eye and mouse data with the saliency metric PCC. We did not find significant consistency between participants' eyes and mouse positions (inter) and between participants' eyes (intra). However, results showed that participants behaved in a more similar way when they had the same task with the same location (reading task). Then, we got deeper with dynamic analyses. We showed that using distance and correlation, we were able to highlight more interesting coordinations between eyes and mouse. We had better results on Y axis than X axis and succeed to demonstrate behaviour differences between tasks. In addition, scroll analyses, clearly showed a relation between eyes position and scroll speed while browsing and amplitude before the scroll. Finally, we made a model for each task able to predict the area around the mouse's cursor in which the eyes had 70% chances to be located in.
Growth in polyethylene glycol (PEG). Upon inoculation in BAP-medium at a 0. 1 OD 600 , growth of F. alni strain ACN14a was inhibited at all concentrations of PEG tested. Nitrogen fixation was active without PEG but even the lowest concentration of PEG completely inhibited it (data not shown). We chose an inoculation density of 0. 1 to have sufficient biomass and after 7 days, growth without PEG had reached an OD 600 of 0. 677, i. e. a 0. 74 fold in density increase. In the presence of PEG 0. 9%, OD 600 reached 0. 42 or a 0. 47 fold increase. Growth in the presence of PEG was thus 63% that without PEG. Hyphae, vesicles and sporangia looked similar under both conditions. Comprehensive proteome coverage of F. alni cells. Thanks to the high speed and high resolution of this analyser, a total of 295,788 high quality MS/MS spectra were recorded.
Amongst these, a set of 208,018 MS/ MS spectra (70. 3%) was assigned to peptide sequences. This dataset allowed detecting a total of 20,825 peptide sequences, which pointed at 2,454 polypeptides, with 2,052 proteins certified with at least two distinct peptide sequences. These proteins and their characteristics are listed in Supplementary Data, Table  S1. On the average, a total of 20,801 spectral counts were measured per sample with a remarkably low standard deviation (5. 3%). With an average of 10 peptides and 100 spectral counts per polypeptide, an extensive coverage of the proteome was reached that compares favourably to most proteomic studies of bacteria  16, 30, 31.
Functional analysis. The functional enrichment analysis applied on the lists of differentially expressed proteins following PEG treatment, covering Gene Ontology and pathway annotations, was performed through the STRING database  32. Among the enriched KEGG pathways within the blue class differentially expressed proteins, we denote the general metabolic pathways (map01100, FDR = 0. 000147), ribosome (map03010, FDR = 0. 000147) and sesquiterpenoid and triterpenoid biosynthesis (map00909, FDR = 0. 0032). Among the most significantly enriched biological processes associated to the blue class of differentially expressed proteins, we found: nitrogen compound metabolic process (GO. 0006807, FDR = 7. 79e -05 ), metabolic process (GO. 0008152, FDR = 0. 00022), protein metabolic process (GO. 0019538, FDR = 0.
Their length determines their molecular weights which are computed as follows: MW = 18. 02 + 44. 05*n, where n in the number of the repeating units. For instance, PEG400 is composed of 8-9 repeating units and PEG8000 is composed of 181-182 repeating units. PEG400 was herein used as a basic model for moderate to long chain PEGs to conduct molecular docking simulations. In fact, the moderate length of PEG400 can be handled by docking programs for accurate calculations while larger sizes (e. g. , PEG8000) would be time consuming with poor quality (data not shown). We hypothesised that data collected with PEG400 along with the different probes will provide insightful results on the PEG-protein interactions for our case study. PEG400 (see Fig. 5 ) was thus docked on the structure model of the FRAAL0095.
Gautam and colleagues  47  brought insightful elements for a better understanding of the interactions of PEG with protein surfaces and reported in particular the corona-like binding mode previously described  [48] [49] [50] [51]. Our findings are consistent with the literature, which adds confidence to our computational approach. In fact, long-chain PEGs are flexible polymers with multiple rotatable bonds and high degrees of freedom, which makes them a challenging case for molecular docking software. In the present work, we established a protocol to circumvent this issue by using the repeating unit of the PEG and its chemical features  as probes in order to infer a simplistic model for docking. Then, the SOM-based method permitted to generate an accurate map of the binding hot spots on the MscL surface, along with a prediction of the overall binding mode.
Peptides were injected onto a reverse phase Acclaim PepMap 100 C18 column (3 µm, 100 Å, 75 µm id × 500 mm) and resolved at a flow rate of 0. 2 µL/min with a 60 min gradient of CH3CN in presence of 0. 1% formic acid. A dataset of 10 nanoLC-MS/MS runs were recorded. The Q-Exactive HF instrument was operated with Top20 standard parameters and a dynamic exclusion of 10 sec as previously described  73. MS/MS spectra were searched using MASCOT 2. 2. 04 software (Matrix Science, London, W1U 7GB, UK) against the F. alni database (5,804 protein sequences) with the following parameters: full-trypsin specificity, maximum of two missed cleavages, mass tolerances of 5 ppm on the parent ion and 0. 02 Da on the higher energy collisional dissociation-induced peptidic fragments, fixed modification of carboxyamidomethylated cysteine (+57.
0215), and oxidised methionine (+15. 9949) as dynamic modifications. Peptide-to-MS/MS spectrum matching with a MASCOT score below a p-value of 0. 05 were selected and assigned to unique peptide sequence following the parsimony principle. A protein was considered valid when at least two different peptides were detected. The false-positive rate for protein identification was estimated by a search with a reverse decoy database to be below 0. 1% using the same parameters. Proteins were quantified based on their spectral counts. The normalised spectral abundance factor (NSAF) was calculated by dividing the spectral count for each observed protein by the polypeptide theoretical mass, as described previously  30  and is presented as percentage of the NSAF sum considering all proteins. Proteome comparison between both conditions was done taking into account the five biological replicates with the TFold module from the PatternLab software and standard normalisation, as previously described  71.
Homology-based functional analysis and Clusters of Orthologous Groups of proteins (COGs) prediction. Protein sequences of the PEG-responding differentially expressed proteins were retrieved from the National Center for Biotechnology Information (NCBI) database  74  and uploaded into the STRING database  32  analysis tool for Gene Ontologies and metabolic pathways functional enrichments. Biological processes and metabolic pathways with False Discovery Rate (FDR) ≤ 0. 005 were considered as significant. F. alni Clusters of Orthologous Groups of proteins (COGs) were inferred for an important set of proteins based on BLASTP  75  best reciprocal hits with Frankia sp. EAN1pec for which these clusters are known and available in the database of COGs  76. Protein-protein interaction network. The sequences of the 294 proteins of F.
alni belonging to the PatternLab blue class (for which identifications satisfied both the fold (>1. 5) and statistical criteria (p-value < 0. 05)) were downloaded and saved in a multi-FASTA file. This file was then uploaded in the STRING database  32  and F. alni was chosen as a query microorganism. A total of 294 hits with 100% identity were detected and their protein-protein interactions were predicted. In order to generate protein-protein interactions integrating the expression data information of each protein, data were integrated into the Cytoscape tool  77. Expression values were imported as node attributes. The same steps were followed to analyse protein-protein interaction networks of the other detected proteins (belonging to the PatternLab orange, green and red classes (see Results section for details)).
All single nodes were not represented with Cytoscape which considers only interacting proteins. Re-annotation of differentially expressed hypothetical proteins. Differentially expressed proteins that were annotated as hypothetical (or conserved hypothetical) were re-annotated. For this, we conducted new analyses using the Position-Specific Iterated BLAST (PSI-BLAST) program  78  at http://blast. ncbi. nlm. nih. gov/ with default parameters against a database of non-redundant protein sequences (nr). Molecular modelling and docking. Comparative modelling. Protein sequence of the FRAAL0095, one of the most up-regulated Frankia genes (4. 30), identified as coding for an osmotic-stress large conductance mechanosensitive channel, was submitted to the Swiss Model Server  34. Search for templates of experimentally determined protein structures within the Swiss-Model Template Library (SMTL) was performed.
Identified templates, ranked according to their sequence identity rate (IR) and their coverage rate (CR) with the target sequence, were examined. The template(s) with the highest IR and CR were selected for model building. The model with the most satisfactory quality assessment scores was considered for the subsequent analysis. Ramachandran diagrams of the selected model were generated using the RAMPAGE server  79  , and used to assess the 3D model quality. Molecular docking and surface mapping. Molecular docking of the PEG and related molecules (herein called ligands) on the structure model of the protein target (herein called receptor) was performed using AutoDock Vina 1. 1. 2  80. The latter program requires input files of the ligands and the receptor in PDBQT (Protein Data Bank  81  , Partial Charge ('Q'), and Atom Type ('T')) format.
Default parameters were used and a maximum of 20 lowest-energy poses were kept for each ligand. Two forms of the PEG were considered: (i) a short form having the formula C 2 H 10 O 3 and is referred to as PEGbasic and (ii) a longer form having the formula C 18 H 38 O 10 to which we refer as PEG400. Additionally, five chemical substructures of the PEG were generated and used as probes to map hot spots on the protein surface. Simplified Molecular Input Line Entry System (SMILES) conformations of PEGbasic, PEG400 and the probes were generated manually, then converted to PDBQT format using the OpenBabel package (http://openbabel. org)  82. The PDB file of the target was used to generate the corresponding PDBQT format by adding hydrogen atoms and atomic partial charges using the OpenBabel package  82.
Each ligand was docked 500 times on the protein. Docking results were analysed using a customised version of the SOM-BSfinder method  83. Herein, input data consisted in atomic coordinates of the docked PEGbasic, PEG400, and the five probes. Otherwise, default parameters were used as described previously  83. PyMol 1. 8. 2. 1 (Schrodinger, LLC)  84  was used to visualise the results and generate the figures. Proteomics data repository. The mass spectrometry proteomic data were deposited at the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository with the data set identifiers PXD007226 and DOI 10. 6019/PXD007226.
Houston This hyperspectral dataset was captured by the Compact Airborne Spectrographic Imager (CASI) over the University of Houston campus and the neighboring urban area in June 2012. This dataset is composed of 144 bands ranging from 0. 38 µm to 1. 05 µm and the spatial resolution is 2. 5 m. The image contains 349 × 1905 pixels. The available groundtruth covers 15 classes of interest. Table  2  gives information on all 15 classes including the number of training and test samples. Figure  3  shows a three-band false color composite image and its corresponding and already-separated training and test samples.
Trento Data The second dataset was captured over a rural area in the south of the city of Trento, Italy. This dataset is of size 600 by 166 pixels with a spatial resolution of 1 m with 63 spectral bands (ranging from 402. 89 to 989. 09 nm) captured by the AISA Eagle sensor. The available groundtruth covers six classes of interest including Building, Woods, Apple trees, Roads, Vineyard, and Ground. Figure  4  illustrates a false color composite representation of the hyperspectral data and the corresponding training and test samples. Table  3  provides information about the corresponding number of training and test samples. The third dataset was taken by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor over an agricultural area located at northwestern Indiana. This dataset is composed of 220 bands ranging from 400 nm to 2500 nm.
The image contains 145 × 145 pixels with a spatial resolution of 20 m per pixel. The available groundtruth covers 16 classes of interest. Table  4  provides detailed information about all 16 classes and the corresponding number of training and test samples. Figure  5  presents a three-band false color composite image and its corresponding training and test samples. The last dataset is the Washington DC mall which is an airborne dataset captured over the Washington DC Mall in August 1995 using the HYDICE sensor (Hyperspectral Digital Imagery Collection Experiment). The sensor provides 210 bands in the 0. 4-2. 4 µm spectral region where each band contains 1280 lines with 307 pixels. After removing noisy bands, the available dataset contains 191 bands. The reference ground truth contains 7 classes of interest, given in Table  5.
, σ 2 p , and σ p is the noise standard deviation in band p. In  [7] , a model and parameter selection criterion was proposed, based on the estimate of MSE by using SURE for X = A ŴM T where Ŵ is given by (  12 ): Rλ,r = H 2 F + n ∑ i=1 r ∑ j=1 2I( b ij > λ) -max 0, b 2 ij -λ 2 -np, ( 15 ) where I is the indicator function. The main advantage of (  15 ) is that model parameters can be selected based on the MSE estimator. As can be seen, (  15 ) is only dependent on the observed signal (H). Therefore,  (15)  lets us select the model (in the form of model (  2 )) and the models parameters (r and λ) w. r. t. the minimum of the estimation of the MSE. Equation (  15 ) is called hyperspectral SURE (HySURE), and is proposed in  [9]  in the context of spectral unmixing to determine the subspace dimension (or the number of endmembers), r, in the absence of the noise free signal X (The Matlab code online available in  [10] ).
HSI Denoising as a Preprocessing Step Despite the considerable progress in HSI restoration techniques, they have usually not been used in HSI analysis as a preprocessing step. This might be due to several reasons such as the computational cost, efficiency, reliability, and automation of the algorithms. The main goal of the denoising-based preprocessing stage is to improve the SNR of the observed dataset. It is of interest to investigate the contribution of the various HSI restoration approaches as a preprocessing step for further HSI analysis, such as change detection, resolution enhancement, classification or unmixing. In this paper, we will address this matter for the classification application. 1. 5. 6. Computational Cost HSI restoration approaches need to be computationally efficient to be useful as a preprocessing step in real-world applications.
Comparison of HSI Denoising Techniques In this section, different HSI denoising methods are compared qualitatively and quantitatively on a simulated and a real dataset. In a first experiment, a simulated noisy dataset is generated by adding zero-mean Gaussian noise (i. e. , N = n ji where n ji ∼ N(0, σ 2 i ) is normally distributed) to a portion (128 × 128 × 136) of the Houston University dataset (Section 1. 2. 1). The variance of the noise (σ 2 i ) variates along the spectral axis according to σ 2 i = σ 2 e -(i-p/2) 2 2η 2 ∑ p j=1 e -(j-p/2) 2 2η 2 , where the power of the noise is controlled by σ, and η behaves like the standard deviation of a Gaussian bell curve  [13]. In the experiment, six HSI denoising techniques are compared: • 2D-Wavelet: 2D wavelet modeling (4)  [14] , using a conventional band by band denoising technique, • 3D-Wavelet: a 3D wavelet model approach (Section 2.
For all the experiments performed in this paper, Daubechies wavelets were used with 2 and 10 coefficients for spectral and spatial bases, respectively. Five decomposition levels were used for the filter banks. The Matlab codes for FORPDN and HyRes are online available in  [68, 69] , respectively. To evaluate the restoration results for the simulated dataset, SNR and MSAD (Mean Spectral Angle Distance) are used. The output SNR, in decibels is given by: SNR out = 10 log 10 X 2 F / X -X 2 F , while the noise input level for the whole cube is given by: SNR in = 10 log 10 X 2 F / X -H 2 F. MSAD, in degrees is given by: MSAD = 1 n n ∑ j=1 cos -1 X j XT j X j 2 Xj 2 × 180 π. Figure  7a  shows the comparison of the HSI denoising techniques based on SNR. The figure shows the level of the denoised hyperspectral signal SNR out compared to the level of the noise added (in dB) to the simulated dataset SNR in.
HyRes generates the highest SNR out. Since the spectral information is higly valuable in HSI analysis, we also use MSAD to compare the spectral preservation of the HSI denoising techniques. Figure  7b  plots the MSAD of the different HSI restoration techniques w. r. t. the input noise power. The results are shown in logarithmic scale for a better visual representation. It can be seen that 2D-Wavelet produces the highest MSAD. In this experiment, 3D-Wavelet improves over LRMR when SNR in ≤ 15 dB, and SNR in ≥ 35 dB, while LRMR and 3D-Wavelet perform similarly when 15 dB ≤ SNR in ≤ 35 dB. The results also show that FORPDN outperforms 2D-Wavelet, 3D-Wavelet and LRMR consistently for all SNR in. It can also be seen that, also based on MSAD, HyRes and NAILRMA outperform the other techniques. A highly corrupted band from the simulated dataset was selected for a visual comparison of the restoration methods in Figure  8.
2D-Wavelet shows a very poor performance, which is not surprising, since denoising is applied on each spectral band individually and the information is highly corrupted in that specific band. 3D-Wavelet considerably improves the visual quality, due to the incorporation of the information from the other bands by 3D modeling and filtering. FORPDN, NAILRMA and HyRes all perform very well. The weak performance of LRMR on this band is due to the fact that it is not designed to cope with the variation of the noise variance throughout the spectral bands. We also applied the denoising methods on a real dataset.
Results In this section, the above-mentioned classifiers (i. e. , ELM, SVM, and RF) have been applied to the four datasets (i. e. , Houston, Trento, Indian Pines, and Washington DC). With reference to Tables  6 7 8 9 , the following points can be observed: 1. In general, the prior use of denoising approaches improves the performance of the subsequent classification technique compared to the use of the input data without denoising step. The improvements reported in the cases of Trento (up to 19. 4% in OA using ELM) and Indian Pines (up to 26. 31% in OA using ELM) confirms the importance of the use of denoising techniques as a preprocessing step for HSI classification. 2. The use of denoising approaches is clearly advantageous for raw data (i. e. , before performing any preprocessing or low SNR bands removal).
For instance, the classification accuracies have been considerably improved for the Indian Pines dataset while the amount of improvement is not that significant for the Houston and Washington DC data whose low SNR bands had been already eliminated. 3. In general, ELM and SVM demonstrate a superior performance for the classification of denoised datasets compared to RF. Figure  10  shows several classification maps obtained by applying ELM on the Indian Pines dataset, without denoising and denoised by using 2D-Wavelet, 3D-Wavelet, FORPDN, LRMR, NAILRMA and HyRes. As can be seen, the classification map obtained by ELM on the raw data dramatically suffers from salt and pepper noise. This issue can be partially addressed using all the denoising approaches investigated in this paper. In particular, LRMR and NAILRMA considerably reduce the salt and pepper noise and produce homogeneous regions in the classification maps.
, SNR in = 5 dB. In more detail, based on the experimental results, one can conclude that conventional band by band denoising techniques have provided the poorest performance compared to 3D filtering and spectral denoising approaches. Additionally, the effects of denoising as a preprocessing step for HSI classification have been investigated on four datasets. The experimental results have shown that the performances of the denoising techniques are consistent for the three classifiers and four datasets used. In general, from the classification experiments one can conclude that exploiting the denoising techniques has improved the classification accuracies. For Trento and Indian Pines datasets the improvements of the OA are very substantial compared to the spectral classification without the prior use of a denoising approach.
The improvements in the OA obtained using ELM to classify the Indian Pines and Trento datasets reach to 26. 31% and 19. 4%, respectively. Despite the developments in HSI denoising, several important challenges remain to be addressed. Future challenges include; further investigation of the contribution of the HSI denoising approaches as a preprocessing step for further HSI analysis such as unmixing, change detection, resolution enhancement etc. , exploring a model selection criterion which is not restricted to the Gaussian noise model, noise parameter estimation, investigating of the influences of the different noise types and indication of the dominant noise, and incorporating high performance computing techniques to obtain computationally efficient implementations. Investigating the performances of the HSI denoising approaches for denoising hyperspectral image sequences  [77]  and thermal hyperspectral images  [78]  (where emissivity becomes important and therefore other type of noise i.
Acknowledgments: The authors would like to express their appreciation to  Lorezone Bruzzone  from  Trento University, Landgrebe from Purdue University , and the  National Center for Airborne Laser Mapping (NCALM)  for providing the Trento dataset, the  Indian Pines dataset , and the Houston dataset, respectively. This work was supported in part by the  Delegation Generale de l' Armement (Project ANR-DGA APHYPIS,  under Grant  ANR-16 ASTR-0027-01 ).
Corpus2graph is an open-source NLPapplication-oriented Python package that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sentences, extract word pairs and define edge weights, but also supports user-customized functions. By using parallelization techniques, it can generate a large word co-occurrence network of the whole English Wikipedia data within hours. And thanks to its nodes-edges-weight three-level progressive calculation design, rebuilding networks with different configurations is even faster as it does not need to start all over again. This tool also works with other graph libraries such as igraph, NetworkX and graph-tool as a front end providing data to boost network generation speed.