dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis The bioinformatics pipeline can be divided into two steps, consisting of the read pre-processing step and the OTU picking/taxonomy assignment step (Fig. S2a ). During the read pre-processing step, sequencing adapters and PCR primers were first removed using the ILLUMINACLIP step in Trimmomatic 20 (v0. 35) run in paired-end mode. Paired-end reads were then joined using FLASh 21 (v1. 2. 11) with a maximum overlap of 250 bp, before quality trimming (qtrim = rl; trimq = 30) using BBDuk of BBTools (v35. 85) and finally filtered for at least 50 bp using the MINLEN step in Trimmomatic run in single-end mode. After preprocessing, 16S and ITS samples were mapped against the SILVA 3 (v. 123) and UNITE  22  databases, respectively. For both pipelines, the pick_closed_reference_otus. py script in QIIME 2 (v1.
8. 0), implementing the usearch  23  (v7. 0. 1090) algorithm, was used for OTU picking, enabling reverse strand matching and requiring a minimum similarity of 99%. SMG samples were analyzed using MetaPh-lAn2  24  (v2. 5. 0) with default parameters. MeTRS samples were analyzed using the following in-house built pipeline. The MeTRS pipeline is composed of two steps, the mapping step and the taxonomy assignment step. In the first step, the sample is mapped against the SILVA database (v. 123) using Bowtie 25 (v1. 1. 2) and the following parameters: -a -v1 --best --strata. Reads are then assigned to a consensus taxonomy of the mapped hits using an in-house built Python (v2. 7) script, called Consensus Taxonomy Tool (ConTxT). Starting at the species level, if >60% of the mapped hits of a read agrees on the same taxonomic term, then that taxonomic term is assigned to the read.
If no agreement is reached, information from the next highest taxonomic level is iteratively interrogated until a consensus is found. If no consensus is achieved even at the highest taxonomic level (i. e. , domain), the read is assigned to the "unknown" domain. All relevant code is available at https:// github. com/normanpavelka/MeTRS. Within-sample microbial diversity (α-diversity) was estimated with the Shannon-Wiener diversity index  26  as implemented in QIIME. To examine the effect of subsampling and to estimate the minimal required sequencing depth for each sequencing technology, rarefactions were performed on the mapped reads of all the background samples using QIIME with five iterations. The samples were rarefied at the same depths and, for the last rarefaction depth, all of the samples' mapped reads rounding down to two significant digits (Table  S6 ).
ACKNOWLEDGEMENTS Authors are thankful to  Alrina Tan  for technical assistance with nucleic acids extraction; to  Giulia Rancati  and lab members for fruitful scientific discussions; to  Snezhana Oliferenko ,  Suzanne Noble  and  Florent Ginhoux  for providing strains; and to  Brian Abel  for critical reading of the manuscript. This work was supported by an  A*STAR Investigatorship  award n. 1437a00117 to  Norman Pavelka , by grant  NMRC/ BnB/0001b/2012  and by core funding from the  Singapore Immunology Network.
miRNA variability is ubiquitous in the brain of control and sCJD cases Isoforms of a mature miRNA have been referred as isomiRs  [30]. They are functionally active and highly abundant in brain tissue, both in control and in neurodegenerative diseases  [15, 31, 32]. In the present study, 2883 and 4075 different isomiRs were found in the CB and FC, respectively (S4 Table ). Furthermore, most of the sequences mapping onto miRNA database (reference miRNAs and IsomiRs) showed 3-50 counts. No major differences were detected between control and sCJD subtype cases regarding their isomiRs profiles (S2 Fig) ,.
Cross-validation of sCJD-miRNA signature in alternative neurodegenerative diseases Several neurodegenerative disorders share pathological hallmarks such as accumulation of protein aggregates and self-propagation, and common pathways seem to contribute to the neurodegenerative mechanisms in different diseases  [50] [51] [52]. Thus, we speculated that an overlap between miRNA sCJD profiling and other dementia-related conditions could exist. In order to select the most appropriate miRNAs we compared the miRNA signatures in the FC of sCJD obtained from the small RNA-seq analysis from this study with the one reported in the pre-frontal cortex (PFC) of AD cases by Lau et al. [20]. Eight (22. 8% max. coincidence) and seven miRNAs (14% max. coincidence) were commonly increased and decreased respectively in both datasets (Fig  6A ).
Among these, miRNA-195-5p, 877-5p and 323a-5p were previously validated in sCJD from our small RNA-Seq dataset and miRNA-195-5p and 877-5p were validated by qPCR (Fig  2A ). To perform a cross-validation study with the corresponding sCJD brain regions and methodologies we extracted RNA from the FC of AD and DLB cases and age-matched controls. Six miRNAs were selected: i) miRNA-195-5p, 877-5p and 323a-5p, commonly regulated in the FC of sCJD and in the PFC of AD, ii) miRNA-146a-5p and miRNA-342-3p, reported to be altered in AD and prion disease models  [22, 23, 53, 54]  and iii) miRNA-5701. The latter was used as a negative control due to its exclusive enrichment in the CB of sCJD. In AD samples, we detected coincident gene expression regulations with sCJD for miRNA-195-5p (increased) and for miRNA-877-5p and 323a-5p (decreased) (Fig  6B ).
Therefore, we highly purified miRNAs from the FC of control, sCJD MM1 and VV2 brains and transfected them into neuroglioma (H4) and differentiated neuroblastoma (SH-SY5Y) cells. Five miRNAs were analyzed with qPCR in both cell lines, resembling the disease subtype profiling in human sCJD brain (S9A Fig). Overexpression of sCJD-MM1 purified miRNAs lead to an increased cell death in neuroblastoma, but not in neuroglia cells (S9B Fig) , indicating that the overexpression of the sCJD regulated miRNAs is able to induce subtype specific cell death in neuron-like cells. Finally, to gain insight into the potential upstream regulators of differential miRNA expression in sCJD a motif enrichment analysis was performed for data generated from FC of sCJD MM1 sequencing. Among the known sCJD related pathways, our analysis revealed a significant enrichment of a STAT3-binding motif for miRNAs with increased expression in sCJD (S6 Table ).
Small RNA data processing and mapping Reads were trimmed to 36 nt and ligation adapters were removed using the adrec. jar program from the seqBuster suite (http://github. com/lpantano/seqbuster) [28]  with the following options: java -jar adrec. jar 1 8 0. 3. Sequences were mapped to the hg19 genome with the command line: bowtie -f -v 1 -a-best-strata. For summing up miRNA read counts we mapped the reads against miRBase version 21 hairpins with the miraligner. jar tool with these options: java-jar miraligner. jar 1 3 3. Out of a total of several million reads, we discarded any reads without a minimum of 10-nt linker subsequence directly adjoining the insert, showing two or less mismatches. Then sequences were mapped to human pre-miRNA and mature miRNA databases provided in the miRBase (http://miRNA.
sanger. ac. uk/sequences/, Release 14), as well as mRNA, ncRNA, repeats and genome databases available at (http://hgdownload. cse. ucsc. edu/goldenPath/hg18/ bogZips/), using Mega BLAST. For motif discovery, deregulated miRNAs were searched for the genomic coordinates of their primary miRNAs. Upstream regions of 1kb size from each miRNA were extracted and exported to BED files and the script findMotifsGenome. pl in HOMER suite was used to find transcription factor binding motifs within promoter regions using genome assembly hg38  [100]. For differential expression analysis we used DESeq2 analysis and log2 transformation of the count data. Padj value was <0. 15 and nominal p values were in all cases <0. 05. We used the count matrix generated by Seqbuster. Only miRNAs consistently expressed (counts > 10) in at least 10 samples out of the 21-26 were included in each analysis (controls versus MM1 or VV2 cases).
The parameters for the alignment to the mRNA and genome databases allowed up to one mismatch and up to three nucleotide additions in the 3'-terminus. For deep characterization of IsomiRs we applied several filters. First, the sequences considered in the analysis presented a frequency above 3. Second, 10 was chosen as the 'Contribution Cut-Off' parameter, meaning that every isomiR considered in the analysis contributes by more than 10% to the total number of variants annotated in the same miRNA locus. Third, we applied the Z-score option to exclude sequencing errors as the possible cause of the nucleotide changes observed in some variants.
RT-qPCR In order to confirm the direction of the miRNA alterations detected by RNA-seq in sCJD cases (increased or decreased levels compared to controls) by an independent methodology qPCR analysis of selected miRNAs was performed. Quantitative real time PCR for miRNAs was performed using the miRCURY LNAmiRNA PCR System (Exiqon) following Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines. RNA was extracted with the mirVana isolation kit (Ambion) following the manufacturer's instructions. PCR amplification and detection were performed with the Roche LightCycler 480 detector, using 2x SYBR GREEN Master Mix. The reaction profile was: Polymerase Activation/Denaturation (95˚C for 10 min) followed by 40 amplification cycles (95˚C-10 sec, 60˚C-20 sec). miRNA levels were calculated using the LightCycler 480 software.
Western blot Human tissues were lysed in lysis buffer: 100 mM Tris pH 7, 100 mM NaCl, 10 mM EDTA, 0. 5% NP-40 and 0. 5% sodium deoxycolate plus protease and phosphatase inhibitors. After centrifugation at 14,000g for 20 min at 4˚C, supernatants were quantified for protein concentration (BCA, Pierce), mixed with SDS-PAGE sample buffer, boiled, and subjected to 8-15% SDS-PAGE. Gels were transferred onto nitrocellulose membranes and processed for specific immunodetection by chemiluminescence (ECL Amersham, US) using the indicated antibodies. Densitometries were carried out with the ImageJ software and values were normalized using β-actin levels.
Methods Molecular dynamics simulations. We present here results from molecular dynamics (MD) simulations coupled with density functional theory (DFT). We used the Vienna ab initio simulation package (VASP)  55  to perform the DFT-MD simulations at constant density and temperature, employing a Nosé thermostat  56, 57. We used a time step of 0. 5-0. 8 fs for a total duration of at least 1 ps. The simulation cell contained from 60 to 144 atoms depending on the material and the phase (see the details in Supplementary Table  1 ). The cell was given the appropriate shape for a given crystallographic symmetry or a simple cubic shape for liquids. We applied periodic boundary conditions. The DFT calculations were performed using the finite temperature Kohn-Sham scheme  58, 59. We employed the Perdew, Burke and Ernzerhof (PBE) generalized gradient approximation (GGA) exchange and correlation functional  60.
We then recomputed the electronic structure using the Abinit code  65  , with a slightly higher energy cut-off (50 Ha), up to a 4  3  Monkhorst-Pack k-points grid (see the details in Supplementary Table  1 ) and additional bands to be able to determine the conductivity up to an excitation energy of 1 Ha. Because the finite size of the cell limits the resolution in the eigen energies, the DC conductivity is not directly computed. We used instead a common method consisting in extrapolating a linear regression at low excitation energy and used this value for the DC conductivity. We do not expect any large changes in the conductivity behavior at very low energy. In Supplementary Figure  5 , we plotted an example of conductivity calculation for MgO at 470 GPa and 12,000 K. The individual calculations fluctuate significantly indicating how strongly the microscopic configuration influences the properties.
Acknowledgements We thank  D. Fratanduono ,  A. Ravasio ,  R. Bolis , and  M. Millot  for the insightful discussions about the experimental measurements. We are grateful to  B. Buffet  for his explanation of the magnetic field generation in the Earth. The present work was supported by the  U. S. National Science Foundation  ( AST 1412646 ) and in part by the  U. S. Department of Energy  ( DE-SC0016248 ). We used the high-performance computing facilities Comet- SDSC  and  Stampede-TACC  from the  XSEDE program  and  Pleiades  from  NAS-NASA.
The combination of the dynamic programming and self-organizing map approaches constitutes a quite innovative point of the present work. The algorithm was validated on a set of about 900 proteins, representative of the sizes and proportions of secondary structures observed in the Protein Data Bank. The algorithm was revealed to be efficient for noise levels up to 40%, obtaining average gaps of about 20% at maximum between ordered and initial matrices. The proposed approach paves the ways toward a method of fold prediction from noisy proximity information, as TM scores larger than 0. 5 have been obtained for ten randomly chosen proteins, in the case of a noise level of 10%. The methods has been also validated on two experimental cases, on which it performed satisfactorily.
Generation of Synthetic Contact Matrices From PDB Structures The proposed method was validated on a set of original contact matrices obtained from the 3111 protein folds present in the file recent. pdb_select25. nsigma3. 0 from the PDBselect database  [50]. PDBselect (homepages. thm. de/~hg12640/pdbselect. html) is a list of representative protein chains displaying mutual sequence similarity smaller than 25%. Eight proteins, for which side-chains coordinates are not available in the PDB structure, were removed from the set. Then a smaller subset of 897 proteins was randomly selected from the proteins smaller than 200 residues in order to respect the same distribution of size as in the full set. Two types of original contact matrices were calculated from the PDB coordinates. The first type was based on the measurement of distances between a carbons of the protein residues, with different cutoffs.
Implementation The longest trail algorithm was implemented in C++, the code is available at gitlri. lri. fr/chuan/LongestTrailAlgo. The random choice of node was completed using the pseudo-random generator rand() from the library cstlib (www. cplusplus. com/reference/cstdlib/rand/). The SOM clustering was implemented in python and is freely available at: github. com/bougui505/SOM.
Properties of the Processed Proteins The processed proteins display a quite uniform distribution of sizes in the range of 50-150 residues, corresponding to small or medium folded proteins (Fig. 5a ). The distributions of a helix and b strand percentages was compared to the global set of Protein Data Bank (PDB) entries to check whether the subset of protein structures used here is representative of the general knowledge on folded proteins. The processed proteins displaying more than 80% of a helix are relatively less numerous, similarly to the PDB entries in which, among the 15,633 entries corresponding to monomeric proteins with number of residues in the range 10-150, only 1434 (9. 2%) contain more than 80% of a secondary structures. The processed proteins display a peak of structures containing no b strand, and the subset of structures with more than 50% of b strand is smaller than the subset of structures displaying less than 50% of b strands.
Similarly, among the 15,633 PDB entries, 5514 entries (35. 2%) contain less than 10% of b secondary structure, and 1372 entries (8. 8%) contain more than 50% of b secondary structure. The set of proteins used here for Fig. 8. Examples of calculations performed on contact matrices All_avg_9 with a noise level of 10%. Each line corresponds to the processing of a protein, illustrated by: (A) cartoon representation of the with a helices colored in magenta, strands colored in yellow and loops colored in green, (B) the original contact matrix A, (C) an example of noisy contact matrix A , where the original data are drawn in green, the added spurious contacts are in blue and the removed contacts are in red, (D) the distribution of Gap values obtained for the 500 noise realizations, with the value obtained from SOM consensus marked using a magenta star, (E) the ordered matrix, result from the proposed algorithm, combining DFS and dynamic programming using 500 instances of noise level, followed by a SOM clustering and Gap consensus calculation.
The corresponding gap value is written in magenta and corresponds to the star mark in the Gap distribution. validation purposes thus displays secondary structure distributions similar to the ones of the whole PDB. To get a deeper insight into the database quality, the PDBselect folds have been compared to the CATH architectures. Only 8 CATH architectures are not represented in PDB select (Table  3 ). These 8 architectures correspond only to 11 topologies over the 1391 topologies of CATH. Thus, the PDB select list corresponds to the majority of CATH folds.
Force-field included terms for bond length, bond angles and improper angles, and non-bonded interactions were treated with a simple repulsive term. A conformational database potential was also used  [54]. Reconstructed conformers were compared with the original PDB structure by computing the TM-scores  [55]  on the full PDB sequence. A TM-score larger than 0. 5 means that the reconstructed conformers and the original PDB structure have the same fold. In Fig. 12A , C, for each selected protein, TM-scores obtained for the original and result contact matrices are compared. In the case of the contact matrices defined with All_avg_9 and 10% noise, 6 proteins over 10 display a TM-score larger than 0. 5 (Fig. 12A ). TM-scores larger than 0. 5 are obtained up to 60 % Gap and the TM-score decreases linearly with increasing Gap values (Fig.
Application to NMR Experimental Data The general workflow for applying of our approach to experimental NMR data is depicted in Fig. 13A. NMR experiments provide correlations between atoms that are close in space. However correlations are recorded in the frequency space, meaning that one has to assign each frequency (i. e. chemical shift) to the corresponding atoms. To alleviate this step, we propose to label chemical shifts arbitrarily (X n ) and construct an NMR correlation matrix from the peaks observed in the NMR correlation spectrum using the chemical shifts labels as indexes. Once re-ordered, the NMR correlation matrix allows to assign chemical shifts X n to amino-acids of the protein sequence. Using these assignments, it becomes feasible to reconstruct 3D protein structures from the re-ordered NMR correlation matrix.
To assess the efficiency of this approach, we applied it to matrices obtained from solid-state NMR data on two proteins: MAVS  [56]  and YadA  [57]. Conveniently, solid-state NMR can detect correlations between Carbon atoms which provide correlation matrices similar to contact matrices but that are incomplete and noisy owing to the experimental resolution. Using experimental NMR restraints from the Protein Data Bank (PDB entries 2MS7 for MAVS and 2LME for YadA), NMR correlation matrices were constructed using random labels (X n ) for assigned amino acids (Fig. 13B, E ). The re-ordering step yielded matrices with Gap values of 39. 3% and 0% for MAVS and YadA, respectively (Fig. 13D, F ). For YadA, all amino-acids could be assigned correctly, while for MAVS only 10. 9% could be correctly assigned but 75.
Fast online video analysis is currently a key issue for dynamic studies in biology; however, very few tools are available for these concerns. Here we present an ImageJ plug-in: HF_IDS_Cam, which allows for video capture at very high speeds using IDS (Imaging Development Systems GmbH) cameras and image analysis software ImageJ. The software has been optimized for real time video analysis with ImageJ native function and other plug-ins and scripts. The plug-in was written in Java and requires ImageJ 1. 47v or higher. HF_IDS_Cam offers a wide range of applications for exploration of dynamic phenomena in biology, from in vitro/ex vivo studies, such as fast fluorescent calcium imaging and voltage optical mapping in cardiac myocytes and neurons, to in-vivo behavioral studies.
An alternative to storage and offline processing is the real-time analysis of the video flow. The well-known image analysis software ImageJ  [1]  is very effective in this use. Micromanager software  [2] , based on ImageJ libraries made video acquisition from a wide range of digital video cameras possible. However, its efficiency is limited for high-speed video acquisitions (e. g. , recording speed indication are not always reliable and subsampling modes are not managed, which limits camera speed performances). Therefore, in order to combine fast video recording, analytical power, and ease of use, we developed HF_IDS_Cam, an ImageJ/µManager plugin for video capture from cost-effective and high-performance IDS (Imaging Development Systems) video cameras. HF_IDS_Cam, written in Java, allows for video acquisition at high spatial and tonal resolutions, from low to very high frame rates (above 1000 fps), thus allowing for a very wide range of biological applications and studies (neurodegenerative diseases, cardiac arrhythmias, animal or cell tracking, plants growth, etc.
Materials and Methods The plug-in for ImageJ was designed to use the drivers from IDS. The Software-Development-Kit (SDK) of the camera only supports C++. However, plug-ins for ImageJ must be developed in Java, so JNA-4. 2. 1 libraries were used for the communication between the plug-in and the SDK. With Java, code can be developed so that it is independent of the platform and directly compatible with 32 bit and 64 bit operating systems. The general workflow of the HF_IDS_Cam is presented in Figure  1. a serial port to synchronize the camera with other devices (electric stimulators, fast switch perfusion systems, lasers, etc. ).
Materials and Methods The plug-in for ImageJ was designed to use the drivers from IDS. The Software-Development-Kit (SDK) of the camera only supports C++. However, plug-ins for ImageJ must be developed in Java, so JNA-4. 2. 1 libraries were used for the communication between the plug-in and the SDK. With Java, code can be developed so that it is independent of the platform and directly compatible with 32 bit and 64 bit operating systems. The general workflow of the HF_IDS_Cam is presented in Figure  1.
Image Acquisition Features and Outputs The video flow from the camera is directly displayed in ImageJ. As shown in Figure  1 , the camera must be selected at program start. At the same time, a log window supplies pieces of information about the camera and its initialization. A dialog box opens, allowing the user to define pixel size and select pixel depth-up to 16 bit-to record fluorescence signals. The user can also use binning and subsampling modes if the camera is compatible. Binning modes allow the user to decrease spatial resolution to increase the maximum acquisition frame rate and the signal amplitude. The subsampling mode can be used to decrease the spatial resolution of the image to reduce its size. Through this dialog box, serial ports can be activated to capture synchronization, and analog signals can be input from other devices (see online information to capture trigger signal at http://pccv.