dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Summary The 68 Miseq samples we considered were those sequenced and released with the IVA publication  [38] , namely accession numbers ERR732065-ERR732132. The short reads were downloaded from the European Nucleotide Archive. The 50 Hiseq samples we considered were newly generated for the BEEHIVE project, from confirmed seroconverters from Europe. RNA was extracted manually from blood samples following the procedure of  [39]. This was amplified using universal primers that define four overlapping amplicons spanning the whole genome, following the procedure of  [40] , then sequenced. For both sets of samples short reads were assembled into contigs using IVA, which has been shown to outperform 5 Figure  4 : Within-host indel polymorphism in our Hiseq sample 17796 3 29: a 21bp insertion in roughly a third of the reads duplicates the GATAMMQ amino acid motif at the boundary between p2 and p7 in gag.
Here shiver stops to allow a visual check of the accuracy of this alignment. Once it is checked, shiver continues (all remaining steps in the program are performed by the second of two commands needed for full processing). The alignment of contigs to existing references is used to create a reference for mapping which is tailored to the sample. This is done by using contig sequence where available, and those existing references that match the contigs most closely to fill in any gaps between contigs. Before mapping, reads are trimmed for low-quality bases, adapter and primer sequences; contaminant read pairs are diagnosed as those matching contaminant contigs more closely than the tailored reference, and are removed. The remaining reads are mapped to the tailored reference, each position in the genome is considered (resolving indel polymorphism) to find the frequen-cies of different bases, and the most common base is called to give the consensus.
Using shiver shiver and its documentation are available at github. com/ChrisHIV/shiver. It was designed to be run in Linux-like environments, including Mac OS. Once dependent packages are installed, shiver itself requires no installation: it is a set of executable scripts. The Genomic Virtual Laboratory  [41]   on the UK Medical Research Centre's Cloud Infrastructure for Microbial Bioinformatics (MRC CLIMB)  [42] , contains all dependencies 1  , allowing shiver to be run immediately. Before processing with shiver, short reads must be assembled into contigs 2. shiver is run from the command line using three commands: a one-off initialisation command, then two commands per sample to be processed. This produces, for each sample, • the mapped reads in BAM format  [45] ; • a plain text file with the counts of the different bases at each position; • the consensus; • a coordinate-translated version of the consensus for a global alignment; • an alignment of the consensus and the contigs, for comparison; • the insert-size distribution; and • a separate BAM file of only the contaminant reads mapped to the HIV reference (illustrating the importance of removing these reads prior to mapping).
We used the 2012 'Compendium' alignment from the Los Alamos National Laboratory HIV database  [52] , which is already manually curated. Custom reference construction begins with contig preprocessing as follows. Matches between the contigs and any existing reference from the alignment are searched for using BLASTN  [53]  with default settings. Contaminant sequence is inevitable in high-throughput NGS; any contig that has no BLASTN hit to any of the HIV references is taken to be contamination, and is put aside for later use, leaving contigs that are putatively HIV. Assembly may produce contigs that are erroneously spliced -concatenating two separated regions of the genome -due to errors in silico or during sequencing as mentioned. We detect such misassemblies from multiple BLASTN hits for a single contig (discarding any hit wholly contained inside another hit), and correct them by cutting.
After cutting, any contig sequence in the opposite orientation is reversecomplemented. (IVA outputs contigs such that the longest open reading frame is on the forward strand. ) Contigs are then aligned to the existing reference alignment using MAFFT  [54] , trying both --add and --addfragments modes and using the one with the smallest maximum gap fraction (the maximum calculated over all contigs in each alignment). The alignment of contigs to the set of existing refer-10 ences should be visually inspected at this point. For HIV sequences, reference  [16]  states that "Algorithmic alignment does not necessarily retrieve the best alignment. It is important to always verify whether the sequence data are aligned unambiguously and, if necessary, manually correct the alignment. " [13]  echoes this for any evolving pathogen: "the 'best' alignment chosen by an alignment program is not necessarily the 'true' alignment.
• AlignBaseFreqFiles. py aligns not two sequences, but two of the csv-format base frequency files output by shiver. Optionally a similarity metric is calculated at each position in the alignment, between 0 (no agreement on which bases/gaps are present) and 1 (perfect agreement on which bases/gaps are present and on their proportions). This allows comparison not just of consensus sequences between two samples but also of minority variants. • ConvertAlnToColourCodes. py converts each base in a sequence alignment into a colour code indicating agreement with the consensus and indels; AlignmentPlotting. R takes such colour codes and visualises the alignment. These two scripts were used to produce the plots of Appendices F and G. • Finally some simple tools for convenience: FindSeqsInFasta. py extracts named sequences from a fasta file, with options including gap stripping, returning only windows of the sequences, and inverting the search; PrintSeqLengths.
Acknowledgements Thanks to  Martin Hunt ,  Dan Frampton  and  Tiziano Gallo Cassarino  for helpful discussions, and to  Simon Burbidge  and  Matt Harvey  for help with  Imperial College London High Performance Cluster computing. This work was funded by  ERC Advanced  Grant  PBDR-339251. This work used the computing resources of the UK MEDical BIOinformatics partnership -aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO) which is supported by the  Medical Research Council  [grant number  MR/L01632X/1 ].
The Long Term Evolution (LTE) is a high data rates and fully All-IP network. It is developed to support well to multimedia services such as Video, VoIP, Gaming, etc. So that, the real-time services such as VoIP, video, etc. need to be optimized. Nevertheless, the deployment of such live stream services having many challenges. Scheduling and allocating radio resource are very important in LTE network, especially with multimedia services such as VoIP. When voice service transmitted over LTE network, it is affected by many network impairments where there are three main factors including packet loss, delay, and jitter. This study proposes a new scheduler which is based on VoIP priority mode, Wideband (WB) E-model, QoS-and Channel-Aware (called WE-MQS-VoIP Priority scheduler) for voice services in LTE downlink direction.
II. THE SYSTEM MODEL A. VoLTE traffic flow 1) Radio protocol stack: The speech frame is encapsulated by network protocols consisting of Real-time Transport Protocol (RTP), User Datagram Protocol (UDP) and Internet Protocol (IP). And then, it will be packetized by other radio protocols including Packet Data Convergence Protocol (PDCP), Radio Control Link (RLC) and Medium Access Control (MAC) at the corresponding layers. At each radio layer, the corresponding header will be added into the speech packet. This leads to data overhead. Therefore, to reduce the data overhead, Robust Header Compression (RoHC) is deployed at the PDCP layer. This allows saving bandwidth as well as enhancing voice transmission in LTE network. RoHC will presses the header size of IP packet from 40 bytes (with IPv4) or 60 bytes (with IPv6) down to 1 to 4 bytes  [13].
Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the 'Hard' category (T0818-D1) are shown as pertinent examples.
Proteins are major biological macromolecules involved in many critical processes. The three dimensional (3D) structure of a protein determines its function, which makes obtaining of protein 3D structures essential for functional and evolutionary studies. Despite the efficiency of experimental methods (X-ray crystallography, NMR spectroscopy, and cryo-EM) to determine the 3D structure of proteins, these techniques are still costly and time-consuming. Moreover, the number of resolved protein structures is growing at a slower rate than the number of protein sequences in databanks (from 2008 to 2016: + 1000% protein sequences and + 100% protein structures)  1, 2. In this context, in silico approaches of protein structure modeling and prediction are a solution to access 3D information directly from sequence.
Therefore, combining sequence and structure information into a hybrid profile is a better approach for the detection of distant homology relationships  22. ORION is a fold recognition method based on the pairwise comparison of profiles combining sequence and structural information recently developed in our group  22. It relies on a better description of the local protein structure to boost distantly protein detection. These descriptors called Protein Blocks (PB) encode a structural alphabet defined by 16 local structural patterns that accurately describe local protein structures  23. PB is currently the most widely used structural alphabet  24. Thanks to PB structural descriptor and hybrid profile-profile comparisons, ORION outperforms, in terms of template detection sensitivity at fold level, profile-sequence methods like PSI-BLAST by 16% more and profile-profile methods like HHsearch by 5% more  22.
ORION algorithm. As with all profile-profile methods, ORION algorithm is divided into three main steps: (i) preparation of the multiple sequence alignment (MSA) of query -potential-homologs, (ii) generation of query profile and (iii) alignment of the query profile to templates profiles from a databank. In the first step, MSA is obtained by three iterations of PSI-BLAST on the non-redundant databank Uniref90 25 with an E-value threshold of 10 -4. Then in the next step, the query amino acid profile (AA profile) is derived from the MSA. It contains the probabilities of each of the 20 amino acids plus an additional probability that describes the gap frequency at this position. Two structural profiles are predicted from this MSA: the Protein Blocks profile (PB profile) and the solvent accessibility profile (SA profile).
The PB profile is predicted using a similar approach to LOCUSTRA  26  , namely a two layer support vector network with the AA profile. This PB profile contains the probabilities of the 16 PB letters at each position. The SA profile is obtained from the solvent accessibility predicted for each residue by PROF software  27  (see recent improvements section). In the last step, the AA, PB and SA query profiles are concatenated to search the selected databank of AA/PB/ SA template profiles. These template profiles have been pre-calculated and contain information of PB and solvent accessibility features computed from the protein 3D coordinates, with a homemade Python script for PB assignment and NACCESS  28, 29  for solvent accessibility. The databank search is then performed using ORION software  22.
Results and Discussion Assessments of ORION. This new version of ORION has been assessed on a benchmark including a balanced test set derived from the HOMSTRAD database containing 1032 targets. These improvements increase the true positive rate (TPR) of template detection by 5% compared to the initial version of ORION for 10% of false positive rate (FPR) (see Fig. 1 ). Indeed, at 10% of FPR, 'ORION+ SA' reaches ~52% of TPR against ~47% of TPR for ORION without SA.
ORION web server. Input and parameters. The user provides a protein query sequence in FASTA or plain text format (see Fig. 2a ). The ORION web server accepts sequences between 15 and 1000 residues, but performs better on sequences containing no more than one protein domain. Therefore, multiple protein domains sequences should be ideally split into single protein domain. If the domain parts are not identified yet, user can use dedicated web servers for this purpose, like DOMAC  33  or SEG-HCA  34. Then, the user chooses the template databank, the alignment mode and the maximum number of hits to display. User can provide an e-mail to get the link to the results page (see Fig. 2b ), which is optional but highly recommended since the process takes tenths of minutes if the queue is free but it can takes hours otherwise.
Three alignment modes are supported ('gloloc' , 'local' and 'global'). In 'gloloc' mode, the query profile is locally aligned along the entire length of the template profile. In 'local' mode, no penalties are added for begin/end gaps on both of the query and template profile and both can be locally aligned. In 'global' mode, query and template profile are entirely aligned. ORION is optimized for the 'gloloc' mode, since databank such as HOMSTRAD contain only protein domains and the query can have one or several domains. The 'local' mode is most suitable for a sensitive search with a large protein query sequence. Users have the choice between five templates profiles databases obtained from three well-known databases: PDB 1 , SCOP  35  and HOMSTRAD 36 database (see Table  1 ). The PDB template database is based on the protein data bank, which contains all available 3D structures of proteins.
SCOP template database is constructed from the manual classification of protein domains based on similarities of their structure and amino acid sequences. For the PDB and SCOP databases, sequence alignments were obtained by three iterations of PSI-BLAST on the non-redundant databank Uniref90  25  with an E-value threshold of 10 -3 and structure profiles were directly computed from the 3D coordinates of the protein chain/domain structure. Contrary to the PDB and SCOP databases, the HOMSTRAD template profiles database is based on structural alignments of homologous proteins. Since the structures of homologous proteins are generally better conserved than their sequences  19  , the HOMSTRAD template database should be most sensitive for detection of low homology relationships. Once the input sequence has been entered and parameters selected, the user launches the job by clicking on the 'submit' button.
The query-template alignments are displayed with the predicted/assigned PB elements and called "pbpred" for the predicted PBs of query sequence and "PB" for the assigned PBs of the template structure. Query and template secondary structure information that is predicted by PSIPRED software  37  ('psipred') and assigned by DSSP software  38  ('DSSP'), are also shown for indicative purposes (see Fig. 2d ). Secondary structure elements are colored in red and green for the two main types: α -helix and β -strand, respectively. PB elements are similarly colored, red for α -helix elements (central α -helix: m and α -helix N/C cap transitions: f, k, l, n, o and p) and in green for β -strand elements (central β -sheet: d and β -sheet N/C cap transitions: b, c and e). Finally, turn/coil elements are colored in blue (PBs a, g, h, i and j).
PBs give an accurate description of the 3D structure using 16 local conformations, contrary to the secondary structure elements, which are composed of only 3 predicted states (α -helix, β -strand and coil). Therefore, PB helps user to analyze more precisely the local structure conformation of the query protein. User can also identify high scoring regions with the scores color scale, which correspond to the ORION scores between the compared positions  22. Additionally, user can select a template and build a protein model. ORION webserver displays the model obtained with MODELLER 39 using the selected ORION query-template alignment. The 3D model can be explored thanks to the PV viewer JavaScript module  40  and can be rendered with different styles (cartoons, tube, line, trace, see Fig. 2e ).
This 134 residues target corresponds to an NTF2-like (Nuclear Transport Factor 2-like) protein from Eubacterieum siraeum (PDB code: 4r1k). T0818-D1 belongs to the 'hard target' level in the 'Template based modeling' category. For this target, a preliminary version of ORION server named ' Alpha-Gelly-Server' , ranked second among 44 servers. Here, we show an example of the structure prediction from this target sequence. Identification of related proteins. The submitted job to ORION web server was done with the following parameters: the search is performed in the PDB95 database with the 'gloloc' alignment mode and a maximum of 100 hits in the results. A summary hit list is displayed with the identified templates. All of these templates share a very low sequence identity with T0818-D1 (mean value is 8.
45%; the maximum value equals to 14. 63%). Nonetheless, some of the best ranked templates belong to the NTF2-like superfamily and so provides insights to the topology of T0818-D1. Protein sequences of NTF2-like superfamily are very diverse  43  and thus are hard to detect based only on a simple sequence or sequence profile search. ORION has the advantage to use accurate structural features in profiles that allow identifying very remote homologous proteins. ORION succeeded to identify several NTF2-like proteins with very close scores. In the first 5 identified templates, we have selected the fourth template, which is the only template with 100% of the query coverage. This template corresponds to the crystal structure of the Putative scyalone dehydratase from Novosphingobium aromaticivorans (PDB code: 3ef8, chain A).
HOMSTRAD 32 A collection of ORION templates profiles obtained from HOMSTRAD families (aligned sequences and structures) from the HOMSTRAD website. Table  1. List and description of the databases used in the ORION webserver. 3D structure prediction. We create a 3D protein model using MODELLER with the T0818-D1-3ef8_A alignment, by clicking on the 'Build 3D model' button. The model obtained is composed of α -and β -regions organized in three α -helices followed by an antiparallel β -sheet of 5 β -strands (Fig. 3 ). The overall quality of the model is estimated as 'medium' with a z-score between -1 and -2 and have a root mean square deviation (RMSD) value of 3. 8 Å with the target structure. Thus, we investigate for the quality of local regions in the model. We notice 3 main low quality regions from residues 35 to 47; 60-77 and 115-132, in which the DOPE score per residue is over the threshold of 0 (Fig.
ORION models were generated using the first ranked template and we checked that the selected template has been released into the PDB before the CAMEO target date prediction, in order to compute models under the same conditions as during the target release date. Since the HHpred server  45  and the SPARKS-X server  46  have been assessed by CAMEO for two and three of the four examples, respectively, we have launched a prediction on HHpred and SPARKS-X server for the missing targets. For the HHpred server, the two missing models were obtained using the 'pdb70_13Apr16' template database with the default parameters and the 'automatic template selection' option. For the SPARKS-X server, the missing model was obtained with the default parameters and  using the first ranked template. We also ensured that the HHpred and SPARKS-X models were based on templates that have been released into the PDB before the CAMEO target date.
The first example is an odorant binding protein (OBP3) from Megoura viciae (PDB code: 4z39, chain A), an all-α protein of 121 residues length, which is classified by CAMEO as 'hard target' (Fig. 6a ). The best model was proposed by Robetta server  51  with a TM-score  54  of 0. 66 and ORION model ranked second with a TM-score of 0. 64. However, the ORION model was obtained after 22 minutes of computation contrary to Robetta server, which took 20 hours to predict the model (Table  2 , left). The second example is a hydrolase (Apo hypoxanthine-guanine phosphoribosyltransferase) protein from Legionella pneumophila (PDB code: 5esw, chain B). 5esw_B is an α + β protein of 197 residues length that is classified as a medium target (Fig. 6b ). The ORION server outperforms Predicted PB ("pbpred") and SS ("psipred") annotation is reported on the query/model sequence as "pbpred" and "psipred", respectively.
Assigned PB and SS annotation is reported on the template sequence as "PB" and "DSSP", respectively. The sequence of the T0818-D1 model is colored in blue while the sequence of 3ef8_A is shown in black. Regions of high structural complexity in the template 3ef8_A that are in the vicinity of poor quality regions in the model are delineated by gray filled squares and located around residue 33, 67 and residue 122. all the compared servers according to the ORION model that has the higher TM-Score (0. 88). Since the SWISS-MODEL  53  server has predicted an incomplete model with 89% of coverage, the ORION model has also the lowest RMSD value for the complete model (3. 37 Å) (Table  2 , right). The two other examples are of a medium level. The first is an α + β protein of 119 residues length from Francisella tularensis (PDB code: 2mu4, chain A) (Fig.
7a ) and the second is a DNA binding domain of CpxR from Escherichia coli (PDB code: 4uht, chain B) of 102 residues length (Fig. 7b ). According to the TM-score, ORION server has predicted the second best model of 2mu4_A (0. 64) in only 21 minutes (Table  3 , left). However, the ORION server does not perform as well as the other targets for 4uht_B. Indeed, the ORION model is ranked sixth over the 12 servers with a TM-Score of 0. 81. The RBO Aleph 52 model has the highest TM-score value (0. 87) and the Robetta model, which is ranked second, has the lowest RMSD value (2. 18 Å) (Table  3 , right). Based on these four examples, ORION server outperforms similar fold recognition servers based on different algorithms such as HHpred, SPARKS-X, RaptorX, Princeton_TEMPLATE and Phyre2. Robetta server is, with I-TASSER  55  server, one of the most powerful and accurate tool for protein structure prediction  4, [56] [57] [58] [59].
Acknowledgements This work was supported by grants from the  French Ministry of Research, University of Paris Diderot-Sorbonne Paris Cité, French National Institute for Blood Transfusion (INTS) ,  French Institute for Health and Medical Research (INSERM). This study was also supported by grant from  Laboratory of Excellence GR-Ex , reference  ANR-11-LABX-0051. The labex GR-Ex is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. AdB also acknowledges the  Indo-French Centre for the Promotion of Advanced Research/CEFIPRA  for collaborative grants (number  5302-2 ).
1b ). To assess the extent of plant responses to HS, we monitored transcript levels from 32,793 A. lyrata and 32,678 A. thaliana protein-coding genes. This revealed significant upregulation (adjusted P <0. 05; DESeq) of 21. 8 % A. lyrata genes (n = 7156) and 18. 9 % A. thaliana genes (n = 6165) after 6 h HS (Fig. 1c ; Additional files 1 and 2). After recovery, we found only 2. 9 % (n = 980) of genes still upregulated in A. lyrata and 0. 6 % (n = 192) in A. thaliana. A. lyrata showed 21. 3 % (n = 6992) downregulated genes after HS and 1. 5 % (n = 491) after recovery (Fig. 1d ). There were 17. 3 % (n = 5650) significantly downregulated genes after HS and only 0. 3 % (n = 89) after recovery in A. thaliana. Hence, HS treatment induced a similar degree of transient transcriptional changes in both species.