dataesr/scientific-paragraphs-categorization huggingface dataset:
Background With the advent of next generation sequencing methods, there has been an explosion of the sequence information available at our disposal. Therefore, it is increasingly important to consolidate this large amount of sequence data and segregate them into functionally meaningful groups. The current approaches to classify proteins operate at the level of protein domains. Several databases  [1, 2]  that present classification of protein domains, have contributed tremendously to our understanding of the relationships between protein domain sequences, structures and functions. Majority of the proteins in sequence databases are multi-domain in nature  [3]. Multi-domain proteins with similar sequential order of domains tend to have a high functional similarity  [4] [5] [6]. Domain unassigned regions can also play critical roles in function and/or regulation of proteins.
Implementation The engine of our classification scheme is the patternmatching algorithm, which measures local similarities between two protein sequences s and s′ as LMS (Local Matching Score). It can be depicted as, LMS s; s 0 ð Þ ¼ X i∈ _ s;ṡ 0 f g M i; i ½ Where, ṡ and ṡ0 are residues from s and s′ that are a part of all matched five-residue fragments between the two sequences. M[i,i] is the BLOSUM62 substitution score. The scores computed are normalized to give a distance measure ranging from 0 to 1. LMSdist s; s 0 ð Þ ¼ 1- 2LMS s; s 0 ð Þ LMS s; s ð Þ þ LMS s 0 ; s 0 ð Þ The pairwise distances computed using LMS are represented as a symmetric square matrix. These distances are subjected to agglomerative clustering based on Ward's minimum variance method  [13]  as employed in hclust module of R  [14].
The hierarchical clustering obtained is represented as a dendrogram that can be parsed at various distance cut-offs (×), ranging from 0 to 1, to obtain distinct clusters. We believe that the clusters generated at a statistically significant cut-off, which maximizes inter-cluster dissimilarity and minimizes intra-cluster dissimilarity, are representative of the subfamily organization in a dataset of protein sequences. The domain architectural similarities and differences of these clusters help in determining subfamily defining features. Figure  1  summarizes the workflow of the web server.
Results and discussion CLAP is a tool for clustering protein sequences that works well with any set of amino acid sequences. The only requirement is the amino acid sequences of the proteins and no information on domain boundaries is required. Another advantage of CLAP is that full-length sequences are taken into account hence utilizing the information contained in accessory domains as well as inter-domain regions. The assessment of the performance of CLAP, in terms of time efficiency as well as whether it generates biologically meaningful clusters, was reported in our previous paper  [12]. In the previous study, two pilot data sets, one of protein kinases and the other of immunoglobulin domain containing proteins were selected from Pfam database  [2]. As kinases are well classified, the first data set helped to study if CLAP could reproduce an existing well-established classification.
With an increase in either the data set size or the length of the protein, the time taken to run these methods increases. Taking small (<100 amino acids), medium (400-600 amino acids) and large (>850 amino acids) single-domain proteins, data-sets containing 500 and 1000 sequences were created. The sequences selected for the analysis are the reviewed entries from Swiss-Prot  [20]  and hence are wellannotated. As seen from Table  1 , the worst that CLAP takes is ~10 minutes in the case of a huge dataset of 1000 sequences, whereas the longest that ClustalW Table  1  Clustering of different data-sets of small, medium and large sized protein sequences using different methods The processing time was computed using the workstation that hosts the CLAP web-server, with a 2. 40 GHz, Intel xeon processor and 16GB RAM running CentOS.
In theory, such uniform sampling patterns are not conducive to CS recovery, so it is experimented with for comparative purposes only. Both of the two MSFAs are easily extendible to any number of channels. From this state-of-the-art, it appears that there are many possibilities to design an MSFA sensor. Intuitively, we can already understand that given the variety of design, data processing will be very dependent and the cost will be high while designing specific arrangements. All hardware implementations of multispectral filters offer solutions with some issues, such as the lack of sensitivity, crossleak problems between channels, inaccuracy measurements, or do not consider the overall system characterization (sensor + filters). As indicated by the state-of-the-art, it is not difficult to find out that the design of MSFA is rather application-specific in terms of the number of bands, spectral transmittances, as well as the geometrical arrangement.
However, these optimized filters are often based on simple theoretical transmittance function, i. e. , Gaussian or Gaussian-based curves. On the other hand, the results of the optimization processes cannot yet be taken as they are. There is a major difference between the definition of the optimal set and what is available as commercial filters, due to manufacturing processes. There is no doubt that in the next decade, this problem will be bypassed with the advancement of technology, but it is still a limiting problem today. Nevertheless, one may use a brute approach to select the best filter set within a database  [98]. This is valid in the case of large-sized filters that cover the sensor, such as a filter wheel. When it comes to the problem of having a mosaic of filters, the technological constraint is even worse.
The frame rate for receiving video through Ethernet is about 17 fps. Software provides the control of most functions and the acquisition of images or video sequences (see Figure  15 ). This software is used to retrieve the video stream from the sensor through the UDPEthernet protocol. A live preview of the resulting image before and after demosaicing is also available for both arrangements FS1 and FS2. To ensure high speed and a real-time preview of the acquisition, only simple interpolation demosaicing has been implemented. We added the possibility of recording a TIFF image with multiple pages, each page corresponding to one channel. We can also save the arrangement of the filters in the metadata of the demosaiced/raw image. On Blocks 1 and 2, we can see the preview of the image acquired on a selected channel.
Prospective Work on Demosaicing This section presents preliminary results on demosaicing and a comparison between the two setups, FS1 and FS2. We simulated our system in MATLAB, creating an environment able to represent all stages of the pipeline treatment. The simulation of the light source, the radiance calculation, the image mosaic reconstruction and the multispectral image reconstruction are the main steps of our simulation software. Future work will focus on a full resolution channel interpolation, dedicated to these spatial arrangement and spectral characteristics. Indeed, we must consider this research to ensure better image quality. The reduced spatial image resolution is a typical problem of CFAs and MSFAs due to their intrinsic property that a certain spectral band is allocated specifically at each location of the array.
Through the simulation of the system in MATLAB, we provide preliminary results on demosaicing from our two arrangements, FS1 and FS2. The demosaicing techniques used are bilinear interpolation and channel difference interpolation  [104]  (by the P5 channel). The simulation steps is shown in Figure  16 , where we use the Skauli hyperspectral database  [102]. We use standard indicators peak signal-to-noise ratio (PSNR) and structural similarity (SSIM)  [105]  to quantitatively measure the quality of reconstruction. These results are presented in Figure  17. We can see that PSNR for channel difference interpolation is better for FS1, due to the existing over-sampled channel (P5). 17c, d, e  shows the results of the demosaicing following two methods for the P4 channel through FS1. Although PSNR and SSIM indicators do not show significant differences, it can be seen that the edges are significantly sharper for the image reconstructed by the channel interpolation method.
Acknowledgments Open Food System is a research project supported by  Vitagora ,  Cap Digital , Imaginove, Aquimer, Microtechnique and Agrimip, financed by the  French State  and the  Franche-Comté Region  as part of The Investments for the Future Programme managed by  Bpifrance , www. openfoodsystem. fr. The authors want to thank  Matthieu Rossé  and  Cédric Clerc , the technician staff members who participated in the electronics' design.
cerevisiae underwent a wholegenome duplication (WGD), followed by extensive loss of one copy of most paralogous genes  (Wolfe and Shields 1997). All hemiascomycetous yeast have experienced extensive intron loss, and on average introns are found in only 5 % of their genes  (Neuveglise et al. 2011). Introns in S. cerevisiae have been well annotated due to the extensive efforts of the yeast scientific community (Saccharomyces Genome Database [SGD]). However, introns in most other yeast species are poorly annotated, and intron analyses therefore rely either on automatic annotation or small manually curated sets of intron orthologs. This impedes the estimation of intron number and subsequent evolutionary studies. Nonetheless, even with limited intron information, interesting evolutionary observations have been made for the Saccharomycotina subphylum.
1b1  (Wheeler and Eddy 2013). Briefly, hmmbuild was used to build the profile HMM from sequence alignments, and then nhmmer was used to search the profile against a DNA database containing all sequences from YGOB. Lists of hits were manually inspected, and potential matches were extracted and aligned against the seed alignment using hmmalign. An iterative procedure of HMMER search, alignment, manual inspection, and editing allowed us to construct full gene alignments, with annotated intron boundaries and BPs. These alignments allowed us to confirm intron presence, perfect deletion, or imperfect deletion resulting in the removal or insertion of additional codons. Where the alignment did not allow us to conclusively prove intron presence, we assumed the intron was removed by an unknown mechanism and, thus, counted it as an intron loss for the subsequent phylogenetic analysis.
Phylogenetic Analysis Syntenic relationships between intron-containing genes were extracted from YGOB. Paralogous genes arising from WGD were grouped to represent one ancestral gene, and multiple introns in the same gene were treated separately. The phylogenetic tree used to map intron gains and losses was taken from YGOB (simplified version from  Hedtke et al. 2006 ). An additional branch reflecting the WGD loci was added creating a tree with both A and B loci for all post-WGD species. For the analysis of each intron alignment, the tree was pruned to include only the species found in that alignment. For each ancestral intron, gains and losses were placed automatically on the pruned yeast tree using the Dollo parsimony method  (Farris 1977)  implemented in the Dollop script from the Phylip package.
Serine-rich (Srr) proteins exposed at the surface of Gram-positive bacteria are a family of adhesins that contribute to the virulence of pathogenic staphylococci and streptococci. Lectin-binding experiments have previously shown that Srr proteins are heavily glycosylated. We report here the first mass-spectrometry analysis of the glycosylation of Streptococcus agalactiae Srr1. After Srr1 enrichment and trypsin digestion, potential glycopeptides were identified in collision induced dissociation spectra using X! Tandem. The approach was then refined using higher energy collisional dissociation fragmentation which led to the simultaneous loss of sugar residues, production of diagnostic oxonium ions and backbone fragmentation for glycopeptides. This feature was exploited in a new open source software tool (SpectrumFinder) developed for this work.
Lectin affinity chromatography was used to enrich Srr1 before trypsin digestion Fig. 1C. Tryptic peptides were then analyzed by nanoLC-tandem MS (MS/MS) using various activation techniques. A dedicated software tool SpectrumFinder (SF), allowing the retrieval of glycopeptides after higher energy collisional dissociation (HCD) fragmentation was developed for this study. This combination of approaches led to the identification of 27 glycopeptides in the N-terminal part of the protein, outside the region containing the highest serine density. We demonstrated that besides glycosylation by N-acetylhexosamine (HexNAc) or hexose (Hex) residues, Srr1 can also be modified by an unexpected sugar characterized as Oacetylated-N-acetylhexosamine (O-AcHexNAc). To localize glycosylation sites, electron transfer dissociation (ETD) experiments were performed.
In the second type of run, only ETD was used for MS/MS including all glycopeptide ions previously identified. The maximum trapping time, the activation time with fluoranthene and the NCE were respectively fixed at 200 ms, 180 ms, and 35%. All ETD spectra were manually inspected. X! Tandem Analysis-The analysis of CID and HCD data was performed with X! Tandem CYCLONE (2011. 12. 01. 1). The S. agalactiae H36B complete database (http://www. ncbi. nlm. nih. gov/ genome/186?project_id ϭ 54315) and a contaminant database (trypsin, keratins, and others classical contaminants) were used. Enzymatic cleavage was set as a trypsin digestion ([RK] (15)) with multi possible miscleavage. Precursor mass and fragment mass tolerance were set to 10 ppm and 0. 5 Da, respectively for LTQ Orbitrap data and to 10 ppm for Q Exactive data.
Methionine oxidation was taken into account as possible modification. A refinement search was performed that accepts semi-enzymatic cleavage of peptide and amino terminus acetylation (ϩ42. 0105). Only peptides with an E value smaller than 0. 05 were considered for identification. To identify glycopeptides in CID data, Ser/Thr/Tyr variable modification with HexNAc (ϩ203. 0793 Da), O-AcHexNAc (ϩ245. 0912 Da), and Hex (ϩ162. 0528 Da) were introduced in the search parameters. In spectra typical of neutral loss events, the intensity of b and y ions generated by peptide backbone fragmentation is much lower than the ion formed by the glycan loss. To take this effect into account, the dynamic range for scoring spectra was set to 1000. Development of a New Software Tool for Spectra Comparison (SpectrumFinder)-The main goal of SpectrumFinder is to compare pairs of MS/MS spectra, taking into account the fact that the same peptide sequence carrying different glycans will lead to very similar fragmentation patterns in HCD after glycan loss.
SpectrumFinder v1. 0 is open source software developed for the need of this study (Fig. 2 ). It is distributed under the terms of the GNU Public License version 3 and available for Linux and Windows operating systems from http://pappso. inra. fr/bioinfo/sf. SpectrumFinder is written in Cϩϩ. It uses a simple XML parameter file to build a collection of reference spectra and compare it to other spectra in mzXML files. The software requires MS/MS spectra recorded on the same instrument. After an initial spectral cleaning step based on background subtraction and low cut-off selection performed on a user-defined number of peaks, all MS2 spectra of a LC run are compared with a reference spectrum of interest (Fig. 2A ) to assess their similarity. The similarity is established on the basis of different criteria Fig.
In this study, the reference library was deliberately restricted to spectra corresponding to potential glycopeptides (Fig. 3 ). In each case, the reference was manually chosen as the best quality spectrum (minimum six fragment ions with high signal to noise ratio). For each Spectrum-Finder output, we systematically checked the corresponding MS2 spectra for the presence of diagnostic oxonium ions: m/z 163. 0606 (Hex), FIG. 2. Workflow of SpectrumFinder processing. A, SpectrumFinder searches similar fragmentation pattern among MS2 spectra. Peaks are extracted from a reference spectrum and compared with an experimental data set. B, Similarities between MS2 spectra are statistically evaluated and a correlation score is calculated C, When a correlation is considered significant, the mass difference between the precursor ions of reference/computed spectra is calculated.
The relative abundance of each glycoform of the [192-211] peptide of Srr1 produced in H36BSrtA* and H36BSrtA*⌬gtfCH strains was calculated by averaging the corresponding XIC peak area measured from three biological replicates. Normalization was done using the areas of non-glycosylated peptides ([335-345]; [373-387]; and [494 -507]). To perform semi-quantitative analysis of peptide glycoforms, the area of each peak corresponding to a parent ion was computed from an extracted ion chromatogram (XIC) with MassChroQ, a software that performs alignment, XIC extraction, peak detection and quantification on mzXML data (59) with a 10 ppm tolerance. The relative proportion of each glycopeptide is reported in Table  II.
S2A-2D ). MS/MS data were also manually inspected, looking for fragmentation spectra dominated by a single ion characteristic of a glycan loss event. This manual search led to the identification of 12 other glycopeptides (Table  I ). The highest diversity was observed for the  [192] [193] [194] [195] [196] [197] [198] [199] [200] [201] [202] [203] [204] [205] [206] [207] [208] [209] [210] [211] peptide found associated with ten different glycan combinations, a feature illustrating the heterogeneity of Srr1 glycosylation (Table I and supplemental Fig. S3A-3K ). Glycosylation with a Hex was found only on peptides carrying at least four HexNAc (or O-AcHexNAc, see below), suggesting that the latter modification is a prerequisite for further Hex addition. The reactivity of Srr1 toward lectin and the monosaccharide composition analysis of full length protein showed that Srr1 was mainly modified with GlcNAc and Glc residues, strongly suggesting that HexNAc and Hex should correspond to these two sugars.
A software was developed to exploit the HCD spectra similarities between different glycoforms of a peptide. The software, coined Spectrum Finder, was dedicated to the automated search of specific peptide fragmentation pattern without a priori on the mass of the precursor (for details see experimental procedures and http://pappso. inra. fr/bioinfo/sf). As compared with X! Tandem, analysis of HCD data with SpectrumFinder revealed additional fragmentation spectra originating from the same peptide backbones. For each spectrum selected by SpectrumFinder, the mass difference between the precursor ion and the unmodified peptide was calculated and fitted to a glycan combination of Hex, HexNAc, and O-AcHexNAc (see experimental procedures and Fig. 2 , Fig. 3 ). All the glycopeptides previously identified in the CID experiments (listed in Table  I ) could be retrieved, demonstrating the robustness of this new approach.
The quantitative analysis of thorax morphology showed that queens fall in two distinct anatomical types. Using parsimony and maximum likelihood (ML) methods, we reconstructed the pattern of thoracic evolution onto the established phylogenetic tree of the ants  (Brady et al. , 2006; Moreau et al. , 2006)  for 54 genera representing 21 subfamilies plus two genera of wasps as outgroups. We also compiled behavioral data on the mode of colony foundation for our exemplar species and tested for correlated evolution between queen thoracic morphology and founding behavior. Our comparisons are drawn from a total of 111 species belonging to 93 genera within Formicidae, representing 20 of the 21 extant subfamilies plus the fossil taxon Sphecomyrminae † among the four extinct subfamilies.
While the use of T1/T2 in (A) emphasizes the distinction between workers and queens and within species variation, T1/TL and T2/TL in (B) enables the distinction between queen types across species with large differences in body size. Measurements and ratios are available in the Dryad data repository under DOI doi: 10. 5061/dryad. d62p2/1 (Keller et al. , 2014)   2 ). Queens can either (A) have a reduced T1 and huge T2-associated wing muscles (represented here by Oecophylla smaragdina), or (B) show a slightly enlarged T1 and associated neck muscles (represented here by Neoponera apicalis). T1, T2, and T3, first, second and third thoracic segments respectively; A1, first abdominal segment. Workers of N. apicalis lack a discernible T3. Internally (right column), the wing muscles in queens (red) fill most of the thoracic cavity, while the T1 muscles (blue) are narrow and close to the thoracic wall.
Phylogenetic sampling We compared the thorax of queens and workers across multiple species representing all major ant lineages. First, we measured the length of the thoracic segments and entire thorax in a sample of individuals belonging to 11 different species from five ant subfamilies (Table  1 ). Second, we used a database of scanning electronic micrographs  (Keller, 2011)  and an online database of light microscopy images (http://www. antweb. org) to further assess the extent of the taxonomic distribution of the traits of interest. We inspected the external thorax of workers belonging to 110 ant species, and of queens belonging to a subset of 47 species where this caste is known or available (listed in Table  2 ). This taxon sampling represents all 21 extant subfamilies with the exception of the dorylomorph subfamily Aenictogitoninae (for which only males have been formally described), and includes the extinct subfamily Sphecomyrminae †.
Morphometrics and anatomical analysis For the quantitative characterization of the thorax, we took dorsal and lateral photographs of pinned specimens (Museum of Comparative Zoology, Harvard University) with a JVC digital camera mounted on a Leica MZ16 binocular microscope (images are deposited in the Dryad data repository under DOI doi: 10. 5061/dryad. d62p2/2). We then measured (ImageJ, http://rsb. info. nih. gov/ij) the dorsal length of the first (T1), second (T2), and third (T3) thoracic segments along the midline, and the total thoracic length (TL) as the diagonal length in profile from the anterior-most point of T1 to the posterior-most point of T3 (Figure  1 -figure supplement 1; measurements are available in the Dryad data repository under http://dx. doi. org/10. 5061/dryad. d62p2/1).
Measurements and ratios are available in the Dryad data repository under DOI doi: 10. 5061/dryad. d62p2/1 (Keller et al. , 2014). Species codes: A. aus = Amblyopone australis; B. lut = Brachyponera lutea; C. vid = Carebara vidua; C. was = Cataulacus wasmanni; L. nig = Lasius niger; L. per = Leptothorax pergandei; M. pha = Monomorium pharaonis; N. api = Neoponera apicalis; P. bar = Pogonomyrmex barbatus; P. lab = Polyrhachis laboriosa; T. aet = Tetraponera aethiops. DOI: 10. 7554/eLife. 01539. 004 The following figure supplements are available for figure 1:.
Additional information Funding Funder Grant reference number  Author Portuguese Science and Technology Foundation (FCT)   SFRH/BPD/65529/2009   Roberto A Keller Portuguese Science and Technology Foundation  ( FCT) PTDC/BIA-BEC/100243/2008   Patrícia Beldade Oeiras Municipality Installation Grant Patrícia Beldade The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.