dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteins observed in at least three of the replicate samples were considered for clustering; the minimum signal required was 5. Cluster health was set at 0. 9, and the minimum number of items per cluster was three. Sequence Analysis-A BLASTp search was performed with the NCBI website facilities using the nonredundant protein sequences. The BlastX algorithm (version 2. 2. 15, GenBank release number 166) was used with a threshold E-value set at 1E Ϫ10. Data Repository-This whole transcriptome project has been deposited in the EMBL Nucleotide Sequence Database under the accession number PRJEB5098 (www. ebi. ac. uk/ena/data/ view/PRJEB5098). The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org/) via the PRIDE partner repository  (32)  with the dataset identifier PXD000576.
General Proteogenomic Strategy for Discovering Key Proteins Involved in Reproduction from a Nonmodel Complex Organism-To represent as accurately as possible the real ecology of gammarids, we sampled heterogeneously sized organisms from a field population located in eastern central France. Because our focus was on proteins involved in reproduction, only sexually mature organisms were chosen, by selecting those in precopulatory mate guarding (Fig. 1, panel  A ). We first established a database of nucleic-acid-derived protein coding sequences that could be a generic platform for ecotoxicology-related proteomic studies on gammarids, as shown in Fig. 2. For this, we extracted total RNAs from four key tissues: the male and female reproductive tissues (Fig. 1 , panels C and D); the cephalon, where several neuroendocrine glands are located (Fig.
1, panel B ); and the hepatopancreatic caeca (Fig. 1 , panel B), involved in xenobiotic detoxification and energy acquisition (digestive enzyme secretion, food absorption, and nutrient storage). Equal amounts of RNA from each tissue were pooled and sequenced in a single Illumina sequencing run (Fig. 2 ). The resulting nucleic acid information was used to create a protein database named GFOSS that could be used for proteogenomic identification of proteins. For this, each mRNA contig was systematically translated into the six possible reading frames, considering all possible putative ORFs. As in previous proteogenomic studies, tandem mass spectrometry data could be assigned with such a database  (23). The protein catalog established from experimental evidence allows the selection of the appropriate reading direction and frame of each protein-coding mRNA contig.
As indicated in Fig. 2 , we recorded a large dataset on the proteomes from three tissues by resolving each sample via OFFGEL electrophoresis and performing 24 nano-LC-MS/MS runs per sample. In order to identify proteins related to reproductive function, we first performed automatic functional prediction based on cross-species homology. To discover new proteins involved in this function, we then compared the male and female reproductive tissues with a label-free comparative approach. We also focused our attention on the proteome dynamics of the male reproductive tissue to indicate new proteins related to spermatogenesis in G. fossarum. Transcriptome Characterization via RNA-Seq-A normalized cDNA library was constructed from the pool of mRNAs extracted from the male and female reproductive tissues, the hepatopancreatic caeca, and the cephalon of sexually mature gammarids.
The library generated 323,731,722 paired-end reads of ϳ100 bp. After low-quality read filtering, de novo assembly of the Illumina sequences was carried out with a two-stage strategy. First, 1,406,956 sequences were preassembled with an N50 of 334 nt using the Velvet program. A dataset of 218,574 contigs with N50 and N25 of 799 nt and 1,221 nt, respectively, was then created with the Mira software program. The average guanine-cytosine (GC) content was 47. 9%. The total length of mRNA sequences was 145,008,280 nt. A total of 386,238 reads referred to as singletons were not incorporated into the final assembly. Among the assembled contigs, 127,332 (58%) produced significant hits against the nonredundant NCBI database when we used an E-value threshold of 1E Ϫ10 and the BLASTp search tool (supplemental Table  S1 ).
We extracted the taxonomic information associated with the best protein homolog for each significant hit. The greatest number of contigs (8,927, or 4%) showed the most similarity to protein sequences from the crustacean D. pulex, and another significant number (4,053, or 2%) were similar to proteins from the beetle Tribolium castaneum. It is noteworthy that few similarities were found to proteins annotated from the amphipod P. hawaiensis using this metric (221 contigs), although the maternal and embryonic transcriptomes are available for this organism  (26). In addition, 33 BLASTp results were annotated as cytochrome c oxidase subunit 1 (CO1) from diverse species, mainly crustacea (supplemental Table  S1 ). Because this gene has been proposed as a genetic marker for species assignment in Gammarus  (33) , we performed a thorough search for CO1 sequences from GFOSS via BLASTn using 376-bp sequences available for organisms from a regional study covering the area of origin of our population (accession numbers X97163-X97185 (34)).
Phylogenetic tree reconstruction (based on the classification proposed by Meyran et al. (34) ) revealed the presence of different G. fossarum lineages concomitant with G. pulex lineages. The wild population sampled for RNA-Seq was thus an assembly of different lineages of the G. pulex/fossarum species complex. Our RNA-Seq-derived database thus constitutes a resource that is potentially of interest for studies in gammarids other than the G. fossarum species. Establishment of Protein Catalogs in G. fossarum-Our proteogenomic experiment led to a global dataset of 1,148,367 recorded MS/MS spectra. These indicated a total of 7,644 peptide sequences and certified 1,873 protein sequences. A total of 218 (12%) proteins were lineage-specific proteins (i. e. orphans) and are described here for the first time.
The assignment rate of the MS/MS spectra was low, especially with the OFFGEL approach (5%) relative to the gelbased shotgun approach when analyzing the same tissue (i. e. testis tissues), but this figure agrees with a previous interpretation of data acquired for two other complex animals  (35). In-deed, the high dynamic range of the proteome in these complex organisms, as well as the diversity of the proteome, leads to noisy MS/MS spectra for low-abundance proteins. Post-translational modifications could be an additional factor of complexity. The Cephalon Proteome-When we used the NCBInr database, a relatively low number of MS/MS spectra (3,103) could be assigned among the 231,098 MS/MS spectra recorded. The 878 unique peptide sequences (supplemental Table  S2 ) proved the presence of 135 proteins (supplemental Table  S3 ).
Using our customized, RNA-Seq-derived database, 10,647 MS/MS spectra could be assigned, indicating the existence of 2,576 distinct peptide sequences (supplemental Table  S4 ). In this case, a total of 546 mRNA-translated products (supplemental Table  S5 ) were identified. Using the BLASTp tool, we observed that 505 (92%) exhibited significant sequence similarities with an already functionally annotated protein from another sequenced organism (E-value less than 1E Ϫ10 ). Of note, relative to the results obtained with the NCBInr database, our customized database led to a large increase in the number of identified peptides (ϳ3-fold) and, consequently, proteins (ϳ4-fold). Ovary and Testis Proteomes-Using the GFOSS database, among the 700,078 MS/MS spectra recorded, we were able to assign a total of 36,516 (supplemental Table  S6 ).
level of orphans is higher (1. 5ϫ) for these reproductive tissues than has been observed for the cephalon proteome, highlighting the lack of knowledge regarding the key molecular players for both reproductive tissues. Furthermore, among the five proteins contributing 10% of the ovary and testis proteomes in terms of quantity, as estimated based on the normalized spectral abundance factor, one (I. D. 68651) does not have any functional annotation. Fig. 4  shows the complementary strategy followed for establishing the protein profiles of male reproductive systems. These were analyzed at seven different spermatogenesis stages, on five biological replicates. Using the GFOSS database, among the 217,191 MS/MS spectra recorded, we were able to assign a total of 50,793 (supplemental Table  S8 ). These validated a large list of peptides (2,699 unique peptide sequences) and allowed label-free quantitation of 914 translated contigs (supplemental Table  S9 ).
Regarding the previous analysis of the male reproductive tissue proteome, 437 additional products were newly identified here. Among the 914 proteins, 853 items (93%) showed significant similarities to previously annotated proteins from other organisms. The other 61 proteins (7%) can be referred to as orphans. In this global dataset, only three proteins contributed 10% of the proteome in terms of quantity: two histones (I. D. 13250 histone H2A and I. D. 65834 histone H3. 2like) and one protein (I. D. 100349) for which the function could not be predicted.
These were grouped into families according to the biological processes in which they might be involved (supplemental Table  S10 ). A total of six contigs were markedly related to enzymes involved in the hormonal regulation of steroids and isoprenoid methyl farnesoate (MF) synthesis. Four were identified as having a high probability of being involved in MF metabolism. Two isoforms of farnesoic acid O-methyltransferase (FAMeT) were identified in our survey; one was exclusively detected in the male reproductive system, and the other was also detected in the cephalon. Based on the phylogenetic analysis by Hui et al. (36) , both contigs are integrated among the crustacean FAMeT family (supplemental Data File S1), sustaining their involvement in MF biosynthesis. Additionally, two proteins that might be involved in sexual hormone catabolism were sequenced from testis.
fossarum protein catalog. Two isoforms were identified in the cephalon, and one isoform was observed in the female reproductive tissue. The four other contigs were identified in testis at different spermatogenesis stages. Among the seven contigs, five were related to the protein family analyzed previously by Huang et al. (38)  (supplemental Data File S1), of which four belonged to protein groups unrelated to the molting process (chitinase 4 and 1) and one (I. D. 12415) belonged to the chitinase 2 group. Remarkably, a large number of oogenesis-related proteins were detected via tandem mass spectrometry. Among these mRNA-translated products, 45 were lipoproteins (i. e. major yolk proteins or potential components of major yolk proteins), and five were receptors involved in the incorporation of the yolk proteins.
5A. Subsequently, for a detailed comparison of the most abundant proteins between both tissues, spectral-count-based quantitative proteomics was performed. A multiple-hypothesis-testing Student's t test was applied on our MS/MS dataset by means of the Tfold module of the PatternLab software program  (40). Supplemental Table  S7  lists the calculated protein variations between male and female reproductive tissues and their p value confidence. Fig. 5B  presents the distribution of the proteins according to their fold change and p value. Taking into account a fold change of at least 2. 0 and a confidence threshold of 0. 95 (p value Ͻ 0. 05), 129 contigs were significantly more frequently detected in the male tissue than the female tissue, and 75 were significantly more frequently detected in the female than the male tissue (Figs.
DISCUSSION Although crustacea are one of the largest and most species-diverse groups of animals, inhabiting all aquatic and terrestrial environments, little comprehensive molecular infor-FIG. 6. Clustered temporal abundance variations of proteins during spermatogenesis, before and after copulation. The different trends were obtained from clustering the spectral count using the TrendQuest module of the PatternLab program and are displayed in A-E. Cluster health was set at 0. 9, and the minimum number of items per cluster was three. mation is available for this major arthropod group. Today, the only available crustacean genome is that of Daphna magna, a Branchiopod Cladocera  (1). According to pancrustacean phylogeny, branchipoda are more closely related to hexapoda (insects) than to malacostraca (including decapods and amphipods)  (41).
We identified 218,574 contigs in the Gammarus transcriptome. We observed that ϳ40% of the contigs could be Gammarus-specific genes, as they presented poor sequence similarity to previously known genes. Thus, as shown with the cephalon proteome, a shotgun proteomic approach based on a dedicated RNA-Seq-derived database greatly improved MS/MS data interpretation relative to the use of current protein sequence databases from public resources. Here, the experimental difficulty of our approach is in the dissection of relatively large quantities of organs from small animals (ϳ1 mm in size) to extract sufficient mRNA and protein material. However, the interpretation of data is relatively straightforward, as the proteins identified and quantified by means of the shotgun nano-LC-MS/MS approach give a relatively comprehensive view of the G.
D. 4227) that showed dynamic changes during spermatogenesis. These proteins, previously identified in the acrosome from vertebrates, are believed to sequester Ca 2ϩ within this organelle, supporting the hypothesis that the acrosome acts as an intracellular Ca 2ϩ storage compartment  (57). In addition, three proteins identified as calcium dependent were classified as male specific: transglutaminase and two isoforms of copine-8. The functions of copine-8 are elusive, but in humans the gene is predominantly expressed in prostate and testis, suggesting an important role in the development and regulation of male sexual characteristics  (58). In conclusion, we have generated a proteogenomic dataset for the amphipod G. fossarum using Illumina-Solexa pyrosequencing and shotgun proteomics that paves the way for several studies related to this ecotoxicological sentinel organism.
Here, we focused our analysis on the reproductive process in order to propose new biomarkers related to its disturbance. To our knowledge, this represents the first extralarge proteogenomic resource for a crustacean. At this stage, we were able to certify via tandem mass spectrometry the presence of 1,873 proteins in G. fossarum. Further analyses are needed to go deeper into this catalog. Although the results of functional annotation by sequence similarities were quite limited because of the absence of other datasets for this lineage, we were able to highlight interesting candidates involved in various processes related to reproduction, such as hormonal metabolism, morphogenesis, sperm viability, and calcium homeostasis. We also found a high diversity of vitellogenin-like proteins in the female organ.
Acknowledgments-We thank  K. Abbaci ,  G. Le Goff ,  G. Miotello ,  G. Steinmetz ,  H. Queau , and the  PRIDE Team  for their technical assistance, as well as  P. C. Carvalho  ( COPPE, Rio de Janeiro, Brazil ) for adapting the export option of the  PatternLab program. * J. Trapp was supported by a fellowship from the  Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (France)  and from the  Program Transversal de Toxicologie of the Commissariat de l'Energie Atomique et aux Energies Alternatives (France). We thank the  Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (France)  and the  Commissariat de l'Energie Atomique et aux Energies Alternatives (France)  for financial support. □ S This article contains supplemental material.
To standardize the cDNA samples, expression of the housekeeping gene gapdh was tested with primer pair (f) 5ʹ-GGCACAGTCAAGGCTGAGAATG-3ʹ and (r) 5ʹ-ATGGTGGTGAAGACGCCAGTA-3ʹ. The PCR products were stained with ethidium bromide (Invitrogen) and analyzed by gel electrophoresis. All densitometry analyses were performed using ImageJ software. Signal intensities were normalized to gapdh used as housekeeping gene.
I. INTRODUCTION An efficient way for understanding the intrinsic phenomenology of a flow relies on the analysis of its constitutive coherent structures. Hence, the identification and extraction of these structures has received the careful attention of many contributions in the fluid mechanics community. Even today, such an aim remains challenging, although crucial steps have been made in the last decades. 2] [3]  Proper orthogonal decomposition is aimed at identifying, in a given dataset of velocity fields, patterns that are the most correlated in space and time, with respect to kinetic energy. Some spatial modes identified by proper orthogonal decomposition are sometimes considered as empirically determined coherent structures of the flow under study. Proper orthogonal decomposition proved to be very efficient in several problems where scales are clearly separated, e.
g. , cylinder wakes, or shear-layer self-sustained oscillations. 4, 5   wever, when several temporal frequencies are relevant, and associated with coherent structures of similar size, it is not guaranteed whether proper orthogonal decomposition is still able to discriminate between the different patterns of the flow, or rather merge them into some "optimal" pattern. Substitutes to proper orthogonal decomposition have been proposed in order to empirically extract coherent structures out of a dataset, based on either eulerian  6  or lagrangian criterions. 7, 8   cently, Schmid  9  and Rowley et al. 10  introduced the Dynamic Mode Decomposition (DMD) as a way for approximating eigenmodes of the Koopman operator of a flow. Dynamic modes, as an approximation of Koopman modes, reveal spatial coherent structures associated with temporal spectral components, including temporal growth rates.
II. SNAPSHOT-BASED ANALYSIS In the context of modal decomposition, the aim of snapshot-based analysis is to find a relevant basis for describing snapshots of the flow fields. Practically, snapshots are realisations of a given measure -i. e. , the observable -of the dynamics. In this section, we give a unified presentation of POD and DMD. Both decompositions are applied to the same dataset, K N 0 , made of N + 1 snapshots ordered in time. The observable u (r, t) -for instance, realisations of the velocity field -belongs to R n p , with r the mesh of the physical space. Realisations are sampled at every time-step δt from 0 to time T = Nδt. Hence, K N 0 := {u (r, 0) , u (r, δt) ,. , u (r, (N -1) δt) , u (r, N δt)} , ( 1 ) is a n p × N matrix. Discrete time iδt and u (r, iδt) will be referred to below as t i ≡ iδt and u i ≡ u (r, iδt), respectively.
Let U be the Koopman operator associated with the dynamical flow φ t. One can verify that U = A • , with U the Koopman operator applied to the projection. Further information about the link between DMD and a Koopman analysis are developed in Ref. 18. The operator A has an important property, when applied to an observable A (u (t 0 + n t)) = A • (x (t 0 + n t)) = • φ t (x (t 0 + n t)) = (x (t 0 + (n + 1) t)) = u (t 0 + (n + 1) t). ( 14 ) A is an evolution operator between snapshots u n and u n + 1. For the sake of readability, u (t 0 + n t) will be noted as u n. The main objective of Sec. II B is to expose the dynamic mode decomposition algorithm, which aims at extracting information from the operator A, i. e. , information of the dynamics itself, by the mean of an experimental dataset.
Construction of a similar operator DMD algorithms rely on well known properties of similar matrices, i. e. , matrices that share spectral features. As A is a priori not known, the only way to build a similar matrix to A is from the dataset itself. If K j i is the dataset of observable, orderly in increasingly time, i. e. , K j i = u i , u i+1 ,. , u j-1 , u j , then AK j i = Au i ,. , Au j = u i+1 ,. , u j+1 = K j+1 i+1. ( 15 ) Therefore, we will assume the relation AK N -1 0 = K N 1. ( 16 ) For reasons that will become clear later, we make use of the companion matrix method. 10 The assumption is made that the (N + 1) th observable is linearly dependant from the first N vectors of the dataset K N 0. This hypothesis is most usually relevant. At some point, adding more snapshots will not add any furthermore information.
0 s 0 1 0. s 1 0 1. s 2. 0. 0. 0 1 s N -1 ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠. ( 20 ) When R is neglected, the matrix S is similar to A := K N -1 0 K N -1 0 † A, the approximation of A in the image space of K N -1 0 AK N -1 0 := K N -1 0 K N -1 0 † AK N -1 0 = K N -1 0 S. ( 21 ) When the companion matrix is built on the fluctuating flow fields, Chen et al. 14  showed that DMD is equivalent to DFT. In that case, the companion matrix has no more unknown coefficients. Equation (17) reduces to N k=0 u k = 0, hence s 0 = s 1 =. = s N -1 = -1. Note that S is a sparse matrix, which may be not well-conditioned with noisy dataset. Alternative algorithms have been proposed to improve the matrix conditioning, for instance, through a singular value decomposition of the dataset matrix, introduced by Schmid,  9  or by constructing a Hessenberg matrix.
A. Experimental setup The cavity scheme is shown in Figure  1(a). The aspect ratio, cavity length L over height H, is L/H = 2, with L = 100 mm. The incoming velocity at the leading wall (x = 0) is U 0 = 1. 90 m/s, corresponding to a Reynolds number around Re L = U 0 L/ν air = 12 700. Timeresolved particle image velocimetry allows to get time and space resolved velocity fields. The sampling frequency is 250 Hz for the velocity fields, to be compared to the dominant frequency in the flow, of the order of 30 Hz. The dataset is made of N = 4096 velocity fields. Much information about the setup can be found in Ref. 28. A power spectrum of the flow, obtained by Laser Doppler Velocimetry (LDV), is shown in Figure  1(b). The time series come from a point near the impinging edge, in the shear layer.
C. DMD analysis Dynamic mode decomposition is performed on the whole dataset. The dynamics is driven by two high frequencies and some main low frequencies, as can be seen in the DMD spectrum of Figure  10. The two leading frequencies correspond to Strouhal numbers St 1 = 1. 02 and St 2 = 1. 38, which are precisely the frequencies of the shear-layer oscillations, as measured by other means. The spatial structure of the dynamical modes associated with those two Strouhal numbers are presented in Figure  11. As expected, they are energetic in the shear layer and exhibit amplified traveling waves (for instance, compare real and imaginary parts for the first dynamic mode, in Figures  11(a ) and 11(b), which are shifted by a phase of about π /2). We can apply the same procedure as in Sec. III B for extracting the wavelength associated with each dynamic mode.
A numerical approach has been developed to predict the near surface residual stresses induced by turning in orthogonal cutting configuration of aluminum alloy AA7075-T651. This approach is based on a Lagrangian formulation using the finite element software Abaqus-Explicit. The calculated residual stress profiles were validated by experimental measurements using X-ray diffraction method on samples turned under different cutting conditions. Using this method, the effect of the cutting speed and the feed on the machining residual stress has been established.