dataesr/scientific-paragraphs-categorization huggingface dataset:
P values: ns: Not significant; *: P<0. 05; **: P<0. 001; ***: P<0. 0001 The results (shown in Table  2 ) demonstrated that cystic cows had significantly low levels of glucose than normal ones. However, the mean values were within the normal range in both groups. Insulin and urea levels were very low in cystic cows and were below the normal range, while in normal cows, the values were in the reference range. In contrast, the cortisol levels were higher in cystic cows compared with normal cows, but the values remain within the reference range. No significant differences in blood concentrations of total protein, cholesterol, triglycerides, creatinine and liver enzymes activity (AST, ALT, and GGT) between cystic and normal cows were recorded and the all values were within the accepted range. According to the data shown in table 1 and 2, OC were characterized by low levels of glucose, total protein, cholesterol and cortisol in cystic fluid while the urea concentrations were high.
The recent proteogenomics concept emerged as a relevant strategy for the discovery of proteins in non-model organisms  [7]. For animals and plants, this approach combines data obtained by high-throughput mRNA sequencing (RNA-seq) for establishing the most appropriate protein sequence database and large-scale protein shotgun sequencing by tandem mass spectrometry. Indeed, peptide sequence identification relies on the comparison of experimental peptide mass spectra obtained by tandem mass spectrometry against all the possible theoretical spectra derived from the protein sequence database. For non-model organisms whose genome have not yet been sequenced, it has been proposed the construction of a theoretical protein sequence database on the basis of the sequenced transcriptome in silico translated to amino acid sequences in all three possible reading frames.
Proteogenomics has already proven its capacity in quickly documenting proteins of non-model sentinel species used for continental water biomonitoring. For example, 1,873 proteins related to the reproductive system of Gammarus fossarum were experimentally validated using proteogenomics  [8]. In order to get insights into the immune proteome of the zebra mussel, proteogenomics was conducted on both hemocytes and plasma compartment. For this, RNAseq and shotgun mass spectrometry with a tandem mass spectrometer equipped with an ultra-high-field Orbitrap analyzer were performed on the D. polymorpha sentinel species. Both data were combined leading to the identification of 3,227 novel proteins. Proteins involved in immune processes and potential immune proteins of both compartments were highlighted and commented.
Total RNAs for each sample were quantified using a Genova Nano spectrophotometer (Jenway, UK) and their qualities were checked using agarose gel. Samples were stored at -80°C until RNAseq library construction. RNA Seq library construction and sequencing were performed by Acobiom (Montpellier, France), using the HiSeq2000 sequencing instrument (Illumina). A total of six RNAseq libraries, corresponding to each tissue sampled, were constructed according to the TrueSeq Stranded (Illumina) protocol with 500 ng of total RNAs. Samples were sequenced using paired-end reads 2×150 bp method and the reads were assembled with the software Trinity pipeline (2. 3. 0 version) following the procedure of Haas et al. [9]. A transcriptome reference, called "Dreissena_reference", was constructed by compiling the 6 RNAseq data.
Proteogenomics database design and MS/MS interpretation The Dreissena_reference transcriptomic database was translated into the six possible reading frames with a homemade python script. The resulting translated contig database contained 5,648,820 polypeptide sequences, totaling 941,472,234 residues. MS/MS spectra were assigned to peptide sequences with the Mascot search engine with the following parameters: full-trypsin specificity, a maximum of two missed cleavages, a mass tolerance of 5 ppm on the parent ion and 0. 02 Da on the MS/MS analysis, and a pvalue for peptide validation at 0. 1. Modifications taken into account were carboxyamidomethylated cysteine (+57. 0215) as static modification and oxidized methionine (+15. 9949) and deamidation of asparagine and glutamine (+0. 9848) as dynamic modifications.
Translated contigs identified with at least one MS/MS certified peptide were selected for creating a reduced database named "Dreissene_cascade". This second database comprised 54097 polypeptide sequences, totaling 20,419,358 residues. Then, a second Mascot round of search was performed with a p value threshold for validating peptide assignment set at 0. 05. At this stage, a protein was validated when at least two unique peptide sequences were detected in at least one of the ten samples used in this study. The false positive rate for protein identification was estimated, through a search with a reverse decoy database, as less than 1% using the same parameters. For each protein and for each replicate, the numbers of spectral counts observed for the different detected peptides and corresponding to a peptide-to-spectrum assignation were summed.
Protein functional annotation and gene ontology analysis Functional annotation of MS/MS identified proteins was derived from a sequence similarity search without taxonomical restriction against (i) the National Center for Biotechnology Information (NCBI) database of non-redundant protein sequences (nr) (131,025,392 sequences released 05-09-2017) and (ii) the Swissprot database (465,089 sequences released, 30-09-2016), using the blastp module of DIAMOND (version 0. 8. 22) with an E-value threshold set at 1E-03. Functional annotations were further confirmed by searching conserved domain (CD) of proteins with the Batch CD-Search tool of NCBI server  [14]. Gene Ontology (GO) assignment for each translated contig was obtained with the GO database (http://www. geneontology. org/). GO assignments were used to classify molecular processes and biological processes of the hemocyte and plasma proteins.
Results and discussion 3. 1 A double round search for proteogenomics identification of proteins from the hemolymph of D. polymorpha. We applied a proteogenomics-derived strategy for quickly identifying the proteins from two fractions of the hemolymph of Dresseina polymorpha physiologically acclimated to laboratory conditions. Figure  1  shows a schematic representation of the experimental strategy and the double round searches for interpreting the tandem mass spectrometry data. First, RNAseq was performed on six samples of mRNAs extracted from six different tissues of zebra mussels: i) total hemolymph, ii) gonad male and iii) female, iv) gills, v) digestive gland and vi) whole tissues, as indicated in Figure  1. A total of 160,869,134 pairedend reads of 150 bp were assembled into transcripts with the Trinity software resulting in a list of 941,470 contigs with a N50 of 594.
A global proteogenomics reference database "Dreissena_reference" was created consisting of the systematic translation of all these contigs into the six possible frames. A dataset that will be described elsewhere (Lepretre et al. , unpublished data) and comprising 3,211,276 MS/MS spectra recorded on the proteomes of various hemocytes and plasma samples was interpreted against the "dreissena_reference" RNAseq-derived database to select relevant contigs for creating a reduced database as previously recommended  [16]. The attribution rate was 30% for hemocytes and 29% for plasma, a result similar to other proteogenomics analysis performed on non-model organisms with inflated RNA-seq proteogenomics database  [17]. The resulting reduced database comprised 54,097 protein sequences, a size comparable to classical animal proteomics based on a sequenced and annotated genome.
Functional annotation of these proteins was performed by sequence similarity search against the NCBInr and Swissprot databases. Most of the proteins (2,948) could be functionally annotated, but a relatively small set of protein sequences (2 %) did not present any significant sequence similarities with known proteins and thus could be considered as orphan proteins. Regarding the species origin of the most similar BLAST hit against NCBInr, the majority of proteins showed as expected strong sequence similarities with proteins from molluscs taxa (Fig. 3 ). The pacific oyster Crassostrea gigas is the most phylogenetic closely-related species based on this comparison with a total of 750 proteins with high similarities to D. polymorpha MS/MS-detected proteins, followed by another marine bivalve Mizuhopecten yessoensis.
PhD work was funded by the University of Reims Champagne-Ardenne (project PROMETHEE). Financial support was provided by the French National Research Agency (ANR) project MOBIDIC (Mollusc bivalves as indicators of contamination of water bodies by protozoan parasites, Project ANR-15-CE34-0005). The authors are grateful to Dr. Antoine LE-GUERNIC, Dr Damien RIOULT and Isabelle BONNARD, technician, for their contribution and advice to this study.
This PhD work was funded by the University of Reims Champagne-Ardenne (project PROMETHEE). Financial support was provided by the French National Research Agency (ANR) project MOBIDIC (Mollusc bivalves as indicators of contamination of water bodies by protozoan parasites, Project ANR-15-CE34-0005). The authors are grateful to Dr. Antoine LE-GUERNIC, Dr Damien RIOULT and Isabelle BONNARD, technician, for their contribution and advice to this study.
We quantify the impact of dry-out effects on wing shape, estimate anisotropic deformation and score local deformation of the entire wing surface while loading wings at various sites using a micro-force transducer. From these data, we calculate the wing's flexural stiffness and spring constants along selected bending lines. The measured data eventually allow us to construct an advanced, numerical model of fly flight using computational fluid dynamics and fluid-structure interaction. This model is currently under development and will provide quantitative results on flow patterns, aerodynamic forces and moments during flapping of model fly wings with a stiffness distribution similar to that of the natural archetype.
Spring and flexural stiffness We estimated wing stiffness by repeatedly applying various forces at the same load point. In Musca and Calliphora this was the anterior cross vein ( point 6, see Materials and Methods). For size reasons, however, it was not feasible to load this vein in Drosophila and we thus used the end of the third longitudinal vein instead ( point 2, see Materials and Methods, Table  2 ). Point 6 is close to the center of aerodynamic force in fly wings at 0. 56 wing length  (Ramamurti and Sandberg, 2007). Left and middle columns of Fig. 4  show that wing deflection, i. e. the difference between loaded and unloaded wings, follows a theoretical, homogenous beam line with an EI fitted to the data (red, Eqn 1). For the examples shown in Fig. 4  we determined Pearson's correlation coefficient between the best fit bending line (red) and the data (gray) that ranges from 0.
Animals and preparation All data were collected from a total of 34 female fruit flies Drosophila melanogaster Meigen, 35 female house flies Musca domestica Linnaeus and 34 female blowflies Calliphora vomitoria Linnaeus. The number of tested flies varied among the three main experiments as shown in Table  1. The animals were cold-anaesthetized, attached to a hypodermic needle and eventually fully coated using eicosane (CAS: 112-95-8, Sigma-Aldrich) to delay desiccation. In experiments on shape changes of attached and detached wings, we used clear nail varnish (essence cosmetics, cosnova GmbH, Sulzbach, Germany) instead of eicosane. The right or left wing was extended and fixed at the base for stability. The animals were tested immediately after preparation and remained alive throughout the experiments.
Experimental setup and procedures We mounted the tethered fly to a holder below an optical profilometer (VR-3000, Keyence Corporation, Osaka, Japan) that recorded local vertical height of the entire wing surface (Fig. 9A-D ). Point forces were applied and measured by a small, cantilever-based force sensor with nanonewton precision (CiS Forschungsinstitut für Mikrosensorik GmbH, Erfurt, Germany). The sensor was attached to an xyz-micro-translation stage (M-111. 12S micro-translation stages, Physik Instrumente, Karlsruhe, Germany) and positioned by software (PIMikroMove). We glued a tungsten wire with a diameter of 50 μm to the sensor's cantilever, painted the wire tip with yellow fluorescent dye to enhance visibility, and used the wire tip to apply point forces. We calibrated the force sensor prior to the experiment by attaching known weights (wire loops) of 0.
8 mg, 1. 3 mg, 2. 09 mg, 3. 1 mg and 5. 18 mg to the cantilever. Mean ambient temperature during the experiments was 26°C and ambient relative humidity 29% (Table  1 ). After aligning the animal, we positioned the force transducer near one of up to six veins (first to fifth longitudinal veins, anterior and posterior cross veins) and five membrane positions (marginal, submarginal and posterior cells) just below the wing surface (Fig. 9E ) and started a modified version of a custom-made software routine written in LabVIEW (National Instruments; software provided by CiS Forschungsinstitut). We grouped the load points into three regions (Fig. 9E):  (1) a leading edge region that includes load points 1, 6, 11 (all tested species); (2) a wing tip region with points 2, 3, 9, 10 (Drosophila) and 2, 3, 10 (Musca, Calliphora); and (3) a trailing edge region with points 4, 5, 7, 8 (Drosophila) and  4, 5, 7, 8, 9 (Musca, Calliphora).
5 samples per second. We scored forces at the earliest 10 s after the sensor stopped moving and the force signal became stable. Owing to heat-induced drift of the force sensor by the profilometer light, we could not reliably determine forces during the deformation measurements and thus averaged the value from up to five consecutive measurement values before and after (red, Fig. 10 ) the profilometer illumination. In cases in which both measures were off by more than 20%, we dismissed the data. For control, profilometer measurements on unloaded wings were conducted between each wing loading experiment. A full optical recording was 15-120 s and total measurement time for a single wing ranged from 45-60 min. All data were processed using the statistical programming language R (Version 3. 3. 3) including various extensions (R Foundation for Statistical Computing, www.
In total, 195 root samples were collected and snap frozen in liquid nitrogen for follow-up analyses (Sample information for the study) available at figshare  7. RNA extraction, library preparation and RNA sequencing. Total RNA was extracted from 195 root samples using the TRIzol reagent (Invitrogen), and treated with DNase I and Oligo (dT) to isolate mRNAs. The concentration and quality were determined using an ultraviolet spectrophotometer and 2% denaturing agarose gels. The cDNA was synthesized using the mRNA fragments as templates. The short fragments (~300 bp) were ligated with adapters and the suitable fragments were selected for PCR amplification. The libraries were paired-end sequenced using the Illumina platform Hiseq 2500  8. RNA-seq data processing. The program FastQC (http://www.
bioinformatics. babraham. ac. uk/projects/ fastqc/) was employed to determine the base quality of the raw reads (in FASTQ format) and we removed the paired-end reads containing more than 5% ambiguous residues (Ns) and those containing >10% bases with a Phred quality score of 10 (Fig. 2 ). Then, the raw reads were trimmed using Trimmomatic, version 0. 32  9. After cleaning and quality reassessment with FastQC, approximately 31. 6-56. 8 million high-quality reads of 90-bp length remained in each sample (Quality check report of 195 sesame transcriptomes under control, waterlogging and drainage treatments) available at figshare  7. The high-quality reads were mapped to the sesame (Sesamum indicum L. ) reference genome v1. 0 (http://ocri-genomics. org/Sinbase/login. htm)  10  using the STAR software  11  , allowing no more than one mis-match in the alignment.
Approximately, 88. 9-97. 4% of the clean reads were uniquely 0. 73 0. 86 1 Color key Fig. 3  Heatmap clustering of the transcriptome data. Correlations were estimated using the Pearson correlation coefficient based on TPM values. In total two main groups of samples were obtained, including control and stressed samples. mapped to the reference genome, with 94. 3-98. 6% of them uniquely mapped to the genic regions (Statistics of the clean read mapping) available at figshare  7. Using the featureCount package  12  , the gene expression levels were calculated based on the number of unique matched reads to the sesame genome v1. 0 10 and were normalized to Transcripts Per Million (TPM).
Data Records The RNA-seq raw data of the 195 samples are deposited to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the accession SRP181800  13. The gene expression data of the 195 RNA-seq samples are deposited to the Gene Expression Omnibus under the accession GSE133186  14. Supplementary files accompanying this manuscript have been deposited to figshare  7.
Technical Validation Quality control. In this project, a total of 195 RNA libraries were prepared and sequenced generating over 8 billion raw reads (Quality check report of 195 sesame transcriptomes under control, waterlogging and drainage treatments) available at figshare  7. By applying FastQC to the whole dataset, we successfully obtained high quality clean data with 97% of bases scoring Q30 and above (Fig. 2 ). Approximately, 98% of reads could be uniquely mapped to the reference genome of S. indicum.
Basic analysis of RNA-seq data. A heatmap for cluster relationships among the samples representing Pearson distance was generated with the TPM values using the R package 'Pheatmap' v. 1. 0. 12 (https:// cran. r-project. org/web/packages/pheatmap/index. html) (Fig. 3 ). It could be observed that samples from the control conditions (CK) and those from the stress treatments formed distinct groups. To further assess sample relationships and the time-course expression patterns, we implemented the tsne reduction scatter plot, which is a non-linear dimensionality reduction method for embedding high dimensional data into a low-dimensional space  15. The analysis was performed with the R package 'tsne' v. 0. 1-3. The results showed a clear separation between samples from control (CK), waterlogging treatment (0-36 h) and drainage phase (36-48 h) (Fig.
Usage Notes This study generated dense and high-resolution gene expression data under waterlogging stress-drainage in a non-model crop. In contrast to many experimental designs relating to temporal transcriptome profiling under stress which set the beginning of the stress application as the control, in the present project, we collected samples at different time points under the control condition. Comparing expression data from samples harvested at the same time point under stress and control conditions will provide more accurate differential gene expression records. Concerning the time points for which samples were uniquely harvested under stress treatments, the users can still use the previous time point in the control condition for gene differential expression analysis. The whole datasets were publicly deposited at NCBI SRA and we anticipate that our RNA-seq data would provide new insights into the molecular basis of waterlogging stress responses in plants.
Acknowledgements The study was supported by the  China Agriculture Research System  ( CARS-14 ), the  Agricultural Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences  ( CAAS-ASTIP-2013- OCRI ),  Wuhan Cutting-edge Application Technology Fund  ( 2018020401011303 ), and the  Fundamental Research Funds for Central Non-profit Scientific Institution  ( 1610172017003 ).
Particle tracking, image analysis and particle contact angle Bright field and fluorescence optical microscopies were used for particle tracking and imaging. Up-right and inverted optical microscopes mounted on anti-vibration tables were used. In typical experiments, images of isolated Janus particles were recorded at a rate of 30 frames per second using objective of different magnifications (×32, ×50, ×63 and ×100). The partial coating of platinum on the colloid modifies the light transmission through the particles or the fluorescence of the particle. It is then possible to evaluate Pt region under adequate illumination. Using IDL (Interactive Data Language) routines, we treated the raw image sequences and detected the Pt-cap of Janus particles by applying a grayscale threshold. The out-of-plane orientation  of the particle with respect to the interface was evaluated by measuring the absolute value of the visible Pt cap area.
(B) and (C). Bright field microscopy experiments of Pt-SiO2 Janus colloids were already described in our previous articles  [27, 29, 32] , where the darker side of the particle corresponds to the Pt cap possessing a complex refractive index of 0. 48 + 4. 87i (at 532 nm wavelength, whereas the silica refractive index is 1. 46 + 0i)  [27, 29, 32]. By analysing the images taken during the tracking of the Brownian motion of Pt-SiO2 Janus colloids at the air-water interface, we were able to evaluate the changes of the absolute area of the platinum cap. We then evaluated the orientation angle  (see Fig. 2(B) ) from the visible Pt absolute area, APt, knowing that APt = 1/2(1cos)R 2 , and calculated the mean square angular displacement MSAD(). In Fig. 3 , we plotted MSAD() as a function of the lag time for three specific datasets corresponding to three different particles for which  varies of  during 30 s.
We fitted the MSAD data by: MSAD() = MSAD0 + 2Dr,t, (1) where the parameter MSAD0 accounts for the noise introduced by the image treatment. From the fits in Fig. 3 , we find that rotational diffusion times r,=1/Dr, = 87 s (triangle), 152 s (circle), and 547 s (square) are larger by one order of magnitude or more than the hydrodynamic prediction for particles half immersed in water with finite slip lengths b, r, =  ,⊥ /kBT = 8. 4 s if b = 1 nm (r,= 11 s if b = 0. 1 nm)  [11]. Note that the rotational diffusion time in the bulk is r,b=1/Dr,b = 6. 3 s, which is of the same order of magnitude of the rotational times calculated with a finite slip length. In order to discuss these results, we assume the hydrodynamic prediction of  ,⊥ for b = 1 nm for half immersed particles  [11] , and associate an Arrhenius form to the rotational diffusion Dr,: 𝐷 𝑟,⊥ = 𝑘𝑇 𝜁 𝑟,⊥ exp (- |Δ𝐸 𝛽 | 𝑘 𝐵 𝑇 ), ( 2 ) where E is the activation energy that is required for the out-of-plane change of orientation.
Averaging on all MSAD data, we found E = 3. 6 ± 0. 9 kBT. Now we turn our attention to the in-plane rotational diffusion, see Fig. 2(C). By tracking the motion of fluorescent MF particles partially covered by platinum, we were able to distinguish the fluorescent cap of the colloid from the platinum cap that strongly reduces the particle fluorescence (see Fig. 2(A) ). Thus, we calculate the angle  between the projected Janus axis and the lab frame.  was evaluated from the orientation of the lever arm L with respect to the laboratory x-y axis in the interfacial plane, see Fig. 2(C ). MSAD() was calculated for different L/R datasets. In Fig. 4(A) , MSAD() data show several slopes in different lag time intervals. We fitted the data in the short time limit, for t < 0. 2 s, by: MSAD() = MSAD0 + 2Dr,||t.