dataesr/scientific-paragraphs-categorization huggingface dataset:
Multi-Modality Multi-Domain Feature Fusion We compare for each feature family different fusion schemes: intensity in the visible domain with the infrared one, intensity in the visible domain with the depth modality, the infrared domain with the depth modality, along with the multi-modal multi-domain scheme in the visible, depth and infrared domains. The results obtained on the Parma-Tetravision dataset are given in Figure  10. The fusion of visible and depth lowers the false positive rate for all feature families in comparison with the performance obtained on the visible domain. These results are consistent with those obtained on the Daimler Multi-Cue dataset. It is to noted that the performances obtained with the visible and depth fusion scheme are still less than those obtained in the infrared domain.
Fusing the infrared domain with the depth modality lowers the false positive rate in comparison with that obtained for the infrared domain. The relative improvement of performance varies from one feature family to another, being approximately 4 for the HOG features, 1. 6 for ISS features, 3. 3 for LBP and with a staggering factor of 96 for LGP features. The fusion of the visible and infrared domains with the depth modality provides the overall best results for all feature families on the Parma-Tetravision dataset. This fusion scheme allows similar improvement for the HOG and ISS features. However, the family of local binary features is much more robust, since the fusion of LBP features computed for visible, depth and IR has a factor of nine less false positives than with a classifier trained on the HOG features computed over the same three modalities.
Discussion In Figure  11  is presented an overview of the results obtained on the RIFIR dataset, while in Figure  12  is presented an overview of the results obtained on Parma-Tetravision. In Section 3, we have defined four main questions for which this article wants to provide answers (Q1-Q4). We can definitely answer Q1, since on both datasets acquired in daytime conditions, the FIR spectrum gives better results than the visible one for all of the considered features. If we compare a multi-domain fusion of features, we can observe that the relative gain is quite similar, whatever the feature family, but it might be different from one dataset to another. To answer Q2, if the state-of-the-art feature families in the visible domain have the same relative performance when tested in the FIR one, we conclude that this is not always the case.
For example, for the RIFIR dataset, for the visible domain, the best performing feature is LBP, followed by ISS, but at a great distance. On the same dataset, for FIR domain, the best performing feature is still LBP, followed this time by LGP. As concerns question Q3, if the ISS feature family is appropriate for pedestrian classification in the considered modalities (intensity, depth, flow) in the visible versus FIR domains, we can observe that the ISS features are less robust than LBP or LGP. Despite the good results obtained in our previous work  [28]  and on Daimler Multi-Cue for depth modality, overall, the LBP/LGP feature families have better performance. To answer Q4 (what is the relative gain of a multi-domain/multi-modality fusion), we can observe from Figure  12  that no matter what family of feature is employed, a fusion of information from visible, FIR and depth will have an important impact one the false positive rate and will provide the best results from all of the fusion strategies tested.
Conclusions This paper has studied the problem of pedestrian classification over two domains, visible and far-infrared, and three modalities, intensity, depth and flow. Moreover, we have proposed a new dataset, RIFIR, which is publicly available, in order to benchmark different features for pedestrian classification. This dataset contains both visible and FIR images, along with correlated pedestrian and non-pedestrian bounding boxes in the two spectra. A comparison between features computed for the visible and FIR spectrum was performed. For the two tested datasets that contain both visible and FIR images, RIFIR and Parma-Tetravision, the FIR domain provided more discriminative features. Furthermore, the fusion of the two domains will further decrease the false positive error rate. Various features have different performances across modalities.
These animals have low maintenance costs and are well adapted to the harsh conditions of these regions (reduced food resources, elevated temperatures during the hot season, abundance of parasites and pathogens). Additionally, local cattle have a strong cultural significance as they reflect a long history of symbiosis with human populations. The aim of the present study was to provide a detailed assessment of the genetic structure of Tunisian local cattle populations and to trace back their origin and history using medium density SNP chips. For this purpose, we first genotyped 50 Tunisian individuals from the three populations using the Illumina BovineSNP50 BeadChip, and we subsequently combined our genotyping data with publicly available data from 34 other populations (bovine Hapmap dataset) representative of African (AFT), European (EUT) and zebu (ZEB) cattle.
Genetic differentiation To get more insight into the genetic relationship between the different populations of the study, we estimated the Fst for pairwise comparisons of all populations, using both Genepop software (Table  S3  in Additional file 3) and the R package StAMPP (Table  S2  in Additional file 3). The latter allows for the computation of Fst 95 % CI intervals (5000 bootstraps). Pairwise Fst values obtained with both software packages were very similar and revealed lower values for BLCAP/BRATF (0. 0246) and BRATF/BRATG (0. 0375) but higher levels of differentiation between BLCAP and BRATG (0. 0638). Tunisian populations had moderate Fst values (i. e. , <=0. 1) with almost all South European breeds and presented high levels of differentiation (i. e. , Fst > 0. 1) with North European breeds.
Phylogenetic analysis To provide additional insight into the origin and evolutionary history of Tunisian populations, we constructed a NeighborNet graph based on Nei genetic distance (Fig. 4 ). Breeds belonging to one of the three main types (AFT, EUT and ZEB) were grouped together. In agreement with Fst and PCA results, Tunisian populations branched between AFT and EUT breeds with BLCAP closer than BRATG to AFT populations. Additionally, Tunisian populations showed short branch lengths indicating a low degree of divergence. Sets of parallel edges (reticulations) on the NeighborNet graph observed between BRATG and BLCAP indicate past hybridization events between the two populations. Finally, we used the Treemix software to model both population splits and gene flow between a subset of 13 populations.
Sampling was carried out by a team from the Livestock and Pasture Office of Tunisia (OEP). Identification of purebred animals was based on specific morphological criteria. Blood samples were collected in EDTA Vacutainer tubes. DNA was extracted using The Wizard® Genomic DNA Purification Kit (Promega) and by phenol-chloroform using a standard protocol  [21]. DNA quantity and quality were evaluated on Nanodrop and agarose gel. DNA samples were then genotyped on the BovineSNP50 BeadChip Ver. 2 (Illumina, San Diego, CA, USA) using standard procedures (http://www. illumina. com) resulting in 52,886 autosomal genotyped SNPs. We also included genotyping data from individuals belonging to 34 other cattle breeds (bovine Hapmap dataset) representative of European taurines (EUT, 15 breeds), African taurines (AFT, eight breeds), indicine (ZEB, seven breeds), and four crossbreed population (AFTXZEB, AFT-XEUT, EUTXZEB and EUTXAFTXZEB).
SNP quality control and marker selection We used PLINK ver. 1. 07 [25]  (http://pngu. mgh. harvard. edu/~purcell/plink/) for genotyping data quality control. Samples genotyped for less than 90 % of markers were excluded from the analysis. SNPs genotyped for less than 90 % of the animals and those with MAF less than 0. 01 were also discarded. Using these criteria, we excluded one individual from the BRATG population due to a low genotyping rate (<90 %). Similarly, 1115 SNPs were excluded because of low genotyping rates, and 5949 SNPs were also excluded as they were monomorphic (MAF <0. 01). An exact test for Hardy-Weinberg Equilibrium (HWE) was then carried out within each population separately on the remaining SNPs using PLINK, which led to 45,691 SNPs retained for further analysis. Genotyping data from Tunisian samples were then merged with the bovine Hapmap dataset.
Analysis of population structure We used various methods to provide a fine-scale assessment of the genetic structure of Tunisian populations. First we computed the genetic distance for each pair of individuals using the ape R package  [26]. The distance between two individuals was defined as the number of loci for which they differ. A neighbor-joining tree was then computed based on the resulting distance matrix using the phyclust R package  [27]. Then a PCA was performed with all the available 38,597 SNPs using the adegenet R package  [28]. In addition, we used DAPC implemented in the adegenet R package  [29]  based on all the available SNP information to provide further details about the genetic structure of Tunisian populations. DAPC is a non model-based method using PCA as a prior step.
It provides a description of genetic clusters using a few discriminant functions. This method identifies an optimal number of genetic clusters that best describe the data by running a k-means clustering algorithm and comparing the different clustering solutions using the Bayesian Information Criterion (BIC). In our DAPC analysis, we first determined the optimal number of retained principal components using the function optim. a. score implemented in adegenet. We retained 60 principal components that cumulatively explained 40 % of the total variance of the data. Finally, we performed a first run of DAPC with an a priori assignment of individuals to their population of origin. This analysis, which is referred to as the predefined DAPC group, provided membership probabilities of each individual for the populations of the study.
This information was used to check if Tunisian animals were correctly assigned to their predefined populations. Then a second DAPC analysis was run using k-means and identified the optimal number of genetic clusters. Individuals were then assigned to these clusters. This analysis is referred to as a posteriori DAPC. Unsupervised hierarchical clustering was carried out using the Admixture 1. 23 software  [6]. Distruct software  [30]  was then used to graphically display ancestry within each individual.
Genetic diversity and population differentiation analysis We used expected heterozygosity (Hs) and inbreeding within subpopulations measured by Fis to assess the genetic diversity of each population. In order to check for an effect of ascertainment bias (due to SNP discovery) on genetic diversity parameters, we estimated (Hs) using either all data (38,597 SNPs) or only SNPs with MAF above 0. 01 in African and indicine populations. Discarding SNPs whose MAF was below 0. 01 is supposed to limit the effect of ascertainment bias. Genetic differentiation between populations was estimated using Genepop 4. 0 software  [31]  using all available SNPs. The R-package StAMMP  [32]  was used to estimate a 95 % confidence interval (CI) of pairwise Fst based on 5000 bootstraps. The R-package hierfstat  [33]  was used to construct a 95 % CI of inbreeding coefficient, measured by Fis, for all populations using a subset of 5000 randomly chosen SNPs and 100000 bootstraps.
Phylogenetic analysis Phylogenetic relationships between populations were investigated using Nei genetic distance. NeighborNet graphs were constructed from the estimated genetic distances using the Splitstree software  [34]. Patterns of splits and mixtures of a subset of 13 populations were carried out using Treemix  [35]  and by setting Nelore (NEL) as a rooting outgroup. First, we built a maximum likelihood tree of the populations with no migration events allowed. Then, we constructed a phylogenetic network for all the selected populations, thereby increasing migration events (modeled as edges) sequentially up to eight migrations. To further evaluate the consistency of migration edges, we ran TreeMix two separate times with eight migration events. The residuals from the fit of the model to the data were visualized using the R script implemented in Treemix.
Cell Surface Labeling of Glycans on HUVECs and Flow Cytometry Analysis After incubation under normoxia or hypoxia (1% O 2 ) for 48 h in the presence of Ac 4 ManNAz (50 M), the medium was removed, and cells were rinsed and incubated with phosphine-FLAG (250 M in PBS, 0. 2% BSA for 1 h at room temperature). After a phosphate-buffered saline wash, HUVECs were detached from dishes by Accutase treatment (Sigma-Aldrich), Accutase was neutralized with medium and serum, HUVECs were labeled with 5 g/ml antibody, either anti-FLAG M2 monoclonal antibody-FITC conjugate (Sigma-Aldrich) or IgG1-FITC isotype control (BD Pharmingen). Flow cytometry experiments were performed on a CyAn TM ADP multicolor flow cytometer (Dako, Trappes, France), gating on 10,000 live cells per data point. Data were analyzed using Summit v4.
Survey full scan MS spectra (from m/z 300 to 1700) were acquired in the Orbitrap with a resolution of 60,000 at m/z: 400. The automatic gain control was set to 1 ϫ 10 6 with a maximum injection time of 500 ms. The most intense ions (up to five) were then isolated for fragmentation in the LTQ linear ion trap using a normalized collision energy of 35% at the default activation q of 0. 25 with an automatic gain control setting of 1 ϫ 10 5 and a maximum injection time of 100 ms. The dynamic exclusion time window was set to 900 s. Samples were injected in triplicate. All m/z values selected for MS/MS during the first LC-MS/MS experiment were excluded from the MS/MS process for the next LC-MS/MS run (for generation of a reject mass list with a 10-ppm m/z window and a 10-min retention time window, see "LC-MS/MS Data Processing").
LC-MS/MS Data Processing LC-MS/MS data acquired using Xcalibur software (version 2. 07, Thermo Fisher Scientific) were processed using in-house Visual Basic program software developed using XRawfile libraries (distributed by Thermo Fisher Scientific). Four different files were generated by this program. The first file corresponds to an MS/MS peak list (Mascot generic format file) that is used for database searching. This Mascot generic format file contains the exact parent mass and the retention time associated with each LTQ MS/MS spectrum. The exact parent mass is the 12 C isotope ion mass of the most intense isotopic pattern detected on the high resolution Orbitrap MS parallel scan and included in the LTQ MS/MS selection window. The retention time is issued from the LTQ MS/MS scan. The second file is an MS/MS log file, which reports for each acquired MS/MS the scan number, 12 C isotope exact mass, retention time, and parent filter (LTQ selection window).
The third file corresponds to the conversion of the high resolution MS raw data file into a comma-separated value format file, which is used for quantitative analysis. The last file generated by the homemade Visual Basic program software is an exclude list text file that contains the 12 C isotope precursor ion mass with the corresponding start/end exclude retention time 10-min window. The Xcalibur software uses this exclude list as the reject mass list (specifies which parent ions cannot trigger a dependent scan) during the second and third LC-MS/MS experiments (see "Nano-LC-MS/MS Analysis").
Database Searching Database searches were done using our internal Mascot server (version 2. 1, Matrix Science, London, UK) using the Swiss-Prot database containing 402,482 entries. The search parameters used for post-translational modifications were a fixed modification of 57. 02146 Da on cysteine residues (carboxyamidomethylation) and dynamic modifications of 15. 99491 Da on methionine residues (oxidation), 42. 010565 Da on protein N-terminal residues (N-terminal acetylation), and Ϫ17. 026549 Da on N-terminal glutamine residues (N-pyro-Glu). The dynamic modification of 0. 984016 Da on asparagine (deamidation) was included for peptides arising from peptide-N-glycosidase F digestion. The precursor mass tolerance was set to 5 ppm, and the fragment ion tolerance was set to 0. 5 Da. The number of missed cleavage sites for trypsin was set to 3.
Quantitative Analysis Differential Fourier Transform Analysis (DIFFTAL) Algorithm Overview-DIFFTAL is a set of software tools developed at Sanofi under the MatLab environment for label-free differential analysis of complex proteomic mixtures dedicated to LTQ/Orbitrap data. DIFFTAL runs in six main steps. These steps consist of the following: 1) feature detection, 2) MS matching, 3) MS/MS annotations, 4) MS/MS matching, 5) peptide quantification report, and 6) protein relative quantifications. Step 1: Feature Detection-Each LC-MS file is treated independently for feature detection. The signal apparition is detected scan by scan by analyzing the evolution of the average signal of three consecutive scans. Feature detection is achieved using the peptide isotopic patterns calculated with the "averagine" algorithm.
In the case of overlapped peptide signals, the solution is a linear combination of theoretical patterns that minimizes the distance with the detected signal. At the end of the process, a matrix of the features detected in the three-dimensional space (m/z, retention time, and intensity) is stored. This matrix contains links to retrieve the corresponding processed signals, which are stored in a temporary data bank. Step 2: MS Matching-All LC-MS data are matched together using a progressive alignment procedure. The most intense detected features are first matched in agreement with m/z and retention time precision windows defined by the user. Then all peptides are used to compute a specific retention time alignment model. A definitive retention time window is calculated according to the dispersion observed between real and calculated retention times.
Finally, every remaining unmatched m/z is checked by going back to the processed signal stored during the feature detection step. This last point allows a very confident determination of the unmatched feature class. Step 3: MS/MS Annotations-This step corresponds to the data bank search reported previously under the "Database Searching. " Step 4: MS/MS Matching-MS/MS spectrum reports exported from Scaffold are matched with the matrix of detected features using the corresponding acquisition MS/MS log files (see "LC-MS/MS Data Processing"). This matching requires starting and ending time points of each feature. Indeed, the retention time feature is the time at the maximum intensity of the observed MS signal, whereas the MS/MS spectrum is recorded at any time during the peptide elution. In the case of ambiguity, the comparison between the exact isotopic profile calculated from the MS/MS sequence and the detected signal at the feature retention time is used for sorting.
Because about 5-10% of the identified peptides are lost during the MS/MS matching process, another routine has been introduced in the software. This routine quantifies only the MS/MS-identified peptides according to the following scheme. The time profiles of the two major isotopes of each identified peptide are computed in a small time window where the MS/MS spectrum was recorded. Only the co-eluted signals of these two isotopes are analyzed to determine the peptide retention time. The threescan averaged signal centered at this time is then compared with the full theoretical peptide isotopic pattern. This additional quantification is compared with the first quantification to generate a final result report. The convergence of these two quantification routines is used to improve the quantification confidence and identification coverage.
RT-PCR After finishing the analysis at 48 hpf, total RNA from 30 embryos per experimental condition was isolated, and semiquantitative RT-PCR with specific primers was done to check gene silencing. RNA purification was carried out in all cases with TRI Reagent solution (Ambion) following the manufacturer's instructions. Two micrograms of RNA were reverse transcribed using SuperScript III RT (Invitrogen), and 1 l of the reaction containing the cDNA was amplified by PCR using Platinum Taq DNA polymerase (Invitrogen). Primer sequences used for amplification and the size of PCR products expected in each case are given in Table  2. When necessary, gene expression levels were normalized with the expression level of GAPDH as the internal control. Quantification of the optical density of the PCR products was done with NIH ImageJ software.
2B ). This result suggests that CD31 could be oversialylated during hypoxia. Novel Hypoxia-regulated Human Sialoproteins and Their Zebrafish Orthologs-The quantitative proteomic analysis identified four sialoglycoproteins that have not been described as being hypoxia-regulated or as having a role in angiogenesis. These were the SDF4, FAM38A (or Piezo-type mechanosensitive ion channel component 1 (Piezo-1)), the solute carrier SLC44A2, and the TMEM16F. Orthologous genes coding for these proteins were retrieved from the Ensembl and Homolo-Gene databases at the European Bioinformatics Institute and the National Center for Biotechnology Information, respectively. These were determined by reciprocal Blast searches against all the available eukaryotic genomes. Orthologs are defined in Ensembl as genes for which the most common ancestor node is a speciation event.
The number of potential proteincoding splice variants was taken from the Havana group annotation in the Vertebrate Genome Annotation (VEGA) database. Evaluation of the Functional Role of Hypoxia-regulated Sialoproteins in Zebrafish Vasculogenesis Model-Zebrafish provide a system for target validation that combines the biological complexity of in vivo models with the capacity for much higher throughput screening. Blood flow begins in the zebrafish embryo at 24 hpf. Shortly afterward, the angiogenic vessels that perfuse the trunk of the embryo (intersegmental vessels) sprout from the vasculogenic vessels. Orthologous zebrafish genes generally share conserved biological function with their mammalian counterparts. Protein knockdown in zebrafish has been effectively achieved by microinjection of synthetic derivatives of DNA, antisense MOs or morpholinos, into developing embryos at the one-to two-cell stage  (17, 18).
Digestion of membrane proteins on beads avoids further loss of yield of the hydrophobic molecules while allowing quantitative analysis as shown by the quantification of CD105 expression under normoxia and hypoxia conditions by Western blotting and label-free quantitative MS. Using our method, we unambiguously identified about 200 sialoglycoproteins by LC-MS/MS. These identified sialoglycoproteins include receptors and transporters represented in very low amounts, demonstrating the high sensitivity of our approach. We then used an in house-developed software for the labelfree quantification of sialoproteins expressed in HUVECs exposed to hypoxia. To the best of our knowledge, this is the first proteomic study of glycoprotein regulation by hypoxic stress. We quantified the abundance of more than 150 sialoproteins in normoxic versus hypoxic conditions and showed that 27 sialoproteins were up-regulated when HUVECs were grown in a low oxygen atmosphere.