dataesr/scientific-paragraphs-categorization huggingface dataset:
14 Data were analyzed using Illumina's GenomeStudio Genotyping Module. Bead intensity data were processed and normalized for each sample in GenomeStudio. The Genome Reference Consortium GRCh37 (hg19; Feb. 2009) map was used (Illumina manifest file Immuno_BeadChip_11419691_B. bpm), and normalized probe intensities were extracted for all samples that passed standard laboratory quality-control thresholds. Genotype calling was performed with the GenomeStudio GenTrain 2. 0 algorithm (Illumina's GenomeStudio data analysis software; Illumina Inc. ). Sample quality control measures included sample call rate, overall heterozygosity, relatedness testing. All patients included in this study also had a genotyping success rate >90%. Duplicated samples and those showing cryptic relatedness were assessed by calculating identity by descent (IBD).
Model Building and Structural-Based Analysis Three-dimensional (3D) modeling of the human wild-type and variant proteins was performed using SWISS-MODEL, an automated homology modeling program. 18, 19 The homologymodeling server SWISS-MODEL was accessed at http://swissmodel. expasy. org/. 18, 19 The 3D models were visualized by the DeepView/Swiss-PdbViewer 4. 1. 0 and OpenAstexViewer 3. 0 softwares. 20, 21 For each 3D model of the native and variant proteins, narrow grooves, hydrophobic patches, and electrostatic potential on charged residues using the Coulomb method were studied using the DeepView/Swiss-PdbViewer 4. 1. 0 software. 21.
Cholecystectomy Risk According to UGT1A1 and UGT1A6 Variants Among the 773 OASI cohort participants from the Troina (Sicily, Enna) and Cerami (Sicily, Enna) communes, standardized collected data regarding gallstone-related cholecystectomy was available on 669 participants. Among them, 8 underwent cholecystectomy for cholelithiasis (1. 20%; 95% CI, 0. 37-2. 02). All UGT1A1 intronic variants (rs887829, rs6742078, and rs4148325) and UGT1A6 exonic variants (rs6759892, rs2070959, and rs1105879) were significantly associated with gallstone-related cholecystectomy risk (Table  3 ). The UGT1A6 variant rs2070959 (p. Thr181Ala) was associated with the highest risk of gallstone-related cholecystectomy (OR, 4. 58; 95% CI, 1. 58-13. 28; P ¼ 3. 21 Â 10 À3 ) (Figure  5 ). None of the 4 low-frequency and rare exonic variants of UGT1A1 (rs4148323, p.
Gly71Arg; rs144398951, p. Ile215-Val; rs35003977, p. Val225Gly; and rs57307513, p. Ser250Pro) has been replicated due to their low MAF. intronic SNPs (rs6742078, rs887829, rs4148324) serve as proxy markers for the low-frequency and rare UGT1A1-coding variants, thereby providing mechanistic explanation to the relationship between UGT1A1 intronic SNPs and the UGT1A1 enzyme activity. Among the 4 low-frequency and rare variants that we found on the exon-1 of the UGT1A1 gene, only 1 (rs4148323, p. Gly71Arg) has been reported to be associated with serum bilirubin level in Asian populations. 22, 29, 33, 35, 36 sing the 1000 genomes database, the MAF of the rs4148323 (p. Gly71Arg) variant was 0. 9% in Europeans versus 17. 3% in East Asians. Regarding the 3 other missense variants that we found on UGT1A1 first exon, the MAFs in Europeans for rs144398951 (p.
The binding with 3  UTR has been well studied; however the binding with 5  UTR needs further investigation to better understand this regulation. Interestingly, recent studies have shown that animal miRNAs are able not only to repress, but also to activate gene expression by acting on mRNA stability and translational regulation  [8]. The binding of RISC complex on 5  UTR is a necessary condition for the translation activation. It has been shown that the association of miRNAs with 5  UTR generally induces translation activation rather than repression  [9]. According to the miRNA database, today 2578 mature and 1872 precursor forms of miRNAs have been identified in humans  [10]. One of the first identified characteristics of the miRNAs is the very well conserved sequences in all species and these are expressed in tissues, in a specific manner  [11].
The standard GWAS consists in comparing variant frequencies in cases and controls, on massive genotypic datasets (tens of thousands of individuals each described by hundreds of thousands up to a few millions of genetic markers). The goal is to identify the loci on the genome for which the distributions of variants are significantly different between cases and controls, using dependence -namely association -tests (e. g. the Chi 2 test). The unit variants, called single nucleotide polymorphisms (SNPs), which refer to single base pair changes in the DNA sequence, represent the most abundant type of variants in human; they are very often used as markers in GWASs. The key to GWASs lies in this interesting phenomenon known as "linkage disequilibrium" (LD) where variants for different SNPs tend to co-occur non-randomly  (Pritchard and Przeworski, 2001)  (the corresponding SNPs are said to be in LD).
Section 3 first offers a brief introduction to Bayesian networks, the kind of probabilistic graphical models FLTM is based upon. Then section 3 provides a broad brush description of the FLTM learning algorithm together with a sketch of a GWAS strategy based on FLTM. Section 4 briefly refers to the native clustering method used in FLTM (CAST bin ) and to its relaxed version (CAST real ); it then motivates the choice of the alternative clustering method (DBSCAN) plugged in the FLTM learning algorithm. Then, section 5 explains the design of the protocols and methods used in our work. First, we discuss the protocol used to assess how much two partitions agree. Second, we justify the use of the Haploview software program to derive the reference partition, supposedly the closest representation of the underlying reality.
Deriving the Reference Partition The reference partition intends to be the closest representation of the underlying reality, that is the haplotype blocks. We used the Haploview software program  (Gabriel et al. , 2002)  for this purpose. This application allows to select commonly used block definitions to partition the genome into regions of strong LD  (Gabriel et al. , 2002; Wang et al. , 2002). As this block generation is dedicated to handle genetical data, Haploview can only be used for the first layer (observed variables). This reason explains why the partitioning method of the Haploview application has not been plugged in the FLTM learning algorithm.
PCR products were electrophoresed with 1. 5% agarose gel including 1 mL EtBr in TAE buffer and visualized under ultraviolet light analyzer. PCR products were sequenced by REFGEN laboratories. DNA sequences were compared to sequences of BLAST database. An S. destruens DNA sample from the United Kingdom (UK) strain was used as a positive control and amplified by PCR in the same conditions. As PCR tests are so sensitive, all PCR work was done with a negative control to address the measure of potential contamination. Some samples were also blind checked at Bournemouth University laboratory, which has ten years of expertise in S. destruens detection using molecular analyses.
Internal transcribed spacer (ITS) sequence analysis The ITS sequence used for comparisons originated from isolates obtained from three North American outbreaks: (i) Chinook salmon in Washington (accession number: FJ440706); (ii) Atlantic salmon in California (accession number: FJ440708); (iii) winter-run Chinook salmon in California (accession number: FJ440702). The sources of these three American cultures are deposited in the American Type Culture Collection (ATCC) under accession numbers 50643, ATCC 50644, and ATCC 50615, respectively. In addition, the ITS sequences for the UK isolates are deposited in GenBankTM under the accession numbers FJ440702-FJ440710. 17  nested PCR was performed to amplify the ITS1 region of the S. destruens isolate from Turkey. PCR reactions were prepared in 50 mL vols.
Products were visualized on a 1. 0% agarose gel containing 0. 1 lg mL  21  ethidium bromide run at 125 V for 30 min. Products were excised from the gel and purified using the Wizard SV Gel and PCR clean-up system (Promega). Purified fragments were cloned using the Topo TA cloning kit (Invitrogen, Life Technologies, Paisley, United Kingdom) and then sequenced with M13 forward and reverse. At least three clones were sequenced for each sample. Sequences were assembled manually using BioEdit  26  and identity verified by alignment to existing S. destruens ITS1 sequences and GenBank by performing a BLAST search. 27 The amplified ITS1 sequence from the Turkish isolate was deposited in GenBank, accession numbers KT361608. For phylogenetic analysis, ITS1 sequences were aligned with Clustal X version 1 and examined by eye in BioEdit.
destruens. It will also help us to check if the contamination of salmonid populations on the west coast of the USA by S. destruens  7, 8  finds its origin in the North Pacific or across the bearing sea where Chinese, Russian, Japanese, and North American salmonids cohabit  35, 36  and where Asian salmonids have been cohabiting with P. parva populations since the seventies due to fish translocations in China. destruens. The bootstrap consensus tree inferred from 1000 replicates  30  and bootstrap values exceeding 50% are shown at nodes. The phylogenetic tree was produced using MEGA 5. 28  Isolate (and clone) origin, designations and GenBank accession numbers are: RA1-3 (FJ440707. 1); RA3-1 (FJ440708. 1); RA3-2 (FJ440709. 1); RA3-3 (FJ440710. 1); RA4-1 (FJ440702. 1); RA4-3 (FJ440703. 1); RA4-4 (FJ440704.
Figure3Phylogenetic tree resulting from maximum likelihood method based on the Tamura 3-parameter model 29  analysis of the ribosomal ITS1 sequence from S. destruens. The bootstrap consensus tree inferred from 1000 replicates 30  and bootstrap values exceeding 50% are shown at nodes. The phylogenetic tree was produced using MEGA 5. 28  Isolate (and clone) origin, designations and GenBank accession numbers are: RA1-3 (FJ440707. 1); RA3-1 (FJ440708. 1); RA3-2 (FJ440709. 1); RA3-3 (FJ440710. 1); RA4-1 (FJ440702. 1); RA4-3 (FJ440703. 1); RA4-4 (FJ440704. 1); and RA-T (to be deposited).
The wavelength of the maximum chemiluminescent emission (λ em ) was determined for all compounds, and the wavelength of maximum fluorescence emission (λ fl ) for the spent reaction mixtures. The absolute emission quantum yields were determined with luminol as a secondary standard. All these data are listed in Table  2. Table  2. Maximum wavelength for the chemiluminescent emission (λ em ) of the compounds 1 and 2, maximum wavelength for the fluorescent emission (λ fl ) of the spent reaction mixture, observed rate constant for the decay of light emission (k obs ) and chemiluminescence quantum yield (Φ CL ) The maximum wavelength for the chemiluminescent emission of all compounds seems to be limited within the range 425-450 nm, right in the blue region of the spectrum, except for the nitro-substituted derivative 1o.
Acknowledgements This work was partially supported by the  São Paulo Research Foundation ,  FAPESP  (L. F. M. L. C. , grant  2012/02428-5 ). S. S. and P. O. are thanking the  University of Oregon  and in particular  M. M. Haley  and  D. W. Johnson. S. S. is thanking the  Friedrich-Ebert-Stiftung  for financial support.
Nevertheless, the drawback of the original WS method is that it can only be applied in the spatial domain. Due to the prevalence of images compressed in the Joint Photographic Experts Group (JPEG) format, how to deal with this kind of images becomes mandatory. Inspired by the prior studies  [12, 14] , the WS steganalyser for JPEG covers was proposed in  [15]. However, the WS steganalyser does not allow one to get a high-detection performance for a low FAR, see  [16] , and its statistical properties remain unknown, which prevents the guarantee of a prescribed FAR. In practical forensic cases, since a large database of images needs to be processed, the getting of a very low FAR is crucial.
6. If a quantized and rounded 'residual noise' takes zero, WF equals 0; If not, WF equals 1. Thus, based on our proposed WF, it is proposed to categorise 'residual noise' set into two subsets: 'non-zero' subset and 'zero' subset. To verify the effectiveness of our improved algorithm, it is proposed to randomly choose ten exemplary images which are compressed to JPEG format images with quality factor 70 and embedding rate R = 0. 05. Also, all the images of the BossBase database  [27]  are used for computing the average value. Table  1  gives the statistical ratio of the data in which the annotations of the table are as follows: • Cover channel selection ratio: denotes the ratio of the 'non-zero' subset to the 'residual noise' set of a cover image. In our proposed statistical test, the number of the selected coefficients for the detection should be kept very close before and after embedding.
i. d. , following a Laplacian distribution with zero mean. The comparison with this test is meaningful as it allows us to measure how much the detection performance is improved by removing the assumption that the DCT coefficients of each subband are i. i. d. The second chosen competitor is the WS  [15]  due to its similarity with the proposed statistical test, see details in Section 4. 5. For a large-scale verification, it is proposed to use the 'break our steganographic system' (BOSS) database, made of 10,000 grayscale images of size 512 × 512 pixels, used with payload R = 0. 05. Prior to our experiments, the images have been compressed in JPEG using the linux command convert which uses the standard quantization table. Note also that all the JSteg steganography was performed using a Matlab source code we developed based on Phil Sallee's Jpeg Toolbox f.
Four denoising methods have been tested to estimate the expectation of each DCT coefficient, namely the K-SVD, the BM3D, the NL means and the wavelet denoising algorithms. Figure  10  shows the detection performances obtained over the BOSS database compressed with quality factor 70. The detection performances are shown as ROC curves, that is the detection power is plotted as a function of false alarm probability. Figure  10a  particularly emphasises that the statistical test based on the Laplacian model does not perform well while the proposed methodology which takes into account that the Laplacian distribution parameters as nuisance parameters allows us to largely improve the performance. Similarly, the WS detector achieves overall good detection performance. However, it can be shown in Figure  10b , which presents the same results using a logarithmic scale, that for low false alarm probabilities, the performance of the WS significantly decreases.
On the opposite, the proposed statistical test still performs well. Among the four denoising algorithms that have been tested, the BM3D achieves the best performance, but it can be observed in Figure  10  that the performances obtained using the K-SVD and using the wavelet denoising methods are also very good. The performance of NL means method is comparable with the WS detector  [15]. To extend the results previously presented, a similar test has been performed over the BOSS database using the quality factor 85. The detection performance obtained by the proposed test and by the competitors are presented in Figure  11. Again, this figure shows that based on the Laplacian model, the statistical test assuming that DCT coefficients of a subband are i. i. d. has an unsatisfactory performance. It can also be noted that even though the WS performs slightly better for low false alarm probability, compared to the results obtained with quality factor 70, it performs much worse than the proposed statistical test.
performance for database with quality factor 70. (a) Comparison of detection performance for BOSS database with quality factor 70 (linear scale). (b) Comparison of detection performance for BOSS database with quality factor 70 (logarithmic scale). WS, weighted stego-image. It is noted that denoising filters are respectively designed based on wavelet, block-matching and 3D (BM3D), dictionary learning (K-SVD) and non-local (NL) means algorithms.
The complex scene conditions such as light change, high density of mobile objects or object occlusion can cause object mis-detections. When a tracker can not recover these mis-detections, the trajectory of an object is fragmented into some short trajectories called tracklets. As a result, tracking quality is reduced remarkably. In this paper, we propose a new approach to improve the tracking quality by a global tracker which merges all tracklets belonging to an object in the whole video. Particularly, we compute descriptor reliability over time based on their discrimination. On the other hand, a motion model is also combined with appearance descriptors in a flexible way to improve the tracking quality. The proposed approach is evaluated on four benchmark datasets. The obtained results show the robustness and effectiveness of our approach compared to tracking as well as tracklet linking approaches from state of the art.
Experimental results We propose to use the output of the tracker in  [6]  as input because this tracker can be considered as a typical one when using a pool of object appearance descriptors to track objects. The proposed approach is tested on some sequences of four public datasets: PET2015, PETS2009, TUD stadtmitte and TUD crossing. The performance of this approach is compared with the tracker in  [6] , some other tracking and tracklet linking approaches from the state of the art. We use CLEAR MOT metrics to evaluate the proposed method. The multiple object tracking precision (MOTP) evaluates the intersection area over the union area of bounding boxes. The multiple object tracking accuracy (MOTA) calculates the accuracy composed of false negatives, false positives and identify switching. In addition, some other metrics are proposed to use.
PETs datasets With the dataset PETs2015, we choose the sequence W 1 AREN A T g T RK RGB 1 to test our approach because of its challenges. This sequence has 240 frames, there are only few people but their sizes are much changed, they have the variation of poses. With the dataset PETS2009, we choose the sequence S2 L1, view 1 which has 794 frames containing 21 mobile objects with many occlusions and objects moving with different directions. Figure  2  (six top images belong to PETs2009 dataset while three bottom images belong to PETs2015) illustrates the tracking performance related to the online computation of discriminative descriptor weights depending on each video scene. With the situation on three top images, tracklet ID3 (shown by yellow bounding box) and tracklet ID14(shown by red bounding box) are mis-detected because they cross each other at frame 140.
1. Therefore, the proposed tracker focuses mainly on dominant color and color histogram descriptors and is able to track objects correctly (see in frame 565). Two objects in dataset PETs2015 also have the similar  appearance while having the different movement direction. In this case, the proposed approach relies mainly on object motion model to recover the trajectory fragmentation in frame 109. Moreover, figure  3  shows our tracker's performance for the re-acquisition challenge when objects (shown by red arrows) leave and re-enter the scene. Instead of considering the moving objects in the frame they have just re-entered, our approach tracks these objects after a sufficient number of frames. Thanks to reliable descriptors which are updated cumulatively, object IDs are correctly retrieved.
TUD datasets We also use TUD datasets (including TUD Stadtmitte and TUD crossing) sequences to evaluate the performance of our approach compared to other recent trackers. Both of these sequences are quite short, with more or less than 200 frames, but they contain challenges for trackers such as low light intensity, crowded environment, frequent occlusions, similar object appearances. Figure  4  illustrates clearly our approach performance when recovering mis-detection in videos that have low light intensity and objects moving in different directions. In these scenes, object appearances are not discriminative with each other. Appearance descriptors have similar discriminative weights around of 0. 2 while the motion model weight is 0. 4. Therefore, based on the motion model of objects, our approach tracks object ID26 (represented by a purple bounding box) correctly after several mis-detection frames.
The comparison is shown in table  1. With PETs2009 dataset, our performance is better than the tracker  [4] -with all its proposed methods in MOTA and MOTP metrics and there is a significant tracking quality improvement when comparing our tracker with our input  [6]. In particular, MOTA from 0. 62 to 0. 86 and 0. 63 to 0. 72 from MOTP, the track fragmentation (FG) reduce a half. Compared with other tracklet merging algorithms (in bold), our approach has a slightly lower results of MOTA and MOTP than  [15]  and  [3]. However, the tracker in  [15]  works offline while our algorithm chooses flexibly object descriptors overtime which is suitable for the real-time applications. The tracker in  [3]  has a better performance than ours when it used its own input. When being tested with the same input (the output of tracker in  [6] ) with ours, our approach has higher results.
Moreover, our performance is also much better than one of the tracker in  [4]  in case of the global association, especially in MOTA, MOTP and FG metrics. On both the TUD datasets, our approach does not lose any object. The obtained ML values are also the best ones compared to other state of the art trackers in both datasets. Our tracker performance increases MT values from 60% to 70% with TUD Stadtmitte and from 46. 2% to 53. 8% with TUD Crossing dataset compared with our input. The results show that the proposed global tracker is able to improve the tracking results by increasing the length and the precision of the tracklet. In particular, object trajecto- ries are more completed (Mostly Track (MT), MOTA and MOTP and FG values increase) while lost object trajectories are reduced (Mostly Lost (MT) values decrease).
Conclusions This paper proposes a new approach to improve the multi-object tracking quality by merging tracklets belonging to the same mobile object. In order to handle the variation of scenes, we propose a discrimination method to estimate the tracket descriptor reliability overtime. This ensures a high precision for tracklet merging process. Moreover, an adaptive combination of motion model and appearance descriptors is proposed to improve the tracker quality. The experimental results show the significant performance improvement of our approach compared to the input tracker, tracking as well as tracklet linking approaches from the state of the art over four experimented benchmark datasets.
In the field of intelligent network,  11  proposes a methodology for matching based on two levels; the similarity calculation level carried out by the elementary and structural based matching and the extraction alignments level. We can assert that the essential role of matching is usually the sharing and integration of new data and promoting the interoperability. In order to build our own system, it was overriding for us to study tools and platforms based on matching algorithms. We enriched the work of 1 which presented a state of art in matching tools. It has been observed that the most common language of ontologies used with these systems is the OWL which is required by OAEI and also there are other languages such as RDF † and Skos ‡ which are generated by the DSSim  12  and Falcon's tools  13.