dataesr/scientific-paragraphs-categorization huggingface dataset:
They present also a larger scale problem with 29, 30 and 33 jobs to scheduling on respectively 3, 10 and 5 machines. The aim of this computational study is to provide the optimal solutions of these well known instances previously adopted in the literature. For each instance, the mathematical programming model has been solved using ILOG CPLEX 11. 0 software. These optimal solutions allow the exact evaluation of these different approached resolutions. We compare also this mathematical formulation with the classical formulation proposed by Rajakumar et al. 14. In their model, the authors have formulated the workload balancing problem among identical parallel machines as a binary mixed integer program with the objective of minimizing the maximum of workloads. Or, the numerical results reported in tables 6 and 7 illustrate that this formulation does not provide the optimal workload repartition.
Numerical tests We have used the software FreeFem++ v 3. 19,  [9]. For all tests, we use the same initial guess for the coincidence set D \ Ω 0 1 = ∅ and D \ Ω 0 2 = ∅. Test 1. We consider the torsion of an elastic-plastic prism studied in  [7]  p. 133 and  [25]  We use a mesh of 39158 triangles, 19836 vertices and size h = 1 128. The tolerance for the stopping test is tol = 10 -3 and the penalization parameter is Our algorithm stops after 6 iterations and the relative error in the H 1 norm at the last iteration is y n -y n-1 H 1 (D) = 1. 5 × 10 -5. In  [18] , we have tested numerically with positive results the stability of a similar algorithm when f the right-hand side in (1) is perturbed.
Results: We identified 202 putative transcription factors by in silico analysis in 40 families in Casuarina glauca (Casuarinaceae) and 195 in 35 families in Alnus glutinosa (Betulaceae) EST databases. Based on published transcriptome datasets and quantitative PCR analysis, we found that 39% and 26% of these transcription factors were regulated during C. glauca and A. glutinosa-Frankia interactions, respectively. Phylogenetic studies confirmed the presence of common key transcription factors such as NSP, NF-YA and ERN-related proteins involved in nodule formation in legumes, which confirm the existence of a common symbiosis signaling pathway in nitrogen-fixing root nodule symbioses. We also identified an actinorhizal-specific transcription factor belonging to the zinc finger C1-2i subfamily we named CgZF1 in C.
Taken together, these data reveal that specific TF orchestrate plant infection and nodule organogenesis in legumes. The objective of this study was to identify the TF that regulate the expression of genes involved in the C. glauca-/A. glutinosa-Frankia actinorhizal symbioses. Among 14,000 unigenes expressed in roots and nodules of each of the two species, we identified 202 and 195 TF distributed in 40 and 35 families in C. glauca and A. glutinosa, respectively. A global analysis of the expression profile of these genes was conducted to identify up-and down-regulated TF encoding genes in nodule versus root, as well as nodule-specific TF. The expression level of several C. glauca and A. glutinosa TF was confirmed by quantitative PCR. Phylogenetic analyses performed in model legumes, species related to actinorhizal plants, and the actinorhizal plants C.
Identification of C. glauca and A. glutinosa transcription factors To identify transcription factors in actinorhizal plants, tBLASTn searches of the C. glauca and A. glutinosa unigene databases were performed using the DNA-binding domain from the TF database of Arabidopsis thaliana as query sequences. These databases contain 14,327 unigenes for A. glutinosa and 14,868 unigenes for C. glauca  [5]. BLAST analysis of the two unigene sets revealed 405 and 358 genes possibly encoding TF in C. glauca and A. glutinosa, respectively. To remove false positives, tBLASTx was performed to check trans-species sequence homologies between Arabidopsis genes and actinorhizal sequences with an e-value cut-off of 1e -10. Using this approach, we narrowed it down to 202 and 195 potential transcription factors distributed in 40 and 35 families in C.
glauca and A. glutinosa, respectively (Additional files 1 and 2). No potential members were identified for thirteen families including M-type, E2F/DP, and GeBP in C. glauca and A. glutinosa unigene databases. Each predicted C. glauca and A. glutinosa TF gene was given an arbitrary number. Additional files 1 and 2 list each predicted gene for both species, together with the accession numbers of all unigenes, the closest Arabidopsis TF, and detailed BLAST information. Among TF families, the MYB superfamily and the ERF family were the largest, totaling 39 TF for each species. The third largest family was the C 2 H 2 family, with 18 members in C. glauca and 20 in A. glutinosa, followed by the WRKY, NAC and bHLH families. The remaining families were represented by 1 to 19 members (Table  1 ).
Expression profile of the putative actinorhizal transcription factors Microarray data was retrieved from previous published studies  [5, 7]. First, we used a simple comparative transcriptomics tool called Casuarina Transcriptome Compendium (CTC) to compare the microarray data and generate expression profiles in different conditions. Using CTC, we identified 54 repressed transcription factors and 25 induced in C. glauca nodules compared to non-inoculated roots with a nodule/root fold change ≥ 2 or ≤ -2 and a p-value ≤ 0. 01 (Figure  1  and Additional file 3). The C 2 H 2 family was the most frequently represented in the up-regulated class followed by the ERF and bHLH families (Additional file 4). Induction of 11 transcription factors in C. glauca nodules was confirmed by quantitative PCR, which also revealed similar induction values than microarray data (Additional file 5).
Induction of CgZF1 was validated by semi-quantitative PCR because its expression was not detectable in the non-inoculated roots needed to calibrate Q-PCR analysis (Additional file 6). Similarly, we combined available microarray data in A. glutinosa to generate an Alnus Transcriptome Compendium (ATC). This allowed us to identify 30 putative transcription factors induced and 22 others repressed in A. glutinosa nodules compared to non-inoculated roots (Figure  1  and Additional file 7). Similar to the results observed in C. glauca nodules most MYB and WRKY were down-regulated while C 2 H 2 were induced in nodules, (Additional file 8). Induction in A. glutinosa nodules of seven transcription factors was also confirmed by quantitative PCR (Additional file 5).
GRAS, NF-YA and ERF transcription factor families in actinorhizal species In legumes, several TFs that play key roles in the transduction of bacterial Nod factors have been identified. These include in particular members of the GRAS, NF-YA and ERF families (i. e. NSP1, NSP2, NF-YA1, NF-YA2, ERN1, ERN2 and ERN3)  [15, 20, 22]. Therefore we first focused on these families. We screened extra databases using the tBLASTn algorithm in order to identify GRAS, NF-YA and ERF TFs in a third actinorhizal species, D. glomerata  [6] , relatives of actinorhizal plants in the Rosales and Cucurbitales orders, in legumes (http://www. phytozome. net/search. php?show=blast&method=Node_rosales-cucurbitales) and in Arabidopsis (TAIR). The data was used to construct a phylogenetic tree of the GRAS protein family based on the alignment of the complete protein sequences using the maximum likelihood method.
Eleven complete C. glauca sequences and eight A. glutinosa were collected from respective datasets. Based on the phylogenetic analyses, four distinct subfamilies were defined: SCARECROW-like (SCL), SCARE-CROW (SCR), SHORTROOT (SHR) and DELLA. The tree topology resembled the one found both in rice and Arabidopsis  [23, 24]. No sequence from C. glauca was close to NSP1 and NSP2. The same result was found for Rosales and Cucurbitales. However, two sequences from A. glutinosa and D. glomerata named AgGRAS7 (AG-J07f_002_D03) and DgNSP1 respectively were identified in the NSP1 group (Figure  3 ). Plant genes belonging to the NF-YA family encode putative TFs that are variable in length  [25]. Their DNA binding domain shares some similarity with the CCT domain of the nuclear flowering time regulator CON-STANS (CO)  [26, 27].
NF-YA proteins are characterized by two domains (protein binding and DNA binding domains) that are strongly conserved in all higher eukaryotes examined to date  [25]. To clarify the phylogenetic situation of the NF-YA genes identified in the transcriptomes, we built a tree using the maximum likelihood method using only the NF-YA domains. Five of the originally collected sequences had incomplete DNA and protein domains in the C. glauca and A. glutinosa datasets and were excluded from further analysis. The NF-YA tree includes 6 major groups (Groups I-VI) also found in legumes (Figure  4 ,  [25] ). Interestingly, Group I containing MtNF-YA1 and MtNF-YA2 from M. truncatula includes CgNF-YA1 from C. glauca. In the present study, Dgcomp3430_c0_seq7 from D. glomerata is in Group II. Several genes belonging to Group II show a strong expression mainly in seeds  [26, 28].
To study the phylogenetic relationships between the ERF genes in the actinorhizal species, multiple alignment analysis was performed using amino acid sequences in the AP2/ERF domain. Ten of the originally collected sequences had incomplete AP2 domains in the C. glauca and A. glutinosa datasets and were excluded from further analysis. The ERF tree distinguished the 10 major groups (Groups I-X) also found in rice, cotton, and Arabidopsis (Figure  5 ,  [29, 30] ). At least one C. glauca and A. glutinosa ERF TF was found in the main groups except for Group VI. Group VII, which included five ERFs from Arabidopsis, was the most represented by the actinorhizal ERF transcription factor with three sequences in C. glauca and five sequences in A. glutinosa. However, several changes in the groups were observed (Figure  5 ).
1. One hundred bootstrap replications were used to evaluate statistical support for branches. Branches with less than 70% bootstrap support were collapsed. nodules (Figure  1 ; Additional file 8). Remarkably, like in the C. glauca TF gene expression dataset, a C 2 H 2 TF, AgZF1, showed the strongest gene induction in A. glutinosa nodules as indicated by the red triangles in Figure  1. The C 2 H 2 family is characterized by the presence of a "zinc finger" DNA binding domain containing one or two "QALGGH" conserved motif(s)  [32]. CgZF1 contains two QALGGH motifs at positions 85-105 and 142-162. Furthermore, a putative EAR (ethylene-responsive element-binding associated amphiphilic repression) repressor domain with the signature (S)/(F)DLN(L)/(F)XP was identified at the C-terminal positions 176 and 180 of CgZF1.
sativa Mszpt2-1 was added to the dataset because it encodes a C 2 H 2 TF and plays a role in legume symbiosis  [35]. The phylogenetic tree distinguished two major groups, Groups I and II. Group I did not contain C. glauca, A. glutinosa and A. thaliana C 2 H 2 TF. Group II was divided into three subgroups named A, B and C. Subgroups II-B and II-C contained seven and five C 2 H 2 TFs from actinorhizal plants, respectively. Interestingly, one cluster in subgroup II-B containing CgZF1 and AgZF1 was only made up of sequences from Rosales, Fagales, and Cucurbitales and did not contain any legume sequence. Our analysis also confirmed that CgZF1 and AgZF1 protein sequences are very close and are most probably orthologs (79% similarity, data not shown; bootstrap value, 95%, Figure  6 ). This suggests that ZF1-like transcription factors evolved after the divergence between Fabales and the Rosales/Cucurbitales/Fagales clade and the Figure  4  Phylogenetic tree of the NF-YA protein family.
truncatula  [38]. These TFs were classified in eight functional groups of genes differentially regulated in M. truncatula nodules in seven experimental conditions after inoculation with the wild type and three Nodmutant strains of Sinorhizobium meliloti. Transcription factors belonging mainly to the C 2 H 2 , MYB, WRKY and ERF families appeared to be nodule-specific, whereas members of the ERF and WRKY families were over-represented in the TF dataset  [38]. Similarly, transcriptome analysis of L. japonicus led to the identification of 20 TF that expression was up-regulated during nodulation showing that members of the ERF family are the most abundant TF  [31]. Remarkably, members of the ERF and WRKY families of TF were also abundant in our study, and expression analysis showed that nine of them were induced in actinorhizal nodules.
Availability of supporting data EST sequences reported in this paper have been deposited in the GenBank (accession nos. CO036851-CO0388878) and EMBL (accession nos. FQ312199-FQ377516) databases. The normalized and raw microarray data values have been deposited in the Gene Expression Omnibus database (www. ncbi. nlm. nih. gov/geo; accession nos. GPL10929 and GSE24153 for C. glauca and A. glutinosa respectively). In addition, all nucleic acid sequences used in this study have been included in Additional files 1 and 2. Proteins sequences used to produce phylogenetic data sets have been added in Additional file 12.
Database search Fifty-eight predicted DNA-binding domain protein sequences from Arabidopsis were used as query sequences for tBLASTn searches against the predicted C. glauca and A. glutinosa proteins in a previously described database (http://www. ncbi. nlm. nih. gov/nucest/; Accession numbers CO036851 to CO0388878 and FQ312199 to FQ377516)  [5]. Arabidopsis DNA-binding domain sequences were retrieved from a multi-alignment available in DATF (Database Arabidopsis Transcription Factors: http:// planttfdb. cbi. edu. cn/ version 2. 0). The TAIR database (http://www. arabidopsis. org/Blast/) was used to confirm the affiliation of each putative transcription factor to one of the 58 families. tBLASTx were carried out to check trans-species sequence homologies between genes with an e-value cut-off of 1e -10 and genes were annotated according to the TAIR database.
Expression data of putative TF in actinorhizal nodules and mycorrhizal roots The putative TF expressed in C. glauca (Accession number GPL10929) and in A. glutinosa (Accession number GSE24153) were retrieved from microarray data  [5, 7]. A transcription factor was considered differentially expressed if its fold change was greater than or equal to 2 and its p-value lower than or equal to 0. 01. For quantitative RT-PCR analyses, total RNA was purified from roots and nodules by ultracentrifugation  [44]  for C. glauca and using the RNeasy plant mini kit (Qiagen, Courtaboeuf, France, see  [45] ) for A. glutinosa. RNA was quantified using a Nanodrop (Thermo Fisher Scientific, Courtaboeuf, France) and analyzed using a Bioanalyzer 2100 according to the manufacturer's instructions (Agilent, Santa Clara, CA, USA).
For C. glauca analyses, reverse transcription were performed on 0. 5-1 μg of RNA using the SuperScript® reverse transcriptase III kit (Invitrogen Life Science, Carlsbad, CA, USA). For A. glutinosa analyses, reverse transcription was performed with 9 μg of RNA using Transcriptor Revers transcriptase and oligo (dT) 15 primer (Roche, Mannheim, Germany). Reverse transcription (RT) and real time quantitative PCR (qRT-PCR) were performed with the same three biological replicates of nodules and non-inoculated roots. Primers were designed using Beacon designer software (Premier Biosoft International, Palo Alto, CA, USA). Quantitative RT-PCR analyses were conducted as previously described  [5]  using primers listed in supporting information (Additional file 13). For C. glauca, amplifications were performed using a Stratagene Mx3005P thermal cycler, (Agilent, Palo Alto, CA, USA) with the Brilliant II SYBR Green QPCR Master Mix (Agilent, Palo Alto, CA, USA) programmed for a pre-denaturation step of 5 min at 95°C followed by 40 cycles of 10 s at 95°C and 30 s at 60°C and 72°C for 15 s.
Phylogenetic relationship between transcription factors Phylogenetic analysis was performed using the maximum likelihood method on protein sequences from the four TF families: C 2 H 2 , ERF, GRAS and NF-YA, identified in C. glauca and A. glutinosa. We used the default algorithms and settings implemented in www. phylogeny. fr. Briefly, a protein-based alignment of full-length sequences or conserved domains was generated using MUSCLE  [46] , this alignment was then curated using the Gblocks  [47]  to remove poorly aligned positions and gaps and a phylogenetic tree was calculated using PhyML  [48]. Trees were edited with FigTree ("http://tree. bio. ed. ac. uk/software/ figtree/"). We used AT5G14010. 1, AP2-R1, AT4G36710. 1 and AT2G37060. 1 to root the C 2 H 2 , ERF, GRAS and NF-YA trees respectively.
Trees were constructed based on sequence alignments of DNA binding domains for ERF and NF-YA and complete sequences for C 2 H 2 and GRAS. The DNA binding domains or protein sequences of these four TF families from A. thaliana (http://planttfdb. cbi. edu. cn/index. php?sp=At), M. truncatula (http://planttfdb_v1. cbi. pku. edu. cn:9010/web/index. php?sp=mt), L. japonicus (http://planttfdb_v1. cbi. pku. edu. cn:9010/web/index. php?sp=lj), G. max (http://planttfdb. cbi. edu. cn/index. php?sp=Gma), Cucumis sativus, Prunus persica, Malus domestica and Fragaria vesca (http://www. phytozome. net/search. php?show=blast&method=Node_ r o sales-cuc ur bi tales) were added to the dataset. The bootstrap values represent a percentage of 100 repetitions.
Acknowledgements ID and AT were supported by a grant from the  IRD Department of Capacity-Building for Southern Scientific Communities (IRD-DPF ). We thank  Dr Mame Oureye Sy  ( UCAD ) for access to the qPCR and  Dr Katharina Pawlowski  who helped us in the search for orthologous sequences for ZF1, ERN, NF-YA,  NSP1  and  NSP2  in the Datisca glomerata database.
ITK scattered data approximation filter The class RobustSolver implements the solver presented in section Finite Element Solver. This solver is a subclass of itk::Solver, which takes the FEMObject as input and output. FEMObject is an ITK data object to store all Finite Element related containers, such as mesh node container, mesh element container, landmark container, etc. We usually prefer a mesh and a feature point set as inputs and a deformation field image as the output. To.
The FEMScatteredDataPointSet-ToImageFilter takes the mesh and feature point as inputs, converts these inputs into a FEMObject for the RobustSolver, and then generates a deformation field image based on the output FEMobject from the RobustSolver. Moreover, FEMScatteredDataPointSetToImageFilter provides a built-in 2D quadrilateral and 3D hexahedron mesh if the input mesh is not available. Given a 2-or 3-D scattered and noisy point set, in which each point is associated with a 2-D or 3-D displacement, RobustSolver is able to approximate the data while rejecting outliers, advance toward interpolation, and ultimately output a deformed FEMObject, as outlined by the flowchart in Figure  3B. RobustSolver also takes into account two optional point sets: the confidence and the structural tensor. The confidence point set describes our confidence for each feature point using a value between 0 and 1 (0: not trustful, 1: completely trustful), which will make the solver behavior like a weighted Least Squares.
SYNTHETIC DATA EVALUATION In this section, we use a lung image of a rat provided by ITK to evaluate FEMScatteredData-PointSetToImageFilter. The size of the lung image is 128 × 128, and the spacing is 1 × 1 mm 2. This filter estimates the deformation field image based on the sparse deformation field. The approximated deformation field image can be further utlilized with itk::WarpImageFilter to produce an aligned image. To produce a sparse deformation field, we perform deformable registration on the lung images of a rat (see Figures  4A, B ) using itk::BSplineDeformableTransform. The resulting deformation field image (ground truth) is shown in Figure  4C. We then perform edge detection in the fixed image (Figure  4A ) to produce the edge image using the ITK canny edge detector. Finally, for all edge points, we perform interpolation in the deformation field to produce a sparse deformation field, which is represented by itk::PointSet.
MRI EVALUATION We conducted experiments on the registration between preoperative MRI and the intra-operative MRI (iMRI). The five datasets come from public cases from SPL of Harvard medical school  (Talos and Archip, 2007). Table  1  lists the patient information including the gender, tumor location, and histopathology. The MRI of the five public cases were acquired with a protocol of whole brain sagittal 3D-SPGR (slice thickness 1. 3 mm,   (Archip et al. , 2007). In Table  2  we show the registration accuracy of the PBNRR filter for the five cases. As a measure of the registration accuracy, we used the one directional Hausdorff Distance (HD) as it is implemented in the vtkHausdorffDistan-cePointSetFilter. The HD(1 → 2) before PBNRR corresponds to the error between edge points in preoperative MRI and intraoperative MRI, while the HD(1 → 2) after PBNRR corresponds to the error between canny edge points in warped preoperative MRI and intra-operative MRI.
CONCLUSION We present an ITK implementation of a PBNRR method. The three filters: MaskFeaturePointSelection, BlockMatching ImageFilter, and FEMScatteredDataPointSetToImage-Filter can be used separately or combined together to conduct registration. MaskFeaturePointSelection identifies small blocks with rich structure information. BlockMatchingImageFilter finds the displacement associated with these blocks in order to produce a sparse deformation field, which is used by FEMScatteredData PointSetToImageFilter to find the deformation field image. For each block, MaskFeaturePointSelection stores the structure tensor, and BlockMatchingImageFilter stores the confidence, i. e. , the cross-correlation value. Both structure tensor and confidence are incorporated into the FEMScatteredDataPointSetToImageFilter to deal with aperture problem.
FUTURE WORK Although a default rectilinear mesh is provided inside, we strongly suggest users providing an anatomically adapted mesh as the input for the PBNRR due to its advantages in the accurate description of the geometry of the object and a small number of mesh nodes (unknowns). In the future, we plan to provide a webservice for image-to-mesh conversion to generate the mesh of the images over the WEB. This service can maintain new functionality as we better understand the needs of the ITK community. Moreover, due to the influence of the outliers to the HD evaluation, we intend to use a modified HD method presented in  Garlapati et al. (2012)  and the landmark to do more rigorous evaluation. Also, we are collecting more clinical MRI data to increase the number of test cases.
ACKNOWLEDGMENTS This work is funded mainly by the  ARRA  funds for the ITK-v4 implementation with grant number:  NLM A2D2 201000586P. In addition, this work is supported in part by  NSF  grants:  CCF-1139864 ,  CCF-1136538 , and  CSI-1136536  and by the  John Simon Guggenheim Foundation  and the  Richard T. Cheng Endowment. We would like to thank  Luis Ibanez ,  Matt McCormick , and  Arnaud Gelas  for their help on ITK coding style. Also, we would like to thank the image-to-mesh conversion team at  CRTC , especially  Panos Foteinos  for providing the Finite Element Meshes for the evaluation of the software and  Dr. Michele Audette  at  ODU  for bringing this opportunity to our attention while he was at Kitware. The staff of W&M at Grants Office have been extremely helpful with the transition of this grant from  W&M  to ODU.