dataesr/scientific-paragraphs-categorization huggingface dataset:
semiclausum (DsIV vankyrin 1 (GenBank #JI257593), viral innexin 1 (GenBank #JI257597), polar residue-rich protein (GenBank #JI257606), unknown protein (GenBank #JI257608), unknown protein (GenBank #JI257609), and unknown protein (GenBank #JI257611) genes)  [26]. Class B (3,RPKM,18) contained 24 HdIV genes (Figure  2 , Time-Class B) and included at least one member of each of the six multigene families conserved in IVs (2 Vank, 2 Vinx, 8 Rep, 2 Cys-motif, 3 PRRP, 2 N-gene genes), and members of the Hd_F3 and Hd_F6 families, together with two single-copy genes (U1_Hd7 and U1_Hd19). Class C (RPKM,3) contained 91 HdIV genes (Figure  2 , Time-Class C). However, based on the RPKM threshold for background (determined for HdIV segment intergenic regions, see Materials & Methods), only 50 HdIV genes were considered to be truly transcribed (RPKM.
Our results also indicate that total HdIV gene transcript levels are low in non-permissive hosts. This may indicate that HdIV infection is ''abnormal'' (e. g. inefficient cell entry and/or gene expression) in these lepidopteran species, resulting in failed parasitism. How is the expression of these genes regulated? The possible mechanisms involve gene-specific promoter sequences and/or a larger number of gene copies (within the virus particles) thanks to classical segment nesting or the existence of overlapping sequences (e. g. Hd2, Hd17, Hd26). Comparisons between different host species also indicated the probable involvement of host factors in regulating expression, because some genes are differentially expressed between host species, even though this is not generally the case. Our preliminary search for characteristics of the upstream sequences of abundantly transcribed genes was not conclusive, probably because the dataset available is still too limited.
Acknowledgments We thank  Clotilde Gibard ,  Gaetan Clabots ,  Mathieu Jambart  and  Aurelien Ausset  for providing insects and/or reagents for this study. We are particularly grateful to  Sylvie Samain  for her precious help with sequencing and sequence analyses and to  Jean-Michel Drezen  for fruitful discussions. We also thank the anonymous reviewers for their helpful comments and suggestions to improve the manuscript. English Language Editing has been performed by  Alex Edelman & Associates  (http://www. alexedelman. com/).
Image analysis protocols Image analysis and quantification can be performed using our software TiQuant (www. msysbio. com/tiquant). This tool allows the quantitative analysis of key parameters of hepatocytes and the sinusoidal as well as bile canalicular networks (Table  1 ). The results in Table  1  are mean values and standard deviations from five mice (c57Bl6/N, male, 8-12 weeks old). TiQuant quantifies large numbers of structures. For example, approximately 1. 4 × 10 3 hepatocytes, 450 first-order sinusoidal branches and 950 secondorder bile canalicular branches are quantified in a single reconstructed tissue block at 20-fold magnification. These high numbers provide excellent conditions for statistical analyses. Moreover, all quantitative parameters of hepatocytes, sinusoids and bile canaliculi can be extracted from the same tissue specimen.
Advantages and limitations The advantages of the described methods are: (1) bile canaliculi and sinusoids can be reconstructed from the same tissue block, which normally encompasses an entire lobule (x-and y-axis) and the margins of the neighbouring lobules (depending on the dataset); (2) besides entire lobules, detailed cell shape approximations and subcellular structures can also be visualized and quantified; (3) structures can be quantified in relation to their position within an individual lobule (e. g. periportal, mid-zonal and pericentral); (4) s-phase positive cells can be quantified in relation to their lobular position; (5) variants of the protocols with distinct antibody combinations have been optimized to visualize liver tissue from humans, mice and pigs and to reproducibly quantify their architectural parameters; (6) the staining protocols can be performed either manually or using an automated system for high throughput; and (7) the technique has been established for approximately 100-μmthick vibratome slices.
Image analysis protocols The TiQuant software is used for image processing of multi-cellular tissues. It uses the open-source image processing and visualization libraries Insight segmentation and registration Toolkit (ITK) and Visualization Toolkit (VTK), as well as Qt, OpenGl/GlUT and HDF5. similar to its precursor software (Hoehme and Drasdo 2010), TiQuant will become part of an open-source software framework which also includes modelling and simulation capabilities. This software framework is able to receive input directly from the image processing and analysis tool provided in this paper.
Equipment setup 1. A computer (64 bit, preferably multi-processor, minimum of 16 GB rAM, 50 MB free disc space) with a Unix/linux or Microsoft Windows operating system. 2. Installation of TiQuant, which is available from our homepage (www. msysbio. com/tiquant). 3. Installation of image processing software capable of reading and visualizing 3D image stacks (e. g. ImageJ). 4. Digitized image stacks in TIFF format prepared according to the above-described staining protocols comprising DAPI, DPPIV/cD26, Gs and DMs channels, imaged using the 60× objective at a voxel resolution of 0. 207 μm × 0. 207 μm × 0. 54 μm and 1,024 pxl × 1,024 pxl in x-y dimension. This set-up shows a fraction of one hepatic lobule. 5. Imaging set-ups meant for the analysis of a complete lobule should be imaged with a 20× objective at a voxel resolution of 0.
Image processing with TiQuant The current version of TiQuant implements a number of socalled image processing pipelines tailored for the segmentation and quantification of veins, hepatic and non-hepatic nuclei, sinusoidal and bile canaliculi networks, as well as hepatocytes and necrotic tissue in 3D, which are obtained by confocal microscopy of liver lobules. These processing pipelines essentially are sequences of image filters and algorithmic units that can be parameterized to compensate for image feature variability. The following sections introduce all processing pipelines and their parameters in a way that will enable users not familiar with image processing to work with the software.
3 Workflow To ensure segmentation and quantification of results that are of high and reproducible quality, it is necessary to follow an iterative procedure. First, all channels of a dataset that are meant to be used for segmentation have to be thoroughly examined and assessed for quality. It is imperative that structures to be segmented are clearly visible and are set apart from the background throughout the whole dataset. If a lack of staining or high background noise compromises a considerably large part of the dataset, then the dataset may not be suitable for quantification. If the dataset is of sufficient quality for quantification, it is recommended that after pre-processing, to use the default parameter set of a processing pipeline be used to yield an initial segmentation. After execution, the quality of the result has to be assessed.
Troubleshooting: Normally, it is not necessary to apply clAHE to the Gs channel. However, if the Gs channel has to be later used for vein segmentation, then it must be cropped. segmentation Necrotic region segmentation 1. select 'segment Necrotic region' in the pipeline selection menu. 2. specify file locations of pre-processed DPPIV and DMs channels. The DPPIV channel will be used for result visualization only. 3. choose the threshold mode. The sample region size of the 'Adaptive Otsu Threshold' should be at least half the diameter of the largest necrotic region along the respective axis. The number of samples regulates at how many points within the dataset a sample region is constructed, and an individual Otsu Threshold is calculated. More samples increase the processing time. The larger the sampling region, the fewer sampling points are needed.
An alternative to the 'Adaptive Otsu Threshold' is the 'Manual Threshold' option, where a threshold for the whole image stack can be manually specified. 4. The first erosion dilation pair is for noise removal and closing of small cavities within the necrotic region. The dilation kernel size should be roughly twice the size of the erosion kernel size. The kernel sizes depend largely on the level of noise within the dataset. 5. The second erosion dilation pair is for the removal of sinusoidal structures. Therefore, kernel size of the erosion operator should be slightly larger than the sinusoidal radius (in pixel). The kernel size of the dilation operator should be slightly larger than the erosion kernel to close the remaining cavities within the necrotic region. 6. remove small isolated objects.
In the preceding step, most of the sinusoidal system was removed. However, the complete structure may not have been removed by the erosion operator if very thick sinusoids and branching structures were present. Therefore, in this step, all objects below a certain volume will be removed. Adjust the volume, in case of very small necrotic regions (e. g. at dataset borders). 7. After execution using the DPPIV and DMs channel and files beginning with the prefix 'necroticregion', the segmentation is saved as a binary mask and two overlay images.
select 'segment sinusoids + Bile canaliculi in 60× Datasets' in the pipeline selection menu. 2. specify file locations of the pre-processed DPPIV and DMs channels. In case the image stack contains necrotic tissue, check the 'Is there a necrotic region' box and specify the necrotic region segmentation file. Adjust voxel spacing settings if the image set-up differs from the default values. 3. select threshold modes for segmentation of sinusoids in DPPIV and DMs channels. recommended is the 'Adaptive Otsu Threshold'. some datasets may require the specification of a manual threshold. A voxel needs to pass thresholds in both channels to be considered a sinusoidal voxel, which helps minimize false positives. However, locally impaired intensity of one channel may prevent correct segmentation; therefore, careful threshold tuning is mandatory.
6. close the remaining cavities and discontinuities and remove small artificial objects. The closing radii should equal the largest remaining cavity radii. Opening radii should be slightly larger than radii of the largest artificial objects. 7. remove isolated objects. All isolated objects of a volume smaller will be removed. 8. remove noise in the DPPIV channel prior to the bile canaliculi segmentation. In case of high noise levels in the DPPIV channel, the median and greyscale opening radii might be increased to a maximum value of 2. 9. select a threshold mode for segmentation of bile canaliculi in DPPIV channel. recommended is the 'Adaptive Otsu Threshold'. some datasets may require a manual threshold. since in this step the focus is on the bile canaliculi, the method and/or value may differ from the one used in step 4.
5. 3 collapse intersection nodes filter: All intersection node paired within a distance smaller than the threshold are considered artificially separated and will thus be merged together. 5. 4 Geometric pruning filter: Vertices that introduce a deviation from a straight line of less than this angle are considered obsolete for maintaining the geometrical structure of the graph and will thus be deleted. Instead of this vertex and the two attached edges, a direct edge between the two other edge defining vertices is introduced. This procedure is applied to vertices with exactly two attached edges. 6. select whether an analysis of the network graph is desired. 7. The 'Output file prefix' will be used as a prefix for the analysis files. It is recommended to use the default value. 8. specify the dataset name.
All quantified objects will contain a reference to the dataset name in the analysis file. 9. specify the file locations of the requested segmentation files. 10. specify the voxel size in case it differs from the default values. 11. Pipeline execution graphs will be visualized, if the 'Display Options' are set accordingly. Graphs are saved in text files beginning with the prefix from point 7. sub-graphs that are not connected are stored in individual, enumerated files.
Nuclei segmentation 1. select 'segment Nuclei in 60× Datasets' in the pipeline selection menu. 2. specify file location of the pre-processed DAPI channel. Adjust the voxel spacing according to the image setup. 3. In case image data have high noise levels, increase the median filter and greyscale opening radii. 4. choose a threshold mode. 'Adaptive Otsu Threshold' will yield superior results in most cases. In case of high noise levels or weakly stained structures of interest, the results of the Otsu method may not be satisfying quality. Try to find a 'Manual Threshold' instead. 5. remove 'salt-and-pepper noise' using 'Inverse Hole Filling'. For high 'salt-and-pepper noise' levels, lower the 'Majority Threshold' and increase the radii. For low noise levels, the 'Majority Threshold' may be increased.
Note: segmented nuclei should contain no inner holes and must be separated and classified correctly. separation of nearly spherical agglomerations of nuclei may fail in some cases. Hepatocyte shape approximation 1. select 'Approximate cell shape' in the pipeline selection menu. 2. specify the dataset file list, ending with '. ias'. This will automatically fill in most of the necessary segmentation files. Fill in the DPPIV channel, check the necrotic region check box if necessary and fill in the voxel spacing. 3. It is advisable to use the default parameterization in an image stack set-up as described above. Adjust the following: 3. 1 'Bile weight' is a measure which balances the influence of bile canaliculi and sinusoids on the one hand and nuclei on the other, with respect to the cell shape approximation.
cells without nuclei and cells with diameters smaller than 'Minimal cell diameter' are considered artificial and will be discarded. 4. After execution, the cell shape approximation results are saved as binary mask and overlay images in files beginning with the prefix 'cellshape'. Note: It is critical that boundaries of approximated hepatocytes align with confining structures. Artefacts in sinusoid or bile canaliculi segmentation may lead to incorrect cell boundaries. At dataset borders, cell boundaries may be incorrect due to incomplete confinement information. These artefacts can be ignored, since only inner cells that are not in contact with dataset borders are used for quantification.
Hepatocyte analysis 1. select 'Analyze cells' in the pipeline selection menu. 2. specify the dataset name. All quantified objects will hold a reference to the dataset name in the analysis file. 3. specify the dataset file list ending on '. ias'. This will create a parameter table with all necessary segmentation files. 4. In case of a different voxel size, amend the voxel spacing parameters. How to handle data with different image settings The effective voxel size of our datasets was 0. 207 μm × 0. 207 μm × 0. 54 μm. The default parameter values of all pipelines are dependent on this dataset set-up. In case the voxel size differs due to e. g. different magnification or voxel resolution, all kernel values have to be rescaled. For example, a closing operator using a kernel of size 2, 2, 1 in our set-up would use a rescaled kernel of size 4, 4, 1 in a set-up with voxel sizes of 0.
Modelling based on TiQuant In the upcoming open-source software framework cellsys, the quantifications obtained from TiQuant can directly be used as an input for the cellsys modelling engine. As an illustration of this application of the processing pipeline, we refer to a model of liver regeneration after ccl 4 -induced liver damage. This model correctly predicted a previously unknown and subsequently experimentally validated order mechanism that was found essential for liver regeneration  (Hoehme et al. PNAs 2010).
Acknowledgments We thank the south  Valley University, Faculty of Veterinary Medicine  for the support of  Dr. seddik Hammad. This study was supported by the  BMBF (German Federal Ministry of Education and research)  project  Virtual liver Network (VlN ) and by the  French National research Agency  ( ANr-13-TEcs-0006-Project Intraoperative Fluorescent liver Optimization Work-up-iFlOW ). Open Access This article is distributed under the terms of the creative commons Attribution license which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Parameters and states are estimated in spaces where they are unbounded. For example, parameters confined to the [0,1] interval are logtransformed and thus also estimated in an unbounded space. Given the priors over parameters and the input sequence, maximum-a-posteriori (MAP) estimates of model parameters were calculated using the HGF toolbox version 2. 1. The code used is freely available as part of the open source software package TAPAS at http://www. translationalneuromodeling. org/ tapas. Optimization was performed using a quasi-Newton optimization algorithm  [33] [34] [35] [36]. The objective function for maximization was the log-joint posterior density over all perceptual and observation parameters, given the data and the generative model. To exclude the possibility that our Gauss-Newton gradient descent optimization could have been influenced by local minima of the log-joint objective function, we used two additional global optimization methods, a Gaussian Process optimization algorithm (GPO)  [37]  and a Markov chain Monte Carlo  [38]  sampling scheme.
Simulations To test the performance of BMS for our particular case, we generated 20 datasets for each of the 4 perceptual models considered under realistic levels of decision noise and using the prior means as parameter values. Thus, we augmented the softmax function in Eq. (  14 ), which describes the mapping of beliefs onto actions, with a noise parameter g~0:5: p y~1Db ð Þ ~bexp log b ð Þ{g ð Þ b exp log b ð Þ{g ð Þ z 1{b ð Þ exp log b ð Þ{g ð Þ ð20Þ We then performed model inversion using the quasi-Newton optimization algorithm (in the same way as for the other models in this paper) and summarized the performance of BMS in terms of a confusion matrix (Figure  S1 ). This matrix depicts the frequency of ''correct cases'' (where the model which generated the data has the highest exceedance probability of all models tested); here, rows denote the model that generated the data, and columns the model that was inverted.
Beyond the results per se and their implications for concepts of social learning, the modeling approach in this paper, with its emphasis on inter-individual variability in inference and decisionmaking, may serve useful for future studies of social learning. To facilitate this, the HGF and the BMS routines are freely available as open source MATLAB code (the HGF can be found at www. translationalneuromodeling. org/tapas; the BMS routines are part of the SPM software package: www. fil. ion. ucl. ac. uk/spm). Finally, we believe that the approach presented here has potential for characterizing mechanisms of maladaptive behavior in individual patients. The present study in healthy volunteers provides a proof of concept how individual mechanisms can be elucidated in the context of social interactions, a domain where many psychiatric disorders, including schizophrenia, are characterized by particularly salient deficiencies  [54].
Materials and Methods Genomic sequence data were obtained through GenBank. For the 7th order, Thermoplasmata-related methanogens, the accession numbers are CP004049. 1 ("Ca. Methanomethylophilus alvus"), CP005934. 1 ("Ca. Methanomassiliicoccus intestinalis"), and NZ CAJE0100001 to NZ CAJE010-0026 (Methanomassiliicoccus luminyensis). For the genomic organization and comparison of pyl genes, genomic sequences were either treated with the RAST annotation server  [39]  or a local Artemis platform  [40]. Blast searches  [41]  were either performed directly on the RAST server, on the nr database at NCBI or locally. Sequence alignment (DNA, RNA, and proteins) was performed using ClustalW  [42] , T-coffee  [43] , and MUSCLE  [44]. For RNAs, the dedicated R-Coffee program was also used  [45] , accessible at http://www.
tcoffee. org/, which generates a multiple sequence alignment using structural information. RNAfold  [46]  (available at http://rna. tbi. univie. ac. at/cgi-bin/RNAfold. cgi) and RNAstructure  [47]  (available at http://rna. urmc. rochester. edu/) were used to determine the secondary structure of tRNA. The structure of the tRNA Pyl was also manually drawn by comparison with the structure of the D. hafniense and M. acetivorans tRNA Pyl  [11, 20]. Phylogenies were both inferred from maximum likelihood and Bayesian procedures. Datasets of protein homologues were aligned by MUSCLE  [44]  with default parameters, and unambiguously aligned positions were automatically selected by using the BMGE software for multiple-alignment trimming  [48]  with a BLOSUM30 substitution matrix. Maximum likelihood trees were calculated by PhyML  [49]  and the LG amino acid substitution model  [50]  with 4 rate categories as suggested by the AIC criterion implemented in Treefinder  [51].
Trees were also calculated by Bayesian analysis with PhyloBayes  [52] , with the LG model (for single gene trees) or the CAT model (for the concatenated dataset) and 4 categories of evolutionary rates. In this case, two MCMC chains were run in parallel until convergence and the consensus tree was calculated by removing the first 25% of trees as burnin. [11, 13, 53] ). On the right is shown the organization of these genes in the 7th order of methanogens.
In the "Ca. M. alvus" genome, the pyl genes occur in single copy and the pylB gene is ∼0. 7 Mb distant from the pylTSCD cluster (Figure  1 ). Usually, these genes are closely associated with the methylamines MT genes (mtmB, mtbB, and mttB) and other genes involved in these pathways (which do not encode Pylcontaining proteins). A similar gathering of the pyl genes cluster with the genes involved in methylotrophic methanogenesis, including mtmB, mtbB, and mttB is observed in the genomes of the 7th order of methanogens (data not shown). Pyl. The tRNA Pyl homologues were retrieved from the three 7th order genomes by using the tRNA Pyl of Methanosarcina barkeri strain MS-DSM 800 (Accession number AY064401. 1) as seed. As mentioned above the "Ca. M. alvus" and "Ca. M. intestinalis" genomes harbor one pylT gene, while three are present in the M.
M e t h a n o m a s s i li i c o c c u s i n t e s t i n a li s " "C a. M et h a n o m et h y lo p h il u s a lv u s" Figure  3 : Phylogenetic trees of the pylSCD geneproducts. The phylogenetical analysis (PhyML) was performed from a concatenation (672 positions). See Sections 2 and 3 for more details. Individual trees are also reported in the additional file (supplementary Figures  S1 to S3  with indication of the corresponding accession numbers). The 7th methanogenic order members are indicated in red, the Methanosarcinaceae in blue, and the bacteria in black. could be found outside of the known Pyl-containing organisms, whereas PylB had a few distant homologues in various bacteria (not shown). Pyl proteins are very well conserved at the sequence level among Archaea and Bacteria (alignments are available on request from the authors).