dataesr/scientific-paragraphs-categorization huggingface dataset:
36 Cl beta spectrum A preliminary measurement of a 36 Cl source was carried out. As expected, the high energy electrons that pass through our 500 µm thick detector induce a folding of the spectrum. The realistic geometry of the experimental setup was implemented in our simulations. We used the experimental shape factor extracted from the measurement of Rotzinger  [12]  for the primary emitted beta spectrum. The data were adjusted using an approximate linear energy calibration. As shown in figure  4 , our simulation reproduces well the main distortion of the measured spectrum. A complete and careful study of the detector response function will provide a precise energy calibration and should improve the agreement with our simulation. 3 Calculation of beta spectra.
Conclusion Our experimental setup is finalized and initial measurements have been carried out with success. Some distortion persists, but owing to the important development and simulation work, we should be able to characterize and quantify them with our Monte Carlo simulations. The next step will be to precisely study the response function of the detector in order to determine an experimental distortion function. Then we will carry out a series of measurements of well-known spectra to validate our method. This particular setup is well suited for measuring beta spectra with a maximum energy of about 1 MeV. It is also easily adjustable to a 5 mm thick Si(Li) detector, for measuring beta spectra up to about 3. 5 MeV. We will then focus on unique and non-unique forbidden beta spectra to validate our code BetaShape.
7% divergence. The mean resequencing accuracy (correctly resequenced bases in comparison to reference sequencing results or database sequences) was 98. 9% (range 87. 1-100. 0%). For all strains analysed by the RMA and the automated filtering approach, at least one sequence targeting a housekeeping gene and one sequence targeting a neurotoxin gene cluster component were successfully retrieved. For all bivalent strains analysed by RMA, two distinct bont gene prototype sequences could be retrieved: bont/B and/F sequences for strain 168. 08 (toxinotype Bf2), and bont/A and/B sequences for strains NCTC 2916 [toxinotype A1(B)] and 1430-11 [toxinotype A5(B9)]. Additionally, sequences of two complete sets of ha and orf-X cluster components were retrieved for strains 168. 08 and NCTC 2916, but as expected only sequences of ha cluster components were recovered for strain 1430-11.
botulinum prototype sequences were retained after filtering, with call-rates ranging between 46. 7 to 97. 7%. In addition, sequences targeting partial C. perfringens-specific housekeeping genes (rpoB, gyrA, and parC) and toxin genes (cpa, cpb2 and pfoR) were retrieved (call-rates ranging between 70. 1 and 98. 3%). All filtered sequences led to the retrieval of at least one BLAST hit and corresponding taxonomic identity. The highest scoring BLAST results obtained for C. perfringens prototype sequences retrieved by RMA all designated C. perfringens strains as taxonomic hit (data not shown). The results of the highest scoring BLAST alignment for each retained C. botulinum sequence (total score, coverage and taxonomic identity) are presented in figure  3. Strains belonging to the toxin subtype A2 were recognized as best hit for most of the retrieved sequences of the neurotoxin gene cluster components.
botulinum strain as well as a co-cultured C. perfringens strain. The results obtained by BLASTing and phylogenetic analysis of the retrieved bont sequence, as well as the characterization of the BoNT gene cluster components, classified the strain as a C. botulinum toxinotype A2. Moreover, the examination of the taxonomic BLAST hits indicated a close relatedness to strain Mascarpone. As the analysis was performed with a partial sequence of the bont gene (200 nt), containing 4% unidentified bases, misclassification of the toxin subtype may have occurred. However, this was unlikely as the retained sequence contained specific determinants found only in bont/A2 genes, and unidentified residues were located in regions that are not discriminant for subtype differentiation. The C. botulinum strain detected by RMA could be isolated from the food sample, and confirmed as subtype A2 by sequencing the specifically PCR-amplified bont gene (genbank accession number JQ954970).
Materials and Methods Design of the Pathogen ID v2. 0 resequencing microarray (RMA) The second generation of a broad-spectrum resequencing microarray (RMA) was used in this study: the PathogenID v2. 0 array, able to detect 124 bacteria, 126 viruses and 673 genes involved in toxin production, pathogenicity or antibiotic resistance  [31]. This array included specific housekeeping genes, namely rrs and rpoB, for detection and characterization of clostridial species. In addition, it included prototype sequences of each component of the neurotoxin gene cluster from all known toxinotypes, as well as several subtypes. Tiled probes targeting these genes were selected using multiple sequence alignments of genome sequences from various toxin subtypes available in GenBank during the time of the RMA design, in order to select conserved regions within toxinotypes for optimal hybridization efficacy.
Bacterial strains and biological samples analysed Eleven well-characterized C. botulinum strains belonging to toxinotypes A through G served as reference: strains Hall (type A1), 1430-11 [type A5(B9)], NCTC 2916 [type A1(B)], Loch Maree (type A3), BL6 (type B2), 161. 08 (type Bf2), NCIB 10658 (type F6), K16 (type E1), 468 (type C1), 1873 (type D) and NCIB 10714 (type G). The characteristics and origin of most strains have been described elsewhere. The C. botulinum type A5(B) strain 1430-11 was isolated from contaminated commercial food (pasta). The C. botulinum strain 161. 08 was isolated from a foodborne botulism case. Pure cultures of Clostridium spp. strains were performed as described previously  [55]. Genome sequences from reference strains were retrieved from the genbank database. The bont sequences for strain 1430-11 (genbank accession numbers KC683799 and KC683800) and strain 161.
08 (genbank accession numbers KC471328 and KC471329) were obtained by sanger sequencing of specifically PCR-amplified bont genes and deposited in genbank. Biological samples included a naturally contaminated food specimen (salad) and a 48 h enrichment culture of this food sample in Fortified Cooked Meat Medium (FCMM) at 37uC in anaerobic conditions  [56]. Presence of C. botulinum toxinotype A in the food samples was confirmed by SYBR green real-time PCR with primers P1646 (59-TCTTACGCGAAATGGTTATGG-39) and P1647 (59-TGCCTGCACCTAAAAGAGGA-39) for bont/A gene, P1648 (59-CCTGGGCCAGTTTTAAATGA-39) and P1649 (59-GCGCCTTTGTTTTCTTGAAC-39) for bont/B gene, P1650 (59-GTGCCCGAAGGTGAAAATAA-39) and P1651 (59-TAATGCTGCTTGCACAGGTT-39) for bont/E gene, P2107 (59-TGCACAATGAATTTTCAAAACA-39) and P2108 (59-TCCAAAAGCATCCATTACTGC-39) for bont/F gene.
Resequencing microarray assay The amplification products obtained from genomic DNA by WGA were quantified by Quantit BR (Invitrogen) according to the manufacturer's instructions. A recommended amount of target DNA was fragmented and labeled according to the GeneChip Resequencing Assay manual (Affymetrix). The obtained products were coded with unrelated numbers by a non-observer to ensure that the study was performed blindly. The microarray hybridization process was carried out according to the protocol recommended by the manufacturer (Affymetrix). The details and parameter settings for the data analysis (essentially conversion of raw image files obtained from scanning of the microarrays into FASTA files containing the sequences of called bases for each tiled region of the microarray) have been described previously  [54].
Automated data analysis A Perl-based program, designated ''TaxFinder'', was used for the automated analysis of re-sequencing data provided by PathogenID v2. 0. The program read the FASTA file generated for each sample, which contains all the sequences read by the GSEQ software (Affymetrix) from the hybridization results. A filtering process, based on the one described by Malanoski et al. [58]  was applied to these sequences. For each sequence, TaxFinder considers the Nb first bases of the sequence, Nb defined by the user in the beginning of the experiment and corresponding to the minimal length accepted for the sequence to keep. If the percentage of No-call in this fragment is inferior to an elongation threshold defined by the user, a base is added to the fragment and the percentage of No-call is recalculated.
As soon as it is higher that the elongation threshold, the No-call at the ends of the fragment are removed. If the length of the nucleotide fragment is still higher than the minimal threshold established, then it is conserved. If the percentage is higher than the threshold, the fragment is skipped, and the program considers the fragment of size Nb and starting position one base upstream than the precedent fragment. This process is reiterated until the end of the sequence is reached. In this study, sequences that did not contain subsequences with a minimum nucleotide length of 20 nt and a maximum undetermined nucleotide content of 10% were discarded. Filtered sequences individually underwent a blastn analysis to search for sequence homologues in the NCBI nucleotide collection (nr/nt database) with adjusted settings to restrict the search to bacteria entries.
Blast + application distributed by NCBI is used for the automated blast research (http://www. ncbi. nlm. nih. gov/books/NBK1763/). The following algorithm parameters were modified: the expected value cut-off was fixed to 100, the minimum word size was set to 7 and the upper limit of displayed descriptions of database sequences per query was decreased to 50. The best BLAST hits were classified according to their total score (the sum of the high-scoring segment pairs) and their corresponding taxonomies were retrieved from the NCBI Taxonomy database. When several hits obtained the highest total score, the script automatically retrieved the taxonomies of the 10 first BLAST hits.
Phylogenetic analysis The resulting bont sequence obtained from the enrichment culture of the C. botulinum strain in a contaminated food specimen was compared with the corresponding bont sequences of reference strains from the 5 known C. botulinum toxinotype A subtypes (GenBank accessed 21/02/2013). Multiple sequence alignment was performed using the CLC Bio software and checked for accuracy by eye. A neighbor-joining tree of these sequences was constructed using the Jukes-Cantor method with the SeaView v4. 2. 1 software. The level of support for each node is provided by 100 bootstrap replications.
8. Post-acquisition analysis. Subsequently, images are analyzed by a computer algorithm that allows automated scoring of the fluorescence signal for each individual cells. Software such as Metamorph 7. 1 or Acapella can be used to create a script for measuring the ratio between the 450nm and 535nm emission signals (available upon request). Of note, the use of a focus calibration device coupled to automated microscopy during the acquisition allows acquiring dozens of different positions in multiple wells at the same time.
Both strains express β-lactamase, and display β-lactamase activity on their surface due to the presence of the AfaE encoding plasmid. Upon HeLa cell infection with the non-invasive BS176 AfaI strain for 1 hr, the CCF4 FRET probe remains intact, as shown in Figure  2A  by the green signal (535 nm). On the contrary, the infection with the virulent M90T AfaI strain for 1 hr leads to a switch of signal towards blue (450 nm) highlighting the cleavage of the probe in the cytosol. In order to quantify this, we developed a script for the Metamorph and Acapella software that allows automated detection of cells and the measurement of intensities in the 535 and 450 nm channels for the determination of the ratiometric signal. As shown in Figure  2B , nuclei and cytosol of cells are segmented using the Draq5 channel.
The script we developed on MetaMorph software detects directly the CCF4 positive population of cells and calculate the ratio between the intensities in 450 and 535 nm channels for each individual cells. Then cells are classified as a function of their ratio using a macro developed in Excel, thus yielding histograms showing the cell distribution. As it has been the case for the other script developed for Acapella, BS176 AfaI infected cells are characterized by low ratios, whereas M90T AfaI infected cells display high ratios using the MetaMorph algorithm on HeLa cells and THP-1 macrophages (Figures  3B  and 4B ). Finally, an adaptation of this method to the study of mycobacteria is shown in Figure  5. Mycobacterium bovis BCG resides in the phagosome for the whole course of the experiment, as reflected by the strong 535 nm signal detected throughout the time course of the experiment.
Building accurate gene regulatory networks (GRNs) from high-throughput gene expression data is a long-standing challenge. However, with the emergence of new algorithms combined with the increase of transcriptomic data availability, it is now reachable. To help biologists to investigate gene regulatory relationships, we developed a web-based computational service to build, analyze and visualize GRNs that govern various biological processes. The web server is preloaded with all available Affymetrix GeneChip-based transcriptomic and annotation data from the three model legume species, i. e. , Medicago truncatula, Lotus japonicus and Glycine max. Users can also upload their own transcriptomic and transcription factor datasets from any other species/organisms to analyze their in-house experiments.
To gain insight into these gene interactions, bioinformatics tools for GRN analysis are needed to generate hypotheses from highthroughput datasets. However, most of the current statistical or computational tools are difficult to access for most biologists. Although several web-based tools  [4, 5, 6, 7, 8]  have been developed to retrieve known or predicted gene-gene interactions based on existing knowledge, most of them are static databases and do not provide a dynamic GRN prediction function according to users' requests. In other words, there is no way for end users to submit their own data or select specific datasets to perform personal GRN inferences. Two exceptions are GenePattern  [9]  and Predictive-Networks  [10]. However, PredictiveNetworks focuses exclusively on human datasets and GenePattern only provides GRN inference but no network query or visualization.
On other hand, numerous computational methods for GRN prediction have been recently proposed or applied. These methods include co-expression or relevance network (RNs)  [11] , graphical Gaussian modelling (GGM)  [12] , Boolean network  [13, 14] , differential equations  [15] , information theory  [16, 17] , Bayesian network (BN)  [18, 19] , regression models  [20] , among many others  [21, 22]. However, two key problems still hinder their successful applications in practical GRN inferences. One is that the quality of network inference is not robust and stable  [23, 24]. To illustrate this point, some studies  [24]  showed that half of algorithms only performed better than random guessing. A second problem is that some sophisticated models (such as, Bayesian networks) are timeconsuming and infeasible for large datasets with several thousand genes and a large number of experiments.
To address these issues, we developed a flexible, open-source, web-based application and data service framework for GRN analysis using gene expression data (http://legumegrn. noble. org). In this web site, we integrated several commonly used GRN prediction algorithms including co-expression, GGMs  [12] , Context Likelihood of Relatedness (CLR)  [16] , GENIE3  [21] , TIGRESS  [20]. We parallelized GENIE3 and TIGRESS to make them feasible for large datasets. In addition, we also proposed a parallel version of the constraint-based BN learning algorithm called the PLPC algorithm, which is able to infer causal relationships or directionalities. To further improve prediction performance, our system is able to integrate prediction results from individual methods into a composite network to provide more accurate results.
In addition to GRN prediction, we also implemented several features for GRN analysis such as GRN comparisons, GRN subnetwork query and GRN visualization. Although users are able to upload their own datasets, a special focus on legumes has been made by preloading into the web server all the Affymetrix GeneChip based gene expression data and annotation files publicly available from the three legume model species, M. truncatula, L. japonicus and G. max. Thus, the web site allows users to simply upload gene or probeset lists, and then select existing gene expression experiments (i. e. , chips) and specific algorithms to perform GRN prediction in these three legume species. An additional feature allows users to finally identify conserved or divergent gene regulatory programs across these three species.
Data Sets To facilitate GRN analysis for legume species, we have collected all M. truncatula, L. japonicus and G. max Affymetrix microarray datasets from public EBI microarray database and from our collaborators. In total, we have collected 670 Medicago chips, 237 Lotus chips and 913 soybean chips. We then normalized all the raw data using Robust Multichip Averaging (RMA) through R and uploaded them into our database. To permit analysis of usergenerated datasets or of data available from other species, we provided options for users to upload their own expression data in tab-delimited text format. To check ''batch effects'' (the systematic error introduced by the different sources of data), we analysed transcriptomes of major plant tissues from experiments carried out at different locations for Medicago.
From Principal Components Analysis (PCA), we observed that all the organs were well grouped in the PCA plot (Figure  S1 ), which demonstrated that ''batch effects'' related to data sources are negligible in major organs of Medicago dataset. This analysis was not possible in Lotus and soybean due to the low redundancy of experiments between data sources. Moreover, to ensure users to compare relevant experiments, we provided correlation coefficients matrices between all experiment pairs for each species (the data are available at http://legumegrn. noble. org/cc. html).
The GRN Inference Algorithms To support custom GRN predictions, we implemented a multialgorithm program that assists in the construction of gene networks for gene expression data. Multiple GRN prediction algorithms can complement each other and compensate for the limitations of a single GRN prediction approach to improve the prediction accuracy. Based on the literature and previous comparisons of GRNs  [9, 25] , we adopted GGM  [12] , coexpression  [11] , GENIE3  [21] , TIGRESS  [26]  and CLR  [16]  as the major algorithms because of their good performances in each category of algorithms when testing over DREAM5 (Dialogue for Reverse Engineering Assessments and Methods) Network Challenge (http://www. the-dream-project. org/), a competition race in reverse engineering of GRNs  [27]. We parallelized two GRN prediction algorithms, TIGRESS and GENIE3, to reduce their computation time for large data sets.
In addition, we also proposed a parallel constraint-based algorithm called PLPC (i. e. , Parallel Low-order PC Algorithm), which is a parallelized version of our previous algorithm  [25]  based on a Bayesian network (BN) model. BNs are well suited for inferring gene networks because of their ability to model causal influence (cause-effect) between variables (i. e. , genes). Most of BN learning algorithms are very time-consuming and hard to scale up to several thousand genes. Thus, we used parallel computing and restrained the highest order (means the size of conditioning set) in conditional independence (CI) tests to achieve feasibilities for large datasets; meanwhile, we also combined the idea used in the PCstable algorithm  [28]  to improve performance. The details about this algorithm can be found in the File S1.
Across Species Comparison Another feature available for legume species is the gene network comparison across different species (i. e. , Medicago, Lotus and soybean). Many components of regulatory networks governing basic cellular functions are highly homologous in diverse species  [11]. This comparative transcriptomics approach will enable detection of evolutionarily conserved GRNs. Comparison of integrated (multi-species) GRNs with single-species GRNs will also help place regulatory subnetworks (modules) into a phylogenetic context. To compare GRNs in different species, we identified pairs of related genes (i. e. , orthologous genes) between these three species. Each orthologous set was defined using a unique identifier, called a metagene ID. Identification of ortholog sets across multiple species was carried out using the BLAST algorithm and protein sequences.
Annotation Data To facilitate GRN analysis, we implemented the web server with different functional annotations, such as KEGG annotation from GeneBins and GO terms  [35, 36]. A module to identify significant enrichment in GO terms of each (sub)network using the chi-square test was developed and added to the web server. This feature may allow the identification of molecular functions or pathways in which most of the genes are involved. Additional gene annotations have been added, such as predicted transcription factor (TF) domains and tentative functional annotations for these three legume species. We also mapped the probeset IDs with related gene IDs obtained from IMGAG v. 3. 5 for Medicago, Lotus v. 2. 5 for Lotus and Soybase (http://soybase. org) for soybean. All these datasets were loaded into our databases to allow users to use Gene IDs or probeset IDs interchangeably as primary inputs.
Software Implementation and GRN Visualization LegumeGRN is a J2EE web application with Tomcat as the web server. The user interface was written in JSP and Servelet with a significant reliance on JavaScript language and JQuery libraries for front-end interactivity. AJAX was used for data retrieval in network visualization. Users can access the web page using any modern browser, including Microsoft Internet Explorer, Google Chrome, Apple Safari and Mozilla Firefox. On the back-end, we have set up a multiple-host cluster and maintained a job scheduler using Oracle Grid Engineer to respond to user requests. We have implemented the GRN prediction algorithms in Matlab and R and deployed them into the cluster. JAVA was used to write the application for processing user requests, such as GRN prediction, subnetwork query, network comparison and integrative analysis.
Utility Personalized GRN analysis and workspace. To facilitate their use of the database, users may create an account in this server, which allows them to log in to submit new analysis tasks, and to access and manage their analysis results from previous sessions. Users can customize the datasets (gene/probeset lists, microarray chips), select suitable algorithms and tune the parameters as they wish, then submit GRN prediction requests. They will get an ID for every submitted analysis request. Afterwards, users can browse all requests in their own workspace and view job running status, query, compare GRNs and retrieve results using the job ID. Primary input. The primary inputs of legumeGRN are a gene expression data file and an optional transcription factor file. Then, users can select the prediction algorithms and tune the algorithm parameters to be used in the GRN construction (Figure  1A ).