dataesr/scientific-paragraphs-categorization huggingface dataset:
These solid-shell elements have been first developed in the framework of isotropic elastic materials and small strains (see  [22] ), and recently coupled with anisotropic elastic-plastic behavior models within the framework of large strains for the modeling of sheet metal forming processes  [23]. In this paper, the formulations of the quadratic SHB15 and SHB20 elements are combined with functionally graded behavior for the modeling of thin FGM plates. To achieve this, the elastic properties of the proposed elements are assumed to vary gradually in the thickness direction according to a power-law volume fraction. The resulting formulations are implemented into the quasi-static ABAQUS/Standard software. The performance of the proposed elements is assessed through the simulation of various nonlinear benchmark problems taken from the literature.
Conclusions In this work, quadratic prismatic and hexahedral solid-shell SHB elements have been proposed for the 3D modeling of thin FGM structures. The formulation of the SHB elements adopts the in-plane reduced-integration technique along with the assumed-strain method to alleviate various locking phenomena. A local (element) frame has been defined for each element, in which the thickness direction is specified. In this local frame, elastic properties of the thin structure are assumed to vary gradually through the thickness according to a power-law volume fraction distribution. The resulting formulations are implemented into the finite element software ABAQUS/Standard in the framework of large displacements and rotations. A series of selective and representative benchmark problems, involving FGM thin structures, has been performed to evaluate the performance of the SHB elements in geometrically nonlinear analysis.
It is noteworthy that the NCBI database composition is biased toward major STEC clones and strains, comprising high numbers (n > 100) of genome sequences of priority serotypes of STEC incl. O157 and O104, whereas multiple genome sequences of less frequently occurring STEC variants included into the HUSEC collection are scarce. Therefore, we had to restrict our analysis to those subgroups for which multiple genome sequences were publicly available and included 14 STEC subgroups according to their O-antigen or their clonal lineage (ST or CC) into our analysis. This set of STEC variants associated with severe illness and/or outbreaks includes the O157, US priority 6 and O104:H4 serotypes, their corresponding CCs as well as O98:H-/OR:H-(ST306) isolates. We determined specific markers and developed a multiplex PCR (mPCR) for typing isolates of these STEC subgroups.
Bioinformatics Pipeline A: Collection of Genomes After an extensive metadata search and BLASTn analysis with a 95% identity cut-off against Shiga toxin 1 + 2 and intiminencoding gene alleles of strains Sakai and EDL-933 a subset of 166 STEC genome sequences were chosen from 10,282 E. coli genome entries (as of December 2014) available from NCBI's Sequence Read Archive (SRA) database to represent different STEC O-serogroups (Figure  1 ). Depending on the metadata available, these strains were grouped according to the disease of the patient into HUS-associated STEC or STEC from patients with diarrhea, but not developing HUS. Additionally, a control test set of 82 non-STEC genome sequences containing intestinal pathogenic E. coli (IPEC) variants, different extraintestinal pathogenic E. coli (ExPEC) isolates and non-pathogenic strains was compiled (Supplementary Table  S1 ).
Bioinformatics Pipeline A: Assembly of Genomes In a first step SRA raw reads were analyzed with the FastQC software (v0. 11. 5)  (Andrews, 2010)  and raw reads with an rejected per base sequence quality were discarded. We compared the assembly results of velvet (v1. 2. 10)  (Zerbino and Birney, 2008)  and SPAdes (v3. 5)  (Bankevich et al. , 2012)  with and without quality trimming. The results showed that assemblies with SPAdes without previous quality trimming gave fewer and longer contigs than any other combination (data not shown). Therefore, the raw reads were finally assembled with SPAdes (v3. 5) with the built-in "-careful" parameter which realigns reads to correct the assembly using the BWA short-read aligner  (Li and Durbin, 2009). Due to the fact that gene finding on short contigs can be problematic as the chance to detect multiple ORFs increases with decreasing sequence length and such predicted ORFs are often artifacts we discarded contigs smaller than 1 kb.
Bioinformatics Pipeline A: Analysis of Genomes All genomes were analyzed with the SeqSphere+ Software v3. 1. 0 (Ridom GmbH, Münster, Germany 1  ) to allocate the corresponding clonal lineages of the isolates based on MLST. Genomes lacking MLST typing results were discarded. The web-based SerotypeFinder 2  was used for in silico serotyping  (Joensen et al. , 2015)  (Figure  1 ). Virulence gene sequence data were downloaded from the VirulenceFinder database  (Joensen et al. , 2014)  and a BLASTn search was performed to identify the 23 stx1, 121 stx2 and 45 intimin (eae) alleles present in the database in the assembled genomes (Supplementary Table  S1 ).
Bioinformatics Pipeline A: Genome Wide Identification of STEC Biomarkers Pipeline A consists of three steps (Figure  1 ). First, the 248 genomes were annotated with Prokka v1. 11  (Seemann, 2014). The Prokka-generated Genbank file was used to extract the coding sequences (CDS) with cds_extractor v0. 7. 1 3   (Leimbach et al. , 2017). In the second step, Proteinortho (v5. 11)  (Lechner et al. , 2011)  was used to detect orthologous proteins within given genomes via a bidirectional BLASTp analysis. BLASTp parameters of 80% identity and 40% coverage were chosen, which can distinguish stx1 and stx2 alleles used in our test set, to determine orthologs. The presence/absence matrix of all orthologs within the strain panel, created by Proteinortho, was used in the last step of the pipeline by a customized Perl script to categorize orthologs according to a maximum of four user-specified genome groups 4  v0.
Bioinformatics Pipeline B: Identification of STEC Biomarkers Through Set Operations on Homologous CDSs In a first step, the similarity of the annotated genomes of 21 HUS-associated STEC, 22 other clinical STEC isolates and 20 non-STEC isolates (Supplementary Table  S1 ) to the genome of reference STEC O157:H7 strain Sakai (NC_002695) was computed using Genostar's WallGene software. WallGene computes similarity using BLASTp to compare all CDSs of E. coli O157:H7 model strain Sakai against all CDSs of the other strains in the dataset. For this analysis, 80% identity and 40% coverage cut-offs were used to assess homology. In a second step, the results of the homology search were used to compute both, the STEC core proteome and the "pan proteome" of the non-STEC control group (Figure  1 ). The core proteome was defined as the set of gene products from the reference strain with orthologs present in at least 80% of the STEC strains.
The control pan proteome consists of the set of gene products of the reference strain with at least one ortholog in any member of the non-STEC control group. The third step represented the identification of candidate biomarkers specific for clinically relevant STEC by extracting CDSs present in the STEC core proteome, but absent from the pan proteome of the non-STEC strain set. The second and third steps of the analysis have been performed using an in-house Python script (available on demand).
In Silico Verification of Biomarkers As a control step after biomarker identification by the two pipelines, the specificity of the biomarker candidates was verified in silico 6. For this purpose the presence of a biomarker candidate protein was investigated by BLASTp analysis. If a BLASTp match displayed >80% identity and >80% coverage in any STEC or non-STEC control genome used in this study the biomarker was discarded. For each of the specific biomarkers, primer pairs were designed with the web-based tool Primer3 7. The designed primer pairs were then tested in silico against all E. coli genomes present in the E. coli collection of reference strains (ECOR) (72 genomes), in the Diarrheagenic E. coli (DEC)-collection (77 genomes) and in the RefSeq database (739 genomes) as well as in the GenBank database (1,951 genomes) (Supplementary Table  S2 ) with the EMBOSS primersearch v6.
6. 0. 0 software (primer sequences in Table  1 )  (Rice et al. , 2000). Additionally, the final biomarkers have been validated in silico by BLASTn analysis against all the genomes of the enterobacterial genera Shigella, Salmonella, Proteus, Klebsiella, Enterobacter, Citrobacter, Serratia, and Yersinia, which are deposited in the NCBI Nucleotide Collection database (Supplementary Table  S3 ).
Primer Design for Shiga Toxin, Intimin and uidA Well-established STEC biomarkers include the Shiga toxin-and intimin-encoding genes. Allelic variants of stx1, stx2, and eae, were downloaded from the VirulenceFinder database  (Joensen et al. , 2014). 23 stx1 allelic variants, the 33 major stx2 allelic variants present in our STEC genome test set (Supplementary Table  S4 ), and all 45 allelic variants of eae were aligned with the command line version of Clustal Omega (clustalo v1. 2. 1)  (Sievers et al. , 2011). As an internal amplification control the E. coli specific beta-D-glucuronidase-encoding gene uidA was used and all 246 uidA sequences present in our set of E. coli genomes were aligned with Clustal Omega (clustalo v1. 2. 1). The alignments were used to create a majority rule-based consensus sequence for each gene with the EMBOSS tool consambig (v6.
Stx-encoding genes were identified as the sole specific STEC biomarkers, thus confirming the results obtained by pipeline A. Similarly, marker genes that distinguish HUS-associated STEC isolates from other clinical and environmental STEC could not be identified by either pipeline (data not shown). Following these results, the distribution of the stx1 and stx2 genes in representative E. coli and STEC strains was determined. Stxencoding genes were detectable in a phylogenetic diverse group of isolates. Additionally, the presence and combination of stx genes was variable even within members of the same serotype or clonal complex (Figure  3 ). Thus, it is not surprising, that except the stx alleles themselves no other discriminatory marker could be detected in the diverse group of STEC strains used for the initial analyses.
Selection of the Most Suitable Marker Genes for the Improvement of PCR-Based STEC Typing Due to the limitation that only three STEC subgroups can be compared with pipeline A at a time, all proteins of an STEC subgroup were used in a custom BLASTp search against all 248 strains excluding the genomes of their specific subgroup 8. Biomarker candidates were discarded as soon as they had any hit in a different O-antigen or MLST subgroup as targeted (Checkpoint 2 in Figure  1  and Table  3 ). For all remaining 85 marker gene candidates primer pairs were designed and checked in silico against the E. coli genome set of the RefSeq and Genbank databases as well as the ECOR and DEC strain collection to validate their specificity (Checkpoint 3 in Figure  1  and Table  3 ). The 54 biomarker candidates with the highest in silico specificity were finally identified for the thirteen STEC subgroups as suitable for in vitro verification.
In four cases it was even below 90% for various reasons. In general, some genomes are poorly annotated in the databases. Additionally, the number of available genome sequences in some groups of reference genomes was quite small (e. g. , O121, ST306, and ST32). Furthermore, certain STEC subgroups exhibit a diverse genomic background (e. g. , CC20). However, all primer pairs that we selected for our study displayed 100% specificity when tested in vitro (Table  4 ). Additionally, we designed consensus primers for the detection of the typical STEC virulence marker genes stx1 and stx2 as well as for eae. As an internal amplification control, we used primers specific for the E. coli beta-D-glucuronidase-encoding gene uidA. Most of these primer pairs exhibit an in silico specificity of more than 96% tested against the previously described 3,087 genomes, except for the stx2 primer pair which cannot detect stx2f and some rare allelic variants of stx2 (Supplementary Table  S4 ).
1 ng template DNA. In contrast, the OneTaq and GoTaq polymerases required 10-100 ng DNA as template to successfully detect most biomarkers (data not shown). In a second experiment different ranges of CFU per PCR reaction were tested from 180,000 CFU/PCR to 1. 8 CFU/PCR. The results of the CFU dilution series corresponded with the DNA dilution series. The Q5 R High-Fidelity DNA polymerase showed the highest sensitivity with a reliable detection limit of about 18 CFU/PCR, when 35 PCR cycles were used. Interestingly, the GoTaq R DNA polymerase failed to detect most marker genes even when high CFU concentrations have been used as template. The OneTaq R DNA polymerase was only able to detect the majority of the biomarkers when a high template concentration (corresponding to 180,000 CFU) was used (data not shown).
Furthermore, the vast majority of database entries represented redundant sequence information of some major STEC serogroups (O157, O26, O111, O145, and O104), whereas only sparse or even single database entries with good sequence quality existed for most minor, but also some major STEC variants (e. g. , O121 and O45) associated with severe disease in humans. Finally, only a relatively small number of WGS data sets (n = 248) was used for our genome comparison. This highlights the limitations of some of the SRA entries and emphasizes the need for regular updates, sufficient sequence quality, and availability of metadata (e. g. , disease type, source of isolate). To initially distinguish between (i) STEC and non-STEC, (ii) different STEC clones or (iii) genomes carrying different stx alleles, we had to download all the E.
coli genome entries and manually detect stx variants, sero-and/or sequence types. Recently, the search for genomes of interest in the SRA has been facilitated by the Bitsliced Genomic Signature Index (BIGSI)  (Bradley et al. , 2018). However, an STEC specific database would be advantageous to remedy these problems  (Franz et al. , 2014). In part this problem is tackled by the GenomeTrakr Database, which aims to collect genomes of four food-borne pathogens (Salmonella, Listeria, E. coli/Shigella, and Campylobacter) together with detailed metadata  (Stevens et al. , 2017). The classification of the downloaded genome sequences was the first step toward a systematic and unbiased screening of whole genome sequence data for discriminatory STEC marker genes. We not only considered STEC genome plasticity by including multiple genomes of representatives of the different STEC subgroups and phylogenetic lineages for an unbiased definition of STEC markers.
Additionally, we used two pipelines to analyze the HTS data. Pipeline A performed a protein-byprotein bidirectional comparison, whereas pipeline B defined the STEC core proteome and compared this against the non-STEC pan proteome. Different outcomes of both pipelines depend mainly on two factors: First, pipeline A is based on ORF finding by Prokka, which may differ from the ORF definition of the NCBI annotated Refseq and Genbank entries used in pipeline B. Second, subgroup-specific analysis in pipeline A includes an all-against-all comparison of all proteins found in up to four subgroups compared, whereas pipeline B requires definition of an STEC core proteome based on a reference strain prior to comparison with the pan proteome of another group, here all non-STEC strains. Because of this, pipeline A may be more suitable for the identification of markers in rare STEC variants with a less congruent genome content relative to the reference strain.
Development of a Multiplex PCR for Rapid Typing of Clinically Relevant STEC Based on our in silico analysis of large genome sets available from publicly databases and the PCR-based screening of a large number of well-characterized clinical STEC isolates, we have identified marker combinations, which allow a reliable differentiation of the priority STEC variants described above (Table  5 ). The comparative genomic analysis of a larger panel of genomes also enabled us to improve the specificity and performance of published uidA-specific primer pairs (data not shown). To verify our set of marker genes, we developed a multiplex screening PCR to identify different STEC strains (Figure  6 ). We tested the analytical sensitivity of the four primer pools with pure cultures or purified genomic DNA of clinical isolates and three different DNA polymerases.
Whole genome sequencing is becoming the state-of-the-art technology for typing of microbial isolates cultivatable as a pure culture. In routine food microbiology, however, where often more complex samples and different food matrices have to be analyzed, use of whole genome sequence-based typing is still under development. Advanced bioinformatic analyses of HTS data sets retrieved from composite bacterial cultures have to be established to enable meaningful genome analysis and bacterial typing of mixed cultures. Until then, multiplexed DNAbased approaches offer advantages for monitoring throughout food production chains in terms of practicability and on-site usage and costs. Thus, future work will have to focus on the use of the identified biomarkers with on-site detection methods, like LAMP-assays in combination with lab-ona-chip-based as well as with nanofluidics-based screening technologies to improve and facilitate the detection of STEC in food.
ACKNOWLEDGMENTS We thank  O. Mantel  (Münster) for technical support and  B. Spilsberg  (Oslo),  G. Johannessen  (Oslo), and  A. Holst-Jensen  (Oslo) for helpful discussion of the project and critical reading of the manuscript. This publication and all its contents reflect the views of the authors only, and the  European Commission  cannot be held responsible for any use which may be made of the information contained herein. The data reported in this study appear in the Ph. D. thesis of MK. Support by the  Münster Graduate School of Evolution (MGSE)  to MK is gratefully acknowledged.
Computational algorithms of adaptive learning We implemented, fitted, tested, and simulated six learning algorithms, including model-free RL and model-based DL algorithms with ad-hoc parameters (Table  1 ). The dataset consisted of the aggregated sequence of all trials played by the 27 subjects with the same prerecorded opponents. The null algorithm, consisting of assigning uniform probability to all outcomes, was run as benchmark. The important parameters were the learning rate (a measure of how much weight was given to recent feedback with respect to older feedback) and the randomness of choice, embodied in the inverse temperature of the softmax function (a measure of degree of action selection randomness) for RL algorithms, and in the dispersion parameters for DL algorithms. The dispersion parameters could be specific to the upper or lower side of the preferred bid, and to the previous trial outcome contingency.
This is a problem for algorithm fitting, and in particular estimating 101 initial actionvalues depletes all useful degrees of freedom during optimization. Therefore, either we simplified the initial action-values using a three-parameter (as opposed to 101) Beta distribution or else we simply used the first round bids as initial conditions. Data were processed with code written in Python with the scientific computing packages Numpy (RRID:SCR_008633), Scipy (RRID:SCR_008058), Matplotlib (RRID:SCR_008624), and Pandas. Purpose-specific code was written to define the maximum likelihood functions used to estimate the parameters of the learning algorithms. The numerical optimizer employed was a bound-constrained version of the Broyden-Fletcher-Goldfarb-Shannon algorithm, a local search technique which approximates local curvature.
fMRI data collection and analysis Data acquisition. The fMRI data were obtained using ascending interleaved slice acquisition with gradient echo T2 * -weighted echo-planar imaging (EPI) sequence in a 3T Magnetom Verio equipped with a 32-channel head coil (Siemens; Erlangen, Germany). Scanning protocol parameters were as follows: TE=30 ms; flip angle=80°; TR=2280 m; slice thickness=3 mm; no gap; slice matrix=64x64; number of axial slices=35; FoV=192 mm; Voxel resolution=3x3x3. 7 mm. High-resolution structural MRI data acquisition used a T 1 -weighted MP-RAGE sequence. Parameters were as follows: TE=2. 47 ms; flip angle=9°; TR=1900 ms; slice thickness=0. 5 mm; slice matrix=512x512x176; number of slices=176; FoV=256 mm; Voxel resolution=0. 508x0. 508x1 mm. These data were used for anatomical localization.
All clusters from all figures are listed in Tables  2, 3 , and 4. We also investigated correlations between neural data and model proxy variables to localize potential brain regions involved in the computation of the economic transactions on a trial-by-trial basis. We derived a time series of expected value and prediction error signals from each of the bidding algorithms by simulating bidding agents with each algorithm and pitting them against the same sequences of stimuli that the human subjects played against. The dataset comprising all the sequences for all subjects was used. Then, we fitted fMRI data to the learning algorithm variables informed by plausible assumptions on the strategies used by players to maximize profit, and we selected the best algorithms based on BIC scores.
19 We standardized all algorithm proxy variables (PBV, pseudo-RPE, DS) as z-scores across subjects before entering them as parametric regressors in the design matrix. In the group-level analysis, we used this analysis to link between-subject differences to significant activations  (Haruno et al. , 2004). Finally, a neural model comparison routine based on a SPM Bayesian model selection module was performed on anatomical ROIs encompassing striatum and inferior posterior parietal cortex. To assess the goodness of fit of both DL and RL algorithms to neural activity, we defined GLMs in OUTCOME, including either DS or RPE parametric modulators, respectively, and then estimated them using Bayesian statistics, which provided a measure of the evidence of the model for each subject. Log evidence was then fed to a BMS random-effect analysis  (Palminteri et al.
In order to test our hunch that DL is used to learn bids in our task, we proceeded to test which DL or (and) RL algorithms could best explain the observed behavior. Adaptive learning algorithm fits and model selection. We fitted six adaptive learning algorithms to the behavioral data. All DL algorithms fitted better than RL algorithms (Figure  2A ). As we expected, the RL algorithms failed to explain the bid evolution in all market types. We believe this was in part due to a difference in the efficiency of the two algorithm classes. The RL algorithms require a large dataset to learn action values to the point where they start being operationally useful. Since our subjects learned to bid successfully in the limited number of played trials, we argue that DL is the more efficient and appropriate learning strategy for the task we consider.