dataesr/scientific-paragraphs-categorization huggingface dataset:
These are annotated as related forms so that the bioactivity data can be seen aggregated in the database. Cyanocobalamin is a cobalt-containing compound which is recorded as a parent and three different isotopes in the database (CHEMBL2110563, CHEMBL2104118, CHEMBL2104381 and CHEMBL2096655). Again, the GetParent module enables their data aggregation. Organometallic compounds do not however have salts removed due to the complexity of how they are often represented in the deposited molfile. For example, this is often achieved by drawing them as disconnected components as is the case for transplatin (CHEMBL1386) which was deposited into the database as N. N. The list of salts used in ChEMBL is based on the USAN Council's list of pharmacological salts  [33]. Additional entities have been added to this list where a significant number of examples have been present in ChEMBL datasets.
The GetParent module will remove salts regardless of: (i) the charge status (e. g. acetic acid or acetate, Clor HCl); (ii) whether or not stereochemistry is depicted (e. g. tartaric acid); (iii) cis/trans isomers (e. g. maleic and fumaric acid). The salts and solvents files are available in the GitHub repository  [28]. Currently, these files contain 162 salts and 9 solvents respectively. This list will be maintained and extended if additional salts and solvents are identified. For the avoidance of any doubt, although parents, salts, solvents, isotopes and mixtures are all identified using the process just described, the bioactivity data recorded in ChEMBL is registered against the form it was measured on. The aggregation by parent structure is undertaken to make it easier to identify all the data for salts and isotopes of a common parent.
For example, paroxetine (CHEMBL490) has bioactivity data determined for the parent molecule, two salts, one salt/solvent mixture and two different isotopes as well as there being an additional salt registered as an FDA approved drug. Another example is amphetamine (CHEMBL405), which has bioactivity data in ChEMBL for eight different salts in addition to the parent amphetamine and an additional two salts that are recorded in drug sources such as the FDA orange book  [34]. The parent aggregation process makes this data easily identified and grouped. This is illustrated in Fig. 1  for these two compounds.
Parent Other Forms Fig. 1  Examples of the multicomponent forms of paroxetine and amphetamine and how they have been aggregated by use of the GetParent component in the new Standardizer and GetParent modules were largely based on the original ChEMBL rules developed over the last 10 years it is difficult to unambiguously quantify this improvement. To provide a consistent starting point, therefore, the current 1. 9 million compounds in the latest release of ChEMBL (ChEMBL 26) have been completely re-standardised and the parents regenerated for the whole database using the new pipeline.
Checker component To investigate the utility of the Checker module three very different compound sets were used. These were: a set of compounds recently extracted (by manual drawing) from the medicinal chemistry literature by the ChEMBL data extractors; a set of compounds from SureChEMBL, the patent database where the chemical structures are obtained by entity recognition followed by name or image to structure conversion  [37]  and a randomly selected compound set from PubChem (SIDs) as initially submitted by the depositors and prior to any compound standardisation. These three datasets are provided as structure data files (sd files) in the Supplementary Information S1,.
The difference between these sets is not surprising given the differences in the data sources, confirming that the Checker is suitable for identifying a range of different issues of differing severity for datasets of diverse origin. A breakdown of the number of compounds from each set which returned penalty scores is shown in Table  3. When more than one score is returned for a compound the highest score is reported. It is worth noting that 75% of the ChEMBL literature set compounds and 60% of both the SureChEMBL and PubChem compound sets showed no issues in the Checker tests. When structures with a penalty score of 2 associated with InChI Warnings such as "omitted undefined stereo" and "InChI: Charges were rearranged" are excluded (as described above) then the proportion of compounds with undesirable features that would benefit from correction falls to between 4 and 11%.
From a ChEMBL perspective the compounds in the latest released version of the database (ChEMBL 26) have also been subjected to the Checker analysis and this has enabled the identification of small sets of compounds with particular problems that were not previously identified. This dataset is available as an sd file on the ChEMBL FTP site  [38]. These have been prioritised for curation for a subsequent ChEMBL release. The numbers of compounds with penalty scores 5 and 6 are summarised in Table  4. Those considered to be the highest priority for curation are compounds with a penalty score of 6 (69 compounds). In future the structures of molecules with penalty scores of 6 will not be added to the database until they have been manually checked and corrected. The ~ 2500 compounds with penalty scores of 5 are placed in a group for second-tier curation with the following penalty annotations being prioritised: • molecule has a crossed bond in a ring (632 compounds).
Standardizer component Examples of applying the Standardizer to some "ChEMBL-like" molecules are shown in Fig. 2. These examples show some of the specific standardisations described in the methods section for which the Standardizer component was designed to correct. Also, as described above, there will be differences between the last two versions of ChEMBL (versions 25 and 26) due to the re-standardisation as well as the manual curation that took place between the two releases. Comparing the structures in these two releases does not give any useful statistics on the effectiveness of the new Standardizer. used by the PubChem database  [39]. However, as the PubChem PUG service was slow to run via their API, only a small set of compounds were used for this comparison and comprised approved small molecule drugs as annotated in ChEMBL 25 (3071 compounds).
This dataset is provided as an sd file in Supplementary Information S4. The ChEMBL 25 structures for these were taken and standardised using the new ChEMBL RDKit Standardizer and also the PubChem standardiser. In total there were 97 compounds that gave different Standard InChIKeys after the respective standardisations. The key difference is that the PubChem standardiser generates a canonical tautomer as part of the process but ChEMBL does not. In many instances this is not reflected by a change to the Standard InChI but for some molecules the InChI does change. There are also some differences in the double bond representation (cis or trans (either)). Furthermore, PubChem generates all structures with explicit hydrogens which are not present in the ChEMBL structures. Some of these examples are shown in Fig.
GetParent component The GetParent module was used to obtain the parent structures for the standardised structures from ChEMBL where these contained more than one component or an isotope. This resulted in over 100,000 compounds where a salt, solvent, isotope or combination thereof could be removed to create a parent structure. Molfiles that are a mixture of components can contain a number of different combinations such as: a parent and its salts; a parent, its salts and solvent; a combination of only salts; or a true mixture. A true mixture, for example, would include compounds such as Co-trimoxazole (CHEMBL58061) which is a marketed product comprising trimethoprim and sulfamethoxazole and which has been tested as a combination in a number of bioactivity assays. Additionally, any of these multicomponent compounds may have been tested for bioactivity or used in a clinical setting as a specific isotopic form.
Another important function of the Get-Parent module is that if a compound is a 2:1 complex with two parent molecules and one salt molecule, when the GetParent module is applied it can recognise that the two components remaining after salt removal are identical and so only one molecule is returned as the Fig. 6  Examples of applying the GetParent module to some representative ChEMBL compounds containing varying combinations of salts, isotopes and solvents. The "Child" is the compound before and "Parent" the compound after the process has been applied parent. Atorvastatin calcium is an example of this as can be seen in Fig. 6.
Future work Now that the components of the curation pipeline are being used in a production context for curation of compounds being added to the ChEMBL database, it will provide opportunities to review and refine any of the rules on the basis of a large consistently standardised dataset. The ChEMBL group will continue to evolve the curation pipeline as further needs are identified. The code is also now freely available to the community and other researchers are encouraged to suggest modifications so that the curation pipeline can improve further over time. Comments and issues can be added to the issues section of the ChEMBL Structure Pipeline GitHub repository  [28].
Conclusion The three components of the structure curation pipeline described here have been developed as an open source project and are now available for researchers to use and adapt for their own applications. These components have been used by the ChEMBL group to produce the chemical structures in the latest release of the ChEMBL database (ChEMBL26). This has resulted in the correction of certain errors in standardisation that were present in previous ChEMBL releases and the identification of other issues that can now be prioritised for future manual curation. Additionally, the pipeline has been tested on less well curated datasets and demonstrated to be sufficiently robust to be used in the automatic structure checking and standardisation of such datasets. A comparison between the standardiser used by PubChem (another large publicly available database) and the one developed here gave only a small percentage of compounds with non-identical structures (defined as having different Standard InChIs).
Instead, to investigate this, the three sets of molecules previously described have been standardised by the new Standardizer module. The critical differences are where the new standardisation results in a different InChI from the one obtained prior to standardisation. The percentage of molecules with changed InChIs are low for all datasets and always < 2% in each case. The majority of the changes occur where the Standardizer has neutralised a previously charged molecule, for example by removing a proton from a positively charged nitrogen or protonating a carboxyl group. A summary.
Availability of data and materials The datasets used for the analyses are provided as sd files in Supplementary Files. S1_surechembl_set, S2_pubchem_set, S3_chembl_literature_set, S4_approved_drug_set and are available from the ChEMBL FTP site: ftp://ftp. ebi. ac. uk/pub/datab ases/chemb l/stand ardis er_data_sets.
FE Simulations The Ogden constitutive equation was first uploaded into the finite element (FE) library of the code ANSYS (ANSYS 13. 0), the utilized advanced commercial FEM-based code (Ansys 13. 0 user's manual, 2009), where a cylindrical geometry was utilized to describe both aorta and PA roots. Thickness, height, and diameter of both aorta and PA conduits were taken from the average undeformed states directly measured on the samples. To ensure accurate results, a hexahedral regular mesh was generated, each finite element being associated with eight nodes with three translational degrees of freedom. In particular, an ad-hoc custom made Ansys procedure, written in APDL parametric language, was developed to reconstruct the FE model of the whole system constituted by the aorta tract connected with the pulmonary autograft by means of the suture region.
e. to answer the questions: what assets should be made available for an integrated experimentation or learning experience? how can the business view be integrated with conceptual and engineering views in a modular environment -deployed as either a research or didactic laboratory? In response to this challenge, a Smart Innovation Environment is hereby presented. The environment is designed and tested in the collaboration network of the Open Models Laboratory (OMiLAB)  [2]  -a digital ecosystem previously presented as a socio-technical ecosystem with a focal point on conceptualisation processes in  [3]. This paper isolates one key asset of the ecosystem -the Smart Innovation Environment for Digital Engineers, an orchestration of software/hardware resources and workspace configurations articulated in an experimental setup to serve either as a didactic or a research laboratory in OMiLAB "nodes" being set up in partner universities or companies.
The Digital Design Thinking workspace This component supports problem-solving or product development teams that need to perform a participatory analysis of business requirements while co-creating innovative digital business models. The popular approach of Design Thinking  [16]  captures results in the form of innovation boards where goals, stakeholders, processes and product-service characteristics are pinned, grouped, colour-coded. In more advanced approaches, graphic figurines can be employed for storyboarding -e. g. SAP Scenes  [17]. The hereby proposed environment supports this practice with a toolkit for the digitisation of Design Thinking results -i. e. scene boards are captured by a Webcam and QR code software and transferred in the Scene2Model modelling environment  [18, 19]  where further annotation and machinereadable structuring can be performed.
The Conceptual Modelling workspace This is the central stage of the entire innovation environment, where co-created ideas are refined into "Smart Models" -i. e. conceptual models that are both humanreadable and machine-readable, acting as a semantic and interactive representation of Digital Twins, further enabling model-driven engineering or interoperability with cyber-physical systems in the experimentation workspace. To enable full control on Digital Twin design and an open-endedness of the domain-specificity, the provided toolkit supports both modelling and metamodelling: • the out-of-the-box BEE-UP tool offers a hybridisation of modelling languages for both engineering and business analysis needs: UML, BPMN, EPC, ER, Petri Nets, Flowcharts and DMN -semantic links across these languages enable a multilayered description of a System of Systems with adequate granularity; • the BEE-UP tool can be extended with richer semantics, additional model types or functionality that may be required for a selected domain/purpose; or, novel modelling methods may be developed from scratch with the help of the Agile Modelling Method Engineering methodology  [20] , applied in tandem with the ADOxx metamodelling platform  [11]  and the CoChaCo tool for managing the domain analysis and modelling method requirements  [9].
A gene regulatory network built using the BRANE Cut software reveals three sub-networks related to iq a positive correlation between lactose concentration and cellulase production, iiq a particular dependence of the lactose onto the β-glucosidase regulation and iiiq a negative regulation of the development process and growth. Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1 , clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains.
RNA-seq library preparation and analysis Library preparation and RNA-seq data acquisition Library preparation and Illumina sequencing were performed at the École normale supérieure Genomic Platform (Paris, France). Messenger (polyA+) RNAs were purified from 1 µg of total RNA using oligo(dT). Libraries were prepared using the strand specific RNA-seq library preparation TruSeq Stranded mRNA kit (Illumina). Libraries were multiplexed by 6 ¢6 flowcell lanes. A 50 bp read sequencing was performed on a HiSeq 1500 device (Illumina). An average of 35 ¨10 millions passing Illumina quality filter reads was obtained for each of the 36 samples. The analysis were performed using the Eoulsan pipeline  [79] , including read filtering, mapping, alignment filtering, read quantification, normalisation and differential analysis.
As a result, the final matrix contains 593 genes, where for each gene the expression profile contains 32 components. This procedure allows us to deal with expression profiles having a sufficient number of components to obtain a more reliable inferred network. Clustering and functional analysis of differentially expressed genes Clustering As previously mentioned, clustering is performed on the 650 genes. Each gene is characterized by its ten-component expression profile. The following approach was completely performed using the Multi Experiment Viewer (MeV) software  [85]. Firstly, a hierarchical clustering allows us to estimate the optimal number K of clusters hidden in the data. By choosing the Euclidean distance metric and the average linkage method, results suggest K 5 clusters. Then, the K-means algorithm (originating in  [86] ) is preferred in order to obtain a final gene classification.
Promoter analysis The promoter analysis was performed using the Regulatory Sequence Analysis Tools (RSAT) software  [63]. From each set of genes to study (linked to a specific TF), promoter sequences from ¡1 to ¡1000 upstream bases are retrieved using the retrieve sequence tool. From these sequences, a detection of over-represented oligonucleotides was performed thanks to the oligo-analysis tool. We used the reference sequence set of Trichoderma reesei as background model. As mentioned in  [89] , this choice is driven by the fact that the input sequences (the query) are a subset of a larger collection (the reference). As a result, we obtain a list of over-expressed oligonucleotides (from hexa-to octo-) and several larger motifs assembled from the previous ones using the pattern assembly tool.
CPG Model The model was developed using the Codyn framework and exported to C code to run on the robot microcontrollers. Only the 25 most rostral axial segments (each comprising 1 left and 1 right hemisegments) are modeled out of the 40 segments that salamanders typically have (for review see,  Chevallier et al. , 2008). Four additional oscillators control the limbs. The 25 axial segments control the active part of the robot. The caudal half of the robot tail is a passive, flexible caudal fin (Figure  1A ). Each axial hemisegmental oscillator and each limb oscillator is modeled as a phase oscillator with controllable amplitude, and the connections between oscillators are functions of the phase difference between sender and receiver: θi = 2πν i + j r j w ij sin θ j -θ i -ϕ ij - s i r i sin θ i ṙi = a (R i -r i ) + s i cos θ i x i = r i (1 + cos θ i ) ν i = d i e i R i = d i P d i , d th i A positive output x i (which determines the muscle activation) is calculated from the instantaneous phase θ i and amplitude r i.
Mechanical Simulation 3D simulations of the robot were performed using the Webots 6 software (Cyberbotics, Switzerland), which is based on the Open Dynamics Engine (ODE, www. ode. org). The physics engine was extended with a hydrodynamics model that includes reactive and resistive forces  (Porez et al. , 2014). The passive tail fin was modeled as a chain of 10 small segments with passive stiffness. The physics was simulated with a time step of 0. 5 ms. The robot controller used a time step of 1 ms, the minimum value supported by Webots. This was just too high for a stable simulation of the muscles, so the physics plugin was used to implement the muscle model and set the joint torques.
Data Processing The joint angles from simulations and robot experiments were calculated in Matlab by fitting the kinematic chain of the robot to the positions of the LEDs. The CPG and kinematic intersegmental phase lags were calculated in Matlab from the CPG output and joint angle oscillations using the same algorithm: The timing of each cycle was defined as the centroid of the positive part of each oscillation  (Knüsel et al. , 2013). These timings were used to calculate a median phase lag (over time) for each pair of consecutive segments (CPG lag) or consecutive joints (kinematic lag). The CPG intersegmental phase lag was calculated using the average of the median phase lags between segments 8 to 12. For simulations, the kinematic intersegmental phase lag was calculated using the average of the median phase lags between joints 3 to 5.
Pfirrmann and Modified Pfirrmann disc grades were determined by an experienced consultant radiologist, blinded to the demographics and clinical data of participants. LDD sum scores were calculated through the summation of Pfirrmann scores from each lumbar disc  [12]. Four groups were identified based upon the presence or absence of pain and LDD (modified Pfirrmann grade ≥ 6). Pain was determined by the presence or absence of recurrent LBP for greater than 3 months. Self-reported clinical outcome tools including the Short Form 36, Version 2 (SF-36), the Oswestry Disability Index (ODI) and the Hospital Anxiety and Depression Scale (HADS) were used to assess quality of life, function and anxiety and depression, respectively.
Statistical shape modelling (SSM) SSM is an image processing method designed to characterise the shape of an object within a series of image  [8]. It uses principal components analysis to identify different 'modes' of variation (principal components) in sagittal spinal shape and computes mode scores (output variables) for each image to quantify that variation by calculating deviation from the mean of all the images  [10, 11, 13]. Seventy consistent mid-sagittal slices (maximum spinal canal width) were selected from the participant's data using Image J software and exported as Bitmap image files. A previously tested 168-point lumbar spine template  [10, 14]  which defined the outline of each vertebral body from the 1st lumbar to 1st sacral vertebrae (L1-S1) was recreated in custom SSM software (Shape software, University of Aberdeen) and images uploaded for analysis.
Statistical analysis Statistical analysis was undertaken using SPSS software (Version 24, IBM SPSS statistics, IBM Corp. ). The normality of the data was assessed using frequency histograms, quantile-quantile plots and the Shapiro-Wilk test. To assess preliminary trends in the data, associations between LDD (LDD sum scores), age, BMI and intrinsic shape (mode scores) were established using Pearson correlations. Levene's Test confirmed homogeneity of variance. One-way repeated-measures analysis of variance (ANOVA) was used to assess the differences between the LDD groups with Games Howell (for samples with unequal variance) or Hochberg's T2 (for samples with equal variance but unequal sample sizes) post-hoc comparisons. In the case of unequal variance Welch's corrected F-ratio was reported.