dataesr/scientific-paragraphs-categorization huggingface dataset:
Semantic Web data language (e. g. RDF) is suitable to represent structured data of MADB  (Rivault et al. , 2019). Moreover, linking raw data to standard medical taxonomies is interesting to enrich the description of cares with formalized expert knowledge (for instance, ICD-10 1 for diagnosis or ATC 2 for drugs). Once care trajectories have been represented in standard Semantic Web format, SPARQL query engines can be used to enumerate all situations that match a query. A query can be seen as a phenotype. However, if the expressiveness of SPARQL is interesting to specify complex care trajectories as a phenotype, the drawback is their com-putation time. In the following, we assume the reader to be familiar with RDF and SPARQL, but a thorough introduction to semantic web can be found in  (Hitzler et al.
, 2009). The example of VTE illustrates that such query may be a complex arrangement of cares in a patient care trajectory. These arrangement involve temporal relations between events (quantitative delays). Thus, we are interested in specifying complex temporal patterns that may occur in care trajectories. Taking into account numerical filters in metric temporal constraints is not efficient in SPARQL queries, and we can not expect to achieve reasonable computational efficiency on large MADB. This article addresses the problem of enumerating the occurrences of a complex temporal pattern in a dataset of care trajectories. We focus on a class of temporal patterns called chronicles and propose a template of SPARQL query that is both expressive and efficient. A chronicle is an expressive temporal pattern.
Related Work In this section, we review some approaches to enumerate occurrences of temporal patterns in sequences of events and their connection to Semantic Web. Temporal databases and querying tools (Snodgrass and llsoo Ahn, 1986) address a part of the problem by extending the notion of database to timestamped data. They cover data representation problems but also specific querying language problems. This family encompasses the temporal extension of relational databases (e. g. TSQL) but also Semantic Web approaches which combine query language (SPARQL) extended to temporal data with Allen's relations  (Wang et al. , 2010). These approaches defines relative temporal constraints between intervals which are not relevant for the MADB query problems  (Pacaci et al. , 2018). Rivault et al. (Rivault et al.
, 2019)  used RDF to represent care trajectories and shown that querying care trajectories can be achieved with the SPARQL query language. Semantic Web is a relevant approach for our problem: it does not explicitly address the problem of timed queries, but it is relevant to deal with data representation and taxonomies querying. RDF also enables ontology management with OWL based on the Description Logic (DL)  (Baader et al. , 2003)  allowing ontology-mediated query answering (OMQA)  (Bienvenu, 2016). For instance, O'Connor et al. (O'Connor et al. , 2009)  developed a tool based on OWL for research data management with a temporal reasoning in a clinical trial system. This aspect could be added to the presented method. Some approaches proposed to extend RDF/SPARQL with temporal queries in a generic way.
Thus, all ε i , i ∈ [1, n] are distinct. The chronicle of Fig. 1  occurs in sequences s 1 and s 6 of the dataset in Table  1. For instance, {(A01AA01, 1), (B01AA01, 3), (C01AA01, 5), (C02AC01, 6)} is an occurrence of C in s 1. This occurrence is the subsequence of s 1 with indices 1, 2, 4, 5. The chronicle does not occur in s 2 neither in s 4 because of unsatisfied temporal constraints. It does not occur in s 5 as there is only one event with a type of class C in the sequence and the chronicle requires two different events. It does not occur in s 3 because there is not an event in the subgroup of A01.
Semantic Web for Chronicle Recognition Semantic Web is a framework designed to represent, share and manipulate structured data. The keystones of Semantic Web are (i) formal data representations, such as the RDF language, and (ii) query languages, such as SPARQL. Semantic Web is particularly suitable to represent taxonomies. Semantic Web is suitable to represent sequences with label events belonging to taxonomies and to encode chronicle enumeration with SPARQL. So, we propose to represent sequences in RDF and to encode a chronicle enumeration in SPARQL. We propose two approaches for chronicle enumeration: the first approach fully uses Semantic Web technologies; the second approach is an hybrid tool combining SPARQL query and a dedicated algorithm.
Sequence Representation in RDF Sequences are represented in a RDF-Graph (Fig. 2 ). We remind that our concrete objective is to query a dataset of sequences, where each patient care trajectory is represented as a sequence. ?evt4 seq:evtDate ?date4. ?evt4 pseq:evtlabel ?atc4. ?atc4 rdfs:subClassOf* atc:C. FILTER ( ?date2 -?date1 >= -1) FILTER ( ?date2 -?date1 <= 3) FILTER ( ?date3 -?date1 >= -3) FILTER ( ?date3 -?date1 <= 5) FILTER ( ?date3 -?date2 >= -2) FILTER ( ?date3 -?date2 <= 2) FILTER ( ?date4 -?date3 >= 1) FILTER ( ?date4 -?date3 <= 3) } In RDF, each event (e j ,t j ) in a sequence s i is encoded by three tuples: • seq:sequencei seq:hasEvent seq:sequenceievtj denotes existence of an event e j in sequence s i • seq:sequenceievtj seq:eventLabel atc:lk denotes event e j has the label l k.
Synthetic datasets generation and plan of experiments Several synthetic datasets have been generated. Each dataset contains a set of sequences where event labels are randomly chosen at the lowest level of ATC taxonomy. The ATC taxonomy contains 1 900 classes. In addition, occurrences of ten 15-sized chronicles 3 are embedded in the dataset. For each chronicle, a constraint is generated for each pair of events without inconsistency between the temporal constraints. 3 A n-sized-chronicle denotes a chronicle of size n. The synthetic dataset generation process ensures that each chronicle occurs in about 20% of the sequences. Chronicles contain event labels from several levels of ATC following this probability: 1 15 level 1 (ex: N), 2 15 level 2 (ex: N02), 3 15 level 3 (ex: N02B), 3 15 level 4 (ex: N02BE), 6 15 level 5 (ex: N02BE01).
We introduce the notation D ns,ne to denote a synthetic dataset with ns sequences and ne care events per sequence (all sequences have the same number of events). For the following experiments, 25 synthetic datasets have been generated where ns ∈ {1 000, 5 000, 10 000, 15 000, 20 000} and ne ∈ {100, 200, 300, 400, 500}. Each dataset is encoded in RDF. The ATC taxonomy is attached to the dataset.
Experiments and Results The following experiments evaluate the impact of two main parameters on execution times of SPARQL and HYCOR: the size of the dataset (number of sequences and number of events per sequence) and the chronicle size. Fig. 6  compares the execution times of SPARQL and HYCOR with respect to the length of the sequences. It shows that HYCOR is at least one order of magnitude more efficient than pure SPARQL. 6 shows that SPARQL does not scale up for datasets containing more than 50 000 sequences. The HYCOR SPARQL query language does not have the same limitation. Indeed, the pure SPARQL query uses filters to deal with temporal constraints on each admissible event while SPARQL HYCOR query only uses values to find admissible event. So, the use of filters on a large scale of admissible events seems to be inefficient in SPARQL for this kind of use.
Fig. 8  presents execution time of HYCOR wrt chronicle size. HYCOR is run on a unique dataset (ns = 10 000 and ne = 100) and seven sets of 10 chronicles with sizes 2 to 14. We observe execution time linearly increases with the chronicle size. HYCOR outperforms pure-SPARQL for the chronicle enumeration task. Its execution time increases with the chronicle sizes and the dataset size, but it still offers impressive results for large datasets. 7 Use case on the SNDS to find patients with thromboembolism Our use case proposes to find patients diagnosed with venous thromboembolism (VTE) in the SNDS. The SNDS is the french national health insurance database, which covers most of the french population (above 65 million inhabitants). The advantage of this database is to gather information about most the reimbursed medical events, from drug deliveries to nurse home cares, specialist consultations, etc.
The range of medical events that are recorded in the database makes it suitable to conduct a wide variety of health studies  (Tuppin et al. , 2017). However, SNDS has been designed for administrative purposes (care reimbursements) and does not contain detailed medical information such as medical reports, laboratory results or diagnosis. This use case uses a geographical-based SNDS subset (the north western French Brittany population) which contains 377 359 individuals. For the use case application, rdf:type have been added in the RDF event triples to speed up access to event labels. We used five different types: DrugDeliveries (e. g. , drug deliveries from pharmacy), Cares (e. g. , nurse assistance, domestic assistance), Medical acts (e. g. , radiology, surgery), Biologies (e. g. , blood withdrawal), Hospitalisations.
Conclusion In this article, we extended the model of chronicle with taxonomies to enumerate complex temporal patterns in sequences. This problem is motivated by the need for phenotyping patients in medicoadministrative databases. We proposed HYCOR, an hybrid approach that combines the expressiveness of SPARQL and the efficiency of a dedicated algorithm. The results show that HYCOR is one order of magnitude faster than pure SPARQL queries on both synthetic and real dataset. As a perspective, chronicles should be extended with negation to denote the absence of event. Furthermore, it could be interesting to compare the efficiency of different SPARQL engines.
The clinical features and the tests performed in the present cohort to diagnose most frequent types of PID according to their category are described in the Supplementary material, which is available online. Among the disease causing (pathogenic) variants, 5 variants out of the 25 found are novel, and the remaining (20/25) have been reported previously in the literature. The variants, p. (Leu482Arg) and p. (Trp563GlyfsTer6) of BTK, p. (Gln1263Ter) of LYST, p. (Tyr186Ter) of RORC and p. (Met329Lys) of STAT3 are novel variants. These variants are not present in the gnomAD population database and are predicted to be disease causing by pathogenicity prediction software. In terms of treatment, only two patients (severe combined immunodeficiency and Wiskott-Aldrich syndrome) underwent hematopoietic stem cell transplantation (HSCT).
Acknowledgements The authors acknowledge  Dr Anne Puel ,  Génétique Humaine des Maladies Infectieuses/Human Genetics of Infectious Diseases INSERM UMR 1163 ,  Université de Paris, Institut Imagine ,  Pièce 421-B1 24  boulevard du Montparnasse, 75015 Paris, France who helped us with genetic testing and comments on this manuscript. Authors' Contributions HPL conceived the study, analyzed the data, and drafted the manuscript. MM, AD, MG, JB, MS, AR, PB, SL, SW, GSLB and KMG performed immunological and genetic tests. FM, KGB, SR, NK helped in data collection. GSLB and KMG reviewed and helped in drafting and editing the manuscript. All authors have read and approved the final version of the manuscript. HPL is the guarantor for this paper.
The authors identified diverse predicted cooperative genetic interactions supporting tumour aggressiveness or invasion. For instance, since FGFR3 and PI3K mutations co-occur in the literature, the authors explored the difference in proliferation for the three mutants in the model: overexpression of PI3K alone, of FGFR3 alone and of both PI3K and FGFR3. They did not find cooperation between both mutants in uncontrolled growth. Instead it appeared that a third mutation, the deletion of CDKN2A, was necessary to unquestionably increase proliferation. Accordingly, co-occurring FGFR3 activating mutations and CDKN2A deletions have been reported in the literature and several datasets. In addition, the model of Remy et al. suggests that to obtain a significant increase in proliferation in the context of a FGFR3 GoF and CDKN2A LoF mutations, a third (activating) mutation in p21CIP is required.
These predictions were supported by TCGA datasets. With this model, the authors could also predict the temporal order of gene alterations favouring proliferation. For instance, the co-occurrence of E2F2 amplification and p53 LoF would be beneficial for the tumour cell when mutations in p53 appear first. More recently, Rossato et al. built a Boolean model of TGFB signalling to identify cooperating mutations that would explain the dual role of TGFB signalling in promoting cell cycle arrest or apoptosis at early Another hallmark of tumour cells, which has motivated a substantial number of logical models is the Epithelial to Mesenchymal Transition (EMT), a representative example of cancer cell plasticity  [36]. A model of EMT driven by TGFB was published by Steinway et al. [37]. Model prediction and experimental validations in hepatocellular carcinoma cell lines indicates that the concomitant activation of the WNT and SHH signalling are required to induce a mesenchymal phenotype downstream of TGFB.
So far, most models have been defined on the basis of extensive literature reviews, and/or from information on interactions stored in dedicated databases. These models are somewhat "generic" as they intend to represent an "average cell", and thus do not account for tumours and patient heterogeneity. Hence, software tools such as CellNOptR  [32, 43]  and PRUNET  [44] , have been defined to contextualise models using high-throughput data, leading to cell type specific models  ([44]  provides and overview of these tools). To confront logical models to clinical data, Béal et al. recently proposed an approach integrating mutation data, copy number alterations, transcriptomic and proteomic data to models  [31]. Finally, it is important to note the coordinated effort in the computational biology community to establish a standard format to store and exchange models  [45] , to define computational methods handling ever larger models, as well as to develop several software tools available to the community (e.
These models could be implemented in clinical settings for personalised medicine, as predictors of effective therapeutic regimens  [71]. Genomic, transcriptomic and proteomic datasets are being obtained from patient tumour biopsies. These data can be used to train logical models in order to recapitulate tumour behaviours and to predict molecular targets reverting detrimental phenotypes to non-cancer ones. These predictions can subsequently be tested on organoids derived from the same tumour material before clinical practice (Figure  4 ).
LPMOs are widespread and have been discovered in all kingdoms of life. Based on their protein sequences, these enzymes are classified into seven distinct families in the CAZy database of carbohydrate-active enzymes (www. cazy. org)auxiliary activity (AA) families AA9 to AA11 and AA13 to AA16  [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]. Much of the research on LPMOs has focussed on understanding how these enzymes bring about the observed boosting of cellulase activity. The molecular basis for this is thought to result from their ability to introduce chain breaks into the crystalline regions of cellulose, thereby introducing new points at which cellulases can act  ([7,9,18-21]  and see  [22]  for a recent review). LPMOs do this by reductively activating oxygen or hydrogen peroxide to specifically attack at either the C1 or C4 carbon of the glucose ring resulting in hydroxylation at that position and subsequent breakage of the glycosidic bond  [7, 9, [18] [19] [20] 23].
Expression and purification of CjX183 CjX183 (bases 1408 to 1674 from the cbp2d gene in Genbank CP000934. 1) was sub-cloned from a previously generated pET-26b construct which incorporated a pelB leader sequence for periplasmic protein export and C-terminal His 6 tag into the pCW-LIC vector (a gift from Cheryl Arrowsmith, Addgene plasmid 26098) using PIPE cloning  [76]. The pCW-CjX183 plasmid was used to transform E. coli BL21(DE3) cells already transformed with the pEC86 plasmid, encoding the heme maturation system  [61]. 1 L cultures of 2TY media (16 g/ L tryptone, 10 g/L yeast extract, 6 g/L NaCl) were grown to an A 600 of 0. 6 at 37°C shaking at 200 rpm, cooled to 20°C and induced with a final concentration of 0. 4 mM IPTG. Cultures were grown for 20 h before harvesting by centrifugation at 5000×g for 20 min at 4°C.
Acknowledgements We gratefully acknowledge  Dr Lucy Crouch ,  Prof Harry Gilbert  ( Newcastle University ) and  Dr Jeffrey Gardner  ( University of Maryland Baltimore ) for providing constructs of Cbp2D as templates to begin this work and for useful discussions. We also thank  Prof Lars Jeuken  and  Dr Theodoros Laftsoglou  for providing the pEC86 plasmid used to help express CjX183. G. R. H would also like to thank  Prof Paul Walton  for reading drafts of the manuscript, for his support with experiments performed in his laboratory, and for his mentorship which has been invaluable in supporting the work of G. R. H. 's laboratory.
The co-registration measurement will characterize the chromatic shifts of our system. These values can be further used to correct the acquired dataset (e. g. for colocalization studies, ratiometric or FRET imaging). However, accurate correction should be performed at the subpixel level and tackling the co-registration is more efficient at the hardware level than using postprocessing. A spatially resolved analysis of co-registration might also help to identify the region of interest of the field of view of the detector where default can be considered as negligible. We used the ratio of measured distance versus theoretical resolution as the metric to evaluate the co-registration quality of the system for each color pair. The shortest wavelength of the color pair was used to calculate the theoretical resolution, as it is the most stringent resolution value.
Metrics The experimental PSF values were calculated using the MetroloJ_QC plugin with the Fiji software. The plugin calculates FWHM along the x, y and z axis of all beads in the field of view. Before analyzing the images with the plugin, we visually inspected the acquisitions. Some images needed to be cropped to remove saturated beads. Alternative options can be used to discard any saturated beads from image. Only beads that were close to the center of the FOV (30% of the entire sensor for sCMOS, zoom of 6 for confocal) were analyzed to avoid aberrations. At least five beads were analyzed. The MetroloJ_QC plugin works as follows: First, the xy coordinates of beads are identified using a find maxima algorithm on a maximum intensity projection of the stack. Beads too close either to the image's edge or to another bead are discarded from the analysis (the user in the plugin can modify this distance).
Then, FWHM is measured: the maximum intensity pixel within the entire 3D data set is determined and an intensity plot along a straight line through this maximum intensity pixel point is extracted in all dimensions. Finally, the bell-like plots/curves are fitted to a Gaussian function using the built-in ImageJ curve fitting algorithm to determine the FWHM. Where λem is the emission wavelength, λex the excitation wavelength, n is the refractive index of the immersion liquid. Resolution formulas are the FWHM of the PSF and not the distance of maximum to the first minimum of the intensity profile of the PSF  37, 38. For spinning disk confocal microscopes, we consider that the pinhole size does not fill 1 AU (it is the case when we use a circular pinhole of size close to 50 μm)  39. The Lateral Asymmetry Ratio (LAR) is also calculated and is defined as : 𝐿𝐴𝑅 = min [𝐹𝑊𝐻𝑀 𝑥 ,𝐹𝑊𝐻𝑀 𝑦 ] max [𝐹𝑊𝐻𝑀 𝑥 ,𝐹𝑊𝐻𝑀 𝑦 ].
Analysis After automation of the FWHM measurements on several beads/datasets, average values are extracted in all three dimensions, and standard deviations are calculated. Aberrant "PSF" (non-bead features that are considered as beads using the algorithms) can be further filtered using the Gaussian fitting parameter R 2 (square R stands for the coefficient of determination, showing the goodness of a fit of simulated vs measured values) for each FWHM measurement. We recommend a value superior to 0. 95. Further inspection of each PSF, using the computed bead signal to background ratio, will help removing aberrant values. Additional parameters are also measured, like the lateral symmetry (named asymmetry parameter), or whether the acquisition meets the Shannon-Nyquist criterion. The figures in that manuscript and the calculations on the statistical significance are prepared with the GraphPad Prism software.
Sample Different strategies for the evaluation of illumination power stability exist. We used the power-meter console (PM100D, Thorlabs) coupled to a microscope slide power photodiode Si sensor, 350-1100 nm sensitive, designed to measure optical powers from 10nW -150mW (S170C, Thorlabs) with a response time of 1 μs. This sensor fits in the microscope slide holders of upright and inverted microscopes and measures the power at the sample plane. The controller console is connected to the computer by USB. The illumination power can be recorded over time with the Power Monitor GUI software at a defined sample rate. When needed, we monitored the temperature with the TSP01 temperature probe (Thorlabs) and recorded it with the TSP01 Application software.
Microscope Lateral and Axial Resolution (PSF) In total, data from 91 objective lenses from more than 10 light microscopy core facilities was analyzed. Ten bead samples were prepared in the same way and distributed to the facilities. 63x/60x lenses were considered reference lenses and two-color PSF measurements were most often acquired (in the DAPI and GFP channels). For the other lenses, both colors were not always acquired because blue bead fluorescence is usually dimmer than green bead fluorescence and it was difficult to achieve good signal-to-noise ratio images when lower NA lenses were used. We first investigated if the proposed procedure was robust enough (i. e. , if the variability of the calculated PSF metrics with the same sample/setup was kept negligible). PSFs of the same bead at five different time-points were acquired (GFP channel).
Co-registration Our co-registration study collected data from setups of the three microscopy techniques using over 70 different lenses. While sub-resolution beads are absolutely needed for resolution assessment, coregistration can be done with beads whose sizes range from sub-resolution (0. 1 μm) to a few microns. We first studied the influence of the size of the beads on the co-registration results. We observed that beads with a size of 1 or 4 μm are ideal for co-registration studies, while coregistration results based on smaller 0. 5 μm and 0. 2 μm beads give a higher dispersion (data not shown). We believe it is harder to achieve a high SBR, as needed to further correctly identify beads and estimate their center coordinates, using these tiny beads. We calculated the ratio of experimental to theoretical distance of the bead center coordinates for all two channel combinations (e.
Three different measurements on the same system were performed during three consecutive days. The drift values were very close, and the velocity dispersion low (data not shown). However, during a month or even a year, drift of a system can change significantly if environmental parameters change, like the room temperature or even the temperature flow in the room. Time monitoring of the drift is then necessary if such changes are frequent. Using the three metrics and this experimental dataset, drift behavior can be classified into three categories according to the specificity experiments and its need for precision (Table  1 ). When the velocity before and after stabilization is identical and really small (lower than 15 nm per minute, which is the theoretical resolution of a high NA objective lens after 10+ min), the drift can be considered negligible (high precision experiments, e.