dataesr/scientific-paragraphs-categorization huggingface dataset:
)  [52, 53]. Additionally, MongoDB has fault tolerance issues, which is true of practically all distributed databases. Moreover, in the proposed deep learning method to deal with missing data, we do not cover hyperparameter fine-tuning  [54] , which is one of the biggest drawbacks to using deep neural networks. This task can be performed using GridSearch  [55]  or other optimization algorithms such as Genetic Algorithms or the Monte Carlo method, which can lead to better results. Despite this, we found promising results, confirming the reliability of the ERA5-Land reanalysis data for our study area, which could lead to the application of this method using several stations in regions with challenging conditions. Finally, for the anomaly detection part, we presented an unsupervised machine learning method that is based on how isolated a measure is with respect to the surrounding neighborhood to alert administrators via SMS and emails, but other efficient methods can be investigated, such as Conformal Anomaly Detection (CAP)  [56, 57].
Artificial intelligence (AI) may be able to facilitate data analysis and exposure prediction, so may play a significant future role in implementation of real-time biosensors  [80]. The ability of computers to perform complex mathematical modelling and statistical analysis has allowed for published antimicrobial population PK models to be integrated with relevant patient-specific data, such as renal function, weight and TDM sample measurements (if using Bayesian forecasting). Termed model-informed precision dosing (MIPD), these types of software can generate a priori dosing recommendations required for initiation of empiric-based therapy. If using a Bayesian component, these software may also provide a posteriori PK parameter estimates that potentially improve future dosing recommendation accuracy.
Additionally, some MIPD software have the ability to input pathogen MIC so that variations in pathogen susceptibility can be accounted for. One issue of particular relevance to the ECMO and RRT population sub-groups of critically ill patients is the generalisability of existing PK models available in MIPD software. As many of these published models have been developed within specific patient populations, caution must be applied when extrapolating MIPD dosing recommendations to other critically ill sub-groups with external validation strongly suggested. Use of MIPD software may result in dosing recommendations that are more likely to achieve PK/PD targets  [81] ; however, the clinical outcome benefits of these software applications are yet to be quantified in critically ill patients with sepsis.
Future studies will firstly need to examine comparative predictive accuracy of dosing recommendations before second, the design of the most sensitive interventions from clinical advantages (if any) of MIPD can occur. When a patient is connected to an extracorporeal circuit/machine, the implementation of real-time TDM biosensors informing MIPD software will have several advantages. Firstly, all circuits are dynamic processes, and real-time monitoring will make it possible to react to the dynamics of the systems as soon as possible. This is of paramount importance as interruptions and/or fluctuations in extracorporeal treatment modalities are very common during clinical care of patients. Secondly, the information collected via the sensor will allow for the MIPD tool to efficiently optimise antimicrobial dosing.
For example, a severe acute kidney injury patient commencing RRT will have an increase in drug CL and the corresponding change in plasma drug concentrations can be detected and dosing can be adjusted more efficiently. Third, as TDM information is gathered at a faster pace with potentially more intensive sampling, there is a possibility for these data to inform and fine-tune population PK models used in MIPD software, making these applications "smarter" in dosing accuracy (Fig. 1 ). As we are still limited in our knowledge of the impact of extracorporeal circuits on drug exposure, there is scope for TDM biosensors to be used in the future research, especially when novel therapeutics are first administered to patients on extracorporeal treatments. Most importantly, clinical implementation of innovations such as MIPD relies heavily on improving clinician understanding of PK/PD concepts and principles.
Without this underlying knowledge, clinicians may not fully appreciate the Fig. 1  Future approach to therapeutic drug monitoring and use of dosing software in the ICU complexities of how changes in drug exposure are influenced by extracorporeal treatment modalities, such as ECMO or RRT, and will likely not be equipped to maximise the use of these technologies. In our opinion, ICU monitoring of antibiotic exposure has the capacity for significant progress in the near future. As a first step, MIPD software will need validated models that can help predict antibiotic exposure in critically ill patients on ECMO and RRT. However, as there are many variables that may influence drug exposure in these patients, and these variables change continuously, we know that MIPD alone is somewhat limited in its ability to improve attainment of target exposures.
To this extent, although TDM remains the central tool to adapt antimicrobial dosing in most centres, TDM processes will need to become streamlined so that the time-lag between analysis and reporting is greatly reduced. Therefore, the ideal picture in 10-15 years would be that, in addition to MIPD software incorporated into the patient dossier, real-time TDM of antimicrobials in these patients can be achieved through a validated and in situ secured biosensor (either via micro-dialysis or the skin). Output data can be used and interpreted by a team composed of a clinical pharmacist/pharmacologist and intensive care specialist (with input from infectious diseases clinicians if required) to optimise antimicrobial doses accordingly. Alternatively, antimicrobial dosing could be automatically corrected by the system through use of smart antibiotic pumps that can adjust infusion rates.
Acknowledgements JAR receives funding from the  Australian National Health and Medical Research Council for a Centre of Research Excellence  ( APP2007007 ) and an  Investigator Grant  ( APP2009736 ), as well as an  Advancing Queensland Clinical Fellowship. KS acknowledges research support from  Metro North Hospital and Health Service. KW acknowledges support from the  National Institutes of Health  for his work in paediatric extracorporeal life support ( 5R01HD097775 ).
Human CRC cohort The processed TCGA COREAD RNA-Seq dataset (n = 577) was downloaded directly from the Guinney et al. , CMSclassifier study via Synapse (ID: syn2023932), where it has been described previously  (1). Gene symbols and entrez IDs were matched using org. Hs. eg. db R package (v3. 8. 2) thereafter CMS classification was performed via 'random forest' (RF) method using CMSclassifier R package (version 1. 0. 0). Biological Process subset of Gene Ontology (GO) gene sets were extracted from the Molecular Signature Database (MSigDB) using msigdbr R package (v7. 0. 1). Subsequently, ontology scores were generated for the TCGA dataset using single sample GSEA (ssGSEA) method from GSVA R package (v1. 26. 0). To determine the CMS-specific GO terms, these ssGSEA scores were averaged for each gene set across samples within each CMS subtype and scaled to Z-scores where the GO with ssGSEA scaled scores above 0 in a CMS, but below 0 on the others, were selected as the enriched GO term for that CMS.
Amirkhah et al. in DESeq2 R package (v1. 32. 0). The models where the batch they were sequenced in was deeply confounded by genotype were removed and data from 51 GEMMs remained. ComBat_seq function in sva R package (v3. 40. 0) was used to correct read counts for batch, thereafter vst function in DESeq2 same as before was used to normalize the data.
Databases CMS curated gene sets signatures (n=79) were obtained from Synapse (ID: syn2321865). Cancer hallmarks (n=50) and Gene Ontology (GO) Biological Process gene set (C5 BP) was extracted from MSigDB using 'msigdbr' R package (v7. 4. 1). Ten signatures to estimate the proportion of the eight immune (NK cells, Cytotoxic lymphocyte, T cells, CD8 T cells, B lineage, Monocytic lineage, Neutrophils, Myeloid dendritic cells) and two stromal (Fibroblasts and Endothelial) cell populations in each human sample across CMS subtypes were obtained using the MCPcounter R package (v1. 2. 0); the mouse version of signatures were retrieved from the mMCPcounter R package (v0. 1. 0). Immune-related genes for human and mouse were downloaded from the NanoString panel (https://canopybiosciences. com/product/immunology/).
Statistical analysis All the statistical analyses were performed in R (v4. 1. 2) using the stats R package, including cor() function with method = 'pearson' for Pearson's correlation. The Student t-test method embedded in the geom_signif() function of ggsignif package (v0. 6. 3) was used to do statistical analysis in violin plots. Boxplots were generated using ggplot2 (v3. 3. 5) R package. The ComplexHeatmap (v2. 8. 0) and circlize (v0. 4. 13) packages were used to display heatmaps. We used glmnet (v4. 1-3) R package to do LASSO regression model analysis. The λ or tuning parameter in the LASSO model was selected through the 10-fold cross-validation. Transcriptome-based stromal and immune scores were generated using the MCPcounter. estimate() function in MCPcounter R package (v1. 2. 0). Single sample gene set enrichment analysis (ssGSEA) was performed using an R package called GSVA (v1.
In this study, we investigated the presence of CMS subtypes in two panels of 18 and 39 GEMMs with 4 and 6 different genotypes, respectively (Table  1 ). The RF method in the CMSclassifier package was designed for human samples and uses 273 genes to assign CMS subtypes. To enable the use of this method with mouse data, we converted the entire mouse gene matrix to human orthologues using biomaRt  (14). During the conversion of the mouse matrix, 16 genes of the 273 gene used to predict CMS calls in human were mismatched in both cohorts (Supplementary Table  S1 ). Applying the RF method to our n=18 and n=39 mouse model matrices produced 56% unknown samples in both datasets (Supplementary Fig. 1b, c ). Of note, to test the functionality of CMScaller in the same mouse cohorts, we next converted the human CMScaller template genes (n= 529; CMS1=126, CMS2=82, CMS3=84, CMS4=237) to mouse orthologues (n=533; CMS1=128, CMS2=80, CMS3=90, CMS4=235), which as anticipated resulted in a small number of dropouts (n=26 missing genes, Supplementary Table  S2 ) due to lack of recognised orthologues, though overall the number of genes in mouse CMS template increased due to the existence of multiple mapping mouse genes for the individual human genes (Fig.
8) as before and compared them to CMS classifications using this new NTP ontology-based method, where we observe 95% concordance with the RF-based calls (Supplementary Excel File 1, Sheet 2). In line with the generation of MmCMS-A, the mouse-equivalent GO-terms of these human gene ontologies were identified using the 'msigdb' R package and used as the MmCMS-B template for CMS classification in mouse data using NTP method (Fig. 1b , yellow background).
Development of mouse CMS template (MmCMS-C) based on combining gene sets/ pathways that best characterise each human CMS subtype in a supervised approach While MmCMS-B is focussed solely on GO-BP signatures, for MmCMS-C we generated a classifier based on four biologically-informed signature collections (Fig. 1d ). First, we compiled the n=79 gene sets used to characterise biological signalling in the original CMS study from the Synapse database (DOI: https://doi. org/10. 7303/syn2623706). As with MmCMS-B, we refined these 79 signatures into only those with individual CMS class-specific expression (t. test; pvalue < 0. 01) and signatures only kept if one subtype was significantly higher when compared to each of the other subtypes in turn, resulting in 48 of the 79 gene sets being used (Fig.
Amirkhah et al. When tested in the same way as MmCMS-A and B, using the NTP method on TCGA data, MmCMS-C was found to have 98% concordance with the RF-based high probability calls, threshold = 0. 8 (Supplementary Excel File 1, Sheet 3). To enable mouse classification, the biomaRt (  14 ) and msigdb (16) R packages were used to obtain the mouse version of 48 gene sets and 21 hallmark pathways, respectively, with mouse MCP signatures retrieved from the mouse-specific mMCP-counter package  (17). Individual orthologues of immune-related genes were obtained from the mouse NanoString panel (Supplementary Table  S3 ), with 39 mouse genes aligned to the 44 human immune genes identified using regression analysis, and then grouped into signature scores as before.
CMS classification of GEM models using 3 options To assess the performance of our 3 options for classifying mouse tumours, two different cohorts of GEMMs as described above were used (Table  1 , Fig. 3a ). As there is no CMS "ground truth" or reference for mouse tumour data, we utilised tumours from n=18 mouse models across four genotypes (KPN, KP, APN, AP; Table  1 ), which we have previously shown to correlate with signalling associated with stromal CMS4 tumours (KPN and KP) or epithelial-rich CMS2/3 tumours (AP and APN). PCA on the dataset revealed distinct groups according to genotype (Supplementary Fig. 3a ). The NTP-based algorithm was employed to predict CMS classification of GEMM tumours, using each of the three templates individually (MmCMS-A, B and C), with an FDR <0. 05 used as a cut off for significant calls.
MmCMS-C is the best option, particularly in calling CMS2-like mouse tissues To test how well our GEMM classifications align to the biological characteristics associated with human CMS subtypes, we next measured the biological traits of immune-related, metabolic, proliferation and stromal signalling associated with CMS calls in human TCGA data and compared them directly to the CMS classification calls according to each of our three MmCMS options in both independent GEMM cohorts. Mean ssGSEA scores were calculated across samples of each human CMS subtype, using the same TCGA samples used in Figure  1 , alongside mean ssGSEA scores for MmCMS-A, B and C predictions in the n=18 and n=39 GEMM cohorts (Fig. 4a-b ). Using the human RF calls as the ground truth, followed by cross-comparison and correlation analysis of samples assigned as CMS2 by all three mouse options, we find strong correlation with MmCMS-B (r=0.
Despite its importance, dual-species classification has been limited by the lack of a reliable and standardised approach, limiting researchers' ability to ensure faithful alignment between human tumours and preclinical models. Therefore, to address this, we developed a series of dual-species CMS classification models, named MmCMS, and an accompanying R package, which allows users to rapidly perform CMS classification of mouse tissue using three different options (A-C) of increasing complexity, from gene-level to biological pathways. To ensure that these new classifier options benefit the field, we developed a publicly available R package for MmCMS, which can be downloaded from https://github. com/MolecularPathologyLab/MmCMS. Although we have focussed on CMS in this study, data presented here provide an ideal template for the development and testing of other dual-species classifications, for subtypes such as CRC intrinsic subtypes (CRIS) (5), Braf mutant subtypes (BM) (20) and many others.
Our gene-level classifier, MmCMS-A, converts the human CMS template, embedded in CMScaller R package, to mouse orthologs and then use the NTP algorithm to carry out mouse CMS classification. The CMScaller package has been developed to enable exploration of the CMS subtypes in human pre-clinical models, particularly in cell lines, organoids and PDX tumours, to overcome the limitation of CMSclassifier's strong dependence on gene expression derived from the tumour microenvironment  (1, 6). As this approach is based on individual genes, any genes lost during the process of obtaining mouse orthologues  (21, 22)  can affect classification performance, resulting in a higher number of inaccurate or unknown calls, compared to biological knowledge-based approaches. In addition to biological differences between mouse and human, the representation and coverage of individual genes required for robust CMS classification may not be equivalent across different transcriptome profiling platforms  (11) , which again can lead to poor classifier performance.
The diversity and the number of factors impacting the conservation of buildings heritage require to compare and confront a wide range of data to understand better the causes of the potential disorders. The HBIM technology and processes have already shown their ability to enhance the diagnostic of historical monuments throughout the past years and is now mature enough to be applied on megalithic heritage. Focused on the case of the cairn of Gavrinis, this survey deals with the creation of the BIM model of the monument and its assets for the diagnostic. The 3D mapping of the disorders within the BIM model is highlighted. The pros of this workflow are also described before a short introduction to the following steps in progress.
RELATED WORKS The possibility to have a model gathering all the data coming from a wide range of sources is very appealing but can be unreachable because of the lack of adapted tools. The strength of the BIM process is to be able to create a 3D model including the database of the building: the model stores as much data as desired. Nevertheless, very rare case of BIM model has been done on megalithic monuments as shows the very few articles found in the open documentation. The complexity and the nature of this kind of heritage can led to consider the BIM technology as inappropriate. The importance to merge as much data as possible in a single model has been highlighted for the past decade and the BIM was not always a chosen alternative. The complexity, the cost or the inaccuracy hampered the use of this technology.
As the location and the quantities of alterations were a key aspect for the understanding of the conservation state of the cairn, a focus has been put on the 3D mapping of disorders and data in general within this monument. This enables to get areas and length with a high accuracy and to spatialize the data collected during this survey. Once again, the BIM technology hasn't been done to deal with 3D mapping, but recent works have shown that some ways are possible  (Barba, Barbato, Di Filippo, Napoletano and Ribera, 2020; Di Paola, 2020). This work is also inspired by former projects led by A-BIME where the same issues were encountered such as the structural diagnostic of the church of Gommecourt, or the diagnostic of the palais Carnolès in Menton. Depending on the the requirements of the surveys led by the company, different kinds of data are included in the BIM model created.
To better understand the volumes of the monument, the monoliths composing the megalithic structure have been reconstructed in The high resolution point cloud made on site has been segmented per stone, meshed and cleaned. For most of the stones, only half of the monolith is visible. In that case the scanned surface was duplicated, mirrored and sewn together to create a closed shell. For the volume reconstruction, the iconography of the monoliths (drawings and plans realized during the late 19 th and the beginning of the 20 th ). has been used to minimize the randomness factor. This element, a volumic element, was then imported in Revit, the BIM design software chosen. In order to stress the difference between the scanned surfaces and the reconstructed ones, a systematic decimation has been carried out on the invisible surfaces.
Data stored in the model As the model is seen as the database of the monument, each element has been associated to numerous properties. The values for each property come either from documents found during the collect of data, or from onsite observation or survey led by A-BIME. The model enables also to store temporal data. The evolution through the ages of the monument can be displayed: every element referred to a temporal phase of creation, and demolition if needed. The stratigraphic units and the BIM elements in general are dated. This enables to be accurate on the authenticity: whether an element have been reconstructed during the restauration program of the 80s or not; and date systematically every data integrated in the model. The engraved stones for instance are characterized by a date, a material, a conservation state, whether they have been displaced or not (Figure  6 ) … The data can be easily displayed either visually through filters applied on the desired properties or through tables.
Data query As seen previously, the BIM model is the database of the monument and can be queried easily and be exported. All the properties described before can be extracted as tables or filtered to enable to clearly visualize the data. But it is also possible to sum and to count quantities or elements queries. As all elements have a volume, an area, or a length, it is easy to get detailed quantities per material, per deterioration phenomenon (Table  1 ), or to count the precise number of elements such as the microbiological samples. The data query can also provide advanced visualization of data in the model. Graphical technical documents can be exported easily from this 3D reconstruction.
Interoperability and further uses The BIM model can be also exported to other software to perform simulations for instance. The interoperable file format, the IFC standard, enables to exchange the model (geometric shapes, and data) between different software. The BIM model becomes the central model from which other models are created for different purposes. This possibility enlarges the uses of it and enables to create new information. For this project, the importance of the structural issue has motivated the export to a structural software to perform a FEM (Finite Element Method) simulation. As no calculation notes have been found in the archives regarding the comportment of the ceiling stone of the chamber without the protection vault of the chapel, an estimation of its behaviour without this contemporary structure is in progress (Figure  11 ).
Thanks to this model, the archaeologists, the competent authorities, and the laboratories have a common relevant knowledge to take decisions. But this model is not only a database of the monument, it generates also new information. The confrontation of data or the requests can create a significant added value. The power of this BIM model is to be interoperable with other BIM software but also with other technical software such as FEM software. This A-BIM model stores efficiently data and can evolve in a large digital environment. The high resolution of the geometry of the model and the 3D mapping enables to have a very accurate quantities of decay and areas that must be treated. This model becomes a powerful tool for conservation planning and monitoring. Nevertheless, the flexibility of the design has still to be improved.
Chatbots represent a promising tool to automate the processing of requests in a business context. However, despite major progress in natural language processing technologies, constructing a dataset deemed relevant by business experts is a manual, iterative, and error-prone process. To assist these experts during modelling and labelling, the authors propose an active learning methodology coined interactive clustering. It relies on interactions between computer-guided segmentation of data in intents and response-driven human annotations imposing constraints on clusters to improve relevance. This article applies interactive clustering on a realistic dataset and measures the optimal settings required for relevant segmentation in a minimal number of annotations. The usability of the method is discussed in terms of computation time and the achieved compromise between business relevance and classification performance during training.