dataesr/scientific-paragraphs-categorization huggingface dataset:
ncbi. nlm. nih. gov/books/NBK547852/; Hoofnagle et al. , 2013). As both compounds showed a risk for developing drug-induced liver injury (DILI) in humans  (Chen et al. , 2016) , we were interested to assess if they share common information in the Cell Painting and L1000 datasets. For the L1000 dataset, although the 2 compounds were tested on 58 different conditions (cell line, dose, time), we collected the gene's deregulation (absolute z-score of >2. 00) on the HepG2 liver cell line at 10 μM and 24 h. As represented in Fig. 2 , amiodarone and prochlorperazine deregulated a total of 532 genes (22 are deregulated by both compounds) among them 396 were expressed in U2OS cells (15 are deregulated by both compounds). About cell painting, both compounds perturbed 11 features (absolute z-score of >2.
00); 6 of them concerning the nucleus and the 5 others for the cytoplasm. This might mean that the genes disrupted by these molecules will be mainly expressed in these compartments. About the enrichment performed with the deregulated genes, we found that the sterol, cholesterol and secondary alcohol biosynthetic processes (respectively GO:0016126, GO:0006695 and GO:1902653), are significantly annotated with the 266 up-regulated genes. Looking at the genes associated with these ontologies (FDFT1, FDPS, HMGCR, HMGCS1, SREBF1, DHCR7, MVD, MSMO1, SQLE, TM7SF2, NSDHL and HSD17B7), we noticed that all of the proteins encoded from these genes are located in the cytoplasm (according to the human protein atlas database) and sometimes in the endoplasmic reticulum membrane, except for DHCR7 which is located in the nuclear membrane.
Results with three drugs (tamoxifen, probenecid and norethindrone) as potential Non-Genotoxic Carcinogens As a third example, we investigated three compounds, tamoxifen (a first-line endocrine treatment for breast cancer  (Dowsett et al. , 2010) ), probenecid (prescribed for gout, as an anticancer or antiviral  (Murray et al. , 2022; Ahmed et al. , 2016) ) and norethindrone (oral contraceptive  (Schoonen et al. , 2000) ). All of them are classified as Non-Genotoxic Carcinogens (NGC) in the liver according to the Chemical Carcinogenesis Research Information System database. As these 3 compounds were tested in the L1000 and Cell Painting datasets, we were interested to see if they share some common features that could be suggested for NGC in the liver. First, we selected transcriptomic data from the tests performed on HepG2 cells (at 10 μM and for 6 h) among the 49 tested conditions.
Suggestions from compounds having similar morphological cell perturbation In the Cell Painting dataset, some compounds deregulate the same morphological features. Using the similarity calculation (see Methods section) we looked at compounds that share a similarity in terms of morphological features perturbation. As an example, the caffeic acid phenethyl ester (CAPE) has a highly similar morphology profile (82% of similarity) to another compound (BRD-K54384984-001-06-5). Identified several decades ago in the propolis (a honeybee product)  (Aviello et al. , 2010) , CAPE has many clinical applications such as antiinflammatory activity, affecting obesity through insulin resistance in mice  (Bezerra et al. , 2012)  and antioxidant properties  (Göçer and Gülçin, 2011). The second compound does not seem to have any known therapeutic function.
It was used in some screening work but showed no activity against the targets tested (notably a human ubiquitin C-terminal hydrolase, available under PubChem AID 588478). Both compounds deregulate 267 and 192 morphological features respectively, of which 154 are common to both compounds. These features mainly concern the whole cell (67), then the cytoplasm (54) and a little less the nucleus (33). According to the L1000 dataset, the CAPE compound deregulates genes. They are significantly involved in 3 molecular functions related to the protein tyrosine kinase activity or protein kinase activity (GO:0004714, GO:0004713 and GO:0019199); 5 biological processes related to the cell projection organisation (GO:0031346), the neuron (GO:0070997, GO:0043524 and GO:0051402) and the learning process (when a relatively lasting adaptive behavioural change occurs as a result of an experience; GO:0007612); 1 cellular compartment related to the cleavage furrow (GO:0032154) in the cytoplasm.
Discussion By collecting transcriptomics data and Cell Painting images for a set of 9989 molecules, we proposed an enrichment analysis that allows us to extract the gene signatures and morphological cell features signatures associated with a compound and that can explain a phenotype or a disease. Image-based profiling can reveal key biological perturbations of the cell morphology and their compartments. Coupled with transcriptomics information, it provides a deeper understanding of the mechanism of action of a chemical and thus allows to suggest insights on its potential therapeutic and/or adverse effects. Unlike previous study that used 978 landmark genes (genes that capture a high proportion of transcriptional variance in L1000 (Subramanian et al. , 2017)), we considered in our analysis a z-score for the 12,328 genes perturbed by any chemicals and any conditions (time, dose, cell type) in the L1000 dataset.
The similarity analysis based on scaffolds, gene expressions or morphological features is also an interesting approach to suggest chemicalphenotype associations for chemicals that share similar properties. Instead of using the datasets separately, the fusion of several modalities and the exploitation of such big matrices using machine learning and deep learning could improve the reliability of the hypotheses about the impact of a compound on a cell. Some analyses are reported in this direction combining chemical structure or transcriptomics data to cell painting  (Seal et al. , 2022; Haghighi et al. , 2022 preprint; Nyffeler et al. , 2022). Using the scaffold and chemical structure similarity is another interesting information to explore in combination with transcriptomics and cell morphology profile.
Conclusion The association of two large collections of transcriptomics and Cell Painting data is an interesting approach to deciphering phenotypes associated with chemicals. It enables to leverage significant relations between the image-based and transcriptomic profiles in response to a chemical and to suggest key gene deregulations and morphological cell perturbations that can explain a phenotype or a disease. Expanding these collections with Cell Painting data on chemicals tested in other cell lines and with dedicated transcriptomics experiments, in addition to the exploration of these data through innovative artificial intelligence approaches could pave the way for phenotypic drug discovery.
Acknowledgements This work was supported by the project  RISK-HUNT3R : RISK assessment of chemicals integrating HUman centric Next generation Testing strategies promoting the 3Rs. RISK-HUNT3R has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  964537. This work was also supported by the project  PARC: Partnership for the Assessment of Risks from Chemicals. PARC has received funding from the  European Union 's  Horizon Europe Research and Innovation Programme  under Grant Agreement No  101057014. Finally, we would like to thank  Vanille Lejal  for assistance with the Cell painting curation and plate normalisation.
Data and Preprocessing Individualized antibody reactivity levels for SARS-CoV-2 antigens induced by mRNA vaccines were quantified using coronavirus antigen microarray (CoVAM) following the procedure described by Hosseinian et al. [27]  Data were downloaded from the GEO database under accession number GSE199668. The samples were divided into four classes according to the time of vaccination: 104 healthcare workers before vaccination, 534 healthcare workers within 60 days after vaccination, 594 healthcare workers between 60 and 180 days after vaccination, and 141 healthcare workers over 180 days after vaccination  [27]. In terms of features, the CoVAM contained 10 SARS-CoV-2 antigens, including nucleocapsid protein (NP) and several varying fragments of the S protein, as well as 4 SARS, 3 MERS, 12 Common CoV, 8 influenza, and 36 other antigens.
Feature Selection Methods Several features were adopted to represent samples. Some of them were important to classify samples into different classes, whereas others were not. In machine learning, the important features can be extracted by feature selection methods. To date, lots of such methods have been proposed. It is challenge to select a proper one to process a given dataset. Generally, one single method can only output a part of essential features as each method has its limitations. In this study, we adopted four feature selection methods: least absolute shrinkage and selection operator (LASSO)  [28, 29] , light gradient boosting machine (LighGBM)  [30] , Monte Carlo feature selection (MCFS)  [31]  and maximum relevance minimum redundancy (mRMR)  [32]. These methods was designed following different principles, meaning that they can overview the given dataset from different aspects.
Thus, more essential features can be obtained by applying them to the same dataset. Their brief descriptions are as follows. Least Absolute Shrinkage and Selection Operator. The LASSO was introduced by Robert Tibshirani in 1996. It is a statistical method used for regularization and feature selection  [28, 29]. This method reduces the regression coefficients of the redundant features to zero. The feature selection phase occurs after the reduction, where non-zero-valued features are sorted in a list by the absolute value of their coefficients. This study adopted the LASSO program obtained from Scikit-learn  [33] , which was run with default parameters. Light Gradient Boosting Machine. The LightGBM is a free and open-source distributed gradient boosting framework for machine learning that was created by Microsoft  [30].
It performs regression and classification by transforming weak decision tree (DT) classifiers into strong learners. In addition to regression and classification, LightGBM ranks features based on their importance, quantified by the number of times they are selected for building DTs. A high ranking is given to features that are used frequently. LightGBM was implemented through a Python module, which can be obtained at h ps://lightgbm. readthedocs. io/en/latest/. It was also performed with default parameters. Monte Carlo Feature Selection. The MCFS is a useful tool for selecting informative features according to their relative importance in building DTs  [31, [34] [35] [36]. Subsets of features are randomly constructed for many times. For each subset, some samples are randomly selected for training, and the others are for testing.
For instance, a DT is built based on 2/3 of the samples that are randomly selected, and the rest is used for testing, which is also repeated for many times. The relative importance (RI) of each feature can then be estimated by considering the number of times they are used to construct the DTs, the information gain of the features, and the weighted accuracy of the DTs. Finally, features can be sorted according to their RI scores. The MCFS program used in this study was retrieved from h p://www. ipipan. eu/staff/m. draminski/mcfs. html. Also, it was executed using default parameters. Maximum Relevance Minimum Redundancy. The mRMR is a classic and powerful feature selection method  [32]. It evaluates the importance of features according to their relevance to class variable and redundancy to other features.
The relevance and redundancy are all measured by mutual information (MI). Similar to above methods, mRMR also generates a feature list to indicate the importance of features. At first, such list is empty. Then, a loop procedure is executed. In each round, one feature with maximum relevance to class variable and minimum redundancy to features in the current list is selected from all remaining features and appended to the current list. The loop procedure stops until all features are in the list. The mRMR program adopted in this study was obtained from h p://home. penglab. com/proj/mRMR/ and it was run with the default se ings. Above four feature selection methods were applied to the dataset mentioned in Section 2. 1, resulting in four feature lists, which were called LASSO, LightGBM, MCFS and mRMR feature lists.
Synthetic Minority Oversampling Technique As mentioned in Section 2. 1, the sizes of four classes were quite different. The classifier directly built on such dataset may produce bias. It should be processed by some advanced computational method. Here, we selected synthetic minority oversampling technique (SMOTE)  [42] [43] [44]. The idea of this method is to generate synthetic samples for each minority classes, thereby balancing the dataset. In detail, SMOTE randomly chooses a sample from a minority class and determines its k nearest neighbors of the same class. One of its neighbors is randomly selected and a synthetic sample is generated by the linear combination of the sample and its chosen neighbor. This new generated sample is put into the minority class, thereby enlarging its size. This procedure can be performed several rounds until the size of the minority class is same as that of the majority class.
Prediction accuracy (ACC) is the most classic measurement to assess the performance of classifiers. It is defined as the proportion of correctly predicted samples. However, ACC is not accurate when the dataset is imbalanced. Ma hew correlation coefficients (MCC)  [56]  is a more balanced measurement than ACC. Two matrices are used to calculate MCC. One is to store the true class of each sample and the other one is to store the predicted class of each sample. MCC assesses the relationship between these two matrices.
Extraction of Essential Features for Each Class Based on IFS method, some essential features can be obtained. However, it is not clear which class they are highly related to. In view of this, we reconstructed the original dataset and applied above feature selection methods on it. For one class, one dataset was generated, in which samples in this class were termed as positive samples and other samples were considered as negative samples. Then, four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) were adopted to investigate this dataset, resulting in four feature lists. From each list, top 20 features were picked up, thereby obtaining four feature subsets. By investigating the overlap of these feature subsets, some essential features that occurred in multiple subsets can be obtained, which were deemed to be highly related to the given class.
Results In this study, a dataset on the antibody reactivity levels for SARS-CoV-2 antigens induced by mRNA vaccines was investigated. The overall computational framework is shown in Figure  1. The results of each step are presented in this section. Flow chart of the entire analytical process. The 73 antigens in samples from four classes were ranked according to feature importance using four feature selection algorithms, namely, LASSO, LightGBM, mRMR, and MCFS. Then, four feature lists were fed into the IFS method. Efficient classifiers were constructed, and the optimal feature subset from each list and the classification rules were extracted. The optimal feature subsets of LASSO, LightGBM, MCFS, and mRMR were investigated to obtain antigens that recurred in multiple subsets. Finally, a biological analysis was performed on the overlapping antigens and classification rules.
Essential Features for Each Class The essential features obtained above may not be highly related to one class. To extract the essential features for each class, four datasets corresponding four classes were constructed, as described in Section 2. 7. Then, LASSO, LightGBM, MCFS and mRMR were applied to each dataset. Four feature lists were obtained. Top 20 features were picked up and taken intersection. A Venn diagram was plo ed for each class, as shown in Figure  5. The specific antigen names are listed in Table  S4. For the first class, namely, unvaccinated healthcare workers, antigens such as SARS. CoV. 2. S1. RBD. mFc and SARS. CoV. S1. HisTag were identified by all four feature selection methods. For the second class, namely, healthcare workers within 60 days after vaccination, SARS. CoV.
Discussion We identified a set of antigen-reactive antibodies as potential features that could reveal the effect of COVID-19 vaccines on anti-viral immune activation and reflect changes in antibody levels in the body over time after vaccination by using data on serum antibody levels in volunteers after receiving COVID-19 vaccines. This confirms the potential of such features to contribute to the development of effective vaccines with long-lasting protection. Recent publications have found that some identified features, as well as the relevant quantification rules, are linked to vaccine-induced anti-viral immune activation and duration. Using the computational methods, we discovered a set of unique viral antigens-reactive antibodies selected by at least three methods. The antigens we analyzed were distinguished according to virus type (CoV-2, MERS, and other common human coronaviruses), expression system (HEK cells, Baculovirus), and protein fusion tags (such as His and Fc).
Conclusions Combining data on serum antibody levels in volunteers after COVID-19 vaccination and advanced machine learning methods, a set of antigen-reactive antibodies were extracted, which could reveal the effect of the vaccine on antiviral immune activation and reflect changes in antibody levels in the body over time after vaccination. In the computational framework, four efficient feature selecting algorithms, namely, LASSO, LightGBM, MCFS, and mRMR, were performed to rank the features according to their contributions to the classification. Then, through the IFS method, the optimal features for four classification algorithms (DT, KNN, RF, SVM) in each feature list were confirmed. Subsequently, the overlapping features by taking the intersection of the optimal feature subsets corresponding to the four feature selection algorithms were identified, such as SARS.
5 cm). Note that background scans were also recorded with the empty gas cell. For each absorption spectrum recorded, the synchrotron beam current was monitored. The accuracy of the VUV absorption cross-section data is obtained by recording the different sections of the spectrum in 5 or 10 nm sections, with an overlap of 10 points between the adjoining sections. ASTRID 2 operates in a "top-up" mode, which guarantees a quasiconstant light intensity as a function of time, thus compensating for beam decay in the storage ring. The small variations in beam current (and therefore light intensity) of up to 2-3% are removed by normalising the data to the beam current in the storage ring. Such a Fig. 3  The VUV photoabsorption cross-section in the 5. 5-7. 0 eV photon energy range for 2(5H)-thiophenone Fig.
Theoretical methods Quantum chemical calculations were performed in the optimised geometry of 2(5H)-thiophenone (Fig. SI1 ), at the CCSD level of calculation with the aug-cc-pVTZ basis set from Dunning et al. [15, 16]  for H, C and O. For the S atom, the modified aug-cc-p(V + d)Z from Dunning et al. [15]  was employed. For the description of Rydberg excited states, the basis set was augmented at the mass centre of the molecule by a set (6s6p4d ) of diffuse orbitals, taken from  [16]. The excited electronic states were obtained for the ground-state optimised molecular geometry, employing the EOM-CCSD method  [17]  and the aug-cc-pV(T + d)Z + R basis set (Tables  1  and SI1 ), using the MOLPRO 2019. 1 code  [18, 19]. The nature of each excitation was assessed by visual inspection of the natural orbitals for each transition and the average value < r 2 > of the electronic cloud.
Acknowledgements SK acknowledges the  Portuguese National Funding Agency (FCT)  through  PD/BD/142831/2018  and  COVID/BD/152673/2022 , and together with PLV the research grant  CEFITEC  ( UIDB/00068/2020 ). This work was also supported by  Radiation Biology and Biophysics Doctoral Training Programme  ( RaBBiT ,  PD/00193/2012 );  UCIBIO  ( UIDB/04378/2020 ). D. D. thanks the support from the  CaPPA Project (Chemical and Physical Properties of the Atmosphere)  funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under Contract No. ANR-10-LABX-005 ; the  Région Hauts de France  and the  Ministère de l'Enseignement Supérieur et de la Recherche (CPER ECRIN)  and the  European Fund for Regional Economic Development  for their financial support.
In the pharmaceutical sector, evergreening is considered a range of practices applied to extend monopoly protection on existing products. Filing several patent applications related to the same active pharmaceutical ingredient (API) is one of the most common manifestations of evergreening. During the COVID-19 pandemic, several health technologies were developed. This study aimed to analyze the extension of evergreening for selected health technologies for SARS-CoV-2 through patent filing strategies. Starting with the selection of three antivirals, one biological and two vaccines, a patent landscape was built based on public and private databases. Regarding these selected technologies, we analyzed some of the evergreening strategies used by different applicants, academic institutions or pharmaceutical companies and found a total of 29 applications (10 after the pandemic) for antivirals, 3 applications for a biological drug (1 after the pandemic), and 41 applications for vaccines (23 after the pandemic).
Building of the patent landscape The patent landscape for the selected technologies was built from existing landscapes publicly available, such as VaxPaL  (25)  and MedsPaL  (26)  databases, and complemented with a search at the commercial CAS Scientific Patent Explorer database  (27) , considering filing publicly available up to May 2023. The scope of the study was limited to patent applications filed through the Patent Cooperation Treaty (PCT) system and did not focus on regional or national patent applications and related status.
Reflectance Data This study used two MODIS reflectance products, including the nadir BRDF-ad justed reflectance data (MCD43A4 V6) and the BRDF and albedo model parameter dat (MCD43A1 V6). They are part of the MODIS BRDF/Albedo suite and provide observation of the anisotropic reflectance properties of the Earth's surface, which is essential for vari ous applications such as vegetation monitoring, land cover mapping, and climate model ing. The MODIS wavelengths and spatial resolution are listed in Table  1   [34]. Th MCD43A4 V6 product is fitted using a BRDF to simulate the values as though they wer observed in the nadir observation direction. The MCD43A1 V6 product offers the thre model weighting parameters (i. e. , isotropic, volumetric, and geometric parameters) fo each of the MODIS bands.
Data 2. 2. 1. Reflectance Data This study used two MODIS reflectance products, including the nadir BRDF-adjusted reflectance data (MCD43A4 V6) and the BRDF and albedo model parameter data (MCD43A1 V6). They are part of the MODIS BRDF/Albedo suite and provide observations of the anisotropic reflectance properties of the Earth's surface, which is essential for various applications such as vegetation monitoring, land cover mapping, and climate modeling. The MODIS wavelengths and spatial resolution are listed in Table 1  [34]. The MCD43A4 V6 product is fitted using a BRDF to simulate the values as though they were observed in the nadir observation direction. The MCD43A1 V6 product offers the three model weighting parameters (i. e. , isotropic, volumetric, and geometric parameters) for each of the MODIS bands.