dataesr/scientific-paragraphs-categorization huggingface dataset:
Image Analysis Image analysis was performed with the Fiji software  [21] , Python Jupyter notebooks  [22]  and Imaris (v 9. 3. 1, Bitplane, Oxford Instruments-Andor, Belfast, UK). We provide the Fiji macros and notebooks for the entire image analysis pipeline above and they can be downloaded from the public code hosting website: https://gitlab. pasteur. fr/ iah-public/ptrmiad-thomaidou (accessed on 29 March 2023). To measure cell body volumes of microglia and astrocytes in 2-photon images, we first applied auto-thresholding using Renyi Entropy  [23]  to segment the cell body. Morphological opening with radii = 5. 5 and 2 pixels in x, y and z was then used to exclude the remaining processes from the segmented objects. The touching cell bodies were split using watershed separation in the MorphoLibJ plugin  [24].
Statistics All quantified data are presented as mean ± standard deviation (SD) values. Statistics were performed using GraphPad Prism 9. 5. 1. Values from all quantifications performed were initially tested for normality using the Shapiro-Wilk test. All data passed successfully the normality test. Data from 2-photon intravital imaging were further analyzed using Repeated Measures one-way ANOVA and data from confocal experiments were analyzed using Ordinary one-way ANOVA. When interactions were detected, group comparisons were performed using t-test assuming unequal variances for confocal data, and Tukey's multiple comparisons test was used for 2-photon imaging data. The level of statistical significance was set at 0. 05.
To this end, cortical microglia and astrocytes of Cx3cr1-EGFP and hGFAP-ECFP transgenic mice were imaged by 2-photon microscopy under baseline conditions (control). For the next 2 days (day -1 and day 0), the same mice received 5 mg/kg of LPS and were imaged at days 4 and 8 after the second dose of LPS injection. The acquired image z-stacks were analyzed using Fiji-based custom-made macros that allowed quantification of microglial and astrocytic 3D cell body volume as well as the extent of contact with blood vessels in multiple-image data sets. the second dose of LPS injection. The acquired image z-stacks were analyzed using Fijibased custom-made macros that allowed quantification of microglial and astrocytic 3D cell body volume as well as the extent of contact with blood vessels in multiple-image data sets.
45% at 8 days p. i. of LPS. Quantification was performed using a customized Fiji macro, as described in = Section 2. 6. Black circles on the bars represent mean values of each mouse measured. Control vs. LPS 4 days ** p = 0. 0052, control vs. LPS 8 days * p = 0. 01 and LPS 4 days vs. LPS 8 days * p = 0. 03. Visualization of contact areas was performed using Imaris 9. 3. 1 software.
0014, LPS 4 days vs. LPS 8 days ns (not significant) and control vs. LPS 8 days ** p = 0. 002. Analysis was performed using the Imaris surfacesurface contact area XTension. (E) Quantification of AQP4 total volume (μm 3 ) was measured using the Imaris v. 9. 3. 1 surfaces module. Control vs. LPS 4 days ** p = 0. 003, LPS 4 days vs. LPS 8 days ns (not significant) and control vs. LPS 8 days ** p = 0. 011.
55 ± 0. 41% in control to 2. 38 ± 0. 32% at 4 days and 2. 8 ± 0. 45% at 8 days p. i. of LPS. Quantification was performed using a customized Fiji macro, as described in = Section 2. 6. Black circles on the bars represent mean values of each mouse measured. Control vs. LPS 4 days ** p = 0. 0052, control vs. LPS 8 days * p = 0. 01 and LPS 4 days vs. LPS 8 days * p = 0. 03. Visualization of contact areas was performed using Imaris 9. 3. 1 software.
Context. The Cassini-Huygens space mission provided a huge amount of data for the Saturnian system. While the mission ended in September 2017, there is still much information to be retrieved from the data. Aims. Here, we infer the interior of the five inner Saturnian moons, Atlas, Prometheus, Pandora, Janus and Epimetheus from their orbital motion. Our results benefit from a recent study of the shape of these moons, which provides a much better estimation of their 3-d shape and volume. Methods. Using Cassini Imaging Science Subsystem (ISS) astrometric data, we reduce again the Cassini ISS images with an improved shape modeling. Then, we integrate the orbital motion of the inner Saturnian moons and solve for a large set of physical parameters for this system, including the moons' masses and physical libration amplitudes.
Astrometric observations We implemented the 3-d shape modeling from  (Rambaux et al. 2022)  in the Caviar software package  (Cooper et al. 2018 ). Following  Lainey et al. (2019) , the data set was split into different subsets depending on the technique used for the determination of the center of figure of the moons in the images. When the apparent diameter of the moon was above typically 3-4 pixels, we were able to provide a position of the center of figure of the satellite using the limb fitting method and complex shape modeling (see Fig. 1 ). In total, 2635 astrometric positions could be recomputed with the updated limb fitting method. For Prometheus, Pandora, Epimetheus and Janus, images taken from relatively large distances were not used. Indeed, first analysis showed that they did not provide any useful signal on the dynamics of these moons taking into account the great accuracy of limb-fitted data.
Dynamical modelling Using the NOE gravitational N-body code  (Lainey et al. 2007; Lainey 2008; Lainey et al. 2009) , we fitted the full equations of motion, equations (1), to the astrometric observations described above, solving for the initial positions, velocities, and physical librations of Prometheus, Pandora, Janus and Epimetheus, in addition to various global parameters, such as the masses, C np and S np gravity coefficients and polar orientation. Our model included (i) gravitational interactions up to degree two in the expansion of the satellites' gravitational potential (Table  2  from  (Lainey et al. 2019 )) and degree 10 for Saturn  (Iess et al. 2019 ); (ii) gravitational perturbations from Mimas, Enceladus, Tethys, Dione Rhea, Titan, Hyperion and Iapetus, based on ephemerides from  Lainey et al.
Weighting the data Each observed astrometric data value has an inherent uncertainty in pixel units originating from the centre of figure determination. The Caviar software makes an estimate of this uncertainty, but this estimate does not take into account the uncertainty in the camera pointing direction, and more importantly, the real meaning of the Caviar uncertainty as a standard deviation (i. e. 1-σ) is unclear. As a consequence, we instead followed the method used in  (Lainey et al. 2019) , to estimate as accurately as possible a 1-σ uncertainty for each data point. In particular, we rescaled the Caviar uncertainties using a scalar chosen to distribute about 66% of astrometric residuals to within a 1-σ level, for each satellite and coordinate value. An additional issue to be taken into account was the redundancy of information coming from some ISS observation sequences : often data points existed within a relatively small time interval of each other (in this case, less than 30 minutes).
In relation to the aforementioned criteria, the Joint Research Center (JRC) published a theoretical SSbD framework for the description of such criteria. 3 The framework provides guiding principles on the SSbD dimensions to support the design phase and aspects and indicators in each dimension to establish criteria that will guide researchers toward SSbD practices. The SSbD dimensions are shown in Figure  1  demonstrating the re-design phase supported by a hypothesis formulation and the dimensional targets of functionality, human, and environmental safety containing intrinsic hazards, human occupational safety (process stage) and human/environmental health (use phase), and the two final steps of sustainability (environmental and economic). The first principle stressed by ref 4 that supports the SSbD framework is the need of findable, accessible, interoperable, and reusable (FAIR) data: each dimension is driven by criteria based on data (experimental or modeled) to promote safe and sustainable research and innovation.
A cornerstone aspect of the implementation and reproducibility of the SSbD concept is data quality and availability, that is, data FAIRness. Data needs to be treated according to FAIR principles to safeguard its long-term use and access. 5, 6 The data management plan has been added as an inherent deliverable of any project that generates, assembles, or processes data according to the guidelines on FAIR data management. The Anticipating Safety Issues at the Design Stage of NAno Product Development (ASINA) project is generating data across the life cycle of NFs with the aim to develop a data-driven decision-making strategy based on the manufacturing of two types of enhanced antimicrobial NEPs, namely, functional textiles and cosmetics applications. 7 ] [10]  In this study, we show one of the fruits of the FAIR data management process and how such an action accelerates the development of new approach methodologies (NAMs).
Dataset: in a second step, data is curated, merged, and processed with various techniques (such as missing value imputations and one hot encoding). Data is analyzed for visualization and insight purposes. ML explorations for QSAR development: various ML models were explored, from regression and classifications algorithms to the construction of a constrained BN based on expert judgment. Models are trained and validated to reveal predictive performance metrics. Rules extraction: finally, the quantitative intrinsic hazard criteria (rules) applicable to the safety dimension of the SSbD framework were extracted from the BN structure (Figure  2 , right). From now on, in this manuscript, the interpretable rules refer to the quantitative intrinsic hazard criteria. This work demonstrates for the first time how QSAR models in combination with FAIR data can support the development and implementation of SSbD paradigm by supporting the knowledge establishment for criteria definition.
e. , cellular viability). Nanodescriptors should reflect not only the substance elemental composition but also other characteristics requested by regulation when reporting a NF, for example, size distribution and other morphological characterization such as crystal structure. 39 Moreover, nanodescriptors should reflect the influence of the system (surrounding environment or experimental conditions) on those properties. One can differentiate between system-independent (intrinsic properties) and system-dependent (extrinsic properties) nanodescriptors. 40 In this study, both are considered. It is worth to notice that the dataset solely refers to AgNFs, rendering those descriptors collectively unique, acting as a fingerprint. This enables NFs belonging in the same elemental composition group to be differentiated.
The inclusion of the different assays as output-related features is also relevant since NFs could interfere with the tests and the final outcome. 60, 61  Intestinal cells are exposed with either digested or non-digested AgNFs at concentrations ranging from 1. 25 to 100 ppm with the WST-1 assay. All experiments refer to a 24 h duration of exposure. 2. 2. Dataset. 2. 2. 1. Dataset Curation. The three different datasets are as follows: toxicological attributes in (i) lung and (ii) intestinal cell lines along with system-dependent features and (iii) system-independent pchem properties were merged. Each row represents one set of experimental testing conditions and related system-dependent nanodescriptors based on the exposure dose and NF pre-treatment (for intestinal assessments). The system-independent inputs are NF specific and independent of experimental conditions.
Data is captured via FAIR principles where the reader can find the origin (institution) of each data, the responsible data creators (experimentalists), the raw measurements, the protocols followed, and the instrumentations used for each experiment. More information regarding the worksheet used for data capturing can be found here. 8. 2. 2. Data Preprocessing. Missing value imputation methodology is commonly used for ML studies since it is a basic assumption that (i) certain relationship exists between the different attributes and (ii) missing value fill-in is a learning process. 62  Missing value imputation: for the missing values of system-dependent nanodescriptors, imputation was performed by linearly interpolating data in cases where the corresponding variable was known in a smaller and larger dose; for example, if the hydrodynamic size at t 0 was known for a 10 and 50 ppm solution, interpolation for the 20 and 40 ppm solution was feasible (neighboring points according to the corresponding values).
The cases above and below those known values were left blank. The missing value interpolation was performed on a dose, cell line, and NF's pretreatment-reliant manner. Meaning, if the All the samples in the dataset have a cubic crystal structure, and the amorphous phase is at the crystalline state. hydrodynamic size at a 50 ppm solution for one specific digested NF was known, the same value did not apply for the non-digested NFs at the same 50 ppm solution. In this manner, we kept the missing value imputation uncertainty at minimum levels. For the systemindependent missing values and for the ones left blank from the interpolation, an iterative sequential imputation process was executed via regression with the Light Gradient Boosting Machine (lightgbm) algorithm. 63 Each feature is modeled as a function of the other features, allowing prior values to be used into predicting subsequent features.
The dataset with the ML-based imputed values can be found in the supplementary material (Supporting Information: tab v01 in the excel). It is worth to notice that during the ML imputation, the ERM codes of the NFs were left in the dataset (dropped after for modeling) since it is the only feature that distinguishes the dataset into fragments, greatly easing the lightgbm algorithm with targeted imputations, lowering the uncertainties. 2. 2. 2. 1. One Hot Encoding. One hot encoding was performed on the categorical attributes for the ML regression models and the BNs. This technique converts categorical features into numerical dummy variables with values 0/1 indicating the absence or presence of the originally feature. 64. 2. 2. 2. SMOTE. For the ML classifiers and BNs, the outcome was discretized into three classes: safe, toxic, and very toxic depending on the corresponding values of cell viability.
Data Analysis and Visualization. 2. 2. 3. 1. UMAP and MAPPER. Uniform manifold approximation and projection (UMAP), like principal component analysis methodology, is a dimension reduction technique for 3D data structure visualization. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. 66 Prior to patching together their local fuzzy simplicial set representations, it first builds a topological representation of the high-dimensional data with local manifold approximations. 67 Similarly, the Mapper algorithm was used for visualization purposes, a method for extracting simple descriptions of the dataset in the forms of simplicial complexes. 68 The methodology is qualitative based on topological ideas and on partial clustering guided by a set of functions defined on the min-max scaled data.
7. 72 Regression algorithms include lightgbm, random forest regressor (rfr), extra trees regressor (etr), Lasso regression (lasso), elastic net (en), linear regression (lr), AdaBoost regressor (ada), and so forth; classification algorithms include gradient boosting classifier (gbc), random forest classifier (rf), extra trees classifier (et), decision tree classifier (dt), ridge classifier (ridge), linear discriminant analysis (lda), and so forth. All models are trained with a randomly split sample containing 80% of the initial dataset, with 20% withheld for an out-of-sample validation. 2. 3. 2. BNs and Rules Extraction. BNs are directed acyclic graphical models where features are nodes and connections are arrows, each of which denotes a conditional reliance of a child to a parent node. 4] [75]  The probability distribution of all nodes is specified by the artifact of all CPTs in the BN model.
For the development of the BN structure and the CPTs, we utilized an open source ML package for probabilistic modeling in Python, pomegranate. 76 We initialized the BN structure development in a two-fold manner. First, the optimal unconstrained structure was built on the basis of the exact algorithm with knowledge learned directly from data without interference. Second, the structure was then refined by guidance upon expert judgment and inclusion of enforced expected dependencies. 77 The BN constructed in this manner encodes the expert's reasoning process and allows the system to explain the inference through interpretable rules. 22, 78 tructure learning and rules extraction are independent with the latter being described as an explainability method. 77 Each rule is followed with a certainty factor (CF), which is the likelihood ratio for and against an outcome (T) when presented with evidence (X): that is, IF (X = 0) THEN T = 0 with CF = 0.
System-dependent variables contain DLS measurements in two different times. The toxicological data contain information related to (1) in vitro characteristics such as the cell line exposed, the cell type, cell origin, and cell number; (2) exposure conditions such as the exposure dose and duration; and (3) output-related information such as the assay for the cellular viability determination. The FAIR dataset is enriched and annotated with information of the origin of the data, the protocols, and instrumentation and can be found in the Supporting Information and in the open repository Zenodo k. F 3. 2. Data Analysis. In Figure  3 , UMAP and Mapper topological network projecting data into lower dimensional spaces demonstrating the structure of the data. Even with missing value imputations, the experimental data are quite sparse with relatively low variance, with the local dimension varying across the data and the dataset uniformly distributed on the Riemannian manifold (Figure  3 , left).
The NFs (ERM identifiers) which are utilized during missing value imputation with the lightgbm algorithm shows high correlation with nearly half of the features in the dataset. Thus, including it during the imputation helped the algorithm to efficiently allocate missing values. Crystallinity is correlated with the C 1s atomic concentration, while Ag 3d atomic concentration is correlated with the core size, coating, and surface area of NFs. The average crystallite sizes are correlated with the coating along with other features such as the C 1s atomic concentration. This information is useful for the reasoning construction of the BN structure since some correlation among features is required. Negative linear correlation is shown among O 1s with Ag 3d and C 1s atomic concentrations (see Supporting Information Figure  S1 : Pearson r correlation).
82 able  2  shows the final modeling features along with their skewness and the transformed bins for the BN training, which also represent the applicability domain of the QSAR model. Skewness quantifies distribution asymmetry, and values between -2 and +2 are acceptable to demonstrate a normal univariate distribution. 83 All features show good skewness except hydrodynamic size at t 24. However, the feature is included since it contributes greatly to the information gain analysis of the dataset (see Supporting Information Table  S1 : attribute selection). All the experiments refer to a 24 h acute in vitro toxicological screening, thus from the exposure conditions, only the exposure dose was considered. Na 1s and N 1s atomic concentrations were not considered for the modeling due to redundant zero vales and the fact that those features are related to the synthesis process and precursors utilized and have no causal effect to hazard effects.
With the above manipulations of the conditional dependencies based on expert judgment, the constrained model displayed slightly higher predictive capacity for some classes, but most importantly, the structure has reasoning, in order for the rules to make sense. Exploring the Weka software for automated BN construction (estimator: simple, a = 0. 5, search algorithm: local hill climber, six parents limit configuration), the unconstrained structure also demonstrated the exposure dose, hydrodynamic size at t 24 , core size, and assay being connected to the outcome, reinforcing the reasoning on some arcs (see Supporting Information Figure  S3 : unconstrained Bayesian structure derived from WEKA software). Figure  6  demonstrates the external validation results of the QSAR BN-based model tested with the 20% test set.
DISCUSSION 4. 1. Data. For the moment, a great amount of information produced by H2020 projects is stored online in private servers, locked to external users, making the data re-usability unfeasible, hindering progress and data integration, especially for modeling purposes. 85 Commmission 4 remarks that research and innovation are needed in open platforms to ensure access and data integration from different databases enabling exchanges between different stakeholders in line with data governance acts, meaning an overarching level. 1  Metadata capturing is not frequently promoted in regular academic practice, despite its importance. This is due to a lack of data management training. This FAIR challenge requires the active involvement, participation, and collaboration of participants with different expertise.
In this work, we used data captured by the data shepherd,  8  which demonstrates that this role is essential in a project where data are generated, modeled, and used. 85 The role of the shepherd is to capture data, protocols, and instrumentations and to help with data reporting, merging, and harmonization. ] [10]  It is outside of the scope of this manuscript to provide details regarding the FAIR initiatives and the efforts in place in the EU. The reader can refer to the following footnotes l,m to get an appreciation of the current initiatives regarding FAIR data. The size of the dataset used in this study is not remarkable (in comparison to common experimental computer science fields), but in comparison to the dataset sizes used in the nanocomputational domain literature, the data size is sufficiently large.
16 To tackle this sparsity, the approach in this work is twofold, the data is augmented by a standard method to oversample sparse data with SMOTE leading to 1682 data points and by applying BN ML algorithm, which is a robust learning paradigm in the sparse data regime. In addition, the interoperability of data is high due to the annotation with ontological identifiers from eNanoMapper and with ERM identifiers which ensure that internal project documentation can later be linked to released data for specific NFs. 35 The dataset contains harmonized features derived from different laboratories; for example, the system-dependent properties reported in the toxicological datasets were similar across the two partners, greatly facilitating the merging. By capturing the measurements at two time points, we were able to account for variations that might occur to the size when dispersed and once when in contact with cellular medium.