dataesr/scientific-paragraphs-categorization huggingface dataset:
47 By incorporating the polydispersity index, we increases the quality of the measurements and consequently the modeling. 45 The data is targeting cellular viability, which is the majority of the re-usable data that exist in the literature and databases,  27, 85  implying a potentiality of further merging. Also, exposure conditions and in vitro characteristics are commonly considered as input features. 59 The exposure aspects are not part of step 1�hazard assessment of the SSbD framework. 3 In this work, we go a step further considering, besides the system and non-system-dependent properties, also the in vitro characteristics and exposure conditions to better represent how those properties are altered depending on the dose and the cellular target. We argue that inherently safer NFs should be cell line (organ)-target specific; that is, NFs safe for skin cells could be harmful for lung cells.
Other techniques have been proposed such as a hybrid missing data imputation method incorporating records similarity using the global correlation structure by using k-nearest neighbors and iterative imputation algorithms  86  or by merits integration of decision trees and fuzzy clustering into an iterative learning approach. 87  quantile-based discretization function was performed in this study to discretize features into bins. For this step, alternative methods have been proposed, for example,  88  introduced a dynamic programming search strategy and a Bayesian score for the evaluation and the discretization of variables. UMAP places related experiments (each row of the dataset) near to one another. Such an approach could hypothetically be helpful to identify the experimentations that should be prioritized during a project, in a data gap filling manner, supporting the application of QSAR modeling.
Since the axes in UMAP are non-dimensional, input features could be used to predict x, y, z values. On a second step, a SHapley Additive exPlanations (SHAP) analysis could reveal the most important features determining the space and were experimentations should focus. 89 In addition, the dimensionality reduction algorithms could be more interpretable only for some cases due to complexity. 19 However, this field is under research, with hyperparameter choice appearing to play an important role. 4. 3. NAMs. QSAR models based on random forest (rf) and extra tress (et) algorithms showed good validation metrics in our study. 1] [92]  Et algorithm generates a large number of unpruned decision trees from the dataset and then combines the predictions. Et similarly to rf randomly samples the features at each split point.
However, et splits the nodes by selecting cut points randomly, in comparison to rf, and fits each decision tree to the entire training dataset whose structures are independent of the output values. 93 he theoretical framework from ref 3 and the recent report by ref 4 both mention NAM approaches as helpful tools in the implementation and validation of the SSbD approach, without providing instructions. This study is a contribution of an iterative consolidation of modeling and experimental domain expertise. We demonstrate how experimentalists in conductions with modelers can act in a complementary manner, accelerating the progress in the nanosafety domain. Bringing the gaps between the three fields (toxicology, material designers, and modelers) demanded strong communication, interaction, while transferring experimental domain knowledge, adopting a multidisciplinary approach.
94 In this work, we demonstrated in a detailed manner how QSAR tools based on BNs coupled with expert judgment can be used for the definition and extraction of quantitative intrinsic hazard criteria. The same approach can be used in datasets targeting different outputs. The modeling approach is unique in some points: (i) the BN model is crafted by expert reasoning integrating systemdependent and -independent nanodescriptors in combination with in vitro experimental conditions derived from a FAIR process to predict a biological effect, (ii) the data refer to NFs that have the same chemical identity but a unique fingerprint that allows a NF-dependent differentiation among the same substance, (iii) the interpretable rules can guide material developers into synthesizing (re-synthesizing) inherently safer NFs, and (iv) the models (BN, regressors, and classifiers) can enable the fast and cost-efficient in silico toxicological screening of previously synthesized NFs and hypothetical scenarios of yet-to be synthesized NFs.
It is worth noting that the methodology strongly improves given variables that material designers have the most control over modifying in the laboratory. For the development of the BN structure and the CPTs, we utilized an open source ML package pomegranate. 76 Other packages for the implementation of BN are documented. 95 n the nanosafety, there are no clear understandings of causal relations among nanodescriptors and hazardous attributes, only statistical relevance information. Such relevance is insufficient to fully capture causal relations. This means that any explanation proven wrong may have to be prohibited within the structure. 78 The BNs can perform an incremental learning, meaning, as more data become available in the nanosafety domain, the existing structure can remain the same, or updated to novel modifications of parameters (inclusion of additional nanodescriptors or hazard endpoints), and even a new structure, to fit the new data.
CONCLUSIONS Collaborative efforts are required among data shepherds, experimentalists, experts, and modelers to merge information in an iterative manner that can reveal valuable information for each SSbD dimension. BNs are promising probabilistic ML tools helpful to (i) derive interpretable rules from FAIR data, (ii) capable and flexible in updating their conditional dependencies from new data while (iii) allowing the quantification of the uncertainties. In addition, they present graphical structures developed from expert reasoning in combination with automated inference. In this work, utilizing system (i. e. , hydrodynamic size and polydispersity index) and non-system (i. e. , elemental composition and core size)dependent nanodescriptors in combination with biological in vitro attributes and experimental conditions, we demonstrate how such a methodology can be used for extracting quantitative intrinsic hazard criteria for silver NFs, synthesized with the intend of antimicrobial/antiviral functional textiles and antimicrobial creams or lotions (cosmetics) applications, which can guide materials designers toward intrinsically safer materials while saving time, effort, and money for the toxicologists.
FAIR dataset used for the modeling can be found in the supplementary file and in open repository Zenodo ( h t t p s : / / z e n o d o. o r g / r e c o r d / 7 3 3 5 0 3 9 #. Y3emqnbMIQ8) (XLSX) Additional data analysis, QSAR development, validation metrics of algorithms, and additional BN rules extracted (PDF) ■ AUTHOR INFORMATION Corresponding Author.
Interpretable rule extraction workflow. From the data collection, to dataset curation, and exploration of ML tools for the QSAR development to the final extrapolation of interpretable rules. hydroxide (NaOH). From a sustainability perception, the one- step synthesis process utilized is an environment-friendly, sol-gel-based technology obtainable at room temperature h. (2) ERM00000552: Powder AgHEC (AgHEC pwd) is derived by spray-freeze-drying the solution without affecting the organic layer producing microparticles with highly porous nanostruc- tures (final composition of 11% Ag and 89% wt HEC). Both solution and powder contain a molar ratio of HEC/Ag = 5. 5 and NaOH/Ag = 2. 8. (3) ERM00000549: Reference uncoated material�Sigma-Aldrich (Ag ref). (4) ERM00000548: Reference material coated with PVP�Sigma- Aldrich (AgPVP ref) in a powder form.
79 "low": [55. 91 → 209. 97], "medium": size t 0 [209. 97 → 363. 31], "high": ≥363. 31 hydrodynamic nm 2. 67 "low": [63. 74 → 149. 34], "medium": size t 24 [149. 34→ 266. 90], "high": ≥266. 90 pol index t 0 -0. 72 "low": [0. 18 → 0. 47], "medium": [0. 47 → 0. 59], "high": ≥0. 59 pol index t 24 0. 31 "low": [0. 04 → 0. 28], "medium": [0. 28 → 0. 49], "high": ≥0. 49 cell line A549, HCT-116 (one hot encoded) exposure dose ppm 0. 32 "low": [0. 0 → 20. 0], "medium": [20. 0→ 58. 35], "high": ≥58. 35 assay WST-1, MTT, Alamar blue (one hot encoded) output feature metric skewness bins [for the BNs structure training] cellular viability % -0. 28 very toxic [0 → 30. 0%], toxic [30. 0 → 70. 0%], safe >70% a 900 rows transformed into the final dataset of 1682 rows through SMOTE implementation for the classification modeling and BNs.
In the last decade, over 50 entries of the POT family were deposited in the Protein Data Bank (PDB), representing eleven bacterial and two mammalian homologs, bound to eight unique natural di-and tripeptides (Ala-Phe, Ala-Glu, Ala-Gln, Ala-Leu, Phe-Ala, Phe-Ala-Gln, Ala-Ala-Ala, alafosfalin), peptidomimetic drugs (valaciclovir, valganciclovir, 5-aminolevulinic acid) and a potent transport inhibitor Lys[Z(NO2)]-Val  [3] [4] [5] [6] [7] [8] 10, 18, [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41]. These efforts revealed the basic principle underlying peptide binding in POTs which can be described as an electrostatic clamping mechanism between the invariable part of peptides (N-and C-termini as well as the peptide backbone) and conserved residues in the transporter (mainly via arginine, lysine, glutamate, asparagine and tyrosine residues).
Structures of DtpB-peptide complexes stabilized by nanobody 132 In order to obtain highly diffracting crystals of DtpB, we selected conformation-specific nanobodies after immunizing a noninbred llama  42. Out of 31 recombinantly expressed and purified nanobodies, 14 bound DtpB with a dissociation constant of 30 nM or lower as evident by bilayer interferometry (BLI) measurements (Figure  1 , Supplementary Figure  1 , Supplementary Tables  1  and 2 ). They were used as crystallization chaperones in subsequent crystallization trials and nanobody 132 (Nb132) emerged as the most promising binder for co-crystallization approaches. DtpB-Nb132 was initially incubated with the tripeptide Ala-Leu-Ala (ALA), and well-diffracting crystals grew using the vapor diffusion method. The structure of DtpB was determined using the atomic model of DtpA  7  (PDB accession number 6GS4) as search model for molecular replacement.
In order to obtain a broader vision of the plasticity of the binding site in response to peptides possessing various chemical groups, the co-crystallization efforts were continued with an in-house library of 82 peptides. Several batches of the DtpB-Nb132 complex were prepared, incubated with a peptide, and then dispensed robotically in 96 well screens containing three sets of crystallization conditions. If the diffraction resolution of an obtained crystal was better than 4 Å, the chemical screens were further refined around the best conditions. In this campaign, X-ray diffraction data were collected and analyzed for more than 2000 crystals with the help of automated crystallography pipelines based on the CrystalDirect technology  43, 44. For each dataset, with a resolution limit higher than 3.
5 Å, the presence or absence of a peptide was initially assessed by using an atomic model of DtpB devoid of any substrate, to calculate a difference electron density map (i. e. , Fobs-Fcalc). There, strong positive peaks within the central cavity indicated the presence of a ligand. The co-crystallized peptide was then modeled inside the positive peak, as a di-or trialanine moiety first, and then mutated to its original sequence, as the signal improved during the refinement steps. In the final validation, OMIT maps excluding the modeled ligands, were calculated for each structure (Supplementary Figure  2 ). In summary, we obtained 14 unique peptide bound datasets in a resolution range between 2. 0 and 2. 8 Å with the following peptides: Ala-Phe (AF), Ala-Ile (AI), Ala-Leu (AL), Ala-Gln (AQ), Ala-Val (AV), Ala-Trp (AW), Lys-Val (KV), Met-Ser (MS), Asn-Val (NV), Ser-Leu (SL), Ala-Leu-Ala (ALA), Ala-Phe-Ala (AFA), Ala-Pro-Phe (APF) and Ala-Trp-Ala (AWA) (Figure  2 , Supplementary Figure  3 , Table  1 ).
DtpB preferentially binds small hydrophobic peptides A more detailed analysis of the peptide KD dataset (Figure  3G ) indicates that DtpB preferentially binds peptides that are hydrophobic with a molecular weight below 300 Da (Supplementary Figure  9A ). Since our dataset accounts for less than 1% of all possible di-and tripeptides made from proteinogenic amino acids (n = 8400), we asked whether the available crystal structures of DtpBpeptide complexes can be used to formulate more precise peptide recognition rules. For this, we performed flexible docking all of possible di-and tripeptides into DtpB by the Rosetta FlexPepDock protocol  63  and used a modified hit selection procedure (see Materials & Methods). This allowed us to predict the placement of the peptide inside DtpB for all 8400 di-and tripeptides (Figure  5A , Supplementary Figure  10 ).
The datasets of DtpB bound to dipeptides AL and NV have not been part of the training set and as such can be used to validate the predicted binding poses. RMSD values of the peptide backbone between the experimental structures and the top ten docking poses (rmsBB_offset see Materials & Methods) were 0. 61 ± 0. 16 Å for AL and 0. 58 ± 0. 15 Å for NV (Supplementary Figure  10 ). AUC of the modified hit selection procedure (when applied to all 200 generated dock poses) was 0. 89 for AL and 0. 9 for AV (Figure  6B ), highlighting that the peptide binding pose inside the DtpB binding pocket can be predicted with high confidence. Furthermore, the RMSD for the peptide side-chains after superposition (rmsSC see Materials & Methods) was 1. 45 ± 0. 07 Å for AL and 1. 25 ± 0. 24 Å for NV, suggesting that the conformation of the peptide can be predicted with good precision.
binder probability were deposited to Zenodo with record ID's 7612027, 7611944, 7612000, and 7586704. Vertical dashed line shows the value of experimental KD that is used to divide peptides into two classes: binders (KD ≤ 1 mM) and non-binders (KD > 1 mM). Horizontal dashed lines correspond to the cut-off values for probabilities reported by the logistic regression classifier: if the binder probability is below 0. 4, then it is very likely that the peptide will not bind to DtpB. On the other hand, with binder probabilities above 0. 6 an interaction with DtpB is to be expected, however, it cannot be excluded that the peptide will still exhibit low affinity. In the probability range between 0. 4 and 0. 6 it is difficult to assign a peptide to any class ('ambiguous' prediction). Statistics for the highest-resolution shell are shown in parentheses.
Acknowledgements We thank the  Sample Preparation and Characterization facility of EMBL Hamburg  for support in this project and the beamlines P13 and P14 at  EMBL Hamburg  for regular access. We acknowledge  Rolf Nielsen  for initial NanoDSF characterization of DtpB and Instruct-ERIC and the  FWO  for their support to the Nb discovery and  Nele Buys  for the technical assistance during nanobody discovery. Funding for the automated crystallography pipelines at  EMBL Grenoble  was provided by the grants  iNEXT  (g. n. 653706 ) and  iNEXT Discovery  (g. n. 871037 ) projects funded by the  Horizon 2020 program  of the  European Commission  and through  Instruct-ERIC. All past and current members of the  Löw group  are acknowledged for their input to this manuscript. This research was supported through the Maxwell computational resources operated at  Deutsches Elektronen-Synchrotron (DESY), Hamburg, Germany.
Data availability statement The structures of the DtpB-peptide complexes were deposited to PDB with the following accession codes: 8B18, 8B19, 8B1A, 8B1B, 8B1C, 8B1D, 8B1E, 8B1F, 8B1G, 8B17, 8B1H, 8B1I, 8B1K, 8B1J. Data and code for the pyranine assay, thermal unfolding assay and docking-based prediction of.
Mastitis is an inflammatory disease in dairy cows, causing economic losses and reducing animal welfare. In order to contribute for the discovery of early and noninvasive indicators, our objective was to determine the effects of a lipopolysaccharide (LPS) challenge on the microRNA profile (miRNome) of milk fat, using microarray analyses in cows. Cows were fed a lactation diet at ad libitum intake (n = 6). At 27 ± 3 days in milk, cows were injected with 50 µg of LPS Escherichia coli in one healthy rear mammary quarter. Milk samples were collected just before LPS challenge (LPS-) and 6. 5 h after LPS challenge (LPS +) from the same cows. Microarray analysis was performed using customized 8 × 60 K ruminant miRNA microarrays to compare LPS-to LPS + miRNome. In silico functional analyses were performed using OmicsNet and Mienturnet software.
Santa Clara, USA). The data are accessible through the GEO series accession number GSE229476. Normalized data obtained with 75th percentile shift date from the microarray assay were analyzed using GeneSpring software (Agilent Technologies, Inc. Santa Clara, USA). We considered the q-value described as a powerful approach  [23]  to control the false discovery rate (FDR), which were obtained using the Storey method  [24]  with curve fitting model and considered significant at the FDR q-value ≤ 0. 05.
In silico functional analyses In silico functional annotations of miRNAs and their predicted targets were performed using OmicsNet tools  [25]. Due to the higher level of annotations in human than in bovine species, we used human tools with miRTarBase (V8. 0) experimentally validated miRNA-gene interactions. OmicsNet analyses also identified functional biological categories related to differentially expressed miRNAs, using Panther Biological Processes database by selecting only validated miRNA-gene interactions. Then, we categorized and grouped manually biological processes. The gene lists were also filtered by previously obtained results using Mienturnet software  [26] http:// userv er. bio. uniro ma1. it/ apps/ mient urnet/. The potential miRNAs-genes networks were identified and visualized using Cytoscape (Version 3.
Functional annotation of the differentially abundant miRNAs The in silico functional annotation by OmicsNet  [25]  with Panther database is presented in Figure  4. The targets are mainly involved in cell life (47%) including apoptosis, cell cycle, death, proliferation, and differentiation. The second class of biological processes affected were those involved in gene expression machinery (31%) including chromatin organization, regulation of translation and transcription (Figure  4 ). One Gene Ontology-Biological Processes (GO-BP) corresponds to a response to toxic substance process (Figure  4 ). Furthermore, the cell life and gene expression machinery were also highlighted by reactome analysis (Figure  5 ). Reactome analysis also identified Interleukin-4 and -13 signaling (Figure  5 ). The network analyses using Cytoscape showed networks that are influenced by 8 known miRNAs (miR-18b-3p, miR-29c-5p, miR-99a-3p, miR-190a-3p, miR-214-3p, miR-362-3p, miR-379-3p, and miR-496) that were affected by LPS injection (Figure  6A ).
Acknowledgements The authors thank the staff at  Herbipôle INRA (UE1414, Theix, France ),  Sebastien Bes ,  Arnaud Delavaud ,  Didier Bany ,  Cyril Labonne  and  Jacques Rouel  for technical assistance, sample collections and laboratory analyses. Pierre-Alexis Billa  is thanked for the first data analysis. Special appreciation is extended to  Pierre Germon ,  Gilles Foucras  and  Pascal Rainard  for their helpful discussions regarding LPS challenge. Microarray analyses were performed at the " PlateForme d'Exploration du Métabolisme" (PFEM, INRAE, Theix, France). The author acknowledges the support received from the  Agence Nationale de la Recherche of the French government  through the program " Investissements d' Avenir"  ( 16-IDEX-0001 CAP 20-25 ).
The importance of improving the FAIRness (findability, accessibility, interoperability, reusability) of research data is undeniable, especially in the face of large, complex datasets currently being produced by omics technologies. Facilitating the integration of a dataset with other types of data increases the likelihood of reuse, and the potential of answering novel research questions. Ontologies are a useful tool for semantically tagging datasets as adding relevant metadata increases the understanding of how data was produced and increases its interoperability. Ontologies provide concepts for a particular domain as well as the relationships between concepts. By tagging data with ontology terms, data becomes both human-and machine-interpretable, allowing for increased reuse and interoperability.
The size and complexity of such experimental designs and the resulting data challenge good data management practices and the preservation of data. Conversely, some investigations result in scarce amounts of data that may be significant if combined with additional datasets, as long as they are properly preserved. For this reason, the FAIR (findable, accessible, interoperable and reusable) principles were designed to guide data producers to maximize good data management practices  (Wilkinson et al. , 2016). FAIR data ensures transparency, reproducibility, and interoperability of plant science research, facilitating collaboration among scientists and enhancing the overall quality and impact of research outcomes. This in turn allows scientists to more easily contribute to, and more rapidly adapt to, the development of sustainable solutions for addressing global challenges such as food security and climate change  (Mayer et al.
, 2021; Arend et al. , 2022). One key component of research data management (RDM) is the comprehensive and accurate description of metadata, or data about data. Metadata provides essential information about the context, content, and characteristics of the data, helping researchers to organize, describe, and understand datasets and their production, enabling effective data discovery, sharing, and reuse  (Wilkinson et al. , 2016). The correct and complete recording of metadata relating to an investigation is especially important for plant research data as environmental conditions can have such a profound influence over the resulting data of sessile organisms  (C ́wiek-Kupczyńska et al. , 2016). With the large amounts of data being generated for a single research project, the potential and benefit of reusing and combining datasets to facilitate novel scientific discoveries is becoming ever greater.
The challenge lies in the ability to find and integrate relevant datasets from different sources. Metadata is crucial for the correct interpretation of experimental data. Consistency in metadata annotation is important to ensure data is interoperable. It is crucial that datasets are both standardized as well as not only human-but also machine-readable, especially in the plant sciences, where diverse types of data are collected, analyzed and integrated  (Shaw et al. , 2020; Pommier et al. , 2023). In recent years, ontologies have emerged as a powerful building block, supporting the standardization and harmonization of data annotation in plant sciences and increasing their FAIRness. Ontologies are systematic descriptions of knowledge used to describe a specific domain  (Jensen and Bork, 2010).