dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The main route of exposure from most cosmetic products is dermal, but oral (e. g. lipstick, toothpaste) and inhalation exposures (e. g. sprays, loose powders) also occur. One of the most challenging steps in human risk assessment is the derivation of safe thresholds. In recent years, due to a combination of concerns over the ethics of animal tests and regulatory bans on animal testing of cosmetic ingredients, there has been a lot of effort to find new ways to assure the safety of cosmetics ingredients in the absence of animal testing data. The Threshold of Toxicological Concern (TTC) concept is one option which can be used for cosmetics ingredients with a limited dataset and sufficiently low consumer exposure (SCCS, 2021), and is a common feature of tiered approaches to non-animal safety assessment  (Berggren et al.
If by using inhalation TTC an acceptable level of risk is achieved in a cosmetic ingredient safety assessment, then additional data would not need to be generated. A virtual workshop organized by Cosmetics Europe was held on 3rd and 4 th November 2020 to share the current state of the science regarding (a) the existing inhalation TTC approaches and (b) propose modifications for a more robust approach to gain scientific and regulatory acceptance. The workshop included the following presentations by experts in the field of inhalation exposure and toxicity: The second part of the workshop was to discuss several key questions and draw conclusions where possible: • Defining the chemical space and domain of applicability: What other data sources are available to grow the inhalation TTC dataset and which chemicals should be included/excluded? • How is the study quality defined for inclusion into the database? • Do we need to include an applicability domain to know when the inhalation TTC concept can be applied, and do we need better tools to predict the applicability domain? The robustness of the various approaches was debated, vulnerabilities were highlighted, along with what new work was needed to be undertaken to manage any gaps.
Derivation of inhalation TTC values from different databases Compared to the database relating to toxicity data derived from oral repeat dose studies in animals where there are several thousand studies  (Patel et al. , 2020) , the number of available high quality repeated dose inhalation exposure studies is limited (e. g. there are only ~400 rodent inhalation studies in the Research Institute for Fragrance Materials (RIFM) database https://rifm. org/rifm-database/). Thus, inhalation TTC values have been typically developed using smaller databases of different origin and containing different chemical spaces. This section describes some of the key work and considerations involved in deriving inhalation TTCs for low throughput (e. g. cosmetics ingredients) verses high throughput (environmental chemicals) purposes.
RepDose derived inhalation TTCs In 2010,  Escher et al. (2010)  published inhalation TTC values derived from the RepDose database  (Bitsch et al. , 2006). They collected inhalation toxicity data for 203 chemicals and expanded the dataset to 296 chemicals (608 studies) later on  (Tluczkiewicz et al. , 2016). The dataset comprised only organic compounds (no metals, inorganic compounds, or polymers) with a defined chemical structure and smiles code. Several quality criteria were applied such as chemical purity >90% and repeated dose toxicity (RDT) rodent studies via the inhalation route with an examination scope comparable to guideline studies. When several high-quality studies per compound were available, the "no observed effect concentration" (NOEC) was derived from the longer-term study, prioritizing chronic (>1 year) studies above sub-chronic and sub-acute studies.
Abbreviations Both publications investigated the impact of DNA reactive (genotoxic) chemicals on inhalation TTC values. Normally, genotoxic chemicals are allocated an oral TTC of 0. 15 μg/person/day  (Kroes et al. , 2004). There were some predicted genotoxic chemicals (based on QSAR tools, thus an approximation and potentially over-predictive) in the databases. When genotoxic chemicals were excluded from the Escher et al. (  2010 ) dataset, the inhalation TTC for Cramer Class 1 increased by 2-fold (from 71 to 180 μg/person/day) while for Cramer Class 3, the inhalation TTC stayed approximately the same (4 μg/person/day). In the larger data set, the inhalation TTC values for the L-and T-classes also stayed the same with genotoxic chemicals removed. The conclusion from this work was that genotoxic chemicals do not have a high impact on the 5th percentiles and the resulting inhalation TTC values.
Carthew et al. approach Carthew et al. (2009)  used publicly available sources of data, specifically from RDT inhalation studies including chronic, sub-chronic and sub-acute (>28 days) studies. The original dataset was small, with 92 industrial chemicals (commonly found in consumer products) tested in rodents for which "no observed adverse effect concentration" (NOAEC) values for specific chemical classes were identified. Carthew et al. differentiated  between local and systemic effects and when a local effect was reported, it was assumed that it was in the respiratory tract. They used various safety adjustment factors from the ECHA 2008 guidance  (REACH Technical Guidance Document, 2008). Inhalation TTCs were derived for local (1400 and 470 μg/person/day for Cramer Class 1 and 3, respectively) and systemic (980 and 170 μg/person/day for Cramer Class 1 and 3, respectively) effects.
Cramer Class 2 contained only 4 chemicals and therefore was defaulted to Cramer Class 3 inhalation TTC limits. Carthew et al. calculated local respiratory TTC levels by taking the 5th percentile NOAEC for chronic local effects normalized to rat lung weight. This was then converted to human exposure by using the human lung weight. This raises the question as to the type of correction needed for local effects and whether they should be derived based on lung weight. Dosimetry within the lung is complex and converting from an air concentration to a deposited dose in the rat and human lung is complex. An exposure duration correction was used to express the exposure per day to show inhaled dose. A conservative yet practical dataset was built with Cramer Class 1, 2 and 3 chemicals. The range of NOAEC values is relatively wide, but the 5th percentile inhalation TTC limits included some quite toxic chemicals in the low end of the distribution.
Development of inhalation TTC by building upon the A joint Procter & Gamble (P&G) and RIFM project aimed to develop an inhalation TTC using the dataset of  Carthew et al. (2009)   ) and ECETOC). There was a clear distinction between values for local and systemic effects for Cramer Class 1 and chemicals; however, the suitability of Cramer Class to distinguish between toxic potency following inhalation was still unresolved (since Cramer Classification system is based on systemic effects derived from oral studies). Also, since the chemical domain within Carthew's original dataset was limited to industrial chemicals commonly found in consumer products, this expanded dataset needs to be analyzed to ensure relevant chemical spaces are captured. Correlation of physicochemical properties with site-specific effects (i.
e. site of contact effects in different parts of the respiratory tract) to identify those properties that drive such effects was considered for evaluating the dataset. However, this was not possible because in most studies observations were reported for general respiratory tract effects (not specifically for upper or lower respiratory effects). A combination of chemical features and toxicological potency was evaluated to determine whether these were more suitable for defining potency groups for inhalation TTC thresholds. This evaluation required advanced machine learning approaches using both, supervised (predicting the cluster labels of chemicals) and unsupervised (for clustering of chemicals) approaches to identify key descriptors defining potency groups for inhalation TTC. The clustering was analyzed using different methods, e.
g. KMeans, MiniBatchKMeans, Gaussian Mixture Model, Agglomerative Clustering and Birch Clustering, which were visualized using hierarchical clustering. The performance of the clusters was assessed and compared according to the accuracy and cross-validation scores, thus providing a measure of confidence. The machine learning features were narrowed from over 900 to 10 to gain confidence. These included the number of atoms, bonds, and carbons; molecule radius; shape and size of a molecule; electronegativity; and ionization potentialall of which are correlated. They identified the minimum features required to reproduce the optimal cross-validation score, which was >80%. This means that at least 80% of the dataset is represented by these minimum features identified in the machine learning analysis.
Derivation of inhalation TTCs for environmentally relevant chemicals The Office of Research & Development of the US Environmental Protection Agency (EPA) were looking to evaluate how the TTC approach could facilitate risk-based prioritization for thousands of chemicals. To that end,  Patlewicz et al. (2018)  proposed an approach of using TTC (based on the  Kroes et al. (2004)  workflow) in conjunction with high throughput exposure predictions  (Wambaugh et al. , 2014)  to facilitate risk-based prioritization of several thousand chemicals. The approach showed promise, but two questions remained: 1) whether the   2019 ), used the EPA's Toxicity Values Database (ToxValDB) to derive new oral TTC values and compared them to the established values from  Munro et al. (1996). The oral TTC values derived from ToxValDB were similar, but not identical to the Munro oral TTC values.
A number of next steps were outlined including: re-considering exclusion criteria and how these are implemented in the  Kroes et al. (2004)  module within Toxtree, since better specificity is needed (this has since been explored and is described in  Patlewicz et al. (2022) ); harmonization of the genotoxicity alerts used as part of the  Kroes et al. (2004)  implementation within Toxtree (if another genotoxicity rule base is used, there could be some differences in the output); the utility of acute oral/inhalation data within TTC could be investigated and an reexamination of the study data used beyond the arbitrary minimum point of departure used.
Defining the chemical space and domain of applicability: what other data sources are available to grow the inhalation TTC dataset and which chemicals should be included/excluded? The group decided that the database should be expanded as much as possible, not limiting the contents to cosmetic products, thus allowing the approach to be used for all types of sprayable consumer products. It was mentioned that substances on the Cosmetics Regulation Annexes, as well as environmental and other organic chemicals should be considered for inclusion into the database. The group pointed out that a vastly expanded database allows better searches for compound classes of special concern, e. g. those that accumulate at the low/high end of the compiled NOEC distribution. Such analyses might help to identify compound classes that share a specific MoA and Fig.
1. Step-wise process approach taken to derive TTCs for inhalation. i) must be excluded because of comparatively high toxicity due to high pharmacological activity, e. g. as done for steroids, the Cohort of Concern in the oral TTC concept; ii) or contain enough members to justify the derivation of a group specific value, e. g. as proposed for organophosphates. The group acknowledged that the definition of these MoAs and exclusion categories (e. g. DNA reactive compounds, sensitisers or organophosphates) would need some consideration (most likely based on structural properties). Thus only chemicals with a defined structure would have a place in the database (rather than unknowns, or UVCBs (unknown variable composition or biological substances)). Although nanoparticles are currently not included in the oral TTC databases, it was suggested to include solid particles in the inhalation database to assess the extent to which (nano)particle-specific limits can be derived.
How is the study quality defined for inclusion into the database? In order to define study quality for inclusion into the database, a starting point for evaluation of a study could be to apply similar inclusion criteria as employed in the oral TTC dataset, and although some of the criteria for inhalation studies have evolved as the inhalation TTC databases have been built, a systematic and transparent way of including studies is needed. One possibility that resolved from the group discussion was the option to consider adopting the ToxRTool (an evolution of Klimisch  (Schneider et al. , 2009) ) for a systematic evaluation of studies. Furthermore, the group decided in the case of studies with less-than-ideal design/scope of examination, but which still provided useful supporting information, that the addition of uncertainty annotation would prevent the exclusion of useful data.
Do we need to include an applicability domain to know when the inhalation TTC concept can be applied, and do we need better tools to predict the applicability domain? It was discussed that applicability domain should be included to know when the inhalation TTC concept can be applied. However, the database would need to identify any unique structures/chemicals that would have a relevant impact on the derivation of inhalation TTC values, e. g. proteins. As for the oral TTC, these should be excluded from the inhalation TTC database and handled separately. While the exclusion of proteins was accepted by the group, the inclusion of chemical respiratory sensitizers was discussed. Since it is mainly epidemiological human data that identifies respiratory sensitisers, with only a small set of chemicals (less than 10) having compelling clinical evidence  (Sadekar et al.
, 2021) , and with the inhalation RDT study databases not containing the appropriate data on respiratory sensitization, the available data are not suitable for the definition of TTC values for chemical respiratory sensitization. Thus, these chemicals would also need to be excluded from inhalation TTC. The group identified that where chemicals are excluded, this would need to be included in the decision tree, e. g. ask if there is any concern for protein content or structural similarity to known respiratory sensitizers, and then exclude from further consideration within the approach.
What is the appropriate approach to discriminate chemicals based on toxicological potency? The group discussions acknowledged that the work to date has shown that there was not good discrimination between the toxicological potencies of chemicals after inhalation based on the Cramer decision tree. It was also clear from the workshop proceedings that there were different ways of classifying chemicals based on toxicological potency. While the group did not decide on an appropriate approach to discriminate chemicals, this provides the opportunity to develop a new classification scheme as industry. Thus, the focus of any future work should be to investigate whether a different scheme can be defined that better discriminates the potencies, for example by considering the most relevant applicability domains for the datasets.
Conclusions In recent years, the inhalation TTC concept (versions published since 2009 are summarized in Table  6 ) has been developed in part to support the safety assessment of substances with a limited toxicity dataset, but for which exposure via the inhalation route is sufficiently low. There are numerous reasons for differences between values including: the number of chemicals and related inhalation RDT studies; chemical spaces covered; study types; exposure duration; differences in study design; derivation of LOEC/NOEC; variability of underlying toxicity data; adjustment/safety factors used; chemical grouping; data curation; overlapping chemicals between categories and limited discrimination of local versus systemic effects. This workshop highlighted the progress made so far in deriving inhalation TTCs, as well as the next steps envisaged to develop them further for regulatory acceptance.
It also paved the way for a larger collaboration between the US-EPA, Fraunhofer ITEM, P&G and RIFM to 1) harmonize the datasets and outline the criteria for evaluating data quality, 2) evaluating observed local and systemic effects and their respective PoDs, and 3) determining a relevant classification system for the database and derivation of inhalation TTC values. This effort is also supported in part by Cosmetics Europe. In addition to the aim of using the inhalation TTC for exposurebased waiving for regulatory purposes, this concept may already be used as a screening/prioritization tool earlier on in a risk assessment framework when the material is of sufficient low-level exposure.
1. Inhalation exposure assessment and hazard plus workshop objectives (Anthony Bowden, Unilever) 2. Introduction to the TTC values derived from the RepDose databases and outlook to other relevant aspects to address inhalation toxicity (Sylvia Escher, Fraunhofer ITEM) 3. Development of inhalation TTC for local respiratory effects -Building upon the Carthew et al. approach (Jane Rose, Procter and Gamble and Nikaeta Sadekar, RIFM) 4. Derivation of new TTC values for exposure via inhalation for environmentally relevant chemicals (Grace Patlewicz, US EPA).
, 2016 machine learning; 28 groups: 19 high, 9 low High toxic: 2 μg/d Schüürmann NOEC RepDose 296 Structural alerts identified for high vs low NOEC, No Low Tox NOEC >12 et al. , 2016 machine learning. Physicochemical properties, ppm bioavailability, metabolism, MoA, to explore High Tox NOEC <0. 75 ppm Hoersch et al. , IFA GESTIS DNEL list 1876 Statistical DNEL distribution (99th percentile, 8 h Yes 50 μg/m 3 2018 occupational exposure) corresponding to 500 μg/worker/d Nelms and ToxVal database (subacute, 4703 (of No Comparable to Patlewicz, subchronic, chronic, reproductive, which 613 (inconclusive) Escher et al. : CC3 2020 developmental, multigeneration used in TTC) CC1: 8. 23 μg/d toxicity studies) CC3: 4. 28 μg/d Baseline: 22. 4 μg/d Reactive: 4. 3 μg/d RIFM/P&G Carthew, RepDos, P&G, RIFM, ECHA 246 Hierarchical clustering with machine learning Yes 5 clusters, 4 features.
Identification is challenging and relies on gene sequencing. The evolutionary history was inferred using the Neighbor-Joining method. The optimal tree with the sum of branch length Z 0. 22078491 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. The tree is not rooted and drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Kimura 2-parameter method and are in the units of the number of base substitutions per site. The analysis included 30 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 1363 positions in the final dataset.
The sow's location in the room (5 areas, Fig. 1 ), posture (standing, sitting, lying), behaviour (sleeping, investigating, ingesting = eating or drinking, observing), and social interactions (negative or positive) were recorded continuously (focus sampling) following the descriptive Table  1. Datasets are availed on the Data INRAE repository following this URL: https://doi. org/10. 57745/ CT8CUW (Data identification number: 10. 57745/CT8CUW).
Statistical calculations and analyzes The inter-observer reliability was estimated using the Kappa coefficient  (Cohen, 1960). Linear mixed effect models were applied using the nlme package on R (R version 4. 0. 2). The sow and the group (B16 or B17) were included as random in the model. The threshold of significance chosen was a P value below 0. 05. When the P value was between 0. 05 and 0. 10, we considered that as a trend towards significance. The duration and frequency of each behavior were calculated for each sow, week, and period. The effects of the week or day (Base, Test1, Test2), and the period of the day (PM, Night), on the different behavioral variables were evaluated. For the analysis, the different areas of the room were merged into three categories, the areas far from the feeder (A1 and A3), the areas near the feeder (A2 and A4), and the central corridor (A5).
Similarly, the recorded behaviors were merged into three categories: observing (which includes investigating), sleeping, and ingesting (eating or drinking) in the automatons. The effect of the period (PM, Night) and type of sound (alarm, animal, human, metal) on the number of changes and time spent (%) in the different areas, postures, and behaviours was evaluated during Test1 and during Test2. For this analysis, the data related to one "human" type of sound (i. e. "metal singer") have been removed as this sound was considered closer to an alarm sound type and therefore an outlier compared to the other human sounds. Time ratios were calculated (time of emission of the type of sound / total duration of emission of the sounds during a day) because the time of emission of each type of sound was variable.
Methods and Materials This study consisted of two surveys of the horse-owning population across the UK and Ireland. Both surveys were advertised via social media and were available through the Joint Information Systems Committee (JISC) online and were 'live' for a designated time. The surveys contained the following statement: 'The survey will take you approximately 10 min to complete. Your participation in this study is entirely voluntary, all data are totally anonymous therefore once the questionnaire has been sent data cannot be identified to be removed. By submitting your results, you are therefore agreeing to the use of your data in this study. ' This survey was approved by the Royal Agricultural University Research Ethics Committee (project approval number: 2020. 0040).
Survey Design Survey 1 (S1) was circulated between November 2019 and February 2020 and was available for completion for 73 days. It was promoted to universities, riding clubs and pony clubs and via Facebook groups. The survey consisted of 21 main questions that gathered data on horse and owner demographics and knowledge, general feeding practices and the management of different feed types. The questions were a mixture of 11 single answer questions, 17 multiple choice questions, for which respondents could select more than one answer, and 2 ranking questions. This meant that there was an overall 'response effort' of thirty-one questions. Survey 2 was circulated from June 2020 to September 2020 and was 'live' for 120 days. It was distributed via social media as well as horse and rider groups over the UK and via online forums (Horse and Hound).
Selection of trials 0] [21] [22] [23]  We searched the PubMed and Web of Science databases using the terms "(loiasis OR loase OR Loa loa) AND (ivermectin$) AND (trial OR therapeuti$)" for papers published in English or French from database inception until Jan 1, 2018. The reference lists of identified studies were also screened by SDP and MB. Eligible studies were clinical or community trials of ivermectin treatment (given as a single standard dose [150-200 μg/kg bodyweight]) that collected data on the MFD of individuals with L loa infection before and after treatment with.
Four additional studies  [24] [25] [26] [27]  were found to have MFD data obtained before and after treatment, but we had no access to the individual data. No new clinical trials meeting those criteria were identified between Jan 1, 2018, and Nov 2, 2022, in a repeated search of the two databases. Details on the trials used in this study can be found in the appendix (pp 1-2). All trials had been conducted in compliance with the regulations in force, and the present retrospective observational study did not require additional ethical clearance.