dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiple examples of the two behaviours are shown with their centroids illustrated as vectors from the m-sphere origin. The m-prints of the vulture shift due to bank angle; where red and orange prints are performed with the opposing bank angle to blue and dark grey prints (clockwise and anti-clockwise turns). The dot product of the centroid vectors from that of the centroid of the NOP provides the average angle of bank in the roll axis adopted over a full turn. For the swimmer, a postural rotation in the double arm stroke creates the ellipse m-print, which moves about the NOP according to heading, hence the dot product of centroids in this case provide a heading (green prints have the opposite heading to orange prints) sphere angular velocity can be calculated up to every 25 milliseconds between successive data points.
Fast fourier transformation on metrics Isolating single m-print trajectories for a repetitive or periodic behaviour can be done manually, as was the case for the examples above, which is onerous, especially if the behaviour is short-lived. Alternatively, each case can be isolated using a Fast Fourier Transformation (FFT). The FFT has commonly been used to identify periodicity in behaviour and for example, applied to identify the frequencies of limb movement from acceleration data (e. g. in the Ethographer 1 software  [48] ) and periodic patterns in depth profiles such as those associated with diel vertical migration (e. g. [49] ). Periodicity in behaviour measured with the TriMag should be readily identifiable with signal processing tools such as the FFT due to the low signal-to-noise ratio (Fig.
10b ). This should allow periodic behaviours to be isolated, even where they do not result in change in either acceleration or environmental parameters (e. g. dive depth). The FFT can be applied to isolate individual m-print trajectories automatically, using a zero-crossing approach to the inverse FFT to select complete oscillations i. e. complete cases for a repeated behaviour. Here, FFT is applied to extract the fundamental waveform and its low harmonics from the data recorded in a TriMag axis, before converting back to TriMag data using an inverse FFT. This removes any direct current components that create vertical offsets in the signal, forcing the waveform to oscillate about zero. Zero crossings (Fig. 10b , vertical lines) of the cyclical motion become easily defined and the m-prints can then be extracted as the data between crossings.
We used this method to extract the m-prints associated with each stroke cycle performed by a human during a front-crawl sea swim (Fig. 9 ), as well as soaring cycles of a wandering albatross, Diomedea exulans, (Fig. 10 )  [50]. These examples encompass single behaviours (a stroke or a soaring cycle) that vary in duration. Isolating prints with the FFT allows the consistency in angular velocity between complete cycles to be assessed. b The isolated m-prints for six double-arm strokes of the front crawl swim. The cases were separated using a Fast Fourier Transformation, FFT (yellow waveform) on the x-axis of the TriMag data (pink waveform). There is high consistency evident in the trajectory of points between the six cases, which for competitive swimmers is a measure of efficiency as they are not expected to show great variance across strokes although some of the observed differences may be due to the effects of wave chop This is a key performance metric in sports.
Conclusion The m-sphere is not a convenient artefact, but an inevitable consequence of plotting orthogonal magnetic field sensor data in tri-axial space. Similarly, m-prints on the m-sphere arise as a consequence of animal body rotations in any of three space axes, and thus provide a picture of animal behaviour over time. Fundamental metrics derived from m-prints, such as individual m-sphere co-ordinates and angular velocity, reduce complex tri-axial data to two or one dimensions, facilitating interpretation. Given that magnetometers are sensitive to almost all rotational movement, TriMag information is particularly valuable for animals that operate in fluid media (air and water) where body pitch and roll angle frequently depart from the horizontal and co-vary with heading, providing ecological context to movement studies where interpretation of acceleration data can be limited.
Funding Funding was provided by the Deanship of Scientific Research at the King Saud University, Saudi Arabia (project number IRG_15-38) and logistic and financial support by the South African National Antarctic Programme through the National Research Foundation. HW is funded by a Swansea University Studentship. This project as a whole was made possible by a generous grant from the Royal Society/Wolfson fund to build the Swansea University Visualization Suite.
Acknowledgements Many fieldwork teams were instrumental in tag deployments on the various animals at different sites around the world. We would like to thank all those involved in organising the fieldwork and deploying the tags for data collection, including  Mads Frost Bertelsen ,  Paul Manger ,  Tobias Wang ,  Osama Mohammed  and  Saudi Wildlife  staff at the  Mahazat As-Sayd Reserve, Saudi Arabia. We would also like to thank  Prince Bandar bin Saud Al-Saud, President of the Saudi Wildlife Authority (SWA)  for his unlimited support with the  Arabian Oryx project. For collection of the albatross data we thank  Christiaan Brink ,  Janine Schoombie ,  Stefan Schoombie ,  Kim Stevens  for their invaluable assistance in the field. Additionally, we thank  Simon Potier  and  Julie Fluhr  for their help with vulture deployments, we are extremely grateful to all the staff at  Rocher des Aigles (Rocamadour, France)  for their support and enthusiasm and of the support of the directors of the centre  D.
In dogs presenting with obvious neurological signs, a neurology specialist performed the neurological examination and recorded any neurological deficit identified through history or physical examination. In dogs without obvious neurological signs, an internal medicine specialist performed the clinical examination. We did not consider autonomic nervous system signs (low body temperature or low heart rate) as neurological signs at the time of the study. The data were analysed with SAS using a p value of 0. 05 to denote significance. Medians and interquartile ranges are reported for quantitative data. We investigated the association between macroadenoma and body temperature and between macroadenoma and heart rate by using a multivariate linear regression, including potential confounders (association with body temperature (or heart rate) with a p value < 0.
Therefore, we purposely collected data only at initial presentation. Standardized methods of measurement of both parameters as well as repetitive measures would require a prospective study. Because the diagnostic value of these parameters will always remain limited in dogs with clear forebrain neurological dysfunction, it would be of interest to evaluate the temperature and heart rate of dogs with macroadenomas and hyperadrenocorticism, but without obvious neurological signs. We had too few such dogs for meaningful comparison. In summary, our data revealed that dogs with macroadenomas had a lower heart rates and body temperatures than dogs with microadenomas. In the context of a case diagnosed with hyperadrenocorticism, the finding of a heart rate ≤ 84 bpm or a body temperature ≤ 38. 3°C should raise the suspicion of the presence of a macroadenoma.
2014; Knudsen et al. 2009; Martin et al. 2009; Sipes et al. 2013). The EPA has also built a Toxicity Reference Database (ToxRefDB), a reference database with over 30 years of legacy animal toxicity studies containing detailed information on over 6000 in vivo animal toxicity studies on over 1100 chemicals  (Judson et al. 2010; Martin et al. 2011; Reif et al. 2010; Shah et al. 2011; Sipes et al. 2011). In addition to the EPA data resources, two other open source databases are available, namely, the Hazard Evaluation Support System (HESS-DB, http://www. nite. go. jp/en/ chem/qsar/hess-e. html), and COSMOS (http://www. cosmostox. eu/what/COSMOSdb/) databases. The HESS-DB was developed to support repeat-dose toxicity assessment and aid in read across and other category approaches. The COSMOS database is a legacy of the SEURAT-1 project, a European research initiative with the long-term goal of achieving "Safety Evaluation Ultimately Replacing Animal Testing".
Data sources and integration Publicly available data sources were collected, filtered, and integrated for developing a predictive model of study-level systemic toxicity effect level. The primary linkage between all data sources was the generic substance identification (gsid; 1:1 with CAS registry number) from DSSTox (http:// www. epa. gov/ncct/dsstox). All combined data and scripts are publicly available at goo. gl/R5XmxQ.
In vivo systemic effect level and study data Study-level systemic effect levels were collected from three resources: ToxRefDB  (Martin et al. 2009) , HESS-DB (http:// www. nite. go. jp/en/chem/qsar/hess-e. html), and COSMOS (http://www. cosmostox. eu/what/COSMOSdb/). For all database sources, studies were filtered based on common "study inclusion criteria": (1) oral dose administration (i. e. , food, water, gavage, and capsule administration methods); (2) more than one dose level; (3) basic adherence to test guideline with acceptable study quality; and (4) testing and observation of systemic effects. Systemic effects, for the purposes of this study, were defined as in-life observation or pathological finding (i. e. , clinical, macroscopic, and microscopic pathology) in repeat-dose exposed first generation adult animals.
Where possible, neurotoxicity findings were excluded including cholinesterase inhibition and neurobehavioral findings. Integration of these data sources was achieved by retrieving or calculating the study-level systemic effect levels along with the corresponding effect level type and qualifier. The available effect level types consisted of lowest effect level (LEL), lowest observed adverse effect level (LOAEL), and no effect level (NEL). LEL were systematically calculated as the lowest dose at which a systemic effect was observed, whereas LOAEL were retrieved from reviewed documents whereby adversity and a specific effect level were determined for systemic toxicity. NEL were set to the lowest dose tested when no treatment-related systemic toxicities were observed. All effect levels were in units of mg/kg/day or were converted from ppm to mg/kg/day using EPA standard conversions based on assumed food and water consumption.
Effect levels were then log10-transformed for all subsequent evaluation and modeling. Additional studylevel covariates were extracted or calculated from the source databases, including the number of dose levels, dose spacing, test substance purity, study year, the type of effect level (i. e. , NEL, LEL, LOAEL), and effect level qualifier. Effect level qualifiers were assigned when the NEL was assigned to the highest dose tested or when the LOAEL or LEL was assigned to the lowest dose tested. The effect level qualifier for LOAEL and LEL was either "less than or equal to" the lowest tested dose or "equal to" based on the dose level in which the effect level was established. Whereas the NEL was only used when no effects were observed in the study and thus given the effect level qualifier of "greater than or equal to" the highest tested dose.
Toxicity reference database (ToxRefDB) ToxRefDB includes study data for over 1100 chemicals evaluated in more than 6000 animal studies  (Martin et al. 2009). Systemic effect levels were queried from ToxRefDB in the form of LEL or LOAEL. LOAEL values were determined for a subset of studies, primarily registrant-submitted studies of pesticide-active ingredients that were reviewed by EPA. Subsequent to applying study inclusion criteria described above, 3752 studies across 836 chemicals were included in the present study.
Hazard evaluation support system database (HESS-DB) HESS-DB was developed to support repeat-dose toxicity assessment and was coupled with other knowledge bases to aid in read-across and other category approaches (http:// www. nite. go. jp/en/chem/qsar/hess-e. html; Sakuratani et  al. 2013). Portions of HESS-DB have been included in the OECD toolbox. HESS-DB houses detailed repeat-dose toxicity study data, including hundreds of otherwise unpublished Japanese governmental studies performed as 28-day repeat-dose rat studies. Subsequent to applying study inclusion criteria described above, 432 studies across 411 chemicals HESS-DB were included in the present study.
COSmetics to optimize safety database (COSMOS-DB) COSMOS was one of seven projects forming the SEURAT-1 cluster  (Gocht et al. 2015). As part of the COSMOS project, the COSMOS relational toxicity database was developed to store regulatory submission and open literature repeat-dose study findings (http://www. cosmostox. eu/what/databases/). Version 1 of the database is publicly available at http:// cosmosdb. cosmostox. eu/. COSMOS database stores over 12,000 toxicity studies spanning 27 endpoints including subchronic and chronic toxicity across approximately 1600 chemicals. Subsequent to applying study inclusion criteria described above, 195 studies across 141 chemicals were included in the present study.
Physicochemical property (physchem) descriptor set A set of physicochemical and environmental fate properties, LogP (logp), fish bioconcentration factor (bcf), water solubility (watersol), Henry's Law constant (Henry), biodegradability (bio), fish biotransformation (biotrns), gasphase reaction rate (aop), melting point (mp), boiling point (bp), carbon-normalized soil sorption (koc), octanol-air coefficient (koa), and vapor pressure (vp), were calculated using OPERA, a free and open source tool developed at NCCT (https://github. com/kmansouri/OPERA. git). The calculated properties are predictions of weighted k-nearest neighbors (kNN) QSAR models adapted from the publicly available PHYSPROP database in EPIsuite. Available at http://esc. syrres. com/interkow/EpiSuiteData_ISIS_SDF. htm, Scientific Databases available at http://www.
srcinc. com/what-we-do/environmental/scientific-databases. html. Prior to modeling, the PHYSPROP database has undergone extensive curation using an automated KNIME workflow designed for the purpose of validating and correcting the chemical structures and their identifiers such as CAS, names, SMILES and MOLBlocks  (Mansouri et al. 2016). The curated PHYSPROP datasets were then processed through a standardization workflow to generate the QSAR-ready structures used for modeling  (Mansouri et al. 2016). The same standardization workflow was used to process the chemicals structures of this study prior to prediction resulting in a total of 12 modeled physchem descriptors.
ToxCast bioactivity profiles ToxCast HTS summarized activity calls (positive or negative) and potency estimates (the modeled 50% activity concentrations, or AC50 values) were compiled in a matrix format, with one row per chemical and columns containing assay endpoint data represented as the negative log10 of the modeled activity concentration at 50% (AC50) divided by one million. To ensure adequate assay and chemical coverage (i. e. , a near complete matrix of data), chemicals with fewer than 800 assay endpoints tested or any assay endpoints with fewer than 500 chemicals tested were removed from the dataset. These assay and chemical coverage cutoffs generally equate to the full ToxCast Phase I and II chemical libraries that were screened in nearly all assays. The numeric cutoffs were used to allow for future updates and expansions to the model without having to explicitly mention the chemical library.
High-throughput toxicokinetic (httk) data Estimated toxicokinetic parameters along with two experimentally derived values, plasma protein binding and hepatic clearance, were combined in a simple model to produce oral equivalent dose values  (Rotroff et al. 2010; Wetmore et al. 2012). Oral equivalent dose values are the amount of daily oral intake required to reach specific steady-state concentrations in the blood. These toxicokinetic models have been extended to also predict area under the curve (AUC), peak, and mean concentrations as well as volume of distribution in the blood assuming a specific dose regimen. The values for all subsequent modeling were calculated using the 'httk' R package under the assumptions of a single daily dose of 1 mg/kg/day for 90 days in humans  (Pearce et al. 2017).
Feature reduction and missing data handling Following removal of descriptors for constant or near constant values, the initial descriptor set totaled 1306 descriptors. To reduce the number of descriptors prior to model development, descriptors that were highly correlated were removed using the findCorrelation function in the "caret" R package  (Kuhn 2008). A correlation cutoff of 0. 9 was used to identify and subsequently remove descriptors across all input descriptor sets, including physical-chemical properties (physchem), PaDEL, ToxPrint, ToxCast, and httk. The initial set of 1306 descriptors was reduced to 740 descriptors by removing 566 highly correlated descriptors. Principal component analysis was performed, using "prcomp" in R with centered and scale data, on the remaining 740 descriptors solely to characterize the remaining descriptor redundancy showing that 25% of the variance is explained in the first 4 components, 50% variance in the first 17 components, and 75% in the first 79 components.
Predictive model development and evaluation A schematic of the model development process as seen in Fig. 1. Study-level, effect level and associated study design parameters were combined by direct linkage to the gsid with the chemical-level descriptors to produce the complete dataset for modeling. All multivariate models were developed using the randomForest package in R. Additional machine learning methods were applied to the dataset and are not presented here as the random forest models were comparable or outperformed other methods in terms of reduced mean squared error (MSE); required less tuning to prevent overfitting, permitted mixed-type data; did not require data scaling; and, provided clear indications of variable importance as indicated by mean decrease in node impurity  (Liaw and Wiener 2002).
Throughout the model development process, two statistical methods were used to compare and evaluate model performance. Root mean squared error (RMSE) and percent variance explained (pseudo R-squared or R 2 for ease of reporting). Both statistics rely on MSE which is calculated as the mean of the squared difference of the predicted value minus the true value. RMSE is the square root of MSE while R 2 is one minus MSE divided by the variance of the observed values. Models were developed using different descriptor and chemical sets (Fig. 1 ). The three chemical sets stratified the study-and chemical-level data based on having only chemical descriptors, chemical and biological descriptors, and chemical, biological and kinetic descriptors; equating to sequentially smaller datasets based on data availability.
Random forest (RF) models The complete dataset was available for modeling, which includes the feature reduced chemical-level descriptors with median imputed replacement of missing values mapped to each study effect level and their associated study-level covariates. RF, as implemented in randomForest for regression in R  (Liaw and Wiener 2002) , bootstraps the data, creating 'in-bag' and 'out-of-bag' sets for each tree. However, the multi-level nature of the data (i. e. , study-level vs. chemicallevel) presented a challenge as the inherent inter-class correlation can introduce bias and overfitting when evaluating the "out-of-bag" performance of each constructed tree, but has been shown to be addressable  (Karpievitch et al. 2009). For example, if bootstrapping is performed, then training a model on study1-chemical1 and testing the performance of the model on study2-chemical1 would lead to a higher and potentially misleading assessment of the performance.
To overcome this potential bias, all model development steps, including cross-validation and external validation, was performed with chemical-level splitting. Specifically, the complete dataset was split into an internal training set and an external validation set with an 80/20 split using an adaptation of the Venetian blinds technique  (Consonni et al. 2009)  where by the dataset was ordered by the mean effect level for each chemical (mean effect level) and every fifth chemical was selected for the external dataset. Training and testing, using the internal dataset only, was performed using randomized fivefold cross-validation and repeated five times (i. e. , 5× bootstrapping). Additionally, each model set was developed against the full dataset as well as subsets of chemicals based on data availability.
Three different chemical sets were also used in the model development process to account for the varying overlap of a specific chemical with a set of descriptors and to evaluate the relative impact of smaller datasets. The randomForest parameter, mtry, was set to onethird the number of input variables (rounded down where fractional) with the number of trees set to 250. Bias correction was performed using the linear regression coefficients (i. e. , slope and intercept) of the training prediction versus observed values to adjust the test prediction values. Final study-level RF regression models were developed using the entire internal dataset with the number of trees set to a high number (i. e. , 2500 trees) to achieve more stable variable importance estimates. Variable importance was measured by the mean decrease in node impurity as indicated by the residual sum of squares  (Liaw and Wiener 2002).
Study and chemical summary statistics The integration of ToxRefDB, HESS-DB and COSMOS resulted in a dataset of 4379 studies across 1247 chemicals. ToxRefDB comprised the largest study set following quality and applicability filters with 3752 studies (836 chemicals). HESS-DB and COSMOS contained 433 and 195 studies (411 and 141 chemicals), respectively. Effect level data relevant to systemic toxicity were extracted from a diverse set of studies spanning multiple study types, species, strains, and routes of administration (Table  1 ). The effect level distribution for all 4379 studies had a mean of 1. 7 log 10 mg/kg/day (~ 50 mg/kg/day) with a standard deviation of 0. 94 log 10 mg/kg/day (Fig. 2 ). The effect levels were truncated between -2 and 4 (0. 01-10,000 mg/ kg/day) to prevent disproportionate influence of extreme values with 95% of the values falling between -0.
3 and 3. 2 log 10 mg/kg/day (~ 0. 5 and 1500 mg/kg/day). The mean of the effect level distribution decreased significantly (p value ⋘ 0. 01) from COSMOS (2. 2 log 10 mg/kg/day or ~ 158 mg/kg/day) as compared to HESS-DB (1. 8 log 10 mg/ kg/day or ~ 55 mg/kg/day) and ToxRefDB (1. 7 log 10 mg/ kg/day or 50 mg/kg/day); this observation was likely driven by differences in the chemical use types between these databases, with enrichment of cosmetic ingredients in COSMOS as compared to industrial chemicals and pesticides in HESS-DB and ToxRefDB, respectively. The distribution of effect levels stratified by the various study-level covariates illustrate the influence those parameters have on the overall effect level distribution (Fig. 3 ). The first and third quartiles (colored segments of boxplots) include the overall median effect level, 1.
e. , conversion) factors based simply on study type, duration and species. The chemical-level dataset (chemical, biological and kinetic descriptor sets) was integrated via chemical to the study-level dataset. However, biological and kinetic descriptor sets were only available for subsets of chemicals and, hence, studies (Table  2 ). The respective study and chemical counts as additional descriptor sets were added to illustrate the tradeoff in expanding the diversity of descriptors at the expense of study and chemical coverage.