dataesr/scientific-paragraphs-categorization huggingface dataset:
The last dataset contains recordings from one author's (HP) project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, near Darlowo, Poland. We used Song Meter SM2 units with weather-resistant, directional Night Flight Calls microphones from Wildlife Acoustics Inc. , mounted on 3-5 m poles. The amount of collected data (>3200 h of recordings) exceeded what human expert can annotate manually in reasonable time. Therefore we subjectively chose and manually annotated the subset consisting of 22 half-hour recordings from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls. No other selection criterion or weather filtering was applied.
(a) trained on a held-out portion (72. 7 %) of the PolandNFC data and tested on the remaining 27. 3%; (b) trained using the main challenge development data and again tested on the 27. 3% of the PolandNFC data. This allowed us to evaluate further the generalisation capability learnt by the network. For variant (a), the training dataset consisted of sixteen 30 min recordings collected over 11 nights (split into 28,784 1 s clips), and the testing dataset had six recordings from 4 nights (split into 10,793 1 s clips). Training and testing recording dates were disjoint sets. The testing set was held the same across variant (a) and (b) to ensure comparability of results. Important to note is the specific structure of the PolandNFC dataset-it contains mostly negatively annotated examples (only 3.
Final scores attained by the 20 highest-scoring submissions. Error bars are estimated by bootstrap sampling. U R E 6 ROC plots for the systems attaining the four highest and one lowest AUC score. mean per-site stratified AUC F I G R E 7 Performance (AUC) of submitted algorithms analysed on a per-site basis. Warblr data (from around the UK) is treated as one site, while the other sites are recorders in the CEZ. Left-hand plot shows the per-site results; right-hand plot shows how the AUC scores compare when calculated over the whole pooled dataset, versus as a mean of the stratified per-site AUCs.
DATA A C C E S S I B I L I T Y • Development datasets: both audio and annotations are available under CC-BY-4. 0 licences. warblrb10kaudio: https://archive. org/details/warblrb10k_public ff1010bird audio: https://archive. org/details/ff1010bird Annotations: https://doi. org/10. 6084/m9. figshare. 3851466. v1 • Testing data (Chernobyl/warblrb10k): audio public, but annotations held back for future challenges. Audio: https://archive. org/details/birdaudiodetectionchallenge_test • PolandNFC data: held back, in preparation for future challenge. • Source code for baseline classifiers: GMM 'smacpy' classifier (MIT licence): https://github. com/danstowell/smacpy. skfl feature-learning is available as supplementary information in Stowell and Plumbley (2014a) (MIT licence). • Source code for the online submission website (MIT licence): https://github.
PolandNFC dataset : dataset The last dataset contains recordings from one author's (HP) project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, near Darlowo, Poland. We used Song Meter SM2 units with weather-resistant, directional Night Flight Calls microphones from Wildlife Acoustics Inc. , mounted on 3-5 m poles. The amount of collected data (>3200 h of recordings) exceeded what human expert can annotate manually in reasonable time. Therefore we subjectively chose and manually annotated the subset consisting of 22 half-hour recordings from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls. No other selection criterion or weather filtering was applied.
Methods for investigating the Mode of Action (MoA) for rodent liver tumors via constitutive androstane receptor (CAR) activation are outlined here, based on current scientific knowledge about CAR and feedback from regulatory agencies globally. The key events (i. e. , CAR activation, altered gene expression, cell proliferation, altered foci and increased adenomas/carcinomas) can be demonstrated by measuring a combination of key events and associative events that are markers for the key events. For crop protection products, a primary dataset typically should include a short-term study in the species/strain that showed the tumor response at dose levels that bracket the tumorigenic and non-tumorigenic dose levels. The dataset may vary depending on the species and the test compound. As examples, Case Studies with nitrapyrin (in mice) and metofluthrin (in rats) are described.
It is important to mention, that the definition of a "Primary" set of experimental data that is commonly used when establishing that a molecule operates via CAR activation reflects a current state of the science view with established methodologies as of 2017. The scientific methods available to researchers, and the understanding of CAR activation and the subsequent processes leading to liver tumors in rodents, will continue to advance, and thus the current Primary set of techniques/measurements may be supplanted by other approaches in the future. The authors recognize this likely transition, particularly in light of the stated desire to reduce in vivo animal testing and embrace in vitro and other alternative approaches  (NAS, 2007). Therefore, some emerging approaches for the demonstration of a CAR MoA are woven into the Tables, the case studies and the narrative that follows, to illustrate the trends that may become more common in future MoA datasets.
Minimum dataset, and further supporting information, for demonstrating a CAR MoA When proposing a CAR MoA for liver tumors induced by a test compound, there are critical parameters to be included in the final mechanistic data package, which should (at a minimum) include demonstration of the molecular initiating event (i. e. , CAR activation, KE1) and the obligatory key event of increased cell proliferation (KE3), while excluding other possible MoAs. If additional studies or assays are incorporated into a package of MoA work, the weight of evidence supporting the MoA in animals can become greater, in accord with the Bradford-Hill criteria that are part of the IPCS Framework, including: • Empirical support (including dose-concordance and time-con- cordance of Key Events and the adverse outcome) • Strength and consistency of the data (e.
The most compelling MoA data will be those generated in the same species, strain and sex of animal in which the tumors were observed following life time treatment with the test compound. If practical, the same test diet and age/source of animal should also be used in the mechanistic studies. In cases where both sexes were affected, in the interest of animal ethics, information gathered using just one sex that is representative of the responses in both males and females should be sufficient. Similarly, if liver tumors are observed for a test material in two species (i. e. , mice and rats), a minimum data set could be generated to demonstrate the full MoA in one species, as long as some evidence is provided that indicates the same MoA is operative in the other species. For example, Associative Events (e.
g. , subchronic liver pathology; liver weight changes; CYP2B induction) may give sufficient evidence of the same MoA being operative, avoiding the need for excessive duplication of data and animal use. Another consideration when designing MoA studies for a suspected CAR activating compound is testing at appropriate dose levels and time intervals. Typically, the dose levels should cover the achieved dose level(s) that caused a tumor outcome in the long-term rodent study, and at least one lower dose where tumors were not observed, along with a concurrent untreated negative control group. Testing at these tumorigenic and non-tumorigenic dose levels is usually necessary in order to satisfy the modified Bradford-Hill criteria, Fig. 1. A mode of action for rodent liver tumors via CAR activation, suitable for establishing a minimum regulatory dataset.
, 2006; Meek et al. , 2014). However, if robust dose concordance can be demonstrated from existing data or via in vitro methods, then minimally the dosing in the MoA study should match the dose level that produced tumors, but not be excessive. The duration of a MoA study or mechanistic measurements within guideline subchronic or chronic toxicity studies should include time intervals when the key events or associative events typically occur, which may differ slightly depending on the test compound or strain of animal. Examples of dose-response concordance and time concordance of key events with known CAR activators and plant protection products are available in published literature  (Elcombe et al. , 2014; Peffer et al. , 2017) , and in this current publication for the model compounds nitrapyrin and metofluthrin (see Case Studies).
A typical minimum dataset would include demonstration of: 3. 4. 1. CAR activation (KE1) and altered gene expression secondary to CAR activation (KE2) Traditionally this is accomplished by measuring gene transcript levels (KE2), by demonstrating AE1, i. e. , showing increased liver enzyme activity and/or protein that is diagnostic of CAR activators, plus evidence for at least one other related key event or associative event (Table  2 ). The expected pattern is that the fold-change increase in Cyp2b mRNA levels or CYP2B enzyme activity will be greater than the increase in Cyp3a mRNA levels or CYP3A enzyme activity. These measurements can be accomplished in liver samples from subchronic toxicity studies (stored frozen), or as part of an in vivo short-term MoA study and can include measurement at the transcript, protein or enzyme activity level.
If other available data (e. g. , increased liver weight coupled with hepatocellular hypertrophy) suggest that the molecule may be a CAR activator, the experimenters may also need to explore changes in mRNA or protein levels, since some CAR activating compounds have been shown to inactivate CYP2B enzymes and thus block the expected increase in PROD/BROD activity, despite large increases in expression of the Cyp2b mRNA and protein  (LaRocca et al. , 2017; LeBaron et al. , 2013). Data supporting both KE1 and KE2 can be obtained by investigating changes in gene expression in the liver of treated mice or rats (i. e. , KE2). The relative expression of mRNA levels can be measured by focusing on specific CAR-responsive genes, such as Cyp2b10 in mice and Cyp2b1/2 in rats by techniques such as qRT-PCR.
Increased hepatocellular proliferation (KE3) As shown in Table  2 , demonstration of KE3 is a Primary endpoint that needs to be established as part of a MoA dataset. Although the increase in proliferation is not unique to the CAR MoA, the temporal profile of cell proliferation can provide indications of a CAR MoA. Specifically, for most CAR activators, the burst of cell proliferation on a whole liver basis is observable early, during days 1-28, but dissipates back to a background labeling index thereafter. However, the overall number of replicating hepatocytes stays elevated due to the increased number of cells in the treated livers as a result of the initial wave of proliferation, and specialized studies have demonstrated that increased cell proliferation signals still occur within altered foci at later time intervals  (Bursch et al.
For example, a chemical that produces a marginally higher number of adenomas/carcinomas after lifetime feeding to mice may not show a statistically significant increase in the pre-neoplastic altered foci. Integral to a mechanistic package is the exclusion of alternative MoAs that may be responsible for the tumors  (Meek et al. , 2014) , and a number of modes of action have been identified for liver carcinogenesis  (Cohen, 2010). A list of these alternative MoAs and the types of data needed to disprove each alternative MoA are provided in Table  4. If these data are not already available in the toxicology database, one may need to generate data to address these alternate MoAs, typically using the species and strain where liver tumors were observed. If CAR knockout models are used, the background rodent strain may be different to that in which the liver tumors were observed.
In this case some confirmation that the wild-type strain upon which the KO model is based is responsive to the early key events is needed. General guidance on the types of data that can be generated for each alternative MoA is included in Table  4. In practice, some alternative MoAs can be excluded if they are not pertinent to the compound in question (e. g. , the compound is not designed to inhibit HMG-CoA-reductase like the statins), or based on findings seen in the relevant repeat dose studies (e. g. , a lack of evidence of cytotoxicity, inflammation, or signs of estrogenic effects). In other words, some of the existing repeated dose data should be re-examined to rule out alternative MoAs. Where possible, additional data from a short-term MoA study that helps to preclude the alternative MoAs via specific enzyme activity measurements, transcriptomic profiles or other clinical measurements can provide even greater assurance in dismissing the alternatives.
Further, experimental data in Syrian hamster, guinea pigs, non-human primates and humans have demonstrated that hepatocytes from these species (unlike mice and rats) do not to CAR activator treatment with an increase in cell proliferation, and lifetime treatment of Syrian hamsters with phenobarbital  (Diwan et al. , 1986)  did not produce any increase in liver tumors (as summarized in  Elcombe et al. , 2014). Based on these established differences between the species, generation of data with a test item to address human non-relevance of CARmediated liver tumors is listed as "Optional" in Table  2. However, until regulatory authorities (e. g. , ECHA, EPA) list the CAR MoA as another example of a MoA that is not relevant to humans, many registrants will want to consider generating test-item data that confirms this species difference.
With time, as more datasets are reviewed that further confirm the qualitative difference between humans and rodents in the key events of this CAR MoA, these additional studies to demonstrate human non-relevance should become unnecessary for each new molecule. As shown in Table  2 , a typical study will consist of primary hepatocyte cultures from the responding species (mouse or rat) and from human liver (either freshly prepared or cryopreserved). A preferred approach is to test up to the highest concentration that does not produce excessive cytotoxicity or compound precipitation, which may be established in a preliminary set of incubations. Endpoints of Cyp2b induction (which may be affected in both rodent and human hepatocytes) and cell proliferation (which is typically only increased in rodent hepatocytes) are then assessed at a range of concentrations.
Estrogenic activity A weight of evidence from the toxicity database and/or published literature may be sufficient to address this MoA, including a lack of effects on estrogen-sensitive tissues and/or estrogen-mediated endpoints. of the experimental design. Newer models that can be used to demonstrate species differences in the response to a CAR activating compound are listed in Table  3 , and discussed further in Supplemental File S1 (Experimental tools). In addition to primary hepatocyte cultures, various in vitro liver co-culture systems are available (e. g. , spheroids, co-cultures with non-parenchymal cells) that provide a system that retains normal hepatocyte morphology for at least 2-3 weeks, whereas primary hepatocyte cultures can only be treated for 4-5 days. However, methods to reliably demonstrate the key endpoints of Cyp2b induction and cell proliferation within these systems will need to be refined and published when exploring the utility of these rodent and human hepatocyte systems.
Overall weight of evidence The preceding sections and supporting Tables have outlined the typical datasets that registrants are able to produce currently to support a MoA for rodent liver tumors via activation of the CAR nuclear receptor. As described in the IPCS Conceptual Framework  (Boobis et al. , 2006; Meek et al. , 2014) , the overall weight of evidence can then be assessed for the following three questions: 1. Is the weight of evidence sufficient to establish a MoA in animals? 2. Can human relevance of the MoA be reasonably excluded on the basis of fundamental, qualitative differences in key events between animals and humans? 3. Can human relevance of the MoA be reasonably excluded on the basis of quantitative differences in either kinetics or dynamic factors between animals and humans? For a particular test item, the first question regarding the animal MoA is assessed taking into account dose concordance, appropriate temporal associations, strength/consistency of the effects across multiple studies, biological plausibility and consideration of alternative MoAs.
However, the series of Key Events and Associative Events depicted in this manuscript (Fig. 1 , Table  1 ) do not include these toxicokinetics and exposure considerations, in the interest of simplifying the approach to this MoA and the needed data to demonstrate it. By keeping the exposure route and dosing in typical MoA studies close to the methods that were used in the long-term oncogenicity studies, the net effect of exposure to the test substance can be assumed to match the effects that led to an increase in liver tumors.
Case study 2metofluthrin tumor MoA in rats The dose concordance and time concordance of data in male Wistar rats that support its MoA via CAR activation are presented in Table  6. Metofluthrin has produced an increase in liver tumors via a CAR activation MoA in male Wistar rats (900 and 1800 ppm) and female Wistar a Nitrapyrin demonstrated to be mechanism-based (suicide) inhibitor of CYP2B10 activity (PROD). b CYP2B10 qualitatively assessed by Western Blot of nitrapyrin-treated liver microsomes. c Data are presented for panlobular (summed) labelling indices. d Incidence of altered foci include basophilic and eosinophilic foci. e Relevant study-specific control incidence of liver adenomas and carcinomas in male mice was 17/50. f In addition to the results shown under KE1 on Day 14, the following endpoints were similarly affected on Day 7: ↑ Cyp2b10 mRNA, ↑ hepatocellular hypertrophy, ↑ relative liver weight (treatment-related effects at 250 and 400 mg/kg/day).
, 2009; Yamada et al. , 2009) , including the analyses shown below. • Genotoxicity: metofluthrin was negative in a series of genotoxicity studies. • Cytotoxicity: metofluthrin was not cytotoxic to hepatocytes (dosed up to 1000 μM) and did not show histopathology evidence of cytotoxic effects in vivo (e. g. , no necrosis, fibrosis, inflammation). • PPARα activation and AhR activation: metofluthrin did not produce CYP induction that would indicate activation of PPARα (e. g. , Cyp4a isoforms) or AhR (e. g. , Cyp1a isoforms) in microarray analysis  (Deguchi et al. , 2009). It also did not induce the formation of peroxisomes in vivo (including examination by electron microscopy). • Estrogenic MoA: across the full database of toxicology studies, me- tofluthrin did not show evidence of alterations that would signal an estrogenic effect.
Values in parenthesis are fold-change vs. controls. a Data from  Deguchi et al. , (2009) , including supplementary data Tables  1 2 3 4 5. b Data from  Yamada et al. , (2009). Control group tumor incidence was 1/50. c Deaths and significant clinical signs occurred at 3600 ppm, indicating 3600 ppm was in excess of MTD. d BrdU labelling index for a 2700 ppm metofluthrin group was significantly increased at 7 days (4-fold), but no different from controls at 14 days  (Deguchi et al. , 2009). suppression of two markers (Cyp2b1 mRNA and Car mRNA) by siRNA co-treatment of rat hepatocytes in vitro. Alternative MoAs can reasonably be excluded for metofluthrin based on additional measurements in the course of the MoA studies. In addition, the human non-relevance of the proposed MoA for metofluthrin was confirmed experimentally based on lack of a cell proliferation response in primary human hepatocytes (in vitro).
Discussion Current methods for investigating the MoA for rodent liver tumors and providing a sufficient weight of evidence to establish a MoA via CAR activation have been outlined in this paper, based on both the current scientific knowledge about CAR activation  (Elcombe et al. , 2014; Lake et al. , 2015)  and experience in exploring MoA studies for the registration of pesticidal active ingredients with regulatory agencies globally. A proscriptive, box-checking approach to the question of what data is sufficient to demonstrate this rodent MoA via CAR cannot be applied uniformly across all species, strains and test items, because of inherent differences that result based on these variables. However, by drawing from a recommended suite of endpoints, assays and studies (both in vivo and in vitro), a suitable dataset for a new molecule suspected of acting via a CAR MoA can be obtained (Table  2 ).
Also, it must be understood that as new methods emerge and become more widely used, the exact set of studies and endpoints that are used to demonstrate the key events and associative events will evolve. In fact, greater scientific understanding of the mechanisms that are operative during short-term through long-term treatments with CAR-activating compounds may result in changes to the sequence of key events that are considered part of the CAR MoA. It is also worth considering that for certain chemical classes (e. g. triazole fungicides), where extensive data have been generated that demonstrate liver tumors occur via a CAR MoA, a much simpler set of studies should be acceptable to confirm that a new molecule in this same chemical class is also operating via a CAR MoA  (Currie et al. , 2014; Oshida et al.
g. , immune deficiency, the need to administer additional agents such as GH, NTBC, etc. ) suggests that they may not be appropriate at present for widespread use when exploring a CAR MoA for a new test compound. The promise of other new technologies to reduce animal use and/or replace them with in vitro tests or analysis of existing in vivo tissues from the subchronic guideline studies is also worth further consideration. In particular, a CAR biomarker signature in mice has been described that relies not just on a limited number of 3-4 genes, but on an 83-gene signature derived from multiple CAR activating compounds  (Oshida et al. , 2015a). As shown in Fig. 2 , this biomarker signature provides a strong statistical match for a known CAR-activating mouse liver tumorigen (cyproconazole) that was not part of the training dataset.
Further, the CAR signature can be applied following treatment of different mouse strains, treatment durations, and genomic platforms with reasonable assurance that a statistical match is likely to be detectable, and that several alternative MoAs (e. g. , AhR, PPARα) can be excluded based on their biomarker signatures (Fig. 2 ;  Oshida et al. , 2015b; Oshida et al. , 2015c). Hopefully, as tools like this become more widely used for regulatory data submissions, the level of confidence to make a conclusion on the MoA being via CAR activation based on its biomarker signature will become greater, if the data sets show good concordance. With careful foresight, it may be possible in the near future to generate the data to establish that a compound is operating via a CAR MoA using properly stored tissues from earlier subacute and subchronic toxicology studies that are part of the normal progression of regulatory studies for that molecule.