dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, the use of supervised learning methods on high-dimensional data allows for the capture of complex relationships and the avoidance of pitfalls associated heuristic or simplistic structure/activity-relationships (SAR) and Quantitative SAR (QSAR) models. This latter novel approach is based on analysis on how different hazards predict each other. The algorithm incorporated this relationship. Figure  7  demonstrates how similarity approaches benefit from the quick generation of many analogs for each chemical in a dataset. This "network" effect allows RASAR models to quickly cover a large number of compounds with a relatively small number of labeled compounds. In each graph, more labeled compounds from REACH Annex VI Table  3. 1 are added to 33 000 compounds selected from the European INventory of Existing Commercial chemical Substances (EINECS).
Connections are shown between unlabeled EINECS compounds (blue) and highly similar Annex compounds (red). The figure helps to visualize how the increasing number of neighbors makes the database cluster as more and more chemicals find a neighbor from the 1 387-chemical list. The X/Y graph shows the coverage of the chemical space. We can see a small number of ANNEX chemicals cover a very large number of EINECs chemicals. By using only 1 387 labeled chemicals we can cover 33 000 unknowns. The bottom right panel of Figure  7  shows how the number of compounds without labeled analogs (termed "lonely EINECS") decreases as more labeled compounds are added. The coverage rate, ie, for how many chemicals sufficiently close neighbors are available to make a call, depends mainly on the number of chemicals with data in the database and is thus improved with any addition of further data-points.
Counter-intuitively, the information gain of adding a single chemical increases the more, the larger the database already is; this is owed to the fact that the new data-point can be paired with all already included, being one reason for the power of big data for machine learning. The figure demonstrates that chemical similarity networks appear to obey this also known as Metcalfe's law (Metcalfe's law states the effect of a telecommunications network is proportional to the square of the number of connected users of the system (n 2 ). Generalized, a network is more valuable the more nodes it has. ). Models that can integrate different kinds of data have a much larger benefit from this effect due to the increase in labeled chemicals. An unfortunate side effect of integrating more data is the loss of a clear explanation for predictions.
For the acute and topical hazards addressed here, some analyses available are in line with our earlier findings  (Luechtefeld et al. , 2016c,d)  and those reported here: The variability of the LLNA was pointed out by  Urbisch et al. (2015) : By retesting 22 LLNA performance standards in the standard LLNA protocol, a reproducibility of only 77% was found  (Kolle et al. , 2011 ). Recently, Hoffmann (2015)  analyzed the variability of the LLNA test, using the NICEATM database. Repeat experiments for more than 60 substances were analyzed in terms of skin sensitization potential, ie, discriminating sensitizer from nonsensitizers: The false positive rate ranged from 14% to 20% (false negative rate 4%-5%). For eye irritation,  Adriaens et al. (2014)  showed by resampling Draize eye test from more than 2000 studies, analyses an overall probability of at least 11% that chemicals classified as category 1 could be equally identified as category 2 and of about 12% for category 2 chemicals to be equally identified as no category.
Hoffmann et al. (2010)  reported somewhat better reproducibility of the acute oral toxicity (LD 50 ) in mice and rats, corresponding to the 94% accuracy observed here and similar in  Luechtefeld et al. (2016b). Noteworthy, TG 401 for acute oral toxicity has been replaced in the meantime by tiered test guidelines, for which no such reproducibility data are available. Noteworthy, the animal test reproducibility should be higher in toxicology than other areas of the life sciences as these highly standardized tests are carried out under Good Laboratory Practice by skilled professionals addressing high doses of substances (often so-called maximum tolerated doses) in healthy animals, ie, without further modeling of a disease as in most drug-related studies. Still mere reproducibility ranges only in the 70s-80s percent as shown here for the first time in a comprehensive way for the most commonly used toxicity tests based on a very robust sample of hundreds of studies used for regulatory purposes.
The benchmarks established here should be of value for decisions on replacing any of these methods by alternatives far beyond the RASAR tool presented here. This sheds another light on the reproducibility crisis in science  (Baker, 2016). Higher reproductions of such data are either due to better reference datasets or over-fitting, ie, an optimization for the given training set, which will then not hold for the later application. The RASAR development described made use of classifications not the underlying raw animal data, ie, expert judgment has already integrated the available knowledge. It can therefore be somewhat better than the reproducibility of the animal test. Relying on both positive and negative neighbors and other covariates also reduces the impact of misclassified neighbors. Noteworthy, preliminary work using more than 1 positive/negative chemical did not improve predictions to relevant extent, probably reflecting the redundancy of this information.
" (https://www. echa. europa. eu/ documents/10162/13609/work_programme_2018_in_brief_en. pdf/ 9412a2bd-64f1-13a8-9c49-177a9f853372; last accessed June 30, 2018). So, if the estimates from 2009 stand, theoretically stopping REACH after the 2013 deadline (the data we are using) and using the data fusion RASAR, we would have saved 2, 8 million animals and 490 million testing costs and received even more reliable <feature>_T for a feature describing the target compound, <feature>_PosAna for a feature describing distance to closest negative analog, and <feature>_NegAna for a feature describing similarity to the closest negative analog. All relative contributions are given as Supplementary Table  1. data. This is a very theoretical calculation based on the assumption that the ECHA test guidance to industry is actually being enforced.
The estimate takes already into consideration available data, waiving, (Q)SAR and in vitro studies. However, as discussed earlier (for reproductive toxicity, the main challenge in REACH and not covered by the RASAR approach yet) industry did in fact only propose a fraction of these studies  (Rovida et al. , 2011). It will be crucial now to see how ECHA enforces its testing guidelines and RASAR-like approaches might still help to fill data-gaps. The critical question is the validity of the approach. The internal validation of both approaches uses an unprecedented number of (tens of) thousands of chemicals for the leave-oneout cross-validation of the RASAR and 5-fold cross-validation of the data fusion RASAR. What the RASAR models lose in mirroring biological complexity, they gain by their automatable nature.
The method is agnostic to the endpoint of interest-whenever there is a sufficiently large curated dataset of a given property of substances it can predict and determine the confidence of the prediction. For example, the addition of aquatic toxicity endpoints and inhalation toxicity complemented the array of prediction models. The critical dependence on availability and quality of data is acknowledged, urging also those with access to such data, ie, industry and regulatory agencies to make such data in suitable formats publicly available. Questions of legitimate access to these data for use registration purposes, if used in aggregated manner for predicting other substances, have fortunately been clarified by ECHA: "A registrant would need permission to use protected data to read-across from a single substance to the target substance,.
Future Directions Every year, about 3 billion Euro are spent for animal tests in toxicology  (Bottini and Hartung, 2009) , mainly the tests addressed here. These results indicate that large parts of this could be carried out by in silico prediction at a fraction of time and costs. In May 2018, several ten thousand additional substances have to be registered for the European REACH legislation to continue marketing them. The data fusion RASAR approach could satisfy the information requirements for the most prominent endpoints for this deadline without using animals and at a fraction of the costs. However, it is coming obviously too late for this process. It might still help to overcome the shortage in laboratory capacities currently experienced in preparation of dossiers for the deadline. Noteworthy, prices for tests have now often tripled because of these shortages (Dr C.
Rovida, personal communication). However, there is not only REACH in Europe. Similar programs from new and emerging policies in the United States (the Toxic Substance Control Act reauthorization, known as the Lautenberg Chemical Safety for the 21st Century Act), Canada, Turkey, Korea, Taiwan, China, India, and more are following. Very important, the method is endpoint-agnostic. Any sufficiently large dataset of organic chemicals with a given property could be subjected to the RASAR, opening up for further hazards such as endocrine disruption  (Juberg et al. , 2014) , but even chemicophysical properties could be predicted, perhaps also forming an interesting dependency label for the prediction. Future optimizations of the approach beside the expansion and curation of the database should address the similarity metrics employed  (Luechtefeld and Hartung, 2017)  and validate prediction for more difficult chemistries such as inorganic molecules, ions and polymers.
Preliminary data show that especially combinations of different metrics are very promising. A key challenge is metabolism of substances. A combination with software to predict metabolites and subjecting them to the same assessment could be envisaged. The RASAR represents an enabling technology with uses beyond regulatory toxicology  (Luechtefeld et al. , 2018) : Green Chemistry (or better here Green Toxicology), ie, the frontloading of toxicological considerations in the chemical and product development is one, the identification of problematic substances in the supply chain and the search for alternative chemicals is another one. Interestingly, these large databases also impact on the derivation of thresholds of toxicological concern (TTC)  (Hartung, 2017b; van Ravenzwaay et al. , 2017) , which might synergize with the in silico approach of a RASAR: the RASAR would prioritize substances from the hazard properties side, while TTC bring in the relevant exposure for the respective toxicological space.
FUNDING Thomas Luechtefeld was supported by an  NIEHS  training grant ( T32 ES007141 ). This work was supported by the  EU- ToxRisk  project  (An Integrated European  " Flagship" Program Driving Mechanism-Based Toxicity Testing  and  Risk Assessment  for the 21st Century) funded by the  European Commission  under the  Horizon 2020 program  (Grant Agreement No. 681002 ).
During development, coordinated cell behaviors orchestrate tissue and organ morphogenesis. Detailed descriptions of cell lineages and behaviors provide a powerful framework to elucidate the mechanisms of morphogenesis. To study the cellular basis of limb development, we imaged transgenic fluorescently-labeled embryos from the crustacean Parhyale hawaiensis with multi-view light-sheet microscopy at high spatiotemporal resolution over several days of embryogenesis. The cell lineage of outgrowing thoracic limbs was reconstructed at single-cell resolution with new software called Massive Multi-view Tracker (MaMuT). In silico clonal analyses suggested that the early limb primordium becomes subdivided into anterior-posterior and dorsalventral compartments whose boundaries intersect at the distal tip of the growing limb.
Wolff, Tinevez, Pietzsch et al. have now combined advanced microscopy with custom computer software, called Massive  Multi-view Tracker (MaMuT)  to investigate this. As limbs develop in Parhyale, the MaMuT software tracks how cells behave, and how they are organized. This analysis revealed that for cells to produce a limb bud, they need to split at an early stage into separate groups. These groups are organized along two body axes, one that goes from head to tail, and one that runs from back to belly. The limb grows perpendicular to these main body axes, along a new 'proximal-distal' axis that goes from nearest to furthest from the body. Wolff et al. found that the cells that contribute to the extremities of the limb divide faster than the ones that stay closer to the body. Finally, the results show that when cells in a limb divide, they mostly divide along the proximal-distal axis, producing one cell that is further from the body than the other.
, 2015; Ingaramo et al. , 2014; Pietzsch et al. , 2015; Preibisch et al. , 2014; Preibisch et al. , 2010; Rubio-Guivernau et al. , 2012; Wu et al. , 2016). These processes should be repeated for hundreds or thousands of time-points to generate a 4D representation of the embryo as it develops over time  (Amat et al. , 2015; Schmied et al. , 2014; Schmied et al. , 2016). Automated approaches for cell segmentation and tracking have also been developed  (Amat et al. , 2014; Du et al. , 2014; Dufour et al. , 2017; Faure et al. , 2016; Schiegg et al. , 2015; Stegmaier et al. , 2016; Ulman et al. , 2017) , however they do not yet reach the precision required for unsupervised extraction of cell lineages. To address this issue, we describe here the Massive Multi-view Tracker (MaMuT) software that allows visualization, annotation, and accurate lineage reconstruction of large multi-dimensional microscopy data.
We quantitatively analyzed Parhyale LSFM datasets with MaMuT to understand the cellular basis of arthropod limb morphogenesis. As revealed by lineage tracing experiments in the leading arthropod model Drosophila melanogaster, the leg and wing primordia become progressively subdivided into distinct cell populations (called compartments when lineage-restricted) along the anterior-posterior (AP) and dorsal-ventral (DV) axes  (Dahmann et al. , 2011; Garcia-Bellido et al. , 1973; Steiner, 1976). Tissue subdivisions acquire distinct cell fates driven by domain-specific expression of patterning genes (called selectors if lineally inherited), as well as by the localized induction of signaling molecules at compartment boundaries (organizers) that control patterning and growth of developing organs  (Garcı ´a-Bellido, 1975; Lawrence and Struhl, 1996; Mann and Carroll, 2002; Restrepo et al.
Results Imaging Parhyale embryogenesis with multi-view LSFM Three-day old transgenic embryos with fluorescently labeled nuclei were mounted for LSFM in low melting agarose with scattered fluorescent beads. Several parameters were optimized to cover all stages of Parhyale appendage development at single-cell resolution with adequate temporal sampling for accurate cell tracking (see Materials and methods). A typical 4 to 5 day long recording was composed of more than 1 million images resulting in >7 TB datasets. The relatively slow tempo of Parhyale development enabled imaging of each embryo from multiple highly overlapping views with minimal displacement of nuclei between views acquired in each time-point (Figure  1A ). As detailed in Materials and methods, development of the entire embryo was reconstructed using the Fiji (Fiji Is Just ImageJ) biological image analysis platform  (Schindelin et al.
MaMuT: a platform for cell tracking in multi-view and multi-terabyte datasets To examine the cellular basis of morphogenesis, we developed a novel Fiji plugin to extract cell lineages from multi-view and multi-terabyte datasets. This tool was dubbed MaMuT for Massive Multiview Tracker (Figure  3 ) and is a hybrid and extension of two existing Fiji plugins: the BigDataViewer visualization engine  (Pietzsch et al. , 2015)  and the TrackMate annotation engine  (Tinevez et al. , 2017). MaMuT can be installed through the Fiji updater and is tightly integrated with the other Fiji Figure  1  continued the embryo at the indicated developmental stage in hours (h) after egg-lay (AEL) and the corresponding time-point (TP) of the recording. Anterior is to the left and dorsal to the top. Abbreviations: first antenna (An1), second antenna (An2), mandible (Mn), maxilla 1 (Mx1), maxilla 2 (Mx2), thoracic appendages 1 to 8 (T1-T8), pleonic (abdominal) appendages 1 to 6 (P1-P6) and telson (Te).
(I) Embryo at stage S22, (J) S23, and (K) S24 showing different phases of appendage segmentation. Dorsal outgrowths at the base of thoracic appendages, namely coxal plates (orange arrowheads) and gills (red arrowheads), are indicated in T2, T3 and T4. Scale bars are 100 mm. DOI: https://doi. org/10. 7554/eLife. 34410. 003 The following videos are available for figure  1 :   plugins for LSFM data processing. The source code for MaMuT is available on GitHub  (Tinevez et al. , 2018 ; copy archived at https://github. com/elifesciences-publications/ MaMuT) and detailed tutorials and training datasets can be found at http://imagej. net/MaMuT. MaMuT is an interactive, user-friendly tool for visualization, annotation, tracking and lineage reconstruction of large multi-dimensional microscopy data (Figure  3  and Figure  3 -figure supplement 1).
It is a versatile platform that can be used either for manual or semi-automated tracking of selected populations of cells of interest, or for visualization and editing of fully automated computational predictions for systems-wide lineage reconstructions. MaMuT can handle multiple data sources but was developed primarily to enable the analysis of LSFM datasets. Its unique feature is the ability to annotate image volumes synergistically from all available input views (detailed in Materials and methods). This functionality of MaMuT allowed us to identify and track all constituent cells in developing limbs continuously from the early germband stages until the later stages of 3D organ outgrowth, when the information from multiple views was required for full reconstructions.
Cellular dynamics underlying limb morphogenesis The first T2 limb (limb#1) was lineaged with the new MaMuT software from a multi-view acquisition of an embryo imaged at 26˚C (Figure  4 ), while the second T2 limb (limb#2) was lineaged with the previously developed SIMI ˚BioCell software  (Hejnol and Scholtz, 2004; Schnabel et al. , 1997 ) from a single-view of another embryo imaged at 29-30˚C (Figure  4 -figure supplement 2). Analysis of the birth sequence of the founder cells in the two reconstructed T2 limbs largely confirmed that the second mitotic wave creating the 4-row-parasegment propagated from anterior to posterior rows and from medial to lateral columns (Figure  5A -C and Figure  5 -source data 1). For example, division of the ab cells in parasegment E4 had already progressed to column 5 or even more laterally before ab3 divided in the next posterior parasegment E5.
Reconstruction of Parhyale embryogenesis with multi-view LSFM and MaMuT The LSFM technology is empowering biologists to image developmental processes with unprecedented spatiotemporal resolution. Together with MaMuT-based lineaging and tracking, various experimental designs can be addressed ranging from analyzing a small subset of objects in the imaged volume to systems-wide analyses of all constituent parts. The lineage reconstructions presented in this article were generated manually and required 2 to 3 months for each limb. More generally, manual lineaging efforts can take anything between few days to several months depending on the number of tracked cells, the complexity of the imaged tissue of interest, the duration of the tracked process, the quality of the image dataset, and the desired accuracy and completeness of the reconstructed lineages.
The main advantage of manual tracking by experts is that the extracted lineage is more likely error-free compared to results of automated trackers that must be manually proofread before any meaningful analysis can be attempted. In addition to allowing reliable biological insights, manually generated lineages serve as important 'ground truth' datasets for the application of machine learning based automated tracking solutions  (Ulman et al. , 2017). Acknowledging that fully manual tracking is a laborious and repetitive task that may be impractical for large-scale comparative lineaging approaches, the latest MaMuT architecture offers, in addition to manual tracking, two functionalities for automated tracking: (i) a semi-automated option where individual nuclei can be selected by the user and tracked computationally over time, and (ii) the option to import into MaMuT fully automated annotations generated by the Tracking with Gaussian Mixture Models (TGMM) software  (Amat et al.
Parhyale can match the spatiotemporal resolution of Drosophila or zebrafish LSFM datasets, even when access to highest-speed instruments is not available. Due to the optical clarity of the embryo and positioning of the appendages on the surface of the developing embryo, all constituent cells can be followed for quantitative analyses. Finally, the stereotyped and ordered organization of the Parhyale ectoderm will allow to identify homologous cells and compare lineages, cell behaviors and associated genes between serially homologous structures in the same embryo, across embryos and even across malacostracan crustaceans  (Browne et al. , 2005; Dohle et al. , 2004; Dohle and Scholtz, 1988; Gerberding et al. , 2002; Hejnol and Scholtz, 2004; Scholtz, 1990; Scholtz et al. , 1994; Wolff and Gerberding, 2015; Wolff and Scholtz, 2002; 2008).
, 2010)  that offered single-sided illumination and single-sided detection with a 40x/0. 8 immersion objective. One side of this embryo was imaged from 3 views 40˚apart (ventral, ventral-left and left) every 7. 5 min over a period of 66 hr. Each view was composed of 150 frames (1388 Â 1080 pixels) with voxel size 0. 366 mm x 0. 366 mm x 2 mm. The embryo was imaged at 29-30˚C and was heatshocked for 1 hr twice a day by perfusing warm FASWA at 37 ˚C. Cell tracking was carried out with the SIMI ˚BioCell software (Hejnol and Scholtz, 2004; Schnabel et al. , 1997) on a single view, the ventral-left view, of this dataset. Lineage reconstruction of limb#2 with SIMI ˚BioCell was complete up to about 22 hr of imaging time (35 hr when scaled to the growth rate of limb#1). After this timepoint, an increasing number of cells in limb#2, in particular the descendant cells from the medial columns, became intractable.
4d reconstruction of Parhyale embryogenesis from multi-view LSFM image datasets Parhyale LSFM acquisitions typically resulted in 192 time-points/240K images/1. 7 TB of raw data per day. Image processing was carried out on a MS Windows 7 Professional 64-bit workstation with 2 Intel Xeon E5-2687W processors, 256 GB RAM (16 X DIMMs 16384 MB 1600 MHz ECC DDR3), 4. 8 TB hard disk space (2 Â 480 GB and 6 Â 960 GB Crucial M500 SATA 6 Gb/s SSD), 2 NVIDIA Quadro K4000 graphics cards (3 GB GDDR5). The workstation was connected through a 10 GB network interface to a MS Windows 2008 Server with 2 Intel Xeon E5-2680 processors, 196 GB RAM (24 X DIMM 8192 MB 1600 MHz ECC DDR3) and 144 TB hard disk space (36 X Seagate Constellation ES. 3 4000 GB 7200 RPM 128 MB Cache SAS 6. 0 Gb/s). All major LSFM image data processing steps were done with software modules available through the Multiview Reconstruction Fiji plugin (http:// imagej.