dataesr/scientific-paragraphs-categorization huggingface dataset:
The total value of 2181 results is less than the 2290 chemicals analyzed since 109 predictions were inconclusive (approximately 5% were either out-of-domain or indeterminate predictions). From this table, it can be seen that 95% of chemicals were either correctly predicted or were assigned to a more conservative category. However, the skewed nature of this dataset, i. e. the higher percentage of low toxic compounds, means that a balanced assessment was also required (see below). An assessment of the performance of the consensus model for each experimental in vivo GHS category is shown in Table  3. Two summary statistics that help to understand whether the model is fit-for-purpose for classification and labelling are presented: (1) the percentage of correctly predicted chemicals or chemicals predicted to be in a more conservative GHS category and (2) the percentage of correctly predicted chemicals or chemicals predicted in an adjacent more conservative category.
Two additional summary statistics were calculated to help understand the accuracy of the model: (1) the percentage of correctly predicted chemicals and (2) the percentage of correctly predicted chemicals or chemicals predicted in an adjacent category. The inconclusive results were not used in calculating the summary statistics. The data collected reflects the typical distribution of GHS categories within corporate collections and as such it is highly imbalanced and weighted towards the less toxic compounds. Therefore, an overall balanced assessment of the 4 summary statistics was calculated alongside a baseline (represented by a random model). The balanced summary statistics were computed by averaging the values for each category, shown in Table  3 , apart from the "Cat 5. or NC values", with the averages reported in Table  4.
This information was not used in this assessment since this category spans two experimental categories. The supplemental material contains analogous information to Tables 2-4 for the assessment of statistical-based and the expert rulebased methodologies (supplemental tables S1-S6) as well as the three industrial sectors analyzed: pharmaceutical, plant protection products and other chemicals (Supplemental tables S8-S18). As previously discussed for analysis of the consensus model on the combined dataset, due to the skewed nature of the datasets towards low toxicity chemicals, the balanced statistics presented therein provide valuable insight into the predictive performance of the different types of models on different kinds of chemicals. Table  S7  summarizes the results for different (Q)SAR methodologies, statistical-based and expert rule-based, along with the consensus from the two methodologies.
The summary statistics include an assessment of the prediction of the correct or one more conservative category to support these decisions. The consensus model predictions were investigated across different industries, i. e. , pharmaceutical, plant protection product and other industrial chemicals, including specialty chemicals (see Supplemental tables S8-S19). The proportion of correct or more conservative predictions across all three sources of data was greater than 93% (see supplemental Table  S19 ) indicating a high reliability for the (Q)SAR models (with balanced statistic greater than 75%, after excluding an unreliable statistic for pharmaceutical category 1 compounds based on only two datapoints -see supplemental tables S10, S15 and S18). Several AOT in silico models have been assessed as part of a publication by Graham and co-authors  (Graham et al.
Use cases and workflows A (Q)SAR assessment of AOT could be utilized to support both transportation and worker safety assessments as well as emergency overdose situations (e. g. poison control) or health hazard assessments for large-scale spills of chemicals that lack AOT data. An example flow-chart for making an AOT GHS assessment based, in part, on (Q)SAR models is shown in Fig. 5. The first step for any test chemical is to identify whether AOT data are available, either within proprietary databases or through a search of publicly available information. Such a search should return chemicals that match the chemical exactly (including different salt forms, tautomers, etc. ). In many situations, the test chemical cannot be submitted to an online service because of intellectual property concerns and so issuing such a query behind a company's firewall is often important.
Ideally, such a search will return an adequate AOT study, including information on species tested, route of exposure, and LD 50 value. Studies not considered adequate or performed via other routes of exposure may be considered as part of the weight of the evidence in the expert review, discussed later. Further assessment of any available studies should focus on whether they are reliable including consideration of whether the Klimisch score  (Klimisch et al. , 1997 ) is 1 or 2 (i. e. , a well-documented and accepted/sufficient study or data from the literature that is performed according to or partially compliant with valid and/or accepted test guidelines, and preferably performed according to good laboratory practices). Regarding the species tested, the rat is the preferred test species (because of the similarity of the genome between rats and humans); however, if AOT data are available in other animal species, expert scientific judgment should be used to select the most appropriate LD 50.
Such a review may take into consideration other information on the chemical's MoA, other hazardous properties, chemical analogs (i. e. , read-across), inspection of the individual model's results, and any mechanistic information including the potential of the chemical to metabolize. In addition, AOT data not deemed to be sufficiently reliable, on their own, may be included in a weight-of-evidence assessment. The expert review process should generate a documented assessment of the assigned GHS category. It may be possible to generate an assessment even if the (Q)SAR models are unable to predict a GHS category, such as when a chemical is out-of-domain (i. e. , the prediction reliability is expected to be lower) or indeterminate (i. e. , there is conflicting information) based on sufficient additional information.
Conclusions As the current standard for acute oral toxicity hazard identification is a test conducted using animals, an AOT in silico model potentially offers a rapid and cost-effective alternative approach. In silico models have the potential to effectively reduce or eliminate the use of in vivo testing, thereby reducing the reliance of industry on these models for AOT hazard identification. Given that in silico models have been developed based on the wealth of publicly available AOT data, it is promising to note that the Leadscope AOT suite was capable of making typically reliable AOT hazard predictions for a broad range of chemical structures, spanning numerous industries. The evaluation presented in this manuscript also points out the importance of an expert review to enable a weight of evidence approach.
In the context of indirect dark matter detection with charged cosmic rays and -rays, several public tools have been developed at LPSC (Laboratoire de Physique Subatomique et Cosmologie). I present a brief overview of these tools, namely USINE for the calculation of astrophysical nuclear fluxes and associated dark matter signals, the cosmic-ray database CRDB to retrieve cosmic-ray data and associated meta-data, and CLUMPY for the calculation of -ray and signals from dark matter structures. I stress the improvements made for the various releases of these tools. I then conclude with ongoing developments and a wish list for future releases. The main emphasis is put on the USINE code and the recent results obtained with it, as it was at the core of the presentation made at this workshop.
Introduction Antiprotons and -rays are among the most constraining channels for WIMP dark matter (DM) candidates in the GeV-TeV mass range  [1]. The modelling of the expected signals depends on the DM distribution, but unambiguously identifying a signal depends on the confidence we have in the measured and modelled astrophysical backgrounds (which involves many uncertain input ingredients). Last but not least, the potential for a discovery or for setting limits on DM is set by the quality of the experimental data at hand. To fully benefit from the advent of a high-precision era for CR and -ray measurements, we must ensure that the modelling precision meets that of the data: this is a key requirement for developing or improving tools for DM indirect searches. Below, I present the USINE code , developed to interpret Galactic Cosmic-Ray (GCR) data for nuclei and anti-nuclei.
The only public code following a semi-analytical approach is the USINE code  [7] , in which the spatial derivatives are solved analytically and the momentum coordinates numerically (with finite differences). The models currently available in USINE are the Leaky-Box  [8]  and two-zone diffusion models (thin disc and thick diffusive halo, in 1D  [9]  or 2D  [10] ). USINE in a nutshell USINE is a C++ code, with many classes for the input ingredients, transport parameters, and models. It is interfaced with ROOT/CERN libraries for displays and minimisations. It also relies on the Function-Parser library to interpret formulae provided by users in ASCII files as formulae in the code. A dedicated class in USINE gathers all user-declared parameters, providing a mechanism to turn any user-defined parameter into a fitable one-this encompasses geometry, transport, source, solar modulation, and nuclear physics parameters.
>. / b i n / u s i n e [ o p t i o n ] [ p a r a m e t e r s ] w i t h [ o p t i o n ] t a k i n g t h e p o s s i t y ) [ -m ] : m i n i m i s a t i o n ( i n t e r f a c e d w i t h M i n u i t ) Typical parameters in the command line are the initialisation file name, the directory in which output files are saved, etc. To get the full list and details on the expected parameters for each option, just type the command line without parameters. To ensure the code stability and reliability, USINE relies on unit and integration tests-with reference test files to check that the installation is successful and ready to run. The code is on PoS(TOOLS2020)004 Git-Lab , with a full Sphinx-based (readthedocs theme ) documentation , as illustrated in Fig. 1. We stress that the syntax for the USINE keywords (in the initialisation file) can be highly non-trivial.
The last development period was triggered by the release of high-precision data by the AMS collaboration. This lead to the first public release (V3. 4 and 3. 5) of the USINE PoS(TOOLS2020)004 USINE, CRDB, and CLUMPY tools David Maurin code in 2019  [7]. Given the strong limitations of the currently implemented models, namely (i) ISM and energy losses and gains restricted to be in a thin disc, (ii) astrophysical sources at best following a radial distribution in the same thin disc, and (iii) propagation of nuclei and anti-nuclei only, users may wonder why they should ever consider using USINE. There may be not many reasons left, but here they are. First, as of today, the simple models in USINE provide good fits to the recent data, though the transport parameters extracted are to some extent effective only.
To illustrate that USINE still enables to address state-of-the-art science questions, here is a brief list of the recent results we obtained: (i) indication of a high-energy break in the diffusion coefficient  [19] , (ii) list of nuclear cross-sections to improve in order to reach a modelling precision on par with the AMS-02 data 3% precision  [20] , (iii) improved methodology to analyse and fit without biases high precision data  [21] , (iv) new benchmark transport parametrisations SLIM/BIG/QUAINT  [22, 23] , (v) consistency of antiproton data with a pure astrophysical origin  [24] , constraints on the diffusive halo size  [25]. Several more studies are on their way, including an update of the Min/Med/Max benchmark parameters and an update of DM constraints from anti-protons.
Future developments and wish list There are not many development directions left with USINE. An important one, in the context of this workshop, is to plug back the module to calculate DM anti-nuclei. Indeed, this module was not upgraded between the unreleased (2008) and public (2019) versions, but it will be part of the next USINE release: contact me if you are interested in using this module now, as it already exists in a development branch (it makes use of PPPC4DMID final state spectra  [26] ). Other improvements for the next release are (i) PyUSINE, a python library to use USINE in notebooks, (ii) the extension of nuclear ingredients to run USINE for CR species > 30, and (iii) a few other surprises! My initial wish list included electrons and positrons, implemented from the pinching method proposed by Mathieu Boudaud in  [27] , but sadly, our dear friend and colleague Mathieu passed away in January 2020; it is not clear yet whether I will be able to implement this calculation in the next release.
CRDB: the cosmic-ray database CR data are the backbones of CR analyses, but in the past, scientists have all too often wasted time collecting again and again the same data for their analyses. To remedy that, gathering data in a single database is the obvious way to go. The first public distribution of these data started from the GALPROP team as a simple and single ASCII file  [28]. As part of the development of the USINE library, I started to push for the development of a database (from data I had gathered independently). CRDB came online in 2013  [29] , including a contextualised presentation of the data and experiments, ASCII file exports, and online plot generation. The Italian Space Agency (ASI) soon after developed its own CR database, originally to host ASI-supported experimental data  [30] , and the initial design and look of CRDB-ASI was strongly influenced by ours.
Since the first release in 2013  [29] , about a quarter of a million queries were made. CRDB contains 44 638 data points from more than 400 publications and 100 experiments. It is hosted by the LPSC laboratory website, running on a LAMP open source solution (Linux operating system, Apache HTTP server, MySQL database, and PHP hypertext pre-processor language) and relying on AJAX (Asynchronous JavaScript and XML) web development techniques. Accessing the data can be achieved either via the web pages (via jquery libraries), or via a REST (representational state transfer) interface; the former allows to access the fully contextualised data and meta-data, while the latter allows users to retrieve the data directly from command lines in their own scripts or codes. Figure  2  shows a snapshot of the CRDB web page, corresponding to CRDB V4.
David Maurin or CR-related quantities. Among the main improvements made in this release were: multi-species and time-series data selection, extension of the data from MeV to ZeV, improvement of the REST interface (a python notebook example with curl or get is provided), simpler format for anyone to submit data to CRDB, etc. Of special interest for this workshop are the inclusion of data upperlimits for anti-deuterons and anti-elements, and the extraction of solar modulation levels for any time period, based on neutron monitor data  [32]. For the next release, we plan to gather more ultra-high energy data, in collaboration with the KCDC team  [33] , and also possibly to include more quantities (neutrino data and dipole anisotropy). And of course, we will continue adding data from the AMS-02, CALET, DAMPE, Nucleon, and other experiments as they are published.
CLUMPY We conclude here very briefly with the CLUMPY code. It has been developed to estimate DM-induced fluxes of -rays and in a large variety of objects (and user-defined configurations) to provide a unified and comprehensive calculation at both the Galactic and extragalactic scales. The first release dates back to 2011  [34]  and focused on the optimised calculation of the astrophysical J-factors-integration of the DM density squared (resp. density) along the line of sight for DM annihilation (resp. decay)-for local objects, including sub-halos, dwarf spheroidal galaxies, the Milky Way, and nearby galaxy clusters. The second release in 2016  [35]  included the full calculation of -ray and spectra (computed from PPPC4DMID), and also a Jeans analysis module to compute the DM profile of dwarf spheroidal galaxies from their kinematic data.
The third release in 2019  [36]  extended the calculation for any object at any cosmological distance, including the mean extragalactic fluxes in a flat ΛCDM universe. So far, beside full numerical simulations from which CLUMPY borrows some end results (to use as inputs), CLUMPY remains the only public code for this task. It has been used in many publications by experimentalists, phenomenologists, and astrophysicists. CLUMPY is a C/C++ code (but no classes), interfaced with ROOT/CERN, CFITSIO , GSL , and HEALPIX  [37]    As CLUMPY and USINE share one developer, the solutions implemented for testing and documentation are similar (see Fig. 3 ), with many examples provided. CLUMPY is not in a development frenzy right now, all the team members being busy with other projects. Nevertheless, a wish list for a future release would be to include a python interface, to enable the drawing of extragalactic clusters to include synthetic skymaps of all DM objects in one go, and to include velocity-dependent particle physics model for all calculations.
Conclusions I have presented several tools developed at LPSC in the (sometimes loose) context of indirect DM detection. The two codes and database presented are at a mature stage, benefiting from many years of development, and one or two decades of expertise in the field. The development, maintenance, and support of these tools takes a significant amount of time, and I thank all the users who acknowledge this effort in their publications/presentations; I encourage those who don't, to make the extra step. To conclude, I stress that some members of these projects were initially mere PhD students or post-docs, having an interest in expending the tools capabilities and also ready to spend time doing it. Do not hesitate to contact me if this is what you would like to try! PoS(TOOLS2020)004.
The basic PCR step was followed by variations such as E-PCR  [35] , PCR-RFLP  [29] , and PCR-SSCP  [39]. Historically, these methods, which are all limited by the success/failure of the primer selection had only been applied to the discovery of the three-domain Cry genes  [48]. Next-generation sequencing opened new possibilities for IPG discovery as novel Cry and Cyt genes in a newly sequenced genome can be found by similarity search against a database of known genes  [52]. In particular, the similarity search based on Hidden Markov Models (HMMs) allows one to reveal more diverged Cry genes than those found using PCR-based methods. However, since Cry genes are rather variable, their HMMs typically represent only the main sequence domains rather than complete Cry genes. Ye et al. [67]  and Zheng et al.
Since each error in a read creates a bubble in the de Bruijn graph (making this graph very complex), SPAdes and metaSPAdes error-correct reads and transform the de Bruijn graph into a simpler assembly graph. In the case of an "ideal" assembly graph, a genome is spelled by a path that visits all edges of the assembly graph. Given a read-set, an assembly graph consists of nodes (k-mers that appear frequently in reads) and edges connecting these nodes that are labeled by substrings from the genome  [17]. In the case of an "ideal" assembly graph, a genome is spelled by a path that visits all edges of the assembly graph. Figure  1  presents a small subgraph of the assembly graph of the SRR6238356 dataset constructed by SPAdes. The entire assembly graph consists of 1732 vertices and 1288 edges (654 of them are long edges with lengths exceeding 1000 bp).
However, since the existing sequence-to-graph alignment approaches lack the ability to align HMMs to metagenomic assembly graphs and do not take into account specific features of IPGs, they are not well suited for IPG discovery. Here, we describe the ORFograph algorithm (the source code is publicly available at https://github. com/ ablab/orf-search) and apply it to all publicly available read-sets representing the Bacillus genus as well as to an underexplored metagenomic datasets. ORFograph uses the SPAligner tool for graph-based sequence alignment  [21]  and the PathRacer tool for graph-based HMM alignment  [62]  to find novel IPGs (scattered over multiple contigs) that evaded detection by previous approaches. ORFograph identified nearly a hundred novel IPGs that evaded detection in all previous studies.
Our work demonstrates that traditional "single contig" genefinding approaches (both ab initio and similarity-based  [46] ) should be complemented by graph-based geneprediction algorithms that use databases of proteins and protein domain models as additional evidence for finding genes in (meta)genomes. These graph-based algorithms can contribute to unmasking gene content and diversity found in (meta)genomes, especially for large and variable gene families. Although ORFograph is currently limited to insecticide toxins, our next goal is to extend it into a general tool for identifying arbitrary protein families in assembly graphs such as glycoside hydrolases and CAS proteins that are often scattered over multiple contigs.
Generation of complete coding sequences (CDSs). Given a graph, we refer to the set of all paths between its vertices v and w (found by bounded exhaustive search limited to generating at most 1000 paths) as Paths(v,w). Given an anchor-path AnchorPath between vertices start(AnchorPath) and end(AnchorPath), a start codon ending at vertex start-codon and a stop-codon starting at vertex stop_codon, ORFograph generates path-sets Fig. 1  A subgraph of the SPAdes assembly graph of the SRR6238356 dataset reveals a large number of potential IPGs. Green edges represent a path that spells one out of ~3000 potential IPGs in this subgraph. All potential IPGs arising from this subgraph form four clusters and represent Cry1-like genes. The SPAdes assembly graph was constructed in the iterative mode with the default k-mer sizes  (21, 33, 55).
Bti1 and Bti2 datasets These two Illumina read-sets (accession numbers SRR8474067 and SRR8467560) correspond to Bti strains ATCC35646 and AM65-52 that are further referred to as Bti1 and Bti2, respectively  [8]. ATCC35646 is a type strain of Bti often designated as ATCC35646. AM65-52 is the main component of the Vectobac®, a waterdispersible powder formulation of Bti for control of mosquito larvae. Both datasets were assembled with SPAdes (Table  1 ). Statistics were generated with QUAST  [27]  and Meta-QUAST  [44]  tools. The number of long edges (defined as edges longer than 1 kb) reflects the complexity of the assembly graph. The reads were trimmed and filtered using Trimmomatic-0. assembled with SPAdes-3. 12 (Bti1 and Bti2) and metaSPAdes-3. 12 (NYCS) with k-mer size 55.
All isolate IPG-containing bacteria in the NCBI database (B ALL dataset) To extract all Bacillus datasets from the NCBI database, we used the search query (((Bacillus[Organism]) AND Illumina) AND WGS[Strategy]) AND Paired[Layout]) that resulted in 2749 datasets. We used the Diamond tool  [9]  to align reads from each of the extracted datasets to known IPGs and detect datasets that contain Cry, Cyt, or Vip toxin genes. We say that an IPG is covered by a read-set if at least 50% of the IPG length is covered by reads alignments with identity exceeding 80%. A read-set was considered for further analysis if its reads covered at least one known IPG, resulting in 342 datasets. Afterward, we assembled each selected dataset using SPAdes and analyzed the resulting assemblies with ORFograph. ORFograph identified 72 datasets (among all 342 B ALL datasets) that have IPGs alignments scattered over multiple contigs (Supplementary Table  S2 ).