dataesr/scientific-paragraphs-categorization huggingface dataset:
It was reported that even small variation in bioassay can significantly influence the evaluation of toxin activity  24. To date, the available results regarding activity of toxin compositions have not yet been assembled in a suitable database. Thus, the comparison and analysis of the toxin activity is highly inconvenient. In the past, a valuable database created by Kees Van Frankenhuyzen and Carl Nystrom was giving insecticidal activities of single Bt Cry and Cyt toxins (https:// www. glfc. cfs. nrcan. gc. ca/ bacil lus). Despite its huge impact on the field  25  , the database did not cover toxicity reports on compositions and unfortunately it has not been available for many years now. Another perspective of the interaction evaluation studies is that recently, some concerns have been raised stating that combinatorial effects noted between Bt toxins in many works may be artifactual and synergism considerably overestimated  26.
Considering the above necessities, a summary and assessment of experiment results obtained so far is much needed. Here we provide a comprehensive analysis of high-quality collection of all publicly available experiments concerning interactions between Cry/Cyt/Vip proteins. Throughout this work, any individual toxin-organismresult association is considered as a separate experiment. Additionally, for present and future applications we created a freely accessible database TOXiTAXi (http:// www. combio. pl/ toxit axi/) to facilitate efficient management of growing experiment results concerning the activity, interaction types, and host range of Bt toxins. Tools created in this work will help to determine the potential of synergistic/antagonistic interactions in designing new biological pesticides.
g. , neonicotinoids, carbamates), fungal spores (e. g. , Metarhizium anisopliae or Beauveria bassiana spores) and scorpion toxins (e. g. , AahIT, BjaIT). In total, the collected dataset includes experiments performed with: 131 various toxic agents, 54 invertebrate test species, 14 toxicity measures (e. g. , LC 50 , LD 50 , IC 50 ), 11 toxin preparation methods (e. g. , purified proteins, parasporal crystals, inclusion bodies), and seven toxin administration methods (e. g. , surface contamination, diet incorporation, leaf dip).
Bt toxin combinatorial research analysis. The analysis of the collected data allowed us to assess the phenomenon of interactions between Bt Cry/Vip/Cyt proteins reported in research papers published for nearly 30 years. In Bt toxin combinatorial experiments, 50 different Bt proteins were used: Cry (82%), Vip (10%) and Cyt (8%). The insecticidal activity bioassays were performed using 38 test species representing two phyla (Arthropoda and Nematoda) and various classes, orders and families. The analysis shows that experiments are highly unequally distributed across taxa (Fig. 1 ). The highest number of arthropod species tested belong to Lepidoptera order (27 species), followed by Diptera (8 species) and Coleoptera (1 species). Within these orders, the most broadly represented is Noctuidae family (Lepidoptera), comprising 12 species.
Invertebrates outside the Arthropod phylum, namely Nematodes, were only marginally tested (2 species). The number of experiments performed on each species is also highly uneven with lepidopterans being the most frequently tested (Fig. 1 ). The most overrepresented species is Helicoverpa armigera (present in almost every third experiment) followed by Culex quinquefasciatus and Aedes aegypti. Two most frequently tested families are Noctuidae and Culicidae, representing Lepidoptera and Diptera orders, respectively. The dataset analysis shows that 118 different combinations between Bt toxins were created and tested for insecticidal activity. The vast majority are Cry/Cry combinations, followed by Cry/Vip, Cry/Cyt and Cyt/Vip (Fig. 2a ). Out of 716 experiments investigating interactions between Bt proteins, the greater part (54%) indicate synergism, then additive effect (32%) and the rest (14%) assume antagonism between components (Fig.
2b ). Therefore, the ratio of synergistic interactions to sum of additive and less than additive interactions is almost equal. However, the share of each interaction type depends strongly on the toxin combination category analyzed (Fig. 2c ). For example, the rate of synergism in Cry/Cry, Cry/Cyt, Cry/Vip and Cyt/Vip compositions is 58%, 86%, 19% and 60%, respectively. Therefore, it appears that in Cry/Cyt combinations synergism is generally much more evident compared to the remaining categories. In Cry/Vip combinations however, synergistic interactions are a minority and the magnitude of SF is relatively low, whereas antagonism is present in nearly 40% of recorded data and the SF values calculated for antagonistic interactions are lower than in other mixture categories. Note however, that the dataset includes additional 84 experiments reported using Bt toxin mixtures, but no interaction type was determined by their authors.
Moreover, high variability of combinatorial effects is noted for mixtures tested against two and more taxa (Fig. 4b ). Only for 33% of these combinations the same combinatorial effect is noted, whereas for the majority of mixtures the interaction type was different depending on the tested organism. The complete list of Bt toxin combinations along with the corresponding tested species is available in Supplementary Dataset S1. In general, two-component mixtures are the most frequently assessed (92. 1% of experiments), however, compositions containing 3, 4, and 5 components are also present in 6. 9%, 0. 4% and 0. 6% of experiments, respectively. Notably, the number of mixtures containing more than two constituents is growing over time (data not shown). To determine the interaction type between Bt toxins, the available reports used three approaches.
The most frequent is a simple similar action model (55. 8% of trials), and independent joint action model (36. 1%), i. e. , used in works performed by Tabashnik  27  and Fernandez-Luna et al. 15  , respectively. Third approach is somewhat intuitive and generally relies on comparison of effects exerted by composition and individual constituents, where one constituent is often inactive or sublethal doses are administered to tested organism-these various empirical methods were applied in a minority of experiments (7. 3%), i. e. , described by Kaikaew et al. 28. To assess the prevalence of comparative data we have determined the number of independent research teams investigating interactions between given toxins, using the same species as the test organism. The experiments were considered to have been performed by independent teams when they were published in journal articles, in which none of the authors was mentioned twice.
A web resource for toxicity of Bacillus thuringiensis protein compositions. To supplement the results of this study, we have launched a novel, publicly accessible web application, TOXiTAXI (http:// www. combio. pl/ toxit axi/) that conveniently gathers and analyzes the existing and future bioassay results on biocidal activity of toxins against various taxonomic groups. The TOXiTAXi database interface is designed to be used by a bench scientist on an everyday basis. Following a simplicity rule, the interface of TOXiTAXi has been built on only one result window and simple two types of querying systems, despite the vast amount and variability of data contained in the database. The data selection process is provided in the form of a hierarchical expandable menu to supply the functionality for selecting any combination of toxins or target species.
In this way, users can select toxins either by marking individual proteins (e. g. , Cry1Ab3 and/or Vip3Aa20) or groups of proteins (e. g. , all Cry and/or Vip proteins) from the tree-like menu reflecting the proteins hierarchical nomenclature or by typing protein names in the input text box. Analogously, users can select individual target species of interest (e. g. , Helicoverpa armigera) or different combinations of taxonomic groups (e. g. , all Lepidoptera and Coleoptera species). The real potential of the TOXiTAXi service is based on its ability to combine different datasets into one display. There is no limit as to the number and type of selected toxins and target species that can be combined in a single query. The experiments found for a given toxin/combination of toxins or target species are summarized in the table (Fig.
Bt toxin combination Target species Estimated combinatorial effect [source article] Team A Team B Team C  ited in the database, separately for synergistic and antagonistic effects. Percentiles as a measure of interaction strength inform the user about a relative standing of a given SF in comparison to all synergistic or antagonistic interactions present in the database. For example, the synergism factor reported for Cry1Ac + Cry1F (SF = 26. 3) scores above the 90th percentile, which places the Cry1Ac + Cry1F interaction among the top 10% of synergistic interactions stored in the database. The percentiles are shown in the "Synergism factor" column of the result tree, following the SF values. Cry4Aa.
Discussion Entomopathogenic properties of B. thuringiensis were observed as early as in 1901, however first genes encoding Bt insecticidal toxins were cloned and sequenced more than 80 years later 2. Because of the fast development of genetic engineering, to date we know the sequences of more than 350 Cry, Cyt and Vip toxins-main molecules responsible for Bt virulence towards target organisms. Some of them were heterologously expressed and separately assessed for their biological activity, but the toxins were usually tested on a rather limited number of taxa  25. Despite a great number of toxicity data obtained so far, still much is to be revealed in this field. Recently however, the attention has been focused on investigating the pesticidal potency of compositions containing various sets of Cry/Cyt/Vip proteins.
In this study we analyzed 59 manuscripts investigating potency of Cry/Cyt/Vip compositions. A considerable diversity of experimental approaches exists in the available works, therefore, the bioassay results are confounded by various factors such as: toxin types tested; protein expression, purification and modification protocols; bioassay duration; toxin administration to test organism, etc. To overcome these issues and allow convenient data analysis, a suitable database is necessary. In this work, dedicated tools were created to easily assemble, summarize and analyze the data on biological potency of toxins. Results of experiments on insecticidal activity of Bt Cry/Cyt/Vip protein mixtures were gathered and incorporated into the database, along with analogous data for individual toxins, present in the studied articles.
This is almost identical to results obtained by Walters et al. 26. Moreover, generally a low magnitude of noted SF values has been shown in both works. However, when analysis is performed on larger dataset and especially on different combination categories, a very complex and diverse picture appears. It is evident that in Cry/Cyt combinations synergism is generally much more evident compared to the other categories-judging on number of experiments noting synergistic interaction as well as on synergism magnitude. In the experiments involving Cry/Cry and Cyt/Vip combinations, synergistic interactions are a majority, however the SF values are relatively low in magnitude, although the latter category is represented by a small number of experiments. In contrast, a high percentage of antagonistic effects in Cry/Vip compositions is evident and the calculated SF values in assays noting antagonism between Cry and Vip toxins are lower than in other composition categories.
Therefore, our comparison indicates that size and selection criteria of the dataset can have significant impact on analysis of interactions between Bt-toxins. Moreover, differences between various categories of compositions as well as tested taxa should be taken into consideration in future studies. It was not a goal of this work to address other matters related to the correctness of approaches utilized to estimate the combinatorial effects between Bt toxins, such as fidelity of interaction assessment models or biological response variation. We would like to look at the Bt-toxin interaction data from a different perspective and highlight another possible challenge. After analysis of our database, it can be observed that in most cases a particular toxin combination was tested against one insect species only by one research team (and furthermore, usually reported in one article).
exigua. Therefore, the available comparative data on Bt toxin combinatorial effects is quite limited, and may not be sufficient to draw reliable conclusions. Moreover, the mechanisms underlying the synergistic/antagonistic interactions between Bt toxins are still only hypothetical  23  , which further adds uncertainty to the area. Concluding, more research is needed to gain confidence regarding the occurrence of interactions between different Bt toxins, and to properly assess the scale of this phenomenon. By successive incorporation of the results generated in the future, the database created in this work should help to monitor this issue. As described above, the dataset assembled in this work generally included research papers investigating insecticidal potency of combinations containing Bt Cry/Cyt/Vip proteins and interactions between constituents.
However, single toxin activity results available in the manuscripts were also incorporated into the database. Currently, TOXiTAXi is the only available bioinformatic tool gathering this type of information. In the past one such database existed, organizing research experiments investigating the biological specificity of individual Bt deltaendotoxins Cry and Cyt 25 but unfortunately it is no longer available, and the toxicities of protein combinations were not included in its dataset. Currently, the database created in this work contains only a limited portion of single Bt toxin specificity info available in the literature, however it is perfectly suitable to process these types of result. In the future, it can be expanded to include a full set of data. Additionally, in order to test the universal potential of the database, the results regarding biocidal activity of agents other than Bt Cry/Cyt/Vip proteins was incorporated into the dataset.
The information includes pesticidal activity of enzymes; insect midgut proteins; biochemical substances and synthetic pesticides. The input data could be conveniently incorporated into the database without any difficulties and loss of information, whereas the web application can provide suitable display and analysis of this information in the same manner as for Cry/Cyt/Vip proteins. In addition, the hierarchy in the expandable tree available for the user can be easily rearranged when new data will demand changes in the ranks of insecticide types. Therefore, the use of the tools created in this work is not limited to Bt protein toxins, but enables incorporation of information on highly heterogeneous substances with biocidal activity. In conclusion, the created database is the first comprehensive assembly of existing experimental data on Bt toxin combinatorial effects.
This database along with the dedicated web tool can be used to further accumulate the existing, as well as future results on toxicity of B. thuringiensis toxins and other biocidal agents of various origins. The application enables effortless analysis of precisely selected, desired information. Tools created in this work can be used not only by the academic community but also units involved in implementation of new solutions and products in the field of biological plant protection. The gathered dataset will be updated soon after new pesticidal toxin combinatorial effects are reported in the literature. To better streamline the process of data acquisition and enhance usability of the TOXiTAXi database, we encourage users to submit their feedback and suggestions of new studies to include, so that we can continue to improve this web resource, tailoring it to the specific needs of the biocontrol-focused community.
Experiment source selection. Research papers investigating interactions between Cry, Cyt and Vip toxins were selected with Scopus database. The search was performed using different combinations of keywords: Cry; Cyt; Vip; thuringiensis; interaction; synergistic; synergism; antagonistic; antagonism. From the initial dataset further exploration was made selecting the manuscripts, which were written in English and contained experiments: (i) investigating the insecticidal activity of Cry/Cyt/Vip toxin compositions; (ii) performed using B. thuringiensis Cry, Cyt or Vip toxins obtained through expression of cloned genes in hosts not producing other pesticidal toxins (typically E. coli or acrystalliferous B. thuringiensis strains); (iii) providing proper quantification of used toxins. Additionally, some papers not found in Scopus database but appearing in the bibliography of selected articles have been included in the data set, if they met the above-mentioned criteria.
In total, 58 manuscripts were initially included in the dataset. From a selected publication, the available results of experiments investigating toxicity towards invertebrates were incorporated into the database. This includes activity of toxin compositions as well as separate Cry/Cyt/Vip toxins. Each recorded experiment includes information regarding: (i) source publication (authorship, publication date, PubMed ID); (ii) tested toxic agent/composition (exact toxin name(s) according to current nomenclature, toxin source, expression host, molecular modifications applied, details on toxin preparation); (iii) tested target organism (species name; strain/laboratory line, developmental stage, recognized resistance to toxins); (iv) bioassay (toxin administration method, bioassay duration, toxicity measure, observed toxicity with units and confidence intervals); and (v) interaction among toxins (expected toxicity of a mixture, interaction type, estimation model of interaction, and synergism factor).
The published bioassay data were incorporated into the database without alterations with the following exceptions: (i) if synergism factor was not provided by the authors, it was calculated when possible; (ii) antagonism factors were recalculated to synergism factors; (iii) toxicity measure units were recalculated to nanograms (e. g. , µg/cm 2 to ng/cm 2 , µg/ml to ng/ml, etc. ). Experimental records were omitted when toxicity bioassays were performed using: wild type Bt strains (usually containing sets of not precisely characterized and/or non-quantified toxins); binary toxins such as Tpp (formerly Bin), Gpp34/Tpp35 (formerly Cry34/Cry35), Vpb1/Vpb2 (formerly Vip1/Vip2); Lysinibacillus sphaericus toxins (e. g. Mtx, Mpp2-4); Bt toxins obtained from transgenic cropsregardless if the above were tested with or without accepted Cry/Cyt/Vip toxins.
Moreover, if for some reason the exact values could not be determined for bioassay parameters (e. g. results presented solely in a chart form), the data was not included. In order to test the broad-use potential of the created database, a selection of arbitrarily chosen manuscripts were additionally added to the dataset. These works investigate biocidal activity of agents other than B. thuringiensis Cry/Cyt/Vip proteins, including: chitinolytic enzymes  [38] [39] [40] [41] [42]  , synthetic and biochemical insecticides  [43] [44] [45]  , insect midgut proteins  [46] [47] [48]  Bacillus thuringiensis Mpp5A (formerly Sip1 proteins)  49, 50  , scorpion toxins  51  and fungal spores  52, 53. Toxin nomenclature. All publications incorporated into the TOXiTAXi dataset use nomenclature developed in 1998 6 for naming B.
thuringiensis insecticidal proteins. Recently however, a revised nomenclature has been proposed  54. Toxins included into the TOXiTAXi database have identical names in both nomenclatures, with the exception of Cry6-type and Cry55-type proteins, currently named App6-type and Xpp55-type, respectively  5. For the sake of clarity, in the analyses presented in this study, these few proteins are considered as Cry. In the web application however, both names (old and new) are given, when applicable. In the current nomenclature, the quaternary rank groups identical or almost identical (a few amino acid substitutions) Bt proteins and the differences are considered biologically insignificant  6, 25. Therefore, throughout this work, toxins are considered unalike, when they differ in tertiary rank.
IT technology. The backend of TOXiTAXi is implemented in Python with Django 2. 2 web framework (https:// www. djang oproj ect. com), dynamically generating web pages using Apache2 with WSGI. The data are stored using a MySQL database, and a modular database schema allows further growth and incorporation of new data types. The web pages are constructed using HTML5, CSS3, JavaScript with jQuery library (https:// jquery. com) and Bootstrap 4. 4 framework (https:// getbo otstr ap. com). Dynamic and interactive elements of the taxon page are developed using SVG markup language with the d3. js library (https:// d3js. org) for toxin visualization, and DataTables (https:// datat ables. net) as a tabular feature viewer, respectively.
The search for a long-term benchmark for land-surface models (LSM) has brought tree-ring data to the attention of the landsurface community as they record growth well before human-induced environmental changes became important. The most comprehensive archive of publicly shared tree-ring data is the International Tree-ring Data Bank (ITRDB). Many records in the ITRDB have, however, been collected almost exclusively with a view on maximizing an environmental target signal (e. g. climate), which has resulted in a biased representation of forested sites and landscapes and thus limits its use as a data source for benchmarking. The aim of this study is to propose advances in land-surface modelling and data processing to enable the land-surface community to re-use the ITRDB data as a much-needed century-long benchmark.
Given that tree-ring width is largely explained by phenology, tree size, and climate sensitivity, LSMs that intend to use it as a benchmark should at least simulate tree phenology, size-dependent growth, differently-sized trees within a stand, and responses to changes in temperature, precipitation and atmospheric CO2 concentrations. Yet, even if LSMs were capable of accurately simulating treering width, sampling biases in the ITRDB need to be accounted for. This study proposes two solutions: exploiting the observation that the variation due to size-related growth by far exceeds the variation due to environmental changes; and simulating a size-structured population of trees. Combining the proposed advances in modelling and data processing resulted in four complementary benchmarks -reflecting different usage of the information contained in the ITRDB -each described by two metrics rooted in statistics that quantify the performance of the benchmark.
, 2004; D'Arrigo et al. , 2008). The most comprehensive archive of publicly shared tree-ring data is the International Tree-ring Data Bank (ITRDB), with more than 4,000 locations from 226 species across most forested biomes  (Grissino-Mayer and Fritts, 1997; Zhao et al. , 2019). However, a shortage of site metadata and the prevailing geographical, species and tree selection sampling biases resulting from targeting climate-sensitive trees has limited the use of the ITRDB archive to infer long-term changes in forest growth (Bowman.