dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the in situ methods also rely upon several assumptions (especially for non-spherical particles) and this contributes to the bias as well (see, for example,  Reid et al. , 2003 , for a discussion about measurement assumptions required for in situ observations of irregularly shaped particles  [92] ). We also compare the total aerosol volumes (or integrated size distributions), which is an underused parameter in the AERONET database. Aerosol volume is important because it provides a link to the mass-based emissions that are the starting point for all aerosol transport models. Although many aerosol transport models produce the correct AOD(550), they do not necessarily produce the correct column mass load. (Correct modeled AOD with incorrect column mass load occurs when size distribution assumptions in the model are incorrect).
GRASP Imaginary Refractive Index as an Indicator of Aerosol Absorption The imaginary refractive index of a material is an intrinsic parameter that is directly related to the bulk absorption coefficient (e. g. ,  [96] [97] [98] ). We test the ability of the GRASP algorithm to quantify aerosol absorption by comparing the GRASP imaginary refractive indices to in situ SSA measurements (expecting a strong anti-correlation). We tested 89 samples that included various proportions of soot and fullerene mixed with ammonium sulfate, ammonium nitrate, and mineral dust, adjusting the carbonaceous fraction to achieve SSAs between 0. 78 and 0. 98. We found the GRASP imaginary refractive index to be highly anti-correlated with the in situ SSA (r = -0. 964), as shown in Figure  12. We conclude that the GRASP imaginary index can be used to determine bulk aerosol absorption properties that are consistent with in situ SSA measurements.
Comparison of single-scatter albedo derived using GRASP to ω • derived directly from PSAP absorption and CAPS extinction measurements. GRASP retrievals use scattering angles identical to data with solar zenith angles of 50 • in the AERONET database. Blue circles indicate GRASP retrievals that have residuals of less than 8%, while white diamonds in the left panel indicate the additional scatter that occurs when residuals are not restricted. Red square is the mean value, and error bars represent two standard deviations of the mean. Data in the right panel is identical to blue circles in the left panel, but the axis ranges are different. In situ ω Linear.
Acknowledgments: We appreciate the efforts of  Bobby Martin  and  Joel Alexa  for processing and analyzing mineral rocks at  NASA LaRC , and  Matthias Schuhbauer  of  B + M Nottenkämper  for providing the Arginotec NX sample. We also appreciate the efforts of those who collected and shared volcanic ash samples, including  Nickolay Krotkov ,  H. Olafsson ,  U. Shumann , and  Philip DeCola.
Funding: This material in this work is supported by the  National Aeronautics and Space Administration  through the  Science Mission Directorate's Earth Science Division , Program Element A. 14:  Atmospheric Composition Laboratory Research. Y. Derimian ,  O. Dubovik. F. Ducos  and  T. Lapyonok  appreciate support by the  CaPPA project (Chemical and Physical Properties of the Atmosphere) , which is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract « ANR-11-LABX-0005-01 » and by the  Regional Council «Hauts-de-France» and the «European Funds for Regional Economic Development» (FEDER).
1. 1, with extinctions ranging from 28 Mm -1 to 110 Mm -1. 1 0. 95 0. 95 0. 9 0. 9 0. 85 0. 85 0. 8 0. 75 0. 8 0. 7 0. 75 0. 65 0. 75 0. 8 0. 85 0. 9 0. 95 1 0. 7 0. 8 0. 9 1 1. 1 1. 2 1Figure5. Comparison of single-scatter albedo derived using GRASP to ω • derived directly from in situ measurements. GRASP retrievals use scattering angles identical to data with solar zenith angles of 50 • in the AERONET database. Left panel uses photo-acoustic absorption coefficients to determine ω • , whereas the right panel uses the integrating nephelometer. Blue circles indicate GRASP retrievals that have residuals of less than 8%, while white diamonds indicate the additional scatter that occurs when residuals are not restricted. Red square is the mean value, and error bars represent two standard deviations of the mean.
g. LD 50 or LC 50 ) from acute and chronic laboratory studies on binary mixtures in honey bees and wild bees (solitary bees and bumble bees) together with available toxicity data and mode of action information from public databases. In addition, dose response from each individual binary mixture experiment are modelled to identify the nature and potency of the combined toxicity (dose addition, synergism, antagonism) and quantify its magnitude using a toxic unit approach and the MIXTOX model. Furthermore, new predictive hazard assessment tools applicable to large binary mixture datasets in bees are developed. The reader should note that exposure assessment (pesticides application rate, crop management, consumption patterns, etc. ) and full risk characterisation are beyond the scope of this quantitative analysis.
Extensive literature searches Extensive Literature Searches (ELS) were performed by two independent reviewers in January 2018 to critically appraise, collect and analyse data on toxicity of mixtures in bee species (EFSA, 2010), using structured search strategies (Appendix S1). ELSs were carried out in  PubMed (1975 PubMed ( -2018)) , in Web of  Science Core Collection (1975 -2018) , including Science Citation Index Expanded, CABI: CAB Abstracts®, Current Contents Connect®, Data Citation Index SM, FSTA® the food science resource, MEDLINE®, SciELO Citation Index, Zoological Record®, Conference Proceedings Citation Index-Science, Book Citation Index-Science, Current Chemical Reactions, Index Chemicus). All records were computed in the EndNote TM software. In addition, bibliographical sources from EFSA studies and database on mixture toxicity in bees were checked thoroughly for completeness  (Quignot et al.
, 2015; Robinson et al. , 2017). In addition, qualitative information on the Mode of Action (MoA) of the individual chemicals were collected from the literature and available databases  (Sparks and Nauen, 2015; Hermann and Stenzel, 2019; Sanchez-bayo, 2012; Johnson et al. , 2012 Johnson et al. , , 2013;; Leroux et al. , 2008; De Castro et al. , 2015; Huang et al. , 2013). Each individual publication retrieved in EndNote TM libraries was screened and assessed using inclusion and exclusion criteria reported in Table  1  in two steps (i) screening of the titles and abstracts and (ii) screening of the full-text of the publications. All included and excluded publications are available under individual EndNote™ libraries.
Data collection Following the Extensive Literature Searches, individual toxicological endpoints (acute and chronic) from laboratory mixture experiments (e. g. LD 50 or LC 50 ) were collected for the oral and contact exposure according to the inclusion criteria, including bee species, sample, size, summary statistics (mean, median, standard error of the mean, standard deviation, confidence intervals) and exposure patterns. Standardised templates were developed to structure the data into an excel database designed with relevant picklists. When papers reported only graphical information, quantitative data were extracted using "Plot Digitizer GNU" software (available at: http://plotdigitizer. sourceforge. net/) or the R software (R Core Team, 2019). In addition, reference points (e. g. LD 50 , LC 50 ) for all individual chemicals i.
e. mostly Plant Protection Products (PPPs) in honey bees were extracted from EFSA's Chemical Hazards database "OpenFoodTox" (available at: https://zenodo. org/record/1252752#. XLg-4Oj7SUm)  (Dorne et al. , 2017; EFSA, 2014b)  and other publicly available databases were consulted including the US-EPA dashboard (https://cfpub. epa. gov/ecotox/), OECD e-ChemPortal (https://www. echemportal. org/echemportal/index. action), PPDB-Pesticide Properties Database (https://sitem. herts. ac. uk/aeru/ppdb/). All binary mixtures data were compiled in an excel database for further analysis (see Section 2. 2. 2).
Quantification of magnitudes of interaction 2. 2. 2. 1. Estimated mean ratios. A comprehensive analysis of magnitude of interactions (as potency or synergism ratios) was performed through the calculation of Estimated Mean Ratios (EMR) for each individual single compound and binary mixture toxicity dataset or for combined toxicity between a single chemical and a non-chemical stressor (biological or nutritional). EMR has been defined as the ratio between the estimated mean toxicity (e. g. LD 50 , LC 50 , EC 50 ) of a given single chemical (chemical A) for which the experimental dose is available (EM A ) and the estimated toxicity of the binary mixture chemical A + chemical B (EM M ) or chemical A + non-chemical stressor  (Quignot et al. , 2015) : = EMR EM EM A M (1) Each EMR for a given binary mixture (EM M ) is expressed on a harmonised scale starting at 1 to reflect changes in combined toxicity either as an increase (+) or a decrease (-)  (Quignot et al.
Toxic Unit approach. Analysis of each experimental binary mixture for acute and chronic (contact or oral) toxicity was conducted using the Toxic Unit approach to standardise applied dose and critical endpoints (i. e. LD 50 ) using matching datasets for each chemical from OpenFoodTox and other databases. The toxic unit approach assumes that mixture toxicity predictions follow the Dose/ Concentration Addition (DA/CA) model given the quantitative composition of each chemical within the mixture in relation to their relative potency  (Jonker et al. , 2005). Toxic Unit for chemical B (i. e. TU B ) is given as the ratio of the dose/concentration of chemical B applied in the binary mixture experiment relative to the selected critical endpoint (e. g. LD 50 ) used as reference as follows: = TU Applied Dose Critical Endpoint B B B (5) TU B = 0.
Comparison of estimated mean ratios and model deviation ratios A polynomial regression model (with formula y ~x + I(x^2) + I (x^3)) was fitted between the EMR from the individual dose response data and the corresponding individual MDR to assess the potential correlation between the two approaches by means of a Pearson productmoment correlation coefficient (R 2 ) (see result 3. 2. 4). R software has been used and R-script is provided in supplementary materials.
For the toxicological MoA in honey bees, classifications schemes from the Insecticide Resistance Action Committee (IRAC) and the Fungicide Resistance Action Committee's (FRAC) covering the specific target sites in target organisms for insecticides, acaricides and fungicides and the scientific literature were reviewed  (Leroux et al. , 2008; Hermann and Stenzel, 2019; Sanchezbayo, 2012; Johnson et al. , 2012 Johnson et al. , , 2013;; Huang et al. , 2013; de Castro et al. , 2015; Sparks and Nauen, 2015). In this context, pyrethroids/    (Leroux et al. , 2008; Hermann and Stenzel, 2019; Sanchez-bayo, 2012; Huang et al. , 2013; Sparks and Nauen, 2015; Johnson et al. , 2012 Johnson et al. , , 2013;; de Castro et al. , 2015). pyrethrins insecticides and conazole fungicides were the most investigated pesticides (≈55%) belonging to the MoA groups of "sodium channel modulators" (≈25%) and "demethylation inhibitors" (≈30%) respectively, and amongst conazoles, triazole fungicides (Demethylation Inhibitors) provided the largest experimental datasets for binary mixtures in honey bees.
Data collection and analysis 3. 2. 1. Data collection 218 individual binary mixtures were collected and included in the statistical analyses with the majority of toxicological endpoints reported as lethal doses or concentrations (e. g. LD 50 , LC 50, ) for pesticides or pesticides and veterinary drugs combinations with 133, 44 and 41 mixtures reporting acute contact toxicity (i. e. topical application), chronic oral toxicity and acute oral toxicity, respectively (tables S1, S2, S3, S7). Combined toxicity data for binary mixtures were available as dose response data in honey bees species  (Johnson et al. , 2009 (Johnson et al. , , 2012 (Johnson et al. , , 2013) )  for acute contact toxicity (n = 92) and acute oral toxicity (n = 15) (tables S3, S7). All toxicity data are available as spreadsheets on EFSA knowledge junction under the DOI: https://doi.
org/10. 5281/ zenodo. 3383713 and as summary tables in supplementary materials (Tables  S1 -S11 ) classified according to route and exposure patterns (i. e. oral, contact, acute and chronic) and toxicological endpoints (e. g. LD 50 , LC 50 ) for the honey bees (Apis mellifera) and wild bee species (Osmia bicornis, Bombus terrestris). All the studies included in this metaanalysis were performed in vivo experimental laboratory tests according to standard toxicity tests as provided by the author(s). In honey bees, acute contact toxicity studies refer to "topical application" and toxicity tests were mainly conducted on group feeding tests  (Johnson et al. , 2013 (Johnson et al. , , 2009 (Johnson et al. , , 2006;; Iwasa et al. , 2004; Biddinger et al. , 2013). Similarly, acute oral studies on honey bees and bumble bees were conducted on group feeding tests through consumption of contaminated food (e.
Quantification of magnitudes of interaction 3. 2. 2. 1. Estimated mean ratios. EMRs were calculated to characterise the magnitude of the combined toxicity for each individual binary mixture and expressed on a harmonised scale starting at 1 to reflect changes in the toxicological endpoint (EM M ) either as an increase (+) or a decrease (-) in combined toxicity  (Quignot et al. , 2015). 3. 2. 2. 1. 1. Acute contact toxicity. The acute contact toxicity database represented the largest database in honey bees with 133 LD 50 for binary mixtures including dose response data (n = 92) (Tables  S1-S3  and S15 ). A comprehensive analysis of the database provided an analysis of Toxic units below, prediction of combined toxicity and calculation of MDRs in Section 3. 2. 2 and comparison of EMRs and MDRs in Section 3.
However, the author did not exclude that synergism may occur under other exposure situations particularly at higher concentrations or with different proportions of individual chemicals. Combined chronic sub-lethal effects of coumaphos (organo-phosphate acaricide) and prochloraz (imidazole fungicide) in honey bee workers were investigated with regards to the molecular immune response at different developmental stages (prepupa, white-eyed pupa, adult)  (Cizelj et al. , 2016). Changes in mRNA level associated with upregulation of a range of genes (e. g. abaecin, defensin-1, cactus and basket) were reported for prochloraz and coumaphos. In addition, our results on mortality data suggest that an increased toxicity (EMR = 70) is observed when adult bees are exposed to coumaphos-prochloraz mixture, thus highlighting strong synergistic effects (MDR = 12.
Toxic Unit approach As described in the method Section 2. 2. 2, the Toxic Unit (TU) approach has been applied to quantify potency of the binary mixture A + B (TUm) versus compound A (TU A ). In addition, the dose of compound B in each experiment (TU B ) has been estimated using matching potency information (LC 50-B ) binary mixtures from available databases (EFSA PPR, 2012; SCCS, SCENHIR and SCHER, 2012;  More et al. , 2019). From the data available, this analysis could only be conducted for acute contact toxicity binary mixtures since no matching datasets for compound B were available for acute and chronic oral toxicity studies (Table  S13-S15 ). OpenFoodTox and other databases (e. g. US-EPA, OECD e-chem portal, PPDB-Pesticide Properties Database, literature) provided 85% and 15% of values for compound B respectively.
This approach is first described for available classes of chemicals namely a. insecticides-P-450 inhibitors and synergists, b. acaricides and insecticides, c. whole database. All individual binary mixtures data and summary statistics are available in supplementary material (Tables  S1-S3 , S13 and S15). a) Insecticides-P-450 inhibitors (conazole fungicides, synergists) Binary mixture experiments between insecticides and P450 inhibitors (e. g. conazole fungicides or synergists such as piperonyl butoxide -PBO) were the most investigated (55%)  (Iwasa et al. , 2004; Biddinger et al. , 2013; Johnson et al. , 2013; Spurgeon et al. , 2016). For insecticides-conazole fungicides, the largest EMR values were observed for the pyrethroid insecticide tau-fluvalinate with prochloraz (≈1980 Fig. 2. Estimated Mean Ratio (EMR) following chronic oral exposure to binary mixtures in different bee species i.
mellifera ligustica following acute contact exposure to coumaphos  (Rinkevich et al. , 2015). c) Whole database Figs. 7-9 compare EMRs for acute contact toxicity studies with their corresponding individual TU for compound B (TU B ) classified according to three different classes: TU B ≤ 0. 10 (Figs. 7  and 8 ), TU B ≤ 0. 11-0. 30 and TU B ≤ 0. 31-0. 60 (Fig. 9 ). For each TU B class, cumulative frequency distribution graphs are developed in order to quantify the sensitivity of the toxicological endpoint for chemical B contributing to the overall binary mixtures toxicity. The distribution of the EMR values against their "reverse cumulative frequency" is plotted and fits are tested with Pearson product-moment correlation coefficient (R 2 ). Results show that TU B values range from 0. 0001 to 0.
(2007). However, in our analysis, we proposed refined MDR thresholds in order to provide more conservative predictions for quantifying deviations from the CA model (Table  4 ). According to our MDR thresholds, from 92 binary mixtures of pesticides, combined toxicity of the binary mixtures was synergistic in 72% (66 datasets with 48 statistically significant), 17% additive (16 datasets) and Fig. 6. Fore stplot comparing honey-bee subspecies sensitivity to combined toxicity of binary mixtures  (Rinkevich et al. , 2015). Estimated Mean Ratio (EMR dots) and related 95% CI (lines) were reported in different honey bee subspecies (A. mellifera carnica, A. mellifera ligustica, A. mellifera primorski) following acute contact exposure to phenotrin with three different chemicals (amitraz, coumaphos and PBO).
11% antagonistic (10 datasets with 2 statistically significant)  (Table S15 and Figs. 10 and 11). Amongst synergies, the most commonly tested binary mixtures were conazole fungicide-insecticides combinations (Table  S15 ). The statistical significance analysis for each binary mixture was performed using non-overlapping 95% CI of the experimental EM A vs 95% CI of the experimental EM M for chemical A + B). 16 out 66 mixtures were classified as statistical synergism according to our MDR thresholds (Table  S15 ) although these were below the generic 2-fold deviation set as generic value by other authors regardless of target organism (e. g. Daphnia spp. , honey bee), mixture (e. g. metals vs pesticides), exposure route (e. g. oral vs contact) and effects measured (e. g. lethal vs sublethal) in the experimental assays  (Belden et al.
Conclusions and implications for risk assessment This manuscript constitutes the first consolidated quantitative review of the available in vivo laboratory experiments on combined toxicity of binary mixtures in bee species to support of hazard assessment. As noted in the introduction, exposure assessment and full risk characterisation are beyond the scope of this paper but their high relevance and implications for risk assessment are highlighted below together with future perspectives. Overall, 218 datasets were analysed with 61%, 20% and 19% reporting acute contact toxicity, chronic oral toxicity and acute oral toxicity respectively. Magnitude of interactions were estimated using EMRs, from experimental studies lacking dose response data (133 acute contact, 54 chronic oral and 41 acute oral datasets).
3. Applications of this analysis include: (a) use of the current open source database (DOI: https://doi. org/10. 5281/zenodo. 3383713) to provide scientific evidence for interactions and their magnitude for estimating mixture uncertainty factors for specific pesticide binary mixtures (see MIXTOX EFSA guidance (More et al. , 2019) ). (b) Development of in silico tools such as Quantitative-structure activity relationship (QSAR) models to predict combined toxicity of mixtures in honey bees for acute contact toxicity and other endpoints (chronic, sub-lethal), bee species (solitary bees, bumble bees) and routes (oral) in the future. Such models have been developed as classifiers for pesticides of different potency/threshold classes in bacteria (Toropova et al. , 2012). 4. Key data gaps have been identified and include the need for: a).
Acknowledgements The views expressed in this manuscript do not reflect the views of the  European Food Safety Authority  and are the authors only. This work has been financed by the  European Food Safety Authority (EFSA)  under the contract number  NP/EFSA/AFSCO/2016/02  ( Edoardo Carnesecchi ),  RC/EFSA/EMRISK/2013/02  ( LASER Analytica Europe: Audrey Grech, Billy Amzal, Nadia Guignot ) and  OC/EFSA/SCER/2018/ 01  ( Mario Negri Institute for Pharmacological Research, S-IN Soluzioni Informatiche, Molecular Networks GmbH and Altamira ). Authors would like also to thank  Lisa Hunston  for the proof reading.
Quantification with the Bayesian optimized PLA signal sorting package We designed a custom MATLAB package Bayesian optimized PLA signal sorting (BOPSS) to analyze PLA signal images  [38]. To use the program, we input a folder containing Brightfield images for single and dual PLA samples and negative controls. In the first step of BOPSS, clusters of noncellular foreground pixels (putative puncta [PP]) are located in all of the images using an approach typically used in the processing of histological samples  [39]. The PP are then randomly split into two groups (training set and test set). The test set of PP is used to calculate two distributions (negative control distribution and PLA signal distribution) for each of the 11 intensity-based and morphological features (11 pairs of distributions in total are generated).
Perfusion Direct immersion 125 mentary Table  2 ). Therefore, as described in the Supplementary data, we obtained virtual images by whole-slide scanning, divided the ROI into a series of sampling areas and only quantified the PLA signal in the areas (named counting loci in this study) that were selected by a systematic random sampling method adapted from Gundersen et al. [37]  (Supplementary Figure  1F ). Therefore, the total analyzed area represented only ∼3% of the ROI (Supplementary Table  2 ) but was composed of a set of representative counting loci evenly distributed within the entire ROI. To efficiently and reproducibly quantify PLA puncta across our large dataset in an unbiased way, we applied and compared three different quantification approaches. Two of these approaches, Analyze Particles (ImageJ)  [46]  and Spot detector (ICY)  [47] , rely on the user to set intensity and size parameters to distinguish clusters of pixels against the background of an image.
In contrast, our custom MATLAB package BOPSS, which we designed to automatically quantify PLA-BF signal, is an unsupervised machine-learning approach and requires no parameters to be set by the user. Analyze particles (Image J) depends on threshold and particle size to detect PLA puncta. The automated threshold (Figure  4 : ImageJ_auto) worked well for the dual PLA signal, but underestimated the signal for single PLA, and showed high counts in the negative controls (Figure  4D-F, P-Q  Manually optimizing the threshold reduced the nonspecific detection in the negative controls but caused severe undercounting for both dual and single PLA and failed to improve S/N (data not shown). Adjusting the size of particles also failed to improve S/N or undercounting of specific PLA signal (data not shown).