dataesr/scientific-paragraphs-categorization huggingface dataset:
BRCA1-HDR dataset. The authors  [16]  performed a Multiplex Homology-Directed Repair Assay with the aim of quantifying the effect of 1056 amino acid substitutions in the BRCA1 N terminus comprising residues 2-192 known to include the ring domain in residues 7-98. As proper folding of the RING domain is required for the stability and function of the full-length protein, the authors analyze whether the mutated BRCA1 protein is able to maintain its DNA repair function in the homology-directed repair (HDR) pathway using, in tissue culture, a green fluorescent protein (GFP) based reporter assay  [17]  in which the functionality of BRCA1 can be detected by identifying green-flourescent cells. The information about the impact of amino acid mutations on the HDR pathway was depicted graphically using a color scale.
An in house R  [22]  script was used to convert the graphical information to a plain text format. Based on the depletion scores (fluorescence drops respect to a subset of cells having a functional GFP allele encoding an active protein) observed across four replicates of the multiplex HDR reporter assay, mutations showing a depletion in none or just one replicate were considered "benign" (negative examples). On the other hand, mutations showing a depletion state in at least three replicates were considered "pathogenic"; mutations showing depletion states involving two replicates were discarded. As a result, the final dataset comprises 59 "Pathogenic" variants (positive examples) and 977 "benign" ones (negative examples). As expected, both datasets turned out to be highly imbalanced with most of the mutations being of the "benign" type.
Inner Consistency Analysis The task of assessing the inner consistency of the p-tools faces the problem of the heterogeneity of their outputs. It is not simply a problem of outputs involving different scales but of their semantic meaning. Usually, p-tools provide categories to classify the impact of mutations on the functionality of a gene. However, these categories are not equally distributed through their original numerical scales, thus conversions made by the tools are not linear. Furthermore, different numerical scales are used, from probabilities and free-energy values, to ad-hoc scores. Hence, normalization approaches do not make sense. We note, however, that once categories are defined for a p-tool, they naturally induce an internal ranking for numerical predictions. Given a pair of p-tools and a dataset of mutations, the agreement between their internal rankings can be used to assess their inner consistency.
K all first decreases and then increases monotonically until the percentage equality threshold reaches 100%. All mutations then become indistinguishable and K all reaches its maximum value (1). To summarize, K all does not show a monotonic behavior with respect to the percentage equality threshold. Supplementary studies were performed to asses the critical percentage equality threshold where K all accomplishes its minimum. Two independent datasets of mutations, namely, the DM-V dataset comprising reported mutations of the Drosophila melanogaster vermilion (V) gene and the CHKV-E2 dataset comprising reported mutations of the Chikongunya virus E2 gene, were used to evaluate the K all index with respect to increasing values of the percentage equality threshold. All p-tools were analyzed except Panther as this only provides a categorical output.
Outer Consistency Analysis Standard information retrieval metrics including the accuracy, the precision, the recall, the F1-score, and the Matthews correlation coefficient (MCC) were considered to evaluate the outer consistency of p-tools: accuracy = TP + TN TP + TN + FP + FN (5) precision = TP TP + FP (6) recall = TP TP + FN (7) F1-score = 2TP 2TP + FP + FN (8) MCC = TP * TN -FN * FP (TP + FN)(TP + FP)(TN + FN)(TN + FP) (9) where TP, TN, FP, and FN stand for the number of true positive, true negative, false positive, and false negative predictions respectively. It is worth noting that special care should be taken with the above metrics when analyzing highly imbalanced datasets like those induced in experiments involving the high throughput screening of genetic mutations. Fortunately, the human being is a highly robust system, thus we expect most of the SNVs to be negative examples (benign mutations).
The elements above the diagonal correspond to the BRCA1-SGE dataset while the elements below it correspond to the BRCA1-HDR dataset. The P-tools abbreviations are: Polyphen(Pph2), Provean (Prov), Align GVGD (Gvgd), Cupsatd (Cupd) , Cupsatt (Cupt), Panther (Pthr), and Strum (Strm). In addition, Table  3  shows the inner consistency measurements accomplished by the Spearman's correlation coefficient. These results show that many of the p-tools are poorly correlated. In principle, this may be attributed to differences in the semantic of predictions in each p-tool scale and/or the sensitivity of the Spearman's correlation coefficient to p-tools with numerical outputs. For both the BRCA1-SGE and BRCA1-HDR datasets, the most correlated p-tools are Provean and Align GVGD, whose correlation coefficients are highlighted in bold.
Outer Consistency The measurement of p-tools outer consistency is shown in Table  4. Only the information about TP and TN predictions is shown together with the MCC and F1-score statistics. Accuracy, precision, and recall metrics are shown in Appendix A. The BRCA1-SGE dataset has a rather imbalanced distribution of positive and negative samples (G = -0. 56). Three of the p-tools, Align GVGD, Cupsatt, and Panther, correctly predict more than 78% of the positive examples (TP). However, only Panther reasonably predicts negative ones (57%). We note, however, that the three p-tools also introduce many false positive predictions (see Appendix B). Based on the MCC and the F1-score, we can say that the best compromise in the prediction performance is achieved by Panther. The BRCA1-HDR dataset is highly imbalanced (G = -0.
88). For this dataset, four of the p-tools, Align GVGD, Cupsatt, Panther, and Strum, correctly predict most of the positive examples (TP). However, only Panther reasonably predicts negative ones (74%). Based on the MCC and the F1-score, none of the p-tools achieved an acceptable prediction performance. This may be due to many false positive predictions (300 on average) with only 59 TP (see Appendix A). Provean does not predict any mutations as positive, making the F1-score and MCC equal to 0. On the whole, Panther achieves the best compromise in prediction performance for the considered p-tools on average. However, its prediction performance remains poor. Finally, our results show that although most of the mutations reported for the BRCA1 gene are of the benign type, p-tools tend to classify them as pathogenic from the observed high rates of false positive predictions.
Using these indices, the most similar and most different prediction tools can be identified. As a result, these indices can help to accelerate the understanding of new prediction tools. Last, these indices can help to design truly diverse ensembles of prediction tools, a fundamental requirement for improving the confidence of individual members of the ensemble. Inner consistency studies were complemented with outer consistency studies focusing on the extent to which predictions matched the experimental results reported in literature. Without loss of generality, experimental data involving the high throughput screening of genetic mutations on the BRCA1 gene were considered. The outer consistency studies confirmed the importance of selecting suitable information retrieval metrics since reference datasets are expected to be highly imbalanced.
In both studies, the HumVar classification model was selected. Provean http://sift. jcvi. org/-(Protein Variation Effect Analyzer v1. 1. 3) is a software tool for predicting whether an amino acid substitution has an impact on the biological function of a human or mouse protein. It is based on the change, caused by a given variation, in the similarity of the query sequence to a set of its related protein sequences. For this prediction, the algorithm is required to compute a semi-global pairwise sequence alignment score between the query sequence and each of the related sequences. This alignment-based score measures the change in sequence similarity of a query sequence to a protein sequence homolog before and after the introduction of an amino acid variation to the query sequence. The output prediction information of this tool is a table with a prediction label column and a score column.
If the score is equal to or below a predefined threshold (e. g. , -2. 5), the protein variant is predicted to have a "deleterious" effect. If the score is above the threshold, the variant is predicted to have a "neutral" effect. Align GVGD http://agvgd. hci. utah. edu/-(Grantham Variation and Grantham Deviation) is a software tool that combines the biophysical characteristics of amino acids and protein multiple sequence alignments to predict where missense substitutions in genes of interest fall in a spectrum from enriched deleterious to enriched neutral. The output prediction information of this tool is a table with a score column that represents an extension of the Grantham difference, to score missense substitutions against the range of variations present at their position in a multiple sequence alignment and a categorical column with seven classes ordered from most likely to interfere with function to least likely.
Strum https://zhanglab. ccmb. med. umich. edu/STRUM/-(Structure based Prediction of Protein Stability Changes Upon Single-point Mutation) is a software tool for predicting the fold stability change (∆∆G) of protein molecules upon single-point mutations. Strum adopts a gradient boosting regression approach to train the Gibbs free-energy changes on a variety of features at different levels of sequence and structure properties. The unique characteristic of Strum is the combination of sequence profiles with low-resolution structure models from protein structure prediction, which helps to enhance the robustness and accuracy of the method and make it applicable to various protein sequences, including those without experimental structures. The output prediction information of this tool is a column with the ∆∆G value of each mutation.
Cupsat http://cupsat. tu-bs. de-(Cologne University Protein Stability Analysis Tool) is a software tool for predicting changes in protein stability upon point mutations. It uses structural environment specific atom potentials and torsion angle potentials to predict ∆∆G, the difference in free energy of unfolding between wild-type and mutant proteins. To improve accuracy and specificity of predictions, the mutations and mean-force potentials were classified according to different structural regions. Initially, the secondary structure specificity of mutations and mean-force potentials was implemented and the amino acids were classified into helices, sheets, and others. Later, the amino acids belonging to each of these secondary structure elements were further subdivided according to their solvent accessibility.
Aircraft data processing The lidar calibration is performed several times during each flight using a normalization of the attenuated backscatter signal the laser and the telescope, the mean altitude dependency of the ratio between PR2 and the molecular backscatter is determined for each flight in the 0-700 m altitude range below the aircraft using only profiles with no cloud and aerosol layers. The calibration accuracy is then of the order of 5-10% due to the mean signal statistical uncertainty (< 3%) and the assumption on the reference scattering ratio being unity in the calibration range (≈ 5%). Aerosol optical depth retrieval is based on the Fernald forward inversion of the calibrated PR2  (Fernald, 1984) , assuming a range independent value of aerosol lidar ratio (LR). The LR value is constrained using the distribution of 10 km MODIS collection 6  (Levy et al.
, 2013)  Aerosol Optical Depth (AOD) in an area of ± 70 km and a time lag of ± 5 h around the aircraft observation. The possible LR values are obtained by an iterative analysis using the 25 th and 75 th percentile of the MODIS AOD distribution to constrain the lidar AOD. To estimate the uncertainty of the retrieved backscatter ratio, 500 inversions were performed using random LR values within the interval deduced from the constraint with MODIS and random backscatter coefficient at the reference altitude within the interval corresponding to the statistical error on the attenuated backscatter at the reference altitude (≈ 5%). The complete methodology for the lidar data analysis is summarized in the upper panel of Fig. 2.
Comparison of airborne lidar and CALIOP aerosol layer data products In this section, aerosol type and LR values derived from the airborne lidar data analysis are compared to nearby CALIOP overpasses. The time of passage and the position of the aircraft do not allow to have an exact coincidence with the footprint of the CALIOP lidar. Therefore CALIOP granules to be compared with the aircraft have been selected using the FLEXPART PES maps discussed in the previous section. For the selected CALIPSO granule, cloud free attenuated backscatter profiles with PES > 0. 8•PES max are averaged to obtain a mean backscatter ratio vertical profile using the methodology described in  Ancellet et al. (2014). The averaging distance is generally of the order of 100-300 km (see Table  2 ). The aerosol type and the range of the LR distribution for the CALIOP profile is then taken from the Version 4.
For the aged forest fires, the differences between the two CALIOP vertical profiles are quite small and the CALIOP profiles are then fairly representative of the airborne lidar observations. The 532 nm AOD calculated for the aircraft and CALIOP profiles (Table 2) are very similar except for the aged urban pollution layers transported from northern China where the AOD differences 380 between CALIOP and the airborne lidar is 0. 16 (+57%). This is mainly due to large difference of lidar ratio (70 sr instead 45 sr) between the CALIOP retrieval and the aircraft data (see hereafter). Therefore the selected CALIOP profiles are suitable to discuss the differences between the CALIOP V4. 2 lidar ratio values compared to the airborne lidar data analysis. The aerosol composition for the six averaged CALIOP profiles are given in Table  2  using the CALIOP level 2 aerosol type and taking into account the thickness of the aerosol layers.
Conclusions Two airborne lidar campaigns were carried out over Siberia in July 2013 and June 2017. Aerosol types and optical properties were derived using FLEXPART, satellite data and airborne in-situ measurements when available. Six aerosol type could be identified in this work: (i) Dusty aerosol mixture (ii) Ob valley industrial emission (iii) fresh boreal forest fire plumes (iv) aged forest fire plumes (v) pollution over the Tomsk/Novosibirsk region (vi) long range transport of Chinese pollution over Yakutsk. The aircraft in-situ measurement, mainly ∆CO and BC have been useful to validate the identification of the aerosol origin using FLEXPART and the satellite observations ; namely large BC concentrations in the fresh forest fire plume and large ∆CO for the long range transport of Eastern Siberian forest fires and of polluted plumes from Northern China.
The lidar ratio (LR) analysis shows that the lowest LR range is obtained for the "Dusty Mix" case (26-40 sr) and the highest for the urban and industrial pollution from the Tomsk/Novosibirsk area (71-90 sr). We found a good agreement of this work analysis of the LR values according to the aerosol classification with previous studies (e. g. ,  Burton et al. , 2013). The range of lidar ratio obtained for gas flaring emission (43-60 sr) is lower than the high values encountered in the Tomsk/Novosibirk urban area and has never been characterized using lidar observations. Airborne lidar backscatter ratio vertical structure, aerosol types and integrated LR derived from the airborne data analysis (section 4) were compared to nearby CALIOP overpasses. We found three main differences with the CALIOP LR and aerosol type classification over Siberia: (i) layer can be classified as Elevated smoke instead of Polluted continental and vice versa, but with little influence on the LR value (ii) aging and transport of aerosol layers effect on the LR value is not always properly accounted for even when the classification is correct (e.
Acknowledgements. The work was supported by  Sorbonne Université  and the  French Centre National d'Etudes Spatiales  (PhD contract  n°2921/2017 ). The work was also supported in part by  Ministry of Science of Education of RF  (Agreement No. 05. 616. 21. 0118 , unique identifier RFMEFI61619X0118). We thank the  European Centre for Medium Range Weather Forecasts (ECMWF)  for the provision of  ERA-Interim reanalysis data  and the FLEXPART development team for the provision of the FLEXPART 9. 2 model version used in this publication. The authors thank the AERIS infrastructure and  NASA/GSFC  for providing the satellite data used in this paper (CO, AOD, CALIOP and fire FRP).
Code availability. The FLEXPART code version 9. 2 was downloaded from the FLEXPART wiki homepage (https://www. flexpart. eu/downloads). Data availability. Airborne lidar data and in-situ measurements are available and can be provided on request (contact : Mikhail Arshinovmichael@iao. ru). The daily MODIS and VIIRS information from the fires were provided by LANCE FIRMS operated by NASA/GSFC/EOSDIS and are available at https://firms. modaps. eosdis. nasa. gov/download/. MODIS AOD product (10 km and gridded 1 o resolution) were downloaded in hdf format at https://ladsweb. modaps. eosdis. nasa. gov/archive/. The 10 µm IASI product was download in NetCDF format at https://ara. lmd. polytechnique. fr/index. php?page=aerosols. CALIOP level L2 data have been downloaded from the ICARE date base (http: //www.
In most occupational settings, several chemical agents are commonly found, and the associated exposure risk for workers must be assessed. For this purpose, air samples can be collected and analyzed. AltrexChimie is a web application that helps industrial hygienists in the organization of the air sampling strategy and in the subsequent phases of data management, analysis, and communication. AltrexChimie contains a database of more than 550 chemical substances and their respective French Occupational Exposure Limit Values (OELV): Custom OELVs can also be defined by the user. AltrexChimie helps with the definition of key features of the sampling strategy, in particular by promoting a methodology for the design of Similar Exposure Groups (SEGs). Once measurement data are entered, they can be analyzed to obtain exposure diagnostics.
In general, exposure to agents with chronic health effects is considered acceptable when less than 5% of all exposure measurements are above the OELV  [5] [6] [7] [8]. One of the mandatory conditions to ensure the quality of the diagnostic is the adequacy between the actual duration of the task performed, the duration of the sampling, and the type of OELV (acute or chronic toxicity) used for the comparison. The computation of statistical indicators of exposure is made easier by the availability of several software tools. SAND and STAND are two software libraries for the R programming language  [9]  which include useful functions for the analysis of occupational data with non-detects. BWStats V3 is a web application that meets the requirements of the EN 689:2018 standard; it also includes many useful graphs and its older Microsoft Excel version V2 can still be downloaded for offline use  [10].
Expostats is another web-based tool that implements Bayesian inference and proposes some innovative data visualizations  [11]. IHStats is an Excel spreadsheet that uses a frequentist approach for compliance with the computation of the 95th percentile of the measurements' distribution  [12]. Hyginist is a downloadable Microsoft Windows executable file that implements a wide range of different statistical indicators to assess OELV compliance  [13]. ProUCL is a Windows executable originally designed for environmental settings but fully useable for occupational health assessments  [14]. IHDA is a Windows executable whose specificity is to allow IHs to include their own expert judgement in the assessment through Bayesian methods  [15]. Finally, AltrexChimie (https://altrex. inrs. fr) was initially developed as a standalone program in 1993 and recently received a major update and migration to a web application.
It computes statistical indicators and it manages exposure measurements at enterprise level. AltrexChimie is also linked to other tools for prior risk assessment such as Seirich  [16]. AltrexChimie was developed at the French Research and Safety Institute for the Prevention of Occupational Accidents and Diseases (INRS), and is available in French and English. The aim of this paper is to show how the new version of AltrexChimie differs from the other tools available to IHs and how it can help them establish an efficient exposure assessment workflow. The first part of the next section focuses on the data management functionalities of AltrexChimie, which represent the core innovation of the new version. The second part focuses on the definition of the sampling strategy and, in particular, on the procedure for SEG design.
The Management of SEGs and Measurements at the Establishment Level For assessing occupational exposure, several SEGs are usually defined per establishment. For each SEG, several substances can be measured, leading to numerous exposure diagnostics. The definition of SEGs, the analysis of results and the diversity of diagnostics produced over years, and often by different people or contractors, represent a lot of information which is difficult to organize, store and retrieve. At the same time, IHs need to communicate efficiently to decision-makers at the enterprise level: Details on the statistical computation of the diagnostic are not of interest, results are. In AltrexChimie, the management of measurements is facilitated by an underlying database structure, linking the SEGs to substances, measurements and the corresponding diagnostics.
The complete online report provided by AltrexChimie contains some additional graphs: The probability density, the log-log graph, and the box and whiskers plot. Data provided in the report include: The number of measurements and their type (short-term/long-term), descriptive statistics (arithmetic and geometric mean and standard deviation, range,. ), the Shapiro-Wilk statistic and its p-value (to assess the log-normality), Spearman's test and its p-value (to assess the temporal drift), an ANOVA for each determinant of exposure and the exposure diagnostic (exceedance fraction and its 70% confidence interval). When multiple substances having the same toxic effects are measured for a SEG on the same day, a sum of the measurement/OELV ratios calculated for each substance can be computed. The result of this computation is a multiple exposure index, which is considered indicative of the possible combined toxic effects of the mixture of substances to which workers could be exposed in the described situation, assuming additive effects  [28].
No other free software provides explicit multiple exposure assessment functionalities. Meanwhile, all of them can be used with multiple exposure indexes instead of measurements. The statistical computations offered by AltrexChimie are among the simpler ones, like the ones proposed by BWStat, IHStats, or the R libraries SAND and STAND. Hyginist offers a greater number of statistical indicators, allowing for a comprehensive assessment for experts. Expostats and IHDA offer Bayesian-based computations, which facilitate the communication of results under a probabilistic perspective; IHDA also allows prior expert judgment to be included in the computation.