dataesr/scientific-paragraphs-categorization huggingface dataset:
MCV and GGT were summarized with descriptive statistics (geometric mean). Mean difference in the compliance with the assigned treatment was tested with a Student's t-test. The effect of the site on the treatment effect was a posteriori investigated with a two-way ANOVA with site-by-treatment interaction for compliance as outcome and with a site-level meta-analysis for CAR as outcome. Dropout and missing data were assumed to be missing not at random and were considered as relapse to alcohol for CAD, CAR, and time to first relapse. This assumption was selected because relapse was the main documented reason for dropout in previous trials  (Balldin et al. , 2003; Geerlings et al. , 1997; Paille et al. , 1995; Pelc et al. , 1997; Poldrugo, 1997; Sass et al. , 1996; Wiesbeck, 2001). MCV and GGT at the end of treatment as well as compliance with assigned treatment were analyzed based on observed values.
A sensitivity analysis was conducted on the primary endpoint with missing data assumed to be missing at random and using multiple imputation. All AEs were coded according to the Medical Dictionary for Regulatory Activities dictionary. The proportions of patients that reported AEs were tabulated by group and compared by means of the Chi-square or Fisher's exact probability test. For additional information on the above analyses, see Supplements 1 and 2. The principal statistical software used was SAS ® , Version 9. 4. PROC MIXED was used for performing fixed-effect ANOVA and mixed-effects models as well as site-level random-effect meta-analysis on the primary endpoint.
There is a convergence of evidence that the duration of abstinence before treatment initiation and/or the baseline alcohol consumption could be moderators of the effect of SMO in AD  (Guiraud et al. , 2021; Scherrer et al. , 2021; van den Brink et al. , 2018). Unfortunately, these baseline data were not collected in the current study. We are aware that also other subgroupings, for example, according to genetic, neurobiological, and other clinical features, might be important as predictors for the SMO treatment effect. They represent decisive factors for course, therapy, and outcome  (Lesch et al. , 2020). Interestingly, SMO has previously shown efficacy with large effect sizes in treatment-resistant AD patients  (Maremmani et al. , 2001)  and also in RCTs conducted in high-severity population, that is, in patient populations with a low response rate to placebo  (van den Brink et al.
Since patients and investigators remained blind to treatment assignment during the study period in both GATE 1 and GATE 2, we do not expect any serious risk of bias in the GATE 2 findings resulting from the recruitment of patients detoxified with SMO. In addition and since criteria for participation were more stringent in GATE 1, patients who were fulfilling GATE 2 inclusion criteria but who were excluded from the GATE 1 study, for instance due to the lack of moderate or severe alcohol withdrawal syndrome, were also invited to participate in the GATE 2 study. The AE profile was as expected from previously published data from pharmacovigilance and clinical studies  (Addolorato et al. , 2020)  and reflects the pharmacological profile of SMO. No significant group differences were found in the incidence of AEs.
The most reported AEs were effects on the nervous system (dizziness) and gastrointestinal apparatus (nausea). No difference in craving for study medication was detected between treatment groups, suggesting a low risk of abuse and dependence to SMO in the study population. One death (murdered) was reported but was not considered to be related to the study medication. Overall, SMO was well-tolerated. In conclusion, SMO showed efficacy in CAD during the 6-month treatment period in this double-blind RCT. The current RCT confirms efficacy and safety of SMO in the treatment of AD reported in previous RCTs and pharmacovigilance database, especially for patient populations with a low placebo response rate. In this subgroup of severe AD patients, additional data are warranted to further support the clinically relevant effect of SMO.
Acknowledgements We thank all patients for their participation in the GATE 2 study, all research staff, and the  GATE 2 Study Group  for their contributions:  Mauro Ceccanti ,  Maciej Djurkowski ,  Elzbieta Filipecka ,  Antoni Florkowski ,  Gilberto Gerra ,  Rudigen Holzbach ,  Jan Horodnicki ,  Werner Platz ,  Bernardo Spazzapan , and  Halina Zblowska. We also like to thank  Koos Zwinderman  for his contribution to the statistical analyses.
It is recognized that a number of alternative methodologies for classification of aquatic toxicants exist but these provide neither comprehensive coverage nor a tool that may readily be used in risk assessment. For instance, Barron et al. 28  described the MOATox system, which is a database of the mode of action classification based on a consensus approach including the Russom scheme and other sources. While each of the Verhaar, Russom, and MechoA schemes have seen implementation in the form of in silico tools permitting the profiling of chemical libraries, the same has not been true of the Sapounidou rule set. Accordingly, the primary purpose of this investigation was to report the development of an appropriate, freely available resource permitting the.
Environmental Science & Technology practical employment of this scheme in the screening of substances for their potential environmental liability. This takes the form of a workflow within the KNIME analytical software, the core of which lies in a series of structural alerts designed to capture essential chemical features defining participation in the identified MIE. Once operational, this profiler was incorporated alongside those of Verhaar, Russom, and MechoA into a secondary study whereby the performance of each with respect to domain coverage was compared. Particular emphasis was placed upon examining the relative merits of first-and second-generation approaches�the latter of which are anticipated to offer advantages in terms of both the mechanistic resolution afforded and the quantity and breadth of data drawn upon in their development.
Each subgroup is anchored in turn within (potentially several) MIEs, which are themselves defined at the finest level by structural alerts. Implementation to form a practical in silico profiling tool was achieved through construction of a workflow within KNIME analytic software (v. 4. 3. 1; www. knime. com). 34 This was constituted such that it returns all accompanying information associated with given assignments�including the domain of taxonomical applicability�as presented in Table  S3. Structural alerts were compiled from expert knowledge of chemistry surrounding those molecular initiating events established, within the literature, as holding relevance to aquatic toxicology. Their form was tailored such that both excessive exclusivity and generality in terms of potentially matched compounds were minimized.
4. 5; https://www. rdkit. org/). Characterization of structural features was achieved through acquisition of ToxPrint chemotypes generated through ChemoTyper software (version 1. 0; Molecular Networks, Erlangen, Germany). 36, 37 he Verhaar rule set was accessed through the OECD QSAR Toolbox 38 (v. 4. 4. 1; www. qsartoolbox. org), the Russom scheme was accessed through Chemprop (v. 7. 1. 0; http:// www. ufz. de/ecochem/chemprop), MechoA was accessed through the MechoA (v. 2. 2) functionality in the iSafeRat Desktop (v. 2. 1. 0; https://isaferat. kreatis. eu/), and the Sapounidou approach was accessed using the KNIME Workflow described in Section 2. 1.
While 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase and glutamine synthetase are each represented by a single alert (the former not depicted), allosteric modulators at acetolactate synthase may take one of the four known forms (A-D)�these corresponding to established herbicide classes. KNIME workflow output relating to these substances is additionally provided to serve as an indication of the extent and layout of information provided through the profiler. This tool is freely available for download through links https://github. com/LJMU-Chemoinformatics/ Sapounidou-mechanistic-profiler (GitHub) or https://zenodo. org/record/7100972#. YysJ93bMLIU (Zenodo). Considering the scheme in its entirety, it was necessary to encode a total of 183 structural rules. While in the great majority of instances it was possible to express key chemical features through the use of single SMARTS strings (as in Figure  1 ), it was necessary that several entries within the narcosis domain be defined by alert sequences.
To provide greater resolution with regard to the functioning of the scheme against chemicals holding similar properties, a further six defined-use inventories (as outlined in Table  S4 ) were individually screened. The proportion of the data sets identified as belonging to each Tier 1 domain are shown in Figure  3. Profiling of the inventory related to cosmetics (COSMOS) revealed that a majority of chemicals (50. 8%) fall within the domain of narcosis. 39 This is largely expected since the inclusion of noninert chemicals within such products would generally be considered highly undesirable. Accordingly, those constituting the set tend to be small in size, possessing only simple functional groups. By contrast, a significant number of compounds within the pesticide dataset (52. 5%) are assigned to the specific domain.
Data High-volume PM 10 and PM 2. 5 quartz filter (Pallflex Tissuquartz 2500QAT-UP) samples were collected using Digitel DA-80H samplers with flow rates of 30 m 3 h -1. Daily sampling ran continuously from midnight to midnight for a 12-month period between 1 June 2018 and 31 May 2019. However, for the quantification of constituents beyond measurements of simple PM mass, punches from every fourth day's filters were taken and analysed. Because the sites form part of the NABEL network, routine flow checks and various tests were regularly conducted in accordance with standard operating procedures. In total, 908 filters were analysed with three OP assays. Eight hundred and ninety-nine valid samples were reported; the missing samples were due to sampling or laboratory issues. Additional filter punches were used for a collection of other laboratory analyses to quantify other PM constituents such as elemental components (with inductively coupled plasma atomic emission spectrometry (ICP-AES) and inductively coupled plasma mass spectroscopy (ICP-MS)), ions (ion chromatography (IC)), elemental and organic carbon (thermal optical transmission (TOT) via the EN16909 method, using the EUSAAR2 temperature protocol; European Committee for Standardization (CEN), 2017), and a collection of additional organics (a high-performance liquid chromatographic method followed by pulsed amperometric detection (HPLC-PAD)).
For PM 2. 5 , the primary biogenic, mineral dust, and aged sea salt sources were not identified. Details on the specific settings, constraints, and process of the extended PMF modelling can be found in the accompanying  Grange et al. (2021)  publication. The PMF input and output data are also available in a persistent data repository for convenience  (Grange, 2021a). The PMF analysis for this particular dataset was challenging because of the existence of fewer than the recommended number of available samples (91 compared to the recommended at least 100;  Norris et al. , 2014) , low signal-to-noise ratios for many variables because of low ambient concentrations, and the inclusion of extra organic species in the PMF models. Despite the many validation steps conducted, the models had a number of limitations, which are discussed fully in the companion paper  Grange et al.
, 2019; Reddy et al. , 2020). Variable importance is a metric that represents the improvement of information gain at each split in the decision tree for a particular independent variable. Therefore, variable importance aids the determination of a subset of useful variables. The permutation variable importance approach was the specific algorithm used; this approach evaluates the prediction accuracy with the sampled (out of bag, OOB) observations and permutes each variable's values to determine the effect on prediction performance  (Wright et al. , 2016). The variables that were consistently identified by random forest as the top 12 most important for explaining OP AA v and OP DTT v were used in further linear modelling work. This dimensionality reduction pre-processing step allowed the dataset to be reduced from over 50 variables to the most important ≈ 15 for two OP assays.
1 nmol min -1 m -3 ) in the dataset. The lack of current standardisation for OP measurement, quantification, and calibration is an issue that the air quality community should address and would allow for reliable comparisons among different locations and times in the future. The AA and DTT assays showed much more seasonal structure than the third DCFH assay, which made the former approaches more useful for data analysis than the latter. An analysis of Switzerland's PM 10 and PM 2. 5 sources identified by PMF models suggested that two major anthropogenic emission sources, namely road traffic and wood combustion, were the most important drivers of OP v in Switzerland. The importance of these two sources for OP has been reported elsewhere too  (Simonetti et al. , 2018). Contrasting this were the inorganic nitrate-and sulfate-rich sources, which generally had low levels of intrinsic OP m across Switzerland, as did the two biogenic sources (primary and secondary).
However, the results are consistent with those found in the literature and give very clear suggestions on where to focus future efforts to identify the linkage between biological mechanisms and OP v. It is also clear that the PM 10 and PM 2. 5 size fractions have different OP v characteristics and the OP v is not simply additive. Furthermore, considering the importance of non-exhaust emissions for the coarse mode, the importance of continued PM 10 monitoring is outlined. Data availability. The data sources used in this work are described and some datasets are publicly accessible in a persistent data repository (Grange, 2021a, https://doi. org/10. 5281/zenodo. 4668158). Additional data and information are available from the authors on reasonable request.
Supplement. A file (op_model_collection_which_ passed_the_filters. csv) containing all linear regression model formulations along with their estimates and model statistics is attached. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-22-7029-2022-supplement. Author contributions. SKG and CH conceived the research questions and wrote the manuscript. SKG conducted the data analysis and GU managed the OP laboratory analyses. GU, SW, and JLJ helped with revising and improving the manuscript. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. The authors thank the wider Projekte Quellenzuordnung Feinstaub team for their contributions. SKG is also supported by the Natural Environment Research Council (NERC) and holds associate status at the University of York. Andrés Alastuey and Xavier Querol from the Institute of Environmental Assessment and Water Research, Consejo Superior de Investigaciones Científicas are thanked for their help with the elemental ICP analysis. Gaëlle Uzu and Jean-Luc Jaffrezo thank the ANR-15-IDEX-02, ANR-19-CE34-0002-01, and the Foundation of the University of Grenoble Alpes for the funding of the instruments on the AirOSol analytical plateau at IGE. Financial support. This research has been supported by the Bundesamt für Umwelt (grant no. 16. 0096. PJ/R152-0739). Review statement. This paper was edited by Nga Lee Ng and reviewed by three anonymous referees.
With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children's hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task.
Yang et al. [5]  focuses on studying the influence of the combination of word, sentence embedding and CNNs in specific phenotype classification performances. Another set of solutions focus on extracting all the phenotypes terms occurring in the clinical reports. This can be seen as both more specific (it can be a step of aforementioned pipelines) and more general (we do not focus on one phenotype of interest). These descriptors can later be used for querying a database for patients fitting a certain profile under investigation (when hypotheses and search for evidence underpinning them are still being made) and without constraining the search on previous assumptions or known cases. They can also be used as a prerequisite to perform a phenome-wide association study as noted in Robinson et al.
Corpora The corpora were made by sampling free-text Electronic Health Records from the Necker's Children hospital's database (containing 2,5. 10⁶ documents in total). The sampling of the documents was stratified on the provider unit to ensure the representativity of the corpus. A maximum of one document per patient could be selected in the sample. The annotation process was carried out by two experts who were tasked with checking and enriching phenotype annotations made in an automated fashion by an exact match and pattern-based method (as implemented in DrWarehouse  [1] ). First, the preliminary automated annotation consisted in using the French portion of the UMLS metathesaurus to get terms belonging to 10 semantic groups (provided as part of the UMLS hierarchy) deemed to relate to phenotypes  [1].
Then, using a brat annotation server  [15]  the experts had to report both true and false positives (i. e. expressions matching a UMLS phenotype term, that either did or did not correspond to a phenotype in context) as well as indicating if the presence of the phenotype was asserted or negated. Given the scarcity of the family annotations it was decided to constitute a separate enriched dataset for this specific task: the collection of clinical reports from Necker's children hospital was searched for documents containing patterns suggesting family related mentions using the method previously implemented in DrWarehouse. In every case, the quality of the annotations was evaluated by having two annotators performing redundant annotations on a subset of documents (n = 100 and n=60, respectively).
e. , if most of the tokens of an entity were predicted to be part of the positive class then the entity was considered to belong to the positive class itself, to the negative class otherwise). Training was done using various combinations of parameters, including the size of the hidden layer of the RNN cells, dropout rates, number of hidden layers in the RNN, learning rate, and negative vs positive class weighting scheme. The evaluation of performances was done using 10 partitions of the dataset in train / validation / test sets. The performance measures are reported for the mutually exclusive test sets, after selection of the parameters (and early stopping) on the validation set (see Table  2 ). Reported metrics are precision, recall, F1 (the harmonic mean of precision and recall with equal weights), Negative Predictive Value (NPV), specificity, and a measure we dubbed F1n: the harmonic mean of NPV and specificity.
Discussion With this work we improved an existing NLP phenotyping pipeline for French clinical reports using deep learning models. The best model for each task is made available through dockerized microservices that can be queried to augment documents contained in the DrWarehouse database. In turn the additional steps will allow for more precise information retrieval for clinicians and researchers using the DrWarehouse database. We also showed that on the tested prediction problems and corpora, using a transformer-based contextual embedding does improve measured performances (for two tasks, while leaving performances relatively untouched for the third one). This is consistent with several results in the NLP literature although it should be noted that it is not always the case as in  [2]. For the subject prediction task, it was decided to assign clinical reports mentioning a foetus and pregnancy to the foetus.
Although this was a decision consistent with the perceived usage of clinical reports in our context (i. e. , following mainly the health of the foetus in a hospital dedicated to children's health), this poses clear limitations to the exploitation of those reports. In ulterior versions of the datasets and models we plan to add specific rules for that case, including a multi referential view of the clinical report (both mother and foetus/child as patients). Another design choice was to annotate pre-detected terms in order to limit the annotation burden. This implies that the recall for phenotype detection is currently capped by the pattern method which is used prior to the attribute prediction. Future iterations of the system may instead include detection using deep learning based NER. This would allow for robustness against spelling mistakes, as well as exotic variations in terms names not contained in the used lexicons.
Individual lionfishes were also photographed where possible, which enabled the later confirmation of species identification. Surveys were conducted between 11 am and 1 pm, to ensure that there were no differences in density due to increased behaviour which may be observed at certain times of day (e. g. ,  McTee & Grubich, 2014). Statistical analyses were conducted within the statistical programme R, version 4. 2. 2 (R Core  Team, 2022)  on RStudio, version 2022. 7. 2. 576  (RStudio Team, 2022). The authors used a generalized linear mixed-effects model (glmer) with a Poisson distribution in the {lme4} package  (Bates et al. , 2015)  to investigate whether lionfish density varied across the different lionfish species and at different survey depths. In the full model, the number of lionfish (i.
e. , count data) was the dependent variable, lionfish species and survey depth as well as the interaction between these two variables were included as fixed effects and survey location was included as a random effect. A topdown approach was used to arrive at the most parsimonious model: there was no significant interaction between lionfish species and survey depth, so the final model included the two fixed factors (species and survey depth) without the interaction term. Model assumptions were examined using the {DHARMa} package  (Hartig, 2020). Post hoc tests were conducted using the {emmeans} package  (Lenth, 2022). Figures were plotted using the {ggplot2} package  (Wickham, 2016). In total, the authors observed 96 adult lionfish from three of the four species known to occur at Moorea [P.
Absorbance was measured at 450 nm for each well with calibrators and test samples, deposited in duplicate in the assay plate. The concentration of insulin in the test samples was obtained by data reduction from the calibration curve using cubic spline regression and taking into account the initial sample dilution. Finally, insulin concentrations were normalized compared to the level of insulin released in cells without any stimulation (CTL) (initially seeded at 3 × 10 5 cells per well in 1 mL of medium).