dataesr/scientific-paragraphs-categorization huggingface dataset:
1). Peaks found in the blank samples and contaminant masses registered for commercial use were removed, as well as unassigned peaks were removed from the dataset. Further, peaks with an intensity < 0. 01% the sum of intensities were set to zero. All peaks were normalized by the sum of the intensities and to obtain robust results only peaks present in at least two out of the three replicates were included. DOM compounds were assigned to compounds categories  40. Optical measurements and PARAFAC model Detailed of optical measurements performed here have been previously described  41. Brie y, absorbance spectra (200 to 800 nm) were measured at 1 nm intervals with a Lambda 40 spectrophotometer (Perkin-Elmer, Waltham, USA) and uorescence excitation-emission matrices (EEM) with a spectro uorometer (SPEX Fluoromax-4, Horiba Jobin Yvon), using a 1 cm quartz cuvette and Milli-Q water as blank.
Excitation wavelengths ranged from 250 nm to 445 nm at intervals of 5 nm, and emission wavelengths from 300 nm to 600 nm at increments of 4 nm. Samples were corrected for instrument-speci c biases, blank-subtracted, inner lter effect corrected  42  and normalized to Raman peak area using the FDOMcorr toolbox for MATLAB (Mathworks, Natick, MA, USA). Parallel Factor Analysis (PARAFAC) was used to identify the main components of the EEMs  43. Six components were identi ed, components C2, C1 and C4 (ordered from longer to shorter emission wavelength) presented a humic-like character, related with terrestrial sources and vegetation, while components C3, C5 and C6 corresponded to tryptophan-like and tyrosine-like uorophores (Supplementary Information 1).
Data analysis FT-ICR MS compound enrichment was calculated as (Intensity Treatment -Intensity Control )/ Intensity Control × 100. When Intensity Control was equal to zero but Intensity Treatment > 0, enrichment was considered to be 100%. We developed a measure of chemodiversity that incorporates chemical similarity. An underlying assumption of this approach is that molecular compounds which share chemical properties have similar functional consequences. To obtain relational information based on chemical properties, we clustered the DOM molecular formulae using Gower's distance and compound-speci c chemical properties (O:C, H:C, molecule mass, modi ed Aromaticity Index and Double Bound Equivalents). We also included the assignment of DOM compounds into categories (oxygen-poor highly unsaturated, oxygen-rich highly unsaturated, polyphenols, oxygen-poor polyphenols, saturated fatty acids (CHO), saturated fatty acids (CHOX), oxygen-rich unsaturated aliphatic and oxygen-poor unsaturated aliphatic compounds) into this cluster analyses.
Acknowledgements This research was supported by the  European Union   Seventh Framework Program  [ FP7/ 2007-2013 ] under grant no  262693 [INTERACT]  to CR and the  Austrian Science Fund (FWF)  through project  P-24098-B22  to MTP and RS. LT acknowledges funding from the  Knut and Alice Wallenberg Foundation  through grant  2018. 0191. NC acknowledges funding from the  Wenner-Gren  foundation and is at present supported by the " Ramón y Cajal " fellowship  RYC-2021-033714-I  from the  Ministry of Science and Innovation of Spain. We thank  Antero Järvinen ,  Rauni Partanen ,  Oula Kalttopää  and  Pirjo Hakala  from  Kilpisjärvi Biological Station  for assistance with the experiment in Finland and  Laurent Moya ,  Fabian Drewes  and  Jolien Scholten  for help during the experiment in Austria.
Biodiversity is a key component of agricultural ecosystems and belongs to one of the exceeded planetary boundaries. In the quest for innovation to mitigate impact, it is essential to have tools to assess the anthropogenic impact on biodiversity. In this study, we developed a new indicator, I-BIO that aimed to predict the impacts of management practices and the influence of the landscape on overall biodiversity at species group level. I-BIO encompassed four groups: microorganisms, vegetation, invertebrates and vertebrates. This predictive indicator was built using DEXi software that facilitates the design of hierarchical decision trees, based on input variables in the form of qualitative classes, aggregated using utility functions. I-BIO was constructed using data from a longterm platform in Scotland and then applied to two data sets in France and one in Scotland.
The results showed that the lowest class is predominant for all branches especially for invertebrates and soil invertebrates. Monte Carlo sampling was used to test for sensitivity. Then, a validation step was carried out to compare I-BIO outputs with real data using data on the four groups from Scottish and French datasets. I-BIO predictions of weed and soil invertebrate abundance conformed to field data. Due to low numbers of samples, validation of microorganisms, flying invertebrates and vertebrates predictions require more data. Future developments of I-BIO will focus on predictions specifically to assess the impact of management on species richness. Avenues for improvement include a better integration of landscape features and the history of agricultural practices as they both have a great influence on biodiversity.
g. semi-natural habitat), measured effect (e. g. number of earthworms) and predictive effect indicators (e. g. operational model). Causal or indirect indicators and measured or direct indicators are most frequently used  (Bockstaller et al. , 2011; Clergue et al. , 2005). Relative feasibility of the former and the direct link to biodiversity state of the latter explain their popularity. Predictive effect indicators are derived from model outputs which require well documented knowledge on causal-effect relationships or statistical relationships with consequent datasets. Such indicators present a major advantage by making possible ex ante assessment to test innovative solutions without heavy field experiments. This modelling approach broadens remarkably the range of possibilities for exploring alternative solutions  (Sadok et al.
, 2009a). With regard to biodiversity, predictive indicators have been developed. While some authors use a scoring system based on scientific knowledge  (Butler et al. , 2009 : Jeanneret et al. 2014)  or decision tree using fuzzy subsets  (Sattler et al. , 2010) , others use DEXi based models, i. e. the MASC method  (Sadok et al. , 2009a; Sadok et al. , 2009b : Craheix et al. , 2012)  the DEXiPM models  (Angevin et al. , 2017; Pelzer et al. , 2012)  and more recently with DEXi-CSC  (Hawes et al. , 2019). DEXi software is a tool that aggregates qualitative or quantitative variables into classes according to if-then decision rules that result in a hierarchical structure with a single overall output at the top level. The various steps needed to develop this kind of model will be described in the material and method section.
DEXi software The new indicator was designed with the DEXi software  (Bohanec, 2009). This software very user friendly allows the construction of hierarchical decision trees with linguistic rules which facilitate communication among non-specialists  (Babuška and Verbruggen, 2003). Input indicators are characterized by their name, a description and a scale i. e. they are defined as, or transformed into, qualitative classes (e. g. low, medium, high). Those input indicators are then aggregated using utility functions based on the "if-then" rules and weightings gathered in decision tables. These latter could be filled in the DEXi software automatically, semi automatically and by hand. For the first two options, weights for every indicator are defined by the user and only two values of aggregation are filled by hand for the automatic option and more than two values for the semi-automatic option.
For example, an indicator with a low class and an indicator with a high class yields the same average class as two indicators with medium class, leading to an information loss. Furthermore, the distribution of outputs may be unbalanced causing a lack of sensitivity. To prevent this, all the utility functions in I-BIO were manually fixed in the DEXi software (Table  3 ). This was achieved using a function of the DEXi software to export the aggregation rules to Excel by transforming the qualitative classes in quantitative ones. The mean of the input variables was then calculated and ranked  (Fig. S1 ). The aggregation rules were then checked and corrected if the output given automatically by DEXi did not match expert knowledge or the peer reviewed literature. DEXi then recalculated the weights according to these new qualitative classes (Fig.
Case studies assessed by the indicator I-BIO was tested on three case studies in Scotland and France, to assess the reliance of the results and the ability of the model to discriminate situations for which different results are a priori expected. For some datasets, validation was carried out by comparing measurements of abundance and/or diversity against model outputs according to  Bockstaller and Girardin (2003). I-BIO outputs were compared with observed data for the four groups (microorganisms, vegetation, invertebrates and vertebrates).
East of Scotland farm (EOSF) dataset This dataset was taken as part of a wider farm-scale survey of arable biodiversity, resilience and crop management conducted during 2007 at 57 farms across the east of Scotland from Mid-Lothian to Invernesshire. Farmers taking part in this survey were volunteers who followed a range of organic, integrated and conventional management practices. At each farm, two fields were selected as representative of different stages in the crop rotation (generally a break crop and a cereal crop). Information on the crop management and rotational history for each field was analysed for 27 farms contributing between them 24 conventionally managed fields, 15 integrated and 15 organic"  (Hillier et al. , 2009; Hawes et al. , 2010). For this study, 42 fields were selected where all the management data required to implement I-BIO scenarios were available to test the vegetation branch.
ENI (nonintended effects) network database The ENI network database  (Andrade et al. , 2021 ) is a French program launched in 2012 to assess side-effects of agricultural practices and in particular pesticide management practices on biodiversity. Several indicator groups were measured, covering abundance or/and diversity of taxonomic groups of interest for farmers (weed, earthworm, beetle and bird). 500 fields were selected across all the country to assure a gradient of landscape and pedoclimatic conditions, with 20% of fields in organic farming. Selected crops represent the main crop types in France (field crops, vineyard, market gardening crops). For several taxonomic groups, the monitoring was done in field margins: plants and beetle (Coleoptera). For our study, 60 fields were randomly selected among the 500 fields of the ENI database.
Distribution of the model outputs Results for the overall biodiversity and main branches of I-BIO, the different taxonomic groups, for the ENI and EOSF datasets are shown in Fig. 2. Globally, the lowest class is largely predominant for overall biodiversity and for three out four taxonomic groups (microorganism, invertebrates and vertebrates). Only the vegetation and the flying invertebrates showed a more even distribution across classes. The vertebrates branch stands out, especially for the ENI dataset, as the medium to high class almost reaches 25% compared to less than 5% for the EOSF dataset. EOSF outputs were tighter around the lowest class than the ENI ones. Thus, the distribution of the invertebrates and soil invertebrates across classes was skewed to only the "Low" class. Last, soil invertebrates and flying invertebrates showed a marked difference -the lowest class of the former exceeds 75% with the ENI dataset, whereas the flying invertebrates had the first three classes around 30% and an additional fourth class "high".
Flying invertebrates is the only branch with four classes although the highest remained largely underrepresented. Similarly, soil invertebrates from the EOSF dataset were all in the Low frequency category whereas the flying invertebrates have a more balanced distribution. Fig. 3  shows example of outputs of I-BIO evaluation for the ENI and EOSF datasets for some farms as the number of assessed farms were too low to show all the results together. For instance, field 311 yield a "medium" result for the overall biodiversity, being characterised by a very low level of farm inputs (fertiliser and pesticides) and no till, while fields 423 and 424 yielded a negative result "very low", due to more agrochemical inputs and tillage for the field 423. This representation makes possible direct comparison of the most aggregated indicator, overall biodiversity, alongside the main sub-indicators with an additional level of aggregation for invertebrates.
Validation of I-BIO Validation of the microorganisms branch was limited as there were only 10 fields within the NOE database with microorganisms biomass data to use (Fig. 5 ). However, according to Chantrel-Valat et al. (  2021 ), the measured value may correspond to a higher class than low to medium, showing that the indicator is not calibrated. The difference between the two classes "low" and "low to medium" is significant (p value = 0. 0050). Comparison of I-BIO with observed data met our expectation for monocotyledon and dicotyledon abundances for the EOSF site (Fig. 6a ). The difference between boxplots is significant for monocotyledons (p value = 0. 0293) and for dicotyledons (p value = 0. 0227). For ENI data, the trend between I-BIO outputs and observed data was the opposite to those predicted (Fig.
The comparison of the flying invertebrates branch with coleoptera abundance from the ENI dataset showed a positive trend (Fig. 8b ) although it was not so clear as for earthworms and not significant. The test with observation of butterfly abundance from the Noé dataset showed a slight positive trend for abundance which is a little more pronounced for butterfly richness (Fig. 9 ). Finally, it was not possible to validate the vertebrates branch, with the Farmland birds data (Fig. 10 ) from either the NOE or ENI datasets. This could be due to the very low number of observations and fields with "medium to high" or "high" classes (respectively 3 and 1 occurrences for the NOE dataset and 14 and 0 for the ENI dataset). Moreover, it's not surprising that birds are less impacted by local management practices compared to earthworm or coleoptera as other factors such as landscape features have a great influence on this group  (Martin et al.
58 for plants and 0. 39 for grasshoppers). In the case of SALCAbd, results concerned species richness and not abundance. The poor performance of I-BIO for the vegetation branch may be due to the fact that these data were observed at the field margin and not in the centre of the field, so less influenced by agronomic practices. Landscape data are typically heterogeneous, especially for the percentage of semi natural habitats and their connectivity. Indeed, some datasets included detailed landscape features i. e. percentage of crops and semi-natural habitats within a radius of 1 km whereas others had little or no data on landscape context. Thus, I-BIO was restricted, in some instances to using the medium class or using expert opinion in the absence of appropriate data. This was especially the case for the East of Scotland farm dataset.
The indicator is useful for relative comparison of biodiversity levels between different sites in the same dataset but it is complicated to compare sites in different datasets. I-BIO allows a relative comparison but does not provide an absolute scale. Besides, the tool is very flexible since it is very easy to change a value and to get a result. This may support discussion between stakeholders by providing quick answers to question like what can happen if I change this management practice. However, the drawbacks are the lack of sensitivity due to its qualitative form and the uneven frequency distribution on the side of the "low class". The design of a quantitative indicator based on fuzzy decision like the CONTRA method would mitigate this issue. Lastly, transparency is supported by the form in which the results are presented by the tool, where basic indicators and all the aggregated results are available and can be traced.
Acknowledgements This study is part of a PhD research project supported by a grant (n •  2019/0020 ) overseen by the  French National Research Technology Agency (ANRT). The authors would like to thank  Enzo Armaroli ,  Daniel Cluzeau ,  Victor Dupuy ,  Isis Poinas ,  Benoît Ricci  and  Laura Schlosser  for the provided datasets and,  Jacques-Eric Bergez  for statistical support.
(2020) Crop diversity 3 6 Zampieri et al. (2020), Keichinger et al. (2021) Crop rotation 3 6 Expert choice (based on the CSC database) AntiMono 1 2 Expert choice (based on (treatment the CSC database) number) AntiDicot 1 2 Expert choice (based on (treatment the CSC database) number) Herbicide timing Preemergence or both post emergence Expert choice (based on the CSC database) Insecticide 1 2 Expert choice (based on the CSC database) Other 1 2 Expert choice (based on the CSC database) All pesticides 3 6 Expert choice (based on the CSC database) Tillage intensity Direct sowing non inversion tillage ploughing Muneret et al. (2022) Mowing 2 3 Buckingham et al. (2015).
Absence of psychiatric or neurological diagnosis was assessed with two questions in the screening survey: "have you ever been diagnosed with a psychiatric condition (e. g. ADHD, depression, anxiety, mood disorders)?" and "Have you ever been diagnosed with a neurological disorder or disease (e. g. epilepsy, stroke, head injury, seizures, brain tumours, brain surgery, Parkinson's disease)?". Participants in the Low and High CT groups were matched for age, gender, estimated IQ, tobacco, cannabis, and alcohol use. A total of six participants were excluded for data analysis due to missing or unusable MRI and/ or questionnaire data. Thus, the final sample included 52 participants, with 28 participants included in the High-CT group and 24 participants included in the Low-CT group (Table  1 ). Written informed consent was obtained from all participants under protocols approved by the Ethical committee of Roehampton University.
The Depression and Anxiety Stress Scale (DASS)  [53]  total score was used in second-level fMRI analyses as a covariate of no interest to control for current levels of negative emotions/states. Intellectual functioning was measured with a validated short version of the Wechsler abbreviated scale of intelligence (WASI II;  [54]  and working memory was assessed with the digit span backward task  [55]. These measures were used to ensure that our High and Low CT groups were matched for estimated IQ and working memory function. Descriptive statistics and statistical comparisons between High and Low CTQ groups on all demographic data and questionnaire's variables are presented in Table  1  and supplementary table S1, see supplementary material for more details.
Data acquisition and analyses Structural and functional MRI images were acquired using a 3 T Siemens Magnetom TIM trio scanner. Full acquisition details can be found in supplementary material. FMRI data were processed and analysed with the Statistical Parametric Mapping 12 (SPM12; Welcome Department of Neuroscience, London, UK) software package. Functional images were realigned, and participants' motion did not exceed 3 mm in any of the six directions. Each participant's structural image was co-registered and segmented. Finally, normalisation was achieved to 1 mm 3 Montreal Neurologic Institute (MNI) space and images were smoothed with a 8 mm at full width half maximum three-dimensional Gaussian Kernel. First-level models were designed to investigate maineffects of interest (P tone blocks vs.
Behavioural task Behavioural data from the AOT task were analysed to determine omission and commission error rates. Omission errors appear when participants miss a response to the standard 1000 Hz tone. A commission error occurs when participants erroneously respond to the target deviant tone (1500 Hz). Response time and percentages of correct and incorrect responses were calculated across all trial types and contrasted between High and Low CT groups using independent sample t-tests.
Participant characteristics, sub-clinical data Participant demographic and clinical data are shown in Table  1. Overall, Low and High CT groups were matched for Age, Sex, Ethnicity, level of education, WRAT estimated IQ, cannabis, alcohol, and tobacco use. By design the High CT groups had significantly higher CTQ scores (total and subscales) relative to the Low CT group. As would be expected, participants in the High CT group also had significantly higher BSI and DASS scores (total and subscales).
e. 1 2 a). We used a nonlinear least square method to fit the Michaelis-Menten model to the data and estimated the mean and standard error of a and b  (Bates & Watts, 1988). Based on these estimates, a time of departure from coastal areas was calculated (Age 50% ) corresponding to the age, in days, at which more than half of the population showed an offshore mesopelagic signature. The analysis was performed using R software (R Core Team, 2021) and the Tidyverse package  (Wickham et al. , 2019).
In practice, all wire paths are actually computed under a slowly varying envelope approximation using to fit the weak variations in the minor radius caused by the irregular patient-specific vascular geometry. This is because the variations for the cases documented herein (by about 11% relative to the average value) have been found to be well modelled by affine transformations, but more complex analytical functions can be specified as well. Examples of generated stents are given on Fig. 10. Computational domain and mesh. The medical imaging data of both patients is segmented using the 3D Slicer software. The entire proximal portion of the parent artery visible in the images is reconstructed, for which 3D Slicer outputs point coordinates and connectivity of the centerline, together with the corresponding vessel radius (as defined by the minimal distance from the centerline to the vessel boundary) and curvature.
A non-shrinking filter  73  is used as an additional step of shape regularization to obtain smooth surface triangulation of the aneurysm lumen and connected vessel walls (which helps mitigate the effect of inner surface roughness). All vessels are truncated at some distance from the aneurysm bulge and extended with straight cylindrical pipes closed perpendicularly to their axis, to allow for flow development and ease the subsequent application of inflow/outflow boundary conditions. For each patient, three-dimensional unstructured isotropic meshes of the vascular domain and stent devices are generated with the Gmsh software  74  , after which the vascular grid is finely and anisotropically refined with the method described in  75. This allows evaluating any stent design sampled by DRL on the same vascular mesh, and thus yields considerable saving in the overall computational time.
Starting from a random mapping f θ 0 from s 0 to the policy determined by the free parameters ini- tialization, the agent gets one attempt per episode at finding the optimal (i. e. , it interacts with the environment only once per episode) before updating the policy. Another subtle difference is that PPO is actor-critic, i. e. , it features an actor network that learns the policy, and a critic network that learns to estimate the advantage. Singlestep PPO works without knowledge of the critic evaluations (and is thus not actor-critic) because the trajectory (10) of state and actions consists of a single pair. The discount factor adjusting the trade-off between immediate and future rewards can thus be set to γ = 1 , in which case the advantage reduces to the whitened reward 48. Our single step PPO method is based on the default open-source implementation of Stable Baselines (https:// github.