dataesr/scientific-paragraphs-categorization huggingface dataset:
We optimized three parameters: the number of trees, the minimum number of observations in the terminal node and the number of covariates drawn randomly from the covariate set at each split. In the machine learning literature, these parameters are usually denoted ntree, node. size and mtry, respectively. We used the model-based optimization procedure described in  Probst et al. (2019)  using the mean square error of the out-of-bag prediction as objective function and 500 iterations. The optimal parameters were ntree = 500, node. size = 12 and mtry = 10. All other parameters where held to their default value. The performance of the model with optimized parameters was evaluated using a standard random 10-fold cross-validation strategy. The dataset was randomly split into 10 folds of size approximately equal, where nine folds were used to calibrate the RF model and the remaining fold was left apart for validation.
This procedure was repeated 10 times, each time setting aside a single fold. The independent observed values and those predicted by the RF model are used to compute usual validation statistics. The predictions did not show systematic over-of under-prediction (mean error was close to 0), and had a root mean square error of 36. 4 t ha -1 and a modelling efficiency coefficient of 0. 31. The final model for prediction is fitted using all available SOC stocks data and the optimized set of RF parameters. Predictions are made for the whole of France at a resolution of 250 m using the spatially exhaustive set of covariates as predictor. This final model along with prediction for the whole of France is used for interpretation with Shapley values.
,p}\{j} |S|! (p -|S|-1) ! p! f * x i,S∪{j} -f * (x i,S ) , (2) and f * (x i,S ) = E X C [ f (x i,S , X C )] is the prediction function where covariates not contained in S are marginalized (similar for S ∪ {j}). The calibrated RF model is defined by f and X is the matrix containing the covariates from the calibration dataset, and X C is a subset of covariates where C represents the set of covariates not included in S. Note that f * x i,S∪{j} -f * (x i,S ) can be interpreted as marginal contribution to the prediction when adding covariate j to the subset of covariates S. Equation  2 has two components: the first is the marginal contribution for a subset of covariates, whereas the second is a weighted average, giving equal weight to each of marginal contributions of all possible subsets of covariates.
The contribution of a covariate to the prediction of a single spatial location is then given by ϕ 0,j. Calculating the exact solution for Eq. 2 is computationally intractable because it requires estimating the sum of the marginal contribution for 2 p -1 combination of covariates. To solve this, we use the approximation algorithm developed by Štrumbelj and Kononenko (  2014 ) and based on Monte-Carlo sampling. In  Štrumbelj and Kononenko (2014) , the covariate effect is approximated by integrating over the observations of the calibration dataset. We refer to  Štrumbelj and Kononenko (2014)  and  Molnar (2020, Chapter 9)  for more detail on the approximation algorithm. A Shapley value should be interpreted as the contribution of a covariate to the difference between the prediction and the average -Average contribution: absolute Shapley values can be summed over the individual observations from calibration dataset to obtain an overall variable contribution to the prediction.
Note that while it is similar to the variable importance plot obtained by permutation and often reported in soil modelling studies with machine learning, average contribution plot has a different interpretation because it is not based on decrease in model accuracy (unlike permutation plots). -Partial dependence: a scatterplot of the average of Shapley values in the calibration dataset against the value of a single covariate give indication of the functional form of the association between the soil property and the covariate (i. e. the partial dependence,  Hastie et al. , 2009). To ease visualization, the individual dots in the scatterplot are fitted with a smooth curve. -Local and spatial evaluation: the local contribution of the covariate to the prediction is obtained by Shapley values and can be used to generate a spatial pattern.
Practical implementation and computational aspects We use the R programming language (R Core Team, 2022) for the SOC stock calculation, covariate pre-processing, model parameter selection, model fitting and prediction, and estimation of the Shapley values. The RF model was fitted using the ranger package  (Wright and Ziegler, 2017 ) and parameter selected with the tuneRanger function from the tuneRanger package  (Probst et al. , 2019). Estimation of Shapley values was made with the fastshap package  (Greenwell, 2020). The procedure for estimating the Shapley values spatially at was parallelized and combine with a subsampling approach to ease computation. A total of 12,495,098 grid cell locations were taken using a systematic grid sample of 800,000 cells, for which the Shapley values were estimated.
Average contribution Figure  2  shows the magnitude of the covariate contribution to the prediction of SOC stocks averaged over the calibration dataset (average of absolute values in Fig. 2 ) or for each individual point of the calibration dataset (Fig. 2 ). Fig. 2b  indicates that Elevation is on average the most important covariate contributing to the prediction of the SOC stocks. NDVI, mean temperature of warmest quarter and annual mean temperature also have a relatively important contribution to the prediction with an average absolute value of nearly 4 and 3 t ha -1. Fig. 2b  further show that the four most important covariates have a wide range of Shapley values. Elevation, for example, has values between -10 to 55 t ha -1. The colour scale in Fig. 2b  also shows that large positive values of Shapley for the covariate elevation are obtained for high elevation, whereas negative contribution of this covariate to the prediction are obtained for low value of elevation.
prediction. One advantage of Shapley values is the set of mathematical axioms of which they are derived, which provide the basis for a rigorous interpretation of their meaning. Further, Shapley values are additive and symmetric: the individual contribution of a covariate to a spatial location can be averaged over a region or a dataset. We give three example uses in the next paragraph. A Shapley value can be obtained for any single value of the calibration dataset and any prediction at unobserved location, in the unit of the prediction (e. g. the soil property of interest). They are usually used to evaluate the individual contribution of a covariate to the prediction of a single location (i. e. to perform a local interpretation the model). Hereafter we describe three uses of the Shapley values for both local and global interpretation.
Peak 1: residues 63-65 (A1) and 53-60 (E1); peak 2: residues 393-394 (A1) and 392-399 (E1). Peak a: residues 746-751 (A1) and 723-734 (E1); peak b: residues 813-826 (A1) and 798-805 (E1); peak c: residues 632-656 (A1) and 601-656 (E1); peak d: residue 1188-1198 (A1). Long-range hydrogen bonds were detected along the trajectories using the Python package MDAnalysis  [37, 38]. The number of hydrogen bonds present more than 60% of the time and involving residues separated by more than 10 residues in the sequence was calculated between and within BoNT domains (Figure  6 ). The number of long-range hydrogen bonds within the LC domain (and, to a lesser extent, within HC CN ) was high in all trajectories (displaying however a slightly decrease in trans models), in agreement with the smaller RMSD values observed for LC and HC CN (Figure  3 ).
Box-and-whisker plot representation of the distributions of distances between the geometric centers of the domains within the botulinum toxins A1 and E1, averaged over the 150-300 ns interval of the MD trajectories. The color code for the boxes is as follows: cyan (A1ope47, E1ope47), blue (E1ope47r), magenta (A1ope70, E1ope70), pink (E1ope70r), orange (A1clo47, E1clo47), brown (A1clo47r), green (A1clo70, E1clo70), and olive green (A1clo70r). The dashed lines mark the separation between open and closed states; see Figure  3. Number of hydrogen bonds between domains of the botulinum toxins A1 and E1, calculated in the 150-300 ns interval of trajectories. Long-range hydrogen bonds were detected along the trajectories by analyzing each frame every 0. 2 ns, using the python package MDAnalysis  [37, 38].
Remarkably, the stretch SXWY is the conserved ganglioside-binding site (GBS), which was identified first in tetanus neurotoxin, as well as in BoNT/A, B, E, F, and G  [39] [40] [41]. Interestingly, in E1 GBS, key interacting residues unique to BoNT/E have been identified, along with a significant rearrangement of loop 1228-1237, upon carbohydrate binding  [42]. In BoNT/B, the LBL is located between the GBS and the receptor binding site, and is significantly exposed to the solvent; in particular the side-chains of W 1248 and W 1249  [43]. In the X-ray crystallographic structures of BoNTs, the regions LBL and GBS are close in 3D space, and correspond to well-defined binding regions (see, e. g. , Figure  S2  for A1 in the open state; PDB entry: 3BTA). The distributions of distances corresponding to backbone hydrogen bonds between the two β strands initially present in the X-ray crystallographic structures were monitored along MD trajectories (Figure  7 ).
Preparation of Starting Conformations of Toxins In order to prepare systems in which BoNTs are formed of two proteins only connected by a disulfide bridge, the structures from PDB entries 3BTA (A1)  [7]  and 3FFZ (E1)  [9]  were cleaved, eliminating the peptides 438 TKSLDKGYNK 447 and 420 GIR 422 ; the numbering being taken from PDB files 3BTA and 3FFZ (MR Popoff, personal communication). Consequently, the BoNT domains are defined, in the present work, according to the ranges of residues given in Table  2. After removing the cleaved sequences, the disulfide bridge was established between C 430 and C 444 in BoNT/A1 (Figure  1D ), and between C 412 and C 423 in BoNT/E1. For the two studied BoNTs, the conformations of the open state were prepared from the cleaved 3BTA structure and the conformations of the closed state using the cleaved 3FFZ structure.
These starting points of the simulations were refined using Modeller  [52, 53] , in order to conduct an energy optimization of the structures and to relax any structural stress due to cleavage in the two chains. These two conformations, corresponding to the open state of BoNT/A1 and the closed state of BoNT/E1, are denoted in this work as cleaved X-ray models. The closed A1 and open E1 conformations of BoNTs were built by homology modeling, using Modeller  [52, 53]  and the sequence alignments obtained by T-Coffee  [54]  (Figures  S7  and S8 ) The percentages of sequence identity between the two toxins were 35% for LC and 43% for HC, according to which, use of the homology approach was valid  [55]. The template structures used for homology modeling were the PDB entries 3BTA (for the open state) and 3FFZ (for the closed state), cleaved as described above.
Figure6. Number of hydrogen bonds between domains of the botulinum toxins A1 and E1, calculated in the 150-300 ns interval of trajectories. Long-range hydrogen bonds were detected along the trajectories by analyzing each frame every 0. 2 ns, using the python package MDAnalysis [37, 38]. The numbers of hydrogen bonds were calculated as those present more than 60% of the time and separated by more than 10 residues in the sequence. The dashed lines mark the separation between open and closed states; see Figure3.
5 % CP). Diet was formulated using the POR-FAL software v. 4. 0. 17 (ITP-INRA, Paris) in order to follow Hubbard broiler nutrition recommendations  [22]. Potential interesting feedstuffs, such as Faba bean, sunflower meal, rapeseed and DDGS were included in the diet based on the results of a former project dedicated to the test of alternative feedstuffs for poultry diets  [31]. Body weight and feed intake were individually and continuously recorded throughout the experiment thanks to electronic feed stations  [11]  and calculated following the procedure presented in Berger et al. [4].
Genetic analyses Heritability and genetic correlations between traits were estimated using VCE6. 0 software  [33, 34]. In addition to data collected during this experiment, data of pHu collected from previous generations of selection were included in the genetic analyses, leading to a variable number of data depending on the trait: 578 for BW, ADG, DFI, DCFCR and 9,541 for pHu. The pedigree file contained all animals from the beginning of the selection experiment, i. e. 23,319 animals. Genetic analyses were conducted separately for each line. The animal model included the fixed effects of the sex (N=2) and batch (N=31) and the direct genetic effect of animal (N=23,319). When the maternal permanent environment effect (N=1,961) was not null, it was also included in the model of analysis.
There are two instruments onboard that sample the lower atmosphere, typically through column integrated optical depths: Emirates eXploration Imager (EXI;  Jones et al. (2021) ) and Emirates Mars InfraRed Spectrometer (EMIRS;  Edwards et al. (2021)). By combining the their data, one captures the full diurnal cycle, and places constraints on the water-ice particle sizes. In addition, EMIRS can explicitly distinguish between water-ice and dust aerosols, while analysis of EXI data must adopt a dust column. In this paper, we focus solely on EXI observations, naturally limited to daytime hours. Observations by EMIRS are described by  Atwood et al. (2022) ;  Smith et al. (2022). We briefly describe the instrument, the data set, and the analysis algorithm. The resulting retrievals of water ice optical depth are presented through highlights of the observed diurnal behavior of the aphelion cloud belt (ACB) structure from early spring through early fall in the northern hemisphere.
Instrument-Data Set Description EXI is a multi-band camera capable of taking 12-megapixel images, described in detail by  Jones et al. (2021). Given the science orbit of EMM, EXI provides a spatial resolution of 2-4 km per native pixel with six discrete bands from the mid-ultraviolet through the visible. Radiometric fidelity is optimized while simplifying the optical design through the separation of the ultraviolet (UV) and visible (VIS) optical paths. The work here focuses on the "f320" band, nearly identical to the MARCI band 7  (Wolff et al. , 2019)  This band has a solar flux weighted-mean centroid of 321 nm, a Full-Width Half-Maximum of 24 nm, and a radiometric accuracy (and precision) better than 5%. The principal observational mode of EXI is the so-called XOS-1 (XOS = eXi Observation Set), which includes the f320 band.
Because there is a direct trade-off between data volume and number of observations, the f320 images are taken in a 2 × 2 binning mode, giving 4-8 km per pixel at nadir. Each image is processed to "Level 2A" (L2A), which includes a calibrated image and associated metadata such as latitude and longitude, photometric angles, range from surface. Keywords are included to transform the DN values to radiance and radiance factor (I/F). The L2A file is the starting point for the cloud retrieval. Availability of EXI products and documentation is described in the Data Availability Statement.
Limitation of τ ice Detection and Surface Ice Filter The retrieval cannot discriminate between atmospheric and surface ice, nor between water and carbon dioxide ice. Given the seasonal, latitude, and local time ranges considered here (L S = 30 -190°, ±50°N, 6-18 hr Local True Solar Time (LTST)), all the derived τ ice is attributed to the atmosphere and associated with H 2 O. CO 2 clouds present within the analyzed data set are likely to be optically thin, particularly given when included in the 2 × 2 binning of the EXI images, for example,  Vincendon et al. (2011). However, we note that the L3 products contain an "ice mask" that provides guidance on the potential for surface ice in a given pixel. The mask is derived from a series of EMIRS observations of surface temperature over several orbits around the EXI observation, and is further described in the EXI Data Guide (Data Availability Statement).
Results The retrieval is applied to all pixels within the L2A images with incidence and emergence angles ≤75° obtained from L S ∼ 30° (shortly before the official start of the prime science mission) through L S ∼ 190° (early northern fall, and the expected annual dissipation of the ACB), which are in Mars Year (MY) 36. In our analyses, we insert the characteristics of each valid retrieved pixel into a simple database. This simplicity comes with a modest performance cost given the file size of ∼4 Gigabytes, but it allows us to generate products with arbitrary spatial and temporal binning.
Connection to MARCI Given the long temporal baseline of the MARCI data set, it is useful to verify the consistency (and connection) of the two data sets. One way is a direct comparison of EXI and MARCI radiance observations for overlapping pixels. Through experimentation, we optimize a set of parameters that define a pixel match: Δtime ≤20 min, Δincidence, emergence, phase ≤4°, and Δlatitude, longitude ≤0. 4°. Time and location restrictions enforce the viewing of the same effective column/ surface, while the angles minimize differences associated with radiative transfer effects. In the UV, the former constraint is particularly key because clouds moving into or out of a pixel introduce large photometric effects. The chosen angles minimize scatter while maximizing overlaps, that is, more restrictive limits do not further reduce scatter but definitely reduce overlaps.
Despite the large number of images, we find only four sets of observations ranging from July 2021 through March 2022 that meet the above requirements, providing about 50 cases of overlapping pixels. The sample of relative (EXI I/F minus MARCI I/F) differences has a mean difference of 0. 072 ± 0. 004, indicating that for EXI-MARCI observations under identical conditions, a MARCI pixel will be 7% darker than one of EXI. This is the origin of the 1. 07 factor for surface reflectance. A second useful comparison is the zonal behavior for τ ice at 14-16 hr LTST during the ACB season, as is shown in Figure  1. Each instrument data set shows very similar τ ice values and behavior. Notable differences between them are related to two factors: (a) the MARCI data are heavily weighted toward the mean time LTST (15h00) while EXI data are essentially uniformly-weighted across the LTST interval.
For other particle sizes: r eff = 1. 5 μm, τ ice = 2. 9τ TES ; and for r eff = 6. 0 or 8. 0 μm, τ ice = 1. 5τ TES. Comparing the EXI and MPCM optical depths highlight areas of both successful modeling and potential improvement. The exaggeration of the modeled south polar hood has been previously identified (e. g. ,  Wolff et al. , 2019). Thus, we confine our comments to the ACB structure. 1. Allowing for the regional dust activity in the EXI data set, MPCM reproduces reasonably well the general diurnal trends in both zonal and spatial products, particularly in the afternoon. This includes a morning-evening asymmetry, though the clouds do not seem to dissipate as quickly in the model. Though not shown here, the model shows a minimum cloudiness near 13 hr. 2. The broad-scale spatial structure of the ACB produced by the MPCM agrees well with that observed by EXI.
These particular differences are currently being analyzed by others members of the EMM science team. In a similar vein, the work reported here represents only a small fraction of the seasonal and spatial coverage obtained at the point of this writing. Clearly, many additional investigations await, including the use of the EXI data set for synergistic collaborations with EMIRS and EMUS, as well as with other missions such as MRO, TGO, and the Curiosity and Perseverance rovers. Given the public nature of the EMM data, including the derived (L3) products, such projects are accessible to the entire community.
Data Availability Statement Data from the Emirates Mars Mission (EMM) are freely and publicly available on the EMM Science Data Center (SDC, http://sdc. emiratesmarsmission. ae). This location is designated as the primary repository for all data products produced by the EMM team and as the long-term repository as required by the UAE Space Agency. The data available (http://sdc. emiratesmarsmission. ae/data) include ancillary spacecraft data, instrument telemetry, Level 1 (raw instrument data) through Level 3 (derived science products), quicklook products, and data users' guides to assist in the analysis of the data. Following the creation of a free login, all EMM data are searchable via parameters such as product file name, solar longitude, acquisition time, sub-spacecraft latitude, longitude, instrument, data product level.
Search Strategy The search terms, databases, and strategy were developed in consultation with a research librarian at McMaster University (Multimedia Appendix 1). We searched MEDLINE, Embase, Emcare, PsycINFO, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials from inception to February 16, 2022. We manually searched reference lists of relevant reviews and included studies for citations that were not captured in our search. Duplicates were removed and citations were uploaded to a secure web-based platform for screening (DistillerSR; Evidence Partners Inc).
Data Extraction and Charting A team of researchers conducted the screening and data extraction (MR, MJ, PA, DS, DF-L, CW). A minimum of 2 reviewers were required to screen titles and abstracts of all potentially eligible studies independently and in duplicate. Articles marked for inclusion by either team member went on to full-text screening, which was completed independently and in duplicate by 2 team members and required consensus for inclusion or exclusion. After confirmation of the included studies, we looked for related publications that met our search dates and inclusion criteria and grouped publications that were based on the same study and intervention. We developed forms that were housed in a web-based systematic review software program. All authors provided feedback and approved the components of these forms.
Type of Technology Used in the Interventions We extracted the details of the technology from the included studies that actively evaluated or tested a digital health technology (n=20 intervention studies). The 3 reviews and 1 focus group study were not included in this extraction of data as the general nature of the reviews and investigative purpose of the focus groups did not lend themselves to such specific categorization. Full data extraction on the components of the digital health technologies of the individual primary papers can be found in Multimedia Appendix 3. Half of the studies (n=13) used computerized treatments, resources, and mobile apps. These were mostly website-based, accessible from many types of devices, and none of these mobile apps were mobile health. These websites served a variety of purposes and included many different features ranging from educational materials to interactive learning modules and discussion boards (Textbox 1).