dataesr/scientific-paragraphs-categorization huggingface dataset:
Method Bioconductor/R package Literature Additive log-ratio Compositions  Aitchison (1982 Aitchison ( , 1986) )   Variance-stabilizing (VST) DESeq2  Love et al. (2014)  rRNA data using compositional transformation techniques. Among the available options, the CLR transformation was identified as the most suitable, as it enables the assessment of each feature's importance in the decision-making process of ML models. Another study by  McKnight et al. (2019)  examined the impact of log transformations commonly employed in normalization procedures. The authors demonstrated that log transformations could distort community comparisons by suppressing significant differences in common taxa while amplifying subtle differences in rare taxa. Thus, despite the advantages, log-ratio approaches have their limitations and drawbacks and are not the only way to deal with compositionality.
Funding This article is based upon work from  COST Action ML4Microbiome "Statistical and machine learning techniques in human microbiome studies " ( CA18131 ), supported by  COST (European Cooperation in Science and Technology) , www. cost. eu. ML acknowledges support by  FCT -Fundação para a Ciência e a Tecnologia , I. P. , with references  UIDB/00297/2020  and  UIDP/00297/2020  ( NOVA Math ),  UIDB/00667/2020  and  UIDP/00667/2020  ( UNIDEMI ), and  CEECINST/00042/2021. KH acknowledges support through the  HiTEc Cost Action   CA21163  and the project  PID2021-123833OB-I00  provided by the  Spanish Ministry of Science and Innovation  ( MCIN/AEI/10 : 13039/501100011033 ) and  ERDF  A way of making Europe. MB acknowledges support through the  Metagenopolis  grant  ANR-11-DPBS-0001. LM-Z is supported by  Juan de la Cierva Grant  ( IJC2019-042188-I ) from the  Spanish State Research Agency of the Spanish Ministerio de Ciencia e Innovación y Ministerio de Universidades.
The Microchannel X-ray Telescope The Microchannel X-ray Telescope has been developed under the CNES responsibility in close collaboration with CEA-Saclay/Irfu , the Univeristy of Leicester, the Max Planck Institut für Extraterrestrische Physik (MPE) in Munich, and the IJCLab in Orsay. It is a light (< 42 kg) and compact (focal length ∼ 1. 15 m) focusing X-ray telescope; its sensitivity below 1 mCrab makes it the ideal instrument to detect, identify and localize down to the arc min level X-ray afterglows of the SVOM GRBs. The MXT (see Fig. 1 ,  Mercier et al. 2022 ) is composed of five main sub-systems: the MOP: the MXT OPtical assembly, based on square Micropore Optics (MPO), -the MCAM: the MXT CAMera hosting a pnCCD, -the MST: the MXT carbon fiber STructure, -the MDPU: the MXT Data Processing Units (in cold redundancy), -the MXT radiator to dissipate the heat generated at focal plane level.
2022  for more details). In addition the MDPU scientific partition is responsible for analysing the MXT data stream on board, by identifying valid X-ray patterns, building sky images, and detecting and localizing afterglow candidates in near-real-time. The afterglow candidate positions are improved during the observation, as long as more data are accumulated, and regularly transmitted to ground (every ∼ 30 s) in order to allow ground based robotic telescopes to look for optical afterglows in a more efficient way. In fact, the MXT sky error areas will be on average ten times smaller than the ones produced by ECLAIRs, highly enhancing the chances for the VT and other telescopes to correctly identify the optical GRB counterparts. The optical identification and the subsequent spectroscopic distance measurement being a critical task in GRB science, the driving requirement for the MXT design was to be able to localize 90 % of the GRBs pointed to after a slew to better than 2 arc min (J2000).
Localization Capabilities The data presented above have been used to estimate the in flight localization capabilities for the MXT. In order to obtain this result, we performed a simulation process that needs as ingredients the PSF shape ( §4. 1), the telescope effective  area ( §4. 2), the expected background in flight, and some hypotheses on the SVOM mission. Concerning the background, two main components need to be considered, the Cosmic X-ray Background (CXB) and the particle induced background. If we restrain ourselves to high galactic latitudes (which is compatible with the SVOM pointing law), there is a good agreement on the CXB measurement in the MXT energy range (e. g. Parmar et al. , 1999). By folding the CXB spectrum with the MXT effective area one expects about 1 count/s over the entire detector for the CXB.
This corresponds roughly to 600 counts of background integrated over the detector. Using the MXT PSF we simulated 1000 sources, distributed uniformly on the MXT FOV and with fluxes comprised between 10 and 10 4 counts, reconstructed their positions, and compared the latter to the injected positions. So we could define R 90 as the radius within which 90 % of the reconstructed positions fall. In other words, if we associate a given R 90 to an MXT reconstructed position we have 90 % of chances that the true source position is within this radius. In order to derive the MXT position we implemented the following method: we cross correlate the data with the MXT PSF, we look for the maximum in the correlated image, we define an area of ± 35 pixels around the maximum, we select the pixels within this area which have at least 90 % of the value of the maximum, and finally we barycenter those pixels 3.
The R 90 vs SNR curve is reported in Fig. 12. Once we determined the relation between the localization accuracy and the MXT SNR, we downloaded from the public Swift/NGO archive the pre-processed XRT light curves  (Evans et al. , 2009)  for the GRBs detected between 2004 and 2017 (1128 GRBs). Among those GRBs we selected the ones for which BAT data and spectral parameters were available. This selection has been done because we needed to check which of the BAT GRBs, would have been detected by ECLAIRs: ECLAIRs is less sensitive than BAT and we wanted to restrain our simulations to those GRBs that have at least 50 % of probability to be detected by ECLAIRs at a SNR level that would trigger a SVOM slew. We then made the hypothesis that the SVOM and Swift slewing capabilities are similar, so that XRT and MXT are on source at the same time and that they experience the same Earth occultations (Swift and SVOM have similar orbital parameters).
Acknowledgements We thank the  PANTER team  for the continuous and invaluable support through all the test campaign. We also warmly thank the  CNES  and  CEA-Irfu  technical staff for setting up the instrument at PANTER and for their fundamental support in operating the telescope. The work performed at the PANTER X-ray test facility has in part been supported by the  European Union 's  Horizon 2020 Program  under the  AHEAD2020 project  (grant agreement n. 871158 ).
Funding  SVOM /MXT  is a project funded by the  French Space Agency (CNES) , with the contribution of  MPE ,  CEA ,  CNRS  and the  University of Leicester. CNES  took in charge the transportation and installation of the MXT telescope at Panter with the support of MPE. MPE provided the technical support to the operations. CNES ,  CEA ,  CNRS , and the  University of Leicester  funded the journeys of all the staff (scientific and technical) in order to achieved the data taking shifts. The work performed at the PANTER X-ray test facility has in part been supported by the  European Union 's  Horizon 2020 Program  under the  AHEAD2020 project  (grant agreement n. 871158 ).
pressure feedback in this scenario and performed the best among the feedback conditions considered in Experiment #1. 3) Subjects: 20 participants took part in the experiment, including 8 women and 12 men (age 24-29 years old). One practice trial per feedback condition was allowed. 10 of them participated in Experiment #1 as well. Each participant repeated the insertion 2 times per feedback condition, one with LF and one with HF tissue characteristics, leading to 8 insertions per subject. 4) Results: We analyzed the data as done in Sec. III-C4. Fig. 4a  shows the F1 score per each condition for the puncturing event. The two-way repeated-measure ANOVA revealed a statistically significant difference for this metric across feedback condition (F(2. 199, 41. 782) = 16. 265, p < 0. 001), but not across tissue environments.
2) Providing elastic and friction force feedback components through co-located diverse cutaneous feedback (vibrotactile and contact pressure) outperforms providing the same information but at different, farther points on the body. 2) Feedback conditions: Elastic (E) and friction (F) force feedback components are again computed as described in Sec. II-A. In this experiment, we employed the Geomagic Touch kinesthetic interface (G), the hRing pressure device (R), and the TouchDIVER fingertip cutaneous device (D). We considered five feedback conditions:   (E G F D ) Elastic and friction force feedback components are provided by the Geomagic Touch interface and the vibrotactile module of the TouchDIVER device, respectively. (E G F G ) E G F G E R F G E D F D E D F G E G F E G F G E R F G E D F D E D F G E G F D * * 0 As before, in all conditions, the Geomagic Touch interface is used to teleoperate the needle insertion inside the soft tissue (see Sec.
II-A). We considered again condition E G F G as it represents the most common solution for currently-available haptic-enabled needle insertion systems, and E R F G because it enables us to compare co-localized vs. de-localized feedback in this scenario and performed the best among the feedback conditions considered in Experiment #2. 3) Subjects: 30 participants took part in the experiment, including 14 women and 16 men (age 24-35 years old). One practice trial per feedback condition was allowed. 20 of them participated in Experiment #2 as well. Each participant repeated the insertion 2 times per feedback condition, one with LF and one with HF tissue characteristics, leading to 10 insertions per subject. 4) Results: We analyzed the data as done in Secs. III-C4 and III-D4. Fig. 5a  shows the F1 score per each condition for the puncturing event.
834, 53. 194) = 5. 620, p = 0. 007; not shown in the Figure, all mean F1 scores higher than 0. 89). Post hoc analysis with Bonferroni adjustments revealed a statistically significant difference between E G F G vs. E R F G (p < 0. 001), E G F G vs. E D F G (p = 0. 001), E R F G vs. E D F D (p = 0. 014), E R F G vs. E G F D (p < 0. 001), E D F D vs. E G F D (p = 0. 017), and E D F G vs. E G F D (p < 0. 001). Fig. G F G vs. E D F D (p = 0. 019), where E D F D outperformed E G F G. 5) Discussion: Results partially support H3. 1, as co-located diverse information (E D F G ) outperforms providing the same information through the kinesthetic channel only (E G F G ). However, this was not true in all cases, as E D F D or E G F D did not perform significantly better than E G F G. This result can be justified by recalling that E D is provided through the contact pressure module of the TouchDIVER while F D by the vibrotactile module of the same device, confirming that vibrotactile feedback is not effective in providing this type of information (as already seen in Experiment #1).
Background and aims Cover crops can efficiently increase nitrogen (N) recycling in agroecosystems. By providing a green-manure effect for the next crop, they allow reduced mineral fertilisation. We developed a decision-support tool, called MERCI, to predict N available from cover crop residues over time, from a single measurement of fresh shoot biomass. Methods We coupled a large experimental database from France with a simulation experiment using the soil-crop model STICS. More than 25 000 measurements of 74 species of cover crops as a sole crop or bispecific mixtures were collected. Linear regression models, at the species, family or entire-database level depending on the data available, were built to predict dry biomass, N amount and C:N ratio. Dynamics of N mineralized and leaching from cover crop residues were predicted at 24 contrasting sites as a function of the biomass, carbon (C):N ratio and termination date.
Approach developed to estimate N release for the next cash crop The general approach coupled field and simulated experiments to estimate, from a cover crop's fresh Plant Soil 1 3 Vol. : (0123456789) biomass at termination, the N available for the next cash crop over time. The four main steps were the following: (1) Build a large database of many cover crop species, as a sole crop and in species mixtures, with contrasting climates, soils and management. (2) Predict shoot and root dry biomass, N and C contents and C:N ratio from fresh shoot biomass. (3) Quantify N mineralization and nitrate leaching from cover crop residues using the STICS soilcrop model for a variety of climates, soils (type and depth) and crop management practices. (4) Predict N available from cover crop residues over time for the next crop for 6 months after cover crop termination and burial in the soil.
Cover crop database Collecting as much data as possible from 48 research and development partners, we built a large database of cover crop experiments throughout France from 1982 to 2020 (Fig. 2a ). The data came from French regions with large areas of annual cropping systems. Data availability also depended on each partner's resources and the location of experimental units in France. Nevertheless, the data covered a wide variety of climates and soils encountered in French agriculture, which more generally represent conditions of temperate countries in western and southern Europe  (Metzger et al. 2005). The data covered a wide range of cover crop species and management. A total of 949 "site × year" combinations and 8 749 situations (i. e. measurements of a given species on a specific date), mainly from conventional agricultural systems, throughout France were collated, covering 78 species of cover crops from seven botanical families (34% Brassicaceae, 33% Fabaceae, 25% Poaceae, 6% Hydrophyllaceae and 1% or less each Asteraceae, Polygonaceae and Linaceae).
Species in 59% of situations were a sole cover crop, while 41% were in a species mixture, usually of two species (only 2% of mixtures had 3-8 species). The sowing, emergence (if available) and measurement dates during cover crop growth or at termination were recorded in the database. The sowing dates in the database ranged from 15 June to 1 November, while measurement dates (at termination or before) ranged from 13 September to 26 April for 98% of situations. The data of interest were fresh and dry biomasses as well as C and N concentrations in shoots and roots. Data were most common for the dry biomass, N concentration and fresh biomass of shoots, followed by C concentrations of shoots, while data for root biomass were generally scarcer (Table  1 ). Some botanical families were represented more often, with ca.
Experimental design for simulations To simulate N mineralization from cover crop residues and N leaching after cover crop termination, we used the 24 sites across France that Constantin et al. (2015a) had previously selected. These sites cover a wide range of climate conditions in the main cropping areas of the country, particularly during the fallow period from August-April, based on mean temperature and on rainfall minus potential evapotranspiration over several years (Fig. 2b ). We selected 2-4 of the most common soils in the region around each site that had contrasting soil available water capacity (67-327 mm), based on the French Geographic Soil Database at 1:1 000 000 scale (v1-21 December 1998. INRA, US 1106 Infosol, Orléans, France). STICS simulated bare soil with or without cover crop residues from 2006 to 2026 using the RCP 8.
Predicting dry biomass, N content and the C:N ratio of cover crops at termination from experimental data To estimate the shoot and total dry biomass, N content and C:N ratio from a simple measurement of fresh shoot biomass by species on a 1 m 2 quadrat, we first analysed correlations within the experimental database (PerformanceAnalytics R package). We also quantified the percentage of variance explained by available variables (e. g. botanical family, species as a sole cover crop or in a mixture, sowing and termination dates, duration of crop development) and quantitative data that were strongly correlated (r > |0. 5|) with the variable to predict. Depending on the results, we built specific meta-models by botanical family and species to predict the total biomass at termination and its total N concentration, and thus its N amount.
When many data were available for a given species or family, the relation used to predict the shoot N amount was also refined at the species level and by class of dry shoot biomass (0-1, 1-2, 2-3, 3-4 or more than 4 t ha -1 ). The percentage of C in shoots and roots was estimated as the mean in the experimental database by species or botanical family, depending on data availability. It was then used to calculate the C:N ratio of cover crops at termination, which STICS needed as input data to predict N mineralization and leaching. For species mixtures, each species was assessed independently to predict the dry biomass, N content and C:N ratio of its shoots and roots at termination. Vol:. (  1234567890 ) Predicting N mineralized and leached from cover crop residues over time with STICS N available from cover crops to the next crop was estimated using STICS predictions, shoot and root N amounts and the C:N ratio of residues of each species estimated during the previous step.
Statistical analysis To estimate the accuracy of estimates of the C:N ratio and total N content of cover crop residues based on fresh biomass in the database, we used three complementary statistical criteria: root mean square error (RMSE), mean deviation (MD) and the Nash-Sutcliffe efficiency coefficient (EF): with E i the estimated value, O i observed value, Ō the mean of the observed values and n the number of values. Analysis of variance (ANOVA) were performed on the experimental and simulation results to estimate the percentage of variance explained by the factors for each variable of interest, using R software (https:// cran. r-proje ct. org). For the experimental database, the factors tested were species, botanical family, cover RMSE = � ∑ n i=1 (Ei-Oi) 2 n MD = n ∑ i=1 E i -O i n EF = 1 - ∑ n i=1 (Ei-Oi) 2 ∑ n i=1 � O i -O � 2 Plant Soil 1 3 Vol.
: (0123456789) crop in a species mixture or not, growing duration and French region to explain dry shoot biomass, total dry biomass and N in shoots and roots. Depending on the variable explained, measured fresh shoot biomass and dry shoot or root biomass were also used. For the simulation database, we tested the site, soil, cover crop biomass at termination, C:N ratio, termination date and number of months since termination as factors to explain the percentage of N mineralized and N leached from cover crop residues.
Description of the cover crop experimental database The experimental database covered a wide range of shoot biomass, reaching almost 100 t ha -1 in fresh and 12 t ha -1 in dry biomass (median = 9. 1 and 1. 4 t ha -1 , respectively) (Fig. 3 ). The corresponding shoot N amount reached 300 kg ha -1 but usually ranged from ca. 130 to 200 kg ha -1. For the situations in which roots had been measured and analysed, median total dry biomass and N amounts were 1. 7 t ha -1 and 67 kg ha -1 , respectively. C shoot and root contents were similar (usually ca. 40 and 43%, respectively). As N content in roots was lower than that in shoots, the median C:N ratio of roots was twice that of shoots (30 vs. 15, respectively). As sample sizes differed among variables, it was difficult to compare shoots and roots since data were not necessarily on the same experiments or fields.
Predicting dry biomass and N content based on experimental data Analysis of the experimental database showed strong and significant correlations between fresh shoot Fig. 3  Range of variables in the entire cover crop database for shoot, root and total concentration or amount depending on the variable. DM and FM mean dry and fresh biomass (in t ha -1 ), respectively, C means carbon (%), N means nitrogen (a in % and b in kg N ha -1 ) and CN means the C:N ratio (no unit). Whiskers equal 1. 5 times the interquartile range (i. e. 25th to 75th percentile) Plant Soil 1 3 Vol:. (  1234567890 ) biomass, dry shoot biomass and total dry biomass, confirming the relevance of using fresh shoot biomass to estimate dry biomass in MERCI (Fig. 4 ). Dry root biomass was also correlated significantly with dry shoot biomass and total dry biomass, but less strongly.
The correlation between N amount and biomass was somewhat weaker, but remained significant for shoots, roots and total biomass and total N in the plant. Although the correlation between fresh shoot biomass and total N in the plant had fewer data points, it was relatively strong (r = 0. 80). ANOVA showed that dry shoot biomass was explained mainly by fresh shoot biomass (76. 0%), followed by species (4. 2%), botanical family (2. 0%) and growing duration (1. 7%). Total dry biomass was explained mainly by dry shoot biomass (92. 0%), followed by species (2. 2%), while dry root biomass was explained by dry shoot biomass (32%), species (20%) and botanical family (6%). As a result, depending on data availability, models were made by species, by botanical family or all families in the entire database combined.
Linear regressions to predict dry shoot biomass from fresh biomass varied (Table  2 ). As the 'a' coefficient for the entire database was 0. 14, the mean water content of cover crops was 86%. This coefficient was somewhat lower for Brassicaceae and Hydrophyllaceae and higher for Fabaceae, Poaceae, Linaceae and Polygonaceae and resulted in higher R 2 , except for Hydrophyllaceae. The mean percentage of roots estimated by the linear regression between dry shoot biomass and total dry biomass was 31% and ranged from 10 to 43%, with the highest percentages for Poaceae. R 2 ranged from 0. 90 to 0. 98, except for Linaceae (as the database contained only one value). ANOVA showed that dry biomass explained 66-77% of the variance in N amount. Species and botanical family explained 2-5% of the N amount variables, except for the total N amount, which was explained more by species (12%).
The mean N amount in dry shoot biomass in the entire database was 26. 5 kg N t -1 (Table  2 ). It was highest in Fabaceae (33. 6 kg N t -1 ), as expected with legumes, due to their Variables are the fresh shoot biomass (FM shoot ); shoot, root and total dry biomass (DM shoot , DM root and DM total , respectively); and shoot, root and total N in the plant (N shoot , N root and N total , respectively). All correlations were significant (p < 0. 001) Plant Soil 1 3 Vol. : (0123456789) symbiotic N 2 fixation. It was lowest for Polygonaceae and Asteraceae (18 kg N t -1 ). The N amount of the other families ranged from 21 to 25 kg N t -1 (Table  2 ). Root N amount (13. 3 kg N t -1 ) was half that of shoot N amount (26. 5 kg N t -1 ). Shoot N amount had the same trend for Fabaceae and was lowest for Poaceae (9.
7 kg N t -1 ). Shoot N for Asteraceae, Linaceae and Polygonaceae was estimated from only 1-4 measurements depending on the family but seemed consistent with those of the other botanical families. Linear regression models based were generally more accurate when they were built by botanical family than based on the entire database (Table  3 ). However, even predictions based on the entire database were good, with EF of the calculation steps ranging from 0. 69 to 0. 92, except for the prediction of dry root biomass from dry shoot biomass, which had a lower EF (0. 3) and a low RMSE (4 kg N ha -1 ). MD was low for all variables, with three of the five equal to 0. RMSE was 0. 4-0. 9 t ha -1 for predictions of dry biomass and 4-21 kg ha -1 for predictions of N variables. When shoot N was estimated directly from fresh shoot biomass, EF was nearly the same as when it was predicted from dry shoot biomass (0.