dataesr/scientific-paragraphs-categorization huggingface dataset:
Previously published paired-plots and chronosequences studies of PALs featuring carbon data are numerous, readily available, and valuable sources of data that can complement well-known, widely used datasets of longterm repeated measurements. They represent past investments in science that should be repurposed, reused, and archived before being lost  19. Valuable secondary soil physicochemical properties (e. g. , nitrogen, phosphorus, particulate and mineral-associated organic matter, etc. ) for modeling drivers of SOC dynamics are also often available within the same study. Collecting this information can improve our inferential capabilities and reduce geographic and statistical biases. By extracting and synthesizing disparate time-stamped SOC and auxiliary data from published studies involving paired-plots and chronosequences of PALs, in addition to sampling new sites in underrepresented regions, we can benchmark and validate models of successional carbon dynamics.
Leveraging PALs as carbon sinks Integrating better maps and bigger time-stamped datasets with new approaches in artificial intelligence-driven modeling will allow us to investigate the carbon cycle impacts of PALs with more accuracy than ever before. Degraded and marginal agricultural lands already represent some of the best candidates for ecosystem restoration globally. These research efforts will provide critical and timely insights for international reforestation and carbon sequestration initiatives. Whenever and wherever PALs do recarbonize, regardless of their carbon sink longevity and permanence, they must be made known and accounted for ref. 22. Of course, not all PALs can be expected to recarbonize naturally. Those that are degraded beyond spontaneous recovery will need a helping hand through assisted restoration practices like native tree planting.
| ITS metabarcoding library filtering and taxonomic assignment The raw library was filtered using a standard toolbox of software. The expected sequencing depth was around 78,000 reads per sample. The quality of the library was checked using FastQC (https:// www. bioin forma tics. babra ham. ac. uk/proje cts/fastq c/), primers were trimmed using cutadapt  (Martin, 2011) , and reads were merged with PEAR  (Zhang et al. , 2014). The remaining filtering and Operational Taxonomic Unit (OTU) clustering (97% of cut-off threshold) was performed using vsearch v2. 8. 2  (Rognes et al. , 2016). At this point, we produced an OTU table. When possible, OTUs were classified until species level using rjson  (Couture-Beil & Couture-Beil, 2018) , taxize R packages  (Chamberlain & Szöcs, 2013) , and a phylogenetic confirmation through IQ-TREE  (Nguyen et al.
| Flowering plant biodiversity data analysis At this point, only the wild bee samples with a minimum of 1000 reads were kept for subsequent analyses. Thus, we estimated the completeness of the sampling by computing a rarefaction curve and individual rarefaction curves for each of the environmental variables and species traits studied using R package vegan  (Oksanen et al. , 2013). As another measure of quality, we calculated the number of OTUs detected in each sample and averaged this number at the population level to determine the number of pollinated plants detected per wild bee specimen. We used the multivariate BIO-ENV procedure  (Clarke & Ainsworth, 1993) , which is a dissimilarity-based method to identify the set of environmental variables that are most correlated with the OTU table, and thus best explain the bee pollination spectrum.
The four environmental variables for which we had records were included in the BIO-ENV procedure: flowering period, farming system, percentage of weeds, and height of sunflower. The flowering plants pollinated by wild bees on sunflower crops were described using two different biodiversity metrics, relative read abundance (RRA) and frequency of occurrence (FOO). RRA refers to the proportion of reads obtained per OTU in each sample, each variable, or for the full dataset; and FOO informs about the number of samples (counts) in which an OTU is present  (Deagle et al. , 2019). The calculation of both metrics, RRA and FOO, for the whole sampling and each of the selected variables' levels was calculated using customized scripts based on R package dplyr  (Wickham et al. , 2021). The importance of environmental variables and wild bee species traits in the pollination spectrum was inferred by fitting them onto an unconstrained ordination and applying envfit analysis  (Oksanen et al.
Statistical significance of both ordinations was tested through a non-parametric multivariate analysis or PERMANOVA (999 iterations) and constrained CAP permutation test, respectively  (Oksanen et al. , 2013). For the case of these two permutational analyses, aside from the main effects, the interaction between the environmental variables and the species traits was also tested. We explored the effects of environmental variables and species traits selected, together with the interaction between them through the computation of a negative binomial generalized linear model (GLM), using a nested model, and applying a log-link function (999 iterations) using mvabund R package  (Wang et al. , 2012).
| Differentially abundant flowering plant For each of the selected environmental variables and species traits, we identified the differentially abundant flowering plant OTUs by applying a DESeq negative binomial Wald test using DESeq2 R package  (Love et al. , 2014) , that is to say, the OTUs, which show a significantly higher abundance in a particular factor within the environmental variables or species traits. DESeq2 analysis is a procedure originally designed for differential expression analyses of genes, however, it is widely used in count/abundance data. The identified plant species (OTUs) were considered indicator species of each factor of the environmental variable or species trait and were further analyzed. The significance of differential abundance among levels of variables was tested through Kruskal-Wallis test, and the RRA of significantly abundant species was visualized through a hierarchically clustering (complete linkage method) using the pheatmap R package  (Kolde & Kolde, 2015).
Funding information " Plan de Ciencia, Tecnologia e Innovacion 2018-2022 " from the  Government of Asturias , Grant/Award Number: Grant  AYUD/2021/58607 ;  ANR  funded project  IMAgHO, Grant/Award Number :  ANR-18-CE32-0002-01 ;  Fundación para el Fomento en Asturias de la Investigación Científica Aplicada y la Tecnología ;  Gobierno del Principado de Asturias.
Resection of large colorectal neoplasia in the rectum can be performed either by endoscopic submucosal dissection (ESD) or by transanal endoscopic microsurgery (TEM). The rate of en bloc resection and recurrences were similar in a recent meta-analysis  [1]  but we are still waiting for the results of the prospective clinical trial performed in France (MUCEM study, NCT02885142) a few years ago. In any case, management of recurrence appears far more complex after TEM than after ESD since the first technique is a fullthickness resection technique although ESD only removes the mucosa and submucosa (▶ Fig. 1 ). Therefore, recurrence at the site of the scar appears similar at the surface, but the deep part of the wall is totally different in the two situations. In the first case, endoscopic resection of the recurrence can be performed by endoscopic submucosal dissection following a deep cut line very close to the muscular layer.
A. Control setup The control architecture is split into two levels. The computationally expensive optimal control resolution is done at 100Hz. In the case of experiments on the real robot this part is carried out by an external computer (fitted with an AMD Ryzen 5950X, 16 cores with 64 GB of RAM). A faster control, based on the gains computed by the MPC can then be run directly on the robot at 2kHz as shown in Eq. (  5 ). The MPC implementation was based on Crocoddyl (Contact RObot COntrol by Differential DYnamic Library). The software architecture is summarized in.
VII. ACKNOWLEDGMENT This work was supported by the cooperation agreement  ROB4FAM  and the  MERLHBOT Région Occitanie  project. The use of the experimental platform TALOS was supported by  ROBOTEX 2. 0  (Grants  ROBOTEX ANR-10-EQPX-44-01  and  TIRREX-ANR-21-ESRE-0015 ). The author would like to thank  Nahuel Villa  for sharing personal notes on whole-body dynamics. He would also like to thank  Ewen Dantec ,  Pierre Ferenbach ,  Maximilien Naveau , and  Guilhem Saurel  for the help they provided during the experiments as well as  Sébastien Boria  and  Noélie Ramuzat  who made testing at Airbus site possible.
Vegetation data and trait selection On each gravel bar, the vegetation was surveyed within twelve 5 x 5 m quadrats (25 m²) positioned along three transects (four quadrats per transect) oriented perpendicularly to the river, in areas with clear elevation gradients relative to the water surface (for details see,  Janssen, Piégay & Evette, 2022b). Transects were spaced approximately 100 m apart and located to cover the upstream, middle and downstream portion of the gravel bars. All vascular plants were surveyed following the Braun-Blanquet abundance-dominance methodology. To characterize the entire plant community and account for plant phenology, two complete surveys were conducted each year (June and July), and to limit observer biases, all surveys were performed by the same person (PJ). For each species present in at least 5% of the quadrats (i.
e. , n = 172 out of a total of 219 species), we collected data on ecological preferences, ecological strategies, morphological traits and life cycle (referred to as "trait" in the manuscript, see also Table  S1  & Figure  S4 ). Ellenberg's indicator values  (Ellenberg et al. , 1992)  for light (L), nitrogen content (N) and soil moisture (F) were extracted from the Baseflor database  (Julve, 1998)  and used to characterize variations in species habitat niche. Grime's values  (Grime, 1977)  for Competitors, Stress-tolerators and Ruderals, i. e. ternary coordinates, were extracted from the list of species available in  Pierce et al. (2017)  and used to characterize variations in the strategy-environment relationships of plants. Westoby's values  (Westoby, 1998)  for specific leaf area (SLA), plant height and seed mass were extracted from the TRY database  (Kattge et al.
Environmental data At each quadrat center, the elevation difference and distance to the water line was characterized using topographical surveys. Topographic measurements were conducted using a laser rangefinder  (TruPulse 200X)  positioned on a tripod at a constant height of 1. 20 m and a target at a height of 2 m. The horizontal and vertical distances between the tripod and the target were measured between the water at the beginning of each transect and the center of the four successive quadrats. Soil textural properties were derived from two soil core samples collected in representative areas of each quadrat and pooled. Each composite soil sample was then dried, weighed and sieved to estimate the proportion of fine sediments (i. e. , <250-µm mesh). Moreover, fine sediment thickness was estimated at the center of each quadrat using a soil corer inserted into the sediment up to the interface with the gravel and measuring the distance from the surface.
This work was cofunded by the Labex DRIIHM, French program "Investissements d'Avenir" (ANR-11-LABX-0010), which is managed by the French National Research Agency (ANR), the OHM Vallée du Rhône, the Agence de l'eau Rhône Méditerranée Corse, the Compagnie Nationale du Rhône (CNR) and INRAE. The work is also performed within the framework of the EUR H2O'Lyon (ANR-17-EURE-0018) of Université de Lyon (UdL) through the "Investissements d'Avenir" program operated by the French National Research Agency (ANR).
After incubation for 3 days at 28 C, mosquitoes were homogenized in passive lysis buffer (Promega) using the Precellys 24 grinder (Bertin Technologies) for 30 sec at 6,000 rpm. Samples were transferred to a 96-well plate and centrifugated for 5 min at 12,000 3 g. Fifty mL of supernatant were transferred to a new plate, and 50 mL LARII reagent added for the first Fluc measurement. Next, 50 mL Stop&Glow reagent was added before the second measurement of Rluc, according to the Dual Luciferase assay reporter system (Promega). Counts of Rluc were used to control for transfection efficiency, and samples with less than 1,000 counts were discarded from further analysis. Data were normalized by calculating the Fluc/Rluc ratio. Small RNA analysis Small RNA library preparation and sequencing Total RNA from pools of 5 to 8 mosquitoes was isolated with TRIzol (Invitrogen).
readthedocs. io/en/stable/). Only reads with a phred score R20 were retained. A second set of graphs was generated by 'FastQC' on the fastq files created by 'cutadapt'. 68  Mapping was performed by 'Bowtie1'  69  with the '-v 1' option (one mismatch between the read and its target) using the following reference sequences: GenBank accession number HG316481 for DENV-1, GenBank accession number LN898104. 1 for CHIKV, and positions 161,180,000-161,236,000 of chromosome 3 in the AaegL5 genome built  70  for the histone transcript cluster. 'Bowtie1' generates results in 'sam' format. All 'sam' files were analyzed by different tools of the package 'samtools'  71  to produce 'bam' indexed files. To analyze these 'bam' files, different kinds of graphs were generated using home-made R scripts with several Bioconductor libraries such as 'Rsamtools' and 'ShortReads'.
ACKNOWLEDGMENTS We thank the  Lambrechts lab and Saleh lab  members, as well as  Gijs Overheul  and  Ronald van Rij , for helpful comments and discussions. We are grateful to  Catherine Lallemand  for assistance with mosquito rearing and to  Catherine Bourgouin  for access to the Femtoject injector. We thank  Davy Jiolle  and  Christophe Paupy  for providing the DENV-3 strain and the  World Reference Center  for Emerging Viruses and Arboviruses at the  University of Texas Medical Branch  for providing the ZIKV strain. We thank  Diego Ayala ,  Davy Jiolle , and  Christophe Paupy  for the initial field sampling of mosquitoes in Bakoumba, Gabon. We thank  Alain Kohl  and  Emilie Pondeville  for sharing the plasmids for the reporter assay. This work was supported by the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence   Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID  to S.
H. M. , L. L. , and M. -C. S. ); the  Agence Nationale de la Recherche  (grant  ANR-18-CE35-0003-01  to L. L. ); and a grant from the  Fondation iXcore -iXlife -iXblue pour la recherche  to M. -C. S. A. Bergman  was supported by a stipend from the  Pasteur -Paris University (PPU)   International PhD Program. S. H. M. and O. R. were supported by a  Pasteur-Roux-Cantarini Fellowship  of the  Institut Pasteur. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Statistical analyses We tested whether queen exposure to pesticide impaired their survival over four months using a Cox proportional hazard model built in the survival package  (Therneau, 2021)  in R 4. 0. 1 (R Core  Team, 2021). We checked for proportionality of hazard ratios using a score test in the "cox. zph" function of the survival package. We also checked visually for outliers using the "ggcoxdiagnostics" function in the survminer package  (Kassambara et al. , 2021). We assessed significance of the treatment variable by the change in deviance after removal of that variable (Likelihood-Ratio Test [LRT]) using a chi-square test. Significant differences between treatments were tested using the pairwise_survdiff function in the R package survival  (Therneau, 2021) , with fdr p-value corrections for multiple comparisons.
The impact of queen exposure to pesticide treatment on colony growth was tested using linear mixed models (LMMs) built in the lme4 R package  (Bates et al. , 2015). Colony growth parameters were frame surface occupied with either worker bees, nectar, pollen, open brood or capped brood. We also tested the effects of treatments on the proportion of open brood surface over total brood surface using a general linear mixed model (GLMM) with a binomial distribution built in the lme4 R package. This ratio should remain constant over time once the colony is established and otherwise could be an indicator of brood production issues. Predictor variables included treatment, time (in months) and their interaction. We included the square of time to test whether the development of colony parameters over time better fitted a quadratic relation.
Queen ID was also included in models as a random effect to consider repeated measures over time. Normal distribution of residuals and homoscedasticity were checked in LMMs, as well as normal distribution of random effects in all models. Model selection followed a backward-stepwise approach: we assessed significance of predictor variables, starting with the interaction, with an LRT using a chi-square test. The interaction was eliminated from the model if P > 0. 05 and when significant, post hoc comparisons with fdr P-value corrections were performed using the emmeans R package  (Lenth, 2022)  to identify when control colonies differed from colonies headed by treated queens. We also investigated whether treatment was associated with varroa intensity using a general linear model (GLM) with a Poisson distribution built in the stats R package.
All reactions were amplified at 95 • C for one 15 min, 30 cycles of 95 • C for 30 s, 58 • C for 90 s, 72 • C for 60 s, and a final extension at 60 • C for 30 min. PCR products were run on an ABI 3730XL automated capillary DNA sequencer by Genoscreen (Lille, France). Microsatellite fragment size was scored using GeneMapper 3. 7 software (Applied Biosystems) and microsatellite data were then analyzed in the program COLONY  (Jones and Wang, 2010)  to compute the total number of different marker sets within a colony (i. e. mating number). We then used the proportion of each subfamily within a colony to calculate queen effective paternity frequency, an unbiased measure corresponding to queen mating number if all males are represented equally within her offspring  (Nielsen et al. , 2003).
Acknowledgements We are grateful to  Carole Moreau-Vauzelle  and  Daniel Raboteau  for their help in collecting field data for this study. We also thank  Isabelle Giraud  for her advices during paternity analyses, and  Victor Desclos Le Peley  and  Elisa Gomes  for their help during laboratory analyses. We also would like to thank  Gilles Bosquet  for the original drawings used in the graphical abstract and three anonymous reviewers for their comments that improved the manuscript.
CHEOPS observations and data reduction CHEOPS  (Benz et al. 2021 ) observed 55 Cnc in the frame of the Guaranteed Time Observations Programme CH_PR100006. The observations acquired between March 2020 and February 2022 consist of 29 visits in total, each comprising 16 CHEOPS orbits (except 25 orbits for visits #28 and #29), with an approximate 100-min duration each. While this study focuses on 55 Cnc e's occultations, a separate paper will detail the phase-curve analysis  (Meier-Valdés et al. , in prep. ). Because of the Sun-synchronous, dawn-to-dusk orbit of CHEOPS combined to the +28 • declination of 55 Cnc, the time spent on source was mostly limited by Earth occultations, South-Atlantic anomaly interruptions, and stray light contamination, leading to a typical duty cycle between 53 and 61%.
Individual frames (integration time of 2. 2 s) were co-added on board into 44. 2 s stacks, which corresponds to the data cadence, to reduce the downlink volume. A summary of all CHEOPS time-series photometric observations of 55 Cnc, out of which the occultations were extracted, is shown in Table  1. The CHEOPS sub-array (200×200 pixels) data were first processed using the mission's data reduction pipeline (DRP, version 13. 1. 0) described in  Hoyer et al. (2020) , which is based on aperture photometry. For all visits, we used the DEFAULT aperture of 25 pixels, which yields the smallest photometric root mean square (RMS) according to the DRP report. We then complemented the DRP output with point spread function (PSF) photometry using the PSF Imagette Photometric Extraction (PIPE) 1  pipeline, which is a Python package developed for CHEOPS  (Brandeker et al.
in prep). A useful benefit of PIPE is that it allows us to quantify the PSF scale dependence on the spacecraft orbital phase. In addition, we employed PIPE to process the 30-pixel radii imagettes that were extracted from the sub-arrays, stacked in pairs on board providing a 2. 2-s cadence. A visual inspection of the full-array frames shows smearing from the neighbouring 53 Cnc (V = 6. 23), which contaminates 55 Cnc's (V = 5. 98) aperture on CHEOPS orbital timescales. The sub-array DRP and PIPE outputs, as well as the PIPE imagette reductions, provide final time series that are corrected from the smearing of 53 Cnc. For each reduction, we then removed data points flagged by the pipeline because of, for example, cosmic rays or stray light contamination and mask background, and X-Y centroid position outliers identified by large median absolute deviations from the median, assuming a Normal distribution and using a consistent estimator of the standard deviation being σ = 1.
Data analysis In this section we describe how the occultation depths were derived from the reduced CHEOPS time series. We first cut the 29 visits into individual data segments with a single occultation each. We defined a data segment as valid if its duration was at least three times the occultation duration, to ensure that an accurate instrumental baseline model could be derived from the out-of-eclipse photometry. This step resulted in a sample of 41 individual occultations. For the treatment of CHEOPS instrumental systematics, we employed a similar approach to the marginalisation method presented by  Gibson (2014)  and used in the past for other facilities (e. g. HST,  Wakeford et al. 2016). The main reason for this approach is that CHEOPS orbital period (100 min), and therefore its associated systematics, is close to 55 Cnc e's occultation duration (∼95 min).
2020 ). We also assumed circular orbits and fixed √ e cos ω and √ e sin ω values to 0  (Nelson et al. 2014). We ran two chains of 100 000 steps (including 20% burn-in) each, and checked their efficient mixing and convergence by visually inspecting the autocorrelation functions of each chain, and by using the  Gelman & Rubin (1992)  statistical test, ensuring that the test values for all fitted parameters were <1. 01. Our third fit was identical to the preceding one, with the exception that each individual occultation was fit independently of the rest of the dataset. Finally, we repeated this entire analysis using PIPE's imagette photometry to assess whether the higher cadence impacted our occultation depth median values and corresponding credible intervals.
Results The global fit, including all 41 orbits (2020-2022), yielded an occultation depth of dF occ = 12 ± 3 ppm (see Fig. 1 ) with BIC = 10 891, compared to BIC = 10 896 without the occultation model included. We thus assess the ∆BIC = 5 as moderate evidence for the detection of 55 Cnc e's occultation in the averaged dataset. We show all individual occultation depths from our third fit (Sect. 2. 2) in Table  3  and they are also depicted in Fig. 2. We note that using sub-arrays or imagettes in the analysis yield results (parameter values and credible intervals) that are indistinguishable from each other. We thus elected to conduct the analysis using the sub-array frames for computational efficiency. We used the same method as in Meier  Valdés et al. (2022)  to compute the corresponding geometric albedo in the CHEOPS bandpass, using a PHOENIX stellar spectrum  (Husser et al.