dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The results presented above confirm that the multi-annual strategy proposed in this paper may be a viable way to provide the reliable mapping of NAAL surfaces based on Sentinel-2 image time series in the sub-humid West African agrosystems. This is especially true if compared with previous trials of direct annual mapping of NAAL on the same area, even if in a larger land cover mapping scenario, in which the fallow class was mostly missed (average F1-scores lower than 0. 2)  (Gaetano et al. , 2019 ). An extensive validation has been provided using multiple versions of the reference data set, showing that if we keep the most reliable information available on NAAL reference surfaces the measured accuracy is very high (> 80%) considering the fine scale targeted. However, coping with possible biases in such data set, results also show that, enabling different amounts of reference data augmentation based on suitable hypothesis on spatial correlation at field level, performances still remain significantly higher (F1-score ranging from 0.
, 2020; Dara et al. , 2018; Rufin et al. , 2022)  which, although mainly focusing on large-scale assessment, do not provide evidences of such local significance at the scale of pixel aggregates. Some room for improvement in spatial accuracy could be achieved by post-processing NAAL maps based on prior information on the location and shape of the detected fields. We are currently carrying on some experiments involving field-scale segmentation and object-based NAAL field validation to prove this point. Despite the mentioned promising outcomes, some limitations have to be pointed out especially concerning the up-scaling potential of this approach. First of all, the availability of a suitable reference database, which must be both precise at field scale and annually updated, is far from standard for the targeted region, and may be costly to produce, also considering that field boundaries are not static in time  (Castro Alvarado, 2022).
In addition, as shown, even when a proper reference data set is available, ensuring its representativity outside the local scale (e. g. robustly with respect to changes in climate, soil types, vegetation cycles, and all the changes in the captured radiometric information that all this imply) may be hard, making the availability of a broad and extensive data set an absolute necessity. One may argue that the building of such data base could be addressed through the use of high resolution imagery and photointerpretation. Yet the feasibility of this strategy relies on the ability of discriminating (i) NAAL from other types natural vegetated spaces and (ii) clear optical differences between NAAL and AAL. These two conditions are rarely met in sub-humid West Africa due to a mainly rainfed-based agriculture and a lack of reliable cropland mapping products (without mentioning the limited availability of exploitable acquisitions during the rainy season).
Any improvement in image spatial resolution or temporal frequency within the current range of available products are therefore unlikely to provide substantial improvements in the construction of an extensive enough reference database. Nonetheless, in this work we provide a first evidence that for a quantification, at least a rough one, of NAAL surfaces the regular (e. g. annual) detection of active cropland vs. other kinds of nonmanaged deciduous vegetation may suffice, since that would enable the application of a trajectory model like the one proposed here. Hence, a possibility that we consider worth exploring to address the issue of large-scale NAAL identification is to resort to unsupervised classification techniques for managed vs. unmanaged LULC mapping, eventually adapted to specific agro-and eco-systems.
One way to proceed could be to exploit the proposed supervised classification workflow to deeply inspect the combination of image-derived variables allowing for the discrimination between these two classes, and design a proper unsupervised methodology to provide such discrimination. In this setting, the deployment of field work for gathering reference data may be based on an agrosystem-scale sampling in representative areas, moving the whole workflow within the range of feasibility. Further investigation are also being carried out to explore the space of deep-learning based classification methods and assess their performances w. r. t. the legacy Random Forest classifier used here, in particular the methods that explicitly consider the temporal correlation of variables. This could be another way to enhance the robustness of the managed vs.
Conclusions Quantification of non-active agricultural land in West Africa is essential to accurately assess agricultural systems on food security and agro-ecological sustainability of current practices. In this study we proposed a pixel-based methodology that makes use of freely available remote sensing data, multiple years of ground truth data registered in situ, and implements a simple 4-class LCLU typology and a multi-annual trajectory model that allows the mapping of Non Active Agricultural Land (NAAL). Our approach showed positive results, improving mapping accuracy of NAAL in the area compared to previous trials and illustrating the importance of the reference data set preprocessing, in time and space, to correctly identify this land use class that is multi-annual in nature. Future works are being put in place to improve annual LULC maps via deep learning techniques and address the issue of the upscaling of the method to larger extents (e.
Acknowledgments Enzo Castro Alvarado  received for his work a fellowship from the  French National Research Agency  under the  Investments for the Future Program   ANR-16-CONV-0004  (DigitAg), and from  CIRAD. The ground data collection was partly funded by  CNES (APR TOSCA). The authors wish also to thank the  European Space Agency (ESA)  for the Sentinel 2 data, Theia pole for the calibration of the Sentinel-2 images, and  Bertin Kaboré  for collecting the 2019-2021 in-situ data.
Sentinel colonies and measurements of colony performance during crop bloom At each site, we used three bumble bee colonies (B. terrestris terrestris for continental Europe and B. terrestris audax for the UK and Ireland) (n = 384), housed in protective structures (Extended Data Fig. 8 ), before focal crop bloom in 2019. Before deployment, we confirmed that each colony had a natal queen and recorded its initial weight (648 ± 70. 9 g (mean ± s. d. ); Extended Data Fig. 5c ). Colonies were weighed again during peak bloom of the focal crop in each country. At the end of the crop bloom, colonies were weighed again, then sealed, retrieved from sites and frozen. Of the 384 colonies initially deployed across 128 sites (64 apple, 64 oilseed rape), we analysed 316 colonies from 106 sites. This reduced sample size is due to colony losses (for example, animal attack and overrun by machinery; n = 5) or colonies not yielding enough stored pollen material for pesticide quantification (n = 63; Supplementary Table  3 ).
The last could potentially be avoided in any future studies by complementing with concurrent collection of returning foragers' corbicular pollen  7, 8. In the laboratory, we removed any wax covering and sorted through the colony structure to count the number of intact and eclosed worker/ male and queen cocoons (Extended Data Fig. 8 ), on the basis of their different size  1. Our approach allowed us to derive two main indices of colony performance: (1) colony weight gain and (2) the total colony production. For weight gain, we calculated the natural-log response ratio for each colony as ln(g max /g initial ), where g initial is weight before bloom and g max is the maximum weight achieved by a colony during its field placement. In most cases (62% of colonies), g max was achieved by the final weighing but, in some cases, g max was achieved at the second (26%) or first (12%) weighing.
Calculation of pesticide risk We use toxicity-weighted concentrations (TWC) as a basis for indicating the direct pesticide risk to bees  7, 8  , where the TWC for each compound (TWC i ) is the ratio of its concentration in bee-collected pollen (µg kg -1 ; c i ) and its respective acute toxicity endpoint (LD 50i -the dose required to cause 50% mortality in the test population). Following a concentration addition approach, the recommended default for mixture environmental risk assessment  31, 57  , we summed TWCs to calculate the additive toxicity-weighted concentration of all compounds within a sample per site (TWC mix ): ∑ c i TWC = LD i n i mix =1 50 We used an average of the acute oral and contact lethal doses LD 50 for each compound sourced from the Pesticide Properties Database  58, 59  to provide an overall indicator of toxicity, reflective of how bees encounter pesticides in the landscape, that is, moving contaminated food in contact with their bodies for oral consumption  60.
Statistical analyses We tested whether pesticide risk (TWC mix ) interacts with crop type and proportion cropland to affect our measures of colony performance (total colony production, weight gain, maximum weight and queen production). Given a strong right skew, we log-transformed (ln(x + 0. 1)) risk values. We centred risk and cropland values to aid the interpretation of interaction terms. For weight gain, we specified an LMM with risk, crop type, proportion cropland and their interactions as fixed effects and with site nested in country as a random effect. We specified a GLMM with a negative binomial error distribution for overdispersed count data of total colony production (dispersion ratio = 54. 98; P < 0. 001). We used the same fixed and random effect structure as above. We analysed two more measures of colony performance: maximum weight and queen cocoon production (total of intact and eclosed queen cocoons).
We specified an LMM as above with weight log-transformed because it improved diagnostics of model residuals and our results are qualitatively similar if weight is untransformed (Extended Data Table  1 ). We specified a GLMM as above for queen cocoon production and with a single, constant zero-inflation parameter (Extended Data Table  1 ). We included initial colony weight (g initial ) as a covariate in the above models to account for variation in colony starting conditions. Models showed little multicollinearity (VIF range 1. 03-3. 28 across all models) and we confirmed that risk and proportion of cropland were independent by means of an LMM with the country as a random effect (marginal R 2 (R 2 m) = 0. 02; χ 2 = 1. 38, P = 0. 24; Extended Data Fig. 9a ). We also confirmed that risk was independent of initial colony weight by means of an LMM with the country as a random effect (R 2 m = 0.
01; χ 2 = 1. 22, P = 0. 27; Extended Data Fig. 9b ). We performed analyses and data visualization using R v. 4. 1. 1. We constructed LMMs with the lme4 package  64  and GLMMs with the glmmTMB package  65. We report R 2 values calculated following the methods of ref. 66. We estimated marginal means with the emmeans package  67. We estimated interaction slopes with the interactions package  68  and evaluated models for overdispersion, normality and multicollinearity using diagnostic functions in the performance package  69  and the DHARMa package  70. Extended Data Fig. 1 |  A simplified view of landscape exposure and resulting pesticide risk to bees. Pesticide use creates potential hazard for non-target organisms. For bees in agricultural landscapes, pesticide risk results when their activity exposes them to this hazard (top left panel).
Acknowledgements We thank farmers and landowners for access to their land. We also thank  A. Bates ,  J. Borth ,  M. Dietenberger ,  M. Cotter ,  R. George ,  L. Junk ,  S. Kivelitz ,  S. Lotz ,  J. Panziera ,  B. Rai ,  B. Schaer ,  G. Svensson  and  A. Turner  for field and laboratory assistance;  O. Burek ,  M. Goliszek ,  P. Łusiak ,  M. Małysiak ,  A. Niewiadowska  and  S. Semeniuk  for laboratory assistance in pesticide residue analysis;  M. T. N. N. Huyen  for pesticide data curation;  A. Dalpiaz ,  K. Ivarsson ,  L. Marchel  and  A. Saccardo  for site selection and design;  A. Neubauer  for graphic design; and  G. Turney  for project management. This project received funding from the  European Horizon 2020 research and innovation programme  under grant agreement no. 773921. C. C. N.
This can be explained by the fact that the 3D data were obtained from ex situ measurements, that is, a different sample was scanned for each selected cycle number. Lattice Strains as a Function of TMF: Figure  10  shows the evolution of lattice strains in the gauge length direction for Al, Si, and Al 7 Cu 4 Ni determined by neutron diffraction as a function of thermal cycle number. The lattice strains were determined with respect to the initial condition before the TMF experiments. While tensile strains tend to build up during   10b, c ), whereas the alloy 1232 shows a continuous increase in lattice strains already from the second thermal cycle onward. The lattice strains remain fairly constant after 100 thermal cycles, with the strains in 1232 reaching more than twice the strain of the other alloys.
The solidification of primary Si starts at %595 °C for 12 431, while for 12 421 and 1232 it begins at about 10 °C lower temperatures, that is, %583 and %581 °C, respectively (green rectangle). In addition, the solidus temperature of 12 431 and 12 421 are %542 and %536 °C, respectively, whereas 1232 has the highest solidus temperature of %555 °C (blue rectangle). This indicates that the solidification ranges of 12 421 and 12 431 (%47 and %53 °C) are %50% larger than for 1232 (%26 °C). In order to understand which elements are responsible for differences in solidification intervals and the onset of solidification of phases described above, a sensitivity analysis was carried out using the software OptiSlang in combination with Thermo-Calc. To this purpose, the solidification intervals and solidification temperature of primary Si and Al were calculated for 2500 compositions varying the concentration of Ni, Cu, and Mg.
The lattice strains are computed according to ε ¼ ðd À d 0 Þ d 0 ( 5 ) where ε indicates the lattice strain, d 0 is the lattice spacing in the initial condition at ambient temperature before TMF, and d is the lattice spacing after thermal cycling. Thermodynamic Calculations: Solidification simulations were conducted using the thermodynamic calculations software Thermo-Calc 2021a  [58]  using the TCAl7 database for Al in combination with the optimization software OptiSlang 8. 1. 0. [59]  A Python interface was used to link the simulation and optimization softwares. This approach was used to determine the correlation between a set of input parameters, in our case the alloying elements, and a set of response variables, in this case the alloy properties calculated in Thermo-Calc. A sensitivity analysis was performed to quantify the importance of the design variables on the model responses.
For this purpose, Space filling Latin Hypercube Sampling was used to define 2500 initial designs with varied chemical compositions for which solidus and phase solidification temperatures were to be calculated. Once a corresponding response value was obtained for each alloy design, sensitivity was measured as correlation coefficients and polynomial-based coefficients of importance are estimated. [59]  In this work, the results of this analysis were presented in the form of 3D response surface plots with superimposed support points indicating the calculated alloy designs. They showed the approximated values for the selected response or objective data function with respect to the two selected input parameters. These calculations were carried out to shed light on the origin of the microstructures of the studied alloys as a function of chemical composition, especially as a function of Ni, Cu, and Mg contents and their interaction with Si.
Acknowledgements This work was part of the " K-Project for Non-Destructive Testing and Tomography Plus " supported by the  COMET-Program  of the  Austrian Research Promotion Agency (FFG)  as well as the  Provinces of Upper Austria (LOÖ)  and  Styria , grant no. 843540. The  ESRF  and  DESY  are acknowledged for the provision of synchrotron facilities at the beamlines ID19 and P05, respectively. FRM II is acknowledged for the provision of neutron facilities at the Stress-Spec beamline. Open Access funding enabled and organized by  Projekt DEAL.
WES of 40 APAs identifies a new CADM1 somatic mutation WES was performed in 40 APAs from predominantly European ancestry patients with PA (Supplementary Table  1 ). The APAs were consecutive, except for two that antedated our previous WES (n = 13)  8  and two that were excluded by prior targeted sequencing; 31 had a known aldosterone-driver somatic mutation, including 11 CACNA1D and nine KCNJ5 variants (Supplementary Tables  1  and 2 ). A small (13 × 7 mm) APA, with dense CYP11B2 expression, had a new mutation in the intramembranous portion of CADM1 (P1 of Fig. 1a-c , Extended Data Fig. 1  and Supplementary Table  3 ). The only prior association of CADM1 with aldosterone synthesis was as one of the transcripts upregulated in APAs  29. All known somatic mutations and the new mutation in CADM1 were confirmed by Sanger sequencing.
Gly379Asp and 442-amino acid isoform. The shRNA experiment confirmed the reduction in CYP11B2 seen previously on qPCR (Supplementary Fig. 10 , Supplementary Table  6b  and Supplementary Note). Gene-annotation enrichment analysis and functional annotation clustering, using the Database for Annotation, Visualization and Integrated Discovery (DAVID), revealed biological rhythms as the most significant process, together with cell junction and synapse (Supplementary Table  7 ). A plot of expression change for each of the 14 biological rhythms genes showed that RORE and E-box genes formed two clusters with, respectively, up-and downregulated transcription (Fig. 6c ). In most instances, small changes in cells transduced by WT CADM1 were in the opposite direction to mutant. Six of the 14 genes have significant diurnal patterns in mouse adrenal, with the peak for RORE genes out of phase with E-box genes (Supplementary Fig.
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Incubation with primary antibodies (listed in Supplementary Table  11 ) was performed at room temperature for 30 min. Slides were also counterstained with Hematoxylin 2 (Thermo Fisher Scientific). The slides were mounted using DPX mounting medium (Merck Milipore) and images were captured using 3DHISTECH Pannaromic MIDI scanner (Software version 1. 18). For IHC scoring, a minimum of three ×20 images in selected areas of the scans that showed recognizable zonation of the adrenal cortex were captured using CaseViewer (Software version 2. 4). All scoring of IHC scans were performed by two histopathologists independently after standardization using images captured from eight adrenals with 0 representing no staining, +1 representing weak staining, +2 representing intermediate staining and +3 representing intense staining (representative images of scores are shown in Supplementary Fig.
1 M pH 7. 4, 10% horse serum and 0. 5% SDS for 60 min (CYP11B1). The slides were incubated with primary antibody (listed in Supplementary Table  11 ) for 1 h at room temperature (CADM1 and AQP2) or overnight at 4 °C (CYP11B2 and CYP11B1). Sections were washed, incubated 30 min with affinity-purified goat anti-rabbit antibody (1/400, Vector Laboratories), and then washed and incubated with an avidin-biotin-peroxidase complex (Vectastain ABC Elite; Vector Laboratories) for 30 min. Slides were developed using diaminobenzidine (Vector Laboratories) and counterstained with hematoxylin (Sigma-Aldrich). Images were acquired using a Vectra automated imaging system software v. 3. 0. 5 (PerkinElmer).
Gene delivery using lentivirus Lentivirus particles were generated using HEK293T cells transfected with pMD2, psPAX (both kindly gifted by Didier Trono; Addgene plasmid, 12259 and 12260), and pLOC vectors using polyethylenimine. Virus particles were concentrated using Amicon-15 filter units (Merck). Titration of the lentivirus vectors was achieved via flow cytometric (FACS) method (BD FACSDiva Software v6. 1. 3, BD Biosciences, and Flowing Software v. 2. 5. 1) utilizing the GFP on the pLOC vectors  75. Transduction was performed with 8 μg ml -1 of Polybrene using a multiplicity of infection (MOI) of 3-5 TU per cell.
Quantification of mRNA expression using RT-PCR One microgram of RNA was reverse transcribed to cDNA using either Applied Biosystem's High Capacity RNA-to-cDNA kit or AMV Reverse Transcriptase System (Promega). qPCR was used to quantify the level of mRNA expression of the genes of interest. This was performed using a C1000 Touch Thermal Cycler machine, CFX Manage TM Software v3. 1 (Bio-Rad) or ABI 7900 Real-time PCR system, 7900 SDS v2. 4. 1 (Thermo Fisher Scientific) and the TaqMan Fast Advanced Gene Expression Master Mix (Applied Biosystems, 4369514). The genes of interest were identified using commercially available probes from Thermo Fisher Scientific, with Assay ID listed in Supplementary Table  16. Results were analyzed using the 2 -ΔΔCT method, using the housekeeping gene 18S rRNA or β-actin for normalization, unless otherwise specified.
The most stable structure of each isoform was joined to the 3D structure of Ig-like domains (residues, K50-T345), which was obtained from ModBase  83  (model ID, 7f7df202e8d6aa361f9ca3847afe4608) by using the PyMOL Moledular Graphics System software (version 2. 1, Schrödinger). The secondary structure of each isoform was analyzed by DSSP  84. The distances between the most separated atoms in the stalk regions were measured by PyMOL. The tilt angle of the TM helix of WT, G379D and V380D with respect to the bilayer was analyzed by hydrophobic mismatch  85. The length of the shortest TM helix (V380D) was used as thickness of the lipid bilayer. The tilt angle of the TM helix, θ, was given by the following formula: θ = sin -1 ( h V380D h ) where h is the length of the TM helix of CADM1. After the confirmation of validity in these CADM1 structures based on the trajectories and 3D quality under the Ramachandran plot, to determine the model structure of cis-homodimer for the 442-and 453-amino acid isoforms of CADM1, docking simulation was performed by using ZDOCK v.
3. 0. 2 software  86. Tetramer models were simulated and analyzed using the same procedure as for dimer modeling. Two thousand docking runs were performed under the definition of Ig-like V-type domain (residues Q45-T139) as a docking site. The docking poses of trans-tetramer for isoform 1 and trans-tetramer for isoform 4 were classified into six groups, respectively. The complex with the highest ZDOCK score was selected for each binding model. Intercell membrane distance deduced by trans-homophilic binding of WT, G379D and V380D were calculated by d = l × sin θ, where l is the length of extracellular domain of CADM1 trans-tetramer and θ is the angle of the ectodomain to the cell membrane defined by equation 1. Statistical analysis was performed using R software. Differences in intercell membrane distances were evaluated using one-way ANOVA with Tukey's post hoc test.