dataesr/scientific-paragraphs-categorization huggingface dataset:
The uncertainty for EDGARv6. 0 is the same as that of v5. 0 and calculated for 2015 as min/max values for the total and each sector (Solazzo et al. , 2021)  and represents the 95 % confidence interval of a lognormal distribution. CAPRI reports uncertainties for the last 3 years as follows: 2014 and 2016 (17. 6 %) and 2018 (17. 8 %). The mean column represents the common overlapping period of 1990-2018 between datasets. The last years of the time series of the respective datasets are 2019 (UNFCCC and FAOSTAT), 2018 (EDGARv6. 0, CAPRI), 2015 (GAINS every 5 years), 2015-2019 (DayCent) and 2020 ECOSSE, with the last 2 years reporting only crop emissions.
8 and 9 in Sect. 3. 1. 5;AMRPmade Figs. 8 and 9; AT, PR, RL and CQ provided input to the review answers; PIP, OT, RLT, CQ, GE, MK, LHI, PR, RL, DBa and WW provided in-depth advice and commented/edited the initial versions of the manuscript; all remaining co-authors provided data and commented on specific parts of the text related to their datasets.
4 and N 2 O anthropogenic and natural emissions from other bottom-up estimates Data from five global datasets and models of CH 4 and N 2 O anthropogenic emission inventories were used, namely CAPRI, DayCent, ECOSSE, FAOSTAT, GAINS and EDGARv6. 0 (Table 3). These estimates are not completely independent from NGHGIs (see Fig. 4 in Petrescu et al. , 2020) as they integrate their own sectorial modeling with the UNFCCC data (e. g. , common activity data and IPCC emis- sion factors) when no other source of information is avail- able. The CH 4 biomass and biofuel burning emissions are included in NGHGI under the UNFCCC LULUCF sector, although they are identified as a separate category by the Global Carbon Project CH 4 budget synthesis (Saunois et al. , 2020). For both CH 4 and N 2 O, CAPRI (Britz and Witzke, 2014; Weiss and Leip, 2012) and FAOSTAT (FAO, 2022) re- port only agricultural emissions.
(2021) datasets version Frame- inversions inversions work (CIF): 2006-2017 2005-2018 CHIMERE, FLEX- PARTv10. 4 (NILU) and FLEXPART (EMPA) (only CH 4 ) Global inversions from the Global Carbon Project CH 4 and N 2 O budgets (Saunois et al. , 2020; Tian et al. , 2020) GCP-CH 4 22 models for LSCE and Saunois et al. (2020) and model-specific references in Ap- Not up- 2019 an- CH 4 inver- GCP-CH 4 pendix B, Table B4 dated thropogenic sions, both contributors partition from SURF and inversions GOSAT 2000-2017 GCP-CH 4 22 models with LSCE Saunois et al. (2020) and model-specific references in Ap- Not up- 2019 optimized wet- pendix B, Table B3 dated natural par- land CH 4 emis- tition from sions inversions 2000-2017 GN 2 OB 2019 Inverse N 2 O GN 2 OB 2019 Thompson et al. (2019) Partially emissions from and contribu- Tian et al.
7; AT provided the data for Figs. 8 and 9 in Sect. 3. 1. 5;AMRPmade Figs. 8 and 9; AT, PR, RL and CQ provided input to the review answers; PIP, OT, RLT, CQ, GE, MK, LHI, PR, RL, DBa and WW provided in-depth advice and commented/edited the initial versions of the manuscript; all remaining co-authors provided data and commented on specific parts of the text related to their datasets. Project Model Prior Wetlands Geological Fire Termites Soil sink Ocean/lakes Wild animals GCP TOMCAT- JULES emis- TOMCAT 2006 GFEDv4 Matthews and Patra et TOMCAT SURF_ECMWF sions from (McNorton et Fung (1987) al. (2011) (2006), McNorton et al. , 2016) Matthews al. (2016) and Fung (1987) -all emission total rescaled to Schwietzke et al. (2016) GCP NTFVAR- VISIT (Ito and Etiope and GFASv1. 2 Ito and Inatomi VISIT (Ito and TransCom- GOSAT_NIES Inatomi, 2012) Milkov (2004) (2012) Inatomi, 2012) CH 4 GCP NTFVAR-SURF_NIES VISIT (Ito and Etiope and GFASv1.
In general, the lamppost geometry should be interpreted as a jet base, possibly a standing shock  40  which determines the corona position, but with material flowing through the standing shock. So the system should be characterized by two independent parameters: its position and bulk velocity. The corona position and its evolution with time could be constrained by the timing analysis  28. In addition to that, the bulk velocity and its evolution with time can be constrained by the spectral analysis. In order to investigate the effect of the bulk motion of the corona on the reflection fraction, we calculate the reflection fraction by applying the package relxilllpionCp of relxill(v1. 4. 0), with the X-ray point source being located at height H above a BH and outflowing with the velocity β = υ/c.
In the current reflection model relxillCp which is used in this work, the constant density is fixed at constant value n e = 10 15 cm -3 , which is much lower than the typical values in the standard thin disk model  48  , n e ≥ 10 19 cm -3. Along with the decay of the outburst studied in this work, the radiation pressure in the inner disk should decrease and thus the gas density should increase  46, 49. Therefore, the spectral fits with the much lower and constant density in the reflection model should lead to artificially supersolar and also increasing iron abundance along with the outburst decay, consistent with the spectral fitting results in this work. Deriving the physical values and evolution of the iron abundance and disk density with the spectral fitting, however, requires new atomic data in highdensity to be implemented in the reflection model, which is beyond the scope of this work.
10 in ref. 39  ); on the other hand, the Compton amplification factor can increase due to the increase of the outflowing velocity β, leading to the decrease of the photon index (see Supplementary Fig. 7b ). In the decay of MAXI J1820 + 070, if the effect of the geometrical factor dominates over that of the outflowing velocity, the resultant photon index will increase, as found in our spectral fitting. On the effect of system parameters. In our spectral fits, the BH spin is fixed at a = 0. 998. However, it is also possible that the BH spin is low, if the accretion disk extends to the innermost stable circular orbit (ISCO), i. e. , R in = R ISCO , provided that the inner radius R ISCO is fitted to be around 5 R g over the decay  33. Spectral fits to NICER data of MAXI J1820 + 070 in the soft state suggests a low spin a < 0.
It turns that, the spectral fits with the standard relativistic reflection model relxillCp also prefer to large values of the inclination, and the evolution of the corona remains (see Supplementary Fig. 8b  and c ), i. e. , the reflection fraction is also small than unity and decreases during the decay phase. Justification of the outflowing corona. Recently, the outflowing velocity of the lamppost X-ray primary source is taken into account in the package relxilllpionCp of relxill(v1. 4. 0). In principle, we could refit the Insight-HXMT data to directly infer the outflowing velocity and its evolution over time. We found that the outflowing velocity and the height cannot be uniquely determined in the spectral fits. Therefore, deriving the evolution of the outflowing velocity may require that the height of the corona can be estimated from other measurements.
5c. In contrast, at the peak of the outburst, e. g. , MJD = 58204 (obsID = P0114661006), the fitted reflection fraction is R f = 1. 2, and the corona prefers to be stationary, using relxilllpionCp model. Therefore, although it is not easy to uniquely determine the height and the outflowing velocity, the resultant reflection fraction decreases during the decay, which is consistent with our results in the spectral fits of Insight-HXMT using relxillCp. Here, we also directly fit the reflection fraction from the spectral fits to NuSTAR data with the relxillCp + xillverCp model, as is done for Insight-HXMT in this work. The inclination angle is fixed at constant large value θ = 63 ∘. The two diskbb components are used to account for the differences between FPMs due to a thermal blanket tear in FPMA  54.
Methods Data reduction. Insight-HXMT consists of three groups of instruments: High-Energy X-ray Telescope (HE, 20-250 keV), Medium Energy X-ray Telescope (ME, 5-30 keV), and Low Energy X-ray Telescope (LE, 1-15 keV). HE contains 18 cylindrical NaI(Tl)/CsI(Na) phoswich detectors with a total detection area of 5000 cm 2 ; ME is composed of 1728 Si-PIN detectors with a total detection area of 952 cm 2 ; and LE uses Swept Charge Device (SCD) with a total detection area of 384 cm 2. There are three types of Field of View (FoV): 1 ∘ × 6 ∘ (FWHM, full-width half maximum) (also called the small FoV), 6 ∘ × 6 ∘ (large FoV), and the blind FoV used to estimate the particle-induced instrumental background (see ref. 55  or The Insight-HXMT Data Reduction Guide for details). Since its launch, Insight-HXMT went through a series of performance verification tests by observing the blank sky, standard sources, and sources of interest.
According to these calibration results, the energy bands for the spectral fits in this work are 2-10 keV for LE, 10-30 keV for ME, and 28-200 keV for HE, with a systematic error of 1. 5% for LE/ME/HE. We use the Insight-HXMT Data Analysis software (HXMTDAS) v2. 0 to analyze all the data, filtering the data with the following criteria: (1) pointing offset angle < 0. 1 ∘ ; (2) pointing direction above Earth > 10 ∘ ; (3) geomagnetic cut-off rigidity value > 8; (4) time since SAA passage > 300 s and time to next SAA passage > 300 s; (5) for LE observations, pointing direction above bright Earth > 30 ∘. We only select events that belong to the small FoV for LE and ME observations, and for HE we use the events from both small and large FoV. To improve the signal-to-noise ratio of HE observation, we combine 17 spectra obtained from 15 small FoV and 2 large FoV together using addascaspec script in HEAsoft v6.
Acknowledgements BY thanks  J. García  and  T. Dauser  for helping with the reflection model relxill, and thanks  Z. Yan ,  X. G. Zheng , and  S. X. Tian  for the discussion of the spectral fitting. This work is supported by the  National Program on Key Research and Development Project  (Grants No. 2016YFA0400803 ) and the  NSFC  ( 11903024 ,  U1931203 ,  11622326 ,  U1838103 ,  U1838201 , and  U1838202 ). This work made use of the data from the Insight-HXMT mission, a project funded by the  China National Space Administration (CNSA)  and the  Chinese Academy of Sciences (CAS).
Code availability The data reduction is done by the use of the software (HXMTDAS) v2. 0 which is available at the Insight-HXMT website (http://en. hxmt. cn/analysis. jhtml). The addascaspec script in HEAsoft v6. 8 is available: https://heasarc. gsfc. nasa. gov/docs/ suzaku/analysis/addascaspec67. html. The model fitting of spectra and lag-energy spectra was completed with XSPEC  56  , which is available at the HEASARC website (https:// heasarc. gsfc. nasa. gov/xanadu/xspec/). The spectral models diskbb  34  and tbabs  35  are used. The reflection model relxill (v1. 4. 0) 32 used to fit the spectrum data is available: http://www. sternwarte. uni-erlangen. de/~dauser/research/relxill/.
After that, PRC1-Hinge-Fc was produced and purified, leading to the determination of its binding kinetic rate constants, its binding specificity, and its effect on FSHR signalling. The data demonstrate that PRC1-Hinge-Fc binds specifically to human FSHR with a KD of 49. 9 nM. No cross-reactivity was found with the other glycoprotein hormone receptor family members LHCGR and TSHR. Unfortunately, no cross-reactivity with the mouse FSHR was measured, hampering in vivo studies in rodents. Transgenic mice expressing the human FSHR in the FSHR knockout background could help alleviate this important limitation if available in the future. Interestingly, we found that PRC1-Hinge-Fc indifferently binds to the native FSHR without FSH (flow cytometry) and to the FSHR ECD/FSH complex (BLI). We also observed that PRC1-Hinge-Fc modulates the Gs/cAMP pathway only in the presence of FSH and that neither PRC1 nor PRC1-Hinge-Fc interferes with FSH binding to FSHR (NanoBiT, Promega, Madison, WI, USA).
NGS Analysis by Germinal Lineage NGS sequences were processed using a homemade pipeline written in SnakeMake (unpublished) which carried out the trimming of sequence extremities, their fusion, their translation, and their annotations (i. e. , FRs and CDRs). The reading frame was determined in the fused sequences by a protein label conserved in the framework 1 (FR1). Sequences were then translated from this frame. After the translation of the sequences, the complementarity-determining regions of the VHHs (CDR) were extracted and concatenated to create the VHH "signature". The whole CDR sequences were processed with the program seqcollapse (https://forgemia. inra. fr/benoit. piegu/seqcollapse (accessed on 3 July 2023)) to obtain a set of non-redundant sequences and a file describing, for each non-redundant sequence, the number of sequences for each round of selection.
The program seq2kmermat (https://forgemia. inra. fr/benoit. piegu/seq2kmermat (accessed on 3 July 2023) was used to encode the sequences from the set of non-redundant sequences as vectors of kmer. From the resulting matrix, a Uniform Manifold Approximate and Projection (UMAP) 36 was created using the R package uwot 37. The different representations of UMAP projection were created using the following parameters: kmer size-5, number of neighbours-15, number of components-2, metric-Euclidean.
Two antibodies having similarity higher than 50 have a 0. 95 probability of sharing the same epitope; the probability is 0. 75 for a score between 30 and 50. For each pair of VHHs, the incoherence between the similarity and epitope predictions is computed as the difference between the similarity (normalised by min and max values) minus the epitope overlap. Consequently, a value of 1 corresponds to a pair of antibodies having a similarity of 1 (identical) and an overlap of 0 (no overlap of predicted epitopes). We used the FSHR ECD (isolated from the FSHR structure, PDB ID 8I2H  [1] ) as the antigen for those analyses.
Funding: This research was funded with the support of the  Institut National de la Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE) , of the  MAbImprove Labex  ( ANR-10-LABX-53 ), of  Région Centre Val de Loire   ARD2020 Biomédicaments GPCRAb  and  SELMAT grants ,  APR-IR INTACT grant , of the  Bill & Melinda Gates Foundation CONTRABODY  grant, and of the  ANR-22-CE14-0050 MOSDER  grant. F. J-A and L. C. are grateful to  Le Studium Loire Valley Institute for Advanced Studies, Orléans and Tours, France , supported through the  ARD CVL Biopharmaceuticals program , funded by the  Centre-Val de Loire region.
The state of the atmosphere, or of the ocean, cannot be exhaustively observed. Crucial parts might remain out of reach of proper monitoring. Also, defining the exact set of equations driving the atmosphere and ocean is virtually impossible because of their complexity. The goal of this paper is to obtain predictions of a partially observed dynamical system without knowing the model equations. In this data-driven context, the article focuses on the Lorenz-63 system, where only the second and third components are observed and access to the equations is not allowed. To account for those strong constraints, a combination of machine learning and data assimilation techniques is proposed. The key aspects are the following: the introduction of latent variables, a linear approximation of the dynamics and a database that is updated iteratively, maximizing the likelihood.
As the M operator is not time-dependent, the predictions are quite similar, close to the persistence. Then, adding one (green) or two (red) hidden components in the M operators creates some nonlinearities in the forecasts. In Fig. 5 , the predictions are evaluated over the whole test dataset for different lead times. By introducing hidden components, the RMSE decreases for both x 2 and x 3 components (panels a and b). For instance, for a lead time of 0. 05, when considering two hidden components, the RMSE is halved when it is compared to the naive linear model without hidden components. The coverage probability metric is also largely improved (panels c and d). Indeed, the results with two hidden components are close to 50 %, the optimal value. E[x t+1 |y 1 ,. , y t ] = ME[x t |y 1 ,. , y t ], (7a) To evaluate where the linear model with [x 2 , x 3 , z 1 , z 2 ] performs better than the one with [x 2 , x 3 ], the Euclidean distances between the forecasts (for a lead time of 0.
1) and the truth are computed. Those errors are evaluated at each time step of the test dataset, in the (x 2 , x 3 ) space. Based on those errors, Fig. 6  shows the relative improvement between the model without and the model with hidden components. When the two models have similar performance, values are close to 0 (white), and when the model including z 1 and z 2 is better, values are close to 1 (red). Figure  6  clearly shows that error reduction is not homogeneous in the attractor. The improvement is moderate on the outside of the wings of the attractor but important in the wing transition. This suggests that the introduction of the hidden components z 1 and z 2 makes it possible to provide information on the position in the attractor and thus to make better predictions, especially in bifurcation regions.
These predictions are obtained using sequential statistical forecasts, as explained in Eq. (  7 ), on an independent test dataset. resent a single missing one. The reason for this mismatch is two-fold and is mainly the linear approximation of the dynamical system, which implies that (1) the true missing component, which does not have to be linear combinations of the observed variables, is impossible to retrieve in our framework and (2) two variables, using combinations of the time derivatives of the observed variables, are needed to accurately represent the complexity of the dynamics. However, it is important to note that, even if two variables are needed to replace a single one, the dynamical evolution of the system is relatively well captured, for short lead times, with our methodology. This correct representation of the evolution might ultimately be the most important (e.
e. , a fixed or adaptive linear surrogate model). We also plan to compare them to nonlinear surrogate models, based on neural network architectures with latent information encoded in an augmented space or in hidden layers (e. g. , long short-term memory -LSTM). In this paper, we have demonstrated the feasibility of the method on an idealized and comprehensive problem using the Lorenz-63 system. In the future, we plan to apply the methodology to more challenging problems, like the Lorenz-96 system or a quasi-geostrophic model. For application to real data, we plan to use a database of observed climate indices and try to find latent variables that help to make datadriven predictions.
Financial support. This work was supported by  ISblue  project,  Interdisciplinary graduate school  for the blue planet ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir" embedded in France 2030. This work was also supported by  LEFE program  ( LEFE IMAGO projects ARVOR ). Review statement. This paper was edited by  Natale Alberto Carrassi  and reviewed by two anonymous referees.
Aphids cause severe damage to agricultural crops, resulting in significant economic losses, and an increased use of pesticides with decreased efficiency. Monitoring aphid infestations through regular field surveys is time-consuming and does not always provide an accurate spatiotemporal representation of the distribution of pests. Therefore, an automated, non-destructive method to detect and evaluate aphid infestation would be beneficial for targeted treatments. In this study, we present a machine learning model to identify and quantify aphids, localizing their spatial distribution over leaves, using a One-Class Support Vector Machine and Laplacian of Gaussians blob detection. To train this model, we built the first large database of aphids' hyperspectral images, which were captured in a controlled laboratory environment.
This database contains more than 160 images of three aphid lines, distinctive in color, shape, and developmental stages, and are displayed laying on leaves or neutral backgrounds. This system exhibits high-quality validation scores, with a Precision of 0. 97, a Recall of 0. 91, an F 1 score of 0. 94, and an AUPR score of 0. 98. Moreover, when assessing this method on new and challenging images, we did not observe any false negatives (and only a few false positives). Our results suggest that a machine learning model of this caliber could be a promising tool to detect aphids for targeted treatments in the field.
Moreover, depending on their output, the sensors have been classified as imaging if they output the spectra for each pixel of the captured image, or non-imaging if they only output the average reflectance over an area  [10]. Finally, optical datasets can be acquired at different spatial scales by satellite-borne, unmanned airborne vehicles (UAVs) and handheld sensors  [11, 12]. Depending on their distance from the target, these sensors can be classified as large-scale distal devices or proximal devices. Large-scale remote sensing often uses non-imaging multi-and hyperspectral sensors to rapidly analyze large plots of land. These sensors detect major crop symptoms and, thus, can indirectly detect the presence of pests. There are currently only a few studies that explore the use of imaging methods for mapping infested areas with low-resolution  [13].
The conclusions highlight the limitations of using a selection of specific wavelengths for detecting pest infestation in complex environments. However, no previous work has addressed the localization and counting of aphids on hyperspectral images. The aim of this study is to introduce an automated, non-destructive machine learningbased method to detect, localize, and quantify aphid infestation on hyperspectral images. To train the machine learning model, we built a large aphid HSI database since no such dataset was readily available. The database developed in this work contains over 160 images captured in a controlled environment. These images feature three distinct aphid lines that vary in color, shape, and developmental stage and are shown either on leaves or on white backgrounds. Previous works have mainly framed the pest detection problem as a binary or multi-class classification task  [36] [37] [38].
Nevertheless, this formalism requires representative examples for all classes in the training dataset. Consequently, when binary or multi-class classifiers are applied to datasets suffering from ill-defined classes (such as under-sampled, absent, or imbalanced), they tend to misclassify testing samples and exhibit poor generalization capabilities. The aforementioned database contains representative examples of the positive class (i. e. , aphids), but the background images captured under controlled conditions are not likely to be representative of negative examples (i. e. , not aphids) in real-world conditions. Therefore, in this study, the aphid detection task was framed as a one-class classification problem, which aims to learn how to identify aphids from positive examples only. One-class classification is an important research domain in the fields of data mining and pattern recognition.
Insects and Plants The aphids used in this study are from parthenogenetic females from field-originated lines. The three aphid lines have different colors and sizes. This diversity provides a broad spectral database to develop an automated aphid detection program (Figure  1 ). Acyrthosiphon pisum lines LL01 (green) and YR2 (pink) were maintained on young broad bean plants (Vicia faba, L. cv. Aquadulce) and Rhopalosiphum padi line LR01 (black, two to three times smaller than A. pisum) on wheat seedlings (Triticum aestivum [L. ] cv. Orvantis). Aphids and their host plants have been reared in controlled conditions in a phytotron at 20 • C ± 1 • C and 60% RH ± 5% with a 16 h photoperiod. Organic seeds of V. faba and T. aestivum were supplied by Graines-Voltz®(Loire-Authion, France) and Nature et Progrès®(Alès, France), respectively.