dataesr/scientific-paragraphs-categorization huggingface dataset:
None of the enzymes cleave sites that are fully methylated at the external cytosine or hemi-or fully methylated internal and external cytosines. Conversely, both enzymes can digest unmethylated '5-CCGG-3' sequences. These enzymes are combined with EcoRI, which is marginally affected by cytosine methylation. EcoRI/HpaII and EcoRI/MspI DNA digests are ligated to specific adapters and ligated fragments undergo rounds of preselective and selective PCR amplification. Selective amplification with specific fluorescently labelled primers produces PCR fragments that are resolved on capillary electrophoresis detection systems. Raw data matrix of presence and absence of fragments are translated into a binary character matrix (0, absence; 1, presence). The binary information of each fragment is associated with its methylation status  (Schulz et al.
Nonetheless, some steps of each protocol are modified and report different NGS data analyses through specific bioinformatic tools. Barańek et al. (2016)  first used a standard MSAP analysis followed by deep amplicon sequencing with NGS technology. Sequence quality was verified, and low-quality reads were bioinformatically excluded. After adaptor trimming, contigs were assembled using Geneious 8 software and compared by blasting. Chwialkowska et al. (2017)  introduced the MSAP-Seq method, which employs an automatic pipeline called MSEQER. After purifying and fragmenting amplicons by sonication, short tags are created for easy library preparation and high-throughput sequencing. Dedicated MSEQER software is used for automated MSAP-Seq data analysis, including mapping preprocessed reads to an appropriate reference genome for identifying specific genomic sequences.
using PCR-based methods. Depending on the priority of adapter ligation and indexing, library preparation methods are categorized as pre-bisulfite or post-bisulfite. Several WGBS library comparisons have been conducted based on datasets, protocols, quantification, and interpretation of methylation data  (Zhou et al. , 2019; Han et al. , 2022). Bisulfite conversion causes DNA degradation of up to 90% of the DNA input  (Grunau et al. , 2001; Holmes et al. , 2014). To reduce noise (bias) in WGBS libraries caused by bisulfite conversion,  Olova et al. (2018)  recommend filtering reads with three or more consecutive unconverted CH cytosines, even in datasets with high overall conversion rates. Researchers can also work with amplification-free libraries to avoid amplification-related bias  (McInroy et al.
, 2016). Examples for library preparation methods with pre-bisulfite strategy are alkaline, heat, KAPA (heat and alkaline), and Am-BS; while methods with post-bisulfite strategy are PBAT (heat), ampPBAT (heat and alkaline), and EpiGnome (heat)  (Olova et al. , 2018). Processing and analyzing WGBS datasets is computationally demanding, requiring significant memory and storage resources. Figure  3  summarizes the pipelines and components for evaluating WGBS datasets. In a recent benchmark study with 14 alignment algorithms for WGBS in mammals,  Gong et al. (2022)  documented that Bwa-meth, BSBolt, BSMAP, Bismark-bwt2-e2e and Walt exhibited higher uniquely mapped reads, mapped precision, recall, and F1 score than others. However, performance statistics for mammalian genomes may not be directly applicable to crop genomes, which are typically more complex.
MC-seq offers several advantages over other sequencing methods, including lower DNA input requirements, cost-and time-effectiveness for large sampling or large genome size organisms, smaller datasets that demand less computational resources for storage and analysis, flexible capture size ranging from kb to Mb, scalability to handle multiple samples/sequencing runs, and the ability to capture specific regions without requiring a high-quality whole reference genome (e. g. with exon capture). In contrast to RRBS, the analyzed regions by MC-seq are not limited to the presence of the restriction site(s). Finally, bioinformatic analysis can be done with adapted WGBS pipelines or with dedicated pipelines  (Vial-Pradel et al. , 2019). MC-seq approach is also limited by some points: the use of bisulfite introduces the same bias as the WGBS method; it requires careful selection of targeted regions (one possibility is to use WGBS data on a limited number of samples before MC-seq) and their bioinformatic analysis for the design of specific probes (if not publicly available that is mostly not the case for plant species), and off-target capture due to homologous genomic sequences in plant genomes (duplicated genomic regions or repeated sequences).
, 2020). ONT sequencing allows direct identification of DNA base modifications at single nucleotide resolution, including 5mC, 5hmC and 6mA (Figure  4 ). Liu et al. (2021)   A single SMRTbell per ZMW is amplified using fluorescent-labelled nucleotides while their emission spectra are collected. The kinetic data is analyzed using bioinformatics to decipher the positions of the methylated cytosines.
evaluated the performance of seven publicly available computational tools for methylation-calling using human Oxford Nanopore sequencing data: Nanopolish  (Simpson et al. , 2017) , Megalodon (https:// github. com/nanoporetech/megalodon), DeepSignal  (Ni et al. , 2019) , Guppy (Oxford Nanopore Technologies: Nanopore sequencing data analysis. 2020), Tombo/Nanoraw  (Stoiber et al. , 2017) , DeepMod  (Liu et al. , 2019)  and METEORE  (Yuen et al. , 2021). METEORE is an ensemble model, which provides predictions based on two or more tools. The tools were compared using four benchmark datasets, including two human Blymphocyte cell lines (NA19240 and NA12878), leukemia cell lines K562, and a clinical specimen of acute promyelocytic  (Liu Y. et al. , 2021). For the DNA methylation ground truth, published WGBS and RRBS datasets from ENCODE are used.
g. repetitive areas) and higher prediction of CG islands, compared to Nanopolish and Guppy. A comprehensive comparison of these tools is available at https://nanome. jax. org/. Detecting DNA methylation using PacBio sequencing data presents limited bioinformatic options, but recent developments have expanded the field. PacBio SMRT Link (v11. 0) software now includes a machine learning approach for 5mC detection in CG contexts  (Wenger et al. , 2019) , while  Tse et al. (2021)  developed holistic kinetic model (HK model), a convolutional neural network that correlates highly with BS results. More recently,  Ni et al. (2022)  published ccsmeth, a deep learning method that detects 5mCs in CG contexts from PacBio circular consensus sequence (CCS) subreads with greater accuracy, than the HK model on amplified and M.
Various image analysis software, either from CLSMs or other free and commercial tools (Image J/Fiji and Photoshop), can be used for this purpose. The 5mdC antibodies also enable the analysis of global levels of DNA methylation among various plant samples or conditions by ELISA-like immunoassays. Although most commercially available kits are designed for animal DNA samples, they can still be used to compare DNA methylation in plant samples. Even though the 5mdC nucleotide is not different between plants and animals, the genomic DNA methylation context is very different between those two groups with plants having methylation in CHG and CHH contexts in addition to CG contexts. Nevertheless, commercial kits allow to compare DNA methylation in multiple samples, providing an accurate quantification of 5mdC in several DNA samples simultaneously and in a short period of time.
ACKNOWLEDGMENTS We are grateful to  Benjamin Audurier ,  Cristian Baldenegro ,  Gian Michele Innocenti ,  Dennis Perepelitsa ,  Martin Rybar  and  Ricardo Vázquez  for fruitful exchanges on the experimental feasibility of rapiditydependent jet substructure measurements. We also thank  Carlota Andrés  for guidance on the use of the nPDF error sets. The authors would like to thank the organizers of the XXIXth International Conference on Ultra-relativistic Nucleus-Nucleus Collisions,  Quark Matter 2022 , which took place in Krakow, Poland, where this collaboration started. DP has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No. 754496. A. S. O. 's work was supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
It is thus required to solve a large number of large-scaled problems, which unfortunately cannot be computed with classical tools such as Matlab Software CVX  [18] , in acceptable time limits. Therefore we use our proposed solution  [19] , which enables to obtain the same optimal ISL solution as CVX at a highly reduced computational cost, by directly solving the dual problem. Finally, performance results are provided both from simulated and realistic re-synthesized SAR images. Different evaluation criteria are presented in order to compare the results. This work provides a deeper analysis of our work in  [20]  as it contributes to a better comprehension of the communication codes and their choice as well as a strong comparison on the different elements of the image, in order to provide a global understanding of the performed work.
B. Fast Computation of the Mismatched Filter The solution of the convex optimization problem (5) can be computed using a generic convex solver, such as the CVX: Matlab Software for Disciplined Convex Programming  [18] , as presented in  [15] ,  [16] ,  [35] ,  [36]. However, such a solver is unsuitable for large-scale problems due to high computational costs, as mentioned by its creators  [18]. In the considered joint SAR-communications framework, we must solve many large-scale convex optimization problems, one per transmitted pulse, so the use of CVX tool becomes infeasible. This led us to find a faster algorithm to solve the optimization problem (5)  [19]. The solution we propose consists in solving the dual problem. We show that the optimization of the ISL problem can be calculated analytically in the dual space so that the solution can be obtained via a simple monodimensional gradient descent algorithm.
IV. SAR Image Reconstruction Algorithm In the following, we will turn our attention to the formation of SAR images. In the absence of real CPFSK data acquisitions, in this section, we create re-synthesized SAR images, in order to provide as realistic results as possible in comparison to real data SAR images. More precisely, the ground reflectivity of a real-data SAR image generated with chirp signals is used to re-create synthesized images from communication codes. In the following, the fundamental SAR definition that expresses mathematically the image creation is given, as well as the details of the reconstruction algorithm that generates the re-synthesized SAR images.
B. Evaluation of the Impulse Responses Before creating the re-synthesized SAR images, we provide in this subsection the impulse response of the chirp and the CPFSK codes obtained for a single point reflector. In both cases, note that the algorithm presented in subsection III-B is used to compute all the needed mismatched filters. The computation time gain is enormous, as the filters are computed 250 times faster than using the classical CVX Matlab Toolbox. More precisely, it took roughly 16 days to compute 6522 filters, instead of 11 years of computational time needed when using the CVX algorithm  [19]. Figure  6  provides the impulse responses for a chirp and the four different CPFSK configurations. The first observation that can be made on the impulse responses is that the single CPFSK case presents range sidelobes concentrated in the range direction.
EDMD The main idea behind the EDMD is to estimate a finite-dimensional approximation of the Koopman operator K given a dictionary of functions and a dataset of snapshot pairs. Formally, we start by choosing a dictionary: D M = {ψ 1 , ψ 2 ,. , ψ M } (7) where ψ i ∈ F : L -→ C for i = 1, 2,. , M. We also define the vector valued function Ψ M : L -→ C M×1 : Ψ M (z t ) = [ψ 1 (z t ) ψ 2 (z t ). ψ M (z t )] T. ( 8 ) The span of D M is a subspace of F, and can be written as follows: F DM = Span{ψ 1 , ψ 2 ,. , ψ M } = {a T M Ψ M : a M ∈ C M×1 } ⊂ F. ( 9 ) By definition, we can write any function ϕ ∈ F DM as: ϕ = i =M ∑ i =1 a i ψ i = a T M Ψ M. ( 10 ) If we apply the Koopman operator on the observable ϕ we have: Kϕ = a T M KΨ M = a T M Ψ M • T. ( 11 ) If we assume that the subspace spanned by D M is invariant under the action of the Koopman operator we can write: K(F DM ) = F DM (12) then Kϕ = b T M Ψ M for some b M ∈ C M×1.
Under this assumption, we can define an exact finite-dimensional Koopman operator K M ∈ R M×M that verifies b T M = a T M K M. The action of this finite-dimensional Koopman operator is defined as: Kϕ = a T M KΨ M = a T M K M Ψ M = a T M Ψ M • T. ( 13 ) In practice, the subspace spanned by D M is not invariant to the Koopman operator. It implies that the action of the Koopman operator on some element of F DM will not lie exactly on F DM i. e. Kϕ = b T M Ψ M + r with r ∈ F. This yields to the following relation: Kϕ = a T M KΨ M = a T M K M Ψ M + r = a T M Ψ M • T. ( 14 ) To determine K M , the EDMD minimizes the following cost function given a dataset of snapshot pairs {(u k , v k )} N k=1 where v k = T (u k ): J = 1 2 N ∑ k=1 |r(u k )| 2 = 1 2 N ∑ k=1 |a T (Ψ M (v k ) -K M Ψ M (u k ))| 2 (15) where | • | denotes the Frobenius norm.
EDMD with a latent dictionary 3. 2. 1. Proposed framework Within an LEDMD framework, the data-driven derivation of numerical approximations of the Koopman operator are stated as an EDMD approximation with latent observables. These latent observables are used to account for both i) situations where the observation operator is not an embedding and ii) closure issues related to the choice of the dictionary in EDMD framework. In the LEDMD approach, we do not assume to access a dataset of snapshot pairs {(u k , v k )} N k=1 that satisfy v k = T (u k ). We only assume observations of the state space model (3), that we conveniently put in the following dataset: {(x k , x k+1 )} N k=1. Similarly to the EDMD, we start by choosing a dictionary D M (7). This dictionary operates on measurements of the state space (3).
( 28 ) Since the subspace spanned by F D d E is not invariant to the Koopman operator, the approximate Koopman operator K dE includes a residual term r as follows: Kϕ M = a T M GKΨ dE = a T M GΨ dE • T = a T M GK dE Ψ dE + r. ( 29 ) Given a dataset of snapshot pairs, {(x k , x k+1 )} N k=1 the LEDMD considers the following cost function: J LEDMD = 1 2 N ∑ k=1 |r(u k )| 2 = 1 2 N ∑ k=1 |a T (GΨ dE ((z k+1 )) -GK dE Ψ dE (z k ))| 2 = 1 2 N ∑ k=1 |a T (Ψ M (H(z k+1 )) -GK dE Ψ dE (z k ))| 2 = 1 2 N ∑ k=1 |a T (Ψ M (x k+1 )) -GK dE Ψ dE (z k ))| 2. ( 30 ) If D W and z k are known, the LEDMD framework falls back into the standard EDMD problem described above. The solution of the minimization of J LEDMD can then be defined similarly to (15) using the pseudoinverse. However, since D W and z k are not known, we propose to minimize J LEDMD with respect to both K dE and {Ψ W (z k )} k=N k=1 : arg min K d E ,{ΨW(z k )} k=N k=1 J LEDMD.
Acknowledgment This work was supported by the  ERC   Synergy  Project  856408-STUOD ,  Labex Cominlabs  (Grant SEACS),  CNES  (Grant OSTST-MANATEE),  Microsoft  ( AI EU Ocean awards ),  ANR Projects Melody and OceaniX. It benefited from  HPC  and GPU resources from  Azure (Microsoft EU Ocean awards ) and  GENCI-IDRIS  (Grant  2020-101030 ).
The method consisted of seeking possible irrigation events (dates, amounts) between two cloud-free Sentinel-2 (S2) optical images and detecting the most probable of these events, responsible for the least difference between the predicted and observed, S2-derived LAI values (LAI S2 ). The approach was first tested with synthetic noisy values to encompass the effects of errors on the observed and modeled LAI values, and these of increased duration between available observations (cases ΔS2 = 5, 10,and 15 days), promoting the possibility to use daily-interpolated LAI values as a starting point for the inverse problem (ΔS2 is fixed to 10 and 15 days then values are interpolated and recorded on a 5 days basis, cases ΔS2 = 5mod 10 and 5mod 15 days, respectively). From the synthetic dataset, irrigation dates detection results showed that the best performance is obtained for ΔS2 = 5 days or when using daily interpolated LAI values when ΔS2 = 5mod 10 or 5mod 15 days with an Fscore near 85%.
, 2008; Mailhol et al. , 2018; Silvestro et al. , 2017; Varella et al. , 2012). For instance,  Mailhol et al. (2011)  conducted a study over irrigated maize plots to analyze the potential of the PILOTE model (the agronomic and the hydrological core of the Optirrig) for crop development and yield prediction. The model allowed satisfactory simulations and prediction of the soil water balance and LAI along the cropping cycle. In fact, PILOTE simulates soil water balance and crop yield at a daily time step by the association of a soil module and a crop module that is based on the LAI simulation and its response to water stress and soil available water. In addition,  Varella et al. (2012)  applied a global sensitivity analysis on the input parameters of the STICS model in order to determine the importance of different soil parameters.
Optirrig model description Optirrig is a crop growth and irrigation model composed of an inner module in charge of the hydrological and agronomical calculations, and an outer module that deals with the generation, analysis an,d optimization of irrigation scenarios. Optirrig takes most of its agronomic part from adaptations of the former Pilote model  (Mailhol et al. , 2011 (Mailhol et al. , , 1997) )  developed within the same research team. Optirrig simulates crop growth and yield as a function of water and nutrients availability and consumption, focusing on the identification of relevant irrigation and fertilization practices and decision rules (see  Cheviron et al. , 2016) , mainly over cereals and horticulture. The model version used here is the one devoted to field crops, whose conceptual scheme appears in Fig.
3. No nitrogen stress is assumed to occur since fertilization was sufficient on the study sites: the nitrogen cycle part of the model is therefore not shown. In its inner hydro-agronomic module, Optirrig performs a daily water balance that encompasses infiltration and drainage, evaporation and transpiration, and changes in soil water reserves (R 1 , R 2 , R 3 ) in the surface, root-zone, and deep reservoirs, respectively. Optirrig requires four daily climatic forcings: precipitations (P), mean air temperature (T), potential evapotranspiration (ET 0 ), and total global solar radiation (Rg). In addition, irrigation (I) could be required for Optirrig as a management forcing parameter. Crop growth is followed through the dynamics of the Leaf Area Index (LAI) which is predicted from thermal time (TT) and possibly hampered by water stresses (S w ), illustrated in Fig.
Sentinel-2 data Numerous cloud-free optical images of Sentinel-2 (S2) were available throughout the cropping seasons of 2017, 2019, and 2020: 15 images on the M1 plot, 21 on the M2 plot, and 17 on the M3 plot, yielding as many "observed" LAI values. The images were downloaded for each study site via the Theia website (https://www. theia-land. fr/) which provides S2 images corrected for atmospheric effects through the Level-2A operational processor that uses algorithms of scene classification and atmospheric correction, described by  Hagolle et al. (2018). The LAI values obtained from S2 (LAI S2 ) were derived using the builtin Biophysical processor, also called Sentinel-2 Level 2 Prototype Processor (SL2P) within the Sentinel Application Platform (SNAP). The Biophysical processor behind the SL2P is the Sentinel-2 Toolbox (S2Tbx), developed by  Weiss and Baret (2016).
Irrigation retrieval approach Our approach aims to retrieve irrigation timing and amounts at the plot scale based on the integration of LAI data derived from Sentinel-2 (LAI S2 ) into Optirrig through an inversion technique. Thus, irrigation events were estimated by comparing LAI S2 to the LAI simulated by Optirrig through the injection of several combinations of irrigation timings and amounts (irrigation combination) applied between two eventual Sentinel-2 images. The irrigation combination that minimizes the difference between the simulated LAI (Optirrig) and the observed LAI (derived from S2) is considered the most probable. In order to assess the impact of the frequency of S2 observations and the window length between two consecutive S2 images (ΔS2 in days) and the errors on the LAI S2 , synthetic experiments were designed and conducted.
Synthetic S2 derived LAI data In order to convey more realistic values and better approximates to the real Sentinel-2 LAI data, an error corresponding to the accuracy of the LAI observations was added to each simulated LAI (i) value, at the t (i) date. This error varies with the value of the LAI obtained from S2 data at the same date and is estimated as follows  (Weiss and Baret, 2016) : αLAI (i) = max(σ 0 , LAI (i) × σ R ) (1) where αLAI (i) is the absolute error on LAI, σ 0 = 0. 3 and σ R = 0. 2. Accordingly, for LAI (i) at each date of S2 acquisition t (i) , 10,000 noise samples (NS) were randomly selected from the zero-mean Gaussian noise distribution with a standard deviation of ± αLAI (i). To filter the outliers of NS at each date, the random draws located at more than two deviations on either side of the median value have been excluded.
Those LAI values were considered as S2 measured values in our methodology likewise cloudfree S2 images were available every 5 days. Therefore, our inversion approach was tested for five cases: 1. Case I. ΔS2 = 5 days  ̅̅→ difference between LAI (j,k) and LAI (i) , for ΔS2 days between two consecutive S2 images obtained at t (i-1) and t (i). 2. Case II. ΔS2 = 10 days 3. Case III. ΔS2 = 15 days 4. Case IV. ΔS2 = 5mod 10 days where the synthetic window length is fixed to 10 days then LAI values were interpolated in order to record LAI values on a 5 days basis 5. Case V. ΔS2 = 5mod 15 days where the synthetic window length is fixed to 15 days then LAI values were interpolated in order to record LAI values on a 5 days basis After testing our methodology with synthetic LAI data and simulated reference irrigation dates (synthetic experiments), results are validated using real LAI measured S2 values and real irrigation dates (d Re ).
Effect of ΔS2 on the detection of irrigation dates and amounts Fig. 11  summarizes the obtained accuracy metrics for the assessment of the irrigation date detection using our inversion approach, for all three experimental plots (M1,M2 and M3) and all five ΔS2 cases (5, 10, 15, 5mod 10 and 5mod 15 ). It represents (in %) the recall (Re) (Fig. 10a ), precision (Pn) (Fig. 11b ) and Fscore (Fig. 11c ) at each plot and tested ΔS2. A detected event (d D ) is considered an irrigation event (TP) if it is detected within an acceptable difference (|Δt| in days) with the reference irrigation date (d R ), of 5 days which corresponds to the S2 revisit time. For the Montpellier dataset M1, the Re-value and Fscore reached 71. 5% and 83. 5%, respectively, for theΔS2 = 5, 10, 5mod 10 and 5mod 15 cases but lower values (57.