dataesr/scientific-paragraphs-categorization huggingface dataset:
Although different fungal pathogens were obtained, including Botryosphaeriaceae, and Phaeoacremonium and Cylindrocarpon-like species, colonies of Fusarium were present in isolations from all the plant samples. Pure cultures of the Fusarium isolates were obtained from single hyphal tips, and were further identified using a phylogenetic approach. After DNA extraction, the translation elongation factor 1-alpha (tef1) and the RNA polymerase II second largest subunit (rpb2) partial gene regions were amplified and sequenced using, respectively, the primers EF1/EF2, 5F2/7cR and 7cF/11aR. Consensus nucleotide sequences were used to search the closest species in the NCBI database using BLAST. Phylogenetic trees revealed 13 Fusarium species, including members of the F. fujikuroi, F. oxysporum, F. solani, F.
DNA meta -barcoding data were generated from leaf samples ('Bianca') collected throughout a growing season at the experimental vineyard Eszterházy Károly Catholic University, Hungary. Quality-filtered sequences were grouped into Amplicon Sequence Variants (ASVs), that were assigned to taxonomic and functional groups using In the rarefied dataset, 911 ASVs of plant pathogenic fungi represented 88 genera, of which 15 are known to be associated with grapevine trunk diseases (GTDs). Phaemoniella, Diplodia, Stereum, Trametes, and Botryosphaeria had the greatest ASV richness. Among non-GTD pathogens, Cladosporium, Aureobasidium, Alternaria, Vishniacozyma, and Epicoccum were the most diverse. The detected richness and abundance of GTDassociated fungi in leaves was noteworthy, and confirms recent results showing that many of these fungi are part of the core grapevine mycobiome.
04 keV). In order to gain depth-resolved information about the coating microstructure, phases, defect density and residual stress development across the coating thickness of the partially oxidized AlCrSi 5 N, a 30 μm thick lamella (in beam direction) of the coating on a sapphire substrate (coating thickness of 4 μm) was investigated by cross-sectional synchrotron X-ray nanodiffraction  [26]  performed at the nanofocus extension of the ID13 beamline at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France  [27]. A monochromatic, point focusing X-ray beam (E = 12. 7 keV) with dimensions of 25 nm x 35 nm adjusted by Multilayer Laue Lenses  [28]  was used in transmission geometry. The sample was moved along the whole coating thickness in steps of 20 nm and the diffraction data for each position were collected by a Dectris Eiger 4M detector.
Detector calibration was performed by a NIST corundum powder and the sample-to-detector distance was evaluated as 134. 927 mm. The resulting data were processed using the PyFAI software package  [ 29 , 30 ]. For every ring, 24 radial data sets were obtained by the azimuthal integration within 15 °wide sections. The full width at half maximum (FWHM) values were evaluated by fitting the diffraction peaks using a Pseudo-Voigt function. The X-ray elastic stress depth distribution was received by analyzing the elliptical shape of the Debye-Scherrer rings  [26]. The elastic constants 1/2 s 113 2 of 0. 3252 × 10 -5 MPa -1 for Al 2 O 3 , 1/2 s 113 2 of 0. 4142 × 10 -5 MPa -1 for Cr 2 O 3  [31]  , 1/2 s 110 2 of 0. 2848 × 10 -5 MPa -1 for mullite  [32]  and 1/2 s 100 2 of 0. 3903 × 10 -5 MPa -1 for AlN  [33]  were used.
Thus, targeting the NS1 protein may provide a good outcome in the treatment of influenza infection  [7] [8] [9]. NS1, a homodimer of two 230 residue polypeptides, is comprised of three structural domains, where only residues 1-203 have been resolved in crystallography. The RNAbinding domain (RBD) is an obligate dimer of residues 1-73, which is connected to the two effector domains (ED, residues 86-203) by the short, unstructured, linker regions  [10]. The RBD is arranged as three pairs of symmetrically positioned antiparallel alpha-helices. Residues 86-203 of each chain fold autonomously into an ED. To explore the NS1 protein as a target for drug design, three dimensional (3D) structures of the protein are needed. However, only four full-length NS1 proteins (from subtypes H6N6, H1N1 and H5N1) have been crystallized and are available as 3D structures in the Protein Data Bank (PDB)  [11].
Materials and Methods 2. 1. Selection of NS1 Full-Length Structures and Study of Their Flexibility 2. 1. 1. Selection of Four Different Forms of the NS1 Protein Three NS1 proteins from subtypes (H6N6, H1N1, H5N1) are considered, co-crystallized in full-length and available as 3D structures in the PDB. Although RBD has been shown to be stable, it is important to perform the full-length protein study to assess the impact of the NS1 polymorphism during pockets tracking. Figure  1A  illustrates a multiple sequence alignment, calculated with the ClustalΩ algorithm  [23] , of H6N6, H1N1 and H5N1 sequences from the UniProt database (Table  S1 ), using the EMBOSS tools  [24]. It can be noted that, despite sequence mutability, there is strong sequence conservation at the α2 and α2 helices in the groove region.
The RBD (1-73 amino acids) and ED (86-203 amino acids) domains connected by short (SL) or long (LL) linker regions (deletion or not of five residues "∆80-84") are indicated in Figure  1B. [24] , showing conservation of the RBD region and particularly the "RBDgroove", where the sequence is boxed in full gray lines. The RBD, ED and linker (long (LL) or short Figure  1. Illustration of the NS1 sequence conservation and the structural polymorphism of the NS1 protein: (A) Alignment of H6N6, H1N1 and H5N1 sequences extracted from the UniProt database (accession number: P03496, A5A5U1, Q20NS3 respectively) [25] using the "showalign" tool of software suite EMBOSS  [24] , showing conservation of the RBD region and particularly the "RBD-groove", where the sequence is boxed in full gray lines. The RBD, ED and linker (long (LL) or short (SL)) are bordered by red, blue and green dotted lines, respectively.
The α-helices are represented by cylinders and the β-sheets by arrows. Only differences relative to the consensus sequence (above and underneath) are shown, and the consensus line can be displayed in a mixture of upper-and lower-case symbols. Upper case indicates strong consensus and lower case indicates weak consensus; hyphens in H5N1 sequence are deletions (∆80-84). (B) Representation of the four NS1 initial structures (4OPA, 4OPH, 5NT2 and 3F5T), colored in black, gray, pink and red, respectively, using the PyMol visualization software  [26]. The "RBD-groove" is bordered by gray, full lines enclosing the two α2 and α2 helices. The boundaries of the RBD, ED and the linker are shown in red, blue and green, respectively. The d(ED-ED') distance between the EDs monomers centers of mass quantifies the structural polymorphism of the different structures, which is on average 27.
8 Å for H6N6_c, 60. 0 Å for H6N6_so_o, 32. 4 Å for H1N1_so_c and 66. 5 Å for H5N1_o. Four full-length NS1 crystallographic structures associated with three different subtypes of H6N6 (pdb codes: 4OPA and 4OPH), H1N1 (pdb code: 5NT2) and H5N1 (pdb code: 3F5T) are currently available in the PDB and were considered in this study  [12, 27, 28]. After the rebuilding and reversion of the engineered mutations, i. e. , R38A/K41A, and specifically the W187A mutation for 5NT2, the complete conformations of the initial structures' templates were obtained. The corresponding four initial structures are described in Table  S1. The PDB structures 4OPA and 4OPH correspond to the H6N6 subtype but are in different forms and linker sizes: 4OPA is a closed form (its ED are separated by a distance of 28 Å) associated with a short linker (five amino acids ∆80-84 are deleted), whereas 4OPH is a semi-open form with open tendency (its two ED are separated by a distance of 60 Å) (Table  S1 ), associated with a long linker (for distance measurement between two EDs, see  Naceri et al.
Sampling of NS1 Conformations during MD Simulation MD simulations were run for 150 ns, in an isothermal-isobaric (NPT) ensemble at 300 K and 1 bar, at pH 7 and using the AMBER-99SB force field with the GROMACS software package V2019. 5  [29]. The detailed protocol is described in  Naceri et al. , 2022 [15]. The sample of 500 frames of the NS1 full-length conformations were stored at regular intervals from each of the four MD simulations generated from the crystallographic structures (H6N6_c, H6N6_so_o, H1N1_so_c and H5N1_o). A resulting set of 2000 NS1 fulllength conformations was obtained, with 500 conformations extracted from MD simulations obtained on each structure.
D2)  [32, 33]  and the R Hclust package  [34]. Dendrogram visualizations were performed using the Heatmaps2 package in R (v 3. 9)  [35]  to illustrate pocket similarity in terms of residues. The difference in terms of residues between pockets and pocket clusters increases with the dendogram branch-lengths (representative of the average binary distances). The resulting classification visualizes the similarity of the pockets in terms of common residues, and allows the identification of different main clusters of similar pockets.
The polar and positively charged amino acids such as SER and ARG, which form strong ionic hydrogen bonds (salt bridges), are known to be the most favorable contacts for protein-RNA interaction  [45] [46] [47]. Recently,  Wacquiez et al. , 2020 , in their study of the NS1 structure and sequence determinants governing the interactions of RNAs in the case of H7N1 (PDB: 6SX2), described how the RBD-groove aligns itself almost perfectly in parallel to the RNA, to bind it. These authors underlined several RBD-groove residues involved in the RNA interaction  [48]. Our three identified binding-sites include six residues (THR5, ASP29, PRO31, ARG35, SER42, ARG46) of the ten described by  Cheng et al. , 2008 , (THR5, ASP29, ASP34, ARG35, ARG38, SER42, ARG46, THR49), as directly involved in the interaction with RNA; the limitation of their study lies in the fact that they did not take into account the NS1 polymorphism  [49].
Figure1. Illustration of the NS1 sequence conservation and the structural polymorphism of the NS1 protein: (A) Alignment of H6N6, H1N1 and H5N1 sequences extracted from the UniProt database (accession number: P03496, A5A5U1, Q20NS3 respectively) [25] using the "showalign" tool of software suite EMBOSS [24] , showing conservation of the RBD region and particularly the "RBDgroove", where the sequence is boxed in full gray lines. The RBD, ED and linker (long (LL) or short.
Moreover it should target "small" departures in the distribution. The significance of the discrepancy can be large, but the signal can be sizable (i. e. , given by a number of events that is large, relative to the Reference model expectation) only in a small (low-probability) region of the features space, or its significance emerge from correlated small differences in a large region. This is because previous experiments and theoretical considerations generically exclude the viability of new physics models that produce a radical deformation of the LHC data distribution, which are furthermore easier to detect. As said, the Reference sample R consists of synthetic instances of the variable x that follow the distribution predicted by the Reference Model. It plays conceptually the same role as the background dataset in regular model-dependent searches and it can be obtained either by a first-principle Monte Carlo simulation based on the fundamental physical laws of the Reference Model, or with data-driven methods.
Overview of the methodology In this work we develop a full treatment of systematic uncertainties within a model-independent search. Our treatment follows closely the canonical high-energy physics profile likelihood approach, reviewed in Ref. [3]. Each source of imperfection in the knowledge of the Reference Model is associated with a nuisance parameter ν. Its (true) value is unknown but statistically constrained by an "auxiliary" dataset A, which produces a ν-dependent multiplicative term in the likelihood, L(ν|A). The Reference Model prediction for the distribution of the variable x depends on the nuisance parameters, which we collect in a vector ν. The Reference Model is thus interpreted as a composite (parameter-dependent) statistical hypothesis R ν , to be identified with the null hypothesis H 0 of the statistical test.
In this way we divide the calculation of the test statistic t in the evaluation of two separate terms. One of them merely consists of the likelihood log-ratio between the nuisance-dependent R ν likelihood maximized over ν, and the likelihood of the central-value R 0 hypothesis. Maximizing the background-only likelihood as a function of the nuisance parameters is a necessary step of any LHC analysis. It serves in the first place to quantify the pull of the best-fit values of the nuisances, that maximize the complete likelihood (including the likelihood of the data of interest and of the auxiliary data, A), relative to their central value estimates and uncertainties as obtained from the auxiliary likelihood alone. Therefore the determination of the first term in t does not pose any novel challenge, and could be in principle performed with the standard strategy of employing a binned approximation of the likelihood after modeling the dependence of the cross section in each bin on the nuisances.
In practice for the numerical experiments performed in this paper we will find more effective and more easy to employ an un-binned likelihood reconstructed by neural networks  [7] [8] [9] [10] [11] [12] [13]. The other term required for the determination of the test statistic t involves the neural network and requires the maximization over the neural network parameters w (and over ν). It will be obtained by neural network training (with simultaneous minimization over ν), with a strategy that is a relatively straightforward generalization of the one we already employed  [1, 2]  in the absence of nuisance parameters. As in Ref. s  [1, 2] , the training data are the observed dataset D and the Reference dataset R. The Reference data are supposed to represent the distribution in the central-value hypothesis R 0 , therefore they are obtained fixing each nuisance parameter to its central value.
They do not contain any information on the variability of the Reference distribution due to the nuisances, which is taken into account by the first term of the test statistic. This avoids employing in the training Reference samples with multiple values of the nuisance parameters. The algorithm is thus not more computationally expensive than the one in the absence of nuisances. Like any other frequentist hypothesis test, the practical feasibility of our strategy is linked to the validity of asymptotic formulae for the distribution of the test statistic t in the null hypothesis R ν , P (t|H 0 ) = P (t|R ν ). In particular the asymptotic formulae are needed to ensure the independence of P (t|R ν ) on the nuisance parameters ν  [3, 14]. The Wilks-Wald Theorem  [5, 6]  predicts a χ 2 distribution for t in the asymptotic (infinite sample) limit, but it gives no quantitative information on how "large" the dataset should be, in order for P (t|R ν ) to be similar to a χ 2.
Furthermore there is obviously no universal lower threshold on the data statistics after which the asymptotic result starts applying. The threshold depends on the problem and, crucially, on the complexity of the statistical model that is being considered. For instance if a simple one-parameter linear model was used for the numerator hypothesis instead of a neural network, a statistics of a few data events might suffice to reach the asymptotic limit accurately. Larger and larger datasets will be needed if the expressivity of the model is increased using neural networks of increasing complexity. One can of course also adopt the opposite viewpoint, which is more convenient in our case where the statistics of the data is fixed, and consider the upper threshold for the model complexity below which the asymptotic limit is reached and the distribution of t starts following the χ 2 distribution.
We need the asymptotic formula to hold in order to eliminate or mitigate the dependence of P (t|R ν ) on ν. On the other hand, we would like our model to be as complex and expressive as possible in order to be sensitive to the largest possible variety of putative new physics effects. Therefore the optimal complexity for the neural network model is right at the threshold of loosing the χ 2 compatibility. In Ref. [2]  we already advocated this χ 2 compatibility criterion for the selection of the neural network model, with the motivation that the t distribution not following the asymptotic formula signals that t is sensitive to low-statistics regions of the dataset, a fact which in turn can be interpreted as "overfitting" in our context. This heuristic motivation remains, but it is accompanied by the stronger technical argument associated with the feasibility of the hypothesis test including nuisance parameters.
Structure of the paper The rest of the paper is organized as follows. In Section 2 we describe the statistical foundations of our method. Namely we show how to turn the mathematical definition of the Maximum Likelihood ratio test statistic into a practical algorithm for its evaluation along the lines described above. The implementation of the algorithm in all its aspects, including the selection of the neural network hyperparameters by the χ 2 compatibility criterion, is described in Section 3 for an illustrative univariate problem. In that section we will obtain a first validation of our method by studying how it reacts to toy datasets generated with values of the nuisance parameters that are different from the central values employed for the Reference training set. We will see that the term in t coming from the neural network is typically large, its distribution over the toys shifts to the right and gets strongly distorted with respect to the distribution one obtains when the toy data are instead generated with central-value nuisances.
The other term in t, associated with the R ν /R 0 likelihood ratio as previously described, engineers a non-trivial cancellation on the total value of t for each individual toy. A χ 2 distribution is eventually recovered for the total t distribution, compatibly with the Wilks-Wald Theorem, regardless of the value of ν used in the generation of the toy data. Similar tests are performed in Section 4 in a slightly more realistic problem with five features (kinematical variables) that represent a dataset that one might encounter in the study of the production of two particles at the LHC. Two common sources of uncertainties are included, and their impact on the sensitivity of our strategy to benchmark putative signals is quantified. We report our Conclusions in Section 5. Appendix A provides an overview of model-independent strategies in connection and comparison with ours.
Hypothesis testing As explained in Section 1, our method consists of a hypothesis test between a null hypothesis H 0 = R ν and an alternative H 1 = H w,ν. We now characterize the two hypotheses in turn, starting from the null R ν Reference (i. e. , the SM) hypothesis. The data collected in the region of interest for the analysis are denoted as D = {x 1 ,. , x N D } and consist of N D instances of a multi-dimensional variable x. For instance, the region of interest for the analysis could be defined as the subset of the entire experimental dataset where a given experimental signature (e. g. , two high-p T muons reconstructed within a certain detector acceptance) has been observed. The features x would then consist of the reconstructed momenta of these particles. The region of interest might be further restricted by selection cuts that define the region X of the phase space (x ∈ X) to which the particle momenta belong.
Each instance of x in D is thrown with a probability distribution that we denote as P (x |R ν ) in the Reference hypothesis R ν. The total number of instances of x, N D , is Poisson-distributed with a mean N(R ν ) that equals the total cross section in the region X times the integrated luminosity. The likelihood of the R ν hypothesis, given the observation of the dataset D, is thus provided by the extended likelihood L(R ν |D) = N(R ν ) N D N D ! e -N(Rν ) x∈D P (x|R ν ) = e -N(Rν ) N D ! x∈D n(x|R ν ). (1) In the previous equation we defined for shortness n(x|R ν ) = N(R ν )P (x|R ν ). (2) We will denote n(x|H), in different hypotheses H, the "distribution" of the variable x. The Reference hypothesis distribution for x depends on a set of nuisance parameters ν. They model all the imperfections in the knowledge of the Reference Model, ranging from theoretical uncertainties like those in the determination of the parton distribution functions, to the calibration of the detector response.
(8) Notice that this definition of the test statistic, and in turn its properties  [5, 6] , assumes that the composite hypothesis in the denominator (H 0 ) is contained in the numerator hypothesis (H 1 ). This holds in our case since the neural network function in eq. (  4 ) is equal to zero when all its weights and biases w vanish. Therefore (H w,ν )| w=0 = R ν. Also notice that the test statistic variable t depends on all the data that are employed in the analysis. In particular it depends on the auxiliary data A as well as on the data of interest D. We now address the problem of evaluating t, once the data are made available either from the actual experiment or artificially by generating toy datasets.
The central-value Reference hypothesis In order to proceed, we consider the special point in the space of nuisance parameters that corresponds to their central-value determination as obtained from the auxiliary data alone. If we call A 0 the observed auxiliary dataset, namely the one that is observed in the actual experiment, the central values of the nuisance parameters are those maximizing the auxiliary likelihood function L(ν|A 0 ). It is always possible to choose the coordinates in the nuisance parameters space such that the central values of all the parameters sit at ν = 0. So we have, by definition max ν [L(ν|A 0 )] = L(0|A 0 ). (9) We stress again that A 0 represents one single outcome of the auxiliary measurements (the one observed in the actual experiment), unlike A (and D) that describe all the possible experimental outcomes.
Therefore A 0 , and in turn the central value of the nuisance parameters that we have set to ν = 0, is not a statistical variable and therefore it will not fluctuate when we will generate toy experiments, unlike A and D. The central-value Reference hypothesis R 0 predicts a distribution for the variable x, n(x|R 0 ), that can be regarded as the "best guess" we can make for the actual SM distribution of x before analyzing the dataset of interest D. Correspondingly, ν = 0 is the best prior guess for the value of the nuisances. The likelihood of R 0 , given by L(R 0 |D, A) = L(R 0 |D) • L(0|A) = e -N(R 0 ) N D ! x∈D n(x|R 0 ) • L(0|A) , (10) is thus conveniently used to "normalize" the likelihoods at the numerator and denominator in eq. (  8 ). Namely we multiply and divide the argument of the log by L(R 0 |D, A) and we obtain t(D, A) = τ (D, A) -∆(D, A) , (11) where τ involves the maximization over the neural network parameters w and over ν τ (D, A) = 2 max w,ν log L(H w,ν |D) L(R 0 |D) • L(ν|A) L(0|A) , (12) while the "correction" term ∆ does not contain the neural network and involves exclusively the Reference hypothesis ∆(D, A) = 2 max ν log L(R ν |D) L(R 0 |D) • L(ν|A) L(0|A).