dataesr/scientific-paragraphs-categorization huggingface dataset:
F I G U R E 9 The Large-Hops benchmark was built from a dataset of large-step scaffold hopping cases extracted from PDBbind for which 499 decoy molecules were gathered. Overall, it comprises 144 cases defined by a pair of active molecules against the same protein target, and their corresponding decoys. decoys that bind to the same protein as the molecules in the pair. However, we assume that such cases are rare and that their potential presence does not change the overall conclusions of the paper.
Testing 2D Morgan fingerprints encoding is a kind of internal control to confirm that our benchmark is an interesting tool for the development of original methods dedicated to large-step scaffold hopping. (2) MACCS keys fingerprints  [40] , that in principle should suffer from the same limitations as the Morgan fingerprints. In fact, since the former encodes the presence or absence of particular chemical groups rather than the molecular graph itself, it is interesting to test if this can be beneficial to the current problem. (3) 2D pharmacophore fingerprints, that encode for the presence and relative positions in the 2D graph of the molecular structure of chemical groups able to drive different types of interactions with the protein, as defined in RDKit  [30]. Although these descriptors implement a notion of 2D (but not 3D) topology, they may improve over MACCS keys fingerprints.
| Performance evaluation of a machine-learning chemogenomic algorithm on the Large-Hops benchmark With the LH benchmark, we tackle large-step scaffold hopping cases with 'hard' targets, for which only one active ligand is known. This setting prevents from training ligand-based prediction models, and the associated computational methods are restricted to similarity measures, as above. Chemogenomics can overcome this limitation if bindings involving other molecules and other proteins are known (these molecule-protein pairs are noted (m, p) pairs in the following). Such (m, p) pairs can be collected from many public databases, such as the PubChem at NCBI  [44]. Basically, the main difference between ligand-based and chemogenomic methods is that the former predict ligands for a query protein given its known ligands (one known active in our case), while the latter predict ligands for a query protein given its known ligands and those known for other proteins.
In the case of the LH benchmark, chemogenomic algorithms can be trained with the (known active, query protein) binding pair and additional (ligand, protein) pairs known to bind, or not, gathered from an external database. Once trained, the prediction model provides a binding probability for the (decoys, query protein) and (unknown active, query protein) pairs, and the unknown active can be ranked among the 499 decoys according to these probabilities. We use a training set derived from the DrugBank database  [45]  to provide the additional (m, p) pairs. Indeed, DrugBank provides high quality bio-activity information regarding approved and experimental drugs, including their targets. It contains around 15. 000 curated drug-target (m, p) pairs for 2. 670 proteins. Although much smaller than other databases like PubChem or ChEMBL, DrugBank appears relevant to the LH benchmark because it contains drug-like ligands.
More precisely, for each pair of active ligands in the LH benchmark, the ML chemogenomic algorithm is trained with the same information regarding the query protein as the ligandbased methods tested above: a single known pair between the query protein and the known active. This pair is added to the DrugBank dataset, if not already present. All other pairs involving the query protein and any other molecule, if present in the DrugBank dataset, are removed. This allows to avoid redundancies between the training set and the tested pairs, and to compare the prediction performances of the chemogenomic method with those of the ligand-based methods tested in the previous sections because both types of algorithms are provided the same ligand information for the query protein. Figure  10  summarizes the difference between the ligandbased and the chemogenomic setups.
| DISCUSSION The scope of the present paper is essentially: (1) to propose a flowchart to cover the need for a publicly available and well-characterized large-step scaffold hopping benchmark for the community; (2) to provide a general assessment of the relative performances of classical 2D and 3D ligand-based methods for solving large-step scaffold hopping problems with 'hard' targets, in a setting that mimics real-life drug discovery applications  [14]. To our knowledge, the LH benchmark is the first public high-quality benchmark dataset for large-step scaffold hopping. Starting from PDBbind, the proposed flowchart requires threshold values for various criteria. These thresholds were chosen in an expert-based manner to exclude irrelevant scaffold hopping cases. Some criteria enable the selection of pairs of highly different molecules, while others ensure that molecules in the same pair share similar binding modes, i.
e. , correspond to 'true' scaffold hopping cases. We use stringent thresholds for both types of criteria, because our goal is to build a high quality large-step scaffold hopping dataset. The resulting size for the LH benchmark is smaller than that reported for other less characterized benchmarks  [12, 13] , but this illustrates that the number of large-step scaffold hopping cases reported is much smaller than that of small-to medium-step scaffold hops. Note however that available benchmarks are not comparable to the LH benchmark, because they were not conceived in a comparable setting. Should a large-step scaffold hopping benchmark of larger size be desired, the same flowchart could be followed with a lower drug-likeliness threshold, a larger range in molecular weights, or a higher threshold for redundancy between the pairs of molecules.
The chemogenomic algorithm leads to the best performances, although the kernel SVM algorithm can be improved. Because our benchmark contained drug-like molecules for proteins belonging to diverse families, we trained the chemogenomic algorithm based on a Drug-Bank-derived dataset. However, other larger training sets can be used, for example derived from larger databases such as PubChem. For more focused problems like scaffold hopping problems involving a protein belonging to a specific well studied family, such as kinases or GPCRs, one can also use other training databases that gather (ligand, protein) molecular interactions known within these families of proteins  [48, 49]. As an illustration, although chemogenomics has been hardly explored in the field of large-step scaffold hopping, this approach was used in one study within the GPCR family, reporting identification of a new scaffold for an antagonist of Vasopressin 1 A  [50].
| Selecting pairs of drug-like molecules The molecules quantitative estimation of drug-likeness (QED) score  [17]  is calculated with RDKit  [30] : all pairs with a QED below 0. 5 are discarded. To discard redundant pairs, which differ by only slight molecular modifications, the Tanimoto similarities based on the Morgan fingerprints of the generic Murcko scaffolds and of the whole molecules are calculated. When two pairs of ligands have one of these Tanimoto coefficient above 0. 5, only one pair is kept in the dataset.
| Selecting pairs of large-scale hops ligands The RDKit package  [30]  was used to provide generic Murcko scaffold of molecules, where all atoms are replaced by carbons, all bonds are switched to single bonds and only the linkers between the rings are conserved. RDKit  [30]  is also used to compute Morgan fingerprints for the Murcko scaffolds and for the whole structures of molecules, and to calculate the resulting Tanimoto similarities. Pairs with Murcko and Morgan Tanimoto similarities respectively below 0. 6 and 0. 3 are selected.
| 3D Similarity-based methods We used two types of 3D similarity methods, based on shape or 3D pharmacophore representations of the molecules. In both cases, this first requires generating 3D molecular conformers. For all pairs of active molecules and their 499 decoys, a pool of 500 conformers is calculated, from which we keep up to ten conformers of local minimal energy that differ from a RMSD value of at least 1. 5 Å, under MMFF94 force field using RDKit  [55]. Then, all conformers of the known active are aligned pairwise with those of the unknown active or those of the decoy molecules, to maximize their overlap. For the 3D-pharmacophore similarity, for each pair of active molecules and their decoys, the freely available Pharao software  [56]  is used to detect the pharmacophore groups for conformers.
| Kernel SVM The chemogenomic approach used in the present study recasts the problem as a supervised learning binary classification over the space of pairs m; p ð Þ of molecules and proteins, to separate binding pairs from a carefully selected set of non-binding pairs. We rely on a kernelized Support Vector Machines (SVM) classifier  [57]  to perform this classification. Briefly, the SVM is trained on a dataset of m; p ð Þ pairs and learns the optimal hyperplane that separates pairs that bind from those that do not. The kernel SVM leverages a kernel K encoding similarities between m; p ð Þ pairs  [58]. A general method to build a kernel on m; p ð Þ pairs is to use the Kronecker product of molecule and protein kernels as done in  [58]  and  [59]. Given a molecule kernel K molecule and a protein kernel K protein , the Kronecker kernel K pair is defined by: K pair ð m; p ð Þ; m 0 ; p 0 À � Þ ¼ K molecule m; m 0 À � � K protein ðp; p 0 Þ where m and m 0 are molecules and p and p 0 are proteins.
| Training dataset To build our training set, we use the DrugBank v1. 5. 1  [64]  which defines a set of ðm; pÞ pairs which bind together (i. e. m targets p). We choose this dataset because it is well curated and composed of FDA-approved drugs. We kept molecules with molecular weights between 100 and 800 g/mol which is in the range of drug-like molecules  [65]. Among these molecules, we select those which target at least one human protein. Thus, the train dataset comprises 5. 071 molecules, 2. 670 proteins and 14. 638 positive bindings. To complete the dataset, we need to select negative pairs. This selection should be designed with care to correct potential statistical bias in the database and reduce the number of false positive predictions. We use the greedy algorithm in  [45] , which randomly chooses the same number (14.
| Training scheme The ML chemogenomic algorithm is trained for each of the 288 scaffold hopping cases in the LH benchmark as follows: one molecule of the pair is considered as the only known active for the query protein. If this pair is not already present in the DrugBank database, it is added to the training set. All other pairs involving the query protein that are present in DrugBank are removed from the training set. This allows us to exclude the m; p ð Þ pair between the unknown active of the pair with the query protein if this pair is in DrugBank. Hence, for each pair of ligands in the LH benchmark, the chemogenomic algorithm is trained with the same information about ligands of the query protein than the ligand-based algorithms: a single known active ligand. Once trained, the algorithm predicts the binding probabilities of (molecule, query protein) pairs involving the 499 decoys and the unknown active molecule.
from the ZINC database, among molecules with physical and chemical properties similar to those of the active molecules, as detailed below. The considered physical and chemical descriptors are: -Number of hydrogen bond donor and acceptor -Number of aromatic and aliphatic rings -Number of consecutive rotatable bonds -Molecular weight -Lipophilicity -Topological polar surface area More precisely, molecules are selected if their physical and chemical descriptors fulfil the following criteria: descriptor molecule 2 ½min descriptor ligands À � À c,max descriptor ligands À � þ c� where c ¼ 1 for integer descriptors, and.
TA B L E 1 1 Overview of the Urostigma fig species at which pollinators were trapped. For all fig species, the Pegoscapus pollinator species that develop in and emerge from them are known. A section sign ( §) indicates Pegoscapus species known to develop in two host species. A minus sign (-) indicates a Pegoscapus species that have become rare over past 20 years. A plus sign (+) indicates a Pegoscapus species that have become more common in this Urostigma species over the past 20 years. Barcoding was conducted using phylogenetic analyses (Figures S1-S11). The pollinator CO1 sequences were aligned using a reference data matrix composed of all Pegoscapus sp. and Tetrapus sp. COI sequences found in the NCBI GenBank database (Benson et al. , 2005). Aligned sequences from each pollinator individual were first translated using the invertebrate mitochondrial genetic code to confirm they were coding sequences and not nuclear pseudogenes.
None of the sequences showed evidence of pseudogenization or frame shifts. Aligned fig pollinator data from each fig species was then analyzed in Geneious Prime v2021. 2. 2 using the neighborjoining algorithm with Tamura-Nei distances (Tamura & Nei, 1993). DNA sequences were assigned the species name associated with GenBank reference sequences they clustered with within the phylogeny, typically corresponding to sequences from the GenBank reference with <2% divergence. In cases where sequences did not cluster with any GenBank reference sequence, they were named "new sp. " and their closest reference sequence or clade was noted. COI sequences were deposited in GenBank (accession numbers OR288903-OR289513). The total number of identified individuals per fig species is listed in Table.
Finally, we wanted to obtain predictions of counts at pollinator species level, which were not directly measured. For this the barcoding results, and the counts of trapped Pegoscapus pollinators were jointly used. Bootstrap resampling from both datasets was used to generate 200 count datasets, and 200 relative abundance datasets. Multiplying these datasets yielded 200 datasets of the number of pollinators per Ficus species for receptive usual hosts, receptive other hosts, vegetative usual hosts, and vegetative other hosts. The 95% confidence intervals based on these resampled datasets were used to compare numbers of pollinators. These intervals were made for six species that are known as the associated pollinators of the fig species we analyzed at the level of fig species. 3 | RE SULTS3. 1 | Pollinator presence at receptive and vegetative Ficus host trees, or at non-Ficus controlsPegoscapus pollinators were abundantly trapped at receptive Urostigma trees; we carried out 126 trapping events lasting 150 days in total during which 7580 pollinators were trapped (Figure2).
Bromelain is a key enzyme found in pineapple (Ananas comosus (L. ) Merr. ); a proteolytic substance with multiple beneficial effects for human health such as anti-inflammatory, immunomodulatory, antioxidant and anticarcinogenic, traditionally used in many countries for its potential therapeutic value. The aim of this updated and comprehensive review focuses on the potential anticancer benefits of bromelain, analyzing the cytotoxic, apoptotic, necrotic, autophagic, immunomodulating, and anti-inflammatory effects in cancer cells and animal models. Detailed information about Bromelain and its anticancer effects at the cellular, molecular and signaling levels were collected from online databases such as PubMed/MedLine, TRIP database, GeenMedical, Scopus, Web of Science and Google Scholar. The results of the analyzed studies showed that Bromelain possesses corroborated pharmacological activities,.
Review methodology To carry out this updated review on the anticancer effects of bromelain, published studies were searched in specialized databases such as PubMed/MedLine, TRIP database, GeenMedical, Scopus, Web of Science and Google Scholar, using the next MeSH terms: 'Animals", "Anticarcinogenic Agents/pharmacology", "Antineoplastic Agents/pharmacology", "Bromelain/ pharmacology", "Bromelain/administration and dosage", "Bromelain/chemistry", "Cell Line, Tumor", "Cell Survival/drug effects", "Dose-Response Relationship", "Drug, Humans", "Nanocapsules/administration and dosage", "Neoplasms/drug therapy", "Neoplasms, Experimental/pathology", "Signal Transduction/drug effects", "Treatment Outcome". Each article published before 1 August 2022, written in English, and their significance was evaluated by analyzing the title and abstract.
Published results obtained in lattice QCD by the present methods for the quark-connected contribution are also reviewed in that section. Section IX collects our concluding remarks. The appendices contain additional material useful for numerical implementations, providing in particular the kernel asymptotics for various special kinematic regimes. The final appendix (E) provides some information about a computer code available for the numerical implementation of our approach. We are interested in the hadronic light-by-light (HLbL) scattering contribution to the anomalous magnetic moment of the muon, see Fig. 1. The basic idea of our approach is to treat the four-point function of hadronic electromagnetic currents, represented by the blob in Fig. 1 , in lattice QCD regularization, while for the remaining QED part with photons and muons, we use continuum, Euclidean position-space perturbation theory in infinite volume  [38] [39] [40].
As sketched at the beginning of this section, given precomputed coefficients of the Gegenbauer polynomial expansion of the weight functions on a grid in |x| and |y|, the remaining computational tasks to obtain the QED kernel at a given (x, y) point are: to evaluate the sums yielding the weight functions (again using the Clenshaw algorithm), interpolate the weight functions (and their derivatives), apply the chain rules to obtain the T (A) αβδ tensors and finally to perform the contraction of these with the G A δ[ρ,σ]µανβλ tensors. We have written a code in the C programming language to perform these tasks. Some implementation details and a link to the code can be found in appendix E.
While no multiplicative renormalization of the conserved vector current is needed, an additive improvement term with a coefficient c V = 1/2 is required to remove O(a) lattice artifacts, but not included here (see e. g. [79] ). Finally, additional lattice artifacts, which scale linearly with the lattice spacing, originate from contact terms. Our line of constant physics is defined by a constant renormalized mass of the lepton in the loop, m = cst. Including O(a) effects, one has m = Z m m 0 (1 + b m am 0 ), with Z m = 1 and b m = -1/2  [78] , so that the bare subtracted quark mass has to be adjusted in the simulation. Finally, we are working with the QCD code and the result must be divided by N c = 3, the number of colors. We have used seven lattices, with the same physical volume L m l = 7.
Acknowledgements We thank  Henryk Czyz  for discussions and providing references about position-space methods,  Hartmut Wittig  and  Georg von Hippel  for many discussions about the status of the (g -2) µ puzzle. This work has been supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  through grant agreement  771971 -SIMDAMA, as well as by the  Deutsche Forschungsgemeinschaft (DFG)  through the  Collaborative Research Centre 1044  and through the  Cluster of Excellence Precision Physics, Fundamental Interactions, and Structure of Matter  ( PRISMA+ EXC 2118/1 ) within the  German Excellence Strategy  (Project  ID   39083149 ). The project leading to this publication has also received funding from the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" programme ,  AMX-18-ACE-005.
CEST MRI methods, such as APT and NOE imaging reveal biomarkers with significant diagnostic potential due to their ability to access molecular tissue information. Regardless of the technique used, CEST MRI data are affected by static magnetic B 0 and radiofrequency B 1 field inhomogeneities that degrade their contrast. For this reason, the correction of B 0 field-induced artefacts is essential, whereas accounting for B 1 field inhomogeneities have shown significant improvements in image readability. In a previous work, an MRI protocol called WASABI was presented, which can map simultaneously B 0 and B 1 field inhomogeneities, while maintaining the same sequence and readout types as used for CEST MRI. Despite the highly satisfactory quality of B 0 and B 1 maps computed from the WASABI data, the post-processing method is based on an exhaustive search of a four-parameter space and an additional four-parameter non-linear model fitting step.
Some sets of parameters yield negative signal that leads to a cusp (red dashed arrows) in the acquired magnitude signal, that makes the fitting complex. (B) To get rid of the unlikely positive values, a polarized dataset is constructed, based on an iterative process that starts from the two extremities of the WASABI-Z-Spectrum where the signal is supposed to be positive (see green dashed). Moving toward the central frequency offsets of the WASABI-Z-Spectrum, it extrapolates its value at the next frequency offset (using the maximum variation of the signal ΔZ MAX ) and checks if the predicted value is likely to be positive or negative. (C) Through the proposed method, data points that are unlikely to be positive are removed and the fitting of the model without absolute value is performed. (D) The fitting-results are then used to refine the sign of the signal on the whole spectra and the fit is reperformed to derive the definitive estimate of ΔB 0 and B 1.
Construct a polarized data set Use the acquired WASABI-Z-Spectra data Z(Δω) to construct a dataset Y(Δω) for which the polarity is known. This is done by selecting the data point for which the polarity is known with a high likelihood and correct the magnitude data by applying the correct polarity, i. e. : Y(Δω) = Z(Δω) if the data has a high likelihood of being positive. Y(Δω) = -Z(Δω) if the data has a high likelihood of being negative. Z(Δω) is discarded if its polarity is unknown. The polarized dataset construction is based on an iterative process that starts from the two extremities of the WASABI-Z-Spectrum where the signal is supposed to be positive (see green dashed arrows in Fig. 1B ). Moving toward the central frequency offsets of the WASABI-Z-Spectrum, it extrapolates its value at the next frequency offset (extrapolation method described later in the manuscript) and checks if the predicted value is likely to be positive or negative.
Use the polarized dataset for a fast estimation of the model parameters The new dataset (Fig. 1C ) is described by the same equation as Z(Δω) for which the absolute values has been removed: Y(Δω) = c -d × f (ΔB 0 , B 1 , Δω) (3) The parameters of this model are obtained by minimizing the sum of square error: [ĉ, d, B0 , B1 ] = argmin c,d,B0,B1 SSE(c, d, ΔB 0 , B 1 ) (4) where SSE(c, d, B 0 , B 1 ) = ∑ Δω (Y(Δω) -(c -d × f (ΔB 0 , B 1 , Δω) ) ) 2 (5) is the sum of square errors. Assuming that ΔB 0 and B 1 are set to constant values (i. e. they are not more variables), the model is linear in c and d, so the model can be rewritten  [19]  in a matrix form as: Y = M(B 0 , B 1 )β (6) where Y = [Y(Δω 1 ) , …, Y(Δω N ) ] T is the vertical vector that contains the observed data points, of size N, M is a matrix of size N × 2 containing the regressors of the model.