dataesr/scientific-paragraphs-categorization huggingface dataset:
Signal and background modelling All signal and background processes are modelled using MC simulations, with the exception of non-prompt-lepton and fake-lepton backgrounds, which are estimated using data-driven methods (see section 4. 4). All simulated samples used EvtGen 1. 6. 0 [69]  to model the decays of heavy hadrons, with the exception of some background samples generated with Sherpa  [70]. Most of the MC samples were processed using a full simulation  [71]  of the detector response with the Geant4 toolkit  [72]. Samples used to estimate systematic uncertainties in the modelling were obtained either by reweighting the default full-simulation samples or by using fast simulation software (ATLFAST-II  [71] ) to parameterise the showers in the calorimeter. To model additional pp interactions from the same or neighbouring bunch crossings, the hard-scattering events were overlaid with a set of minimum-bias interactions generated using Pythia 8  [73]  and the MSTW2008lo  [74]  parton distribution function (PDF) set with the A3  [75]  tuned parameter settings.
The virtual QCD corrections for matrix elements at NLO were provided by the OpenLoops library. The calculation was performed in the G µ scheme, ensuring an optimal description of pure EW interactions at the EW scale. The production of t tV and t tH events was modelled using the Mad-Graph5_aMC@NLO 2. 3. 3  [117]  and Powheg Box v2 generators, respectively. The generators provide matrix elements at NLO in α s , with the NNPDF3. 0nlo PDF set. An exception is the simulation of t tH events corresponding to data collected in 2018, modelled using the MadGraph5_aMC@NLO 2. 6. 0 generator. For t tV and t tH production, the events were interfaced with Pythia 8. 210  [73]  and Pythia 8. 230, respectively. Each used the A14 tune and the NNPDF2. 3lo PDF set. The rare SM processes leading to tW Z and tZ production are also considered in the dilepton channel.
Resolved topology The single-lepton resolved topology requires at least four small-R jets with p T > 25 GeV. Events which also pass the boosted selection (see section 5. 2. 2) are removed. The challenge in reconstructing an event in the single-lepton resolved topology is to correctly assign individual selected jets to the corresponding partons from the decaying top quarks. For this purpose a boosted decision tree (BDT), implemented within the TMVA package  [121]  is designed. The BDT combines kinematic variables and b-tagging information with weight information from the Kinematic Likelihood Fitter (KLFitter)  [122] , into a single discriminant. Each permutation of jet-to-parton assignments is evaluated and the permutation with the highest BDT score is used for the t t kinematic reconstruction.
[55, 131]. Uncertainties associated with the small-R jet selection arise from the jet energy scale (JES), the JVT requirement and the jet energy resolution (JER). The uncertainty in the JES is assessed in data  [60] , using MC-based corrections and in situ techniques. It is broken down into a set of 29 decorrelated nuisance parameters, with contributions from pile-up, jet-flavour composition, single-particle response, and punch-through. Each parameter has different jet p T and η dependencies  [132]. The uncertainty in the JVT efficiency comes from the estimation of the residual contamination from pile-up jets after pile-up suppression, and a systematic uncertainty assessed by using different MC generators for simulation of Z → µµ and t t events  [61]. The uncertainty in the JER is determined by an eigenvector decomposition strategy similar to that for the JES systematic uncertainties.
Eight nuisance parameters take into account various effects evaluated by comparing simulation with data. The overall effect of the JER uncertainty is parameterised in jet p T and η  [132]. The efficiency of the flavour-tagging algorithm is measured for each jet flavour using control samples in data and simulation. The uncertainties are related to the b-jet tagging calibration for b-jets, c-jets and light-jets, and comprise nine, four and five eigenvector variations to the tagging efficiencies, respectively  [62]. The uncertainties due to the highp T extrapolation of the b-jet and c-jet efficiencies are accounted for by two additional nuisance parameters.
Method uncertainties Given the finite number of events in the t t signal sample, the response matrix is estimated with limited statistical precision. To estimate the resulting uncertainty in A C , an Asimov dataset  [143]  unfolding is repeated 500 times with smeared response matrices (according to the MC statistics) to obtain a distribution of pseudo-experiment results for A C. The width of this distribution is considered as the uncertainty, which is then summed in quadrature with the total uncertainty obtained from the unfolding. The Gaussian smearing is applied to each element of the response matrix. To evaluate any source of bias from the unfolding procedure, the response of the unfolding is determined from eight pseudo-datasets generated with Protos. Each pseudodataset is composed of the nominal t t signal, reweighted to simulate a specific injected asymmetry.
C results The combined results of the A t t C measurement are interpreted in the SMEFT. In this formalism the SM Lagrangian is extended with higher-dimensional operators that encode the effect of new-physics phenomena at a scale beyond the direct reach of the experiment. A general effective Lagrangian is written as follows: L eff = L SM + 1 Λ 2 i C i O i + O Λ -4 , where L SM represents the SM Lagrangian, Λ represents the characteristic new-physics scale and C i are the Wilson coefficients of the dimension-six operators O i. Any observable receives contributions proportional to Λ -2 , from the interference of dimension-six operators with SM amplitudes. The squares of dimension-six operators are also computed, and these give rise to contributions of order Λ -4. However, further terms of the same order, from the interference of dimension-eight operators with the SM, are ignored.
Also, T A ≡ λ A /2 where λ A are Gell-Mann matrices; τ I represents Pauli matrices, φ represents the Higgs doublet ϕ with the antisymmetric SU(  2 ) tensor by = iτ 2 and φ = ϕ *. The CP-violating imaginary parts of operator coefficients are not considered. The charge asymmetry is affected by a particular linear combination of operator coefficients  [151] [152] [153]. This measurement is therefore complementary to cross-section measurements and very valuable in disentangling blind directions in global SMEFT fits  [151]. The operators are implemented in the SMEFT@NLO UFO model  [154]  in the Mad-Graph5_aMC@NLO package  [117]. The relation between the operator coefficients and the charge asymmetry is determined at NLO accuracy in the strong coupling constant and is parameterised with an analytic function  [155]   6.
For the inclusive measurement, the linear fit yields tighter bounds on the Wilson coefficients for octet operators, which enter at tree level, than for the coefficients of singlet operators that only affect t t production at JHEP08(2023)077  tu /Λ 2 in the SMEFT. The bounds are derived from the charge asymmetry measurement presented in this paper, combining the single-lepton and dilepton channels. For the quadratic fit, two solutions were found by the χ 2 minimisation in some cases. The theory uncertainty corresponds to the NNLO QCD + NLO EW calculation. The impact of dimension-six operators is parameterised at NLO accuracy in QCD using the SMEFT@NLO package in MadGraph5_aMC@NLO. Bounds are also shown from the forward-backward asymmetry measurements in √ s = 1. 96 TeV pp collisions at the Tevatron and the charge asymmetry measurements in 8 TeV pp collisions in LHC Run 1.
A further asymmetry measurement was proposed in refs. [159, 160]. The energy asymmetry in boosted t t production in association with an energetic jet provides complementary information that can eliminate a blind direction of the charge asymmetry measurement. Such a measurement was performed in t tj production by the ATLAS Collaboration  [46]. For the energy asymmetry, the SM prediction was obtained from simulations of t tj production at NLO with MadGraph5_aMC@NLO+Pythia 8, while the impact of dimension-six operators is parameterised at LO accuracy in QCD using the SMEFT@NLO package in MadGraph5_aMC@NLO. The covariance matrix used in the χ 2 fit contains the experimental uncertainties as well as the statistical uncertainty of the Monte Carlo prediction and the theory uncertainty due to its scale dependence.
Individual 68% (solid line) and 95% (dashed line) CL limits on the Wilson coefficients C i /Λ 2 in the SMEFT from the differential charge asymmetry measurements versus m t t. Only one Wilson coefficient is varied at a time while the others are kept at 0. The bounds are derived from the charge asymmetry measurement presented in this paper, combining the single-lepton and dilepton channels. The theory uncertainty corresponds to the NNLO QCD + NLO EW calculation. The impact of dimension-six operators is parameterised at NLO accuracy in QCD using the SMEFT@NLO package in MadGraph5_aMC@NLO. -37 -.
-38 -JHEP08(2023)077 The full and dashed lines show the 68% and 95% CL limits, respectively. The bounds are derived from a comparison of the measured asymmetries with the SMEFT predictions using the quadratic parameterisation. The SM prediction of A E is obtained from simulations of t tj production at NLO with MadGraph5_aMC@NLO+Pythia 8, while the impact of dimension-six operators is parameterised at LO accuracy in QCD using the SMEFT@NLO package in MadGraph5_aMC@NLO. For A t t C the SM prediction is calculated up to NNLO QCD and NLO EW accuracy and the impact of the dimension-six operators is parameterised at NLO accuracy in QCD using the SMEFT@NLO package in MadGraph5_aMC@NLO. 4 2 0 2 4 C 1,1 Qq / 2 [.
JHEP08(2023)077 9 Conclusion The charge asymmetry in top-quark pair production is measured in the single-lepton and dilepton decay channels using 139 fb -1 of √ s = 13 TeV proton-proton collision data collected by the ATLAS experiment at the LHC. A fully Bayesian unfolding method is used to correct for detector resolution and acceptance effects. The resolved and boosted topologies of the single-lepton channels are unfolded simultaneously with the same-and opposite-flavour dilepton channels. Differential measurements are performed as a function of the invariant mass, transverse momentum and longitudinal boost of the t t system. In comparison with previous results obtained at  8  TeV, the differential variables benefit from a finer binning at larger values which are sensitive to possible enhancements of the charge asymmetry due to new-physics phenomena beyond the Standard Model.
Main Text: A scalable approach for interpreting the effects of human genetic variants and their impact on disease risk is urgently needed to realize the promise of personalized genomic medicine  (1) (2) (3). Out of more than 70 million possible protein-altering variants in the human genome, only ~0. 1% are annotated in clinical variant databases such as ClinVar  (4) , with the remainder being variants of uncertain clinical significance  (5, 6). Despite collaborative efforts by the scientific community, the rarity of most human genetic variants has meant that progress towards deciphering personal genomes has been incremental  (7, 8). Consequently, clinical sequencing tests frequently return without definitive diagnoses, a frustrating outcome for both patients and clinicians  (9, 10). In certain cases, patients have needed to be recontacted and diagnoses reversed when the presumed pathogenic variant was later found to be a common variant in previously understudied human populations  (11) (12) (13).
A database of 4. 3 million benign missense variants across the primate lineage To expand upon this strategy, we sequenced 703 individuals from 211 primate species  (19) , and aggregated these with data from previous studies  (19) (20) (21) (22) (23) (24) (25) (26) , yielding a total of 809 individuals from 233 species. We identified 4. 3 million unique missense (protein-altering) variants and 6. 7 million unique synonymous (non-protein altering) variants (Fig. 1A ), after excluding variants at positions that lacked unambiguous 1:1 mapping with human, or which resulted in nonconcordant amino acid translation outcomes because of changes at neighboring nucleotides (fig. S1 ). The species selected for sequencing represent close to half of the 521 extant primate species on Earth  (27)  and cover all major primate families, from Old World monkeys and New World monkeys to lemurs and tarsiers.
We targeted a small number of individuals per species (3. 5 on average) to ensure that we primarily sampled common variants that have been filtered by natural selection rather than rare mutations (fig. S2 ). Compared to the genome Aggregation Database (gnomAD) cohort of 141,456 human individuals from diverse populations  (28, 29) , the primate sequencing cohort contained ~20% more exome variants despite sequencing 1/175th the number of individuals (Fig. 1A  and fig. S3 ), attesting to the remarkable genetic diversity present in non-human primate species  (19, 30) , many of which are critically endangered  (31). The overlap of primate variants with gnomAD was low, consistent with independent mutational origins in each species (fig. S3 ). Out of the 22 million possible synonymous variants in the human genome, 30% were observed in the primate cohort, compared to just 6% of possible missense mutations (Fig.
1B ). Because de novo mutations would have laid down unbiased proportions of missense and synonymous variants, the observed depletion of missense mutations in the primate cohort is consistent with the majority of newly-arising human missense mutations being removed by natural selection due to their deleteriousness  (8, (32) (33) (34). The surviving missense variants are seen at high frequencies in primate populations, and represent a subset of missense variants that have tolerated filtering by natural selection and are unlikely to be pathogenic  (35). Missense variants from the primate cohort are strongly enriched for benign consequence in the ClinVar clinical variant database (Fig. 1C ). Amongst ClinVar variants with higher review levels (2-star and above, indicating consensus by multiple submitters) (4), missense variants found in at least one non-human primate species were Benign or Likely Benign ~99% of the time, compared to 63% for ClinVar missense variants in general, and 80% for missense variants seen in gnomAD (Fig.
1D ) and CREBBP (fig. S4 ), genes responsible for familial epilepsy  (41, 42)  and Rubinstein-Taybi syndrome  (43, 44). Missense variants in the gnomAD cohort were partially depleted within these same critical regions (Fig. 1D  and fig. S4 ), indicating that humans and primates experience similar selective pressures. However, deleterious variants were incompletely removed in humans, consistent with the shorter amount of time they were exposed to natural selection. Prior to using primate data as an indicator of benign consequence in a diagnostic setting, it is vital to understand why a handful of human pathogenic ClinVar variants appear as tolerated common variants in primates. Our clinical laboratory independently reviewed evidence for each of the 36 ClinVar pathogenic variants that appeared in the primate cohort, according to ACMG guidelines  (14).
By expanding our cohort to sequence a large number of individuals per species, we would definitively exclude rare variation from our catalogue of primate variation, as well as grow the database of benign variants to improve clinical variant interpretation. As evolutionary distance from human increases, cases where the surrounding sequence context has changed sufficiently to alter the effect of the variant should also increase, until common variants in more distant species could no longer be reliably counted on as benign in human. We examined variation in each major branch of the primate tree, as well as variation from mammals (mouse, rat, cow, dog), chicken, and zebrafish, and evaluated their pathogenicity in ClinVar (Fig. 1F ). Common variants from species throughout the primate lineage, including more distant branches such as lemurs and tarsiers, varied from 98.
6% to 99% benign in the human ClinVar database, but this dropped to 87% for placental mammals, and 71% for chicken. The high fraction of variants that are pathogenic in human, yet tolerated as common variants in more distant vertebrates, indicates that selection on orthologous variants diverges substantially in distantly-related species, as a consequence of changes in the surrounding sequence context and other differences in the species' biology (fig. S5 ). We have made the primate population variant database, which contains over 4. 3 million likely benign missense variants, publicly available at https://primad. basespace. illumina. com as a reference for the genomics community. Overall, this resource is over 50 times larger than ClinVar in terms of number of annotated missense variants, and consists almost entirely of variants of previously unknown significance.
Common primate variants used for training and annotated ClinVar pathogenic variants from separate parts of the linear sequence form distinct clusters in 3D space. Although ClinVar variants are shown for illustration, it is important to note that the network was not trained on either human-engineered features or annotated variants from clinical variant databases, thereby avoiding potential human biases in variant annotation. Rather, it learns to infer pathogenicity based on the local enrichment or depletion of common primate variants, taking only the protein's multiple sequence alignment and 3D structure as inputs. PrimateAI-3D can utilize protein structures from either experimental sources or computational prediction  (72) (73) (74) (75) (76) ; we used AlphaFold DB  (72, 73)  and HHpred  (74)  predicted structures for the broadest coverage across human genes.
For training data, we incorporated all common missense variants from the 233 non-human primate species  (17) , and common human missense variants (allele frequency > 0. 1% across populations) in gnomAD  (28, 29) , TOPMed  (77, 78) , and UK Biobank  (79, 80) , resulting in a total of 4. 5 million unique missense variants of likely benign consequence. This dataset covers 6. 34% of all possible human missense variants, and is over 50fold larger than the current ClinVar database (79,381 missense variants after excluding variants of uncertain significance and those with conflicting annotations), greatly enlarging the training dataset available for machine learning approaches. Because the training dataset consists only of variants labeled as benign, we created a control set of randomly selected variants that were matched to the common variants by trinucleotide mutation rate, and trained PrimateAI-3D to separate common variants from matched controls as a semi-supervised learning task.
In parallel with the variant classification task, we generated amino acid substitution probabilities for each position in the protein by masking the residue and using the sequence context to predict the missing amino acid, borrowing from language model architectures that are trained to predict missing words in sentences  (81, 82). We trained both a 3D convolutional "fill-in-the-blank" model, which tasked the network with predicting the missing amino acid in a gap in the voxelized 3D protein structure, and separately, a language model utilizing the transformer architecture to predict the missing amino acid using the surrounding multiple sequence alignment as context  (83). We implemented these models as additional loss functions to further refine the PrimateAI-3D predictions (fig. S13 ). We also trained a variational autoencoder (67) on multiple sequence alignments and found that it performed comparably to our transformer architecture (fig.
In each cohort, we cataloged de novo missense mutations that appeared in affected probands but were absent in their parents, as well as de novo missense mutations that appeared in a set of shared healthy controls. We evaluated the ability of each classifier to separate the de novo missense mutations that appear in cases versus controls on the basis of their prediction scores, using the Mann-Whitney U test to measure performance. PrimateAI-3D outperformed all other classifiers at distinguishing pathogenic from benign variants in the four patient cohorts we tested (UKBB, DDD, ASD, CHD); it was also the top performer at separating pathogenic from benign variants in the ClinVar annotation database, and had the highest average correlation with the deep mutational scan assays (Fig. 3D  and fig. S15 ).
After PrimateAI-3D, there was no clear runner-up, with second place occupied by six different classifiers in the six different benchmarks. We observed a moderate correlation between the performance of different classifiers in UKBB and DDD (Spearman r = 0. 556; Fig. 3C ), which are the two largest clinical cohorts and therefore likely the most robust for benchmarking (with 200,643 and 33,613 patients, respectively), but outside of PrimateAI-3D, strong performance of a classifier on one task had limited generalizability to other tasks. Our results underscore the importance of validating machine learning classifiers along multiple dimensions, particularly in large real-world cohorts, to avoid overgeneralizing a classifier's performance based upon an impressive showing along a single axis. PrimateAI-3D's top-ranked performance at separating benign and pathogenic missense variants in ClinVar was unexpected, since the other machine learning classifiers (with the exception of EVE) were trained either directly on ClinVar, or on other variant annotation databases with a high degree of content overlap.
Because they are primarily based on variants described in the literature, clinical variant databases are subject to ascertainment bias  (12, 106, 107) , which may have contributed to supervised classifiers picking up on tendencies of human variant annotation that are unrelated to the task of separating benign from pathogenic variants (figs. S16, S17, and S18). Given the challenges with human annotation, we also investigated whether PrimateAI-3D could assist in revising incorrectly labeled ClinVar variants, by comparing annotations in the current ClinVar database and those from a September 2017 snapshot. Disagreement between PrimateAI-3D and the 2017 version of ClinVar was highly predictive of future revision and the odds of revision increased with PrimateAI-3D confidence (fig. S19 ). Among variants with the 10% most confident PrimateAI-3D predictions, the odds of revision were 10-fold elevated if PrimateAI-3D was in disagreement with the ClinVar label (P < 10 -14 ).
The performance of PrimateAI-3D on clinical variant benchmarks scaled directly with training dataset size, indicating that additional primate sequencing data will be the key to unlocking further gains (Fig. 4  and fig. S20 ). The current primate cohort already covers 30% of all possible synonymous variants in the human genome, despite containing only 809 individuals from 233 species (Fig. 4B ). By increasing the number of species and the number of individuals sequenced per species, we expect to saturate the majority of the remaining tolerated substitutions in the human genome (fig. S21 ), including both coding and noncoding variation, leaving the remaining deleterious variants to be deduced by process of elimination.