dataesr/scientific-paragraphs-categorization huggingface dataset:
The analysis of sequence features revealed a slight increase of CDR3 length in COVID-associated versus generic clonotypes (S4 Fig). A few V and J genes were enriched or depleted in COVID-associated TCR (Fig 5C  and 5E ): TRBV5-1, TRBV20-1 and TRBV6-2, TRBJ1-1 are considerably underrepresented in the COVID-associated repertoire, while TRBV27, TRBJ2-1 and TRBJ1-2 are enriched, consistent with previous reports  [43, 74]. To better understand the structure of the COVID-specific TCR repertoire, we next performed a network and clustering analysis of COVID-associated clonotypes. As before, we built a graph by putting edges between clonotypes with at most two differences in CDR3 aminoacid identity or in V and J gene usage. Clonotypes found in the MIRA dataset of  [70]  are colored according to their antigen specificity: yellow for the surface glycoprotein (S), red for the nucleocapsid phosphoprotein (N), brown for the transmembrane protein (M) and orange for the envelope protein (E); and the seven putative accessory proteins (ORF1ab (light green), ORF3a (light blue), ORF6 (magenta), ORF7a (black), ORF7b (dark green), ORF8 (light pink), and ORF10 (dark blue).
Sequence preprocessing IgM and IgG repertoires. The analysis of healthy BCR heavy-chain (IGH) repertoires was performed on several large datasets comprising a total of over 3 × 10 8 productive sequences from 10 healthy adult subjects  [13]. The sequence isotype, IgM or IgG, was identified during primer removal and pairing of the two reads. We used the pre-processed data provided by the initial analysis of the dataset  [13]  with the exception of the two largest individuals (326651 and 326713). For these two donors, we applied a custom pipeline to find high-quality out-of-frame transcripts required for inference of the generative model of V(D)J recombination. Most outof-frame sequences are expected to originate from a frameshift during recombination, rather than from hypermutations or sequencing errors, insertions and deletions.
Those occur with rates lower than 10 -4 per base pair  [92]  or with a frequency lower than 0. 5% in the CDR3, compared to the total 5. 6% of unproductive sequences. We aligned raw reads using pRESTO of the Immcantation environment  [93]  with a setup allowing for correcting for errors in UMIs and dealing with insufficient UMI diversity. Since reads begin with the CDR1, 74% of the V gene and the entirety of the J gene are covered, leading to unambiguous gene assignment. We grouped reads with the same UMI together, annotating each consensus sequence with a corresponding number of reads, which is a proxy for sequence quality. Pre-processed data were then aligned to V, D and J templates from IMGT  [94]  database using IgBlast  [95]. We filtered the set of productive IgM sequences to keep only sequences with at most one mutation.
Using a subset of non-productive, naive IgM sequences of high quality (with at least 3 reads per consensus sequence), we used IGoR  [19]  to infer the statistics of the generative process, and to build a model P gen (σ) that can be used to generate synthetic sequences with no mutations, and free of selection effects that affect real productive sequences. To include selection effects, we learned a sequence-specific selection factor Q(σ) that modifies the probability of occurrences of sequences relative to the generation model. The resulting post-selection probability model reads: P post (σ) = Q(σ)P gen (σ). The training of the function Q (σ) was done using IgM productive sequences with at most 1 hypermutation using the SONIA software  [20]. Clonal lineage reconstruction in IgG repertoires. A large fraction of the IgG repertoire has already undergone successive stages of differentiation and specialization after antigen activation, meaning that statistics describing naive IgM sequences features are no longer valid for IgG sequences.
TCRβ repertoires. Healthy T cell β-chain repertoires were taken from a cohort of 666 donors  [16] , 641 of which were serotyped for cytomegalovirus (CMV): 352 subjects were CMV negative (CMV-) and 289 were CMV positive (CMV+), with a mean number of unique β chains � 180,000 per donor. COVID-19 repertoires were obtained from a cohort of 1414 SARS-CoV-2 positive donors sequenced by Adaptive Biotechnologies, as described in  [70]. Both datasets were already preprocessed. We separated non-productive and productive sequences from healthy donors. We inferred a generative model from the nonproductive sequences using IGoR  [19] , and a selection model from the productive sequences using soNNia  [18] , an artificial neural network based extension of SONIA.
Control cohort with matching P post distribution When comparing COVID-associated TCRβ to sequences from bulk repertoires, we observed differences in their P post distribution reflecting the relationship between overlap and P post. To make comparisons without that possibly confounding factor, we built a healthy control by histogramming the log P post distribution of significantly shared sequences in COVID-19 patients into n equal-width bins. For each bin of width w and height h, we randomly drew clonotypes from healthy repertoires falling in that P post -bin, until we reached frequency h. The resulting dataset has the same distribution of P post as SARS-CoV-2 sequences, but with sequences from the control repertoire.
To avoid overfitting, we learn P data on a training set. PðdatajH 0 Þ ¼ Y s2i\S ð1 À e À N i P � post ðsÞ Þ Y s2Sni e À N i P � post ðsÞ ; ð9Þ PðdatajH 1 Þ ¼ Y s2i\S ð1 The set S can be adjusted by taking the n top clones (ranked from lowest to highest value of the score of Eq 8), and letting n vary. Individuals with score L > c are called positives, and L < c negative, with c an adjustable threshold, which is set to achieve a given sensitivity on the training dataset. For the purpose of TCR we turned to logistic regression, which uses a more general form of the score given by the logistic function: L i logit ¼ log PðH 1 jiÞ 1 À PðH 1 jiÞ ¼ b 0 þ X s2S b s x i s ð11Þ where x i s 2 f0; 1g denotes the absence of presence of sequence σ from S in individual i. Note that the likelihood ratio test is a particular case, with b s ¼ logðe N i P � data ðsÞ À 1Þ À logðe N i P � post ðsÞ À 1Þ.
By constrast, in logistic regression the parameters β σ are trained on labeled data, i. e. repertoires of individuals with know COVID-19 status, denotes by y i = 1 if the person was SARS-CoV-2 positive, and y i = 0 otherwise. Logistic regression is performed by minimizing the cross-entropy over the β σ : H ¼ X i ½y i log PðH 1 jiÞ þ ð1 À y i Þ logð1 À PðH 1 jiÞÞ� þ C À 1 X s jb s j; ð12Þ where the last term is an ℓ 1 regularization. The parameters n (controling the size of S) and C were optimized to optimize performance (see S6 Fig). With n = 7000 and C = 0. 0005, the logistic classifier achieved 83% specificity and 92% precision on held-out TCR. The dataset was separated between training and testing sets as described in the main text.
The distribution of sharing numbers is compared with the model prediction obtained previously, this time showing departures from the model, due to the enriched sharing of public COVIDassociated clonotypes. These clonotypes are identified as those that are significantly more frequent in the cohort than predicted by the theory. The sequence features and organization of COVID-associated clonotypes are then analyzed and validated against databases of receptors with known SARS-CoV-2 specificity. https://doi. org/10. 1371/journal. pgen. 1010652. g001.
636 ± 0. 005, while the P post works well. (C) Comparison of the sharing number distribution between two equal-size cohorts of IgM and IgG repertoires. For an identical sequencing depth, IgG repertoires present a higher level of sharing, suggesting stronger convergent selection than in IgM repertoires. (D) Distribution of the sharing number of CDR3 amino-acid sequences of TCRβ from 666 patients. Models predictions are shown for P gen and P post , with or without a correction factor q. The correction factor is q = 0. 037 for P gen , and q = 0. 472 ± 0. 002 for P post , indicating a better accuracy of the latter. In addition, the corrected P gen model (black line) overestimates the number of sequences shared by all individuals relative to the data and to the corrected P post prediction (orange line).
This work was supported by the  Marie Skłodowska-Curie Actions   H2020-MSCA-ITN-2017  program no 764698 from the  European Union  (T. M. ), and by a  European Research Council   Consolidator grant  no  724208  (A. M. W. ). The salary of M. R. O. was provided in part by the  Marie Skłodowska-Curie Actions   H2020-MSCA-ITN-2017  program no 764698 from the  European Union  and the  European Research Council Consolidator  grant no  724208. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The datasets used in this study can be downloaded at the following sources: https://github. com/briney/grp_paper https://clients. adaptivebiotech. com/pub/emerson-2017-natgen https://clients. adaptivebiotech. com/ pub/covid-2020 https://www. ncbi. nlm. nih. gov/ bioproject/?term=PRJNA625995 https://www. ncbi. nlm. nih. gov/bioproject/PRJNA645245 https:// zenodo. org/record/3886395#. YgWMI33MLQ0.
The P gen model is again inaccurate, requiring a correction factor q = 0. 636 ± 0. 005, while the P post works well. (C) Comparison of the sharing number distribution between two equal-size cohorts of IgM and IgG repertoires. For an identical sequencing depth, IgG repertoires present a higher level of sharing, suggesting stronger convergent selection than in IgM repertoires. (D) Distribution of the sharing number of CDR3 amino-acid sequences of TCRβ from 666 patients. Models predictions are shown for P gen and P post , with or without a correction factor q. The correction factor is q = 0. 037 for P gen , and q = 0. 472 ± 0. 002 for P post , indicating a better accuracy of the latter. In addition, the corrected P gen model (black line) overestimates the number of sequences shared by all individuals relative to the data and to the corrected P post prediction (orange line).
DNA Integrity Assessment with Acridine Orange (AO) Staining The AO assay enables the analysis of DNA susceptibility to damage. Data were obtained as the percentage of sperm with double-stranded DNA (dsDNA, green fluorescence, normal) and single-stranded DNA (ssDNA, red fluorescence). The percentage of sperm with dsDNA in the swim-up fractions incubated for 2 and 4 h with F 4 -NeuroPs did not differ from that in the control samples (both up 98%).
Statistical Analysis Statistical analysis was performed using IBM SPSS version 23. 0 for Windows software package (IBM Corp. , Armonk, NY, USA). The appropriate analysis in this study, where the variables size was less than 30, was to use non-parametric tests. To determine the homogeneity of the variances, Levene's test was applied. The Mann-Whitney U test was used to compare differences between two independent groups. The Kruskal-Wallis test (one-way ANOVA on ranks) was performed for multiple comparisons of more than two independent groups, followed by post-hoc comparisons using the Dunnett test or Tukey test. The variables were reported as median (interquartile range [IQR]). A value of p < 0. 05 was considered statistically significant.
Regression Model Setup LON metrics serve as the independent variables in the algorithm performance models. The dependent variable is runtime of iterated local search; the modelling is therefore a regression task. As the learning algorithm, Random Forest in R is used. For each individual model, the observations include metrics from LONs constructed using one of the 30 random seeds, combined with ILS runtime for the problem instance as the dependent variable. To be clear, the same ILS performance data is used regardless of the LON random seed: for example, the runtime value for bur26a is mapped to all 30 of its LONs. Because we use one row per QAP instance to build the models, there are limited number of observations in the datasets: 40. The one-in-ten rule  [5]  stipulates that roughly ten observations are required per independent variable.
For future work, we will consider a larger QAP instance set. In particular, an instance generator (such as  [10] ) could be used to obtain an expansive set of similar problems. This should remove the variation seen between instances from different classes. We would also like to further remove noise from the performance modelling pipeline; consider the study of other problems, including pseudoboolean spaces; and include other heuristics or alternative LON sampling methods such as snowballing  [39]  and those relating to recombination  [8, 41]. The data from this work are available at https://doi. org/10. 5281/zenodo. 7900303.
Financial Support: E. S. was supported by grants of the  German Federal Ministry of Health  ( ZMVI1-52 2518FSB705 ) and from the  German Research Foundation  (DFG, grant number:  398066876-GRK 53 2485/1  and grant number:  STE 1954/12-1 ). A. -K. M. and R. M. F. were supported by grants from the  54   DFG  (project number  272983813 SFB/TRR 179  and  467724337 , respectively). D. T. was supported by  55  grants from the  DFG  (project number  448974291 ) and the  German Ministry of Education and Research   56  ( BMBF , project  VirBio ;  01KI2106 ). V. K. was supported by a  Forschungspool grant  of the Carl von Page 4 of 52  John Wiley & Sons  i e w 3 57  Ossietzky University Oldenburg, Germany  (Grant  2021-062 ). V. L. D. T. , P. B. and E. S. were funded by 58 a grant of the  German Centre for Infection Research (DZIF).
1: EGFR is expressed endogenously in primary human liver cells. (A) T-distributed stochastic EGFR kinase activity 370 361 389 581 Figure Legends 582 583 neighbor-embedding (t-SNE) plots highlighting mRNA expression of ALB and EGFR across all cells 584 of healthy human liver tissue 32. The color of each cell represents the gene expression according to the 585 corresponding legend as log2 value of the expression. Cell type annotation was transferred from 586 Aizarani et al. 32 (B) Violin plot showing the normalized expression of proposed HEV host factors 587 EGFR, TSG101 and Rab5. The color code depicts the different cell types present in the dataset. 588 589 Fig. 599 (C) Representative immunofluorescence images stained for ORF2 protein in HEVcc (p6) infected 600 HepG2 cells under EGFR modulator treatment.
In this letter, we use LHC data from the Drell-Yan processes pp → i j (with i = j) to derive modelindependent upper limits on lepton-flavor-violating meson decays. Our analysis is based on an Effective Field Theory (EFT) approach and it does not require a specific assumption regarding the basis of effective operators. We find that current LHC data (140 fb -1 ) already provides competitive limits on B(B → π eτ ) and B(B → πμτ ) with respect to the ones obtained through experimental searches at the B-factories. Moreover, we derive upper limits on several decays that have not been searched for experimentally yet, such as D 0 → eτ in the charm sector, and various semileptonic decays such as B → ρμτ , B s → K μτ and B s → φμτ. Lastly, we discuss the validity of the EFT description of LHC data and the impact of loop corrections in our analysis.
fr d e-mail: olcyr. sumensari@ijclab. in2p3. fr (corresponding author) The LHC is currently leading the effort in the search for new-physics particles in the high-energy frontier. Besides performing direct searches for particles that might be light enough to be produced on-shell, the LHC can also be used to look for indirect effects from new particles that are too heavy to be produced in proton collisions  [2]. The latter strategy is based on the study of the high-energy tails of kinematic distributions, and it has been proven to be extremely useful to constrain flavor transition through LHC data on the Drell-Yan processes pp → i j and pp → i ν at highp T , see Refs. [3, 4]  and references therein. By exploiting the five quark flavors that are available in the proton, several Effective Field Theory (EFT) studies have been performed in the literature, deriving LHC bounds on specific semileptonic d = 6 operators that were compared to low-energy constraints  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20].
Although less accurate in most cases, the energy enhancement of the partonic cross sections induced by the effective operators can lead to LHC bounds that outperform the ones derived from low-energy processes for specific operators and quark/lepton flavors, as shown, for instance, for semileptonic LFV transitions in Ref. [19]  and for charm-meson decays in Refs. [19, 20]. In this letter, we will demonstrate that Drell-Yan data can be used to set fully model-independent upper limits on LFV meson decays. In other words, we will derive upper limits on the branching fractions for these processes without making any assumptions regarding the choice of effective operators. We will only assume that the EFT approach is valid to describe the LHC data  [21]. It will be fundamental for our derivation that Drell-Yan processes are inclusive, probing operators with all possible Lorentz structures and all possible combinations of the five quark flavors at tree level.
This is in contrast to exclusive meson decays which are sensitive to specific quark-flavor transitions and to operators with selected Lorentz structures, depending on the quantum numbers of the mesons in the initial and final states, as depicted in Fig. 1. We will illustrate the above procedure for the decays B (s) → i j and B (s) → M i j , with i = j, where M denotes a pseudoscalar or vector meson  [22]. We will show that current LHC data is already sufficient to provide competitive bounds for specific channels compared to the direct limits from flavor experiments, which will be further improved with the high-luminosity phase of the LHC. We will also use LHC data to constrain decays that have not yet been searched for experimentally, such as D 0 → eτ in the charm sector and various semileptonic decays of B (s) -mesons.
We consider the constraints on the SM EFT Lagrangian provided in the HighPT package  [3, 4] , which have been obtained through the recast of the CMS searches  [29]  using the PDF4LHC15_nnlo_mc PDF set  [30]. After minimizing the χ 2 -distribution describing the invariant-mass tails, we can write the resulting LHC constraints at 2σ in the following general form, n ∈ bins α n ( C † A n C -1) 2 ≤ 1, (11) where the summation spans over all experimental dilepton invariant-mass bins, α n are real coefficients and A n are Hermitian matrices determined for the nth bin. The vector C comprises all SM EFT Wilson coefficients that violate lepton flavor, defined at the scale μ high ≈ 1 TeV. To simplify our discussion, we will focus on the limits extracted from a single mass bin at large invariant mass.
Numerical results In this section, we apply the method outlined in Sect. 4 to the LFV decays of B (s) -mesons detailed above. We will focus only on the processes leading to τ e or τ μ pairs, since LHC bounds cannot be competitive with the very stringent lowenergy limits on the decays with μe in the final state, as already shown in Ref. [19]. We will present our final results by solving the optimization problem numerically, considering the whole invariant-mass spectrum provided in Ref. ). The last column corresponds to the direct experimental limit obtained experimentally by BaBar  [34] , Belle  [35, 36, 53]  and LHCb  [37, 38]. The highlighted cells correspond to the most stringent limits with current (bold) or future (italic) data Observable LHC (140 fb -1 ) HL-LHC (3 ab -1 ) Exp. limit B(B 0 → μ ± τ ∓ ) 8 × 10 -4 1.
The 95% CL constraints derived from current LHC data (140 fb -1 ) and their projection to HL-LHC (3 ab -1 ) are depicted by the dark-and light-orange regions On the EFT validity Before discussing the application of the method to other processes, we briefly comment on the validity of the EFT description of LHC data  [21]. For the EFT to be valid, the EFT cutoff ( ) must be much larger than the energy scale (E) of the relevant LHC events. Since we are constraining combinations of the type C/ 2 , it is clear that for a search of given sensitivity, our limits will only apply for C above a certain value. The maximal EFT cutoff max for which our results are applicable can be estimated by requiring that the 2 → 2 amplitude does not exceed the 4π perturbativity bound  [2]. Assuming that the effective operators are generated at tree level, max is given by max = √ 4π C max / 2 , ( 31 ) which must satisfy < max , where C max denotes the largest effective coefficient that maximizes the observable in Eq.
Summary and outlook In this letter, we revisited the constraints on LFV meson decays by using LHC data on the Drell-Yan processes pp → i j (with i = j) at highp T. By relying on the EFT approach, we have shown that it is possible to derive upper bounds on such processes in a model-independent way by marginalizing over the complete set of SM EFT operators compatible with such LHC bounds. In particular, this optimization problem can be reduced to an eigenvalue problem that can be solved analytically, provided a single bin at large di-lepton invariant-mass is considered for the LHC data. As an illustration of this approach, we have derived indirect limits on B (s) -meson LFV decays based on the transitions b → dτ and b → sτ , with = e, μ. We find that our upper limits on the decays B → π eτ and B → πμτ are already 7 We notice that even more stringent bounds can be used by using the purely leptonic decays τ → μμμ  [39]  competitive with the current limits from the B-factories and that the expected sensitivity at the HL-LHC will supersede them.
Acknowledgements This project has received support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  agreement No 860881-HIDDeN. IP receives funding from " P2IO LabEx  ( ANR-10-LABX-0038 )" in the framework " Investissements d'Avenir"  ( ANR-11-IDEX-0003-01 ) managed by the  Agence Nationale de la Recherche (ANR) ,  France. The work of DAF is supported by the  US Department of Energy  under Grant  DOE-DE-SC0010008.
2008; Gruberbauer et al. 2013; Rendle et al. 2019). In this study, we first investigate the fit of the individual frequencies, with a focus on the impact of the surface effects. Then, we test a more elaborate technique that uses frequency separation ratios coupled with a mean density inversion. This technique has been shown to be effective  (Buldgen et al. 2019a; Bétrisey et al. 2022 ). We also investigate the impact of the correlations between the inverted mean density and the frequency separation ratios, which were neglected in the past studies. For all the minimisations, we used the AIMS software  (Rendle et al. 2019 ), and we applied the two modelling strategies on synthetic targets from  Sonoi et al. (2015; models A to F). The frequencies of these simulated targets were computed with the MAD oscillation code.
4. 2 for further details). These issues did not occur with the models of this section. We tested three prescriptions to include the inverted mean density in the constraints: we did not include it, we included it as a classical constraint, or we included it as a seismic constraint. By classical constraints, we imply that the likelihoods were computed assuming that the true value of the observations were perturbed by some Gaussian-distributed random noise (and we note that other distributions are also supported by the software), while AIMS accounts for all the correlations for a seismic constraint. A comparison of the three prescriptions is shown in Table  4. When the inverted mean density is added to the constraints, the precision of the stellar mass and radius is significantly improved. The precision of the stellar age is mostly dominated by the seismic information contained in the ratios, and a gain in precision is likely to be an indirect consequence of a gain in the precision of the stellar mass and radius.